From cd17680c71b3f66583eaa0d208015322b946a55d Mon Sep 17 00:00:00 2001 From: volaya Date: Mon, 30 Jan 2017 09:41:38 +0100 Subject: [PATCH 1/4] [processing] removed otb and lidartools providers --- .../processing/algs/lidar/CMakeLists.txt | 6 - .../algs/lidar/LidarToolsAlgorithmProvider.py | 260 ------ .../plugins/processing/algs/lidar/__init__.py | 0 .../processing/algs/lidar/fusion/ASCII2DTM.py | 87 -- .../algs/lidar/fusion/CMakeLists.txt | 3 - .../algs/lidar/fusion/CanopyMaxima.py | 104 --- .../algs/lidar/fusion/CanopyModel.py | 154 ---- .../processing/algs/lidar/fusion/Catalog.py | 130 --- .../processing/algs/lidar/fusion/ClipData.py | 100 --- .../algs/lidar/fusion/CloudMetrics.py | 96 --- .../processing/algs/lidar/fusion/Cover.py | 116 --- .../processing/algs/lidar/fusion/Csv2Grid.py | 60 -- .../processing/algs/lidar/fusion/DTM2ASCII.py | 64 -- .../processing/algs/lidar/fusion/DTM2TIF.py | 65 -- .../algs/lidar/fusion/DensityMetrics.py | 97 --- .../algs/lidar/fusion/FilterData.py | 76 -- .../algs/lidar/fusion/FirstLastReturn.py | 67 -- .../algs/lidar/fusion/FusionAlgorithm.py | 61 -- .../algs/lidar/fusion/FusionUtils.py | 89 -- .../algs/lidar/fusion/GridMetrics.py | 148 ---- .../algs/lidar/fusion/GridSurfaceCreate.py | 141 ---- .../algs/lidar/fusion/GridSurfaceStats.py | 105 --- .../algs/lidar/fusion/GroundFilter.py | 77 -- .../algs/lidar/fusion/ImageCreate.py | 99 --- .../algs/lidar/fusion/IntensityImage.py | 98 --- .../processing/algs/lidar/fusion/MergeDTM.py | 92 --- .../processing/algs/lidar/fusion/MergeData.py | 64 -- .../algs/lidar/fusion/MergeRaster.py | 80 -- .../algs/lidar/fusion/OpenViewerAction.py | 54 -- .../algs/lidar/fusion/PolyClipData.py | 85 -- .../algs/lidar/fusion/ReturnDensity.py | 97 --- .../processing/algs/lidar/fusion/SplitDTM.py | 71 -- .../algs/lidar/fusion/SurfaceStats.py | 73 -- .../algs/lidar/fusion/TinSurfaceCreate.py | 103 --- .../algs/lidar/fusion/TopoMetrics.py | 102 --- .../processing/algs/lidar/fusion/TreeSeg.py | 118 --- .../processing/algs/lidar/fusion/__init__.py | 0 .../algs/lidar/lastools/CMakeLists.txt | 3 - .../algs/lidar/lastools/LAStoolsAlgorithm.py | 444 ---------- .../algs/lidar/lastools/LAStoolsUtils.py | 79 -- .../algs/lidar/lastools/__init__.py | 0 .../algs/lidar/lastools/blast2dem.py | 77 -- .../algs/lidar/lastools/blast2demPro.py | 87 -- .../algs/lidar/lastools/blast2iso.py | 90 --- .../algs/lidar/lastools/blast2isoPro.py | 100 --- .../algs/lidar/lastools/flightlinesToCHM.py | 162 ---- .../lidar/lastools/flightlinesToDTMandDSM.py | 144 ---- .../lastools/flightlinesToSingleCHMpitFree.py | 264 ------ .../algs/lidar/lastools/hugeFileClassify.py | 140 ---- .../lidar/lastools/hugeFileGroundClassify.py | 118 --- .../algs/lidar/lastools/hugeFileNormalize.py | 130 --- .../processing/algs/lidar/lastools/las2dem.py | 82 -- .../algs/lidar/lastools/las2demPro.py | 88 -- .../processing/algs/lidar/lastools/las2iso.py | 94 --- .../algs/lidar/lastools/las2lasPro_filter.py | 56 -- .../algs/lidar/lastools/las2lasPro_project.py | 122 --- .../lidar/lastools/las2lasPro_transform.py | 84 -- .../algs/lidar/lastools/las2las_filter.py | 59 -- .../algs/lidar/lastools/las2las_project.py | 116 --- .../algs/lidar/lastools/las2las_transform.py | 77 -- .../processing/algs/lidar/lastools/las2shp.py | 71 -- .../processing/algs/lidar/lastools/las2tin.py | 54 -- .../processing/algs/lidar/lastools/las2txt.py | 66 -- .../algs/lidar/lastools/las2txtPro.py | 68 -- .../algs/lidar/lastools/lasboundary.py | 91 --- .../algs/lidar/lastools/lasboundaryPro.py | 93 --- .../algs/lidar/lastools/lascanopy.py | 156 ---- .../algs/lidar/lastools/lascanopyPro.py | 166 ---- .../algs/lidar/lastools/lasclassify.py | 58 -- .../algs/lidar/lastools/lasclassifyPro.py | 60 -- .../processing/algs/lidar/lastools/lasclip.py | 87 -- .../algs/lidar/lastools/lascolor.py | 64 -- .../algs/lidar/lastools/lascontrol.py | 89 -- .../processing/algs/lidar/lastools/lasdiff.py | 78 -- .../algs/lidar/lastools/lasduplicate.py | 76 -- .../algs/lidar/lastools/lasduplicatePro.py | 79 -- .../processing/algs/lidar/lastools/lasgrid.py | 81 -- .../algs/lidar/lastools/lasgridPro.py | 87 -- .../algs/lidar/lastools/lasground.py | 86 -- .../algs/lidar/lastools/lasgroundPro.py | 86 -- .../algs/lidar/lastools/lasgroundPro_new.py | 105 --- .../algs/lidar/lastools/lasground_new.py | 99 --- .../algs/lidar/lastools/lasheight.py | 82 -- .../algs/lidar/lastools/lasheightPro.py | 89 -- .../lidar/lastools/lasheightPro_classify.py | 126 --- .../algs/lidar/lastools/lasheight_classify.py | 120 --- .../algs/lidar/lastools/lasindex.py | 67 -- .../algs/lidar/lastools/lasindexPro.py | 69 -- .../processing/algs/lidar/lastools/lasinfo.py | 116 --- .../algs/lidar/lastools/lasinfoPro.py | 113 --- .../algs/lidar/lastools/lasmerge.py | 96 --- .../algs/lidar/lastools/lasmergePro.py | 55 -- .../algs/lidar/lastools/lasnoise.py | 91 --- .../algs/lidar/lastools/lasnoisePro.py | 97 --- .../algs/lidar/lastools/lasoverage.py | 75 -- .../algs/lidar/lastools/lasoveragePro.py | 81 -- .../algs/lidar/lastools/lasoverlap.py | 90 --- .../algs/lidar/lastools/lasoverlapPro.py | 100 --- .../algs/lidar/lastools/lasprecision.py | 59 -- .../algs/lidar/lastools/laspublish.py | 125 --- .../algs/lidar/lastools/laspublishPro.py | 125 --- .../algs/lidar/lastools/lasquery.py | 80 -- .../processing/algs/lidar/lastools/lassort.py | 63 -- .../algs/lidar/lastools/lassortPro.py | 69 -- .../algs/lidar/lastools/lassplit.py | 78 -- .../processing/algs/lidar/lastools/lasthin.py | 93 --- .../algs/lidar/lastools/lasthinPro.py | 99 --- .../processing/algs/lidar/lastools/lastile.py | 80 -- .../algs/lidar/lastools/lastilePro.py | 94 --- .../algs/lidar/lastools/lasvalidate.py | 60 -- .../algs/lidar/lastools/lasvalidatePro.py | 60 -- .../processing/algs/lidar/lastools/lasview.py | 81 -- .../algs/lidar/lastools/lasviewPro.py | 76 -- .../processing/algs/lidar/lastools/laszip.py | 71 -- .../algs/lidar/lastools/laszipPro.py | 78 -- .../processing/algs/lidar/lastools/shp2las.py | 69 -- .../processing/algs/lidar/lastools/txt2las.py | 121 --- .../algs/lidar/lastools/txt2lasPro.py | 123 --- .../processing/algs/otb/CMakeLists.txt | 28 - .../processing/algs/otb/OTBAlgorithm.py | 354 -------- .../algs/otb/OTBAlgorithmProvider.py | 109 --- .../algs/otb/OTBSpecific_XMLLoading.py | 358 -------- .../plugins/processing/algs/otb/OTBUtils.py | 293 ------- .../plugins/processing/algs/otb/__init__.py | 0 .../algs/otb/description/5.0.0/BandMath.xml | 41 - .../algs/otb/description/5.0.0/BandMathX.xml | 55 -- .../BinaryMorphologicalOperation-closing.xml | 72 -- .../BinaryMorphologicalOperation-dilate.xml | 90 --- .../BinaryMorphologicalOperation-erode.xml | 72 -- .../BinaryMorphologicalOperation-opening.xml | 72 -- .../5.0.0/ClassificationMapRegularization.xml | 64 -- .../5.0.0/ColorMapping-continuous.xml | 98 --- .../description/5.0.0/ColorMapping-custom.xml | 65 -- .../description/5.0.0/ColorMapping-image.xml | 88 -- .../5.0.0/ColorMapping-optimal.xml | 63 -- .../otb/description/5.0.0/CompareImages.xml | 75 -- .../5.0.0/ComputeConfusionMatrix-raster.xml | 57 -- .../5.0.0/ComputeConfusionMatrix-vector.xml | 67 -- .../5.0.0/ComputeImagesStatistics.xml | 30 - .../5.0.0/ComputeModulusAndPhase-OneEntry.xml | 49 -- .../ComputeModulusAndPhase-TwoEntries.xml | 56 -- .../ComputeOGRLayersFeaturesStatistics.xml | 31 - .../5.0.0/ComputePolylineFeatureFromImage.xml | 56 -- .../description/5.0.0/ConcatenateImages.xml | 31 - .../5.0.0/ConcatenateVectorData.xml | 22 - .../5.0.0/ConnectedComponentSegmentation.xml | 66 -- .../algs/otb/description/5.0.0/Convert.xml | 78 -- .../algs/otb/description/5.0.0/DEMConvert.xml | 20 - .../otb/description/5.0.0/Despeckle-frost.xml | 60 -- .../otb/description/5.0.0/Despeckle-lee.xml | 60 -- .../5.0.0/DimensionalityReduction-ica.xml | 80 -- .../5.0.0/DimensionalityReduction-maf.xml | 55 -- .../5.0.0/DimensionalityReduction-napca.xml | 80 -- .../5.0.0/DimensionalityReduction-pca.xml | 62 -- .../5.0.0/EdgeExtraction-gradient.xml | 51 -- .../5.0.0/EdgeExtraction-sobel.xml | 51 -- .../5.0.0/EdgeExtraction-touzi.xml | 60 -- .../otb/description/5.0.0/ExtractROI-fit.xml | 58 -- .../description/5.0.0/ExtractROI-standard.xml | 78 -- ...FusionOfClassifications-dempstershafer.xml | 75 -- ...FusionOfClassifications-majorityvoting.xml | 52 -- ...rayScaleMorphologicalOperation-closing.xml | 72 -- ...GrayScaleMorphologicalOperation-dilate.xml | 72 -- .../GrayScaleMorphologicalOperation-erode.xml | 72 -- ...rayScaleMorphologicalOperation-opening.xml | 72 -- .../5.0.0/HaralickTextureExtraction.xml | 116 --- .../5.0.0/HooverCompareSegmentation.xml | 89 -- .../otb/description/5.0.0/ImageClassifier.xml | 53 -- .../otb/description/5.0.0/ImageEnvelope.xml | 39 - .../5.0.0/KMeansClassification.xml | 79 -- .../algs/otb/description/5.0.0/KmzExport.xml | 52 -- .../description/5.0.0/LSMSSegmentation.xml | 88 -- .../5.0.0/LSMSSmallRegionsMerging.xml | 55 -- .../description/5.0.0/LSMSVectorization.xml | 45 -- .../5.0.0/LineSegmentDetection.xml | 28 - .../5.0.0/LocalStatisticExtraction.xml | 48 -- .../description/5.0.0/MeanShiftSmoothing.xml | 89 -- .../5.0.0/MultivariateAlterationDetector.xml | 37 - .../description/5.0.0/OGRLayerClassifier.xml | 46 -- .../description/5.0.0/OpticalCalibration.xml | 167 ---- .../5.0.0/OrthoRectification-epsg.xml | 115 --- .../5.0.0/OrthoRectification-fit-to-ortho.xml | 100 --- .../OrthoRectification-lambert-WGS84.xml | 108 --- .../5.0.0/OrthoRectification-utm.xml | 122 --- .../description/5.0.0/Pansharpening-bayes.xml | 67 -- .../description/5.0.0/Pansharpening-lmvm.xml | 67 -- .../description/5.0.0/Pansharpening-rcs.xml | 49 -- .../description/5.0.0/RadiometricIndices.xml | 124 --- .../description/5.0.0/Rasterization-image.xml | 78 -- .../5.0.0/Rasterization-manual.xml | 134 --- .../otb/description/5.0.0/ReadImageInfo.xml | 57 -- .../algs/otb/description/5.0.0/Rescale.xml | 48 -- .../5.0.0/RigidTransformResample-id.xml | 83 -- .../5.0.0/RigidTransformResample-rotation.xml | 92 --- .../RigidTransformResample-translation.xml | 101 --- .../5.0.0/SFSTextureExtraction.xml | 84 -- .../description/5.0.0/SOMClassification.xml | 143 ---- .../otb/description/5.0.0/Segmentation-cc.xml | 152 ---- .../description/5.0.0/Segmentation-edison.xml | 180 ----- .../5.0.0/Segmentation-meanshift.xml | 188 ----- .../5.0.0/Segmentation-mprofiles.xml | 179 ---- .../5.0.0/Segmentation-watershed.xml | 161 ---- .../description/5.0.0/Smoothing-anidif.xml | 69 -- .../description/5.0.0/Smoothing-gaussian.xml | 51 -- .../otb/description/5.0.0/Smoothing-mean.xml | 51 -- .../algs/otb/description/5.0.0/SplitImage.xml | 29 - .../otb/description/5.0.0/StereoFramework.xml | 315 -------- .../otb/description/5.0.0/Superimpose.xml | 91 --- .../algs/otb/description/5.0.0/TileFusion.xml | 40 - .../5.0.0/TrainImagesClassifier-ann.xml | 247 ------ .../5.0.0/TrainImagesClassifier-bayes.xml | 125 --- .../5.0.0/TrainImagesClassifier-boost.xml | 167 ---- .../5.0.0/TrainImagesClassifier-dt.xml | 184 ----- .../5.0.0/TrainImagesClassifier-gbt.xml | 161 ---- .../5.0.0/TrainImagesClassifier-knn.xml | 134 --- .../5.0.0/TrainImagesClassifier-libsvm.xml | 156 ---- .../5.0.0/TrainImagesClassifier-rf.xml | 188 ----- .../5.0.0/TrainImagesClassifier-svm.xml | 209 ----- .../5.0.0/TrainOGRLayersClassifier.xml | 46 -- .../5.0.0/VectorDataExtractROI.xml | 38 - .../5.0.0/VectorDataReprojection-image.xml | 57 -- .../5.0.0/VectorDataReprojection-user.xml | 91 --- .../description/5.0.0/VectorDataTransform.xml | 83 -- .../otb/description/5.0.0/doc/BandMath.html | 6 - .../otb/description/5.0.0/doc/BandMathX.html | 98 --- .../BinaryMorphologicalOperation-closing.html | 5 - .../BinaryMorphologicalOperation-dilate.html | 5 - .../BinaryMorphologicalOperation-erode.html | 5 - .../BinaryMorphologicalOperation-opening.html | 5 - .../doc/BinaryMorphologicalOperation.html | 5 - .../description/5.0.0/doc/BlockMatching.html | 5 - .../5.0.0/doc/BundleToPerfectSensor.html | 5 - .../doc/ClassificationMapRegularization.html | 7 - .../5.0.0/doc/ColorMapping-continuous.html | 13 - .../5.0.0/doc/ColorMapping-custom.html | 13 - .../5.0.0/doc/ColorMapping-image.html | 13 - .../5.0.0/doc/ColorMapping-optimal.html | 13 - .../description/5.0.0/doc/ColorMapping.html | 13 - .../description/5.0.0/doc/CompareImages.html | 5 - .../doc/ComputeConfusionMatrix-raster.html | 5 - .../doc/ComputeConfusionMatrix-vector.html | 5 - .../5.0.0/doc/ComputeConfusionMatrix.html | 5 - .../5.0.0/doc/ComputeImagesStatistics.html | 5 - .../ComputeOGRLayersFeaturesStatistics.html | 5 - .../doc/ComputePolylineFeatureFromImage.html | 5 - .../5.0.0/doc/ConcatenateImages.html | 5 - .../5.0.0/doc/ConcatenateVectorData.html | 5 - .../doc/ConnectedComponentSegmentation.html | 5 - .../otb/description/5.0.0/doc/Convert.html | 6 - .../5.0.0/doc/ConvertCartoToGeoPoint.html | 5 - .../5.0.0/doc/ConvertSensorToGeoPoint.html | 5 - .../otb/description/5.0.0/doc/CookBook.css | 200 ----- .../otb/description/5.0.0/doc/DEMConvert.html | 5 - .../5.0.0/doc/DSFuzzyModelEstimation.html | 5 - .../5.0.0/doc/Despeckle-frost.html | 5 - .../description/5.0.0/doc/Despeckle-lee.html | 5 - .../otb/description/5.0.0/doc/Despeckle.html | 5 - .../doc/DimensionalityReduction-ica.html | 5 - .../doc/DimensionalityReduction-maf.html | 5 - .../doc/DimensionalityReduction-napca.html | 5 - .../doc/DimensionalityReduction-pca.html | 5 - .../5.0.0/doc/DimensionalityReduction.html | 5 - .../5.0.0/doc/DisparityMapToElevationMap.html | 5 - .../5.0.0/doc/DownloadSRTMTiles.html | 5 - .../5.0.0/doc/EdgeExtraction-gradient.html | 5 - .../5.0.0/doc/EdgeExtraction-sobel.html | 5 - .../5.0.0/doc/EdgeExtraction-touzi.html | 5 - .../description/5.0.0/doc/EdgeExtraction.html | 5 - .../description/5.0.0/doc/ExtractROI-fit.html | 5 - .../5.0.0/doc/ExtractROI-standard.html | 5 - .../otb/description/5.0.0/doc/ExtractROI.html | 5 - .../5.0.0/doc/FineRegistration.html | 5 - ...usionOfClassifications-dempstershafer.html | 9 - ...usionOfClassifications-majorityvoting.html | 9 - .../5.0.0/doc/FusionOfClassifications.html | 9 - .../5.0.0/doc/GeneratePlyFile.html | 5 - .../5.0.0/doc/GenerateRPCSensorModel.html | 5 - ...ayScaleMorphologicalOperation-closing.html | 5 - ...rayScaleMorphologicalOperation-dilate.html | 5 - ...GrayScaleMorphologicalOperation-erode.html | 5 - ...ayScaleMorphologicalOperation-opening.html | 5 - .../doc/GrayScaleMorphologicalOperation.html | 5 - .../5.0.0/doc/GridBasedImageResampling.html | 5 - .../5.0.0/doc/HaralickTextureExtraction.html | 5 - .../5.0.0/doc/HomologousPointsExtraction.html | 5 - .../5.0.0/doc/HooverCompareSegmentation.html | 7 - .../5.0.0/doc/HyperspectralUnmixing.html | 8 - .../5.0.0/doc/ImageClassifier.html | 5 - .../description/5.0.0/doc/ImageEnvelope.html | 5 - .../5.0.0/doc/KMeansClassification.html | 5 - .../otb/description/5.0.0/doc/KmzExport.html | 5 - .../5.0.0/doc/LSMSSegmentation.html | 5 - .../5.0.0/doc/LSMSSmallRegionsMerging.html | 5 - .../5.0.0/doc/LSMSVectorization.html | 5 - .../5.0.0/doc/LineSegmentDetection.html | 7 - .../5.0.0/doc/LocalStatisticExtraction.html | 5 - .../5.0.0/doc/MeanShiftSmoothing.html | 5 - .../5.0.0/doc/MultiResolutionPyramid.html | 5 - .../doc/MultivariateAlterationDetector.html | 21 - .../5.0.0/doc/OGRLayerClassifier.html | 5 - .../description/5.0.0/doc/OSMDownloader.html | 6 - .../5.0.0/doc/ObtainUTMZoneFromGeoPoint.html | 5 - .../5.0.0/doc/OpticalCalibration.html | 60 -- .../5.0.0/doc/OrthoRectification-epsg.html | 7 - .../doc/OrthoRectification-fit-to-ortho.html | 7 - .../doc/OrthoRectification-lambert-WGS84.html | 7 - .../5.0.0/doc/OrthoRectification-utm.html | 7 - .../5.0.0/doc/OrthoRectification.html | 7 - .../5.0.0/doc/Pansharpening-bayes.html | 5 - .../5.0.0/doc/Pansharpening-lmvm.html | 5 - .../5.0.0/doc/Pansharpening-rcs.html | 5 - .../description/5.0.0/doc/Pansharpening.html | 5 - .../otb/description/5.0.0/doc/PixelValue.html | 6 - .../otb/description/5.0.0/doc/Quicklook.html | 7 - .../5.0.0/doc/RadiometricIndices.html | 25 - .../5.0.0/doc/Rasterization-image.html | 6 - .../5.0.0/doc/Rasterization-manual.html | 6 - .../description/5.0.0/doc/Rasterization.html | 6 - .../description/5.0.0/doc/ReadImageInfo.html | 5 - .../5.0.0/doc/RefineSensorModel.html | 5 - .../otb/description/5.0.0/doc/Rescale.html | 5 - .../5.0.0/doc/RigidTransformResample-id.html | 5 - .../doc/RigidTransformResample-rotation.html | 5 - .../RigidTransformResample-translation.html | 5 - .../5.0.0/doc/RigidTransformResample.html | 5 - .../5.0.0/doc/SFSTextureExtraction.html | 5 - .../5.0.0/doc/SOMClassification.html | 5 - .../5.0.0/doc/SarRadiometricCalibration.html | 5 - .../5.0.0/doc/Segmentation-cc.html | 11 - .../5.0.0/doc/Segmentation-meanshift.html | 11 - .../5.0.0/doc/Segmentation-mprofiles.html | 11 - .../5.0.0/doc/Segmentation-watershed.html | 11 - .../description/5.0.0/doc/Segmentation.html | 11 - .../5.0.0/doc/Smoothing-anidif.html | 5 - .../5.0.0/doc/Smoothing-gaussian.html | 5 - .../description/5.0.0/doc/Smoothing-mean.html | 5 - .../otb/description/5.0.0/doc/Smoothing.html | 5 - .../otb/description/5.0.0/doc/SplitImage.html | 5 - .../5.0.0/doc/StereoFramework.html | 16 - .../doc/StereoRectificationGridGenerator.html | 5 - .../description/5.0.0/doc/Superimpose.html | 5 - .../5.0.0/doc/TestApplication.html | 5 - .../otb/description/5.0.0/doc/TileFusion.html | 5 - .../5.0.0/doc/TrainImagesClassifier-ann.html | 11 - .../doc/TrainImagesClassifier-bayes.html | 11 - .../doc/TrainImagesClassifier-boost.html | 11 - .../5.0.0/doc/TrainImagesClassifier-dt.html | 11 - .../5.0.0/doc/TrainImagesClassifier-gbt.html | 11 - .../5.0.0/doc/TrainImagesClassifier-knn.html | 11 - .../doc/TrainImagesClassifier-libsvm.html | 11 - .../5.0.0/doc/TrainImagesClassifier-rf.html | 11 - .../5.0.0/doc/TrainImagesClassifier-svm.html | 11 - .../5.0.0/doc/TrainImagesClassifier.html | 11 - .../5.0.0/doc/TrainOGRLayersClassifier.html | 5 - .../5.0.0/doc/VectorDataDSValidation.html | 5 - .../5.0.0/doc/VectorDataExtractROI.html | 5 - .../doc/VectorDataReprojection-image.html | 7 - .../doc/VectorDataReprojection-user.html | 7 - .../5.0.0/doc/VectorDataReprojection.html | 7 - .../5.0.0/doc/VectorDataSetField.html | 5 - .../5.0.0/doc/VectorDataTransform.html | 5 - .../5.0.0/doc/VertexComponentAnalysis.html | 5 - .../algs/otb/description/5.4.0/BandMath.xml | 43 - .../BinaryMorphologicalOperation-closing.xml | 77 -- .../BinaryMorphologicalOperation-dilate.xml | 97 --- .../BinaryMorphologicalOperation-erode.xml | 77 -- .../BinaryMorphologicalOperation-opening.xml | 77 -- .../5.4.0/ClassificationMapRegularization.xml | 69 -- .../5.4.0/ColorMapping-continuous.xml | 104 --- .../description/5.4.0/ColorMapping-custom.xml | 68 -- .../description/5.4.0/ColorMapping-image.xml | 94 --- .../5.4.0/ColorMapping-optimal.xml | 67 -- .../otb/description/5.4.0/CompareImages.xml | 81 -- .../5.4.0/ComputeConfusionMatrix-raster.xml | 60 -- .../5.4.0/ComputeConfusionMatrix-vector.xml | 71 -- .../5.4.0/ComputeImagesStatistics.xml | 31 - .../ComputeOGRLayersFeaturesStatistics.xml | 30 - .../5.4.0/ComputePolylineFeatureFromImage.xml | 60 -- .../description/5.4.0/ConcatenateImages.xml | 32 - .../5.4.0/ConcatenateVectorData.xml | 23 - .../5.4.0/ConnectedComponentSegmentation.xml | 72 -- .../algs/otb/description/5.4.0/Convert.xml | 83 -- .../algs/otb/description/5.4.0/DEMConvert.xml | 20 - .../otb/description/5.4.0/Despeckle-frost.xml | 64 -- .../description/5.4.0/Despeckle-gammamap.xml | 64 -- .../otb/description/5.4.0/Despeckle-kuan.xml | 64 -- .../otb/description/5.4.0/Despeckle-lee.xml | 64 -- .../5.4.0/DimensionalityReduction-ica.xml | 85 -- .../5.4.0/DimensionalityReduction-maf.xml | 58 -- .../5.4.0/DimensionalityReduction-napca.xml | 85 -- .../5.4.0/DimensionalityReduction-pca.xml | 65 -- .../5.4.0/EdgeExtraction-gradient.xml | 54 -- .../5.4.0/EdgeExtraction-sobel.xml | 54 -- .../5.4.0/EdgeExtraction-touzi.xml | 64 -- .../otb/description/5.4.0/ExtractROI-fit.xml | 61 -- .../description/5.4.0/ExtractROI-standard.xml | 84 -- ...FusionOfClassifications-dempstershafer.xml | 79 -- ...FusionOfClassifications-majorityvoting.xml | 55 -- ...rayScaleMorphologicalOperation-closing.xml | 77 -- ...GrayScaleMorphologicalOperation-dilate.xml | 77 -- .../GrayScaleMorphologicalOperation-erode.xml | 77 -- ...rayScaleMorphologicalOperation-opening.xml | 77 -- .../5.4.0/HaralickTextureExtraction.xml | 126 --- .../5.4.0/HooverCompareSegmentation.xml | 95 --- .../otb/description/5.4.0/ImageClassifier.xml | 72 -- .../otb/description/5.4.0/ImageEnvelope.xml | 42 - .../5.4.0/KMeansClassification.xml | 84 -- .../algs/otb/description/5.4.0/KmzExport.xml | 54 -- .../description/5.4.0/LSMSSegmentation.xml | 94 --- .../5.4.0/LSMSSmallRegionsMerging.xml | 58 -- .../description/5.4.0/LSMSVectorization.xml | 47 -- .../5.4.0/LineSegmentDetection.xml | 30 - .../5.4.0/LocalStatisticExtraction.xml | 51 -- .../description/5.4.0/MeanShiftSmoothing.xml | 96 --- .../5.4.0/MultivariateAlterationDetector.xml | 38 - .../description/5.4.0/OGRLayerClassifier.xml | 48 -- .../5.4.0/OrthoRectification-epsg.xml | 124 --- .../5.4.0/OrthoRectification-fit-to-ortho.xml | 107 --- .../OrthoRectification-lambert-WGS84.xml | 116 --- .../5.4.0/OrthoRectification-utm.xml | 132 --- .../description/5.4.0/Pansharpening-bayes.xml | 71 -- .../description/5.4.0/Pansharpening-lmvm.xml | 71 -- .../description/5.4.0/Pansharpening-rcs.xml | 51 -- .../description/5.4.0/RadiometricIndices.xml | 131 --- .../description/5.4.0/Rasterization-image.xml | 83 -- .../5.4.0/Rasterization-manual.xml | 146 ---- .../otb/description/5.4.0/ReadImageInfo.xml | 62 -- .../algs/otb/description/5.4.0/Rescale.xml | 51 -- .../5.4.0/RigidTransformResample-id.xml | 89 -- .../5.4.0/RigidTransformResample-rotation.xml | 99 --- .../RigidTransformResample-translation.xml | 109 --- .../5.4.0/SFSTextureExtraction.xml | 91 --- .../description/5.4.0/SOMClassification.xml | 155 ---- .../otb/description/5.4.0/Segmentation-cc.xml | 165 ---- .../5.4.0/Segmentation-meanshift.xml | 205 ----- .../5.4.0/Segmentation-mprofiles.xml | 195 ----- .../5.4.0/Segmentation-watershed.xml | 175 ---- .../description/5.4.0/Smoothing-anidif.xml | 74 -- .../description/5.4.0/Smoothing-gaussian.xml | 54 -- .../otb/description/5.4.0/Smoothing-mean.xml | 54 -- .../otb/description/5.4.0/StereoFramework.xml | 344 -------- .../otb/description/5.4.0/Superimpose.xml | 97 --- .../algs/otb/description/5.4.0/TileFusion.xml | 42 - .../5.4.0/TrainImagesClassifier-ann.xml | 268 ------ .../5.4.0/TrainImagesClassifier-bayes.xml | 134 --- .../5.4.0/TrainImagesClassifier-boost.xml | 180 ----- .../5.4.0/TrainImagesClassifier-dt.xml | 200 ----- .../5.4.0/TrainImagesClassifier-gbt.xml | 174 ---- .../5.4.0/TrainImagesClassifier-knn.xml | 144 ---- .../5.4.0/TrainImagesClassifier-rf.xml | 204 ----- .../5.4.0/TrainOGRLayersClassifier.xml | 48 -- .../5.4.0/VectorDataExtractROI.xml | 40 - .../5.4.0/VectorDataReprojection-image.xml | 59 -- .../5.4.0/VectorDataReprojection-user.xml | 97 --- .../description/5.4.0/VectorDataTransform.xml | 90 --- .../otb/description/5.4.0/doc/BandMath.html | 6 - .../BinaryMorphologicalOperation-closing.html | 5 - .../BinaryMorphologicalOperation-dilate.html | 5 - .../BinaryMorphologicalOperation-erode.html | 5 - .../BinaryMorphologicalOperation-opening.html | 5 - .../doc/BinaryMorphologicalOperation.html | 5 - .../description/5.4.0/doc/BlockMatching.html | 5 - .../5.4.0/doc/BundleToPerfectSensor.html | 5 - .../doc/ClassificationMapRegularization.html | 7 - .../5.4.0/doc/ColorMapping-continuous.html | 13 - .../5.4.0/doc/ColorMapping-custom.html | 13 - .../5.4.0/doc/ColorMapping-image.html | 13 - .../5.4.0/doc/ColorMapping-optimal.html | 13 - .../description/5.4.0/doc/ColorMapping.html | 13 - .../description/5.4.0/doc/CompareImages.html | 5 - .../doc/ComputeConfusionMatrix-raster.html | 5 - .../doc/ComputeConfusionMatrix-vector.html | 5 - .../5.4.0/doc/ComputeConfusionMatrix.html | 5 - .../5.4.0/doc/ComputeImagesStatistics.html | 5 - .../ComputeOGRLayersFeaturesStatistics.html | 5 - .../doc/ComputePolylineFeatureFromImage.html | 5 - .../5.4.0/doc/ConcatenateImages.html | 5 - .../5.4.0/doc/ConcatenateVectorData.html | 5 - .../doc/ConnectedComponentSegmentation.html | 5 - .../otb/description/5.4.0/doc/Convert.html | 6 - .../5.4.0/doc/ConvertCartoToGeoPoint.html | 5 - .../5.4.0/doc/ConvertSensorToGeoPoint.html | 5 - .../otb/description/5.4.0/doc/DEMConvert.html | 5 - .../5.4.0/doc/DSFuzzyModelEstimation.html | 5 - .../5.4.0/doc/Despeckle-frost.html | 5 - .../5.4.0/doc/Despeckle-gammamap.html | 5 - .../description/5.4.0/doc/Despeckle-kuan.html | 5 - .../description/5.4.0/doc/Despeckle-lee.html | 5 - .../otb/description/5.4.0/doc/Despeckle.html | 5 - .../doc/DimensionalityReduction-ica.html | 5 - .../doc/DimensionalityReduction-maf.html | 5 - .../doc/DimensionalityReduction-napca.html | 5 - .../doc/DimensionalityReduction-pca.html | 5 - .../5.4.0/doc/DimensionalityReduction.html | 5 - .../5.4.0/doc/DisparityMapToElevationMap.html | 5 - .../5.4.0/doc/DownloadSRTMTiles.html | 5 - .../5.4.0/doc/EdgeExtraction-gradient.html | 5 - .../5.4.0/doc/EdgeExtraction-sobel.html | 5 - .../5.4.0/doc/EdgeExtraction-touzi.html | 5 - .../description/5.4.0/doc/EdgeExtraction.html | 5 - .../description/5.4.0/doc/ExtractROI-fit.html | 5 - .../5.4.0/doc/ExtractROI-standard.html | 5 - .../otb/description/5.4.0/doc/ExtractROI.html | 5 - .../5.4.0/doc/FineRegistration.html | 5 - ...usionOfClassifications-dempstershafer.html | 9 - ...usionOfClassifications-majorityvoting.html | 9 - .../5.4.0/doc/FusionOfClassifications.html | 9 - .../5.4.0/doc/GeneratePlyFile.html | 5 - .../5.4.0/doc/GenerateRPCSensorModel.html | 5 - ...ayScaleMorphologicalOperation-closing.html | 5 - ...rayScaleMorphologicalOperation-dilate.html | 5 - ...GrayScaleMorphologicalOperation-erode.html | 5 - ...ayScaleMorphologicalOperation-opening.html | 5 - .../doc/GrayScaleMorphologicalOperation.html | 5 - .../5.4.0/doc/GridBasedImageResampling.html | 5 - .../5.4.0/doc/HaralickTextureExtraction.html | 5 - .../5.4.0/doc/HomologousPointsExtraction.html | 5 - .../5.4.0/doc/HooverCompareSegmentation.html | 7 - .../5.4.0/doc/HyperspectralUnmixing.html | 8 - .../5.4.0/doc/ImageClassifier.html | 16 - .../description/5.4.0/doc/ImageEnvelope.html | 5 - .../5.4.0/doc/KMeansClassification.html | 5 - .../otb/description/5.4.0/doc/KmzExport.html | 5 - .../5.4.0/doc/LSMSSegmentation.html | 5 - .../5.4.0/doc/LSMSSmallRegionsMerging.html | 5 - .../5.4.0/doc/LSMSVectorization.html | 5 - .../5.4.0/doc/LineSegmentDetection.html | 7 - .../5.4.0/doc/LocalStatisticExtraction.html | 5 - .../description/5.4.0/doc/ManageNoData.html | 5 - .../5.4.0/doc/MeanShiftSmoothing.html | 5 - .../5.4.0/doc/MultiResolutionPyramid.html | 5 - .../doc/MultivariateAlterationDetector.html | 21 - .../5.4.0/doc/OGRLayerClassifier.html | 5 - .../description/5.4.0/doc/OSMDownloader.html | 6 - .../5.4.0/doc/ObtainUTMZoneFromGeoPoint.html | 5 - .../5.4.0/doc/OrthoRectification-epsg.html | 7 - .../doc/OrthoRectification-fit-to-ortho.html | 7 - .../doc/OrthoRectification-lambert-WGS84.html | 7 - .../5.4.0/doc/OrthoRectification-utm.html | 7 - .../5.4.0/doc/OrthoRectification.html | 7 - .../5.4.0/doc/Pansharpening-bayes.html | 5 - .../5.4.0/doc/Pansharpening-lmvm.html | 5 - .../5.4.0/doc/Pansharpening-rcs.html | 5 - .../description/5.4.0/doc/Pansharpening.html | 5 - .../otb/description/5.4.0/doc/PixelValue.html | 6 - .../5.4.0/doc/PolygonClassStatistics.html | 12 - .../5.4.0/doc/PredictRegression.html | 5 - .../otb/description/5.4.0/doc/Quicklook.html | 7 - .../5.4.0/doc/RadiometricIndices.html | 25 - .../5.4.0/doc/Rasterization-image.html | 6 - .../5.4.0/doc/Rasterization-manual.html | 6 - .../description/5.4.0/doc/Rasterization.html | 6 - .../description/5.4.0/doc/ReadImageInfo.html | 5 - .../5.4.0/doc/RefineSensorModel.html | 5 - .../otb/description/5.4.0/doc/Rescale.html | 5 - .../5.4.0/doc/RigidTransformResample-id.html | 5 - .../doc/RigidTransformResample-rotation.html | 5 - .../RigidTransformResample-translation.html | 5 - .../5.4.0/doc/RigidTransformResample.html | 5 - .../description/5.4.0/doc/SARCalibration.html | 7 - .../5.4.0/doc/SARDecompositions.html | 15 - .../5.4.0/doc/SARPolarMatrixConvert.html | 32 - .../description/5.4.0/doc/SARPolarSynth.html | 32 - .../5.4.0/doc/SFSTextureExtraction.html | 5 - .../5.4.0/doc/SOMClassification.html | 5 - .../5.4.0/doc/SarRadiometricCalibration.html | 7 - .../5.4.0/doc/Segmentation-cc.html | 11 - .../5.4.0/doc/Segmentation-meanshift.html | 11 - .../5.4.0/doc/Segmentation-mprofiles.html | 11 - .../5.4.0/doc/Segmentation-watershed.html | 11 - .../description/5.4.0/doc/Segmentation.html | 11 - .../5.4.0/doc/Smoothing-anidif.html | 5 - .../5.4.0/doc/Smoothing-gaussian.html | 5 - .../description/5.4.0/doc/Smoothing-mean.html | 5 - .../otb/description/5.4.0/doc/Smoothing.html | 5 - .../otb/description/5.4.0/doc/SplitImage.html | 5 - .../5.4.0/doc/StereoFramework.html | 16 - .../doc/StereoRectificationGridGenerator.html | 5 - .../description/5.4.0/doc/Superimpose.html | 5 - .../5.4.0/doc/TestApplication.html | 5 - .../otb/description/5.4.0/doc/TileFusion.html | 5 - .../5.4.0/doc/TrainImagesClassifier-ann.html | 8 - .../doc/TrainImagesClassifier-bayes.html | 8 - .../doc/TrainImagesClassifier-boost.html | 8 - .../5.4.0/doc/TrainImagesClassifier-dt.html | 8 - .../5.4.0/doc/TrainImagesClassifier-gbt.html | 8 - .../5.4.0/doc/TrainImagesClassifier-knn.html | 8 - .../5.4.0/doc/TrainImagesClassifier-rf.html | 8 - .../5.4.0/doc/TrainImagesClassifier.html | 8 - .../5.4.0/doc/TrainOGRLayersClassifier.html | 5 - .../5.4.0/doc/TrainRegression.html | 7 - .../5.4.0/doc/VectorDataDSValidation.html | 5 - .../5.4.0/doc/VectorDataExtractROI.html | 5 - .../doc/VectorDataReprojection-image.html | 7 - .../doc/VectorDataReprojection-user.html | 7 - .../5.4.0/doc/VectorDataReprojection.html | 7 - .../5.4.0/doc/VectorDataSetField.html | 5 - .../5.4.0/doc/VectorDataTransform.html | 5 - .../5.4.0/doc/VertexComponentAnalysis.html | 5 - .../algs/otb/description/5.6.0/BandMath.xml | 42 - .../BinaryMorphologicalOperation-closing.xml | 77 -- .../BinaryMorphologicalOperation-dilate.xml | 97 --- .../BinaryMorphologicalOperation-erode.xml | 77 -- .../BinaryMorphologicalOperation-opening.xml | 77 -- .../5.6.0/ClassificationMapRegularization.xml | 69 -- .../5.6.0/ColorMapping-continuous.xml | 104 --- .../description/5.6.0/ColorMapping-custom.xml | 68 -- .../description/5.6.0/ColorMapping-image.xml | 94 --- .../5.6.0/ColorMapping-optimal.xml | 67 -- .../otb/description/5.6.0/CompareImages.xml | 91 --- .../5.6.0/ComputeConfusionMatrix-raster.xml | 60 -- .../5.6.0/ComputeConfusionMatrix-vector.xml | 70 -- .../5.6.0/ComputeImagesStatistics.xml | 31 - .../ComputeOGRLayersFeaturesStatistics.xml | 30 - .../5.6.0/ComputePolylineFeatureFromImage.xml | 57 -- .../description/5.6.0/ConcatenateImages.xml | 32 - .../5.6.0/ConcatenateVectorData.xml | 22 - .../5.6.0/ConnectedComponentSegmentation.xml | 68 -- .../algs/otb/description/5.6.0/Convert.xml | 83 -- .../algs/otb/description/5.6.0/DEMConvert.xml | 20 - .../otb/description/5.6.0/Despeckle-frost.xml | 64 -- .../description/5.6.0/Despeckle-gammamap.xml | 64 -- .../otb/description/5.6.0/Despeckle-kuan.xml | 64 -- .../otb/description/5.6.0/Despeckle-lee.xml | 64 -- .../5.6.0/DimensionalityReduction-ica.xml | 85 -- .../5.6.0/DimensionalityReduction-maf.xml | 58 -- .../5.6.0/DimensionalityReduction-napca.xml | 85 -- .../5.6.0/DimensionalityReduction-pca.xml | 65 -- .../5.6.0/EdgeExtraction-gradient.xml | 54 -- .../5.6.0/EdgeExtraction-sobel.xml | 54 -- .../5.6.0/EdgeExtraction-touzi.xml | 64 -- .../otb/description/5.6.0/ExtractROI-fit.xml | 61 -- .../description/5.6.0/ExtractROI-standard.xml | 84 -- ...FusionOfClassifications-dempstershafer.xml | 79 -- ...FusionOfClassifications-majorityvoting.xml | 55 -- ...rayScaleMorphologicalOperation-closing.xml | 77 -- ...GrayScaleMorphologicalOperation-dilate.xml | 77 -- .../GrayScaleMorphologicalOperation-erode.xml | 77 -- ...rayScaleMorphologicalOperation-opening.xml | 77 -- .../5.6.0/HaralickTextureExtraction.xml | 126 --- .../5.6.0/HooverCompareSegmentation.xml | 95 --- .../otb/description/5.6.0/ImageClassifier.xml | 72 -- .../otb/description/5.6.0/ImageEnvelope.xml | 40 - .../5.6.0/KMeansClassification.xml | 84 -- .../algs/otb/description/5.6.0/KmzExport.xml | 54 -- .../description/5.6.0/LSMSSegmentation.xml | 94 --- .../5.6.0/LSMSSmallRegionsMerging.xml | 58 -- .../description/5.6.0/LSMSVectorization.xml | 47 -- .../5.6.0/LineSegmentDetection.xml | 29 - .../5.6.0/LocalStatisticExtraction.xml | 51 -- .../otb/description/5.6.0/ManageNoData.xml | 101 --- .../description/5.6.0/MeanShiftSmoothing.xml | 96 --- .../5.6.0/MultivariateAlterationDetector.xml | 38 - .../description/5.6.0/OGRLayerClassifier.xml | 47 -- .../description/5.6.0/OpticalCalibration.xml | 180 ----- .../5.6.0/OrthoRectification-epsg.xml | 124 --- .../5.6.0/OrthoRectification-fit-to-ortho.xml | 107 --- .../OrthoRectification-lambert-WGS84.xml | 116 --- .../5.6.0/OrthoRectification-utm.xml | 132 --- .../description/5.6.0/Pansharpening-bayes.xml | 71 -- .../description/5.6.0/Pansharpening-lmvm.xml | 71 -- .../description/5.6.0/Pansharpening-rcs.xml | 51 -- .../5.6.0/PolygonClassStatistics.xml | 64 -- .../description/5.6.0/PredictRegression.xml | 54 -- .../description/5.6.0/RadiometricIndices.xml | 131 --- .../description/5.6.0/Rasterization-image.xml | 82 -- .../5.6.0/Rasterization-manual.xml | 145 ---- .../otb/description/5.6.0/ReadImageInfo.xml | 58 -- .../algs/otb/description/5.6.0/Rescale.xml | 51 -- .../5.6.0/RigidTransformResample-id.xml | 89 -- .../5.6.0/RigidTransformResample-rotation.xml | 99 --- .../RigidTransformResample-translation.xml | 109 --- .../otb/description/5.6.0/SARCalibration.xml | 56 -- .../description/5.6.0/SARDecompositions.xml | 78 -- .../otb/description/5.6.0/SARPolarSynth.xml | 106 --- .../5.6.0/SFSTextureExtraction.xml | 91 --- .../description/5.6.0/SOMClassification.xml | 155 ---- .../description/5.6.0/SampleExtraction.xml | 89 -- .../otb/description/5.6.0/SampleSelection.xml | 146 ---- .../otb/description/5.6.0/Segmentation-cc.xml | 161 ---- .../5.6.0/Segmentation-meanshift.xml | 202 ----- .../5.6.0/Segmentation-mprofiles.xml | 192 ----- .../5.6.0/Segmentation-watershed.xml | 172 ---- .../description/5.6.0/Smoothing-anidif.xml | 74 -- .../description/5.6.0/Smoothing-gaussian.xml | 54 -- .../otb/description/5.6.0/Smoothing-mean.xml | 54 -- .../otb/description/5.6.0/StereoFramework.xml | 343 -------- .../otb/description/5.6.0/Superimpose.xml | 97 --- .../algs/otb/description/5.6.0/TileFusion.xml | 42 - .../5.6.0/TrainImagesClassifier-ann.xml | 266 ------ .../5.6.0/TrainImagesClassifier-bayes.xml | 133 --- .../5.6.0/TrainImagesClassifier-boost.xml | 179 ---- .../5.6.0/TrainImagesClassifier-dt.xml | 199 ----- .../5.6.0/TrainImagesClassifier-gbt.xml | 173 ---- .../5.6.0/TrainImagesClassifier-knn.xml | 143 ---- .../5.6.0/TrainImagesClassifier-libsvm.xml | 190 ----- .../5.6.0/TrainImagesClassifier-rf.xml | 203 ----- .../5.6.0/TrainOGRLayersClassifier.xml | 47 -- .../description/5.6.0/TrainRegression-ann.xml | 233 ------ .../description/5.6.0/TrainRegression-dt.xml | 166 ---- .../description/5.6.0/TrainRegression-gbt.xml | 155 ---- .../description/5.6.0/TrainRegression-knn.xml | 124 --- .../5.6.0/TrainRegression-libsvm.xml | 176 ---- .../description/5.6.0/TrainRegression-rf.xml | 170 ---- .../5.6.0/TrainVectorClassifier-ann.xml | 237 ------ .../5.6.0/TrainVectorClassifier-bayes.xml | 104 --- .../5.6.0/TrainVectorClassifier-boost.xml | 150 ---- .../5.6.0/TrainVectorClassifier-dt.xml | 170 ---- .../5.6.0/TrainVectorClassifier-gbt.xml | 144 ---- .../5.6.0/TrainVectorClassifier-knn.xml | 114 --- .../5.6.0/TrainVectorClassifier-libsvm.xml | 161 ---- .../5.6.0/TrainVectorClassifier-rf.xml | 174 ---- .../5.6.0/VectorDataExtractROI.xml | 39 - .../5.6.0/VectorDataReprojection-image.xml | 59 -- .../5.6.0/VectorDataReprojection-user.xml | 97 --- .../description/5.6.0/VectorDataTransform.xml | 89 -- .../5.6.0/doc/ApplicationExample.html | 5 - .../otb/description/5.6.0/doc/BandMath.html | 10 - .../BinaryMorphologicalOperation-closing.html | 5 - .../BinaryMorphologicalOperation-dilate.html | 5 - .../BinaryMorphologicalOperation-erode.html | 5 - .../BinaryMorphologicalOperation-opening.html | 5 - .../doc/BinaryMorphologicalOperation.html | 5 - .../description/5.6.0/doc/BlockMatching.html | 5 - .../5.6.0/doc/BundleToPerfectSensor.html | 5 - .../doc/ClassificationMapRegularization.html | 7 - .../5.6.0/doc/ColorMapping-continuous.html | 13 - .../5.6.0/doc/ColorMapping-custom.html | 13 - .../5.6.0/doc/ColorMapping-image.html | 13 - .../5.6.0/doc/ColorMapping-optimal.html | 13 - .../description/5.6.0/doc/ColorMapping.html | 13 - .../description/5.6.0/doc/CompareImages.html | 5 - .../doc/ComputeConfusionMatrix-raster.html | 5 - .../doc/ComputeConfusionMatrix-vector.html | 5 - .../5.6.0/doc/ComputeConfusionMatrix.html | 5 - .../5.6.0/doc/ComputeImagesStatistics.html | 5 - .../ComputeOGRLayersFeaturesStatistics.html | 5 - .../doc/ComputePolylineFeatureFromImage.html | 5 - .../5.6.0/doc/ConcatenateImages.html | 5 - .../5.6.0/doc/ConcatenateVectorData.html | 5 - .../doc/ConnectedComponentSegmentation.html | 5 - .../otb/description/5.6.0/doc/Convert.html | 6 - .../5.6.0/doc/ConvertCartoToGeoPoint.html | 5 - .../5.6.0/doc/ConvertSensorToGeoPoint.html | 5 - .../otb/description/5.6.0/doc/DEMConvert.html | 5 - .../5.6.0/doc/DSFuzzyModelEstimation.html | 5 - .../5.6.0/doc/Despeckle-frost.html | 5 - .../5.6.0/doc/Despeckle-gammamap.html | 5 - .../description/5.6.0/doc/Despeckle-kuan.html | 5 - .../description/5.6.0/doc/Despeckle-lee.html | 5 - .../otb/description/5.6.0/doc/Despeckle.html | 5 - .../doc/DimensionalityReduction-ica.html | 5 - .../doc/DimensionalityReduction-maf.html | 5 - .../doc/DimensionalityReduction-napca.html | 5 - .../doc/DimensionalityReduction-pca.html | 5 - .../5.6.0/doc/DimensionalityReduction.html | 5 - .../5.6.0/doc/DisparityMapToElevationMap.html | 5 - .../5.6.0/doc/DownloadSRTMTiles.html | 5 - .../5.6.0/doc/EdgeExtraction-gradient.html | 5 - .../5.6.0/doc/EdgeExtraction-sobel.html | 5 - .../5.6.0/doc/EdgeExtraction-touzi.html | 5 - .../description/5.6.0/doc/EdgeExtraction.html | 5 - .../description/5.6.0/doc/ExtractROI-fit.html | 5 - .../5.6.0/doc/ExtractROI-standard.html | 5 - .../otb/description/5.6.0/doc/ExtractROI.html | 5 - .../5.6.0/doc/FineRegistration.html | 5 - ...usionOfClassifications-dempstershafer.html | 9 - ...usionOfClassifications-majorityvoting.html | 9 - .../5.6.0/doc/FusionOfClassifications.html | 9 - .../5.6.0/doc/GeneratePlyFile.html | 5 - .../5.6.0/doc/GenerateRPCSensorModel.html | 5 - ...ayScaleMorphologicalOperation-closing.html | 5 - ...rayScaleMorphologicalOperation-dilate.html | 5 - ...GrayScaleMorphologicalOperation-erode.html | 5 - ...ayScaleMorphologicalOperation-opening.html | 5 - .../doc/GrayScaleMorphologicalOperation.html | 5 - .../5.6.0/doc/GridBasedImageResampling.html | 5 - .../5.6.0/doc/HaralickTextureExtraction.html | 5 - .../5.6.0/doc/HomologousPointsExtraction.html | 5 - .../5.6.0/doc/HooverCompareSegmentation.html | 7 - .../5.6.0/doc/HyperspectralUnmixing.html | 8 - .../5.6.0/doc/ImageClassifier.html | 16 - .../description/5.6.0/doc/ImageEnvelope.html | 5 - .../5.6.0/doc/KMeansClassification.html | 5 - .../otb/description/5.6.0/doc/KmzExport.html | 5 - .../5.6.0/doc/LSMSSegmentation.html | 5 - .../5.6.0/doc/LSMSSmallRegionsMerging.html | 5 - .../5.6.0/doc/LSMSVectorization.html | 5 - .../5.6.0/doc/LineSegmentDetection.html | 7 - .../5.6.0/doc/LocalStatisticExtraction.html | 5 - .../description/5.6.0/doc/ManageNoData.html | 5 - .../5.6.0/doc/MeanShiftSmoothing.html | 5 - .../5.6.0/doc/MultiResolutionPyramid.html | 5 - .../doc/MultivariateAlterationDetector.html | 21 - .../5.6.0/doc/OGRLayerClassifier.html | 5 - .../description/5.6.0/doc/OSMDownloader.html | 6 - .../5.6.0/doc/ObtainUTMZoneFromGeoPoint.html | 5 - .../5.6.0/doc/OpticalCalibration.html | 60 -- .../5.6.0/doc/OrthoRectification-epsg.html | 7 - .../doc/OrthoRectification-fit-to-ortho.html | 7 - .../doc/OrthoRectification-lambert-WGS84.html | 7 - .../5.6.0/doc/OrthoRectification-utm.html | 7 - .../5.6.0/doc/OrthoRectification.html | 7 - .../5.6.0/doc/Pansharpening-bayes.html | 5 - .../5.6.0/doc/Pansharpening-lmvm.html | 5 - .../5.6.0/doc/Pansharpening-rcs.html | 5 - .../description/5.6.0/doc/Pansharpening.html | 5 - .../otb/description/5.6.0/doc/PixelValue.html | 6 - .../5.6.0/doc/PolygonClassStatistics.html | 12 - .../5.6.0/doc/PredictRegression.html | 5 - .../otb/description/5.6.0/doc/Quicklook.html | 7 - .../5.6.0/doc/RadiometricIndices.html | 25 - .../5.6.0/doc/Rasterization-image.html | 6 - .../5.6.0/doc/Rasterization-manual.html | 6 - .../description/5.6.0/doc/Rasterization.html | 6 - .../description/5.6.0/doc/ReadImageInfo.html | 5 - .../5.6.0/doc/RefineSensorModel.html | 5 - .../otb/description/5.6.0/doc/Rescale.html | 5 - .../5.6.0/doc/RigidTransformResample-id.html | 5 - .../doc/RigidTransformResample-rotation.html | 5 - .../RigidTransformResample-translation.html | 5 - .../5.6.0/doc/RigidTransformResample.html | 5 - .../description/5.6.0/doc/SARCalibration.html | 7 - .../5.6.0/doc/SARDecompositions.html | 15 - .../5.6.0/doc/SARPolarMatrixConvert.html | 32 - .../description/5.6.0/doc/SARPolarSynth.html | 32 - .../5.6.0/doc/SFSTextureExtraction.html | 5 - .../5.6.0/doc/SOMClassification.html | 5 - .../5.6.0/doc/SampleExtraction.html | 5 - .../5.6.0/doc/SampleSelection.html | 34 - .../5.6.0/doc/SarRadiometricCalibration.html | 7 - .../5.6.0/doc/Segmentation-cc.html | 11 - .../5.6.0/doc/Segmentation-meanshift.html | 11 - .../5.6.0/doc/Segmentation-mprofiles.html | 11 - .../5.6.0/doc/Segmentation-watershed.html | 11 - .../description/5.6.0/doc/Segmentation.html | 11 - .../5.6.0/doc/Smoothing-anidif.html | 5 - .../5.6.0/doc/Smoothing-gaussian.html | 5 - .../description/5.6.0/doc/Smoothing-mean.html | 5 - .../otb/description/5.6.0/doc/Smoothing.html | 5 - .../otb/description/5.6.0/doc/SplitImage.html | 5 - .../5.6.0/doc/StereoFramework.html | 16 - .../doc/StereoRectificationGridGenerator.html | 5 - .../description/5.6.0/doc/Superimpose.html | 5 - .../5.6.0/doc/TestApplication.html | 5 - .../otb/description/5.6.0/doc/TileFusion.html | 5 - .../5.6.0/doc/TrainImagesClassifier-ann.html | 8 - .../doc/TrainImagesClassifier-bayes.html | 8 - .../doc/TrainImagesClassifier-boost.html | 8 - .../5.6.0/doc/TrainImagesClassifier-dt.html | 8 - .../5.6.0/doc/TrainImagesClassifier-gbt.html | 8 - .../5.6.0/doc/TrainImagesClassifier-knn.html | 8 - .../doc/TrainImagesClassifier-libsvm.html | 8 - .../5.6.0/doc/TrainImagesClassifier-rf.html | 8 - .../5.6.0/doc/TrainImagesClassifier.html | 8 - .../5.6.0/doc/TrainOGRLayersClassifier.html | 5 - .../5.6.0/doc/TrainRegression-ann.html | 7 - .../5.6.0/doc/TrainRegression-dt.html | 7 - .../5.6.0/doc/TrainRegression-gbt.html | 7 - .../5.6.0/doc/TrainRegression-knn.html | 7 - .../5.6.0/doc/TrainRegression-libsvm.html | 7 - .../5.6.0/doc/TrainRegression-rf.html | 7 - .../5.6.0/doc/TrainRegression.html | 7 - .../5.6.0/doc/TrainVectorClassifier-ann.html | 5 - .../doc/TrainVectorClassifier-bayes.html | 5 - .../doc/TrainVectorClassifier-boost.html | 5 - .../5.6.0/doc/TrainVectorClassifier-dt.html | 5 - .../5.6.0/doc/TrainVectorClassifier-gbt.html | 5 - .../5.6.0/doc/TrainVectorClassifier-knn.html | 5 - .../doc/TrainVectorClassifier-libsvm.html | 5 - .../5.6.0/doc/TrainVectorClassifier-rf.html | 5 - .../5.6.0/doc/TrainVectorClassifier.html | 5 - .../5.6.0/doc/VectorDataDSValidation.html | 5 - .../5.6.0/doc/VectorDataExtractROI.html | 5 - .../doc/VectorDataReprojection-image.html | 7 - .../doc/VectorDataReprojection-user.html | 7 - .../5.6.0/doc/VectorDataReprojection.html | 7 - .../5.6.0/doc/VectorDataSetField.html | 5 - .../5.6.0/doc/VectorDataTransform.html | 5 - .../5.6.0/doc/VertexComponentAnalysis.html | 5 - .../algs/otb/description/5.8.0/BandMath.xml | 42 - .../algs/otb/description/5.8.0/BandMathX.xml | 56 -- .../BinaryMorphologicalOperation-closing.xml | 77 -- .../BinaryMorphologicalOperation-dilate.xml | 97 --- .../BinaryMorphologicalOperation-erode.xml | 77 -- .../BinaryMorphologicalOperation-opening.xml | 77 -- .../5.8.0/ClassificationMapRegularization.xml | 87 -- .../5.8.0/ColorMapping-continuous.xml | 104 --- .../description/5.8.0/ColorMapping-custom.xml | 68 -- .../description/5.8.0/ColorMapping-image.xml | 94 --- .../5.8.0/ColorMapping-optimal.xml | 67 -- .../otb/description/5.8.0/CompareImages.xml | 101 --- .../5.8.0/ComputeConfusionMatrix-raster.xml | 60 -- .../5.8.0/ComputeConfusionMatrix-vector.xml | 70 -- .../5.8.0/ComputeImagesStatistics.xml | 41 - .../ComputeOGRLayersFeaturesStatistics.xml | 30 - .../5.8.0/ComputePolylineFeatureFromImage.xml | 57 -- .../description/5.8.0/ConcatenateImages.xml | 32 - .../5.8.0/ConcatenateVectorData.xml | 22 - .../5.8.0/ConnectedComponentSegmentation.xml | 78 -- .../algs/otb/description/5.8.0/Convert.xml | 83 -- .../algs/otb/description/5.8.0/DEMConvert.xml | 20 - .../otb/description/5.8.0/Despeckle-frost.xml | 64 -- .../description/5.8.0/Despeckle-gammamap.xml | 64 -- .../otb/description/5.8.0/Despeckle-kuan.xml | 64 -- .../otb/description/5.8.0/Despeckle-lee.xml | 64 -- .../5.8.0/DimensionalityReduction-ica.xml | 95 --- .../5.8.0/DimensionalityReduction-maf.xml | 68 -- .../5.8.0/DimensionalityReduction-napca.xml | 95 --- .../5.8.0/DimensionalityReduction-pca.xml | 75 -- .../5.8.0/EdgeExtraction-gradient.xml | 54 -- .../5.8.0/EdgeExtraction-sobel.xml | 54 -- .../5.8.0/EdgeExtraction-touzi.xml | 64 -- .../otb/description/5.8.0/ExtractROI-fit.xml | 61 -- .../description/5.8.0/ExtractROI-standard.xml | 84 -- ...FusionOfClassifications-dempstershafer.xml | 79 -- ...FusionOfClassifications-majorityvoting.xml | 55 -- ...rayScaleMorphologicalOperation-closing.xml | 77 -- ...GrayScaleMorphologicalOperation-dilate.xml | 77 -- .../GrayScaleMorphologicalOperation-erode.xml | 77 -- ...rayScaleMorphologicalOperation-opening.xml | 77 -- .../5.8.0/HaralickTextureExtraction.xml | 126 --- .../5.8.0/HooverCompareSegmentation.xml | 95 --- .../otb/description/5.8.0/ImageClassifier.xml | 72 -- .../otb/description/5.8.0/ImageEnvelope.xml | 40 - .../5.8.0/KMeansClassification.xml | 84 -- .../algs/otb/description/5.8.0/KmzExport.xml | 54 -- .../description/5.8.0/LSMSSegmentation.xml | 94 --- .../5.8.0/LSMSSmallRegionsMerging.xml | 68 -- .../description/5.8.0/LSMSVectorization.xml | 57 -- .../5.8.0/LineSegmentDetection.xml | 39 - .../5.8.0/LocalStatisticExtraction.xml | 51 -- .../otb/description/5.8.0/ManageNoData.xml | 101 --- .../description/5.8.0/MeanShiftSmoothing.xml | 96 --- .../5.8.0/MultiImageSamplingRate.xml | 89 -- .../5.8.0/MultivariateAlterationDetector.xml | 38 - .../description/5.8.0/OGRLayerClassifier.xml | 47 -- .../description/5.8.0/OpticalCalibration.xml | 180 ----- .../5.8.0/OrthoRectification-epsg.xml | 124 --- .../5.8.0/OrthoRectification-fit-to-ortho.xml | 107 --- .../OrthoRectification-lambert-WGS84.xml | 116 --- .../5.8.0/OrthoRectification-utm.xml | 132 --- .../description/5.8.0/Pansharpening-bayes.xml | 71 -- .../description/5.8.0/Pansharpening-lmvm.xml | 71 -- .../description/5.8.0/Pansharpening-rcs.xml | 51 -- .../5.8.0/PolygonClassStatistics.xml | 64 -- .../description/5.8.0/PredictRegression.xml | 54 -- .../description/5.8.0/RadiometricIndices.xml | 131 --- .../description/5.8.0/Rasterization-image.xml | 82 -- .../5.8.0/Rasterization-manual.xml | 145 ---- .../otb/description/5.8.0/ReadImageInfo.xml | 58 -- .../algs/otb/description/5.8.0/Rescale.xml | 51 -- .../5.8.0/RigidTransformResample-id.xml | 89 -- .../5.8.0/RigidTransformResample-rotation.xml | 99 --- .../RigidTransformResample-translation.xml | 109 --- .../otb/description/5.8.0/SARCalibration.xml | 56 -- .../description/5.8.0/SARDecompositions.xml | 78 -- .../otb/description/5.8.0/SARPolarSynth.xml | 106 --- .../5.8.0/SFSTextureExtraction.xml | 91 --- .../description/5.8.0/SOMClassification.xml | 155 ---- .../description/5.8.0/SampleExtraction.xml | 89 -- .../otb/description/5.8.0/SampleSelection.xml | 168 ---- .../otb/description/5.8.0/Segmentation-cc.xml | 161 ---- .../5.8.0/Segmentation-meanshift.xml | 202 ----- .../5.8.0/Segmentation-mprofiles.xml | 192 ----- .../5.8.0/Segmentation-watershed.xml | 172 ---- .../description/5.8.0/Smoothing-anidif.xml | 74 -- .../description/5.8.0/Smoothing-gaussian.xml | 54 -- .../otb/description/5.8.0/Smoothing-mean.xml | 54 -- .../otb/description/5.8.0/StereoFramework.xml | 343 -------- .../otb/description/5.8.0/Superimpose.xml | 97 --- .../algs/otb/description/5.8.0/TileFusion.xml | 42 - .../5.8.0/TrainImagesClassifier-ann.xml | 266 ------ .../5.8.0/TrainImagesClassifier-bayes.xml | 133 --- .../5.8.0/TrainImagesClassifier-boost.xml | 179 ---- .../5.8.0/TrainImagesClassifier-dt.xml | 199 ----- .../5.8.0/TrainImagesClassifier-gbt.xml | 173 ---- .../5.8.0/TrainImagesClassifier-knn.xml | 143 ---- .../5.8.0/TrainImagesClassifier-libsvm.xml | 190 ----- .../5.8.0/TrainImagesClassifier-rf.xml | 203 ----- .../5.8.0/TrainOGRLayersClassifier.xml | 47 -- .../description/5.8.0/TrainRegression-ann.xml | 233 ------ .../description/5.8.0/TrainRegression-dt.xml | 166 ---- .../description/5.8.0/TrainRegression-gbt.xml | 155 ---- .../description/5.8.0/TrainRegression-knn.xml | 124 --- .../5.8.0/TrainRegression-libsvm.xml | 176 ---- .../description/5.8.0/TrainRegression-rf.xml | 170 ---- .../5.8.0/TrainVectorClassifier-ann.xml | 237 ------ .../5.8.0/TrainVectorClassifier-bayes.xml | 104 --- .../5.8.0/TrainVectorClassifier-boost.xml | 150 ---- .../5.8.0/TrainVectorClassifier-dt.xml | 170 ---- .../5.8.0/TrainVectorClassifier-gbt.xml | 144 ---- .../5.8.0/TrainVectorClassifier-knn.xml | 114 --- .../5.8.0/TrainVectorClassifier-libsvm.xml | 161 ---- .../5.8.0/TrainVectorClassifier-rf.xml | 174 ---- .../5.8.0/VectorDataExtractROI.xml | 39 - .../5.8.0/VectorDataReprojection-image.xml | 59 -- .../5.8.0/VectorDataReprojection-user.xml | 97 --- .../description/5.8.0/VectorDataTransform.xml | 89 -- .../otb/description/5.8.0/doc/BandMath.html | 10 - .../otb/description/5.8.0/doc/BandMathX.html | 98 --- .../BinaryMorphologicalOperation-closing.html | 5 - .../BinaryMorphologicalOperation-dilate.html | 5 - .../BinaryMorphologicalOperation-erode.html | 5 - .../BinaryMorphologicalOperation-opening.html | 5 - .../doc/BinaryMorphologicalOperation.html | 5 - .../description/5.8.0/doc/BlockMatching.html | 5 - .../5.8.0/doc/BundleToPerfectSensor.html | 5 - .../doc/ClassificationMapRegularization.html | 7 - .../5.8.0/doc/ColorMapping-continuous.html | 13 - .../5.8.0/doc/ColorMapping-custom.html | 13 - .../5.8.0/doc/ColorMapping-image.html | 13 - .../5.8.0/doc/ColorMapping-optimal.html | 13 - .../description/5.8.0/doc/ColorMapping.html | 13 - .../description/5.8.0/doc/CompareImages.html | 5 - .../doc/ComputeConfusionMatrix-raster.html | 5 - .../doc/ComputeConfusionMatrix-vector.html | 5 - .../5.8.0/doc/ComputeConfusionMatrix.html | 5 - .../5.8.0/doc/ComputeImagesStatistics.html | 5 - .../ComputeOGRLayersFeaturesStatistics.html | 5 - .../doc/ComputePolylineFeatureFromImage.html | 5 - .../5.8.0/doc/ConcatenateImages.html | 5 - .../5.8.0/doc/ConcatenateVectorData.html | 5 - .../doc/ConnectedComponentSegmentation.html | 5 - .../otb/description/5.8.0/doc/Convert.html | 6 - .../5.8.0/doc/ConvertCartoToGeoPoint.html | 5 - .../5.8.0/doc/ConvertSensorToGeoPoint.html | 5 - .../otb/description/5.8.0/doc/DEMConvert.html | 5 - .../5.8.0/doc/DSFuzzyModelEstimation.html | 5 - .../5.8.0/doc/Despeckle-frost.html | 5 - .../5.8.0/doc/Despeckle-gammamap.html | 5 - .../description/5.8.0/doc/Despeckle-kuan.html | 5 - .../description/5.8.0/doc/Despeckle-lee.html | 5 - .../otb/description/5.8.0/doc/Despeckle.html | 5 - .../doc/DimensionalityReduction-ica.html | 5 - .../doc/DimensionalityReduction-maf.html | 5 - .../doc/DimensionalityReduction-napca.html | 5 - .../doc/DimensionalityReduction-pca.html | 5 - .../5.8.0/doc/DimensionalityReduction.html | 5 - .../5.8.0/doc/DisparityMapToElevationMap.html | 5 - .../5.8.0/doc/DownloadSRTMTiles.html | 5 - .../5.8.0/doc/EdgeExtraction-gradient.html | 5 - .../5.8.0/doc/EdgeExtraction-sobel.html | 5 - .../5.8.0/doc/EdgeExtraction-touzi.html | 5 - .../description/5.8.0/doc/EdgeExtraction.html | 5 - .../description/5.8.0/doc/ExtractROI-fit.html | 5 - .../5.8.0/doc/ExtractROI-standard.html | 5 - .../otb/description/5.8.0/doc/ExtractROI.html | 5 - .../5.8.0/doc/FineRegistration.html | 5 - ...usionOfClassifications-dempstershafer.html | 11 - ...usionOfClassifications-majorityvoting.html | 11 - .../5.8.0/doc/FusionOfClassifications.html | 11 - .../5.8.0/doc/GeneratePlyFile.html | 5 - .../5.8.0/doc/GenerateRPCSensorModel.html | 5 - ...ayScaleMorphologicalOperation-closing.html | 5 - ...rayScaleMorphologicalOperation-dilate.html | 5 - ...GrayScaleMorphologicalOperation-erode.html | 5 - ...ayScaleMorphologicalOperation-opening.html | 5 - .../doc/GrayScaleMorphologicalOperation.html | 5 - .../5.8.0/doc/GridBasedImageResampling.html | 5 - .../5.8.0/doc/HaralickTextureExtraction.html | 5 - .../5.8.0/doc/HomologousPointsExtraction.html | 5 - .../5.8.0/doc/HooverCompareSegmentation.html | 7 - .../5.8.0/doc/HyperspectralUnmixing.html | 8 - .../5.8.0/doc/ImageClassifier.html | 16 - .../description/5.8.0/doc/ImageEnvelope.html | 5 - .../5.8.0/doc/KMeansClassification.html | 5 - .../otb/description/5.8.0/doc/KmzExport.html | 5 - .../5.8.0/doc/LSMSSegmentation.html | 5 - .../5.8.0/doc/LSMSSmallRegionsMerging.html | 5 - .../5.8.0/doc/LSMSVectorization.html | 5 - .../5.8.0/doc/LineSegmentDetection.html | 0 .../5.8.0/doc/LocalStatisticExtraction.html | 5 - .../description/5.8.0/doc/ManageNoData.html | 5 - .../5.8.0/doc/MeanShiftSmoothing.html | 5 - .../5.8.0/doc/MultiImageSamplingRate.html | 56 -- .../5.8.0/doc/MultiResolutionPyramid.html | 5 - .../doc/MultivariateAlterationDetector.html | 21 - .../5.8.0/doc/OGRLayerClassifier.html | 5 - .../description/5.8.0/doc/OSMDownloader.html | 6 - .../5.8.0/doc/ObtainUTMZoneFromGeoPoint.html | 5 - .../5.8.0/doc/OpticalCalibration.html | 0 .../5.8.0/doc/OrthoRectification-epsg.html | 7 - .../doc/OrthoRectification-fit-to-ortho.html | 7 - .../doc/OrthoRectification-lambert-WGS84.html | 7 - .../5.8.0/doc/OrthoRectification-utm.html | 7 - .../5.8.0/doc/OrthoRectification.html | 7 - .../5.8.0/doc/Pansharpening-bayes.html | 5 - .../5.8.0/doc/Pansharpening-lmvm.html | 5 - .../5.8.0/doc/Pansharpening-rcs.html | 5 - .../description/5.8.0/doc/Pansharpening.html | 5 - .../otb/description/5.8.0/doc/PixelValue.html | 6 - .../5.8.0/doc/PolygonClassStatistics.html | 12 - .../5.8.0/doc/PredictRegression.html | 5 - .../otb/description/5.8.0/doc/Quicklook.html | 7 - .../5.8.0/doc/RadiometricIndices.html | 25 - .../5.8.0/doc/Rasterization-image.html | 6 - .../5.8.0/doc/Rasterization-manual.html | 6 - .../description/5.8.0/doc/Rasterization.html | 6 - .../description/5.8.0/doc/ReadImageInfo.html | 5 - .../5.8.0/doc/RefineSensorModel.html | 5 - .../otb/description/5.8.0/doc/Rescale.html | 5 - .../5.8.0/doc/RigidTransformResample-id.html | 5 - .../doc/RigidTransformResample-rotation.html | 5 - .../RigidTransformResample-translation.html | 5 - .../5.8.0/doc/RigidTransformResample.html | 5 - .../description/5.8.0/doc/SARCalibration.html | 7 - .../5.8.0/doc/SARDecompositions.html | 15 - .../5.8.0/doc/SARPolarMatrixConvert.html | 32 - .../description/5.8.0/doc/SARPolarSynth.html | 35 - .../5.8.0/doc/SFSTextureExtraction.html | 5 - .../5.8.0/doc/SOMClassification.html | 5 - .../5.8.0/doc/SampleExtraction.html | 5 - .../5.8.0/doc/SampleSelection.html | 41 - .../5.8.0/doc/SarRadiometricCalibration.html | 7 - .../5.8.0/doc/Segmentation-cc.html | 11 - .../5.8.0/doc/Segmentation-meanshift.html | 11 - .../5.8.0/doc/Segmentation-mprofiles.html | 11 - .../5.8.0/doc/Segmentation-watershed.html | 11 - .../description/5.8.0/doc/Segmentation.html | 11 - .../5.8.0/doc/Smoothing-anidif.html | 5 - .../5.8.0/doc/Smoothing-gaussian.html | 5 - .../description/5.8.0/doc/Smoothing-mean.html | 5 - .../otb/description/5.8.0/doc/Smoothing.html | 5 - .../otb/description/5.8.0/doc/SplitImage.html | 5 - .../5.8.0/doc/StereoFramework.html | 18 - .../doc/StereoRectificationGridGenerator.html | 5 - .../description/5.8.0/doc/Superimpose.html | 5 - .../5.8.0/doc/TestApplication.html | 5 - .../otb/description/5.8.0/doc/TileFusion.html | 5 - .../5.8.0/doc/TrainImagesClassifier-ann.html | 8 - .../doc/TrainImagesClassifier-bayes.html | 8 - .../doc/TrainImagesClassifier-boost.html | 8 - .../5.8.0/doc/TrainImagesClassifier-dt.html | 8 - .../5.8.0/doc/TrainImagesClassifier-gbt.html | 8 - .../5.8.0/doc/TrainImagesClassifier-knn.html | 8 - .../doc/TrainImagesClassifier-libsvm.html | 8 - .../5.8.0/doc/TrainImagesClassifier-rf.html | 8 - .../5.8.0/doc/TrainImagesClassifier.html | 8 - .../5.8.0/doc/TrainOGRLayersClassifier.html | 5 - .../5.8.0/doc/TrainRegression-ann.html | 7 - .../5.8.0/doc/TrainRegression-dt.html | 7 - .../5.8.0/doc/TrainRegression-gbt.html | 7 - .../5.8.0/doc/TrainRegression-knn.html | 7 - .../5.8.0/doc/TrainRegression-libsvm.html | 7 - .../5.8.0/doc/TrainRegression-rf.html | 7 - .../5.8.0/doc/TrainRegression.html | 7 - .../5.8.0/doc/TrainVectorClassifier-ann.html | 5 - .../doc/TrainVectorClassifier-bayes.html | 5 - .../doc/TrainVectorClassifier-boost.html | 5 - .../5.8.0/doc/TrainVectorClassifier-dt.html | 5 - .../5.8.0/doc/TrainVectorClassifier-gbt.html | 5 - .../5.8.0/doc/TrainVectorClassifier-knn.html | 5 - .../doc/TrainVectorClassifier-libsvm.html | 5 - .../5.8.0/doc/TrainVectorClassifier-rf.html | 5 - .../5.8.0/doc/TrainVectorClassifier.html | 5 - .../5.8.0/doc/VectorDataDSValidation.html | 5 - .../5.8.0/doc/VectorDataExtractROI.html | 5 - .../doc/VectorDataReprojection-image.html | 7 - .../doc/VectorDataReprojection-user.html | 7 - .../5.8.0/doc/VectorDataReprojection.html | 7 - .../5.8.0/doc/VectorDataSetField.html | 5 - .../5.8.0/doc/VectorDataTransform.html | 5 - .../5.8.0/doc/VertexComponentAnalysis.html | 5 - .../generate_application_descriptors.py | 414 ---------- .../algs/otb/maintenance/OTBHelper.py | 733 ----------------- .../maintenance/OTBSpecific_XMLcreation.py | 763 ------------------ .../algs/otb/maintenance/OTBTester.py | 442 ---------- .../processing/algs/otb/maintenance/README.md | 69 -- .../algs/otb/maintenance/TestOTBAlgorithms.py | 208 ----- .../algs/otb/maintenance/black_list.xml | 119 --- .../algs/otb/maintenance/parsing.py | 190 ----- .../algs/otb/maintenance/white_list.xml | 257 ------ 1173 files changed, 60370 deletions(-) delete mode 100644 python/plugins/processing/algs/lidar/CMakeLists.txt delete mode 100644 python/plugins/processing/algs/lidar/LidarToolsAlgorithmProvider.py delete mode 100644 python/plugins/processing/algs/lidar/__init__.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/ASCII2DTM.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/CMakeLists.txt delete mode 100644 python/plugins/processing/algs/lidar/fusion/CanopyMaxima.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/CanopyModel.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/Catalog.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/ClipData.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/CloudMetrics.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/Cover.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/Csv2Grid.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/DTM2ASCII.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/DTM2TIF.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/DensityMetrics.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/FilterData.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/FirstLastReturn.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/FusionAlgorithm.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/FusionUtils.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/GridMetrics.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/GridSurfaceCreate.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/GridSurfaceStats.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/GroundFilter.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/ImageCreate.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/IntensityImage.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/MergeDTM.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/MergeData.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/MergeRaster.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/OpenViewerAction.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/PolyClipData.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/ReturnDensity.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/SplitDTM.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/SurfaceStats.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/TinSurfaceCreate.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/TopoMetrics.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/TreeSeg.py delete mode 100644 python/plugins/processing/algs/lidar/fusion/__init__.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/CMakeLists.txt delete mode 100644 python/plugins/processing/algs/lidar/lastools/LAStoolsAlgorithm.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/LAStoolsUtils.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/__init__.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/blast2dem.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/blast2demPro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/blast2iso.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/blast2isoPro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/flightlinesToCHM.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/flightlinesToDTMandDSM.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/flightlinesToSingleCHMpitFree.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/hugeFileClassify.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/hugeFileGroundClassify.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/hugeFileNormalize.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/las2dem.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/las2demPro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/las2iso.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/las2lasPro_filter.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/las2lasPro_project.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/las2lasPro_transform.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/las2las_filter.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/las2las_project.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/las2las_transform.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/las2shp.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/las2tin.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/las2txt.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/las2txtPro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasboundary.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasboundaryPro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lascanopy.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lascanopyPro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasclassify.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasclassifyPro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasclip.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lascolor.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lascontrol.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasdiff.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasduplicate.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasduplicatePro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasgrid.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasgridPro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasground.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasgroundPro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasgroundPro_new.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasground_new.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasheight.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasheightPro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasheightPro_classify.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasheight_classify.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasindex.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasindexPro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasinfo.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasinfoPro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasmerge.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasmergePro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasnoise.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasnoisePro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasoverage.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasoveragePro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasoverlap.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasoverlapPro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasprecision.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/laspublish.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/laspublishPro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasquery.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lassort.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lassortPro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lassplit.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasthin.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasthinPro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lastile.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lastilePro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasvalidate.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasvalidatePro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasview.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/lasviewPro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/laszip.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/laszipPro.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/shp2las.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/txt2las.py delete mode 100644 python/plugins/processing/algs/lidar/lastools/txt2lasPro.py delete mode 100644 python/plugins/processing/algs/otb/CMakeLists.txt delete mode 100644 python/plugins/processing/algs/otb/OTBAlgorithm.py delete mode 100644 python/plugins/processing/algs/otb/OTBAlgorithmProvider.py delete mode 100644 python/plugins/processing/algs/otb/OTBSpecific_XMLLoading.py delete mode 100644 python/plugins/processing/algs/otb/OTBUtils.py delete mode 100644 python/plugins/processing/algs/otb/__init__.py delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/BandMath.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/BandMathX.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/BinaryMorphologicalOperation-closing.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/BinaryMorphologicalOperation-dilate.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/BinaryMorphologicalOperation-erode.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/BinaryMorphologicalOperation-opening.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/ClassificationMapRegularization.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/ColorMapping-continuous.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/ColorMapping-custom.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/ColorMapping-image.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/ColorMapping-optimal.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/CompareImages.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/ComputeConfusionMatrix-raster.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/ComputeConfusionMatrix-vector.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/ComputeImagesStatistics.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/ComputeModulusAndPhase-OneEntry.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/ComputeModulusAndPhase-TwoEntries.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/ComputeOGRLayersFeaturesStatistics.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/ComputePolylineFeatureFromImage.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/ConcatenateImages.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/ConcatenateVectorData.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/ConnectedComponentSegmentation.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/Convert.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/DEMConvert.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/Despeckle-frost.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/Despeckle-lee.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/DimensionalityReduction-ica.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/DimensionalityReduction-maf.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/DimensionalityReduction-napca.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/DimensionalityReduction-pca.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/EdgeExtraction-gradient.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/EdgeExtraction-sobel.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/EdgeExtraction-touzi.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/ExtractROI-fit.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/ExtractROI-standard.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/FusionOfClassifications-dempstershafer.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/FusionOfClassifications-majorityvoting.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/GrayScaleMorphologicalOperation-closing.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/GrayScaleMorphologicalOperation-dilate.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/GrayScaleMorphologicalOperation-erode.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/GrayScaleMorphologicalOperation-opening.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/HaralickTextureExtraction.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/HooverCompareSegmentation.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/ImageClassifier.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/ImageEnvelope.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/KMeansClassification.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/KmzExport.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/LSMSSegmentation.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/LSMSSmallRegionsMerging.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/LSMSVectorization.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/LineSegmentDetection.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/LocalStatisticExtraction.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/MeanShiftSmoothing.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/MultivariateAlterationDetector.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/OGRLayerClassifier.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/OpticalCalibration.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/OrthoRectification-epsg.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/OrthoRectification-fit-to-ortho.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/OrthoRectification-lambert-WGS84.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/OrthoRectification-utm.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/Pansharpening-bayes.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/Pansharpening-lmvm.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/Pansharpening-rcs.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/RadiometricIndices.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/Rasterization-image.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/Rasterization-manual.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/ReadImageInfo.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/Rescale.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/RigidTransformResample-id.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/RigidTransformResample-rotation.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/RigidTransformResample-translation.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/SFSTextureExtraction.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/SOMClassification.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/Segmentation-cc.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/Segmentation-edison.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/Segmentation-meanshift.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/Segmentation-mprofiles.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/Segmentation-watershed.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/Smoothing-anidif.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/Smoothing-gaussian.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/Smoothing-mean.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/SplitImage.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/StereoFramework.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/Superimpose.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/TileFusion.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-ann.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-bayes.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-boost.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-dt.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-gbt.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-knn.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-libsvm.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-rf.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-svm.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/TrainOGRLayersClassifier.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/VectorDataExtractROI.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/VectorDataReprojection-image.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/VectorDataReprojection-user.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/VectorDataTransform.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/BandMath.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/BandMathX.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/BinaryMorphologicalOperation-closing.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/BinaryMorphologicalOperation-dilate.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/BinaryMorphologicalOperation-erode.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/BinaryMorphologicalOperation-opening.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/BinaryMorphologicalOperation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/BlockMatching.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/BundleToPerfectSensor.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ClassificationMapRegularization.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ColorMapping-continuous.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ColorMapping-custom.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ColorMapping-image.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ColorMapping-optimal.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ColorMapping.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/CompareImages.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ComputeConfusionMatrix-raster.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ComputeConfusionMatrix-vector.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ComputeConfusionMatrix.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ComputeImagesStatistics.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ComputeOGRLayersFeaturesStatistics.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ComputePolylineFeatureFromImage.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ConcatenateImages.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ConcatenateVectorData.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ConnectedComponentSegmentation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/Convert.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ConvertCartoToGeoPoint.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ConvertSensorToGeoPoint.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/CookBook.css delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/DEMConvert.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/DSFuzzyModelEstimation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/Despeckle-frost.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/Despeckle-lee.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/Despeckle.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/DimensionalityReduction-ica.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/DimensionalityReduction-maf.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/DimensionalityReduction-napca.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/DimensionalityReduction-pca.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/DimensionalityReduction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/DisparityMapToElevationMap.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/DownloadSRTMTiles.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/EdgeExtraction-gradient.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/EdgeExtraction-sobel.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/EdgeExtraction-touzi.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/EdgeExtraction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ExtractROI-fit.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ExtractROI-standard.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ExtractROI.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/FineRegistration.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/FusionOfClassifications-dempstershafer.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/FusionOfClassifications-majorityvoting.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/FusionOfClassifications.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/GeneratePlyFile.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/GenerateRPCSensorModel.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/GrayScaleMorphologicalOperation-closing.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/GrayScaleMorphologicalOperation-dilate.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/GrayScaleMorphologicalOperation-erode.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/GrayScaleMorphologicalOperation-opening.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/GrayScaleMorphologicalOperation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/GridBasedImageResampling.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/HaralickTextureExtraction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/HomologousPointsExtraction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/HooverCompareSegmentation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/HyperspectralUnmixing.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ImageClassifier.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ImageEnvelope.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/KMeansClassification.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/KmzExport.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/LSMSSegmentation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/LSMSSmallRegionsMerging.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/LSMSVectorization.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/LineSegmentDetection.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/LocalStatisticExtraction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/MeanShiftSmoothing.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/MultiResolutionPyramid.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/MultivariateAlterationDetector.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/OGRLayerClassifier.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/OSMDownloader.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ObtainUTMZoneFromGeoPoint.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/OpticalCalibration.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/OrthoRectification-epsg.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/OrthoRectification-fit-to-ortho.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/OrthoRectification-lambert-WGS84.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/OrthoRectification-utm.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/OrthoRectification.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/Pansharpening-bayes.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/Pansharpening-lmvm.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/Pansharpening-rcs.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/Pansharpening.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/PixelValue.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/Quicklook.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/RadiometricIndices.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/Rasterization-image.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/Rasterization-manual.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/Rasterization.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/ReadImageInfo.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/RefineSensorModel.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/Rescale.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/RigidTransformResample-id.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/RigidTransformResample-rotation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/RigidTransformResample-translation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/RigidTransformResample.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/SFSTextureExtraction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/SOMClassification.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/SarRadiometricCalibration.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/Segmentation-cc.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/Segmentation-meanshift.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/Segmentation-mprofiles.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/Segmentation-watershed.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/Segmentation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/Smoothing-anidif.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/Smoothing-gaussian.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/Smoothing-mean.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/Smoothing.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/SplitImage.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/StereoFramework.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/StereoRectificationGridGenerator.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/Superimpose.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/TestApplication.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/TileFusion.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-ann.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-bayes.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-boost.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-dt.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-gbt.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-knn.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-libsvm.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-rf.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-svm.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/TrainOGRLayersClassifier.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataDSValidation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataExtractROI.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataReprojection-image.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataReprojection-user.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataReprojection.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataSetField.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataTransform.html delete mode 100644 python/plugins/processing/algs/otb/description/5.0.0/doc/VertexComponentAnalysis.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/BandMath.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/BinaryMorphologicalOperation-closing.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/BinaryMorphologicalOperation-dilate.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/BinaryMorphologicalOperation-erode.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/BinaryMorphologicalOperation-opening.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/ClassificationMapRegularization.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/ColorMapping-continuous.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/ColorMapping-custom.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/ColorMapping-image.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/ColorMapping-optimal.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/CompareImages.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/ComputeConfusionMatrix-raster.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/ComputeConfusionMatrix-vector.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/ComputeImagesStatistics.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/ComputeOGRLayersFeaturesStatistics.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/ComputePolylineFeatureFromImage.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/ConcatenateImages.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/ConcatenateVectorData.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/ConnectedComponentSegmentation.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/Convert.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/DEMConvert.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/Despeckle-frost.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/Despeckle-gammamap.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/Despeckle-kuan.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/Despeckle-lee.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/DimensionalityReduction-ica.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/DimensionalityReduction-maf.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/DimensionalityReduction-napca.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/DimensionalityReduction-pca.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/EdgeExtraction-gradient.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/EdgeExtraction-sobel.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/EdgeExtraction-touzi.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/ExtractROI-fit.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/ExtractROI-standard.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/FusionOfClassifications-dempstershafer.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/FusionOfClassifications-majorityvoting.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/GrayScaleMorphologicalOperation-closing.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/GrayScaleMorphologicalOperation-dilate.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/GrayScaleMorphologicalOperation-erode.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/GrayScaleMorphologicalOperation-opening.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/HaralickTextureExtraction.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/HooverCompareSegmentation.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/ImageClassifier.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/ImageEnvelope.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/KMeansClassification.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/KmzExport.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/LSMSSegmentation.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/LSMSSmallRegionsMerging.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/LSMSVectorization.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/LineSegmentDetection.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/LocalStatisticExtraction.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/MeanShiftSmoothing.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/MultivariateAlterationDetector.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/OGRLayerClassifier.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/OrthoRectification-epsg.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/OrthoRectification-fit-to-ortho.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/OrthoRectification-lambert-WGS84.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/OrthoRectification-utm.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/Pansharpening-bayes.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/Pansharpening-lmvm.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/Pansharpening-rcs.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/RadiometricIndices.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/Rasterization-image.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/Rasterization-manual.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/ReadImageInfo.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/Rescale.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/RigidTransformResample-id.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/RigidTransformResample-rotation.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/RigidTransformResample-translation.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/SFSTextureExtraction.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/SOMClassification.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/Segmentation-cc.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/Segmentation-meanshift.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/Segmentation-mprofiles.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/Segmentation-watershed.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/Smoothing-anidif.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/Smoothing-gaussian.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/Smoothing-mean.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/StereoFramework.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/Superimpose.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/TileFusion.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-ann.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-bayes.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-boost.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-dt.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-gbt.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-knn.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-rf.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/TrainOGRLayersClassifier.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/VectorDataExtractROI.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/VectorDataReprojection-image.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/VectorDataReprojection-user.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/VectorDataTransform.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/BandMath.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/BinaryMorphologicalOperation-closing.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/BinaryMorphologicalOperation-dilate.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/BinaryMorphologicalOperation-erode.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/BinaryMorphologicalOperation-opening.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/BinaryMorphologicalOperation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/BlockMatching.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/BundleToPerfectSensor.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ClassificationMapRegularization.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ColorMapping-continuous.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ColorMapping-custom.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ColorMapping-image.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ColorMapping-optimal.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ColorMapping.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/CompareImages.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ComputeConfusionMatrix-raster.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ComputeConfusionMatrix-vector.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ComputeConfusionMatrix.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ComputeImagesStatistics.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ComputeOGRLayersFeaturesStatistics.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ComputePolylineFeatureFromImage.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ConcatenateImages.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ConcatenateVectorData.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ConnectedComponentSegmentation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Convert.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ConvertCartoToGeoPoint.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ConvertSensorToGeoPoint.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/DEMConvert.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/DSFuzzyModelEstimation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Despeckle-frost.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Despeckle-gammamap.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Despeckle-kuan.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Despeckle-lee.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Despeckle.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/DimensionalityReduction-ica.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/DimensionalityReduction-maf.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/DimensionalityReduction-napca.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/DimensionalityReduction-pca.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/DimensionalityReduction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/DisparityMapToElevationMap.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/DownloadSRTMTiles.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/EdgeExtraction-gradient.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/EdgeExtraction-sobel.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/EdgeExtraction-touzi.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/EdgeExtraction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ExtractROI-fit.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ExtractROI-standard.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ExtractROI.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/FineRegistration.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/FusionOfClassifications-dempstershafer.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/FusionOfClassifications-majorityvoting.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/FusionOfClassifications.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/GeneratePlyFile.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/GenerateRPCSensorModel.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/GrayScaleMorphologicalOperation-closing.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/GrayScaleMorphologicalOperation-dilate.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/GrayScaleMorphologicalOperation-erode.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/GrayScaleMorphologicalOperation-opening.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/GrayScaleMorphologicalOperation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/GridBasedImageResampling.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/HaralickTextureExtraction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/HomologousPointsExtraction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/HooverCompareSegmentation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/HyperspectralUnmixing.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ImageClassifier.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ImageEnvelope.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/KMeansClassification.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/KmzExport.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/LSMSSegmentation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/LSMSSmallRegionsMerging.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/LSMSVectorization.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/LineSegmentDetection.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/LocalStatisticExtraction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ManageNoData.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/MeanShiftSmoothing.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/MultiResolutionPyramid.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/MultivariateAlterationDetector.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/OGRLayerClassifier.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/OSMDownloader.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ObtainUTMZoneFromGeoPoint.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/OrthoRectification-epsg.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/OrthoRectification-fit-to-ortho.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/OrthoRectification-lambert-WGS84.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/OrthoRectification-utm.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/OrthoRectification.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Pansharpening-bayes.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Pansharpening-lmvm.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Pansharpening-rcs.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Pansharpening.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/PixelValue.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/PolygonClassStatistics.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/PredictRegression.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Quicklook.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/RadiometricIndices.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Rasterization-image.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Rasterization-manual.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Rasterization.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/ReadImageInfo.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/RefineSensorModel.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Rescale.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/RigidTransformResample-id.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/RigidTransformResample-rotation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/RigidTransformResample-translation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/RigidTransformResample.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/SARCalibration.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/SARDecompositions.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/SARPolarMatrixConvert.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/SARPolarSynth.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/SFSTextureExtraction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/SOMClassification.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/SarRadiometricCalibration.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Segmentation-cc.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Segmentation-meanshift.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Segmentation-mprofiles.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Segmentation-watershed.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Segmentation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Smoothing-anidif.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Smoothing-gaussian.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Smoothing-mean.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Smoothing.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/SplitImage.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/StereoFramework.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/StereoRectificationGridGenerator.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/Superimpose.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/TestApplication.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/TileFusion.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-ann.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-bayes.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-boost.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-dt.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-gbt.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-knn.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-rf.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/TrainOGRLayersClassifier.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/TrainRegression.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataDSValidation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataExtractROI.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataReprojection-image.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataReprojection-user.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataReprojection.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataSetField.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataTransform.html delete mode 100644 python/plugins/processing/algs/otb/description/5.4.0/doc/VertexComponentAnalysis.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/BandMath.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/BinaryMorphologicalOperation-closing.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/BinaryMorphologicalOperation-dilate.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/BinaryMorphologicalOperation-erode.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/BinaryMorphologicalOperation-opening.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/ClassificationMapRegularization.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/ColorMapping-continuous.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/ColorMapping-custom.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/ColorMapping-image.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/ColorMapping-optimal.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/CompareImages.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/ComputeConfusionMatrix-raster.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/ComputeConfusionMatrix-vector.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/ComputeImagesStatistics.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/ComputeOGRLayersFeaturesStatistics.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/ComputePolylineFeatureFromImage.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/ConcatenateImages.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/ConcatenateVectorData.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/ConnectedComponentSegmentation.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/Convert.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/DEMConvert.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/Despeckle-frost.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/Despeckle-gammamap.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/Despeckle-kuan.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/Despeckle-lee.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/DimensionalityReduction-ica.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/DimensionalityReduction-maf.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/DimensionalityReduction-napca.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/DimensionalityReduction-pca.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/EdgeExtraction-gradient.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/EdgeExtraction-sobel.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/EdgeExtraction-touzi.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/ExtractROI-fit.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/ExtractROI-standard.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/FusionOfClassifications-dempstershafer.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/FusionOfClassifications-majorityvoting.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/GrayScaleMorphologicalOperation-closing.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/GrayScaleMorphologicalOperation-dilate.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/GrayScaleMorphologicalOperation-erode.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/GrayScaleMorphologicalOperation-opening.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/HaralickTextureExtraction.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/HooverCompareSegmentation.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/ImageClassifier.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/ImageEnvelope.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/KMeansClassification.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/KmzExport.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/LSMSSegmentation.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/LSMSSmallRegionsMerging.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/LSMSVectorization.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/LineSegmentDetection.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/LocalStatisticExtraction.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/ManageNoData.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/MeanShiftSmoothing.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/MultivariateAlterationDetector.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/OGRLayerClassifier.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/OpticalCalibration.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/OrthoRectification-epsg.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/OrthoRectification-fit-to-ortho.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/OrthoRectification-lambert-WGS84.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/OrthoRectification-utm.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/Pansharpening-bayes.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/Pansharpening-lmvm.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/Pansharpening-rcs.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/PolygonClassStatistics.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/PredictRegression.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/RadiometricIndices.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/Rasterization-image.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/Rasterization-manual.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/ReadImageInfo.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/Rescale.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/RigidTransformResample-id.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/RigidTransformResample-rotation.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/RigidTransformResample-translation.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/SARCalibration.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/SARDecompositions.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/SARPolarSynth.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/SFSTextureExtraction.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/SOMClassification.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/SampleExtraction.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/SampleSelection.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/Segmentation-cc.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/Segmentation-meanshift.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/Segmentation-mprofiles.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/Segmentation-watershed.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/Smoothing-anidif.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/Smoothing-gaussian.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/Smoothing-mean.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/StereoFramework.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/Superimpose.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TileFusion.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-ann.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-bayes.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-boost.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-dt.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-gbt.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-knn.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-libsvm.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-rf.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TrainOGRLayersClassifier.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-ann.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-dt.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-gbt.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-knn.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-libsvm.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-rf.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-ann.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-bayes.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-boost.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-dt.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-gbt.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-knn.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-libsvm.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-rf.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/VectorDataExtractROI.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/VectorDataReprojection-image.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/VectorDataReprojection-user.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/VectorDataTransform.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ApplicationExample.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/BandMath.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/BinaryMorphologicalOperation-closing.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/BinaryMorphologicalOperation-dilate.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/BinaryMorphologicalOperation-erode.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/BinaryMorphologicalOperation-opening.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/BinaryMorphologicalOperation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/BlockMatching.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/BundleToPerfectSensor.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ClassificationMapRegularization.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ColorMapping-continuous.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ColorMapping-custom.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ColorMapping-image.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ColorMapping-optimal.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ColorMapping.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/CompareImages.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ComputeConfusionMatrix-raster.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ComputeConfusionMatrix-vector.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ComputeConfusionMatrix.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ComputeImagesStatistics.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ComputeOGRLayersFeaturesStatistics.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ComputePolylineFeatureFromImage.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ConcatenateImages.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ConcatenateVectorData.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ConnectedComponentSegmentation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Convert.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ConvertCartoToGeoPoint.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ConvertSensorToGeoPoint.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/DEMConvert.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/DSFuzzyModelEstimation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Despeckle-frost.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Despeckle-gammamap.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Despeckle-kuan.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Despeckle-lee.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Despeckle.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/DimensionalityReduction-ica.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/DimensionalityReduction-maf.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/DimensionalityReduction-napca.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/DimensionalityReduction-pca.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/DimensionalityReduction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/DisparityMapToElevationMap.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/DownloadSRTMTiles.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/EdgeExtraction-gradient.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/EdgeExtraction-sobel.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/EdgeExtraction-touzi.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/EdgeExtraction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ExtractROI-fit.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ExtractROI-standard.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ExtractROI.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/FineRegistration.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/FusionOfClassifications-dempstershafer.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/FusionOfClassifications-majorityvoting.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/FusionOfClassifications.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/GeneratePlyFile.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/GenerateRPCSensorModel.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/GrayScaleMorphologicalOperation-closing.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/GrayScaleMorphologicalOperation-dilate.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/GrayScaleMorphologicalOperation-erode.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/GrayScaleMorphologicalOperation-opening.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/GrayScaleMorphologicalOperation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/GridBasedImageResampling.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/HaralickTextureExtraction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/HomologousPointsExtraction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/HooverCompareSegmentation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/HyperspectralUnmixing.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ImageClassifier.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ImageEnvelope.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/KMeansClassification.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/KmzExport.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/LSMSSegmentation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/LSMSSmallRegionsMerging.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/LSMSVectorization.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/LineSegmentDetection.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/LocalStatisticExtraction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ManageNoData.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/MeanShiftSmoothing.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/MultiResolutionPyramid.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/MultivariateAlterationDetector.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/OGRLayerClassifier.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/OSMDownloader.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ObtainUTMZoneFromGeoPoint.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/OpticalCalibration.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/OrthoRectification-epsg.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/OrthoRectification-fit-to-ortho.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/OrthoRectification-lambert-WGS84.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/OrthoRectification-utm.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/OrthoRectification.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Pansharpening-bayes.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Pansharpening-lmvm.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Pansharpening-rcs.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Pansharpening.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/PixelValue.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/PolygonClassStatistics.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/PredictRegression.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Quicklook.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/RadiometricIndices.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Rasterization-image.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Rasterization-manual.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Rasterization.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/ReadImageInfo.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/RefineSensorModel.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Rescale.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/RigidTransformResample-id.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/RigidTransformResample-rotation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/RigidTransformResample-translation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/RigidTransformResample.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/SARCalibration.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/SARDecompositions.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/SARPolarMatrixConvert.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/SARPolarSynth.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/SFSTextureExtraction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/SOMClassification.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/SampleExtraction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/SampleSelection.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/SarRadiometricCalibration.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Segmentation-cc.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Segmentation-meanshift.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Segmentation-mprofiles.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Segmentation-watershed.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Segmentation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Smoothing-anidif.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Smoothing-gaussian.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Smoothing-mean.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Smoothing.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/SplitImage.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/StereoFramework.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/StereoRectificationGridGenerator.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/Superimpose.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TestApplication.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TileFusion.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-ann.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-bayes.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-boost.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-dt.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-gbt.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-knn.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-libsvm.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-rf.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainOGRLayersClassifier.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-ann.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-dt.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-gbt.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-knn.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-libsvm.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-rf.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-ann.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-bayes.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-boost.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-dt.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-gbt.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-knn.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-libsvm.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-rf.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataDSValidation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataExtractROI.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataReprojection-image.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataReprojection-user.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataReprojection.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataSetField.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataTransform.html delete mode 100644 python/plugins/processing/algs/otb/description/5.6.0/doc/VertexComponentAnalysis.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/BandMath.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/BandMathX.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/BinaryMorphologicalOperation-closing.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/BinaryMorphologicalOperation-dilate.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/BinaryMorphologicalOperation-erode.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/BinaryMorphologicalOperation-opening.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/ClassificationMapRegularization.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/ColorMapping-continuous.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/ColorMapping-custom.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/ColorMapping-image.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/ColorMapping-optimal.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/CompareImages.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/ComputeConfusionMatrix-raster.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/ComputeConfusionMatrix-vector.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/ComputeImagesStatistics.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/ComputeOGRLayersFeaturesStatistics.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/ComputePolylineFeatureFromImage.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/ConcatenateImages.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/ConcatenateVectorData.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/ConnectedComponentSegmentation.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/Convert.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/DEMConvert.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/Despeckle-frost.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/Despeckle-gammamap.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/Despeckle-kuan.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/Despeckle-lee.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/DimensionalityReduction-ica.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/DimensionalityReduction-maf.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/DimensionalityReduction-napca.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/DimensionalityReduction-pca.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/EdgeExtraction-gradient.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/EdgeExtraction-sobel.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/EdgeExtraction-touzi.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/ExtractROI-fit.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/ExtractROI-standard.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/FusionOfClassifications-dempstershafer.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/FusionOfClassifications-majorityvoting.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/GrayScaleMorphologicalOperation-closing.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/GrayScaleMorphologicalOperation-dilate.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/GrayScaleMorphologicalOperation-erode.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/GrayScaleMorphologicalOperation-opening.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/HaralickTextureExtraction.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/HooverCompareSegmentation.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/ImageClassifier.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/ImageEnvelope.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/KMeansClassification.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/KmzExport.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/LSMSSegmentation.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/LSMSSmallRegionsMerging.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/LSMSVectorization.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/LineSegmentDetection.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/LocalStatisticExtraction.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/ManageNoData.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/MeanShiftSmoothing.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/MultiImageSamplingRate.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/MultivariateAlterationDetector.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/OGRLayerClassifier.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/OpticalCalibration.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/OrthoRectification-epsg.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/OrthoRectification-fit-to-ortho.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/OrthoRectification-lambert-WGS84.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/OrthoRectification-utm.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/Pansharpening-bayes.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/Pansharpening-lmvm.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/Pansharpening-rcs.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/PolygonClassStatistics.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/PredictRegression.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/RadiometricIndices.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/Rasterization-image.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/Rasterization-manual.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/ReadImageInfo.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/Rescale.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/RigidTransformResample-id.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/RigidTransformResample-rotation.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/RigidTransformResample-translation.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/SARCalibration.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/SARDecompositions.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/SARPolarSynth.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/SFSTextureExtraction.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/SOMClassification.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/SampleExtraction.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/SampleSelection.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/Segmentation-cc.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/Segmentation-meanshift.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/Segmentation-mprofiles.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/Segmentation-watershed.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/Smoothing-anidif.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/Smoothing-gaussian.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/Smoothing-mean.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/StereoFramework.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/Superimpose.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TileFusion.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-ann.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-bayes.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-boost.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-dt.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-gbt.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-knn.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-libsvm.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-rf.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TrainOGRLayersClassifier.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-ann.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-dt.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-gbt.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-knn.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-libsvm.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-rf.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-ann.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-bayes.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-boost.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-dt.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-gbt.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-knn.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-libsvm.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-rf.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/VectorDataExtractROI.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/VectorDataReprojection-image.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/VectorDataReprojection-user.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/VectorDataTransform.xml delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/BandMath.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/BandMathX.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/BinaryMorphologicalOperation-closing.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/BinaryMorphologicalOperation-dilate.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/BinaryMorphologicalOperation-erode.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/BinaryMorphologicalOperation-opening.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/BinaryMorphologicalOperation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/BlockMatching.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/BundleToPerfectSensor.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ClassificationMapRegularization.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ColorMapping-continuous.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ColorMapping-custom.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ColorMapping-image.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ColorMapping-optimal.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ColorMapping.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/CompareImages.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ComputeConfusionMatrix-raster.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ComputeConfusionMatrix-vector.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ComputeConfusionMatrix.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ComputeImagesStatistics.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ComputeOGRLayersFeaturesStatistics.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ComputePolylineFeatureFromImage.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ConcatenateImages.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ConcatenateVectorData.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ConnectedComponentSegmentation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Convert.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ConvertCartoToGeoPoint.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ConvertSensorToGeoPoint.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/DEMConvert.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/DSFuzzyModelEstimation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Despeckle-frost.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Despeckle-gammamap.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Despeckle-kuan.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Despeckle-lee.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Despeckle.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/DimensionalityReduction-ica.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/DimensionalityReduction-maf.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/DimensionalityReduction-napca.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/DimensionalityReduction-pca.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/DimensionalityReduction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/DisparityMapToElevationMap.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/DownloadSRTMTiles.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/EdgeExtraction-gradient.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/EdgeExtraction-sobel.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/EdgeExtraction-touzi.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/EdgeExtraction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ExtractROI-fit.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ExtractROI-standard.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ExtractROI.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/FineRegistration.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/FusionOfClassifications-dempstershafer.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/FusionOfClassifications-majorityvoting.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/FusionOfClassifications.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/GeneratePlyFile.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/GenerateRPCSensorModel.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/GrayScaleMorphologicalOperation-closing.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/GrayScaleMorphologicalOperation-dilate.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/GrayScaleMorphologicalOperation-erode.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/GrayScaleMorphologicalOperation-opening.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/GrayScaleMorphologicalOperation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/GridBasedImageResampling.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/HaralickTextureExtraction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/HomologousPointsExtraction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/HooverCompareSegmentation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/HyperspectralUnmixing.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ImageClassifier.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ImageEnvelope.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/KMeansClassification.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/KmzExport.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/LSMSSegmentation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/LSMSSmallRegionsMerging.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/LSMSVectorization.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/LineSegmentDetection.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/LocalStatisticExtraction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ManageNoData.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/MeanShiftSmoothing.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/MultiImageSamplingRate.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/MultiResolutionPyramid.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/MultivariateAlterationDetector.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/OGRLayerClassifier.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/OSMDownloader.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ObtainUTMZoneFromGeoPoint.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/OpticalCalibration.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/OrthoRectification-epsg.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/OrthoRectification-fit-to-ortho.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/OrthoRectification-lambert-WGS84.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/OrthoRectification-utm.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/OrthoRectification.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Pansharpening-bayes.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Pansharpening-lmvm.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Pansharpening-rcs.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Pansharpening.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/PixelValue.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/PolygonClassStatistics.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/PredictRegression.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Quicklook.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/RadiometricIndices.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Rasterization-image.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Rasterization-manual.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Rasterization.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/ReadImageInfo.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/RefineSensorModel.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Rescale.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/RigidTransformResample-id.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/RigidTransformResample-rotation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/RigidTransformResample-translation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/RigidTransformResample.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/SARCalibration.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/SARDecompositions.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/SARPolarMatrixConvert.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/SARPolarSynth.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/SFSTextureExtraction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/SOMClassification.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/SampleExtraction.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/SampleSelection.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/SarRadiometricCalibration.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Segmentation-cc.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Segmentation-meanshift.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Segmentation-mprofiles.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Segmentation-watershed.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Segmentation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Smoothing-anidif.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Smoothing-gaussian.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Smoothing-mean.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Smoothing.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/SplitImage.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/StereoFramework.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/StereoRectificationGridGenerator.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/Superimpose.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TestApplication.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TileFusion.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-ann.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-bayes.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-boost.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-dt.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-gbt.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-knn.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-libsvm.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-rf.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainOGRLayersClassifier.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-ann.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-dt.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-gbt.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-knn.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-libsvm.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-rf.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-ann.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-bayes.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-boost.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-dt.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-gbt.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-knn.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-libsvm.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-rf.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataDSValidation.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataExtractROI.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataReprojection-image.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataReprojection-user.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataReprojection.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataSetField.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataTransform.html delete mode 100644 python/plugins/processing/algs/otb/description/5.8.0/doc/VertexComponentAnalysis.html delete mode 100644 python/plugins/processing/algs/otb/helper/generate_application_descriptors.py delete mode 100644 python/plugins/processing/algs/otb/maintenance/OTBHelper.py delete mode 100644 python/plugins/processing/algs/otb/maintenance/OTBSpecific_XMLcreation.py delete mode 100644 python/plugins/processing/algs/otb/maintenance/OTBTester.py delete mode 100644 python/plugins/processing/algs/otb/maintenance/README.md delete mode 100644 python/plugins/processing/algs/otb/maintenance/TestOTBAlgorithms.py delete mode 100644 python/plugins/processing/algs/otb/maintenance/black_list.xml delete mode 100644 python/plugins/processing/algs/otb/maintenance/parsing.py delete mode 100644 python/plugins/processing/algs/otb/maintenance/white_list.xml diff --git a/python/plugins/processing/algs/lidar/CMakeLists.txt b/python/plugins/processing/algs/lidar/CMakeLists.txt deleted file mode 100644 index d4e252561a8c..000000000000 --- a/python/plugins/processing/algs/lidar/CMakeLists.txt +++ /dev/null @@ -1,6 +0,0 @@ -FILE(GLOB PY_FILES *.py) - -ADD_SUBDIRECTORY(fusion) -ADD_SUBDIRECTORY(lastools) - -PLUGIN_INSTALL(processing ./algs/lidar ${PY_FILES}) diff --git a/python/plugins/processing/algs/lidar/LidarToolsAlgorithmProvider.py b/python/plugins/processing/algs/lidar/LidarToolsAlgorithmProvider.py deleted file mode 100644 index 590245da09b6..000000000000 --- a/python/plugins/processing/algs/lidar/LidarToolsAlgorithmProvider.py +++ /dev/null @@ -1,260 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - LidarToolsAlgorithmProvider.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com - --------------------- - Date : April, October 2014 and May 2016 - Copyright : (C) 2014 - 2016 by Martin Isenburg - Email : martin near rapidlasso point com - --------------------- - Date : June 2014 - Copyright : (C) 2014 by Agresta S. Coop - Email : iescamochero at agresta dot org -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -from qgis.PyQt.QtGui import QIcon -from processing.core.AlgorithmProvider import AlgorithmProvider -from processing.core.ProcessingConfig import ProcessingConfig, Setting -from processing.tools.system import isWindows - -from .lastools.LAStoolsUtils import LAStoolsUtils -from .lastools.lasground import lasground -from .lastools.lasheight import lasheight -from .lastools.lasclassify import lasclassify -from .lastools.laszip import laszip -from .lastools.lasindex import lasindex -from .lastools.lasclip import lasclip -from .lastools.lasquery import lasquery -from .lastools.lascolor import lascolor -from .lastools.lasthin import lasthin -from .lastools.lasnoise import lasnoise -from .lastools.lassort import lassort -from .lastools.lastile import lastile -from .lastools.lasgrid import lasgrid -from .lastools.lasview import lasview -from .lastools.lasboundary import lasboundary -from .lastools.lasinfo import lasinfo -from .lastools.las2dem import las2dem -from .lastools.blast2dem import blast2dem -from .lastools.las2iso import las2iso -from .lastools.las2tin import las2tin -from .lastools.las2las_filter import las2las_filter -from .lastools.las2las_project import las2las_project -from .lastools.las2las_transform import las2las_transform -from .lastools.blast2iso import blast2iso -from .lastools.lasprecision import lasprecision -from .lastools.lasvalidate import lasvalidate -from .lastools.lasduplicate import lasduplicate -from .lastools.las2txt import las2txt -from .lastools.txt2las import txt2las -from .lastools.las2shp import las2shp -from .lastools.shp2las import shp2las -from .lastools.lasmerge import lasmerge -from .lastools.lassplit import lassplit -from .lastools.lascanopy import lascanopy -from .lastools.lasoverage import lasoverage -from .lastools.lasoverlap import lasoverlap -from .lastools.laspublish import laspublish -from .lastools.lasground_new import lasground_new -from .lastools.lascontrol import lascontrol -from .lastools.lasdiff import lasdiff -from .lastools.lasheight_classify import lasheight_classify - -from .lastools.lastilePro import lastilePro -from .lastools.lasgroundPro import lasgroundPro -from .lastools.las2demPro import las2demPro -from .lastools.lasheightPro import lasheightPro -from .lastools.laszipPro import laszipPro -from .lastools.lasgridPro import lasgridPro -from .lastools.lasduplicatePro import lasduplicatePro -from .lastools.lassortPro import lassortPro -from .lastools.lasclassifyPro import lasclassifyPro -from .lastools.lasthinPro import lasthinPro -from .lastools.lasnoisePro import lasnoisePro -from .lastools.lasindexPro import lasindexPro -from .lastools.lascanopyPro import lascanopyPro -from .lastools.blast2demPro import blast2demPro -from .lastools.lasboundaryPro import lasboundaryPro -from .lastools.lasinfoPro import lasinfoPro -from .lastools.las2lasPro_filter import las2lasPro_filter -from .lastools.las2lasPro_project import las2lasPro_project -from .lastools.las2lasPro_transform import las2lasPro_transform -from .lastools.lasoveragePro import lasoveragePro -from .lastools.txt2lasPro import txt2lasPro -from .lastools.las2txtPro import las2txtPro -from .lastools.blast2isoPro import blast2isoPro -from .lastools.lasvalidatePro import lasvalidatePro -from .lastools.lasmergePro import lasmergePro -from .lastools.lasviewPro import lasviewPro -from .lastools.lasoverlapPro import lasoverlapPro -from .lastools.laspublishPro import laspublishPro -from .lastools.lasgroundPro_new import lasgroundPro_new -from .lastools.lasheightPro_classify import lasheightPro_classify - -from .lastools.flightlinesToDTMandDSM import flightlinesToDTMandDSM -from .lastools.flightlinesToCHM import flightlinesToCHM -from .lastools.flightlinesToSingleCHMpitFree import flightlinesToSingleCHMpitFree -from .lastools.hugeFileClassify import hugeFileClassify -from .lastools.hugeFileGroundClassify import hugeFileGroundClassify -from .lastools.hugeFileNormalize import hugeFileNormalize - -from .fusion.OpenViewerAction import OpenViewerAction -from .fusion.ASCII2DTM import ASCII2DTM -from .fusion.CanopyMaxima import CanopyMaxima -from .fusion.CanopyModel import CanopyModel -from .fusion.Catalog import Catalog -from .fusion.ClipData import ClipData -from .fusion.CloudMetrics import CloudMetrics -from .fusion.Cover import Cover -from .fusion.DTM2TIF import DTM2TIF -from .fusion.DTM2ASCII import DTM2ASCII -from .fusion.FirstLastReturn import FirstLastReturn -from .fusion.GridMetrics import GridMetrics -from .fusion.GridSurfaceCreate import GridSurfaceCreate -from .fusion.TinSurfaceCreate import TinSurfaceCreate -from .fusion.Csv2Grid import Csv2Grid -from .fusion.GroundFilter import GroundFilter -from .fusion.MergeData import MergeData -from .fusion.FilterData import FilterData -from .fusion.PolyClipData import PolyClipData -from .fusion.ImageCreate import ImageCreate -from .fusion.IntensityImage import IntensityImage -from .fusion.DensityMetrics import DensityMetrics -from .fusion.MergeDTM import MergeDTM -from .fusion.TopoMetrics import TopoMetrics -from .fusion.TreeSeg import TreeSeg -from .fusion.SplitDTM import SplitDTM -from .fusion.MergeRaster import MergeRaster -from .fusion.SurfaceStats import SurfaceStats -from .fusion.ReturnDensity import ReturnDensity # spellok -from .fusion.GridSurfaceStats import GridSurfaceStats -from .fusion.FusionUtils import FusionUtils - - -class LidarToolsAlgorithmProvider(AlgorithmProvider): - - def __init__(self): - super().__init__() - self.activate = False - - def _loadAlgorithms(self): - self.algs = [] - - # LAStools for processing single files - - if (isWindows() or LAStoolsUtils.hasWine()): - lastools = [ - lasground(), lasheight(), lasclassify(), lasclip(), lastile(), - lascolor(), lasgrid(), las2dem(), blast2dem(), las2iso(), blast2iso(), - lasview(), lasboundary(), lasinfo(), lasprecision(), las2tin(), - lasvalidate(), lasduplicate(), las2txt(), txt2las(), laszip(), - lasindex(), lasthin(), lassort(), lascanopy(), lasmerge(), - las2shp(), shp2las(), lasnoise(), lassplit(), las2las_filter(), - las2las_project(), las2las_transform(), lasoverage(), lasoverlap(), - lasquery(), laspublish(), lasground_new(), lascontrol(), lasdiff(), - lasheight_classify() - ] - else: - lastools = [ - lasinfo(), lasprecision(), lasvalidate(), las2txt(), txt2las(), - laszip(), lasindex(), lasmerge(), las2las_filter(), las2las_project(), - las2las_transform(), lasquery(), lasdiff() - ] - self.algs.extend(lastools) - - # LAStools Production for processing folders of files - - if (isWindows() or LAStoolsUtils.hasWine()): - lastoolsPro = [ - lastilePro(), lasgroundPro(), las2demPro(), lasheightPro(), laszipPro(), - lasduplicatePro(), lasgridPro(), lassortPro(), lasclassifyPro(), lasthinPro(), - lasnoisePro(), lasindexPro(), lascanopyPro(), blast2demPro(), lasboundaryPro(), - lasinfoPro(), las2lasPro_filter(), las2lasPro_project(), las2lasPro_transform(), - lasoveragePro(), txt2lasPro(), las2txtPro(), blast2isoPro(), lasvalidatePro(), - lasmergePro(), lasviewPro(), lasoverlapPro(), laspublishPro(), lasgroundPro_new(), - lasheightPro_classify() - ] - else: - lastoolsPro = [ - laszipPro(), lasindexPro(), lasinfoPro(), las2lasPro_filter(), las2lasPro_project(), - las2lasPro_transform(), txt2lasPro(), las2txtPro(), lasvalidatePro(), lasmergePro() - ] - self.algs.extend(lastoolsPro) - - # some examples for LAStools Pipelines - - if (isWindows() or LAStoolsUtils.hasWine()): - lastoolsPipe = [ - flightlinesToDTMandDSM(), flightlinesToCHM(), flightlinesToSingleCHMpitFree(), hugeFileClassify(), - hugeFileGroundClassify(), hugeFileNormalize() - ] - else: - lastoolsPipe = [] - self.algs.extend(lastoolsPipe) - - # FUSION - - if isWindows(): - self.actions.append(OpenViewerAction()) - fusiontools = [ - Catalog(), CloudMetrics(), CanopyMaxima(), CanopyModel(), ClipData(), - Csv2Grid(), Cover(), FilterData(), GridMetrics(), GroundFilter(), - GridSurfaceCreate(), MergeData(), TinSurfaceCreate(), PolyClipData(), - DTM2TIF(), DTM2ASCII(), FirstLastReturn(), ASCII2DTM(), ImageCreate(), - IntensityImage(), DensityMetrics(), MergeDTM(), TopoMetrics(), TreeSeg(), - SplitDTM(), MergeRaster(), SurfaceStats(), ReturnDensity(), GridSurfaceStats() # spellok - ] - for alg in fusiontools: - alg.group, alg.i18n_group = alg.trAlgorithm('Fusion') - self.algs.extend(fusiontools) - - def initializeSettings(self): - AlgorithmProvider.initializeSettings(self) - ProcessingConfig.addSetting(Setting( - self.name(), - LAStoolsUtils.LASTOOLS_FOLDER, - self.tr('LAStools folder'), LAStoolsUtils.LAStoolsPath(), - valuetype=Setting.FOLDER)) - ProcessingConfig.addSetting(Setting( - self.name(), - FusionUtils.FUSION_FOLDER, - self.tr('Fusion folder'), FusionUtils.FusionPath(), - valuetype=Setting.FOLDER)) - if not isWindows(): - ProcessingConfig.addSetting(Setting( - self.name(), - LAStoolsUtils.WINE_FOLDER, - self.tr('Wine folder'), '', valuetype=Setting.FOLDER)) - - def id(self): - return 'lidartools' - - def name(self): - return self.tr('Tools for LiDAR data') - - def icon(self): - return QIcon(os.path.dirname(__file__) + '/../../images/tool.png') - - def getSupportedOutputTableExtensions(self): - return ['csv'] diff --git a/python/plugins/processing/algs/lidar/__init__.py b/python/plugins/processing/algs/lidar/__init__.py deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/python/plugins/processing/algs/lidar/fusion/ASCII2DTM.py b/python/plugins/processing/algs/lidar/fusion/ASCII2DTM.py deleted file mode 100644 index f51b74069f35..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/ASCII2DTM.py +++ /dev/null @@ -1,87 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - ASCII2DTM.py - --------------------- - Date : May 2014 - Copyright : (C) 2014 by Niccolo' Marchi - Email : sciurusurbanus at hotmail dot it -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = "Niccolo' Marchi" -__date__ = 'May 2014' -__copyright__ = "(C) 2014 by Niccolo' Marchi" - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterNumber -from processing.core.outputs import OutputFile -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils - - -class ASCII2DTM(FusionAlgorithm): - - INPUT = 'INPUT' - OUTPUT = 'OUTPUT' - COORDSYS = 'COORDSYS' - XYUNITS = 'XYUNITS' - ZUNITS = 'ZUNITS' - UNITS = ['Meter', 'Feet'] - ZONE = 'ZONE' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('ASCII to DTM') - self.group, self.i18n_group = self.trAlgorithm('Conversion') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input ESRI ASCII layer'), optional=False)) - self.addParameter(ParameterSelection( - self.XYUNITS, self.tr('XY Units'), self.UNITS)) - self.addParameter(ParameterSelection( - self.ZUNITS, self.tr('Z Units'), self.UNITS)) - self.addParameter(ParameterSelection( - self.COORDSYS, self.tr('Coordinate system'), ['unknown', 'UTM', 'state plane'])) - self.addParameter(ParameterNumber( - self.ZONE, self.tr("Coordinate system zone ('0' for unknown)"), 0, None, 0)) - - self.addOutput(OutputFile( - self.OUTPUT, self.tr('Output surface'), 'dtm')) - - self.addAdvancedModifiers() - - def processAlgorithm(self, feedback): - commands = [os.path.join(FusionUtils.FusionPath(), 'ASCII2DTM.exe')] - commands.append('/verbose') - self.addAdvancedModifiersToCommand(commands) - outFile = self.getOutputValue(self.OUTPUT) - commands.append(outFile) - commands.append(self.UNITS[self.getParameterValue(self.XYUNITS)][0]) - commands.append(self.UNITS[self.getParameterValue(self.ZUNITS)][0]) - commands.append(str(self.getParameterValue(self.COORDSYS))) - commands.append(str(self.getParameterValue(self.ZONE))) - commands.append('0') - commands.append('0') - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - FusionUtils.runFusion(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/fusion/CMakeLists.txt b/python/plugins/processing/algs/lidar/fusion/CMakeLists.txt deleted file mode 100644 index 2a29e1d91430..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/CMakeLists.txt +++ /dev/null @@ -1,3 +0,0 @@ -FILE(GLOB PY_FILES *.py) - -PLUGIN_INSTALL(processing ./algs/lidar/fusion ${PY_FILES}) diff --git a/python/plugins/processing/algs/lidar/fusion/CanopyMaxima.py b/python/plugins/processing/algs/lidar/fusion/CanopyMaxima.py deleted file mode 100644 index 0657791cd52e..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/CanopyMaxima.py +++ /dev/null @@ -1,104 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - CanopyMaxima.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterBoolean -from processing.core.outputs import OutputFile -from .FusionUtils import FusionUtils -from .FusionAlgorithm import FusionAlgorithm - - -class CanopyMaxima(FusionAlgorithm): - - INPUT = 'INPUT' - OUTPUT = 'OUTPUT' - THRESHOLD = 'THRESHOLD' - GROUND = 'GROUND' - SUMMARY = 'SUMMARY' - PARAM_A = 'PARAM_A' - PARAM_C = 'PARAM_C' - SHAPE = 'SHAPE' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('Canopy Maxima') - self.group, self.i18n_group = self.trAlgorithm('Points') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input PLANS DTM canopy height model'), - optional=False)) - self.addParameter(ParameterFile( - self.GROUND, self.tr('Input ground PLANS DTM layer [optional]'))) - self.addParameter(ParameterNumber( - self.THRESHOLD, self.tr('Limit analysis to areas above this height threshold'), 0, None, 10.0)) - - self.addParameter(ParameterNumber( - self.PARAM_A, self.tr('Variable window size: parameter A'), 0, None, 2.51503)) - self.addParameter(ParameterNumber( - self.PARAM_C, self.tr('Parameter C'), 0, None, 0.00901)) - summary = ParameterBoolean( - self.SUMMARY, self.tr('Tree height summary statistics'), False) - summary.isAdvanced = True - self.addParameter(summary) - shape = ParameterBoolean( - self.SHAPE, self.tr('Create output shapefiles'), False) - shape.isAdvanced = True - self.addParameter(shape) - - self.addOutput(OutputFile( - self.OUTPUT, self.tr('Output file with maxima'), 'csv')) - - self.addAdvancedModifiers() - - def processAlgorithm(self, feedback): - commands = [os.path.join(FusionUtils.FusionPath(), 'CanopyMaxima.exe')] - commands.append('/verbose') - commands.append('/wse:' + unicode(self.getParameterValue(self.PARAM_A)) + ',0,' + unicode(self.getParameterValue(self.PARAM_C)) + ',0') - ground = self.getParameterValue(self.GROUND) - if ground: - gfiles = self.getParameterValue(self.GROUND).split(';') - if len(gfiles) == 1: - commands.append('/ground:' + str(ground)) - else: - FusionUtils.createGroundList(gfiles) - commands.append('/ground:' + str(FusionUtils.tempGroundListFilepath())) - commands.append('/threshold:' + str(self.getParameterValue(self.THRESHOLD))) - if self.getParameterValue(self.SUMMARY): - commands.append('/summary') - self.addAdvancedModifiersToCommand(commands) - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - commands.append(self.getOutputValue(self.OUTPUT)) - - FusionUtils.runFusion(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/fusion/CanopyModel.py b/python/plugins/processing/algs/lidar/fusion/CanopyModel.py deleted file mode 100644 index cdec4d9d9c1c..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/CanopyModel.py +++ /dev/null @@ -1,154 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - CanopyModel.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com - --------------------- - Date : June 2014 - Copyright : (C) 2014 by Agresta S. Coop. - Email : iescamochero at agresta dot org -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterString -from processing.core.outputs import OutputFile -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils - - -class CanopyModel(FusionAlgorithm): - - INPUT = 'INPUT' - OUTPUT_DTM = 'OUTPUT_DTM' - ASPECT = 'ASPECT' - CELLSIZE = 'CELLSIZE' - XYUNITS = 'XYUNITS' - ZUNITS = 'ZUNITS' - UNITS = ['Meter', 'Feet'] - GROUND = 'GROUND' - MEDIAN = 'MEDIAN' - SMOOTH = 'SMOOTH' - SLOPE = 'SLOPE' - CLASS = 'CLASS' - RETURN = 'RETURN' - ASCII = 'ASCII' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('Canopy Model') - self.group, self.i18n_group = self.trAlgorithm('Points') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input LAS layer'), - optional=False)) - self.addParameter(ParameterNumber( - self.CELLSIZE, self.tr('Cell Size'), 0, None, 10.0)) - self.addParameter(ParameterSelection( - self.XYUNITS, self.tr('XY Units'), self.UNITS)) - self.addParameter(ParameterSelection( - self.ZUNITS, self.tr('Z Units'), self.UNITS)) - self.addOutput(OutputFile( - self.OUTPUT_DTM, self.tr('.dtm output surface'), 'dtm')) - ground = ParameterFile( - self.GROUND, self.tr('Input ground PLANS DTM layer'), False, True) - ground.isAdvanced = True - self.addParameter(ground) - median = ParameterString( - self.MEDIAN, self.tr('Median'), '', False, True) - median.isAdvanced = True - self.addParameter(median) - smooth = ParameterString( - self.SMOOTH, self.tr('Smooth'), '', False, True) - smooth.isAdvanced = True - self.addParameter(smooth) - class_var = ParameterString( - self.CLASS, self.tr('Select specific class'), '', False, True) - class_var.isAdvanced = True - self.addParameter(class_var) - ret_num = ParameterString( - self.RETURN, self.tr('Select specific return'), '', False, True) - ret_num.isAdvanced = True - self.addParameter(ret_num) - slope = ParameterBoolean( - self.SLOPE, self.tr('Calculate slope'), False) - slope.isAdvanced = True - self.addParameter(slope) - aspec = ParameterBoolean( - self.ASPECT, self.tr('Calculate aspect'), False) - aspec.isAdvanced = True - self.addParameter(aspect) - self.addParameter(ParameterBoolean( - self.ASCII, self.tr('Add an ASCII output'), False)) - self.addAdvancedModifiers() - - def processAlgorithm(self, feedback): - commands = [os.path.join(FusionUtils.FusionPath(), 'CanopyModel.exe')] - commands.append('/verbose') - ground = self.getParameterValue(self.GROUND) - if str(ground).strip(): - gfiles = self.getParameterValue(self.GROUND).split(';') - if len(gfiles) == 1: - commands.append('/ground:' + str(ground)) - else: - FusionUtils.createGroundList(gfiles) - commands.append('/ground:' + str(FusionUtils.tempGroundListFilepath())) - median = self.getParameterValue(self.MEDIAN) - if str(median).strip(): - commands.append('/median:' + str(median)) - smooth = self.getParameterValue(self.SMOOTH) - if str(smooth).strip(): - commands.append('/smooth:' + str(smooth)) - slope = self.getParameterValue(self.SLOPE) - if slope: - commands.append('/slope') - aspect = self.getParameterValue(self.ASPECT) - if aspect: - commands.append('/aspect') - class_var = self.getParameterValue(self.CLASS) - if str(class_var).strip(): - commands.append('/class:' + str(class_var)) - ret_num = self.getParameterValue(self.RETURN) - if str(ret_num).strip(): - commands.append('/return:' + str(ret_num)) - use_ascii = self.getParameterValue(self.ASCII) - if use_ascii: - commands.append('/ascii') - self.addAdvancedModifiersToCommand(commands) - commands.append(self.getOutputValue(self.OUTPUT_DTM)) - commands.append(str(self.getParameterValue(self.CELLSIZE))) - commands.append(self.UNITS[self.getParameterValue(self.XYUNITS)][0]) - commands.append(self.UNITS[self.getParameterValue(self.ZUNITS)][0]) - commands.append('0') - commands.append('0') - commands.append('0') - commands.append('0') - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - FusionUtils.runFusion(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/fusion/Catalog.py b/python/plugins/processing/algs/lidar/fusion/Catalog.py deleted file mode 100644 index 512da8ca6795..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/Catalog.py +++ /dev/null @@ -1,130 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - Catalog.py - --------------------- - Date : June 2014 - Copyright : (C) 2014 by Agresta S. Coop - Email : iescamochero at agresta dot org -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Agresta S. Coop - www.agresta.org' -__date__ = 'June 2014' -__copyright__ = '(C) 2014, Agresta S. Coop' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterString -from processing.core.parameters import ParameterBoolean -from processing.core.outputs import OutputFile -from .FusionUtils import FusionUtils -from .FusionAlgorithm import FusionAlgorithm - - -class Catalog(FusionAlgorithm): - - INPUT = 'INPUT' - OUTPUT = 'OUTPUT' - DENSITY = 'DENSITY' - FIRSTDENSITY = 'FIRSTDENSITY' - INTENSITY = 'INTENSITY' - INDEX = 'INDEX' - IMAGE = 'IMAGE' - DRAWTILES = 'DRAWTILES' - COVERAGE = 'COVERAGE' - CRETURNS = 'CRETURNS' - ADVANCED_MODIFIERS = 'ADVANCED_MODIFIERS' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('Catalog') - self.group, self.i18n_group = self.trAlgorithm('Points') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input LAS layer'), - optional=False)) - self.addOutput(OutputFile(self.OUTPUT, self.tr('Output files'))) - density = ParameterString( - self.DENSITY, - self.tr('Density - area, min, max (set blank if not used)'), - '', False, True) - density.isAdvanced = True - self.addParameter(density) - firest_density = ParameterString( - self.FIRSTDENSITY, - self.tr('First Density - area, min, max (set blank if not used)'), - '', False, True) - firest_density.isAdvanced = True - self.addParameter(firest_density) - intensity = ParameterString( - self.INTENSITY, - self.tr('Intensity - area, min, max (set blank if not used)'), - '', False, True) - intensity.isAdvanced = True - self.addParameter(intensity) - self.addParameter(ParameterBoolean(self.INDEX, - self.tr('Create LIDAR data file indexes'), False)) - self.addParameter(ParameterBoolean(self.IMAGE, - self.tr('Create image files showing the coverage area for each LIDAR file'), False)) - self.addParameter(ParameterBoolean(self.DRAWTILES, - self.tr('Draw data file extents and names on the intensity image'), False)) - self.addParameter(ParameterBoolean(self.COVERAGE, - self.tr('Create one image that shows the nominal coverage area'), False)) - self.addParameter(ParameterBoolean(self.CRETURNS, - self.tr('Adds count return columns in the CSV and HTML output'), False)) - advanced_modifiers = ParameterString( - self.ADVANCED_MODIFIERS, - self.tr('Additional modifiers'), '', False, True) - advanced_modifiers.isAdvanced = True - self.addParameter(advanced_modifiers) - - def processAlgorithm(self, feedback): - commands = [os.path.join(FusionUtils.FusionPath(), 'Catalog.exe')] - commands.append('/verbose') - intensity = self.getParameterValue(self.INTENSITY) - if str(intensity).strip(): - commands.append('/intensity:' + str(intensity)) - density = self.getParameterValue(self.DENSITY) - if str(density).strip(): - commands.append('/density:' + str(density)) - firstdensity = self.getParameterValue(self.FIRSTDENSITY) - if str(firstdensity).strip(): - commands.append('/firstdensity:' + str(firstdensity)) - index = self.getParameterValue(self.INDEX) - if str(index).strip(): - commands.append('/index') - drawtiles = self.getParameterValue(self.IMAGE) - if str(drawtiles).strip(): - commands.append('/drawtiles') - coverage = self.getParameterValue(self.DRAWTILES) - if str(coverage).strip(): - commands.append('/coverage') - image = self.getParameterValue(self.COVERAGE) - if str(image).strip(): - commands.append('/image') - creturns = self.getParameterValue(self.COVERAGE) - if str(creturns).strip(): - commands.append('/countreturns') - advanced_modifiers = str(self.getParameterValue(self.ADVANCED_MODIFIERS)).strip() - if advanced_modifiers: - commands.append(advanced_modifiers) - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - commands.append(self.getOutputValue(self.OUTPUT)) - FusionUtils.runFusion(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/fusion/ClipData.py b/python/plugins/processing/algs/lidar/fusion/ClipData.py deleted file mode 100644 index 57e498646b95..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/ClipData.py +++ /dev/null @@ -1,100 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - ClipData.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterExtent -from processing.core.parameters import ParameterSelection -from processing.core.outputs import OutputFile -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils - - -class ClipData(FusionAlgorithm): - - INPUT = 'INPUT' - OUTPUT = 'OUTPUT' - EXTENT = 'EXTENT' - SHAPE = 'SHAPE' - DTM = 'DTM' - HEIGHT = 'HEIGHT' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('Clip Data') - self.group, self.i18n_group = self.trAlgorithm('Points') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input LAS layer'), - optional=False)) - self.addParameter(ParameterExtent(self.EXTENT, self.tr('Extent'), optional=False)) - self.addParameter(ParameterSelection( - self.SHAPE, self.tr('Shape of the sample area'), ['Rectangle', 'Circle'])) - self.addOutput(OutputFile( - self.OUTPUT, self.tr('Output clipped LAS file'))) - dtm = ParameterFile( - self.DTM, self.tr('Ground file for height normalization')) - dtm.isAdvanced = True - self.addParameter(dtm) - height = ParameterBoolean( - self.HEIGHT, self.tr("Convert point elevations into heights above ground (used with the above command)"), False) - height.isAdvanced = True - self.addParameter(height) - self.addAdvancedModifiers() - - def processAlgorithm(self, feedback): - commands = [os.path.join(FusionUtils.FusionPath(), 'ClipData.exe')] - commands.append('/verbose') - self.addAdvancedModifiersToCommand(commands) - commands.append('/shape:' + str(self.getParameterValue(self.SHAPE))) - dtm = self.getParameterValue(self.DTM) - if dtm: - gfiles = self.getParameterValue(self.DTM).split(';') - if len(gfiles) == 1: - commands.append('/ground:' + str(dtm)) - else: - FusionUtils.createGroundList(gfiles) - commands.append('/ground:' + str(FusionUtils.tempGroundListFilepath())) - height = self.getParameterValue(self.HEIGHT) - if height: - commands.append('/height') - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - outFile = self.getOutputValue(self.OUTPUT) - commands.append(outFile) - extent = str(self.getParameterValue(self.EXTENT)).split(',') - commands.append(extent[0]) - commands.append(extent[2]) - commands.append(extent[1]) - commands.append(extent[3]) - FusionUtils.runFusion(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/fusion/CloudMetrics.py b/python/plugins/processing/algs/lidar/fusion/CloudMetrics.py deleted file mode 100644 index 968baca9e408..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/CloudMetrics.py +++ /dev/null @@ -1,96 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - CloudMetrics.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com - --------------------- - Date : June 2014 - Copyright : (C) 2014 by Agresta S. Coop. - Email : iescamochero at agresta dot org -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterBoolean -from processing.core.outputs import OutputFile -from .FusionUtils import FusionUtils -from .FusionAlgorithm import FusionAlgorithm - - -class CloudMetrics(FusionAlgorithm): - - INPUT = 'INPUT' - OUTPUT = 'OUTPUT' - ABOVE = 'ABOVE' - FIRSTIMPULSE = 'FIRSTIMPULSE' - FIRSTRETURN = 'FIRSTRETURN' - HTMIN = 'HTMIN' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('Cloud Metrics') - self.group, self.i18n_group = self.trAlgorithm('Points') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input LAS layer'), - optional=False)) - self.addOutput(OutputFile( - self.OUTPUT, self.tr('Output file with tabular metric information'), 'csv')) - above = ParameterNumber(self.ABOVE, self.tr('Compute cover statistics above the following heightbreak:'), 0, None, 0.0) - above.isAdvanced = True - self.addParameter(above) - firstImpulse = ParameterBoolean( - self.FIRSTIMPULSE, self.tr('First Impulse'), False) - firstImpulse.isAdvanced = True - self.addParameter(firstImpulse) - firstReturn = ParameterBoolean( - self.FIRSTRETURN, self.tr('First Return'), False) - firstReturn.isAdvanced = True - self.addParameter(firstReturn) - htmin = ParameterNumber(self.HTMIN, self.tr('Use only returns above this minimum height:'), 0, None, 0) - htmin.isAdvanced = True - self.addParameter(htmin) - - def processAlgorithm(self, feedback): - commands = [os.path.join(FusionUtils.FusionPath(), 'CloudMetrics.exe')] - commands.append('/verbose') - above = self.getParameterValue(self.ABOVE) - if above != 0.0: - commands.append('/above:' + str(above)) - firstImpulse = self.getParameterValue(self.FIRSTIMPULSE) - if firstImpulse: - commands.append('/firstinpulse') - firstReturn = self.getParameterValue(self.FIRSTRETURN) - if firstReturn: - commands.append('/firstreturn') - htmin = self.getParameterValue(self.HTMIN) - if htmin != 0.0: - commands.append('/minht:' + str(htmin)) - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - commands.append(self.getOutputValue(self.OUTPUT)) - FusionUtils.runFusion(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/fusion/Cover.py b/python/plugins/processing/algs/lidar/fusion/Cover.py deleted file mode 100644 index 31f0484c342e..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/Cover.py +++ /dev/null @@ -1,116 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - Cover.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -import subprocess -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterSelection -from processing.core.outputs import OutputRaster -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils - - -class Cover(FusionAlgorithm): - - INPUT = 'INPUT' - OUTPUT = 'OUTPUT' - CELLSIZE = 'CELLSIZE' - HEIGHTBREAK = 'HEIGHTREAK' - GROUND = 'GROUND' - ALLRETS = 'ALLRETS' - PENETRATION = 'PENETRATION' - XYUNITS = 'XYUNITS' - ZUNITS = 'ZUNITS' - UNITS = ['Meter', 'Feet'] - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('Cover') - self.group, self.i18n_group = self.trAlgorithm('Points') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input LAS layer'), - optional=False)) - self.addParameter(ParameterFile( - self.GROUND, self.tr('Input ground PLANS DTM layer'), - optional=False)) - self.addParameter(ParameterNumber( - self.CELLSIZE, self.tr('Cell Size'), 0, None, 10.0)) - self.addParameter(ParameterNumber( - self.HEIGHTBREAK, self.tr('Heightbreak for the cover calculation (see help)'), 0, None, 10.0)) - self.addParameter(ParameterSelection( - self.XYUNITS, self.tr('XY Units'), self.UNITS)) - self.addParameter(ParameterSelection( - self.ZUNITS, self.tr('Z Units'), self.UNITS)) - self.addParameter(ParameterBoolean( - self.ALLRETS, self.tr('Use all returns instead of only first'), False)) - self.addParameter(ParameterBoolean( - self.PENETRATION, self.tr('Compute the proportion of returns close to the ground surface'), False)) - self.addOutput(OutputFile(self.OUTPUT, self.tr('Cover output file'))) - self.addAdvancedModifiers() - - def processAlgorithm(self, feedback): - commands = [os.path.join(FusionUtils.FusionPath(), 'Cover.exe')] - commands.append('/verbose') - allrets = self.getParameterValue(self.ALLRETS) - if str(allrets).strip() != '': - commands.append('/all') - penetration = self.getParameterValue(self.PENETRATION) - if penetration: - commands.append('/penetration') - self.addAdvancedModifiersToCommand(commands) - ground = self.getParameterValue(self.GROUND).split(';') - if len(ground) == 1: - commands.append(self.getParameterValue(self.GROUND)) - else: - FusionUtils.createGroundList(ground) - commands.append(FusionUtils.tempGroundListFilepath()) - outFile = self.getOutputValue(self.OUTPUT) + '.dtm' - commands.append(outFile) - commands.append(str(self.getParameterValue(self.HEIGHTBREAK))) - commands.append(str(self.getParameterValue(self.CELLSIZE))) - commands.append(self.UNITS[self.getParameterValue(self.XYUNITS)][0]) - commands.append(self.UNITS[self.getParameterValue(self.ZUNITS)][0]) - commands.append('0') - commands.append('0') - commands.append('0') - commands.append('0') - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - FusionUtils.runFusion(commands, feedback) - commands = [os.path.join(FusionUtils.FusionPath(), 'DTM2ASCII.exe')] - commands.append(outFile) - commands.append(self.getOutputValue(self.OUTPUT)) - p = subprocess.Popen(commands, shell=True) - p.wait() diff --git a/python/plugins/processing/algs/lidar/fusion/Csv2Grid.py b/python/plugins/processing/algs/lidar/fusion/Csv2Grid.py deleted file mode 100644 index 279dc4eb4e9f..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/Csv2Grid.py +++ /dev/null @@ -1,60 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - Csv2Grid.py - --------------------- - Date : June 2014 - Copyright : (C) 2014 by Agresta S. Coop - Email : iescamochero at agresta dot org -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Agresta S. Coop - www.agresta.org' -__date__ = 'June 2014' -__copyright__ = '(C) 2014, Agresta S. Coop' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterNumber -from processing.core.outputs import OutputFile -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils - - -class Csv2Grid(FusionAlgorithm): - - INPUT = 'INPUT' - COLUMN = 'COLUMN' - OUTPUT = 'OUTPUT' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('Csv2Grid') - self.group, self.i18n_group = self.trAlgorithm('Points') - self.addParameter(ParameterFile(self.INPUT, self.tr('CSV Files'), optional=False)) - self.addParameter(ParameterNumber(self.COLUMN, self.tr('Column'), 0, None, 0)) - self.addOutput(OutputFile(self.OUTPUT, self.tr('Raster Output file'), 'asc')) - - def processAlgorithm(self, feedback): - commands = [os.path.join(FusionUtils.FusionPath(), 'CSV2Grid.exe')] - commands.append('/verbose') - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - commands.append(self.getParameterValue(self.COLUMN)) - commands.append(self.getOutputValue(self.OUTPUT)) - FusionUtils.runFusion(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/fusion/DTM2ASCII.py b/python/plugins/processing/algs/lidar/fusion/DTM2ASCII.py deleted file mode 100644 index bd7dadd602e4..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/DTM2ASCII.py +++ /dev/null @@ -1,64 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - DTM2ASCII.py - --------------------- - Date : May 2014 - Copyright : (C) 2014 by Niccolo' Marchi - Email : sciurusurbanus at hotmail dot it -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = "Niccolo' Marchi" -__date__ = 'May 2014' -__copyright__ = "(C) 2014 by Niccolo' Marchi" - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterSelection -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils - - -class DTM2ASCII(FusionAlgorithm): - - INPUT = 'INPUT' - SWITCH = 'SWITCH' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('DTM to ASCII') - self.group, self.i18n_group = self.trAlgorithm('Points') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input canopy surface (.dtm)'), - optional=False)) - self.addParameter(ParameterSelection( - self.SWITCH, self.tr('Output format'), ['raster (ASCII)', 'csv'])) - - def processAlgorithm(self, feedback): - commands = [os.path.join(FusionUtils.FusionPath(), 'DTM2ASCII.exe')] - commands.append('/verbose') - if self.getParameterValue(self.SWITCH) == 0: - commands.append('/raster') - else: - commands.append('/csv') - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - FusionUtils.runFusion(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/fusion/DTM2TIF.py b/python/plugins/processing/algs/lidar/fusion/DTM2TIF.py deleted file mode 100644 index b816aa4315df..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/DTM2TIF.py +++ /dev/null @@ -1,65 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - DTM2TIF.py - --------------------- - Date : May 2014 - Copyright : (C) 2014 by Niccolo' Marchi - Email : sciurusurbanus at hotmail dot it -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = "Niccolo' Marchi" -__date__ = 'May 2014' -__copyright__ = "(C) 2014 by Niccolo' Marchi" - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterFile -from processing.core.outputs import OutputRaster -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils - - -class DTM2TIF(FusionAlgorithm): - - INPUT = "INPUT" - OUTPUT = "OUTPUT" - CSV = 'CSV' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('DTM to TIF') - self.group, self.i18n_group = self.trAlgorithm('Conversion') - self.addParameter(ParameterFile( - self.INPUT, self.tr("Input .dtm layer"), - optional=False)) - self.addOutput(OutputRaster(self.OUTPUT, self.tr('Output file name'))) - self.addAdvancedModifiers() - - def processAlgorithm(self, feedback): - commands = [os.path.join(FusionUtils.FusionPath(), "DTM2TIF.exe")] - commands.append("/verbose") - self.addAdvancedModifiersToCommand(commands) - files = self.getParameterValue(self.INPUT).split(";") - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - outFile = self.getOutputValue(self.OUTPUT) - commands.append(outFile) - - FusionUtils.runFusion(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/fusion/DensityMetrics.py b/python/plugins/processing/algs/lidar/fusion/DensityMetrics.py deleted file mode 100644 index 3920bf29efe8..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/DensityMetrics.py +++ /dev/null @@ -1,97 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - DensityMetrics.py - --------------------- - Date : August 2016 - Copyright : (C) 2016 by Niccolo' Marchi - Email : sciurusurbanus at hotmail dot it -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" - -__author__ = "Niccolo' Marchi" -__date__ = 'August 2016' -__copyright__ = "(C) 2016 by Niccolo' Marchi" - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -import subprocess -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterNumber -from processing.core.outputs import OutputFile -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils - - -class DensityMetrics(FusionAlgorithm): - - INPUT = 'INPUT' - OUTPUT = 'OUTPUT' - CELLSIZE = 'CELLSIZE' - SLICE = 'SLICE' - GROUND = 'GROUND' - FIRST = 'FIRST' - NOCSV = 'NOCSV' - HTLIM = 'HTLIM' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('Density Metrics') - self.group, self.i18n_group = self.trAlgorithm('Points') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input LAS layer'), optional=False)) - self.addParameter(ParameterFile( - self.GROUND, self.tr('Input ground PLANS DTM layer'), optional=False)) - self.addParameter(ParameterNumber( - self.CELLSIZE, self.tr('Cellsize'), 0, None, 5.0)) - self.addParameter(ParameterNumber( - self.SLICE, self.tr('Slice thickness'), 0, None, 2.0)) - self.addParameter(ParameterNumber( - self.HTLIM, self.tr('Maximum height limit'), 0, None, 50.0)) - self.addParameter(ParameterBoolean( - self.FIRST, self.tr('Use only first returns'), False)) - self.addParameter(ParameterBoolean( - self.NOCSV, self.tr('Do not create a CSV output file for cell metrics'), False)) - self.addOutput(OutputFile( - self.OUTPUT, self.tr('Base name for output files'))) - self.addAdvancedModifiers() - - def processAlgorithm(self, progress): - commands = [os.path.join(FusionUtils.FusionPath(), 'DensityMetrics.exe')] - commands.append('/verbose') - first = self.getParameterValue(self.FIRST) - if first: - commands.append('/first') - nocsv = self.getParameterValue(self.NOCSV) - if nocsv: - commands.append('/nocsv') - commands.append('/maxsliceht:' + unicode(self.getParameterValue(self.HTLIM))) - self.addAdvancedModifiersToCommand(commands) - ground = self.getParameterValue(self.GROUND).split(';') - if len(ground) == 1: - commands.append(self.getParameterValue(self.GROUND)) - else: - FusionUtils.createGroundList(ground) - commands.append(FusionUtils.tempGroundListFilepath()) - commands.append(unicode(self.getParameterValue(self.CELLSIZE))) - commands.append(unicode(self.getParameterValue(self.SLICE))) - outFile = self.getOutputValue(self.OUTPUT) - commands.append(outFile) - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - FusionUtils.runFusion(commands, progress) diff --git a/python/plugins/processing/algs/lidar/fusion/FilterData.py b/python/plugins/processing/algs/lidar/fusion/FilterData.py deleted file mode 100644 index 1d13240d7173..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/FilterData.py +++ /dev/null @@ -1,76 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - FilterData.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterNumber -from processing.core.outputs import OutputFile -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils - - -class FilterData(FusionAlgorithm): - - INPUT = 'INPUT' - OUTPUT = 'OUTPUT' - VALUE = 'VALUE' - SHAPE = 'SHAPE' - WINDOWSIZE = 'WINDOWSIZE' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('Filter Data outliers') - self.group, self.i18n_group = self.trAlgorithm('Points') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input LAS layer'), - optional=False)) - self.addParameter(ParameterNumber( - self.VALUE, self.tr('Standard Deviation multiplier'))) - self.addParameter(ParameterNumber( - self.WINDOWSIZE, self.tr('Window size'), None, None, 10)) - self.addOutput(OutputFile( - self.OUTPUT, self.tr('Output filtered LAS file'))) - self.addAdvancedModifiers() - - def processAlgorithm(self, feedback): - commands = [os.path.join(FusionUtils.FusionPath(), 'FilterData.exe')] - commands.append('/verbose') - self.addAdvancedModifiersToCommand(commands) - commands.append('outlier') - commands.append(str(self.getParameterValue(self.VALUE))) - commands.append(str(self.getParameterValue(self.WINDOWSIZE))) - outFile = self.getOutputValue(self.OUTPUT) - commands.append(outFile) - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - FusionUtils.runFusion(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/fusion/FirstLastReturn.py b/python/plugins/processing/algs/lidar/fusion/FirstLastReturn.py deleted file mode 100644 index eb6211731b02..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/FirstLastReturn.py +++ /dev/null @@ -1,67 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - FirstLastReturn.py - --------------------- - Date : May 2014 - Copyright : (C) 2014 by Niccolo' Marchi - Email : sciurusurbanus at hotmail dot it -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = "Niccolo' Marchi" -__date__ = 'May 2014' -__copyright__ = "(C) 2014 by Niccolo' Marchi" - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterBoolean -from processing.core.outputs import OutputFile -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils - - -class FirstLastReturn(FusionAlgorithm): - - INPUT = 'INPUT' - OUTPUT = 'OUTPUT' - SWITCH = 'SWITCH' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('First&Last Return') - self.group, self.i18n_group = self.trAlgorithm('Points') - self.addParameter(ParameterFile(self.INPUT, self.tr('Input LAS layer'))) - self.addParameter(ParameterBoolean( - self.SWITCH, self.tr('Use LAS info'), True)) - self.addOutput(OutputFile(self.OUTPUT, self.tr('Output layers'))) - self.addAdvancedModifiers() - - def processAlgorithm(self, feedback): - commands = [os.path.join(FusionUtils.FusionPath(), 'FirstLastReturn.exe')] - commands.append('/verbose') - if self.getParameterValue(self.SWITCH): - commands.append('/uselas') - self.addAdvancedModifiersToCommand(commands) - outFile = self.getOutputValue(self.OUTPUT) - commands.append(outFile) - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - FusionUtils.runFusion(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/fusion/FusionAlgorithm.py b/python/plugins/processing/algs/lidar/fusion/FusionAlgorithm.py deleted file mode 100644 index dbde09feb6d9..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/FusionAlgorithm.py +++ /dev/null @@ -1,61 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - FusionAlgorithm.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -from qgis.PyQt.QtGui import QIcon -from processing.core.GeoAlgorithm import GeoAlgorithm -from processing.core.parameters import ParameterString -from .FusionUtils import FusionUtils - - -class FusionAlgorithm(GeoAlgorithm): - - ADVANCED_MODIFIERS = 'ADVANCED_MODIFIERS' - - def getIcon(self): - filepath = os.path.dirname(__file__) + '/../../../images/tool.png' - return QIcon(filepath) - - def checkBeforeOpeningParametersDialog(self): - path = FusionUtils.FusionPath() - if path == '': - return self.tr('Fusion folder is not configured.\nPlease ' - 'configure it before running Fusion algorithms.') - - def addAdvancedModifiers(self): - param = ParameterString( - self.ADVANCED_MODIFIERS, self.tr('Additional modifiers'), '', optional=True) - param.isAdvanced = True - self.addParameter(param) - - def addAdvancedModifiersToCommand(self, commands): - s = str(self.getParameterValue(self.ADVANCED_MODIFIERS)).strip() - if s != '': - commands.append(s) diff --git a/python/plugins/processing/algs/lidar/fusion/FusionUtils.py b/python/plugins/processing/algs/lidar/fusion/FusionUtils.py deleted file mode 100644 index fd185a4f3644..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/FusionUtils.py +++ /dev/null @@ -1,89 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - FusionUtils.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from builtins import object - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -import subprocess -from qgis.PyQt.QtCore import QCoreApplication -from processing.core.ProcessingLog import ProcessingLog -from processing.core.ProcessingConfig import ProcessingConfig -from processing.tools.system import userFolder - - -class FusionUtils(object): - - FUSION_FOLDER = 'FUSION_FOLDER' - - @staticmethod - def FusionPath(): - folder = ProcessingConfig.getSetting(FusionUtils.FUSION_FOLDER) - if folder is None: - folder = '' - - return folder - - @staticmethod - def tempFileListFilepath(): - filename = 'fusion_files_list.txt' - filepath = os.path.join(userFolder(), filename) - return filepath - - @staticmethod - def createFileList(files): - with open(FusionUtils.tempFileListFilepath(), 'w') as out: - for f in files: - out.write(f + '\n') - - @staticmethod - def runFusion(commands, feedback): - loglines = [] - loglines.append( - QCoreApplication.translate('FusionUtils', - 'Fusion execution console output')) - proc = subprocess.Popen( - commands, - shell=True, - stdout=subprocess.PIPE, - stdin=subprocess.DEVNULL, - stderr=subprocess.STDOUT, - universal_newlines=False, - ).stdout - for line in iter(proc.readline, ''): - loglines.append(line) - ProcessingLog.addToLog(ProcessingLog.LOG_INFO, loglines) - - @staticmethod - def tempGroundListFilepath(): - filename = 'fusion_groundFiles_list.txt' - filepath = os.path.join(userFolder(), filename) - return filepath - - @staticmethod - def createGroundList(gfiles): - with open(FusionUtils.tempGroundListFilepath(), 'w') as outg: - for f in gfiles: - outg.write(f + '\n') diff --git a/python/plugins/processing/algs/lidar/fusion/GridMetrics.py b/python/plugins/processing/algs/lidar/fusion/GridMetrics.py deleted file mode 100644 index 271a750a39ae..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/GridMetrics.py +++ /dev/null @@ -1,148 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - GridMetrics.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com - --------------------- - Date : June 2014 - Copyright : (C) 2014 by Agresta S. Coop. - Email : iescamochero at agresta dot org -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterString -from processing.core.outputs import OutputFile -from .FusionUtils import FusionUtils -from .FusionAlgorithm import FusionAlgorithm - - -class GridMetrics(FusionAlgorithm): - - INPUT = 'INPUT' - OUTPUT_CSV_ELEVATION = 'OUTPUT_CSV_ELEVATION' - OUTPUT_CSV_INTENSITY = 'OUTPUT_CSV_INTENSITY' - OUTPUT_TXT_ELEVATION = 'OUTPUT_TXT_ELEVATION' - OUTPUT_TXT_INTENSITY = 'OUTPUT_TXT_INTENSITY' - GROUND = 'GROUND' - HEIGHT = 'HEIGHT' - CELLSIZE = 'CELLSIZE' - OUTLIER = 'OUTLIER' - FIRST = 'FIRST' - FUEL = 'FUEL' - MINHT = 'MINHT' - CLASS = 'CLASS' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('Grid Metrics') - self.group, self.i18n_group = self.trAlgorithm('Points') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input LAS layer'), - optional=False)) - self.addParameter(ParameterFile( - self.GROUND, self.tr('Input ground PLANS DTM layer'), - optional=False)) - self.addParameter(ParameterNumber( - self.HEIGHT, self.tr('Height break'))) - self.addParameter(ParameterNumber( - self.CELLSIZE, self.tr('Cell Size'))) - self.addParameter(ParameterBoolean( - self.FIRST, self.tr('Use only first returns'), False)) - self.addParameter(ParameterBoolean( - self.FUEL, self.tr('Apply fuel parameter models (cannot be used with /first switch)'), False)) - - self.addOutput(OutputFile( - self.OUTPUT_CSV_ELEVATION, self.tr('Output table with grid metrics'))) - - output_csv_intensity = OutputFile( - self.OUTPUT_CSV_INTENSITY, self.tr('OUTPUT CSV INTENSITY')) - output_csv_intensity.hidden = True - self.addOutput(output_csv_intensity) - - output_txt_elevation = OutputFile( - self.OUTPUT_TXT_ELEVATION, self.tr('OUTPUT CSV INTENSITY')) - output_txt_elevation.hidden = True - self.addOutput(output_txt_elevation) - - output_txt_intensity = OutputFile( - self.OUTPUT_TXT_INTENSITY, self.tr('OUTPUT CSV INTENSITY')) - output_txt_intensity.hidden = True - self.addOutput(output_txt_intensity) - - outlier = ParameterString( - self.OUTLIER, self.tr('Outlier:low,high'), '', False, True) - outlier.isAdvanced = True - self.addParameter(outlier) - minht = ParameterString(self.MINHT, self.tr('Htmin'), '', False, True) - minht.isAdvanced = True - self.addParameter(minht) - class_var = ParameterString( - self.CLASS, self.tr('Class (set blank if not used)'), '', False, True) - class_var.isAdvanced = True - self.addParameter(class_var) - self.addAdvancedModifiers() - - def processAlgorithm(self, feedback): - commands = [os.path.join(FusionUtils.FusionPath(), 'GridMetrics.exe')] - commands.append('/verbose') - outlier = self.getParameterValue(self.OUTLIER) - if str(outlier).strip() != '': - commands.append('/outlier:' + str(outlier)) - first = self.getParameterValue(self.FIRST) - if first: - commands.append('/first') - fuel = self.getParameterValue(self.FUEL) - if fuel: - commands.append('/fuel') - minht = self.getParameterValue(self.MINHT) - if str(minht).strip() != '': - commands.append('/minht:' + str(minht)) - class_var = self.getParameterValue(self.CLASS) - if str(class_var).strip() != '': - commands.append('/class:' + str(class_var)) - self.addAdvancedModifiersToCommand(commands) - ground = self.getParameterValue(self.GROUND).split(';') - if len(ground) == 1: - commands.append(self.getParameterValue(self.GROUND)) - else: - FusionUtils.createGroundList(ground) - commands.append(FusionUtils.tempGroundListFilepath()) - commands.append(str(self.getParameterValue(self.HEIGHT))) - commands.append(str(self.getParameterValue(self.CELLSIZE))) - commands.append(self.getOutputValue(self.OUTPUT_CSV_ELEVATION)) - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - FusionUtils.runFusion(commands, feedback) - basePath = self.getOutputValue(self.OUTPUT_CSV_ELEVATION) - basePath = os.path.join(os.path.dirname(basePath), os.path.splitext(os.path.basename(basePath))[0]) - self.setOutputValue(self.OUTPUT_CSV_ELEVATION, basePath + '_all_returns_elevation_stats.csv') - self.setOutputValue(self.OUTPUT_CSV_INTENSITY, basePath + '_all_returns_intensity_stats.csv') - self.setOutputValue(self.OUTPUT_TXT_ELEVATION, basePath + '_all_returns_elevation_stats_ascii_header.txt') - self.setOutputValue(self.OUTPUT_TXT_INTENSITY, basePath + '_all_returns_intensity_stats_ascii_header.txt') diff --git a/python/plugins/processing/algs/lidar/fusion/GridSurfaceCreate.py b/python/plugins/processing/algs/lidar/fusion/GridSurfaceCreate.py deleted file mode 100644 index 084eed969ef7..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/GridSurfaceCreate.py +++ /dev/null @@ -1,141 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - GridSurfaceCreate.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com - --------------------- - Date : June 2014 - Copyright : (C) 2014 by Agresta S. Coop. - Email : iescamochero at agresta dot org -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterSelection -from processing.core.outputs import OutputFile -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils -from processing.core.parameters import ParameterString - - -class GridSurfaceCreate(FusionAlgorithm): - - INPUT = 'INPUT' - OUTPUT_DTM = 'OUTPUT_DTM' - CELLSIZE = 'CELLSIZE' - XYUNITS = 'XYUNITS' - ZUNITS = 'ZUNITS' - UNITS = ['Meter', 'Feet'] - SPIKE = 'SPIKE' - MEDIAN = 'MEDIAN' - SMOOTH = 'SMOOTH' - SLOPE = 'SLOPE' - MINIMUM = 'MINIMUM' - CLASS = 'CLASS' - ADVANCED_MODIFIERS = 'ADVANCED_MODIFIERS' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('Grid Surface Create') - self.group, self.i18n_group = self.trAlgorithm('Surface') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input LAS layer'), - optional=False)) - self.addParameter(ParameterNumber( - self.CELLSIZE, self.tr('Cell Size'), 0, None, 10.0)) - self.addParameter(ParameterSelection( - self.XYUNITS, self.tr('XY Units'), self.UNITS)) - self.addParameter(ParameterSelection( - self.ZUNITS, self.tr('Z Units'), self.UNITS)) - self.addOutput(OutputFile( - self.OUTPUT_DTM, self.tr('DTM Output Surface'), 'dtm')) - spike = ParameterNumber( - self.SPIKE, self.tr('Filter final surface to remove spikes with slopes greater than # percent'), 0, None, 0.0) - spike.isAdvanced = True - self.addParameter(spike) - median = ParameterNumber( - self.MEDIAN, self.tr('Apply median filter to model using # by # neighbor window'), 0, None, 0.0) - median.isAdvanced = True - self.addParameter(median) - smooth = ParameterNumber( - self.SMOOTH, self.tr('Apply mean filter to model using # by # neighbor window'), 0, None, 0.0) - smooth.isAdvanced = True - self.addParameter(smooth) - slope = ParameterNumber( - self.SLOPE, self.tr('Filter areas from the surface with slope greater than # percent'), 0, None, 0.0) - slope.isAdvanced = True - self.addParameter(slope) - minimum = ParameterBoolean( - self.MINIMUM, self.tr('Use the lowest point in each cell as the surface elevation'), False) - minimum.isAdvanced = True - self.addParameter(minimum) - class_var = ParameterString( - self.CLASS, self.tr('Class(es)'), '', False, True) - class_var.isAdvanced = True - self.addParameter(class_var) - advance_modifiers = ParameterString( - self.ADVANCED_MODIFIERS, self.tr('Additional modifiers'), '', False, True) - advance_modifiers.isAdvanced = True - self.addParameter(advance_modifiers) - - def processAlgorithm(self, feedback): - commands = [os.path.join(FusionUtils.FusionPath(), 'GridSurfaceCreate.exe')] - commands.append('/verbose') - spike = self.getParameterValue(self.SPIKE) - if spike != 0.0: - commands.append('/spike:' + str(spike)) - median = self.getParameterValue(self.MEDIAN) - if median != 0.0: - commands.append('/median:' + str(median)) - smooth = self.getParameterValue(self.SMOOTH) - if smooth != 0.0: - commands.append('/smooth:' + str(smooth)) - slope = self.getParameterValue(self.SLOPE) - if slope != 0.0: - commands.append('/slope:' + str(slope)) - minimum = self.getParameterValue(self.MINIMUM) - if str(minimum).strip(): - commands.append('/minimum:' + str(minimum)) - class_var = self.getParameterValue(self.CLASS) - if str(class_var).strip(): - commands.append('/class:' + str(class_var)) - advance_modifiers = str(self.getParameterValue(self.ADVANCED_MODIFIERS)).strip() - if advance_modifiers: - commands.append(advance_modifiers) - commands.append(self.getOutputValue(self.OUTPUT_DTM)) - commands.append(str(self.getParameterValue(self.CELLSIZE))) - commands.append(self.UNITS[self.getParameterValue(self.XYUNITS)][0]) - commands.append(self.UNITS[self.getParameterValue(self.ZUNITS)][0]) - commands.append('0') - commands.append('0') - commands.append('0') - commands.append('0') - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - FusionUtils.runFusion(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/fusion/GridSurfaceStats.py b/python/plugins/processing/algs/lidar/fusion/GridSurfaceStats.py deleted file mode 100644 index a5f7b812c25d..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/GridSurfaceStats.py +++ /dev/null @@ -1,105 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - GridSurfaceStats.py - --------------------- - Date : November 2016 - Copyright : (C) 2016 by Niccolo' Marchi - Email : sciurusurbanus at hotmail dot it -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" - -__author__ = "Niccolo' Marchi" -__date__ = 'November 2016' -__copyright__ = "(C) 2016 by Niccolo' Marchi" - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterBoolean -from processing.core.outputs import OutputFile -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils - - -class GridSurfaceStats(FusionAlgorithm): - - INPUT = 'INPUT' - OUTPUT = 'OUTPUT' - SAFACT = 'SAFACT' - AREA = 'AREA' - ASCII = 'ASCII' - SVONLY = 'SVONLY' - GROUND = 'GROUND' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('Grid Surface Stats') - self.group, self.i18n_group = self.trAlgorithm('Surface') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input PLANS DTM canopy height model'), optional=False)) - self.addParameter(ParameterNumber( - self.SAFACT, self.tr('Multiplier for outputfile cell size'), 0, None, 1.0)) - self.addOutput(OutputFile( - self.OUTPUT, self.tr('Output file'))) - area = ParameterBoolean( - self.AREA, self.tr('Compute the surface area of inputfile instead of the surface area divided by the flat cell area'), False) - area.isAdvanced = True - self.addParameter(area) - ascii = ParameterBoolean( - self.ASCII, self.tr('Output all files in ASCII raster format instead of PLANS DTM ones'), False) - ascii.isAdvanced = True - self.addParameter(ascii) - svonly = ParameterBoolean( - self.SVONLY, self.tr('Output only the surface volume metric layer'), False) - svonly.isAdvanced = True - self.addParameter(svonly) - - ground = ParameterFile( - self.GROUND, self.tr('Use the specified surface model to represent the ground surface'), False, True) - ground.isAdvanced = True - self.addParameter(ground) - - self.addAdvancedModifiers() - - def processAlgorithm(self, progress): - commands = [os.path.join(FusionUtils.FusionPath(), 'GridSurfaceStats.exe')] - commands.append('/verbose') - area = self.getParameterValue(self.AREA) - if area: - commands.append('/area') - ascii = self.getParameterValue(self.ASCII) - if ascii: - commands.append('/ascii') - svonly = self.getParameterValue(self.SVONLY) - if svonly: - commands.append('/svonly') - ground = self.getParameterValue(self.GROUND) - if ground: - gfiles = self.getParameterValue(self.GROUND).split(';') - if len(gfiles) == 1: - commands.append('/ground:' + unicode(ground)) - else: - FusionUtils.createGroundList(gfiles) - commands.append('/ground:' + unicode(FusionUtils.tempGroundListFilepath())) - self.addAdvancedModifiersToCommand(commands) - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - commands.append(self.getOutputValue(self.OUTPUT)) - commands.append(unicode(self.getParameterValue(self.SAFACT))) - FusionUtils.runFusion(commands, progress) diff --git a/python/plugins/processing/algs/lidar/fusion/GroundFilter.py b/python/plugins/processing/algs/lidar/fusion/GroundFilter.py deleted file mode 100644 index db8fa6b83313..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/GroundFilter.py +++ /dev/null @@ -1,77 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - GroundFilter.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterNumber -from processing.core.outputs import OutputFile -from .FusionUtils import FusionUtils -from .FusionAlgorithm import FusionAlgorithm - - -class GroundFilter(FusionAlgorithm): - - INPUT = 'INPUT' - OUTPUT = 'OUTPUT' - CELLSIZE = 'CELLSIZE' - SURFACE = 'SURFACE' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('Ground Filter') - self.group, self.i18n_group = self.trAlgorithm('Points') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input LAS layer'), - optional=False)) - self.addParameter(ParameterNumber(self.CELLSIZE, - self.tr('Cell size for intermediate surfaces'), 0, None, 10)) - self.addOutput(OutputFile( - self.OUTPUT, self.tr('Output ground LAS file'))) - self.addParameter(ParameterBoolean( - self.SURFACE, self.tr('Create .dtm surface'), False)) - self.addAdvancedModifiers() - - def processAlgorithm(self, feedback): - commands = [os.path.join(FusionUtils.FusionPath(), 'GroundFilter.exe')] - commands.append('/verbose') - self.addAdvancedModifiersToCommand(commands) - surface = self.getParameterValue(self.SURFACE) - if surface: - commands.append('/surface') - outFile = self.getOutputValue(self.OUTPUT) - commands.append(outFile) - commands.append(str(self.getParameterValue(self.CELLSIZE))) - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - FusionUtils.runFusion(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/fusion/ImageCreate.py b/python/plugins/processing/algs/lidar/fusion/ImageCreate.py deleted file mode 100644 index 23555fdf2c23..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/ImageCreate.py +++ /dev/null @@ -1,99 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - ImageCreate.py - --------------------- - Date : January 2016 - Copyright : (C) 2016 by Niccolo' Marchi - Email : sciurusurbanus at hotmail dot it -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = "Niccolo' Marchi" -__date__ = 'January 2016' -__copyright__ = "(C) 2016 by Niccolo' Marchi" - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterNumber -from processing.core.outputs import OutputFile -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils - - -class ImageCreate(FusionAlgorithm): - - INPUT = 'INPUT' - COLOROPTION = 'COLOROPTION' - GROUND = 'GROUND' - PIXEL = 'PIXEL' - RGB = 'RGB' - SWITCH = 'SWITCH' - OUTPUT = 'OUTPUT' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('ImageCreate') - self.group, self.i18n_group = self.trAlgorithm('Points') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input LAS'), - optional=False)) - self.addParameter(ParameterSelection( - self.COLOROPTION, self.tr('Method to assign color'), - ['Intensity', 'Elevation', 'Height'])) - self.addParameter(ParameterFile( - self.GROUND, self.tr("Ground file (used with 'Height' method)"), 'dtm')) - self.addParameter(ParameterBoolean( - self.RGB, self.tr('Use RGB color model to create the color ramp'), False)) - self.addParameter(ParameterNumber( - self.PIXEL, self.tr('Pixel size'), 0, None, 1.0)) - self.addParameter(ParameterSelection( - self.SWITCH, self.tr('Output format'), ['JPEG', 'Bitmap'])) - self.addOutput(OutputFile(self.OUTPUT, 'Output image')) - self.addAdvancedModifiers() - - def processAlgorithm(self, feedback): - commands = [os.path.join(FusionUtils.FusionPath(), 'ImageCreate.exe')] - commands.append('/verbose') - commands.append('/coloroption:' + str(self.getParameterValue(self.COLOROPTION))) - ground = self.getParameterValue(self.GROUND) - if ground: - gfiles = self.getParameterValue(self.GROUND).split(';') - if len(gfiles) == 1: - commands.append('/ground:' + str(ground)) - else: - FusionUtils.createGroundList(gfiles) - commands.append('/ground:' + str(FusionUtils.tempGroundListFilepath())) - if self.getParameterValue(self.RGB): - commands.append('/rgb') - if self.getParameterValue(self.SWITCH) == 0: - commands.append('/jpg') - else: - commands.append('/bmp') - self.addAdvancedModifiersToCommand(commands) - outFile = self.getOutputValue(self.OUTPUT) - commands.append(outFile) - commands.append(str(self.getParameterValue(self.PIXEL))) - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - FusionUtils.runFusion(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/fusion/IntensityImage.py b/python/plugins/processing/algs/lidar/fusion/IntensityImage.py deleted file mode 100644 index 232cf67ce8bf..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/IntensityImage.py +++ /dev/null @@ -1,98 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - IntensityImage.py - --------------------- - Date : January 2016 - Copyright : (C) 2016 by Niccolo' Marchi - Email : sciurusurbanus at hotmail dot it -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = "Niccolo' Marchi" -__date__ = 'January 2016' -__copyright__ = "(C) 2016 by Niccolo' Marchi" - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterNumber -from processing.core.outputs import OutputFile -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils - - -class IntensityImage(FusionAlgorithm): - - INPUT = 'INPUT' - ALLRET = 'ALLRET' - LOWEST = 'LOWEST' - HIST = 'HIST' - PIXEL = 'PIXEL' - SWITCH = 'SWITCH' - FALIGN = 'FALIGN' - OUTPUT = 'OUTPUT' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('IntensityImage') - self.group, self.i18n_group = self.trAlgorithm('Points') - - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input file'), - optional=False)) - self.addParameter(ParameterBoolean( - self.ALLRET, self.tr('Use all returns instead of only first'), False)) - self.addParameter(ParameterBoolean( - self.LOWEST, self.tr('Use the lowest return in pixel area to assign the intensity value'), False)) - self.addParameter(ParameterBoolean( - self.HIST, self.tr('Produce a CSV intensity histogram data file'), False)) - self.addParameter(ParameterNumber( - self.PIXEL, self.tr('Pixel size'), 0, None, 1.0)) - self.addParameter(ParameterSelection( - self.SWITCH, self.tr('Output format'), ['Bitmap', 'JPEG'])) - falign = ParameterBoolean( - self.FALIGN, self.tr('Force alignment to match other raster products'), False) - falign.isAdvanced = True - self.addParameter(falign) - self.addOutput(OutputFile(self.OUTPUT, 'Output image')) - self.addAdvancedModifiers() - - def processAlgorithm(self, feedback): - commands = [os.path.join(FusionUtils.FusionPath(), 'IntensityImage.exe')] - commands.append('/verbose') - if self.getParameterValue(self.ALLRET): - commands.append('/allreturns') - if self.getParameterValue(self.LOWEST): - commands.append('/lowest') - if self.getParameterValue(self.HIST): - commands.append('/hist') - if self.getParameterValue(self.SWITCH) == 1: - commands.append('/jpg') - if self.getParameterValue(self.FALIGN): - commands.append('/rasterorigin') - commands.append(unicode(self.getParameterValue(self.PIXEL))) - outFile = self.getOutputValue(self.OUTPUT) - commands.append(outFile) - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - FusionUtils.runFusion(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/fusion/MergeDTM.py b/python/plugins/processing/algs/lidar/fusion/MergeDTM.py deleted file mode 100644 index 88f98d7de3e4..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/MergeDTM.py +++ /dev/null @@ -1,92 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - MergeDTM.py - --------------------- - Date : August 2016 - Copyright : (C) 2016 by Niccolo' Marchi - Email : sciurusurbanus at hotmail dot it -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" - -__author__ = "Niccolo' Marchi" -__date__ = 'August 2016' -__copyright__ = "(C) 2016 by Niccolo' Marchi" - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterNumber -from processing.core.outputs import OutputFile -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils - - -class MergeDTM(FusionAlgorithm): - - INPUT = 'INPUT' - OUTPUT = 'OUTPUT' - CELLSIZE = 'CELLSIZE' - EXTENT = 'EXTENT' - DISK = 'DISK' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('Merge PLANS DTM files') - self.group, self.i18n_group = self.trAlgorithm('Points') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input PLANS DTM files'), optional=False)) - self.addOutput(OutputFile( - self.OUTPUT, self.tr('Output merged file'))) - - cellsize = ParameterNumber( - self.CELLSIZE, self.tr('Resample the input DTM data to the following cellsize'), 0, None, 0.0) - cellsize.isAdvanced = True - self.addParameter(cellsize) - - extent = ParameterBoolean( - self.EXTENT, self.tr('Preserve the exact extent of the input models'), False) - extent.isAdvanced = True - self.addParameter(extent) - - disk = ParameterBoolean( - self.DISK, self.tr('Merge the files to a disk file. USE ONLY IF DEFAULT METHOD FAILS'), False) - disk.isAdvanced = True - self.addParameter(disk) - - self.addAdvancedModifiers() - - def processAlgorithm(self, progress): - commands = [os.path.join(FusionUtils.FusionPath(), 'MergeDTM.exe')] - commands.append('/verbose') - - cellsize = self.getParameterValue(self.CELLSIZE) - if cellsize != 0.0: - commands.append('/cellsize:' + unicode(self.getParameterValue(self.CELLSIZE))) - extent = self.getParameterValue(self.EXTENT) - if extent: - commands.append('/exactextent') - disk = self.getParameterValue(self.DISK) - if disk: - commands.append('/disk') - self.addAdvancedModifiersToCommand(commands) - outFile = self.getOutputValue(self.OUTPUT) - commands.append(outFile) - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - FusionUtils.runFusion(commands, progress) diff --git a/python/plugins/processing/algs/lidar/fusion/MergeData.py b/python/plugins/processing/algs/lidar/fusion/MergeData.py deleted file mode 100644 index 294fde3fbe6e..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/MergeData.py +++ /dev/null @@ -1,64 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - MergeData.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterFile -from processing.core.outputs import OutputFile -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils - - -class MergeData(FusionAlgorithm): - - INPUT = 'INPUT' - OUTPUT = 'OUTPUT' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('Merge LAS Files') - self.group, self.i18n_group = self.trAlgorithm('Points') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input LAS files'), - optional=False)) - self.addAdvancedModifiers() - self.addOutput(OutputFile( - self.OUTPUT, self.tr('Output merged LAS file'))) - - def processAlgorithm(self, feedback): - commands = [os.path.join(FusionUtils.FusionPath(), 'MergeData.exe')] - commands.append('/verbose') - self.addAdvancedModifiersToCommand(commands) - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - outFile = self.getOutputValue(self.OUTPUT) - commands.append(outFile) - FusionUtils.runFusion(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/fusion/MergeRaster.py b/python/plugins/processing/algs/lidar/fusion/MergeRaster.py deleted file mode 100644 index 902297eb1454..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/MergeRaster.py +++ /dev/null @@ -1,80 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - MergeRaster.py - --------------------- - Date : November 2016 - Copyright : (C) 2016 by Niccolo' Marchi - Email : sciurusurbanus at hotmail dot it -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" - -__author__ = "Niccolo' Marchi" -__date__ = 'November 2016' -__copyright__ = "(C) 2016 by Niccolo' Marchi" - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterString -from processing.core.outputs import OutputFile -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils - - -class MergeRaster(FusionAlgorithm): - - INPUT = 'INPUT' - OUTPUT = 'OUTPUT' - OVERL = 'OVERL' - COMP = 'COMP' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('Merge ASCII files') - self.group, self.i18n_group = self.trAlgorithm('Points') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input ASCII files'), optional=False)) - self.addOutput(OutputFile( - self.OUTPUT, self.tr('Output file'), 'asc')) - - overl = ParameterString( - self.OVERL, self.tr('Specify how overlap areas should be treated'), '', False, True) - overl.isAdvanced = True - self.addParameter(overl) - comp = ParameterBoolean( - self.COMP, self.tr('Compare values in cells common to two or more input files'), False) - comp.isAdvanced = True - self.addParameter(comp) - self.addAdvancedModifiers() - - def processAlgorithm(self, progress): - commands = [os.path.join(FusionUtils.FusionPath(), 'MergeRaster.exe')] - commands.append('/verbose') - overl = self.getParameterValue(self.OVERL) - if overl: - commands.append('/overlap:' + unicode(overl)) - comp = self.getParameterValue(self.COMP) - if comp: - commands.append('/compare') - self.addAdvancedModifiersToCommand(commands) - outFile = self.getOutputValue(self.OUTPUT) - commands.append(outFile) - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - FusionUtils.runFusion(commands, progress) diff --git a/python/plugins/processing/algs/lidar/fusion/OpenViewerAction.py b/python/plugins/processing/algs/lidar/fusion/OpenViewerAction.py deleted file mode 100644 index 4f93d01a5c11..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/OpenViewerAction.py +++ /dev/null @@ -1,54 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - OpenViewerAction.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -import subprocess -from qgis.PyQt.QtGui import QIcon -from qgis.PyQt.QtWidgets import QMessageBox -from processing.gui.ToolboxAction import ToolboxAction -from .FusionUtils import FusionUtils - - -class OpenViewerAction(ToolboxAction): - - def __init__(self): - self.name, self.i18n_name = self.trAction('Open Fusion LAS viewer') - self.group, self.i18n_group = self.trAction('Visualization') - - def getIcon(self): - return QIcon(os.path.dirname(__file__) + '/../../../images/tool.png') - - def execute(self): - f = os.path.join(FusionUtils.FusionPath(), 'pdq.exe') - if os.path.exists(f): - subprocess.Popen(f) - else: - QMessageBox.critical(None, - self.tr('Unable to open viewer'), - self.tr('The current Fusion folder does not contain the ' - 'viewer executable.\nPlease check the configuration ' - 'in the Processing settings dialog.')) diff --git a/python/plugins/processing/algs/lidar/fusion/PolyClipData.py b/python/plugins/processing/algs/lidar/fusion/PolyClipData.py deleted file mode 100644 index ff6a885048e4..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/PolyClipData.py +++ /dev/null @@ -1,85 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - PolyClipData.py - --------------------- - Date : May 2014 - Copyright : (C) 2014 by Niccolo' Marchi - Email : sciurusurbanus at hotmail dot it -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = "Niccolo' Marchi" -__date__ = 'May 2014' -__copyright__ = "(C) 2014 by Niccolo' Marchi" - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterString -from processing.core.outputs import OutputFile -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils - - -class PolyClipData(FusionAlgorithm): - - INPUT = 'INPUT' - OUTPUT = 'OUTPUT' - SHAPE = 'SHAPE' - MASK = 'MASK' - FIELD = 'FIELD' - VALUE = 'VALUE' - MULTIFILE = 'MULTIFILE' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('Poly Clip Data') - self.group, self.i18n_group = self.trAlgorithm('Points') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input LAS layer'), - optional=False)) - self.addParameter(ParameterFile(self.MASK, self.tr('Mask layer'), optional=False)) - self.addOutput(OutputFile(self.OUTPUT, - self.tr('Output clipped LAS file'), 'las')) - self.addParameter(ParameterString(self.SHAPE, self.tr("Use Shape attribute (shp column number,value)"), optional=True)) - self.addParameter(ParameterString(self.FIELD, - self.tr('Shape field index'))) - self.addParameter(ParameterString(self.VALUE, self.tr("Shape value"))) - self.addParameter(ParameterBoolean(self.MULTIFILE, self.tr('Create a file per each polygon'), False)) - self.addAdvancedModifiers() - - def processAlgorithm(self, feedback): - commands = [os.path.join(FusionUtils.FusionPath(), 'PolyClipData.exe')] - commands.append('/verbose') - if self.getParameterValue(self.SHAPE): - commands.append('/shape:' + str(self.getParameterValue(self.FIELD)) + ',' + str(self.getParameterValue(self.VALUE))) - multiFile = self.getParameterValue(self.MULTIFILE) - if multiFile: - commands.append('/multifile') - self.addAdvancedModifiersToCommand(commands) - commands.append(self.getParameterValue(self.MASK)) - outFile = self.getOutputValue(self.OUTPUT) - commands.append(outFile) - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - - FusionUtils.runFusion(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/fusion/ReturnDensity.py b/python/plugins/processing/algs/lidar/fusion/ReturnDensity.py deleted file mode 100644 index 75fecd5dbd4c..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/ReturnDensity.py +++ /dev/null @@ -1,97 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - ReturnDensity.py #spellok - --------------------- - Date : November 2016 - Copyright : (C) 2016 by Niccolo' Marchi - Email : sciurusurbanus at hotmail dot it -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" - -__author__ = "Niccolo' Marchi" -__date__ = 'November 2016' -__copyright__ = "(C) 2016 by Niccolo' Marchi" - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterString -from processing.core.outputs import OutputFile -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils - - -class ReturnDensity(FusionAlgorithm): # spellok - - INPUT = 'INPUT' - OUTPUT = 'OUTPUT' - CELLSIZE = 'CELLSIZE' - FIRST = 'FIRST' - ASCII = 'ASCII' - CLASS = 'CLASS' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('Return Density') - self.group, self.i18n_group = self.trAlgorithm('Surface') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input LAS layer'), optional=False)) - self.addParameter(ParameterNumber( - self.CELLSIZE, self.tr('Cellsize'), 0, None, 10.0)) - self.addOutput(OutputFile( - self.OUTPUT, self.tr('Output file'))) - first = ParameterBoolean( - self.FIRST, self.tr('Use only first returns when computing return counts'), False) - first.isAdvanced = True - self.addParameter(first) - ascii = ParameterBoolean( - self.ASCII, self.tr('Output raster data in ASCII raster format instead of PLANS DTM format'), False) - ascii.isAdvanced = True - self.addParameter(ascii) - - class_var = ParameterString( - self.CLASS, self.tr('LAS class'), '', False, True) - class_var.isAdvanced = True - self.addParameter(class_var) - self.addAdvancedModifiers() - - def processAlgorithm(self, progress): - commands = [os.path.join(FusionUtils.FusionPath(), 'ReturnDensity.exe')] # spellok - commands.append('/verbose') - - first = self.getParameterValue(self.FIRST) - if first: - commands.append('/first') - - ascii = self.getParameterValue(self.ASCII) - if ascii: - commands.append('/ascii') - - class_var = self.getParameterValue(self.CLASS) - if class_var: - commands.append('/class:' + unicode(class_var)) - - self.addAdvancedModifiersToCommand(commands) - commands.append(self.getOutputValue(self.OUTPUT)) - commands.append(unicode(self.getParameterValue(self.CELLSIZE))) - - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - FusionUtils.runFusion(commands, progress) diff --git a/python/plugins/processing/algs/lidar/fusion/SplitDTM.py b/python/plugins/processing/algs/lidar/fusion/SplitDTM.py deleted file mode 100644 index 52bc7b7784b6..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/SplitDTM.py +++ /dev/null @@ -1,71 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - SplitDTM.py - --------------------- - Date : November 2016 - Copyright : (C) 2016 by Niccolo' Marchi - Email : sciurusurbanus at hotmail dot it -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" - -__author__ = "Niccolo' Marchi" -__date__ = 'November 2016' -__copyright__ = "(C) 2016 by Niccolo' Marchi" - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterString -from processing.core.outputs import OutputFile -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils - - -class SplitDTM(FusionAlgorithm): - - INPUT = 'INPUT' - OUTPUT = 'OUTPUT' - COLUMNS = 'COLUMNS' - ROWS = 'ROWS' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('Split PLANS DTM files') - self.group, self.i18n_group = self.trAlgorithm('Points') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input PLANS DTM file'), optional=False)) - self.addParameter(ParameterNumber( - self.COLUMNS, self.tr('Number of columns of tiles'), 0, None, 1)) - self.addParameter(ParameterNumber( - self.ROWS, self.tr('Number of rows of tiles'), 0, None, 1)) - self.addOutput(OutputFile( - self.OUTPUT, self.tr('Output files'))) - self.addAdvancedModifiers() - - def processAlgorithm(self, progress): - commands = [os.path.join(FusionUtils.FusionPath(), 'SplitDTM.exe')] - commands.append('/verbose') - self.addAdvancedModifiersToCommand(commands) - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - outFile = self.getOutputValue(self.OUTPUT) - commands.append(outFile) - commands.append(unicode(self.getParameterValue(self.COLUMNS))) - commands.append(unicode(self.getParameterValue(self.ROWS))) - FusionUtils.runFusion(commands, progress) diff --git a/python/plugins/processing/algs/lidar/fusion/SurfaceStats.py b/python/plugins/processing/algs/lidar/fusion/SurfaceStats.py deleted file mode 100644 index a929e6d4c53a..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/SurfaceStats.py +++ /dev/null @@ -1,73 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - SurfaceStats.py - --------------------- - Date : November 2016 - Copyright : (C) 2016 by Niccolo' Marchi - Email : sciurusurbanus at hotmail dot it -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" - -__author__ = "Niccolo' Marchi" -__date__ = 'November 2016' -__copyright__ = "(C) 2016 by Niccolo' Marchi" - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterFile -from processing.core.outputs import OutputFile -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils - - -class SurfaceStats(FusionAlgorithm): - - INPUT = "INPUT" - OUTPUT = "OUTPUT" - GROUND = 'GROUND' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('Surface Statistics') - self.group, self.i18n_group = self.trAlgorithm('Surface') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input PLANS DTM layer'), optional=False)) - self.addOutput(OutputFile(self.OUTPUT, self.tr('Output file name'), 'csv')) - ground = ParameterFile( - self.GROUND, self.tr('Use the specified surface model to represent the ground surface')) - ground.isAdvanced = True - self.addParameter(ground) - self.addAdvancedModifiers() - - def processAlgorithm(self, progress): - commands = [os.path.join(FusionUtils.FusionPath(), "SurfaceStats.exe")] - commands.append('/verbose') - ground = self.getParameterValue(self.GROUND) - if ground: - gfiles = self.getParameterValue(self.GROUND).split(';') - if len(gfiles) == 1: - commands.append('/ground:' + unicode(ground)) - else: - FusionUtils.createGroundList(gfiles) - commands.append('/ground:' + unicode(FusionUtils.tempGroundListFilepath())) - self.addAdvancedModifiersToCommand(commands) - files = self.getParameterValue(self.INPUT).split(";") - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - outFile = self.getOutputValue(self.OUTPUT) - commands.append(outFile) - FusionUtils.runFusion(commands, progress) diff --git a/python/plugins/processing/algs/lidar/fusion/TinSurfaceCreate.py b/python/plugins/processing/algs/lidar/fusion/TinSurfaceCreate.py deleted file mode 100644 index 2647a623126d..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/TinSurfaceCreate.py +++ /dev/null @@ -1,103 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - TINSurfaceCreate.py - --------------------- - Date : June 2014 - Copyright : (C) 2014 by Agresta S. Coop - Email : iescamochero at agresta dot org -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Agresta S. Coop - www.agresta.org' -__date__ = 'June 2014' -__copyright__ = '(C) 2014, Agresta S. Coop' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -import subprocess -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterString -from processing.core.outputs import OutputFile -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils - - -class TinSurfaceCreate(FusionAlgorithm): - - INPUT = 'INPUT' - OUTPUT = 'OUTPUT' - CELLSIZE = 'CELLSIZE' - XYUNITS = 'XYUNITS' - ZUNITS = 'ZUNITS' - UNITS = ['Meter', 'Feet'] - CLASS = 'CLASS' - RETURN = 'RETURN' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('Tin Surface Create') - self.group, self.i18n_group = self.trAlgorithm('Surface') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input LAS layer'), - optional=False)) - self.addParameter(ParameterNumber(self.CELLSIZE, - self.tr('Cell Size'), 0, None, 10.0)) - self.addParameter(ParameterSelection(self.XYUNITS, - self.tr('XY Units'), self.UNITS)) - self.addParameter(ParameterSelection(self.ZUNITS, - self.tr('Z Units'), self.UNITS)) - self.addOutput(OutputFile(self.OUTPUT, - self.tr('.dtm output surface'), 'dtm')) - class_var = ParameterString(self.CLASS, - self.tr('Class'), '', False, True) - class_var.isAdvanced = True - self.addParameter(class_var) - return_sel = ParameterString(self.RETURN, - self.tr('Select specific return'), '', False, True) - return_sel.isAdvanced = True - self.addParameter(return_sel) - - def processAlgorithm(self, feedback): - commands = [os.path.join(FusionUtils.FusionPath(), 'TINSurfaceCreate.exe')] - commands.append('/verbose') - class_var = self.getParameterValue(self.CLASS) - if str(class_var).strip(): - commands.append('/class:' + str(class_var)) - return_sel = self.getParameterValue(self.RETURN) - if str(return_sel).strip(): - commands.append('/return:' + str(return_sel)) - outFile = self.getOutputValue(self.OUTPUT) - commands.append(outFile) - commands.append(str(self.getParameterValue(self.CELLSIZE))) - commands.append(self.UNITS[self.getParameterValue(self.XYUNITS)][0]) - commands.append(self.UNITS[self.getParameterValue(self.ZUNITS)][0]) - commands.append('0') - commands.append('0') - commands.append('0') - commands.append('0') - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - commands.extend(files) - FusionUtils.runFusion(commands, feedback) - commands = [os.path.join(FusionUtils.FusionPath(), 'DTM2ASCII.exe')] - commands.append('/raster') - commands.append(outFile) - commands.append(self.getOutputValue(self.OUTPUT)) - p = subprocess.Popen(commands, shell=True) - p.wait() diff --git a/python/plugins/processing/algs/lidar/fusion/TopoMetrics.py b/python/plugins/processing/algs/lidar/fusion/TopoMetrics.py deleted file mode 100644 index ce568e4f818c..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/TopoMetrics.py +++ /dev/null @@ -1,102 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - TopoMetrics.py - --------------------- - Date : August 2016 - Copyright : (C) 2016 by Niccolo' Marchi - Email : sciurusurbanus at hotmail dot it -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" - -__author__ = "Niccolo' Marchi" -__date__ = 'August 2016' -__copyright__ = "(C) 2016 by Niccolo' Marchi" - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterNumber -from processing.core.outputs import OutputFile -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils - - -class TopoMetrics(FusionAlgorithm): - - INPUT = 'INPUT' - OUTPUT = 'OUTPUT' - CELLSIZE = 'CELLSIZE' - POINTSP = 'POINTSP' - LATITUDE = 'LATITUDE' - TPI = 'TPI' - SQUARE = 'SQUARE' - DISK = 'DISK' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('Topographic Metrics') - self.group, self.i18n_group = self.trAlgorithm('Points') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input PLANS DTM surface files'), optional=False)) - self.addOutput(OutputFile( - self.OUTPUT, self.tr('Output file'))) - - self.addParameter(ParameterNumber( - self.CELLSIZE, self.tr('Size of the cell used to report topographic metrics'), 0, None, 5.0)) - self.addParameter(ParameterNumber( - self.POINTSP, self.tr('Spacing for the 3 by 3 array of points used to compute the metrics'), 0, None, 0.0)) - self.addParameter(ParameterNumber( - self.LATITUDE, self.tr('Latitude'), 2, None, 45.0)) - self.addParameter(ParameterNumber( - self.TPI, self.tr('TPI window size'), 0, None, 5.0)) - - square = ParameterBoolean( - self.SQUARE, self.tr('Use a square window for TPI'), False) - square.isAdvanced = True - self.addParameter(square) - - disk = ParameterBoolean( - self.DISK, self.tr('Do not load ground surface models into memory'), False) - disk.isAdvanced = True - self.addParameter(disk) - - self.addAdvancedModifiers() - - def processAlgorithm(self, progress): - commands = [os.path.join(FusionUtils.FusionPath(), 'TopoMetrics.exe')] - commands.append('/verbose') - - square = self.getParameterValue(self.SQUARE) - if square: - commands.append('/square') - disk = self.getParameterValue(self.DISK) - if disk: - commands.append('/disk') - self.addAdvancedModifiersToCommand(commands) - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - - commands.append(unicode(self.getParameterValue(self.CELLSIZE))) - commands.append(unicode(self.getParameterValue(self.POINTSP))) - commands.append(unicode(self.getParameterValue(self.LATITUDE))) - commands.append(unicode(self.getParameterValue(self.TPI))) - - outFile = self.getOutputValue(self.OUTPUT) - commands.append(outFile) - FusionUtils.runFusion(commands, progress) diff --git a/python/plugins/processing/algs/lidar/fusion/TreeSeg.py b/python/plugins/processing/algs/lidar/fusion/TreeSeg.py deleted file mode 100644 index a1e261cb884d..000000000000 --- a/python/plugins/processing/algs/lidar/fusion/TreeSeg.py +++ /dev/null @@ -1,118 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - TreeSeg.py - --------------------- - Date : November 2016 - Copyright : (C) 2016 by Niccolo' Marchi - Email : sciurusurbanus at hotmail dot it -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" - -__author__ = "Niccolo' Marchi" -__date__ = 'November 2016' -__copyright__ = "(C) 2016 by Niccolo' Marchi" - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterNumber -from processing.core.outputs import OutputFile -from .FusionAlgorithm import FusionAlgorithm -from .FusionUtils import FusionUtils - - -class TreeSeg(FusionAlgorithm): - - INPUT = 'INPUT' - GROUND = 'GROUND' - HTTH = 'HTTH' - OUTPUT = 'OUTPUT' - HEIGHT = 'HEIGHT' - SHAPE = 'SHAPE' - ALIGN = 'ALIGN' - BUFF = 'BUFF' - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('Tree Segmentation') - self.group, self.i18n_group = self.trAlgorithm('Points') - self.addParameter(ParameterFile( - self.INPUT, self.tr('Input Canopy Height Model (in PLANS DTM format)'), optional=False)) - self.addParameter(ParameterNumber( - self.HTTH, self.tr('Minimum height for object segmentation'), 0, None, 2.0)) - self.addOutput(OutputFile( - self.OUTPUT, self.tr('Base name for output files'))) - - ground = ParameterFile( - self.GROUND, self.tr('Ground file for height normalization')) - ground.isAdvanced = True - self.addParameter(ground) - height = ParameterBoolean( - self.HEIGHT, self.tr("Normalize height model using ground model (select if a ground file is provided)"), False) - height.isAdvanced = True - self.addParameter(height) - buff = ParameterNumber( - self.BUFF, self.tr('Add a buffer to the data extent when segmenting'), 0, None, 0.0) - buff.isAdvanced - self.addParameter(buff) - shape = ParameterBoolean( - self.SHAPE, self.tr('Create output shapefiles'), False) - shape.isAdvanced = True - self.addParameter(shape) - align = ParameterBoolean( - self.ALIGN, self.tr('Align output grid to the input extent'), False) - align.isAdvanced = True - self.addParameter(align) - - self.addAdvancedModifiers() - - def processAlgorithm(self, progress): - commands = [os.path.join(FusionUtils.FusionPath(), 'TreeSeg.exe')] - commands.append('/verbose') - - ground = self.getParameterValue(self.GROUND) - if ground: - gfiles = self.getParameterValue(self.GROUND).split(';') - if len(gfiles) == 1: - commands.append('/ground:' + unicode(ground)) - else: - FusionUtils.createGroundList(gfiles) - commands.append('/ground:' + unicode(FusionUtils.tempGroundListFilepath())) - height = self.getParameterValue(self.HEIGHT) - if height: - commands.append('/height') - buff = self.getParameterValue(self.BUFF) - if buff != 0.0: - commands.append('/buffer:' + unicode(self.getParameterValue(self.BUFF))) - shape = self.getParameterValue(self.SHAPE) - if shape: - commands.append('/shape') - align = self.getParameterValue(self.ALIGN) - if align: - commands.append('/align:' + unicode(self.getParameterValue(self.INPUT))) - self.addAdvancedModifiersToCommand(commands) - - files = self.getParameterValue(self.INPUT).split(';') - if len(files) == 1: - commands.append(self.getParameterValue(self.INPUT)) - else: - FusionUtils.createFileList(files) - commands.append(FusionUtils.tempFileListFilepath()) - - commands.append(unicode(self.getParameterValue(self.HTTH))) - - outFile = self.getOutputValue(self.OUTPUT) - commands.append(outFile) - FusionUtils.runFusion(commands, progress) diff --git a/python/plugins/processing/algs/lidar/fusion/__init__.py b/python/plugins/processing/algs/lidar/fusion/__init__.py deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/python/plugins/processing/algs/lidar/lastools/CMakeLists.txt b/python/plugins/processing/algs/lidar/lastools/CMakeLists.txt deleted file mode 100644 index c2249094617a..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/CMakeLists.txt +++ /dev/null @@ -1,3 +0,0 @@ -FILE(GLOB PY_FILES *.py) - -PLUGIN_INSTALL(processing ./algs/lidar/lastools ${PY_FILES}) diff --git a/python/plugins/processing/algs/lidar/lastools/LAStoolsAlgorithm.py b/python/plugins/processing/algs/lidar/lastools/LAStoolsAlgorithm.py deleted file mode 100644 index 3c49ed3395f0..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/LAStoolsAlgorithm.py +++ /dev/null @@ -1,444 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - LAStoolsAlgorithm.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com - --------------------- - Date : April 2014 and May 2016 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from qgis.PyQt import QtGui -from processing.core.GeoAlgorithm import GeoAlgorithm - -from .LAStoolsUtils import LAStoolsUtils - -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterString -from processing.core.parameters import ParameterSelection -from processing.core.outputs import OutputFile -from processing.core.outputs import OutputRaster -from processing.core.outputs import OutputVector - - -class LAStoolsAlgorithm(GeoAlgorithm): - - VERBOSE = "VERBOSE" - GUI = "GUI" - CORES = "CORES" - INPUT_LASLAZ = "INPUT_LASLAZ" - INPUT_DIRECTORY = "INPUT_DIRECTORY" - INPUT_WILDCARDS = "INPUT_WILDCARDS" - MERGED = "MERGED" - OUTPUT_LASLAZ = "OUTPUT_LASLAZ" - OUTPUT_DIRECTORY = "OUTPUT_DIRECTORY" - OUTPUT_APPENDIX = "OUTPUT_APPENDIX" - OUTPUT_POINT_FORMAT = "OUTPUT_POINT_FORMAT" - OUTPUT_POINT_FORMATS = ["laz", "las"] - OUTPUT_RASTER = "OUTPUT_RASTER" - OUTPUT_RASTER_FORMAT = "OUTPUT_RASTER_FORMAT" - OUTPUT_RASTER_FORMATS = ["tif", "bil", "img", "dtm", "asc", "xyz", "png", "jpg"] - OUTPUT_VECTOR = "OUTPUT_VECTOR" - OUTPUT_VECTOR_FORMAT = "OUTPUT_VECTOR_FORMAT" - OUTPUT_VECTOR_FORMATS = ["shp", "wkt", "kml", "txt"] - ADDITIONAL_OPTIONS = "ADDITIONAL_OPTIONS" - TEMPORARY_DIRECTORY = "TEMPORARY_DIRECTORY" - HORIZONTAL_FEET = "HORIZONTAL_FEET" - VERTICAL_FEET = "VERTICAL_FEET" - FILES_ARE_FLIGHTLINES = "FILES_ARE_FLIGHTLINES" - APPLY_FILE_SOURCE_ID = "APPLY_FILE_SOURCE_ID" - STEP = "STEP" - - FILTER_RETURN_CLASS_FLAGS1 = "FILTER_RETURN_CLASS_FLAGS1" - FILTER_RETURN_CLASS_FLAGS2 = "FILTER_RETURN_CLASS_FLAGS2" - FILTER_RETURN_CLASS_FLAGS3 = "FILTER_RETURN_CLASS_FLAGS3" - FILTERS_RETURN_CLASS_FLAGS = ["---", "keep_last", "keep_first", "keep_middle", "keep_single", "drop_single", - "keep_double", "keep_class 2", "keep_class 2 8", "keep_class 8", "keep_class 6", - "keep_class 9", "keep_class 3 4 5", "keep_class 3", "keep_class 4", "keep_class 5", - "keep_class 2 6", "drop_class 7", "drop_withheld", "drop_synthetic"] - FILTER_COORDS_INTENSITY1 = "FILTER_COORDS_INTENSITY1" - FILTER_COORDS_INTENSITY2 = "FILTER_COORDS_INTENSITY2" - FILTER_COORDS_INTENSITY3 = "FILTER_COORDS_INTENSITY3" - FILTER_COORDS_INTENSITY1_ARG = "FILTER_COORDS_INTENSITY1_ARG" - FILTER_COORDS_INTENSITY2_ARG = "FILTER_COORDS_INTENSITY2_ARG" - FILTER_COORDS_INTENSITY3_ARG = "FILTER_COORDS_INTENSITY3_ARG" - FILTERS_COORDS_INTENSITY = ["---", "drop_x_above", "drop_x_below", "drop_y_above", "drop_y_below", "drop_z_above", - "drop_z_below", "drop_intensity_above", "drop_intensity_below", "drop_gps_time_above", - "drop_gps_time_below", "drop_scan_angle_above", "drop_scan_angle_below", "keep_point_source", - "drop_point_source", "drop_point_source_above", "drop_point_source_below", "keep_user_data", - "drop_user_data", "drop_user_data_above", "drop_user_data_below", "keep_every_nth", - "keep_random_fraction", "thin_with_grid"] - - TRANSFORM_COORDINATE1 = "TRANSFORM_COORDINATE1" - TRANSFORM_COORDINATE2 = "TRANSFORM_COORDINATE2" - TRANSFORM_COORDINATE1_ARG = "TRANSFORM_COORDINATE1_ARG" - TRANSFORM_COORDINATE2_ARG = "TRANSFORM_COORDINATE2_ARG" - TRANSFORM_COORDINATES = ["---", "translate_x", "translate_y", "translate_z", "scale_x", "scale_y", "scale_z", "clamp_z_above", "clamp_z_below"] - - TRANSFORM_OTHER1 = "TRANSFORM_OTHER1" - TRANSFORM_OTHER2 = "TRANSFORM_OTHER2" - TRANSFORM_OTHER1_ARG = "TRANSFORM_OTHER1_ARG" - TRANSFORM_OTHER2_ARG = "TRANSFORM_OTHER2_ARG" - TRANSFORM_OTHERS = ["---", "scale_intensity", "translate_intensity", "clamp_intensity_above", "clamp_intensity_below", - "scale_scan_angle", "translate_scan_angle", "translate_gps_time", "set_classification", "set_user_data", - "set_point_source", "scale_rgb_up", "scale_rgb_down", "repair_zero_returns"] - - IGNORE_CLASS1 = "IGNORE_CLASS1" - IGNORE_CLASS2 = "IGNORE_CLASS2" - IGNORE_CLASSES = ["---", "unclassified (1)", "ground (2)", "veg low (3)", "veg mid (4)", "veg high (5)", "buildings (6)", "noise (7)", "keypoint (8)", "water (9)"] - - def getIcon(self): - filepath = os.path.dirname(__file__) + "/../../../images/tool.png" - return QtGui.QIcon(filepath) - - def checkBeforeOpeningParametersDialog(self): - path = LAStoolsUtils.LAStoolsPath() - if path == "": - return self.tr('LAStools folder is not configured.\nPlease ' - 'configure it before running LAStools algorithms.') - - def addParametersVerboseGUI(self): - self.addParameter(ParameterBoolean(LAStoolsAlgorithm.VERBOSE, self.tr("verbose"), False)) - self.addParameter(ParameterBoolean(LAStoolsAlgorithm.GUI, self.tr("open LAStools GUI"), False)) - - def addParametersVerboseCommands(self, commands): - if self.getParameterValue(LAStoolsAlgorithm.VERBOSE): - commands.append("-v") - if self.getParameterValue(LAStoolsAlgorithm.GUI): - commands.append("-gui") - - def addParametersCoresGUI(self): - self.addParameter(ParameterNumber(LAStoolsAlgorithm.CORES, self.tr("number of cores"), 1, 32, 4)) - - def addParametersCoresCommands(self, commands): - cores = self.getParameterValue(LAStoolsAlgorithm.CORES) - if cores != 1: - commands.append("-cores") - commands.append(str(cores)) - - def addParametersPointInputGUI(self): - self.addParameter(ParameterFile(LAStoolsAlgorithm.INPUT_LASLAZ, self.tr("input LAS/LAZ file"), False, False)) - - def addParametersPointInputCommands(self, commands): - input = self.getParameterValue(LAStoolsAlgorithm.INPUT_LASLAZ) - if input is not None: - commands.append("-i") - commands.append('"' + input + '"') - - def addParametersPointInputFolderGUI(self): - self.addParameter(ParameterFile(LAStoolsAlgorithm.INPUT_DIRECTORY, self.tr("input directory"), True, False)) - self.addParameter(ParameterString(LAStoolsAlgorithm.INPUT_WILDCARDS, self.tr("input wildcard(s)"), "*.laz")) - - def addParametersPointInputFolderCommands(self, commands): - input = self.getParameterValue(LAStoolsAlgorithm.INPUT_DIRECTORY) - wildcards = self.getParameterValue(LAStoolsAlgorithm.INPUT_WILDCARDS).split() - for wildcard in wildcards: - commands.append("-i") - if input is not None: - commands.append('"' + input + "\\" + wildcard + '"') - else: - commands.append('"' + wildcard + '"') - - def addParametersPointInputMergedGUI(self): - self.addParameter(ParameterBoolean(LAStoolsAlgorithm.MERGED, self.tr("merge all input files on-the-fly into one"), False)) - - def addParametersPointInputMergedCommands(self, commands): - if self.getParameterValue(LAStoolsAlgorithm.MERGED): - commands.append("-merged") - - def addParametersGenericInputFolderGUI(self, wildcard): - self.addParameter(ParameterFile(LAStoolsAlgorithm.INPUT_DIRECTORY, self.tr("input directory"), True, False)) - self.addParameter(ParameterString(LAStoolsAlgorithm.INPUT_WILDCARDS, self.tr("input wildcard(s)"), wildcard)) - - def addParametersGenericInputFolderCommands(self, commands): - input = self.getParameterValue(LAStoolsAlgorithm.INPUT_DIRECTORY) - wildcards = self.getParameterValue(LAStoolsAlgorithm.INPUT_WILDCARDS).split() - for wildcard in wildcards: - commands.append("-i") - if input is not None: - commands.append('"' + input + "\\" + wildcard + '"') - else: - commands.append('"' + wildcard + '"') - - def addParametersHorizontalFeetGUI(self): - self.addParameter(ParameterBoolean(LAStoolsAlgorithm.HORIZONTAL_FEET, self.tr("horizontal feet"), False)) - - def addParametersHorizontalFeetCommands(self, commands): - if self.getParameterValue(LAStoolsAlgorithm.HORIZONTAL_FEET): - commands.append("-feet") - - def addParametersVerticalFeetGUI(self): - self.addParameter(ParameterBoolean(LAStoolsAlgorithm.VERTICAL_FEET, self.tr("vertical feet"), False)) - - def addParametersVerticalFeetCommands(self, commands): - if self.getParameterValue(LAStoolsAlgorithm.VERTICAL_FEET): - commands.append("-elevation_feet") - - def addParametersHorizontalAndVerticalFeetGUI(self): - self.addParametersHorizontalFeetGUI() - self.addParametersVerticalFeetGUI() - - def addParametersHorizontalAndVerticalFeetCommands(self, commands): - self.addParametersHorizontalFeetCommands(commands) - self.addParametersVerticalFeetCommands(commands) - - def addParametersFilesAreFlightlinesGUI(self): - self.addParameter(ParameterBoolean(LAStoolsAlgorithm.FILES_ARE_FLIGHTLINES, self.tr("files are flightlines"), False)) - - def addParametersFilesAreFlightlinesCommands(self, commands): - if self.getParameterValue(LAStoolsAlgorithm.FILES_ARE_FLIGHTLINES): - commands.append("-files_are_flightlines") - - def addParametersApplyFileSourceIdGUI(self): - self.addParameter(ParameterBoolean(LAStoolsAlgorithm.APPLY_FILE_SOURCE_ID, self.tr("apply file source ID"), False)) - - def addParametersApplyFileSourceIdCommands(self, commands): - if self.getParameterValue(LAStoolsAlgorithm.APPLY_FILE_SOURCE_ID): - commands.append("-apply_file_source_ID") - - def addParametersStepGUI(self): - self.addParameter(ParameterNumber(LAStoolsAlgorithm.STEP, self.tr("step size / pixel size"), 0, None, 1.0)) - - def addParametersStepCommands(self, commands): - step = self.getParameterValue(LAStoolsAlgorithm.STEP) - if step != 0.0: - commands.append("-step") - commands.append(str(step)) - - def getParametersStepValue(self): - step = self.getParameterValue(LAStoolsAlgorithm.STEP) - return step - - def addParametersPointOutputGUI(self): - self.addOutput(OutputFile(LAStoolsAlgorithm.OUTPUT_LASLAZ, self.tr("output LAS/LAZ file"), "laz")) - - def addParametersPointOutputCommands(self, commands): - output = self.getOutputValue(LAStoolsAlgorithm.OUTPUT_LASLAZ) - if output is not None: - commands.append("-o") - commands.append('"' + output + '"') - - def addParametersPointOutputFormatGUI(self): - self.addParameter(ParameterSelection(LAStoolsAlgorithm.OUTPUT_POINT_FORMAT, self.tr("output format"), LAStoolsAlgorithm.OUTPUT_POINT_FORMATS, 0)) - - def addParametersPointOutputFormatCommands(self, commands): - format = self.getParameterValue(LAStoolsAlgorithm.OUTPUT_POINT_FORMAT) - commands.append("-o" + LAStoolsAlgorithm.OUTPUT_POINT_FORMATS[format]) - - def addParametersRasterOutputGUI(self): - self.addOutput(OutputRaster(LAStoolsAlgorithm.OUTPUT_RASTER, self.tr("Output raster file"))) - - def addParametersRasterOutputCommands(self, commands): - commands.append("-o") - commands.append(self.getOutputValue(LAStoolsAlgorithm.OUTPUT_RASTER)) - - def addParametersRasterOutputFormatGUI(self): - self.addParameter(ParameterSelection(LAStoolsAlgorithm.OUTPUT_RASTER_FORMAT, self.tr("output format"), LAStoolsAlgorithm.OUTPUT_RASTER_FORMATS, 0)) - - def addParametersRasterOutputFormatCommands(self, commands): - format = self.getParameterValue(LAStoolsAlgorithm.OUTPUT_RASTER_FORMAT) - commands.append("-o" + LAStoolsAlgorithm.OUTPUT_RASTER_FORMATS[format]) - - def addParametersVectorOutputGUI(self): - self.addOutput(OutputVector(LAStoolsAlgorithm.OUTPUT_VECTOR, self.tr("Output vector file"))) - - def addParametersVectorOutputCommands(self, commands): - commands.append("-o") - commands.append(self.getOutputValue(LAStoolsAlgorithm.OUTPUT_VECTOR)) - - def addParametersVectorOutputFormatGUI(self): - self.addParameter(ParameterSelection(LAStoolsAlgorithm.OUTPUT_VECTOR_FORMAT, self.tr("output format"), LAStoolsAlgorithm.OUTPUT_VECTOR_FORMATS, 0)) - - def addParametersVectorOutputFormatCommands(self, commands): - format = self.getParameterValue(LAStoolsAlgorithm.OUTPUT_VECTOR_FORMAT) - commands.append("-o" + LAStoolsAlgorithm.OUTPUT_VECTOR_FORMATS[format]) - - def addParametersOutputDirectoryGUI(self): - self.addParameter(ParameterFile(LAStoolsAlgorithm.OUTPUT_DIRECTORY, self.tr("output directory"), True)) - - def addParametersOutputDirectoryCommands(self, commands): - odir = self.getParameterValue(LAStoolsAlgorithm.OUTPUT_DIRECTORY) - if odir != "": - commands.append("-odir") - commands.append(odir) - - def addParametersOutputAppendixGUI(self): - self.addParameter(ParameterString(LAStoolsAlgorithm.OUTPUT_APPENDIX, self.tr("output appendix"), optional=True)) - - def addParametersOutputAppendixCommands(self, commands): - odix = self.getParameterValue(LAStoolsAlgorithm.OUTPUT_APPENDIX) - if odix != "": - commands.append("-odix") - commands.append(odix) - - def addParametersTemporaryDirectoryGUI(self): - self.addParameter(ParameterFile(LAStoolsAlgorithm.TEMPORARY_DIRECTORY, self.tr("empty temporary directory"), True, False)) - - def addParametersTemporaryDirectoryAsOutputDirectoryCommands(self, commands): - odir = self.getParameterValue(LAStoolsAlgorithm.TEMPORARY_DIRECTORY) - if odir != "": - commands.append("-odir") - commands.append(odir) - - def addParametersTemporaryDirectoryAsInputFilesCommands(self, commands, files): - idir = self.getParameterValue(LAStoolsAlgorithm.TEMPORARY_DIRECTORY) - if idir != "": - commands.append("-i") - commands.append(idir + '\\' + files) - - def addParametersAdditionalGUI(self): - self.addParameter(ParameterString(LAStoolsAlgorithm.ADDITIONAL_OPTIONS, self.tr("additional command line parameter(s)"), optional=True)) - - def addParametersAdditionalCommands(self, commands): - additional_options = self.getParameterValue(LAStoolsAlgorithm.ADDITIONAL_OPTIONS).split() - for option in additional_options: - commands.append(option) - - def addParametersFilter1ReturnClassFlagsGUI(self): - self.addParameter(ParameterSelection(LAStoolsAlgorithm.FILTER_RETURN_CLASS_FLAGS1, self.tr("filter (by return, classification, flags)"), - LAStoolsAlgorithm.FILTERS_RETURN_CLASS_FLAGS, 0)) - - def addParametersFilter1ReturnClassFlagsCommands(self, commands): - filter1 = self.getParameterValue(LAStoolsAlgorithm.FILTER_RETURN_CLASS_FLAGS1) - if filter1 != 0: - commands.append("-" + LAStoolsAlgorithm.FILTERS_RETURN_CLASS_FLAGS[filter1]) - - def addParametersFilter2ReturnClassFlagsGUI(self): - self.addParameter(ParameterSelection(LAStoolsAlgorithm.FILTER_RETURN_CLASS_FLAGS2, self.tr("second filter (by return, classification, flags)"), - LAStoolsAlgorithm.FILTERS_RETURN_CLASS_FLAGS, 0)) - - def addParametersFilter2ReturnClassFlagsCommands(self, commands): - filter2 = self.getParameterValue(LAStoolsAlgorithm.FILTER_RETURN_CLASS_FLAGS2) - if filter2 != 0: - commands.append("-" + LAStoolsAlgorithm.FILTERS_RETURN_CLASS_FLAGS[filter2]) - - def addParametersFilter3ReturnClassFlagsGUI(self): - self.addParameter(ParameterSelection(LAStoolsAlgorithm.FILTER_RETURN_CLASS_FLAGS3, self.tr("third filter (by return, classification, flags)"), - LAStoolsAlgorithm.FILTERS_RETURN_CLASS_FLAGS, 0)) - - def addParametersFilter3ReturnClassFlagsCommands(self, commands): - filter3 = self.getParameterValue(LAStoolsAlgorithm.FILTER_RETURN_CLASS_FLAGS3) - if filter3 != 0: - commands.append("-" + LAStoolsAlgorithm.FILTERS_RETURN_CLASS_FLAGS[filter3]) - - def addParametersFilter1CoordsIntensityGUI(self): - self.addParameter(ParameterSelection(LAStoolsAlgorithm.FILTER_COORDS_INTENSITY1, self.tr("filter (by coordinate, intensity, GPS time, ...)"), - LAStoolsAlgorithm.FILTERS_COORDS_INTENSITY, 0)) - self.addParameter(ParameterString(LAStoolsAlgorithm.FILTER_COORDS_INTENSITY1_ARG, self.tr("value for filter (by coordinate, intensity, GPS time, ...)"))) - - def addParametersFilter1CoordsIntensityCommands(self, commands): - filter1 = self.getParameterValue(LAStoolsAlgorithm.FILTER_COORDS_INTENSITY1) - filter1_arg = self.getParameterValue(LAStoolsAlgorithm.FILTER_COORDS_INTENSITY1_ARG) - if filter1 != 0 and filter1_arg is not None: - commands.append("-" + LAStoolsAlgorithm.FILTERS_COORDS_INTENSITY[filter1]) - commands.append(filter1_arg) - - def addParametersFilter2CoordsIntensityGUI(self): - self.addParameter(ParameterSelection(LAStoolsAlgorithm.FILTER_COORDS_INTENSITY2, self.tr("second filter (by coordinate, intensity, GPS time, ...)"), LAStoolsAlgorithm.FILTERS_COORDS_INTENSITY, 0)) - self.addParameter(ParameterString(LAStoolsAlgorithm.FILTER_COORDS_INTENSITY2_ARG, self.tr("value for second filter (by coordinate, intensity, GPS time, ...)"))) - - def addParametersFilter2CoordsIntensityCommands(self, commands): - filter2 = self.getParameterValue(LAStoolsAlgorithm.FILTER_COORDS_INTENSITY2) - filter2_arg = self.getParameterValue(LAStoolsAlgorithm.FILTER_COORDS_INTENSITY2_ARG) - if filter2 != 0 and filter2_arg is not None: - commands.append("-" + LAStoolsAlgorithm.FILTERS_COORDS_INTENSITY[filter2]) - commands.append(filter2_arg) - - def addParametersTransform1CoordinateGUI(self): - self.addParameter(ParameterSelection(LAStoolsAlgorithm.TRANSFORM_COORDINATE1, - self.tr("transform (coordinates)"), LAStoolsAlgorithm.TRANSFORM_COORDINATES, 0)) - self.addParameter(ParameterString(LAStoolsAlgorithm.TRANSFORM_COORDINATE1_ARG, - self.tr("value for transform (coordinates)"))) - - def addParametersTransform1CoordinateCommands(self, commands): - transform1 = self.getParameterValue(LAStoolsAlgorithm.TRANSFORM_COORDINATE1) - transform1_arg = self.getParameterValue(LAStoolsAlgorithm.TRANSFORM_COORDINATE1_ARG) - if transform1 != 0 and transform1_arg is not None: - commands.append("-" + LAStoolsAlgorithm.TRANSFORM_COORDINATES[transform1]) - commands.append(transform1_arg) - - def addParametersTransform2CoordinateGUI(self): - self.addParameter(ParameterSelection(LAStoolsAlgorithm.TRANSFORM_COORDINATE2, - self.tr("second transform (coordinates)"), LAStoolsAlgorithm.TRANSFORM_COORDINATES, 0)) - self.addParameter(ParameterString(LAStoolsAlgorithm.TRANSFORM_COORDINATE2_ARG, - self.tr("value for second transform (coordinates)"))) - - def addParametersTransform2CoordinateCommands(self, commands): - transform2 = self.getParameterValue(LAStoolsAlgorithm.TRANSFORM_COORDINATE2) - transform2_arg = self.getParameterValue(LAStoolsAlgorithm.TRANSFORM_COORDINATE2_ARG) - if transform2 != 0 and transform2_arg is not None: - commands.append("-" + LAStoolsAlgorithm.TRANSFORM_COORDINATES[transform2]) - commands.append(transform2_arg) - - def addParametersTransform1OtherGUI(self): - self.addParameter(ParameterSelection(LAStoolsAlgorithm.TRANSFORM_OTHER1, - self.tr("transform (intensities, scan angles, GPS times, ...)"), LAStoolsAlgorithm.TRANSFORM_OTHERS, 0)) - self.addParameter(ParameterString(LAStoolsAlgorithm.TRANSFORM_OTHER1_ARG, - self.tr("value for transform (intensities, scan angles, GPS times, ...)"))) - - def addParametersTransform1OtherCommands(self, commands): - transform1 = self.getParameterValue(LAStoolsAlgorithm.TRANSFORM_OTHER1) - transform1_arg = self.getParameterValue(LAStoolsAlgorithm.TRANSFORM_OTHER1_ARG) - if transform1 != 0: - commands.append("-" + LAStoolsAlgorithm.TRANSFORM_OTHERS[transform1]) - if transform1 < 11 and transform1_arg is not None: - commands.append(transform1_arg) - - def addParametersTransform2OtherGUI(self): - self.addParameter(ParameterSelection(LAStoolsAlgorithm.TRANSFORM_OTHER2, - self.tr("second transform (intensities, scan angles, GPS times, ...)"), LAStoolsAlgorithm.TRANSFORM_OTHERS, 0)) - self.addParameter(ParameterString(LAStoolsAlgorithm.TRANSFORM_OTHER2_ARG, - self.tr("value for second transform (intensities, scan angles, GPS times, ...)"))) - - def addParametersTransform2OtherCommands(self, commands): - transform2 = self.getParameterValue(LAStoolsAlgorithm.TRANSFORM_OTHER2) - transform2_arg = self.getParameterValue(LAStoolsAlgorithm.TRANSFORM_OTHER2_ARG) - if transform2 != 0: - commands.append("-" + LAStoolsAlgorithm.TRANSFORM_OTHERS[transform2]) - if transform2 < 11 and transform2_arg is not None: - commands.append(transform2_arg) - - def addParametersIgnoreClass1GUI(self): - self.addParameter(ParameterSelection(LAStoolsAlgorithm.IGNORE_CLASS1, - self.tr("ignore points with this classification"), LAStoolsAlgorithm.IGNORE_CLASSES, 0)) - - def addParametersIgnoreClass1Commands(self, commands): - ignore1 = self.getParameterValue(LAStoolsAlgorithm.IGNORE_CLASS1) - if ignore1 != 0: - commands.append("-ignore_class") - commands.append(str(ignore1)) - - def addParametersIgnoreClass2GUI(self): - self.addParameter(ParameterSelection(LAStoolsAlgorithm.IGNORE_CLASS2, - self.tr("also ignore points with this classification"), LAStoolsAlgorithm.IGNORE_CLASSES, 0)) - - def addParametersIgnoreClass2Commands(self, commands): - ignore2 = self.getParameterValue(LAStoolsAlgorithm.IGNORE_CLASS2) - if ignore2 != 0: - commands.append("-ignore_class") - commands.append(str(ignore2)) diff --git a/python/plugins/processing/algs/lidar/lastools/LAStoolsUtils.py b/python/plugins/processing/algs/lidar/lastools/LAStoolsUtils.py deleted file mode 100644 index fe87fb7f5b0e..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/LAStoolsUtils.py +++ /dev/null @@ -1,79 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - LAStoolsUtils.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com - --------------------- - Date : October 2014 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from builtins import object - - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -import subprocess - -from qgis.PyQt.QtCore import QCoreApplication - -from processing.core.ProcessingLog import ProcessingLog -from processing.core.ProcessingConfig import ProcessingConfig -from processing.tools.system import isWindows - - -class LAStoolsUtils(object): - - LASTOOLS_FOLDER = "LASTOOLS_FOLDER" - WINE_FOLDER = "WINE_FOLDER" - - @staticmethod - def hasWine(): - wine_folder = ProcessingConfig.getSetting(LAStoolsUtils.WINE_FOLDER) - return wine_folder is not None and wine_folder != "" - - @staticmethod - def LAStoolsPath(): - lastools_folder = ProcessingConfig.getSetting(LAStoolsUtils.LASTOOLS_FOLDER) - if lastools_folder is None: - lastools_folder = "" - if isWindows(): - wine_folder = "" - else: - wine_folder = ProcessingConfig.getSetting(LAStoolsUtils.WINE_FOLDER) - if wine_folder is None or wine_folder == "": - folder = lastools_folder - else: - folder = wine_folder + "/wine " + lastools_folder - return folder - - @staticmethod - def runLAStools(commands, feedback): - loglines = [] - commandline = " ".join(commands) - loglines.append(QCoreApplication.translate("LAStoolsUtils", "LAStools command line")) - loglines.append(commandline) - loglines.append(QCoreApplication.translate("LAStoolsUtils", "LAStools console output")) - proc = subprocess.Popen(commandline, shell=True, stdout=subprocess.PIPE, stdin=subprocess.DEVNULL, - stderr=subprocess.STDOUT, universal_newlines=False).stdout - for line in iter(proc.readline, ""): - loglines.append(line) - feedback.pushConsoleInfo(line) - ProcessingLog.addToLog(ProcessingLog.LOG_INFO, loglines) diff --git a/python/plugins/processing/algs/lidar/lastools/__init__.py b/python/plugins/processing/algs/lidar/lastools/__init__.py deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/python/plugins/processing/algs/lidar/lastools/blast2dem.py b/python/plugins/processing/algs/lidar/lastools/blast2dem.py deleted file mode 100644 index b242d9fcdf32..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/blast2dem.py +++ /dev/null @@ -1,77 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - blast2dem.py - --------------------- - Date : September 2013 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Martin Isenburg' -__date__ = 'September 2013' -__copyright__ = '(C) 2013, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterBoolean - - -class blast2dem(LAStoolsAlgorithm): - - ATTRIBUTE = "ATTRIBUTE" - PRODUCT = "PRODUCT" - ATTRIBUTES = ["elevation", "slope", "intensity", "rgb"] - PRODUCTS = ["actual values", "hillshade", "gray", "false"] - USE_TILE_BB = "USE_TILE_BB" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('blast2dem') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParametersFilter1ReturnClassFlagsGUI() - self.addParametersStepGUI() - self.addParameter(ParameterSelection(blast2dem.ATTRIBUTE, - self.tr("Attribute"), blast2dem.ATTRIBUTES, 0)) - self.addParameter(ParameterSelection(blast2dem.PRODUCT, - self.tr("Product"), blast2dem.PRODUCTS, 0)) - self.addParameter(ParameterBoolean(blast2dem.USE_TILE_BB, - self.tr("Use tile bounding box (after tiling with buffer)"), False)) - self.addParametersRasterOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "blast2dem")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - self.addParametersFilter1ReturnClassFlagsCommands(commands) - self.addParametersStepCommands(commands) - attribute = self.getParameterValue(blast2dem.ATTRIBUTE) - if attribute != 0: - commands.append("-" + blast2dem.ATTRIBUTES[attribute]) - product = self.getParameterValue(blast2dem.PRODUCT) - if product != 0: - commands.append("-" + blast2dem.PRODUCTS[product]) - if (self.getParameterValue(blast2dem.USE_TILE_BB)): - commands.append("-use_tile_bb") - self.addParametersRasterOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/blast2demPro.py b/python/plugins/processing/algs/lidar/lastools/blast2demPro.py deleted file mode 100644 index 564eda5c3062..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/blast2demPro.py +++ /dev/null @@ -1,87 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - blast2demPro.py - --------------------- - Date : October 2014 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterBoolean - - -class blast2demPro(LAStoolsAlgorithm): - - ATTRIBUTE = "ATTRIBUTE" - PRODUCT = "PRODUCT" - ATTRIBUTES = ["elevation", "slope", "intensity", "rgb"] - PRODUCTS = ["actual values", "hillshade", "gray", "false"] - USE_TILE_BB = "USE_TILE_BB" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('blast2demPro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParametersPointInputMergedGUI() - self.addParametersFilter1ReturnClassFlagsGUI() - self.addParametersStepGUI() - self.addParameter(ParameterSelection(blast2demPro.ATTRIBUTE, - self.tr("Attribute"), blast2demPro.ATTRIBUTES, 0)) - self.addParameter(ParameterSelection(blast2demPro.PRODUCT, - self.tr("Product"), blast2demPro.PRODUCTS, 0)) - self.addParameter(ParameterBoolean(blast2demPro.USE_TILE_BB, - self.tr("Use tile bounding box (after tiling with buffer)"), False)) - self.addParametersOutputDirectoryGUI() - self.addParametersOutputAppendixGUI() - self.addParametersRasterOutputFormatGUI() - self.addParametersRasterOutputGUI() - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "blast2dem")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - self.addParametersPointInputMergedCommands(commands) - self.addParametersFilter1ReturnClassFlagsCommands(commands) - self.addParametersStepCommands(commands) - attribute = self.getParameterValue(blast2demPro.ATTRIBUTE) - if attribute != 0: - commands.append("-" + blast2demPro.ATTRIBUTES[attribute]) - product = self.getParameterValue(blast2demPro.PRODUCT) - if product != 0: - commands.append("-" + blast2demPro.PRODUCTS[product]) - if (self.getParameterValue(blast2demPro.USE_TILE_BB)): - commands.append("-use_tile_bb") - self.addParametersOutputDirectoryCommands(commands) - self.addParametersOutputAppendixCommands(commands) - self.addParametersRasterOutputFormatCommands(commands) - self.addParametersRasterOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/blast2iso.py b/python/plugins/processing/algs/lidar/lastools/blast2iso.py deleted file mode 100644 index c22618ca90f0..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/blast2iso.py +++ /dev/null @@ -1,90 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - blast2iso.py - --------------------- - Date : September 2013 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'September 2013' -__copyright__ = '(C) 2013, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterNumber - - -class blast2iso(LAStoolsAlgorithm): - - SMOOTH = "SMOOTH" - ISO_EVERY = "ISO_EVERY" - SIMPLIFY_LENGTH = "SIMPLIFY_LENGTH" - SIMPLIFY_AREA = "SIMPLIFY_AREA" - CLEAN = "CLEAN" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('blast2iso') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParameter(ParameterNumber(blast2iso.SMOOTH, - self.tr("smooth underlying TIN"), 0, None, 0)) - self.addParameter(ParameterNumber(blast2iso.ISO_EVERY, - self.tr("extract isoline with a spacing of"), 0, None, 10.0)) - self.addParameter(ParameterNumber(blast2iso.CLEAN, - self.tr("clean isolines shorter than (0 = do not clean)"), - None, None, 0.0)) - self.addParameter(ParameterNumber(blast2iso.SIMPLIFY_LENGTH, - self.tr("simplify segments shorter than (0 = do not simplify)"), - None, None, 0.0)) - self.addParameter(ParameterNumber(blast2iso.SIMPLIFY_AREA, - self.tr("simplify segments pairs with area less than (0 = do not simplify)"), - None, None, 0.0)) - self.addParametersVectorOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "blast2iso")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - smooth = self.getParameterValue(blast2iso.SMOOTH) - if smooth != 0: - commands.append("-smooth") - commands.append(str(smooth)) - commands.append("-iso_every") - commands.append(str(self.getParameterValue(blast2iso.ISO_EVERY))) - simplify_length = self.getParameterValue(blast2iso.SIMPLIFY_LENGTH) - if simplify_length != 0: - commands.append("-simplify_length") - commands.append(str(simplify_length)) - simplify_area = self.getParameterValue(blast2iso.SIMPLIFY_AREA) - if simplify_area != 0: - commands.append("-simplify_area") - commands.append(str(simplify_area)) - clean = self.getParameterValue(blast2iso.CLEAN) - if clean != 0: - commands.append("-clean") - commands.append(str(clean)) - self.addParametersVectorOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/blast2isoPro.py b/python/plugins/processing/algs/lidar/lastools/blast2isoPro.py deleted file mode 100644 index e9f9845b4c13..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/blast2isoPro.py +++ /dev/null @@ -1,100 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - blast2isoPro.py - --------------------- - Date : October 2014 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterNumber - - -class blast2isoPro(LAStoolsAlgorithm): - - SMOOTH = "SMOOTH" - ISO_EVERY = "ISO_EVERY" - SIMPLIFY_LENGTH = "SIMPLIFY_LENGTH" - SIMPLIFY_AREA = "SIMPLIFY_AREA" - CLEAN = "CLEAN" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('blast2isoPro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParametersPointInputMergedGUI() - self.addParameter(ParameterNumber(blast2isoPro.SMOOTH, - self.tr("smooth underlying TIN"), 0, None, 0)) - self.addParameter(ParameterNumber(blast2isoPro.ISO_EVERY, - self.tr("extract isoline with a spacing of"), 0, None, 10.0)) - self.addParameter(ParameterNumber(blast2isoPro.CLEAN, - self.tr("clean isolines shorter than (0 = do not clean)"), - None, None, 0.0)) - self.addParameter(ParameterNumber(blast2isoPro.SIMPLIFY_LENGTH, - self.tr("simplify segments shorter than (0 = do not simplify)"), - None, None, 0.0)) - self.addParameter(ParameterNumber(blast2isoPro.SIMPLIFY_AREA, - self.tr("simplify segments pairs with area less than (0 = do not simplify)"), - None, None, 0.0)) - self.addParametersOutputDirectoryGUI() - self.addParametersOutputAppendixGUI() - self.addParametersVectorOutputFormatGUI() - self.addParametersVectorOutputGUI() - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "blast2iso")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - self.addParametersPointInputMergedCommands(commands) - smooth = self.getParameterValue(blast2isoPro.SMOOTH) - if smooth != 0: - commands.append("-smooth") - commands.append(str(smooth)) - commands.append("-iso_every") - commands.append(str(self.getParameterValue(blast2isoPro.ISO_EVERY))) - simplify_length = self.getParameterValue(blast2isoPro.SIMPLIFY_LENGTH) - if simplify_length != 0: - commands.append("-simplify_length") - commands.append(str(simplify_length)) - simplify_area = self.getParameterValue(blast2isoPro.SIMPLIFY_AREA) - if simplify_area != 0: - commands.append("-simplify_area") - commands.append(str(simplify_area)) - clean = self.getParameterValue(blast2isoPro.CLEAN) - if clean != 0: - commands.append("-clean") - commands.append(str(clean)) - self.addParametersOutputDirectoryCommands(commands) - self.addParametersOutputAppendixCommands(commands) - self.addParametersVectorOutputFormatCommands(commands) - self.addParametersVectorOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/flightlinesToCHM.py b/python/plugins/processing/algs/lidar/lastools/flightlinesToCHM.py deleted file mode 100644 index 4593b11b597d..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/flightlinesToCHM.py +++ /dev/null @@ -1,162 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - flightlinesToCHM.py - --------------------- - Date : May 2014 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'May 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterString - - -class flightlinesToCHM(LAStoolsAlgorithm): - - TILE_SIZE = "TILE_SIZE" - BUFFER = "BUFFER" - TERRAIN = "TERRAIN" - TERRAINS = ["wilderness", "nature", "town", "city", "metro"] - BEAM_WIDTH = "BEAM_WIDTH" - BASE_NAME = "BASE_NAME" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('flightlinesToCHM') - self.group, self.i18n_group = self.trAlgorithm('LAStools Pipelines') - self.addParametersPointInputFolderGUI() - self.addParameter(ParameterNumber(flightlinesToCHM.TILE_SIZE, - self.tr("tile size (side length of square tile)"), - 0, None, 1000.0)) - self.addParameter(ParameterNumber(flightlinesToCHM.BUFFER, - self.tr("buffer around each tile (avoids edge artifacts)"), - 0, None, 25.0)) - self.addParameter(ParameterSelection(flightlinesToCHM.TERRAIN, - self.tr("terrain type"), flightlinesToCHM.TERRAINS, 1)) - self.addParameter(ParameterNumber(flightlinesToCHM.BEAM_WIDTH, - self.tr("laser beam width (diameter of laser footprint)"), - 0, None, 0.2)) - self.addParametersStepGUI() - self.addParametersTemporaryDirectoryGUI() - self.addParametersOutputDirectoryGUI() - self.addParameter(ParameterString(flightlinesToCHM.BASE_NAME, - self.tr("tile base name (using 'sydney' creates sydney_274000_4714000...)"), "tile")) - self.addParametersRasterOutputFormatGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - # first we tile the data - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lastile")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - commands.append("-files_are_flightlines") - tile_size = self.getParameterValue(flightlinesToCHM.TILE_SIZE) - commands.append("-tile_size") - commands.append(str(tile_size)) - buffer = self.getParameterValue(flightlinesToCHM.BUFFER) - if buffer != 0.0: - commands.append("-buffer") - commands.append(str(buffer)) - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - base_name = self.getParameterValue(flightlinesToCHM.BASE_NAME) - if base_name == "": - base_name = "tile" - commands.append("-o") - commands.append(base_name) - commands.append("-olaz") - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we ground classify the tiles - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasground")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, base_name + "*.laz") - method = self.getParameterValue(flightlinesToCHM.TERRAIN) - if method != 1: - commands.append("-" + flightlinesToCHM.TERRAINS[method]) - if method > 2: - commands.append("-ultra_fine") - elif method > 1: - commands.append("-extra_fine") - elif method > 0: - commands.append("-fine") - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - commands.append("-odix") - commands.append("_g") - commands.append("-olaz") - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we height-normalize the tiles - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasheight")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, base_name + "*_g.laz") - commands.append("-replace_z") - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - commands.append("-odix") - commands.append("h") - commands.append("-olaz") - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we thin and splat the tiles - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasthin")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, base_name + "*_gh.laz") - beam_width = self.getParameterValue(flightlinesToCHM.BEAM_WIDTH) - if beam_width != 0.0: - commands.append("-subcircle") - commands.append(str(beam_width / 2)) - step = self.getParametersStepValue() - commands.append("-step") - commands.append(str(step / 4)) - commands.append("-highest") - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - commands.append("-odix") - commands.append("t") - commands.append("-olaz") - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we rasterize the classified tiles into CHMs - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2dem")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, base_name + "*_ght.laz") - self.addParametersStepCommands(commands) - commands.append("-use_tile_bb") - self.addParametersOutputDirectoryCommands(commands) - commands.append("-ocut") - commands.append("4") - commands.append("-odix") - commands.append("_chm") - self.addParametersRasterOutputFormatCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/flightlinesToDTMandDSM.py b/python/plugins/processing/algs/lidar/lastools/flightlinesToDTMandDSM.py deleted file mode 100644 index 8f92f0ba17a6..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/flightlinesToDTMandDSM.py +++ /dev/null @@ -1,144 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - flightlinesToDTMandDSM.py - --------------------- - Date : April 2014 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'May 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterString - - -class flightlinesToDTMandDSM(LAStoolsAlgorithm): - - TILE_SIZE = "TILE_SIZE" - BUFFER = "BUFFER" - TERRAIN = "TERRAIN" - TERRAINS = ["wilderness", "nature", "town", "city", "metro"] - BASE_NAME = "BASE_NAME" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('flightlinesToDTMandDSM') - self.group, self.i18n_group = self.trAlgorithm('LAStools Pipelines') - self.addParametersPointInputFolderGUI() - self.addParameter(ParameterNumber(flightlinesToDTMandDSM.TILE_SIZE, - self.tr("tile size (side length of square tile)"), - 0, None, 1000.0)) - self.addParameter(ParameterNumber(flightlinesToDTMandDSM.BUFFER, - self.tr("buffer around each tile (avoids edge artifacts)"), - 0, None, 25.0)) - self.addParameter(ParameterSelection(flightlinesToDTMandDSM.TERRAIN, - self.tr("terrain type"), flightlinesToDTMandDSM.TERRAINS, 1)) - self.addParametersStepGUI() - self.addParametersTemporaryDirectoryGUI() - self.addParametersOutputDirectoryGUI() - self.addParameter(ParameterString(flightlinesToDTMandDSM.BASE_NAME, - self.tr("tile base name (using 'sydney' creates sydney_274000_4714000...)"), "tile")) - self.addParametersRasterOutputFormatGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - # first we tile the data - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lastile")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - commands.append("-files_are_flightlines") - tile_size = self.getParameterValue(flightlinesToDTMandDSM.TILE_SIZE) - commands.append("-tile_size") - commands.append(str(tile_size)) - buffer = self.getParameterValue(flightlinesToDTMandDSM.BUFFER) - if buffer != 0.0: - commands.append("-buffer") - commands.append(str(buffer)) - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - base_name = self.getParameterValue(flightlinesToDTMandDSM.BASE_NAME) - if base_name == "": - base_name = "tile" - commands.append("-o") - commands.append(base_name) - commands.append("-olaz") - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we ground classify the tiles - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasground")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, base_name + "*.laz") - method = self.getParameterValue(flightlinesToDTMandDSM.TERRAIN) - if method != 1: - commands.append("-" + flightlinesToDTMandDSM.TERRAINS[method]) - if method > 2: - commands.append("-ultra_fine") - elif method > 1: - commands.append("-extra_fine") - elif method > 0: - commands.append("-fine") - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - commands.append("-odix") - commands.append("_g") - commands.append("-olaz") - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we rasterize the classified tiles into DTMs - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2dem")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, base_name + "*_g.laz") - commands.append("-keep_class") - commands.append("2") - self.addParametersStepCommands(commands) - commands.append("-use_tile_bb") - self.addParametersOutputDirectoryCommands(commands) - commands.append("-ocut") - commands.append("2") - commands.append("-odix") - commands.append("_dtm") - self.addParametersRasterOutputFormatCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we rasterize the classified tiles into DSMs - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2dem")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, base_name + "*_g.laz") - commands.append("-first_only") - self.addParametersStepCommands(commands) - commands.append("-use_tile_bb") - self.addParametersOutputDirectoryCommands(commands) - commands.append("-ocut") - commands.append("2") - commands.append("-odix") - commands.append("_dsm") - self.addParametersRasterOutputFormatCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/flightlinesToSingleCHMpitFree.py b/python/plugins/processing/algs/lidar/lastools/flightlinesToSingleCHMpitFree.py deleted file mode 100644 index 87b8a659eebe..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/flightlinesToSingleCHMpitFree.py +++ /dev/null @@ -1,264 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - flightlinesToSingleCHMpitFree.py - --------------------- - Date : May 2014 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'May 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterNumber - - -class flightlinesToSingleCHMpitFree(LAStoolsAlgorithm): - - TILE_SIZE = "TILE_SIZE" - BUFFER = "BUFFER" - TERRAIN = "TERRAIN" - TERRAINS = ["wilderness", "nature", "town", "city", "metro"] - BEAM_WIDTH = "BEAM_WIDTH" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('flightlinesToSingleCHMpitFree') - self.group, self.i18n_group = self.trAlgorithm('LAStools Pipelines') - self.addParametersPointInputFolderGUI() - self.addParameter(ParameterNumber(flightlinesToSingleCHMpitFree.TILE_SIZE, - self.tr("tile size (side length of square tile)"), - 0, None, 1000.0)) - self.addParameter(ParameterNumber(flightlinesToSingleCHMpitFree.BUFFER, - self.tr("buffer around each tile (avoids edge artifacts)"), - 0, None, 25.0)) - self.addParameter(ParameterSelection(flightlinesToSingleCHMpitFree.TERRAIN, - self.tr("terrain type"), flightlinesToSingleCHMpitFree.TERRAINS, 1)) - self.addParameter(ParameterNumber(flightlinesToSingleCHMpitFree.BEAM_WIDTH, - self.tr("laser beam width (diameter of laser footprint)"), 0, None, 0.2)) - self.addParametersStepGUI() - self.addParametersTemporaryDirectoryGUI() - self.addParametersRasterOutputGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - # needed for thinning and killing - step = self.getParametersStepValue() - - # first we tile the data - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lastile")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - commands.append("-files_are_flightlines") - tile_size = self.getParameterValue(flightlinesToSingleCHMpitFree.TILE_SIZE) - commands.append("-tile_size") - commands.append(str(tile_size)) - buffer = self.getParameterValue(flightlinesToSingleCHMpitFree.BUFFER) - if buffer != 0.0: - commands.append("-buffer") - commands.append(str(buffer)) - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - commands.append("-o") - commands.append("tile.laz") - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we ground classify the tiles - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasground")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, "tile*.laz") - method = self.getParameterValue(flightlinesToSingleCHMpitFree.TERRAIN) - if method != 1: - commands.append("-" + flightlinesToSingleCHMpitFree.TERRAINS[method]) - if method > 2: - commands.append("-ultra_fine") - elif method > 1: - commands.append("-extra_fine") - elif method > 0: - commands.append("-fine") - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - commands.append("-odix") - commands.append("_g") - commands.append("-olaz") - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we height-normalize the tiles - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasheight")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, "tile*_g.laz") - commands.append("-replace_z") - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - commands.append("-odix") - commands.append("h") - commands.append("-olaz") - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we thin and splat the tiles - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasthin")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, "tile*_gh.laz") - beam_width = self.getParameterValue(flightlinesToSingleCHMpitFree.BEAM_WIDTH) - if beam_width != 0.0: - commands.append("-subcircle") - commands.append(str(beam_width / 2)) - commands.append("-step") - commands.append(str(step / 4)) - commands.append("-highest") - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - commands.append("-odix") - commands.append("t") - commands.append("-olaz") - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we rasterize the classified tiles into the partial CHMs at level 00 - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2dem")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, "tile*_ght.laz") - self.addParametersStepCommands(commands) - commands.append("-use_tile_bb") - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - commands.append("-ocut") - commands.append("4") - commands.append("-odix") - commands.append("_chm00") - commands.append("-obil") - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we rasterize the classified tiles into the partial CHMs at level 02 - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2dem")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, "tile*_ght.laz") - commands.append("-drop_z_below") - commands.append("2") - self.addParametersStepCommands(commands) - commands.append("-kill") - commands.append(str(step * 3)) - commands.append("-use_tile_bb") - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - commands.append("-ocut") - commands.append("4") - commands.append("-odix") - commands.append("_chm02") - commands.append("-obil") - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we rasterize the classified tiles into the partial CHMs at level 05 - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2dem")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, "tile*_ght.laz") - commands.append("-drop_z_below") - commands.append("5") - self.addParametersStepCommands(commands) - commands.append("-kill") - commands.append(str(step * 3)) - commands.append("-use_tile_bb") - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - commands.append("-ocut") - commands.append("4") - commands.append("-odix") - commands.append("_chm05") - commands.append("-obil") - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we rasterize the classified tiles into the partial CHMs at level 10 - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2dem")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, "tile*_ght.laz") - commands.append("-drop_z_below") - commands.append("10") - self.addParametersStepCommands(commands) - commands.append("-kill") - commands.append(str(step * 3)) - commands.append("-use_tile_bb") - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - commands.append("-ocut") - commands.append("4") - commands.append("-odix") - commands.append("_chm10") - commands.append("-obil") - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we rasterize the classified tiles into the partial CHMs at level 15 - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2dem")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, "tile*_ght.laz") - commands.append("-drop_z_below") - commands.append("15") - self.addParametersStepCommands(commands) - commands.append("-kill") - commands.append(str(step * 3)) - commands.append("-use_tile_bb") - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - commands.append("-ocut") - commands.append("4") - commands.append("-odix") - commands.append("_chm15") - commands.append("-obil") - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we rasterize the classified tiles into the partial CHMs at level 20 - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2dem")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, "tile*_ght.laz") - commands.append("-drop_z_below") - commands.append("20") - self.addParametersStepCommands(commands) - commands.append("-kill") - commands.append(str(step * 3)) - commands.append("-use_tile_bb") - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - commands.append("-ocut") - commands.append("4") - commands.append("-odix") - commands.append("_chm20") - commands.append("-obil") - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we combine the partial CHMs into a single output CHM - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasgrid")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, "tile_chm*.bil") - commands.append("-highest") - self.addParametersStepCommands(commands) - self.addParametersRasterOutputCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/hugeFileClassify.py b/python/plugins/processing/algs/lidar/lastools/hugeFileClassify.py deleted file mode 100644 index e56652789788..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/hugeFileClassify.py +++ /dev/null @@ -1,140 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - hugeFileClassify.py - --------------------- - Date : May 2014 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'May 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterNumber - - -class hugeFileClassify(LAStoolsAlgorithm): - - TILE_SIZE = "TILE_SIZE" - BUFFER = "BUFFER" - AIRBORNE = "AIRBORNE" - TERRAIN = "TERRAIN" - TERRAINS = ["wilderness", "nature", "town", "city", "metro"] - GRANULARITY = "GRANULARITY" - GRANULARITIES = ["coarse", "default", "fine", "extra_fine", "ultra_fine"] - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('hugeFileClassify') - self.group, self.i18n_group = self.trAlgorithm('LAStools Pipelines') - self.addParametersPointInputGUI() - self.addParameter(ParameterNumber(hugeFileClassify.TILE_SIZE, - self.tr("tile size (side length of square tile)"), - 0, None, 1000.0)) - self.addParameter(ParameterNumber(hugeFileClassify.BUFFER, - self.tr("buffer around each tile (avoids edge artifacts)"), - 0, None, 25.0)) - self.addParameter(ParameterBoolean(hugeFileClassify.AIRBORNE, - self.tr("airborne LiDAR"), True)) - self.addParameter(ParameterSelection(hugeFileClassify.TERRAIN, - self.tr("terrain type"), hugeFileClassify.TERRAINS, 1)) - self.addParameter(ParameterSelection(hugeFileClassify.GRANULARITY, - self.tr("preprocessing"), hugeFileClassify.GRANULARITIES, 1)) - self.addParametersTemporaryDirectoryGUI() - self.addParametersPointOutputGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - # first we tile the data with option '-reversible' - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lastile")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - tile_size = self.getParameterValue(hugeFileClassify.TILE_SIZE) - commands.append("-tile_size") - commands.append(str(tile_size)) - buffer = self.getParameterValue(hugeFileClassify.BUFFER) - if buffer != 0.0: - commands.append("-buffer") - commands.append(str(buffer)) - commands.append("-reversible") - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - commands.append("-o") - commands.append("hugeFileClassify.laz") - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we ground classify the reversible tiles - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasground")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, "hugeFileClassify*.laz") - airborne = self.getParameterValue(hugeFileClassify.AIRBORNE) - if not airborne: - commands.append("-not_airborne") - method = self.getParameterValue(hugeFileClassify.TERRAIN) - if method != 1: - commands.append("-" + hugeFileClassify.TERRAINS[method]) - granularity = self.getParameterValue(hugeFileClassify.GRANULARITY) - if granularity != 1: - commands.append("-" + hugeFileClassify.GRANULARITIES[granularity]) - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - commands.append("-odix") - commands.append("_g") - commands.append("-olaz") - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we compute the height for each points in the reversible tiles - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasheight")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, "hugeFileClassify*_g.laz") - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - commands.append("-odix") - commands.append("h") - commands.append("-olaz") - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we classify buildings and trees in the reversible tiles - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasclassify")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, "hugeFileClassify*_gh.laz") - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - commands.append("-odix") - commands.append("c") - commands.append("-olaz") - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we reverse the tiling - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lastile")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, "hugeFileClassify*_ghc.laz") - commands.append("-reverse_tiling") - self.addParametersPointOutputCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/hugeFileGroundClassify.py b/python/plugins/processing/algs/lidar/lastools/hugeFileGroundClassify.py deleted file mode 100644 index 489490008977..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/hugeFileGroundClassify.py +++ /dev/null @@ -1,118 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - hugeFileGroundClassify.py - --------------------- - Date : May 2014 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'May 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterNumber - - -class hugeFileGroundClassify(LAStoolsAlgorithm): - - TILE_SIZE = "TILE_SIZE" - BUFFER = "BUFFER" - AIRBORNE = "AIRBORNE" - TERRAIN = "TERRAIN" - TERRAINS = ["wilderness", "nature", "town", "city", "metro"] - GRANULARITY = "GRANULARITY" - GRANULARITIES = ["coarse", "default", "fine", "extra_fine", "ultra_fine"] - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('hugeFileGroundClassify') - self.group, self.i18n_group = self.trAlgorithm('LAStools Pipelines') - self.addParametersPointInputGUI() - self.addParameter(ParameterNumber( - hugeFileGroundClassify.TILE_SIZE, - self.tr("tile size (side length of square tile)"), - 0, None, 1000.0)) - self.addParameter(ParameterNumber(hugeFileGroundClassify.BUFFER, - self.tr("buffer around each tile (avoids edge artifacts)"), - 0, None, 25.0)) - self.addParameter(ParameterBoolean(hugeFileGroundClassify.AIRBORNE, - self.tr("airborne LiDAR"), True)) - self.addParameter(ParameterSelection(hugeFileGroundClassify.TERRAIN, - self.tr("terrain type"), hugeFileGroundClassify.TERRAINS, 1)) - self.addParameter(ParameterSelection(hugeFileGroundClassify.GRANULARITY, - self.tr("preprocessing"), hugeFileGroundClassify.GRANULARITIES, 1)) - self.addParametersTemporaryDirectoryGUI() - self.addParametersPointOutputGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - - # first we tile the data with option '-reversible' - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lastile")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - tile_size = self.getParameterValue(hugeFileGroundClassify.TILE_SIZE) - commands.append("-tile_size") - commands.append(str(tile_size)) - buffer = self.getParameterValue(hugeFileGroundClassify.BUFFER) - if buffer != 0.0: - commands.append("-buffer") - commands.append(str(buffer)) - commands.append("-reversible") - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - commands.append("-o") - commands.append("hugeFileGroundClassify.laz") - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we ground classify the reversible tiles - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasground")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, "hugeFileGroundClassify*.laz") - airborne = self.getParameterValue(hugeFileGroundClassify.AIRBORNE) - if not airborne: - commands.append("-not_airborne") - method = self.getParameterValue(hugeFileGroundClassify.TERRAIN) - if method != 1: - commands.append("-" + hugeFileGroundClassify.TERRAINS[method]) - granularity = self.getParameterValue(hugeFileGroundClassify.GRANULARITY) - if granularity != 1: - commands.append("-" + hugeFileGroundClassify.GRANULARITIES[granularity]) - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - commands.append("-odix") - commands.append("_g") - commands.append("-olaz") - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we reverse the tiling - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lastile")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, "hugeFileGroundClassify*_g.laz") - commands.append("-reverse_tiling") - self.addParametersPointOutputCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/hugeFileNormalize.py b/python/plugins/processing/algs/lidar/lastools/hugeFileNormalize.py deleted file mode 100644 index b2777c6d329f..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/hugeFileNormalize.py +++ /dev/null @@ -1,130 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - hugeFileNormalize.py - --------------------- - Date : May 2014 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'May 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterNumber - - -class hugeFileNormalize(LAStoolsAlgorithm): - - TILE_SIZE = "TILE_SIZE" - BUFFER = "BUFFER" - AIRBORNE = "AIRBORNE" - TERRAIN = "TERRAIN" - TERRAINS = ["wilderness", "nature", "town", "city", "metro"] - GRANULARITY = "GRANULARITY" - GRANULARITIES = ["coarse", "default", "fine", "extra_fine", "ultra_fine"] - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('hugeFileNormalize') - self.group, self.i18n_group = self.trAlgorithm('LAStools Pipelines') - self.addParametersPointInputGUI() - self.addParameter(ParameterNumber(hugeFileNormalize.TILE_SIZE, - self.tr("tile size (side length of square tile)"), - 0, None, 1000.0)) - self.addParameter(ParameterNumber(hugeFileNormalize.BUFFER, - self.tr("buffer around each tile (avoids edge artifacts)"), - 0, None, 25.0)) - self.addParameter(ParameterBoolean(hugeFileNormalize.AIRBORNE, - self.tr("airborne LiDAR"), True)) - self.addParameter(ParameterSelection(hugeFileNormalize.TERRAIN, - self.tr("terrain type"), hugeFileNormalize.TERRAINS, 1)) - self.addParameter(ParameterSelection(hugeFileNormalize.GRANULARITY, - self.tr("preprocessing"), hugeFileNormalize.GRANULARITIES, 1)) - self.addParametersTemporaryDirectoryGUI() - self.addParametersPointOutputGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - - # first we tile the data with option '-reversible' - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lastile")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - tile_size = self.getParameterValue(hugeFileNormalize.TILE_SIZE) - commands.append("-tile_size") - commands.append(str(tile_size)) - buffer = self.getParameterValue(hugeFileNormalize.BUFFER) - if buffer != 0.0: - commands.append("-buffer") - commands.append(str(buffer)) - commands.append("-reversible") - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - commands.append("-o") - commands.append("hugeFileNormalize.laz") - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we ground classify the reversible tiles - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasground")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, "hugeFileNormalize*.laz") - airborne = self.getParameterValue(hugeFileNormalize.AIRBORNE) - if not airborne: - commands.append("-not_airborne") - method = self.getParameterValue(hugeFileNormalize.TERRAIN) - if method != 1: - commands.append("-" + hugeFileNormalize.TERRAINS[method]) - granularity = self.getParameterValue(hugeFileNormalize.GRANULARITY) - if granularity != 1: - commands.append("-" + hugeFileNormalize.GRANULARITIES[granularity]) - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - commands.append("-odix") - commands.append("_g") - commands.append("-olaz") - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we height-normalize each points in the reversible tiles - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasheight")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, "hugeFileNormalize*_g.laz") - self.addParametersTemporaryDirectoryAsOutputDirectoryCommands(commands) - commands.append("-replace_z") - commands.append("-odix") - commands.append("h") - commands.append("-olaz") - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) - - # then we reverse the tiling - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lastile")] - self.addParametersVerboseCommands(commands) - self.addParametersTemporaryDirectoryAsInputFilesCommands(commands, "hugeFileNormalize*_gh.laz") - commands.append("-reverse_tiling") - self.addParametersPointOutputCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/las2dem.py b/python/plugins/processing/algs/lidar/lastools/las2dem.py deleted file mode 100644 index 31d7210f066c..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/las2dem.py +++ /dev/null @@ -1,82 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - las2dem.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com - --------------------- - Date : September 2013 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os - -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterBoolean - - -class las2dem(LAStoolsAlgorithm): - - ATTRIBUTE = "ATTRIBUTE" - PRODUCT = "PRODUCT" - ATTRIBUTES = ["elevation", "slope", "intensity", "rgb", "edge_longest", "edge_shortest"] - PRODUCTS = ["actual values", "hillshade", "gray", "false"] - USE_TILE_BB = "USE_TILE_BB" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('las2dem') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParametersFilter1ReturnClassFlagsGUI() - self.addParametersStepGUI() - self.addParameter(ParameterSelection(las2dem.ATTRIBUTE, - self.tr("Attribute"), las2dem.ATTRIBUTES, 0)) - self.addParameter(ParameterSelection(las2dem.PRODUCT, - self.tr("Product"), las2dem.PRODUCTS, 0)) - self.addParameter(ParameterBoolean(las2dem.USE_TILE_BB, - self.tr("use tile bounding box (after tiling with buffer)"), False)) - self.addParametersRasterOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2dem")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - self.addParametersFilter1ReturnClassFlagsCommands(commands) - self.addParametersStepCommands(commands) - attribute = self.getParameterValue(las2dem.ATTRIBUTE) - if attribute != 0: - commands.append("-" + las2dem.ATTRIBUTES[attribute]) - product = self.getParameterValue(las2dem.PRODUCT) - if product != 0: - commands.append("-" + las2dem.PRODUCTS[product]) - if (self.getParameterValue(las2dem.USE_TILE_BB)): - commands.append("-use_tile_bb") - self.addParametersRasterOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/las2demPro.py b/python/plugins/processing/algs/lidar/lastools/las2demPro.py deleted file mode 100644 index 520ee9c9a1f0..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/las2demPro.py +++ /dev/null @@ -1,88 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - las2demPro.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com - --------------------- - Date : September 2013 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os - -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterBoolean - - -class las2demPro(LAStoolsAlgorithm): - - ATTRIBUTE = "ATTRIBUTE" - PRODUCT = "PRODUCT" - ATTRIBUTES = ["elevation", "slope", "intensity", "rgb", "edge_longest", "edge_shortest"] - PRODUCTS = ["actual values", "hillshade", "gray", "false"] - USE_TILE_BB = "USE_TILE_BB" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('las2demPro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParametersFilter1ReturnClassFlagsGUI() - self.addParametersStepGUI() - self.addParameter(ParameterSelection(las2demPro.ATTRIBUTE, - self.tr("attribute (what to interpolate)"), las2demPro.ATTRIBUTES, 0)) - self.addParameter(ParameterSelection(las2demPro.PRODUCT, - self.tr("product (how to output per pixel)"), las2demPro.PRODUCTS, 0)) - self.addParameter(ParameterBoolean(las2demPro.USE_TILE_BB, - self.tr("use tile bounding box (after tiling with buffer)"), False)) - self.addParametersOutputDirectoryGUI() - self.addParametersOutputAppendixGUI() - self.addParametersRasterOutputFormatGUI() - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2dem")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - self.addParametersFilter1ReturnClassFlagsCommands(commands) - self.addParametersStepCommands(commands) - attribute = self.getParameterValue(las2demPro.ATTRIBUTE) - if attribute != 0: - commands.append("-" + las2demPro.ATTRIBUTES[attribute]) - product = self.getParameterValue(las2demPro.PRODUCT) - if product != 0: - commands.append("-" + las2demPro.PRODUCTS[product]) - if (self.getParameterValue(las2demPro.USE_TILE_BB)): - commands.append("-use_tile_bb") - self.addParametersOutputDirectoryCommands(commands) - self.addParametersOutputAppendixCommands(commands) - self.addParametersRasterOutputFormatCommands(commands) - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/las2iso.py b/python/plugins/processing/algs/lidar/lastools/las2iso.py deleted file mode 100644 index 92a8f6683c17..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/las2iso.py +++ /dev/null @@ -1,94 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - las2iso.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com - --------------------- - Date : September 2013 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterNumber - - -class las2iso(LAStoolsAlgorithm): - - SMOOTH = "SMOOTH" - ISO_EVERY = "ISO_EVERY" - SIMPLIFY_LENGTH = "SIMPLIFY_LENGTH" - SIMPLIFY_AREA = "SIMPLIFY_AREA" - CLEAN = "CLEAN" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('las2iso') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParameter(ParameterNumber(las2iso.SMOOTH, - self.tr("smooth underlying TIN"), 0, None, 0)) - self.addParameter(ParameterNumber(las2iso.ISO_EVERY, - self.tr("extract isoline with a spacing of"), 0, None, 10.0)) - self.addParameter(ParameterNumber(las2iso.CLEAN, - self.tr("clean isolines shorter than (0 = do not clean)"), - None, None, 0.0)) - self.addParameter(ParameterNumber(las2iso.SIMPLIFY_LENGTH, - self.tr("simplify segments shorter than (0 = do not simplify)"), - None, None, 0.0)) - self.addParameter(ParameterNumber(las2iso.SIMPLIFY_AREA, - self.tr("simplify segments pairs with area less than (0 = do not simplify)"), - None, None, 0.0)) - self.addParametersVectorOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2iso")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - smooth = self.getParameterValue(las2iso.SMOOTH) - if smooth != 0: - commands.append("-smooth") - commands.append(str(smooth)) - commands.append("-iso_every") - commands.append(str(self.getParameterValue(las2iso.ISO_EVERY))) - simplify_length = self.getParameterValue(las2iso.SIMPLIFY_LENGTH) - if simplify_length != 0: - commands.append("-simplify_length") - commands.append(str(simplify_length)) - simplify_area = self.getParameterValue(las2iso.SIMPLIFY_AREA) - if simplify_area != 0: - commands.append("-simplify_area") - commands.append(str(simplify_area)) - clean = self.getParameterValue(las2iso.CLEAN) - if clean != 0: - commands.append("-clean") - commands.append(str(clean)) - self.addParametersVectorOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/las2lasPro_filter.py b/python/plugins/processing/algs/lidar/lastools/las2lasPro_filter.py deleted file mode 100644 index 63347be1f1d5..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/las2lasPro_filter.py +++ /dev/null @@ -1,56 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - las2lasPro_filter.py - --------------------- - Date : October 2014 and May 2016 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - - -class las2lasPro_filter(LAStoolsAlgorithm): - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('las2lasPro_filter') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParametersFilter1ReturnClassFlagsGUI() - self.addParametersFilter2ReturnClassFlagsGUI() - self.addParametersFilter1CoordsIntensityGUI() - self.addParametersFilter2CoordsIntensityGUI() - self.addParametersPointOutputGUI() - - def processAlgorithm(self, feedback): - if (LAStoolsUtils.hasWine()): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2las.exe")] - else: - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2las")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - self.addParametersFilter1ReturnClassFlagsCommands(commands) - self.addParametersFilter2ReturnClassFlagsCommands(commands) - self.addParametersFilter1CoordsIntensityCommands(commands) - self.addParametersFilter2CoordsIntensityCommands(commands) - self.addParametersPointOutputCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/las2lasPro_project.py b/python/plugins/processing/algs/lidar/lastools/las2lasPro_project.py deleted file mode 100644 index 577fe0da625f..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/las2lasPro_project.py +++ /dev/null @@ -1,122 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - las2lasPro_project.py - --------------------- - Date : October 2014 and May 2016 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterSelection - - -class las2lasPro_project(LAStoolsAlgorithm): - - STATE_PLANES = ["---", "AK_10", "AK_2", "AK_3", "AK_4", "AK_5", "AK_6", "AK_7", "AK_8", "AK_9", "AL_E", "AL_W", "AR_N", "AR_S", "AZ_C", "AZ_E", "AZ_W", "CA_I", "CA_II", "CA_III", "CA_IV", "CA_V", "CA_VI", "CA_VII", "CO_C", "CO_N", "CO_S", "CT", "DE", "FL_E", "FL_N", "FL_W", "GA_E", "GA_W", "HI_1", "HI_2", "HI_3", "HI_4", "HI_5", "IA_N", "IA_S", "ID_C", "ID_E", "ID_W", "IL_E", "IL_W", "IN_E", "IN_W", "KS_N", "KS_S", "KY_N", "KY_S", "LA_N", "LA_S", "MA_I", "MA_M", "MD", "ME_E", "ME_W", "MI_C", "MI_N", "MI_S", "MN_C", "MN_N", "MN_S", "MO_C", "MO_E", "MO_W", "MS_E", "MS_W", "MT_C", "MT_N", "MT_S", "NC", "ND_N", "ND_S", "NE_N", "NE_S", "NH", "NJ", "NM_C", "NM_E", "NM_W", "NV_C", "NV_E", "NV_W", "NY_C", "NY_E", "NY_LI", "NY_W", "OH_N", "OH_S", "OK_N", "OK_S", "OR_N", "OR_S", "PA_N", "PA_S", "PR", "RI", "SC_N", "SC_S", "SD_N", "SD_S", "St.Croix", "TN", "TX_C", "TX_N", "TX_NC", "TX_S", "TX_SC", "UT_C", "UT_N", "UT_S", "VA_N", "VA_S", "VT", "WA_N", "WA_S", "WI_C", "WI_N", "WI_S", "WV_N", "WV_S", "WY_E", "WY_EC", "WY_W", "WY_WC"] - - UTM_ZONES = ["---", "1 (north)", "2 (north)", "3 (north)", "4 (north)", "5 (north)", "6 (north)", "7 (north)", "8 (north)", "9 (north)", "10 (north)", "11 (north)", "12 (north)", "13 (north)", "14 (north)", "15 (north)", "16 (north)", "17 (north)", "18 (north)", "19 (north)", "20 (north)", "21 (north)", "22 (north)", "23 (north)", "24 (north)", "25 (north)", "26 (north)", "27 (north)", "28 (north)", "29 (north)", "30 (north)", "31 (north)", "32 (north)", "33 (north)", "34 (north)", "35 (north)", "36 (north)", "37 (north)", "38 (north)", "39 (north)", "40 (north)", "41 (north)", "42 (north)", "43 (north)", "44 (north)", "45 (north)", "46 (north)", "47 (north)", "48 (north)", "49 (north)", "50 (north)", "51 (north)", "52 (north)", "53 (north)", "54 (north)", "55 (north)", "56 (north)", "57 (north)", "58 (north)", "59 (north)", "60 (north)", "1 (south)", "2 (south)", "3 (south)", "4 (south)", "5 (south)", "6 (south)", "7 (south)", "8 (south)", "9 (south)", "10 (south)", "11 (south)", "12 (south)", "13 (south)", "14 (south)", "15 (south)", "16 (south)", "17 (south)", "18 (south)", "19 (south)", "20 (south)", "21 (south)", "22 (south)", "23 (south)", "24 (south)", "25 (south)", "26 (south)", "27 (south)", "28 (south)", "29 (south)", "30 (south)", "31 (south)", "32 (south)", "33 (south)", "34 (south)", "35 (south)", "36 (south)", "37 (south)", "38 (south)", "39 (south)", "40 (south)", "41 (south)", "42 (south)", "43 (south)", "44 (south)", "45 (south)", "46 (south)", "47 (south)", "48 (south)", "49 (south)", "50 (south)", "51 (south)", "52 (south)", "53 (south)", "54 (south)", "55 (south)", "56 (south)", "57 (south)", "58 (south)", "59 (south)", "60 (south)"] - - PROJECTIONS = ["---", "utm", "sp83", "sp27", "longlat", "latlong", "ecef"] - - SOURCE_PROJECTION = "SOURCE_PROJECTION" - SOURCE_UTM = "SOURCE_UTM" - SOURCE_SP = "SOURCE_SP" - - TARGET_PROJECTION = "TARGET_PROJECTION" - TARGET_UTM = "TARGET_UTM" - TARGET_SP = "TARGET_SP" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('las2lasPro_project') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParameter(ParameterSelection(las2lasPro_project.SOURCE_PROJECTION, - self.tr("source projection"), las2lasPro_project.PROJECTIONS, 0)) - self.addParameter(ParameterSelection(las2lasPro_project.SOURCE_UTM, - self.tr("source utm zone"), las2lasPro_project.UTM_ZONES, 0)) - self.addParameter(ParameterSelection(las2lasPro_project.SOURCE_SP, - self.tr("source state plane code"), las2lasPro_project.STATE_PLANES, 0)) - self.addParameter(ParameterSelection(las2lasPro_project.TARGET_PROJECTION, - self.tr("target projection"), las2lasPro_project.PROJECTIONS, 0)) - self.addParameter(ParameterSelection(las2lasPro_project.TARGET_UTM, - self.tr("target utm zone"), las2lasPro_project.UTM_ZONES, 0)) - self.addParameter(ParameterSelection(las2lasPro_project.TARGET_SP, - self.tr("target state plane code"), las2lasPro_project.STATE_PLANES, 0)) - self.addParametersOutputDirectoryGUI() - self.addParametersOutputAppendixGUI() - self.addParametersPointOutputFormatGUI() - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - if (LAStoolsUtils.hasWine()): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2las.exe")] - else: - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2las")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - source_projection = self.getParameterValue(las2lasPro_project.SOURCE_PROJECTION) - if source_projection != 0: - if source_projection == 1: - source_utm_zone = self.getParameterValue(las2lasPro_project.SOURCE_UTM) - if source_utm_zone != 0: - commands.append("-" + las2lasPro_project.PROJECTIONS[source_projection]) - if source_utm_zone > 60: - commands.append(str(source_utm_zone - 60) + "M") - else: - commands.append(str(source_utm_zone) + "N") - elif source_projection < 4: - source_sp_code = self.getParameterValue(las2lasPro_project.SOURCE_SP) - if source_sp_code != 0: - commands.append("-" + las2lasPro_project.PROJECTIONS[source_projection]) - commands.append(las2lasPro_project.STATE_PLANES[source_sp_code]) - else: - commands.append("-" + las2lasPro_project.PROJECTIONS[source_projection]) - target_projection = self.getParameterValue(las2lasPro_project.TARGET_PROJECTION) - if target_projection != 0: - if target_projection == 1: - target_utm_zone = self.getParameterValue(las2lasPro_project.TARGET_UTM) - if target_utm_zone != 0: - commands.append("-target_" + las2lasPro_project.PROJECTIONS[target_projection]) - if target_utm_zone > 60: - commands.append(str(target_utm_zone - 60) + "M") - else: - commands.append(str(target_utm_zone) + "N") - elif target_projection < 4: - target_sp_code = self.getParameterValue(las2lasPro_project.TARGET_SP) - if target_sp_code != 0: - commands.append("-target_" + las2lasPro_project.PROJECTIONS[target_projection]) - commands.append(las2lasPro_project.STATE_PLANES[target_sp_code]) - else: - commands.append("-target_" + las2lasPro_project.PROJECTIONS[target_projection]) - self.addParametersOutputDirectoryCommands(commands) - self.addParametersOutputAppendixCommands(commands) - self.addParametersPointOutputFormatCommands(commands) - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/las2lasPro_transform.py b/python/plugins/processing/algs/lidar/lastools/las2lasPro_transform.py deleted file mode 100644 index de7f816e3f1d..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/las2lasPro_transform.py +++ /dev/null @@ -1,84 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - las2lasPro_transform.py - --------------------- - Date : October 2014 and May 2016 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterString -from processing.core.parameters import ParameterSelection - - -class las2lasPro_transform(LAStoolsAlgorithm): - - OPERATION = "OPERATION" - OPERATIONS = ["---", "set_point_type", "set_point_size", "set_version_minor", "set_version_major", "start_at_point", "stop_at_point", "remove_vlr", "week_to_adjusted", "adjusted_to_week", "auto_reoffset", "scale_rgb_up", "scale_rgb_down", "remove_all_vlrs", "remove_extra", "clip_to_bounding_box"] - OPERATIONARG = "OPERATIONARG" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('las2lasPro_transform') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParametersTransform1CoordinateGUI() - self.addParametersTransform2CoordinateGUI() - self.addParametersTransform1OtherGUI() - self.addParametersTransform2OtherGUI() - self.addParameter(ParameterSelection(las2lasPro_transform.OPERATION, - self.tr("operations (first 8 need an argument)"), - las2lasPro_transform.OPERATIONS, 0)) - self.addParameter(ParameterString(las2lasPro_transform.OPERATIONARG, - self.tr("argument for operation"))) - self.addParametersOutputDirectoryGUI() - self.addParametersOutputAppendixGUI() - self.addParametersPointOutputFormatGUI() - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - if (LAStoolsUtils.hasWine()): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2las.exe")] - else: - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2las")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - self.addParametersTransform1CoordinateCommands(commands) - self.addParametersTransform2CoordinateCommands(commands) - self.addParametersTransform1OtherCommands(commands) - self.addParametersTransform2OtherCommands(commands) - operation = self.getParameterValue(las2lasPro_transform.OPERATION) - if operation != 0: - commands.append("-" + las2lasPro_transform.OPERATIONS[operation]) - if operation > 8: - commands.append(self.getParameterValue(las2lasPro_transform.OPERATIONARG)) - self.addParametersOutputDirectoryCommands(commands) - self.addParametersOutputAppendixCommands(commands) - self.addParametersPointOutputFormatCommands(commands) - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/las2las_filter.py b/python/plugins/processing/algs/lidar/lastools/las2las_filter.py deleted file mode 100644 index 6bef3a6b2a8e..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/las2las_filter.py +++ /dev/null @@ -1,59 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - las2las_filter.py - --------------------- - Date : September 2013 and May 2016 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" - -__author__ = 'Martin Isenburg' -__date__ = 'September 2013' -__copyright__ = '(C) 2013, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - - -class las2las_filter(LAStoolsAlgorithm): - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('las2las_filter') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParametersFilter1ReturnClassFlagsGUI() - self.addParametersFilter2ReturnClassFlagsGUI() - self.addParametersFilter1CoordsIntensityGUI() - self.addParametersFilter2CoordsIntensityGUI() - self.addParametersPointOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - if (LAStoolsUtils.hasWine()): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2las.exe")] - else: - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2las")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - self.addParametersFilter1ReturnClassFlagsCommands(commands) - self.addParametersFilter2ReturnClassFlagsCommands(commands) - self.addParametersFilter1CoordsIntensityCommands(commands) - self.addParametersFilter2CoordsIntensityCommands(commands) - self.addParametersPointOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/las2las_project.py b/python/plugins/processing/algs/lidar/lastools/las2las_project.py deleted file mode 100644 index ffd1f384fb7b..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/las2las_project.py +++ /dev/null @@ -1,116 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - las2las_project.py - --------------------- - Date : September 2013 and May 2016 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'September 2013' -__copyright__ = '(C) 2013, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterSelection - - -class las2las_project(LAStoolsAlgorithm): - - STATE_PLANES = ["---", "AK_10", "AK_2", "AK_3", "AK_4", "AK_5", "AK_6", "AK_7", "AK_8", "AK_9", "AL_E", "AL_W", "AR_N", "AR_S", "AZ_C", "AZ_E", "AZ_W", "CA_I", "CA_II", "CA_III", "CA_IV", "CA_V", "CA_VI", "CA_VII", "CO_C", "CO_N", "CO_S", "CT", "DE", "FL_E", "FL_N", "FL_W", "GA_E", "GA_W", "HI_1", "HI_2", "HI_3", "HI_4", "HI_5", "IA_N", "IA_S", "ID_C", "ID_E", "ID_W", "IL_E", "IL_W", "IN_E", "IN_W", "KS_N", "KS_S", "KY_N", "KY_S", "LA_N", "LA_S", "MA_I", "MA_M", "MD", "ME_E", "ME_W", "MI_C", "MI_N", "MI_S", "MN_C", "MN_N", "MN_S", "MO_C", "MO_E", "MO_W", "MS_E", "MS_W", "MT_C", "MT_N", "MT_S", "NC", "ND_N", "ND_S", "NE_N", "NE_S", "NH", "NJ", "NM_C", "NM_E", "NM_W", "NV_C", "NV_E", "NV_W", "NY_C", "NY_E", "NY_LI", "NY_W", "OH_N", "OH_S", "OK_N", "OK_S", "OR_N", "OR_S", "PA_N", "PA_S", "PR", "RI", "SC_N", "SC_S", "SD_N", "SD_S", "St.Croix", "TN", "TX_C", "TX_N", "TX_NC", "TX_S", "TX_SC", "UT_C", "UT_N", "UT_S", "VA_N", "VA_S", "VT", "WA_N", "WA_S", "WI_C", "WI_N", "WI_S", "WV_N", "WV_S", "WY_E", "WY_EC", "WY_W", "WY_WC"] - - UTM_ZONES = ["---", "1 (north)", "2 (north)", "3 (north)", "4 (north)", "5 (north)", "6 (north)", "7 (north)", "8 (north)", "9 (north)", "10 (north)", "11 (north)", "12 (north)", "13 (north)", "14 (north)", "15 (north)", "16 (north)", "17 (north)", "18 (north)", "19 (north)", "20 (north)", "21 (north)", "22 (north)", "23 (north)", "24 (north)", "25 (north)", "26 (north)", "27 (north)", "28 (north)", "29 (north)", "30 (north)", "31 (north)", "32 (north)", "33 (north)", "34 (north)", "35 (north)", "36 (north)", "37 (north)", "38 (north)", "39 (north)", "40 (north)", "41 (north)", "42 (north)", "43 (north)", "44 (north)", "45 (north)", "46 (north)", "47 (north)", "48 (north)", "49 (north)", "50 (north)", "51 (north)", "52 (north)", "53 (north)", "54 (north)", "55 (north)", "56 (north)", "57 (north)", "58 (north)", "59 (north)", "60 (north)", "1 (south)", "2 (south)", "3 (south)", "4 (south)", "5 (south)", "6 (south)", "7 (south)", "8 (south)", "9 (south)", "10 (south)", "11 (south)", "12 (south)", "13 (south)", "14 (south)", "15 (south)", "16 (south)", "17 (south)", "18 (south)", "19 (south)", "20 (south)", "21 (south)", "22 (south)", "23 (south)", "24 (south)", "25 (south)", "26 (south)", "27 (south)", "28 (south)", "29 (south)", "30 (south)", "31 (south)", "32 (south)", "33 (south)", "34 (south)", "35 (south)", "36 (south)", "37 (south)", "38 (south)", "39 (south)", "40 (south)", "41 (south)", "42 (south)", "43 (south)", "44 (south)", "45 (south)", "46 (south)", "47 (south)", "48 (south)", "49 (south)", "50 (south)", "51 (south)", "52 (south)", "53 (south)", "54 (south)", "55 (south)", "56 (south)", "57 (south)", "58 (south)", "59 (south)", "60 (south)"] - - PROJECTIONS = ["---", "utm", "sp83", "sp27", "longlat", "latlong", "ecef"] - - SOURCE_PROJECTION = "SOURCE_PROJECTION" - SOURCE_UTM = "SOURCE_UTM" - SOURCE_SP = "SOURCE_SP" - - TARGET_PROJECTION = "TARGET_PROJECTION" - TARGET_UTM = "TARGET_UTM" - TARGET_SP = "TARGET_SP" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('las2las_project') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParameter(ParameterSelection(las2las_project.SOURCE_PROJECTION, - self.tr("source projection"), las2las_project.PROJECTIONS, 0)) - self.addParameter(ParameterSelection(las2las_project.SOURCE_UTM, - self.tr("source utm zone"), las2las_project.UTM_ZONES, 0)) - self.addParameter(ParameterSelection(las2las_project.SOURCE_SP, - self.tr("source state plane code"), las2las_project.STATE_PLANES, 0)) - self.addParameter(ParameterSelection(las2las_project.TARGET_PROJECTION, - self.tr("target projection"), las2las_project.PROJECTIONS, 0)) - self.addParameter(ParameterSelection(las2las_project.TARGET_UTM, - self.tr("target utm zone"), las2las_project.UTM_ZONES, 0)) - self.addParameter(ParameterSelection(las2las_project.TARGET_SP, - self.tr("target state plane code"), las2las_project.STATE_PLANES, 0)) - self.addParametersPointOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - if (LAStoolsUtils.hasWine()): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2las.exe")] - else: - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2las")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - source_projection = self.getParameterValue(las2las_project.SOURCE_PROJECTION) - if source_projection != 0: - if source_projection == 1: - source_utm_zone = self.getParameterValue(las2las_project.SOURCE_UTM) - if source_utm_zone != 0: - commands.append("-" + las2las_project.PROJECTIONS[source_projection]) - if source_utm_zone > 60: - commands.append(str(source_utm_zone - 60) + "M") - else: - commands.append(str(source_utm_zone) + "N") - elif source_projection < 4: - source_sp_code = self.getParameterValue(las2las_project.SOURCE_SP) - if source_sp_code != 0: - commands.append("-" + las2las_project.PROJECTIONS[source_projection]) - commands.append(las2las_project.STATE_PLANES[source_sp_code]) - else: - commands.append("-" + las2las_project.PROJECTIONS[source_projection]) - target_projection = self.getParameterValue(las2las_project.TARGET_PROJECTION) - if target_projection != 0: - if target_projection == 1: - target_utm_zone = self.getParameterValue(las2las_project.TARGET_UTM) - if target_utm_zone != 0: - commands.append("-target_" + las2las_project.PROJECTIONS[target_projection]) - if target_utm_zone > 60: - commands.append(str(target_utm_zone - 60) + "M") - else: - commands.append(str(target_utm_zone) + "N") - elif target_projection < 4: - target_sp_code = self.getParameterValue(las2las_project.TARGET_SP) - if target_sp_code != 0: - commands.append("-target_" + las2las_project.PROJECTIONS[target_projection]) - commands.append(las2las_project.STATE_PLANES[target_sp_code]) - else: - commands.append("-target_" + las2las_project.PROJECTIONS[target_projection]) - self.addParametersPointOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/las2las_transform.py b/python/plugins/processing/algs/lidar/lastools/las2las_transform.py deleted file mode 100644 index b1c78357492b..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/las2las_transform.py +++ /dev/null @@ -1,77 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - las2las_transform.py - --------------------- - Date : September 2013 and May 2016 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Martin Isenburg' -__date__ = 'September 2013' -__copyright__ = '(C) 2013, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterString -from processing.core.parameters import ParameterSelection - - -class las2las_transform(LAStoolsAlgorithm): - - OPERATION = "OPERATION" - OPERATIONS = ["---", "set_point_type", "set_point_size", "set_version_minor", "set_version_major", "start_at_point", "stop_at_point", "remove_vlr", "week_to_adjusted", "adjusted_to_week", "auto_reoffset", "scale_rgb_up", "scale_rgb_down", "remove_all_vlrs", "remove_extra", "clip_to_bounding_box"] - OPERATIONARG = "OPERATIONARG" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('las2las_transform') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParametersTransform1CoordinateGUI() - self.addParametersTransform2CoordinateGUI() - self.addParametersTransform1OtherGUI() - self.addParametersTransform2OtherGUI() - self.addParameter(ParameterSelection(las2las_transform.OPERATION, - self.tr("operations (first 8 need an argument)"), las2las_transform.OPERATIONS, 0)) - self.addParameter(ParameterString(las2las_transform.OPERATIONARG, - self.tr("argument for operation"))) - self.addParametersPointOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - if (LAStoolsUtils.hasWine()): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2las.exe")] - else: - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2las")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - self.addParametersTransform1CoordinateCommands(commands) - self.addParametersTransform2CoordinateCommands(commands) - self.addParametersTransform1OtherCommands(commands) - self.addParametersTransform2OtherCommands(commands) - operation = self.getParameterValue(las2las_transform.OPERATION) - if operation != 0: - commands.append("-" + las2las_transform.OPERATIONS[operation]) - if operation > 8: - commands.append(self.getParameterValue(las2las_transform.OPERATIONARG)) - self.addParametersPointOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/las2shp.py b/python/plugins/processing/algs/lidar/lastools/las2shp.py deleted file mode 100644 index 8271c0bf3eba..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/las2shp.py +++ /dev/null @@ -1,71 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - las2shp.py - --------------------- - Date : September 2013 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - - -__author__ = 'Martin Isenburg' -__date__ = 'September 2013' -__copyright__ = '(C) 2013, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterNumber -from processing.core.outputs import OutputVector - - -class las2shp(LAStoolsAlgorithm): - - POINT_Z = "POINT_Z" - RECORD_SIZE = "RECORD_SIZE" - OUTPUT = "OUTPUT" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('las2shp') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParameter(ParameterBoolean(las2shp.POINT_Z, - self.tr("use PointZ instead of MultiPointZ"), False)) - self.addParameter(ParameterNumber(las2shp.RECORD_SIZE, - self.tr("number of points per record"), 0, None, 1024)) - self.addOutput(OutputVector(las2shp.OUTPUT, - self.tr("Output SHP file"))) - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2shp")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - if self.getParameterValue(las2shp.POINT_Z): - commands.append("-single_points") - record_size = self.getParameterValue(las2shp.RECORD_SIZE) - if record_size != 1024: - commands.append("-record_size") - commands.append(str(record_size)) - commands.append("-o") - commands.append(self.getOutputValue(las2shp.OUTPUT)) - self.addParametersAdditionalCommands(commands) - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/las2tin.py b/python/plugins/processing/algs/lidar/lastools/las2tin.py deleted file mode 100644 index 68422962c8b1..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/las2tin.py +++ /dev/null @@ -1,54 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - las2tin.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com - --------------------- - Date : March 2014 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - - -class las2tin(LAStoolsAlgorithm): - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('las2tin') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParametersFilter1ReturnClassFlagsGUI() - self.addParametersVectorOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2tin")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - self.addParametersFilter1ReturnClassFlagsCommands(commands) - self.addParametersVectorOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/las2txt.py b/python/plugins/processing/algs/lidar/lastools/las2txt.py deleted file mode 100644 index 051acebc7abc..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/las2txt.py +++ /dev/null @@ -1,66 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - las2txt.py - --------------------- - Date : September 2013 and May 2016 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Martin Isenburg' -__date__ = 'September 2013' -__copyright__ = '(C) 2013, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterString -from processing.core.outputs import OutputFile - - -class las2txt(LAStoolsAlgorithm): - - PARSE = "PARSE" - OUTPUT = "OUTPUT" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('las2txt') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParameter(ParameterString(las2txt.PARSE, - self.tr("parse string"), "xyz")) - self.addOutput(OutputFile(las2txt.OUTPUT, self.tr("Output ASCII file"))) - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - if (LAStoolsUtils.hasWine()): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2txt.exe")] - else: - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2txt")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - parse = self.getParameterValue(las2txt.PARSE) - if parse != "xyz": - commands.append("-parse") - commands.append(parse) - commands.append("-o") - commands.append(self.getOutputValue(las2txt.OUTPUT)) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/las2txtPro.py b/python/plugins/processing/algs/lidar/lastools/las2txtPro.py deleted file mode 100644 index fd4da28d5643..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/las2txtPro.py +++ /dev/null @@ -1,68 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - las2txtPro.py - --------------------- - Date : October 2014 and May 2016 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterString - - -class las2txtPro(LAStoolsAlgorithm): - - PARSE = "PARSE" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('las2txtPro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParameter(ParameterString(las2txtPro.PARSE, - self.tr("parse string"), "xyz")) - self.addParametersOutputDirectoryGUI() - self.addParametersOutputAppendixGUI() - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - if (LAStoolsUtils.hasWine()): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2txt.exe")] - else: - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "las2txt")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - parse = self.getParameterValue(las2txtPro.PARSE) - if parse != "xyz": - commands.append("-parse") - commands.append(parse) - self.addParametersOutputDirectoryCommands(commands) - self.addParametersOutputAppendixCommands(commands) - commands.append("-otxt") - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasboundary.py b/python/plugins/processing/algs/lidar/lastools/lasboundary.py deleted file mode 100644 index 03f7feefd358..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasboundary.py +++ /dev/null @@ -1,91 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasboundary.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com - --------------------- - Date : September 2013 and May 2016 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterNumber - - -class lasboundary(LAStoolsAlgorithm): - - MODE = "MODE" - MODES = ["points", "spatial index (the *.lax file)", "bounding box", "tile bounding box"] - CONCAVITY = "CONCAVITY" - DISJOINT = "DISJOINT" - HOLES = "HOLES" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasboundary') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParametersFilter1ReturnClassFlagsGUI() - self.addParameter(ParameterSelection(lasboundary.MODE, - self.tr("compute boundary based on"), lasboundary.MODES, 0)) - self.addParameter(ParameterNumber(lasboundary.CONCAVITY, - self.tr("concavity"), 0, None, 50.0)) - self.addParameter(ParameterBoolean(lasboundary.HOLES, - self.tr("interior holes"), False)) - self.addParameter(ParameterBoolean(lasboundary.DISJOINT, - self.tr("disjoint polygon"), False)) - self.addParametersVectorOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasboundary")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - self.addParametersFilter1ReturnClassFlagsCommands(commands) - mode = self.getParameterValue(lasboundary.MODE) - if (mode != 0): - if (mode == 1): - commands.append("-use_lax") - elif (mode == 2): - commands.append("-use_bb") - else: - commands.append("-use_tile_bb") - else: - concavity = self.getParameterValue(lasboundary.CONCAVITY) - commands.append("-concavity") - commands.append(str(concavity)) - if self.getParameterValue(lasboundary.HOLES): - commands.append("-holes") - if self.getParameterValue(lasboundary.DISJOINT): - commands.append("-disjoint") - self.addParametersVectorOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasboundaryPro.py b/python/plugins/processing/algs/lidar/lastools/lasboundaryPro.py deleted file mode 100644 index 1e634fb1040a..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasboundaryPro.py +++ /dev/null @@ -1,93 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasboundaryPro.py - --------------------- - Date : October 2014 and May 2016 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterNumber - - -class lasboundaryPro(LAStoolsAlgorithm): - - MODE = "MODE" - MODES = ["points", "spatial index (the *.lax file)", "bounding box", "tile bounding box"] - CONCAVITY = "CONCAVITY" - DISJOINT = "DISJOINT" - HOLES = "HOLES" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasboundaryPro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParametersFilter1ReturnClassFlagsGUI() - self.addParameter(ParameterSelection(lasboundaryPro.MODE, - self.tr("compute boundary based on"), lasboundaryPro.MODES, 0)) - self.addParameter(ParameterNumber(lasboundaryPro.CONCAVITY, - self.tr("concavity"), 0, None, 50.0)) - self.addParameter(ParameterBoolean(lasboundaryPro.HOLES, - self.tr("interior holes"), False)) - self.addParameter(ParameterBoolean(lasboundaryPro.DISJOINT, - self.tr("disjoint polygon"), False)) - self.addParametersOutputDirectoryGUI() - self.addParametersOutputAppendixGUI() - self.addParametersVectorOutputFormatGUI() - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasboundary")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - self.addParametersFilter1ReturnClassFlagsCommands(commands) - mode = self.getParameterValue(lasboundaryPro.MODE) - if (mode != 0): - if (mode == 1): - commands.append("-use_lax") - elif (mode == 2): - commands.append("-use_bb") - else: - commands.append("-use_tile_bb") - else: - concavity = self.getParameterValue(lasboundaryPro.CONCAVITY) - commands.append("-concavity") - commands.append(str(concavity)) - if self.getParameterValue(lasboundaryPro.HOLES): - commands.append("-holes") - if self.getParameterValue(lasboundaryPro.DISJOINT): - commands.append("-disjoint") - self.addParametersOutputDirectoryCommands(commands) - self.addParametersOutputAppendixCommands(commands) - self.addParametersVectorOutputFormatCommands(commands) - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lascanopy.py b/python/plugins/processing/algs/lidar/lastools/lascanopy.py deleted file mode 100644 index 27e616b8fa4c..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lascanopy.py +++ /dev/null @@ -1,156 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lascanopy.py - --------------------- - Date : September 2013 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'September 2013' -__copyright__ = '(C) 2013, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterString -from processing.core.parameters import ParameterSelection - - -class lascanopy(LAStoolsAlgorithm): - - PLOT_SIZE = "PLOT_SIZE" - HEIGHT_CUTOFF = "HEIGHT_CUTOFF" - ATTRIBUTE = "ATTRIBUTE" - PRODUCT1 = "PRODUCT1" - PRODUCT2 = "PRODUCT2" - PRODUCT3 = "PRODUCT3" - PRODUCT4 = "PRODUCT4" - PRODUCT5 = "PRODUCT5" - PRODUCT6 = "PRODUCT6" - PRODUCT7 = "PRODUCT7" - PRODUCT8 = "PRODUCT8" - PRODUCT9 = "PRODUCT9" - PRODUCTS = ["---", "min", "max", "avg", "std", "ske", "kur", "qav", "cov", "dns", "all", - "p 1", "p 5", "p 10", "p 25", "p 50", "p 75", "p 90", "p 99", - "int_min", "int_max", "int_avg", "int_std", "int_ske", "int_kur", - "int_p 1", "int_p 5", "int_p 10", "int_p 25", "int_p 50", "int_p 75", "int_p 90", "int_p 99"] - COUNTS = "COUNTS" - DENSITIES = "DENSITIES" - USE_TILE_BB = "USE_TILE_BB" - FILES_ARE_PLOTS = "FILES_ARE_PLOTS" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lascanopy') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParameter(ParameterNumber(lascanopy.PLOT_SIZE, - self.tr("square plot size"), 0, None, 20)) - self.addParameter(ParameterNumber(lascanopy.HEIGHT_CUTOFF, - self.tr("height cutoff / breast height"), 0, None, 1.37)) - self.addParameter(ParameterSelection(lascanopy.PRODUCT1, - self.tr("create"), lascanopy.PRODUCTS, 0)) - self.addParameter(ParameterSelection(lascanopy.PRODUCT2, - self.tr("create"), lascanopy.PRODUCTS, 0)) - self.addParameter(ParameterSelection(lascanopy.PRODUCT3, - self.tr("create"), lascanopy.PRODUCTS, 0)) - self.addParameter(ParameterSelection(lascanopy.PRODUCT4, - self.tr("create"), lascanopy.PRODUCTS, 0)) - self.addParameter(ParameterSelection(lascanopy.PRODUCT5, - self.tr("create"), lascanopy.PRODUCTS, 0)) - self.addParameter(ParameterSelection(lascanopy.PRODUCT6, - self.tr("create"), lascanopy.PRODUCTS, 0)) - self.addParameter(ParameterSelection(lascanopy.PRODUCT7, - self.tr("create"), lascanopy.PRODUCTS, 0)) - self.addParameter(ParameterSelection(lascanopy.PRODUCT8, - self.tr("create"), lascanopy.PRODUCTS, 0)) - self.addParameter(ParameterSelection(lascanopy.PRODUCT9, - self.tr("create"), lascanopy.PRODUCTS, 0)) - self.addParameter(ParameterString(lascanopy.COUNTS, - self.tr("count rasters (e.g. 2.0 5.0 10.0 20.0)"), "")) - self.addParameter(ParameterString(lascanopy.DENSITIES, - self.tr("density rasters (e.g. 2.0 5.0 10.0 20.0)"), "")) - self.addParameter(ParameterBoolean(lascanopy.USE_TILE_BB, - self.tr("use tile bounding box (after tiling with buffer)"), False)) - self.addParameter(ParameterBoolean(lascanopy.FILES_ARE_PLOTS, - self.tr("input file is single plot"), False)) - self.addParametersRasterOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lascanopy")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - plot_size = self.getParameterValue(lascanopy.PLOT_SIZE) - if plot_size != 20: - commands.append("-step") - commands.append(str(plot_size)) - height_cutoff = self.getParameterValue(lascanopy.HEIGHT_CUTOFF) - if height_cutoff != 1.37: - commands.append("-height_cutoff") - commands.append(str(height_cutoff)) - product = self.getParameterValue(lascanopy.PRODUCT1) - if product != 0: - commands.append("-" + lascanopy.PRODUCTS[product]) - product = self.getParameterValue(lascanopy.PRODUCT2) - if product != 0: - commands.append("-" + lascanopy.PRODUCTS[product]) - product = self.getParameterValue(lascanopy.PRODUCT3) - if product != 0: - commands.append("-" + lascanopy.PRODUCTS[product]) - product = self.getParameterValue(lascanopy.PRODUCT4) - if product != 0: - commands.append("-" + lascanopy.PRODUCTS[product]) - product = self.getParameterValue(lascanopy.PRODUCT5) - if product != 0: - commands.append("-" + lascanopy.PRODUCTS[product]) - product = self.getParameterValue(lascanopy.PRODUCT6) - if product != 0: - commands.append("-" + lascanopy.PRODUCTS[product]) - product = self.getParameterValue(lascanopy.PRODUCT7) - if product != 0: - commands.append("-" + lascanopy.PRODUCTS[product]) - product = self.getParameterValue(lascanopy.PRODUCT8) - if product != 0: - commands.append("-" + lascanopy.PRODUCTS[product]) - product = self.getParameterValue(lascanopy.PRODUCT9) - if product != 0: - commands.append("-" + lascanopy.PRODUCTS[product]) - array = self.getParameterValue(lascanopy.COUNTS).split() - if (len(array) > 1): - commands.append("-c") - for a in array: - commands.append(a) - array = self.getParameterValue(lascanopy.DENSITIES).split() - if (len(array) > 1): - commands.append("-d") - for a in array: - commands.append(a) - if (self.getParameterValue(lascanopy.USE_TILE_BB)): - commands.append("-use_tile_bb") - if (self.getParameterValue(lascanopy.FILES_ARE_PLOTS)): - commands.append("-files_are_plots") - self.addParametersRasterOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lascanopyPro.py b/python/plugins/processing/algs/lidar/lastools/lascanopyPro.py deleted file mode 100644 index 3863a071ad0c..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lascanopyPro.py +++ /dev/null @@ -1,166 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lascanopyPro.py - --------------------- - Date : October 2014 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterString -from processing.core.parameters import ParameterSelection - - -class lascanopyPro(LAStoolsAlgorithm): - - PLOT_SIZE = "PLOT_SIZE" - HEIGHT_CUTOFF = "HEIGHT_CUTOFF" - ATTRIBUTE = "ATTRIBUTE" - PRODUCT1 = "PRODUCT1" - PRODUCT2 = "PRODUCT2" - PRODUCT3 = "PRODUCT3" - PRODUCT4 = "PRODUCT4" - PRODUCT5 = "PRODUCT5" - PRODUCT6 = "PRODUCT6" - PRODUCT7 = "PRODUCT7" - PRODUCT8 = "PRODUCT8" - PRODUCT9 = "PRODUCT9" - PRODUCTS = ["---", "min", "max", "avg", "std", "ske", "kur", "qav", "cov", "dns", "all", - "p 1", "p 5", "p 10", "p 25", "p 50", "p 75", "p 90", "p 99", - "int_min", "int_max", "int_avg", "int_std", "int_ske", "int_kur", - "int_p 1", "int_p 5", "int_p 10", "int_p 25", "int_p 50", "int_p 75", "int_p 90", "int_p 99"] - COUNTS = "COUNTS" - DENSITIES = "DENSITIES" - USE_TILE_BB = "USE_TILE_BB" - FILES_ARE_PLOTS = "FILES_ARE_PLOTS" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lascanopyPro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParametersPointInputMergedGUI() - self.addParameter(ParameterNumber(lascanopyPro.PLOT_SIZE, - self.tr("square plot size"), 0, None, 20)) - self.addParameter(ParameterNumber(lascanopyPro.HEIGHT_CUTOFF, - self.tr("height cutoff / breast height"), 0, None, 1.37)) - self.addParameter(ParameterSelection(lascanopyPro.PRODUCT1, - self.tr("create"), lascanopyPro.PRODUCTS, 0)) - self.addParameter(ParameterSelection(lascanopyPro.PRODUCT2, - self.tr("create"), lascanopyPro.PRODUCTS, 0)) - self.addParameter(ParameterSelection(lascanopyPro.PRODUCT3, - self.tr("create"), lascanopyPro.PRODUCTS, 0)) - self.addParameter(ParameterSelection(lascanopyPro.PRODUCT4, - self.tr("create"), lascanopyPro.PRODUCTS, 0)) - self.addParameter(ParameterSelection(lascanopyPro.PRODUCT5, - self.tr("create"), lascanopyPro.PRODUCTS, 0)) - self.addParameter(ParameterSelection(lascanopyPro.PRODUCT6, - self.tr("create"), lascanopyPro.PRODUCTS, 0)) - self.addParameter(ParameterSelection(lascanopyPro.PRODUCT7, - self.tr("create"), lascanopyPro.PRODUCTS, 0)) - self.addParameter(ParameterSelection(lascanopyPro.PRODUCT8, - self.tr("create"), lascanopyPro.PRODUCTS, 0)) - self.addParameter(ParameterSelection(lascanopyPro.PRODUCT9, - self.tr("create"), lascanopyPro.PRODUCTS, 0)) - self.addParameter(ParameterString(lascanopyPro.COUNTS, - self.tr("count rasters (e.g. 2.0 5.0 10.0 20.0)"), "")) - self.addParameter(ParameterString(lascanopyPro.DENSITIES, - self.tr("density rasters (e.g. 2.0 5.0 10.0 20.0)"), "")) - self.addParameter(ParameterBoolean(lascanopyPro.USE_TILE_BB, - self.tr("use tile bounding box (after tiling with buffer)"), False)) - self.addParameter(ParameterBoolean(lascanopyPro.FILES_ARE_PLOTS, - self.tr("input file is single plot"), False)) - self.addParametersOutputDirectoryGUI() - self.addParametersOutputAppendixGUI() - self.addParametersRasterOutputFormatGUI() - self.addParametersRasterOutputGUI() - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lascanopy")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - self.addParametersPointInputMergedCommands(commands) - plot_size = self.getParameterValue(lascanopyPro.PLOT_SIZE) - if plot_size != 20: - commands.append("-step") - commands.append(str(plot_size)) - height_cutoff = self.getParameterValue(lascanopyPro.HEIGHT_CUTOFF) - if height_cutoff != 1.37: - commands.append("-height_cutoff") - commands.append(str(height_cutoff)) - product = self.getParameterValue(lascanopyPro.PRODUCT1) - if product != 0: - commands.append("-" + lascanopyPro.PRODUCTS[product]) - product = self.getParameterValue(lascanopyPro.PRODUCT2) - if product != 0: - commands.append("-" + lascanopyPro.PRODUCTS[product]) - product = self.getParameterValue(lascanopyPro.PRODUCT3) - if product != 0: - commands.append("-" + lascanopyPro.PRODUCTS[product]) - product = self.getParameterValue(lascanopyPro.PRODUCT4) - if product != 0: - commands.append("-" + lascanopyPro.PRODUCTS[product]) - product = self.getParameterValue(lascanopyPro.PRODUCT5) - if product != 0: - commands.append("-" + lascanopyPro.PRODUCTS[product]) - product = self.getParameterValue(lascanopyPro.PRODUCT6) - if product != 0: - commands.append("-" + lascanopyPro.PRODUCTS[product]) - product = self.getParameterValue(lascanopyPro.PRODUCT7) - if product != 0: - commands.append("-" + lascanopyPro.PRODUCTS[product]) - product = self.getParameterValue(lascanopyPro.PRODUCT8) - if product != 0: - commands.append("-" + lascanopyPro.PRODUCTS[product]) - product = self.getParameterValue(lascanopyPro.PRODUCT9) - if product != 0: - commands.append("-" + lascanopyPro.PRODUCTS[product]) - array = self.getParameterValue(lascanopyPro.COUNTS).split() - if (len(array) > 1): - commands.append("-c") - for a in array: - commands.append(a) - array = self.getParameterValue(lascanopyPro.DENSITIES).split() - if (len(array) > 1): - commands.append("-d") - for a in array: - commands.append(a) - if (self.getParameterValue(lascanopyPro.USE_TILE_BB)): - commands.append("-use_tile_bb") - if (self.getParameterValue(lascanopyPro.FILES_ARE_PLOTS)): - commands.append("-files_are_plots") - self.addParametersOutputDirectoryCommands(commands) - self.addParametersOutputAppendixCommands(commands) - self.addParametersRasterOutputFormatCommands(commands) - self.addParametersRasterOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasclassify.py b/python/plugins/processing/algs/lidar/lastools/lasclassify.py deleted file mode 100644 index a8f894d4f400..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasclassify.py +++ /dev/null @@ -1,58 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasclassify.py - --------------------- - Date : August 2012 and May 2016 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com - --------------------- - Date : September 2013 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - - -class lasclassify(LAStoolsAlgorithm): - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasclassify') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParametersIgnoreClass1GUI() - self.addParametersIgnoreClass2GUI() - self.addParametersHorizontalAndVerticalFeetGUI() - self.addParametersPointOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasclassify")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - self.addParametersIgnoreClass1Commands(commands) - self.addParametersIgnoreClass2Commands(commands) - self.addParametersHorizontalAndVerticalFeetCommands(commands) - self.addParametersPointOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasclassifyPro.py b/python/plugins/processing/algs/lidar/lastools/lasclassifyPro.py deleted file mode 100644 index 57ffddb5c4e3..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasclassifyPro.py +++ /dev/null @@ -1,60 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasclassifyPro.py - --------------------- - Date : October 2014 and May 2016 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - - -class lasclassifyPro(LAStoolsAlgorithm): - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasclassifyPro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParametersIgnoreClass1GUI() - self.addParametersIgnoreClass2GUI() - self.addParametersHorizontalAndVerticalFeetGUI() - self.addParametersOutputDirectoryGUI() - self.addParametersOutputAppendixGUI() - self.addParametersPointOutputFormatGUI() - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasclassify")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - self.addParametersIgnoreClass1Commands(commands) - self.addParametersIgnoreClass2Commands(commands) - self.addParametersHorizontalAndVerticalFeetCommands(commands) - self.addParametersOutputDirectoryCommands(commands) - self.addParametersOutputAppendixCommands(commands) - self.addParametersPointOutputFormatCommands(commands) - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasclip.py b/python/plugins/processing/algs/lidar/lastools/lasclip.py deleted file mode 100644 index e875edb166ec..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasclip.py +++ /dev/null @@ -1,87 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasclip.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com - --------------------- - Date : September 2013 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterVector -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterSelection - -from processing.tools import dataobjects - - -class lasclip(LAStoolsAlgorithm): - - POLYGON = "POLYGON" - INTERIOR = "INTERIOR" - OPERATION = "OPERATION" - OPERATIONS = ["clip", "classify"] - CLASSIFY_AS = "CLASSIFY_AS" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasclip') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParameter(ParameterVector(lasclip.POLYGON, - self.tr("Input polygon(s)"), [dataobjects.TYPE_VECTOR_POLYGON])) - self.addParameter(ParameterBoolean(lasclip.INTERIOR, - self.tr("interior"), False)) - self.addParameter(ParameterSelection(lasclip.OPERATION, - self.tr("what to do with points"), lasclip.OPERATIONS, 0)) - self.addParameter(ParameterNumber(lasclip.CLASSIFY_AS, - self.tr("classify as"), 0, None, 12)) - self.addParametersPointOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasclip")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - poly = self.getParameterValue(lasclip.POLYGON) - if poly is not None: - commands.append("-poly") - commands.append(poly) - if self.getParameterValue(lasclip.INTERIOR): - commands.append("-interior") - operation = self.getParameterValue(lasclip.OPERATION) - if operation != 0: - commands.append("-classify") - classify_as = self.getParameterValue(lasclip.CLASSIFY_AS) - commands.append(str(classify_as)) - self.addParametersPointOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lascolor.py b/python/plugins/processing/algs/lidar/lastools/lascolor.py deleted file mode 100644 index 6799a3092f5d..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lascolor.py +++ /dev/null @@ -1,64 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasclip.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com - --------------------- - Date : March 2014 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterRaster - - -class lascolor(LAStoolsAlgorithm): - - ORTHO = "ORTHO" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lascolor') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParameter(ParameterRaster(lascolor.ORTHO, - self.tr("Input ortho"))) - self.addParametersPointOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lascolor")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - ortho = self.getParameterValue(lascolor.ORTHO) - if ortho is not None: - commands.append("-image") - commands.append(ortho) - self.addParametersPointOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lascontrol.py b/python/plugins/processing/algs/lidar/lastools/lascontrol.py deleted file mode 100644 index 6821fe041100..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lascontrol.py +++ /dev/null @@ -1,89 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lascontrol.py - --------------------- - Date : May 2016 - Copyright : (C) 2016 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from __future__ import absolute_import -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'May 2016' -__copyright__ = '(C) 2016, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterString -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterSelection - - -class lascontrol(LAStoolsAlgorithm): - - CONTROL_POINT_FILE = "CONTROL_POINT_FILE" - PARSE_STRING = "PARSE_STRING" - USE_POINTS = "USE_POINTS" - USE_POINTS_LIST = ["all", "ground (2)", "ground (2) and keypoints (8)", "ground (2), buildings (6), and keypoints (8)"] - ADJUST_Z = "ADJUST_Z" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lascontrol') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParameter(ParameterFile(lascontrol.CONTROL_POINT_FILE, - self.tr("ASCII text file of control points"), False, False)) - self.addParameter(ParameterString(lascontrol.PARSE_STRING, - self.tr("parse string marking which columns are xyz (use 's' for skip)"), "sxyz", False, False)) - self.addParameter(ParameterSelection(lascontrol.USE_POINTS, - self.tr("which points to use for elevation checks"), lascontrol.USE_POINTS_LIST, 0)) - self.addParameter(ParameterBoolean(lascontrol.ADJUST_Z, - self.tr("adjust z elevation by translating away the average error"), False)) - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lascontrol")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - file = self.getParameterValue(lascontrol.CONTROL_POINT_FILE) - if file is not None: - commands.append("-cp") - commands.append('"' + file + '"') - parse = self.getParameterValue(lascontrol.PARSE_STRING) - if parse is not None: - commands.append("-parse") - commands.append(parse) - use_point = self.getParameterValue(lascontrol.USE_POINTS) - if use_point > 0: - commands.append("-keep_class") - commands.append(str(2)) - if use_point > 1: - commands.append(str(8)) - if use_point > 2: - commands.append(str(6)) - if self.getParameterValue(lascontrol.ADJUST_Z): - commands.append("-adjust_z") - commands.append("-odix _adjusted") - commands.append("-olaz") - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasdiff.py b/python/plugins/processing/algs/lidar/lastools/lasdiff.py deleted file mode 100644 index ca7507a98cca..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasdiff.py +++ /dev/null @@ -1,78 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasdiff.py - --------------------- - Date : May 2016 - Copyright : (C) 2016 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from __future__ import absolute_import -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Martin Isenburg' -__date__ = 'May 2016' -__copyright__ = '(C) 2016, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterSelection - - -class lasdiff(LAStoolsAlgorithm): - - OTHER_POINT_FILE = "OTHER_POINT_FILE" - CREATE_DIFFERENCE_FILE = "CREATE_DIFFERENCE_FILE" - SHUTUP = "SHUTUP" - SHUTUP_AFTER = ["5", "10", "50", "100", "1000", "10000", "50000"] - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasdiff') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParameter(ParameterFile(lasdiff.OTHER_POINT_FILE, - self.tr("other input LAS/LAZ file"), False, False)) - self.addParameter(ParameterSelection(lasdiff.SHUTUP, - self.tr("stop reporting difference after this many points"), lasdiff.SHUTUP_AFTER, 0)) - self.addParameter(ParameterBoolean(lasdiff.CREATE_DIFFERENCE_FILE, - self.tr("create elevation difference file (if points are in the same order)"), False)) - self.addParametersPointOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - if (LAStoolsUtils.hasWine()): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasdiff.exe")] - else: - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasdiff")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - file = self.getParameterValue(lasdiff.OTHER_POINT_FILE) - if file is not None: - commands.append("-i") - commands.append('"' + file + '"') - shutup = self.getParameterValue(lasdiff.SHUTUP) - if (shutup != 0): - commands.append("-shutup") - commands.append(lasdiff.SHUTUP_AFTER[shutup]) - if self.getParameterValue(lasdiff.CREATE_DIFFERENCE_FILE): - self.addParametersPointOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasduplicate.py b/python/plugins/processing/algs/lidar/lastools/lasduplicate.py deleted file mode 100644 index 60a7199e640b..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasduplicate.py +++ /dev/null @@ -1,76 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasduplicate.py - --------------------- - Date : September 2013 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Martin Isenburg' -__date__ = 'September 2013' -__copyright__ = '(C) 2013, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterFile - - -class lasduplicate(LAStoolsAlgorithm): - - LOWEST_Z = "LOWEST_Z" - UNIQUE_XYZ = "UNIQUE_XYZ" - SINGLE_RETURNS = "SINGLE_RETURNS" - RECORD_REMOVED = "RECORD_REMOVED" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasduplicate') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParameter(ParameterBoolean(lasduplicate.LOWEST_Z, - self.tr("keep duplicate with lowest z coordinate"), False)) - self.addParameter(ParameterBoolean(lasduplicate.UNIQUE_XYZ, - self.tr("only remove duplicates in x y and z"), False)) - self.addParameter(ParameterBoolean(lasduplicate.SINGLE_RETURNS, - self.tr("mark surviving duplicate as single return"), False)) - self.addParameter(ParameterFile(lasduplicate.RECORD_REMOVED, - self.tr("record removed duplicates to LAS/LAZ file"))) - self.addParametersPointOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasduplicate")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - if self.getParameterValue(lasduplicate.LOWEST_Z): - commands.append("-lowest_z") - if self.getParameterValue(lasduplicate.UNIQUE_XYZ): - commands.append("-unique_xyz") - if self.getParameterValue(lasduplicate.SINGLE_RETURNS): - commands.append("-single_returns") - record_removed = self.getParameterValue(lasduplicate.RECORD_REMOVED) - if record_removed is not None and record_removed != "": - commands.append("-record_removed") - commands.append(record_removed) - self.addParametersPointOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasduplicatePro.py b/python/plugins/processing/algs/lidar/lastools/lasduplicatePro.py deleted file mode 100644 index d9b535f0cbad..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasduplicatePro.py +++ /dev/null @@ -1,79 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasduplicate.py - --------------------- - Date : October 2014 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean - - -class lasduplicatePro(LAStoolsAlgorithm): - - LOWEST_Z = "LOWEST_Z" - UNIQUE_XYZ = "UNIQUE_XYZ" - SINGLE_RETURNS = "SINGLE_RETURNS" - RECORD_REMOVED = "RECORD_REMOVED" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasduplicatePro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParameter(ParameterBoolean(lasduplicatePro.LOWEST_Z, - self.tr("keep duplicate with lowest z coordinate"), False)) - self.addParameter(ParameterBoolean(lasduplicatePro.UNIQUE_XYZ, - self.tr("only remove duplicates in x y and z"), False)) - self.addParameter(ParameterBoolean(lasduplicatePro.SINGLE_RETURNS, - self.tr("mark surviving duplicate as single return"), False)) - self.addParameter(ParameterBoolean(lasduplicatePro.RECORD_REMOVED, - self.tr("record removed duplicates"), False)) - self.addParametersOutputDirectoryGUI() - self.addParametersOutputAppendixGUI() - self.addParametersPointOutputFormatGUI() - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasduplicate")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - if self.getParameterValue(lasduplicatePro.LOWEST_Z): - commands.append("-lowest_z") - if self.getParameterValue(lasduplicatePro.UNIQUE_XYZ): - commands.append("-unique_xyz") - if self.getParameterValue(lasduplicatePro.SINGLE_RETURNS): - commands.append("-single_returns") - if self.getParameterValue(lasduplicatePro.RECORD_REMOVED): - commands.append("-record_removed") - self.addParametersOutputDirectoryCommands(commands) - self.addParametersOutputAppendixCommands(commands) - self.addParametersPointOutputFormatCommands(commands) - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasgrid.py b/python/plugins/processing/algs/lidar/lastools/lasgrid.py deleted file mode 100644 index 479f77c81b77..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasgrid.py +++ /dev/null @@ -1,81 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasgrid.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com - --------------------- - Date : September 2013 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterBoolean - - -class lasgrid(LAStoolsAlgorithm): - - ATTRIBUTE = "ATTRIBUTE" - METHOD = "METHOD" - ATTRIBUTES = ["elevation", "intensity", "rgb", "classification"] - METHODS = ["lowest", "highest", "average", "stddev"] - USE_TILE_BB = "USE_TILE_BB" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasgrid') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParametersFilter1ReturnClassFlagsGUI() - self.addParametersStepGUI() - self.addParameter(ParameterSelection(lasgrid.ATTRIBUTE, - self.tr("Attribute"), lasgrid.ATTRIBUTES, 0)) - self.addParameter(ParameterSelection(lasgrid.METHOD, - self.tr("Method"), lasgrid.METHODS, 0)) - self.addParameter(ParameterBoolean(lasgrid.USE_TILE_BB, - self.tr("use tile bounding box (after tiling with buffer)"), False)) - self.addParametersRasterOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasgrid")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - self.addParametersFilter1ReturnClassFlagsCommands(commands) - self.addParametersStepCommands(commands) - attribute = self.getParameterValue(lasgrid.ATTRIBUTE) - if attribute != 0: - commands.append("-" + lasgrid.ATTRIBUTES[attribute]) - method = self.getParameterValue(lasgrid.METHOD) - if method != 0: - commands.append("-" + lasgrid.METHODS[method]) - if (self.getParameterValue(lasgrid.USE_TILE_BB)): - commands.append("-use_tile_bb") - self.addParametersRasterOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasgridPro.py b/python/plugins/processing/algs/lidar/lastools/lasgridPro.py deleted file mode 100644 index 8f2784f75914..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasgridPro.py +++ /dev/null @@ -1,87 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasgridPro.py - --------------------- - Date : October 2014 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterBoolean - - -class lasgridPro(LAStoolsAlgorithm): - - ATTRIBUTE = "ATTRIBUTE" - METHOD = "METHOD" - ATTRIBUTES = ["elevation", "intensity", "rgb", "classification"] - METHODS = ["lowest", "highest", "average", "stddev"] - USE_TILE_BB = "USE_TILE_BB" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasgridPro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParametersPointInputMergedGUI() - self.addParametersFilter1ReturnClassFlagsGUI() - self.addParametersStepGUI() - self.addParameter(ParameterSelection(lasgridPro.ATTRIBUTE, - self.tr("Attribute"), lasgridPro.ATTRIBUTES, 0)) - self.addParameter(ParameterSelection(lasgridPro.METHOD, - self.tr("Method"), lasgridPro.METHODS, 0)) - self.addParameter(ParameterBoolean(lasgridPro.USE_TILE_BB, - self.tr("use tile bounding box (after tiling with buffer)"), False)) - self.addParametersOutputDirectoryGUI() - self.addParametersOutputAppendixGUI() - self.addParametersRasterOutputFormatGUI() - self.addParametersRasterOutputGUI() - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasgrid")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - self.addParametersPointInputMergedCommands(commands) - self.addParametersFilter1ReturnClassFlagsCommands(commands) - self.addParametersStepCommands(commands) - attribute = self.getParameterValue(lasgridPro.ATTRIBUTE) - if attribute != 0: - commands.append("-" + lasgridPro.ATTRIBUTES[attribute]) - method = self.getParameterValue(lasgridPro.METHOD) - if method != 0: - commands.append("-" + lasgridPro.METHODS[method]) - if (self.getParameterValue(lasgridPro.USE_TILE_BB)): - commands.append("-use_tile_bb") - self.addParametersOutputDirectoryCommands(commands) - self.addParametersOutputAppendixCommands(commands) - self.addParametersRasterOutputFormatCommands(commands) - self.addParametersRasterOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasground.py b/python/plugins/processing/algs/lidar/lastools/lasground.py deleted file mode 100644 index 59b02885e7a3..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasground.py +++ /dev/null @@ -1,86 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasground.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com - --------------------- - Date : September 2013 and May 2016 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterSelection - - -class lasground(LAStoolsAlgorithm): - - NO_BULGE = "NO_BULGE" - BY_FLIGHTLINE = "BY_FLIGHTLINE" - TERRAIN = "TERRAIN" - TERRAINS = ["wilderness", "nature", "town", "city", "metro"] - GRANULARITY = "GRANULARITY" - GRANULARITIES = ["coarse", "default", "fine", "extra_fine", "ultra_fine"] - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasground') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParametersIgnoreClass1GUI() - self.addParametersHorizontalAndVerticalFeetGUI() - self.addParameter(ParameterBoolean(lasground.NO_BULGE, - self.tr("no triangle bulging during TIN refinement"), False)) - self.addParameter(ParameterBoolean(lasground.BY_FLIGHTLINE, - self.tr("classify flightlines separately (needs point source IDs populated)"), False)) - self.addParameter(ParameterSelection(lasground.TERRAIN, - self.tr("terrain type"), lasground.TERRAINS, 1)) - self.addParameter(ParameterSelection(lasground.GRANULARITY, - self.tr("preprocessing"), lasground.GRANULARITIES, 1)) - self.addParametersPointOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasground")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - self.addParametersIgnoreClass1Commands(commands) - self.addParametersHorizontalAndVerticalFeetCommands(commands) - if (self.getParameterValue(lasground.NO_BULGE)): - commands.append("-no_bulge") - if (self.getParameterValue(lasground.BY_FLIGHTLINE)): - commands.append("-by_flightline") - method = self.getParameterValue(lasground.TERRAIN) - if (method != 1): - commands.append("-" + lasground.TERRAINS[method]) - granularity = self.getParameterValue(lasground.GRANULARITY) - if (granularity != 1): - commands.append("-" + lasground.GRANULARITIES[granularity]) - self.addParametersPointOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasgroundPro.py b/python/plugins/processing/algs/lidar/lastools/lasgroundPro.py deleted file mode 100644 index a9aad6e11847..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasgroundPro.py +++ /dev/null @@ -1,86 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasgroundPro.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com - --------------------- - Date : April 2014 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterSelection - - -class lasgroundPro(LAStoolsAlgorithm): - - NO_BULGE = "NO_BULGE" - TERRAIN = "TERRAIN" - TERRAINS = ["wilderness", "nature", "town", "city", "metro"] - GRANULARITY = "GRANULARITY" - GRANULARITIES = ["coarse", "default", "fine", "extra_fine", "ultra_fine"] - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasgroundPro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParametersHorizontalAndVerticalFeetGUI() - self.addParameter(ParameterBoolean(lasgroundPro.NO_BULGE, - self.tr("no triangle bulging during TIN refinement"), False)) - self.addParameter(ParameterSelection(lasgroundPro.TERRAIN, - self.tr("terrain type"), lasgroundPro.TERRAINS, 1)) - self.addParameter(ParameterSelection(lasgroundPro.GRANULARITY, - self.tr("preprocessing"), lasgroundPro.GRANULARITIES, 1)) - self.addParametersOutputDirectoryGUI() - self.addParametersOutputAppendixGUI() - self.addParametersPointOutputFormatGUI() - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasground")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - self.addParametersHorizontalAndVerticalFeetCommands(commands) - if (self.getParameterValue(lasgroundPro.NO_BULGE)): - commands.append("-no_bulge") - method = self.getParameterValue(lasgroundPro.TERRAIN) - if (method != 1): - commands.append("-" + lasgroundPro.TERRAINS[method]) - granularity = self.getParameterValue(lasgroundPro.GRANULARITY) - if (granularity != 1): - commands.append("-" + lasgroundPro.GRANULARITIES[granularity]) - self.addParametersCoresCommands(commands) - self.addParametersOutputDirectoryCommands(commands) - self.addParametersOutputAppendixCommands(commands) - self.addParametersPointOutputFormatCommands(commands) - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasgroundPro_new.py b/python/plugins/processing/algs/lidar/lastools/lasgroundPro_new.py deleted file mode 100644 index 345fcd925fb9..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasgroundPro_new.py +++ /dev/null @@ -1,105 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasgroundPro_new.py - Date : May 2016 - Copyright : (C) 2016 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from __future__ import absolute_import -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'May 2016' -__copyright__ = '(C) 2016 by Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterSelection - - -class lasgroundPro_new(LAStoolsAlgorithm): - TERRAIN = "TERRAIN" - TERRAINS = ["wilderness", "nature", "town", "city", "metro", "custom"] - GRANULARITY = "GRANULARITY" - GRANULARITIES = ["coarse", "default", "fine", "extra_fine", "ultra_fine", "hyper_fine"] - STEP = "STEP" - BULGE = "BULGE" - SPIKE = "SPIKE" - DOWN_SPIKE = "DOWN_SPIKE" - OFFSET = "OFFSET" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasgroundPro_new') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersVerboseGUI() - self.addParametersPointInputFolderGUI() - self.addParametersIgnoreClass1GUI() - self.addParametersHorizontalAndVerticalFeetGUI() - self.addParameter(ParameterSelection(lasgroundPro_new.TERRAIN, - self.tr("terrain type"), lasgroundPro_new.TERRAINS, 1)) - self.addParameter(ParameterSelection(lasgroundPro_new.GRANULARITY, - self.tr("preprocessing"), lasgroundPro_new.GRANULARITIES, 1)) - self.addParameter(ParameterNumber(lasgroundPro_new.STEP, - self.tr("step (for 'custom' terrain only)"), 25.0)) - self.addParameter(ParameterNumber(lasgroundPro_new.BULGE, - self.tr("bulge (for 'custom' terrain only)"), 2.0)) - self.addParameter(ParameterNumber(lasgroundPro_new.SPIKE, - self.tr("spike (for 'custom' terrain only)"), 1.0)) - self.addParameter(ParameterNumber(lasgroundPro_new.DOWN_SPIKE, - self.tr("down spike (for 'custom' terrain only)"), 1.0)) - self.addParameter(ParameterNumber(lasgroundPro_new.OFFSET, - self.tr("offset (for 'custom' terrain only)"), 0.05)) - self.addParametersOutputDirectoryGUI() - self.addParametersOutputAppendixGUI() - self.addParametersPointOutputFormatGUI() - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasground_new")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - self.addParametersIgnoreClass1Commands(commands) - self.addParametersHorizontalAndVerticalFeetCommands(commands) - method = self.getParameterValue(lasgroundPro_new.TERRAIN) - if (method == 5): - commands.append("-step") - commands.append(str(self.getParameterValue(lasgroundPro_new.STEP))) - commands.append("-bulge") - commands.append(str(self.getParameterValue(lasgroundPro_new.BULGE))) - commands.append("-spike") - commands.append(str(self.getParameterValue(lasgroundPro_new.SPIKE))) - commands.append("-spike_down") - commands.append(str(self.getParameterValue(lasgroundPro_new.DOWN_SPIKE))) - commands.append("-offset") - commands.append(str(self.getParameterValue(lasgroundPro_new.OFFSET))) - else: - commands.append("-" + lasgroundPro_new.TERRAINS[method]) - granularity = self.getParameterValue(lasgroundPro_new.GRANULARITY) - if (granularity != 1): - commands.append("-" + lasgroundPro_new.GRANULARITIES[granularity]) - self.addParametersOutputDirectoryCommands(commands) - self.addParametersOutputAppendixCommands(commands) - self.addParametersPointOutputFormatCommands(commands) - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasground_new.py b/python/plugins/processing/algs/lidar/lastools/lasground_new.py deleted file mode 100644 index 23d32d52b0b4..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasground_new.py +++ /dev/null @@ -1,99 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasground_new.py - Date : May 2016 - Copyright : (C) 2016 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from __future__ import absolute_import -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'May 2016' -__copyright__ = '(C) 2016 by Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterSelection - - -class lasground_new(LAStoolsAlgorithm): - TERRAIN = "TERRAIN" - TERRAINS = ["wilderness", "nature", "town", "city", "metro", "custom"] - GRANULARITY = "GRANULARITY" - GRANULARITIES = ["coarse", "default", "fine", "extra_fine", "ultra_fine", "hyper_fine"] - STEP = "STEP" - BULGE = "BULGE" - SPIKE = "SPIKE" - DOWN_SPIKE = "DOWN_SPIKE" - OFFSET = "OFFSET" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasground_new') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParametersIgnoreClass1GUI() - self.addParametersHorizontalAndVerticalFeetGUI() - self.addParameter(ParameterSelection(lasground_new.TERRAIN, - self.tr("terrain type"), lasground_new.TERRAINS, 1)) - self.addParameter(ParameterSelection(lasground_new.GRANULARITY, - self.tr("preprocessing"), lasground_new.GRANULARITIES, 1)) - self.addParameter(ParameterNumber(lasground_new.STEP, - self.tr("step (for 'custom' terrain only)"), 25.0)) - self.addParameter(ParameterNumber(lasground_new.BULGE, - self.tr("bulge (for 'custom' terrain only)"), 2.0)) - self.addParameter(ParameterNumber(lasground_new.SPIKE, - self.tr("spike (for 'custom' terrain only)"), 1.0)) - self.addParameter(ParameterNumber(lasground_new.DOWN_SPIKE, - self.tr("down spike (for 'custom' terrain only)"), 1.0)) - self.addParameter(ParameterNumber(lasground_new.OFFSET, - self.tr("offset (for 'custom' terrain only)"), 0.05)) - self.addParametersPointOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasground_new")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - self.addParametersIgnoreClass1Commands(commands) - self.addParametersHorizontalAndVerticalFeetCommands(commands) - method = self.getParameterValue(lasground_new.TERRAIN) - if (method == 5): - commands.append("-step") - commands.append(str(self.getParameterValue(lasground_new.STEP))) - commands.append("-bulge") - commands.append(str(self.getParameterValue(lasground_new.BULGE))) - commands.append("-spike") - commands.append(str(self.getParameterValue(lasground_new.SPIKE))) - commands.append("-spike_down") - commands.append(str(self.getParameterValue(lasground_new.DOWN_SPIKE))) - commands.append("-offset") - commands.append(str(self.getParameterValue(lasground_new.OFFSET))) - else: - commands.append("-" + lasground_new.TERRAINS[method]) - granularity = self.getParameterValue(lasground_new.GRANULARITY) - if (granularity != 1): - commands.append("-" + lasground_new.GRANULARITIES[granularity]) - self.addParametersPointOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasheight.py b/python/plugins/processing/algs/lidar/lastools/lasheight.py deleted file mode 100644 index 1475bec37aec..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasheight.py +++ /dev/null @@ -1,82 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasheight.py - --------------------- - Date : September 2013 and May 2016 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'September 2013' -__copyright__ = '(C) 2013, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterNumber - - -class lasheight(LAStoolsAlgorithm): - - REPLACE_Z = "REPLACE_Z" - DROP_ABOVE = "DROP_ABOVE" - DROP_ABOVE_HEIGHT = "DROP_ABOVE_HEIGHT" - DROP_BELOW = "DROP_BELOW" - DROP_BELOW_HEIGHT = "DROP_BELOW_HEIGHT" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasheight') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParametersIgnoreClass1GUI() - self.addParametersIgnoreClass2GUI() - self.addParameter(ParameterBoolean(lasheight.REPLACE_Z, - self.tr("replace z"), False)) - self.addParameter(ParameterBoolean(lasheight.DROP_ABOVE, - self.tr("drop above"), False)) - self.addParameter(ParameterNumber(lasheight.DROP_ABOVE_HEIGHT, - self.tr("drop above height"), None, None, 100.0)) - self.addParameter(ParameterBoolean(lasheight.DROP_BELOW, - self.tr("drop below"), False)) - self.addParameter(ParameterNumber(lasheight.DROP_BELOW_HEIGHT, - self.tr("drop below height"), None, None, -2.0)) - self.addParametersPointOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasheight")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - self.addParametersIgnoreClass1Commands(commands) - self.addParametersIgnoreClass2Commands(commands) - if self.getParameterValue(lasheight.REPLACE_Z): - commands.append("-replace_z") - if self.getParameterValue(lasheight.DROP_ABOVE): - commands.append("-drop_above") - commands.append(str(self.getParameterValue(lasheight.DROP_ABOVE_HEIGHT))) - if self.getParameterValue(lasheight.DROP_BELOW): - commands.append("-drop_below") - commands.append(str(self.getParameterValue(lasheight.DROP_BELOW_HEIGHT))) - self.addParametersPointOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasheightPro.py b/python/plugins/processing/algs/lidar/lastools/lasheightPro.py deleted file mode 100644 index f70ded567efa..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasheightPro.py +++ /dev/null @@ -1,89 +0,0 @@ - -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasheightPro.py - --------------------- - Date : October 2014 and May 2016 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterNumber - - -class lasheightPro(LAStoolsAlgorithm): - - REPLACE_Z = "REPLACE_Z" - DROP_ABOVE = "DROP_ABOVE" - DROP_ABOVE_HEIGHT = "DROP_ABOVE_HEIGHT" - DROP_BELOW = "DROP_BELOW" - DROP_BELOW_HEIGHT = "DROP_BELOW_HEIGHT" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasheightPro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParametersIgnoreClass1GUI() - self.addParametersIgnoreClass2GUI() - self.addParameter(ParameterBoolean(lasheightPro.REPLACE_Z, - self.tr("replace z"), False)) - self.addParameter(ParameterBoolean(lasheightPro.DROP_ABOVE, - self.tr("drop above"), False)) - self.addParameter(ParameterNumber(lasheightPro.DROP_ABOVE_HEIGHT, - self.tr("drop above height"), None, None, 100.0)) - self.addParameter(ParameterBoolean(lasheightPro.DROP_BELOW, - self.tr("drop below"), False)) - self.addParameter(ParameterNumber(lasheightPro.DROP_BELOW_HEIGHT, - self.tr("drop below height"), None, None, -2.0)) - self.addParametersOutputDirectoryGUI() - self.addParametersOutputAppendixGUI() - self.addParametersPointOutputFormatGUI() - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasheight")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - self.addParametersIgnoreClass1Commands(commands) - self.addParametersIgnoreClass2Commands(commands) - if self.getParameterValue(lasheightPro.REPLACE_Z): - commands.append("-replace_z") - if self.getParameterValue(lasheightPro.DROP_ABOVE): - commands.append("-drop_above") - commands.append(str(self.getParameterValue(lasheightPro.DROP_ABOVE_HEIGHT))) - if self.getParameterValue(lasheightPro.DROP_BELOW): - commands.append("-drop_below") - commands.append(str(self.getParameterValue(lasheightPro.DROP_BELOW_HEIGHT))) - self.addParametersOutputDirectoryCommands(commands) - self.addParametersOutputAppendixCommands(commands) - self.addParametersPointOutputFormatCommands(commands) - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasheightPro_classify.py b/python/plugins/processing/algs/lidar/lastools/lasheightPro_classify.py deleted file mode 100644 index ab8033aae9ad..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasheightPro_classify.py +++ /dev/null @@ -1,126 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasheightPro_classify.py - --------------------- - Date : May 2016 - Copyright : (C) 2016 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from __future__ import absolute_import -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'May 2016' -__copyright__ = '(C) 2016, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterSelection - - -class lasheightPro_classify(LAStoolsAlgorithm): - - REPLACE_Z = "REPLACE_Z" - CLASSIFY_BELOW = "CLASSIFY_BELOW" - CLASSIFY_BELOW_HEIGHT = "CLASSIFY_BELOW_HEIGHT" - CLASSIFY_BETWEEN1 = "CLASSIFY_BETWEEN1" - CLASSIFY_BETWEEN1_HEIGHT_FROM = "CLASSIFY_BETWEEN1_HEIGHT_FROM" - CLASSIFY_BETWEEN1_HEIGHT_TO = "CLASSIFY_BETWEEN1_HEIGHT_TO" - CLASSIFY_BETWEEN2 = "CLASSIFY_BETWEEN2" - CLASSIFY_BETWEEN2_HEIGHT_FROM = "CLASSIFY_BETWEEN2_HEIGHT_FROM" - CLASSIFY_BETWEEN2_HEIGHT_TO = "CLASSIFY_BETWEEN2_HEIGHT_TO" - CLASSIFY_ABOVE = "CLASSIFY_ABOVE" - CLASSIFY_ABOVE_HEIGHT = "CLASSIFY_ABOVE_HEIGHT" - - CLASSIFY_CLASSES = ["---", "unclassified (1)", "ground (2)", "veg low (3)", "veg mid (4)", "veg high (5)", "buildings (6)", "noise (7)", "keypoint (8)", "water (9)", "water (9)", "rail (10)", "road (11)", "overlap (12)"] - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasheightPro_classify') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParametersIgnoreClass1GUI() - self.addParametersIgnoreClass2GUI() - self.addParameter(ParameterBoolean(lasheightPro_classify.REPLACE_Z, - self.tr("replace z"), False)) - self.addParameter(ParameterSelection(lasheightPro_classify.CLASSIFY_BELOW, - self.tr("classify below height as"), lasheightPro_classify.CLASSIFY_CLASSES, 0)) - self.addParameter(ParameterNumber(lasheightPro_classify.CLASSIFY_BELOW_HEIGHT, - self.tr("below height"), None, None, -2.0)) - self.addParameter(ParameterSelection(lasheightPro_classify.CLASSIFY_BETWEEN1, - self.tr("classify between height as"), lasheightPro_classify.CLASSIFY_CLASSES, 0)) - self.addParameter(ParameterNumber(lasheightPro_classify.CLASSIFY_BETWEEN1_HEIGHT_FROM, - self.tr("between height ... "), None, None, 0.5)) - self.addParameter(ParameterNumber(lasheightPro_classify.CLASSIFY_BETWEEN1_HEIGHT_TO, - self.tr("... and height"), None, None, 2.0)) - self.addParameter(ParameterSelection(lasheightPro_classify.CLASSIFY_BETWEEN2, - self.tr("classify between height as"), lasheightPro_classify.CLASSIFY_CLASSES, 0)) - self.addParameter(ParameterNumber(lasheightPro_classify.CLASSIFY_BETWEEN2_HEIGHT_FROM, - self.tr("between height ..."), None, None, 2.0)) - self.addParameter(ParameterNumber(lasheightPro_classify.CLASSIFY_BETWEEN2_HEIGHT_TO, - self.tr("... and height"), None, None, 5.0)) - self.addParameter(ParameterSelection(lasheightPro_classify.CLASSIFY_ABOVE, - self.tr("classify above"), lasheightPro_classify.CLASSIFY_CLASSES, 0)) - self.addParameter(ParameterNumber(lasheightPro_classify.CLASSIFY_ABOVE_HEIGHT, - self.tr("classify above height"), None, None, 100.0)) - self.addParametersOutputDirectoryGUI() - self.addParametersOutputAppendixGUI() - self.addParametersPointOutputFormatGUI() - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasheight")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - self.addParametersIgnoreClass1Commands(commands) - self.addParametersIgnoreClass2Commands(commands) - if self.getParameterValue(lasheightPro_classify.REPLACE_Z): - commands.append("-replace_z") - classify = self.getParameterValue(lasheightPro_classify.CLASSIFY_BELOW) - if (classify != 0): - commands.append("-classify_below") - commands.append(str(self.getParameterValue(lasheightPro_classify.CLASSIFY_BELOW_HEIGHT))) - commands.append(str(classify)) - classify = self.getParameterValue(lasheightPro_classify.CLASSIFY_BETWEEN1) - if (classify != 0): - commands.append("-classify_between") - commands.append(str(self.getParameterValue(lasheightPro_classify.CLASSIFY_BETWEEN1_HEIGHT_FROM))) - commands.append(str(self.getParameterValue(lasheightPro_classify.CLASSIFY_BETWEEN1_HEIGHT_TO))) - commands.append(str(classify)) - classify = self.getParameterValue(lasheightPro_classify.CLASSIFY_BETWEEN2) - if (classify != 0): - commands.append("-classify_between") - commands.append(str(self.getParameterValue(lasheightPro_classify.CLASSIFY_BETWEEN2_HEIGHT_FROM))) - commands.append(str(self.getParameterValue(lasheightPro_classify.CLASSIFY_BETWEEN2_HEIGHT_TO))) - commands.append(str(classify)) - classify = self.getParameterValue(lasheightPro_classify.CLASSIFY_ABOVE) - if (classify != 0): - commands.append("-classify_above") - commands.append(str(self.getParameterValue(lasheightPro_classify.CLASSIFY_ABOVE_HEIGHT))) - commands.append(str(classify)) - self.addParametersOutputDirectoryCommands(commands) - self.addParametersOutputAppendixCommands(commands) - self.addParametersPointOutputFormatCommands(commands) - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasheight_classify.py b/python/plugins/processing/algs/lidar/lastools/lasheight_classify.py deleted file mode 100644 index d2fae442349e..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasheight_classify.py +++ /dev/null @@ -1,120 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasheight_classify.py - --------------------- - Date : May 2016 - Copyright : (C) 2016 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from __future__ import absolute_import -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'May 2016' -__copyright__ = '(C) 2016, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterSelection - - -class lasheight_classify(LAStoolsAlgorithm): - - REPLACE_Z = "REPLACE_Z" - CLASSIFY_BELOW = "CLASSIFY_BELOW" - CLASSIFY_BELOW_HEIGHT = "CLASSIFY_BELOW_HEIGHT" - CLASSIFY_BETWEEN1 = "CLASSIFY_BETWEEN1" - CLASSIFY_BETWEEN1_HEIGHT_FROM = "CLASSIFY_BETWEEN1_HEIGHT_FROM" - CLASSIFY_BETWEEN1_HEIGHT_TO = "CLASSIFY_BETWEEN1_HEIGHT_TO" - CLASSIFY_BETWEEN2 = "CLASSIFY_BETWEEN2" - CLASSIFY_BETWEEN2_HEIGHT_FROM = "CLASSIFY_BETWEEN2_HEIGHT_FROM" - CLASSIFY_BETWEEN2_HEIGHT_TO = "CLASSIFY_BETWEEN2_HEIGHT_TO" - CLASSIFY_ABOVE = "CLASSIFY_ABOVE" - CLASSIFY_ABOVE_HEIGHT = "CLASSIFY_ABOVE_HEIGHT" - - CLASSIFY_CLASSES = ["---", "unclassified (1)", "ground (2)", "veg low (3)", "veg mid (4)", "veg high (5)", "buildings (6)", "noise (7)", "keypoint (8)", "water (9)", "water (9)", "rail (10)", "road (11)", "overlap (12)"] - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasheight_classify') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParametersIgnoreClass1GUI() - self.addParametersIgnoreClass2GUI() - self.addParameter(ParameterBoolean(lasheight_classify.REPLACE_Z, - self.tr("replace z"), False)) - self.addParameter(ParameterSelection(lasheight_classify.CLASSIFY_BELOW, - self.tr("classify below height as"), lasheight_classify.CLASSIFY_CLASSES, 0)) - self.addParameter(ParameterNumber(lasheight_classify.CLASSIFY_BELOW_HEIGHT, - self.tr("below height"), None, None, -2.0)) - self.addParameter(ParameterSelection(lasheight_classify.CLASSIFY_BETWEEN1, - self.tr("classify between height as"), lasheight_classify.CLASSIFY_CLASSES, 0)) - self.addParameter(ParameterNumber(lasheight_classify.CLASSIFY_BETWEEN1_HEIGHT_FROM, - self.tr("between height ... "), None, None, 0.5)) - self.addParameter(ParameterNumber(lasheight_classify.CLASSIFY_BETWEEN1_HEIGHT_TO, - self.tr("... and height"), None, None, 2.0)) - self.addParameter(ParameterSelection(lasheight_classify.CLASSIFY_BETWEEN2, - self.tr("classify between height as"), lasheight_classify.CLASSIFY_CLASSES, 0)) - self.addParameter(ParameterNumber(lasheight_classify.CLASSIFY_BETWEEN2_HEIGHT_FROM, - self.tr("between height ..."), None, None, 2.0)) - self.addParameter(ParameterNumber(lasheight_classify.CLASSIFY_BETWEEN2_HEIGHT_TO, - self.tr("... and height"), None, None, 5.0)) - self.addParameter(ParameterSelection(lasheight_classify.CLASSIFY_ABOVE, - self.tr("classify above"), lasheight_classify.CLASSIFY_CLASSES, 0)) - self.addParameter(ParameterNumber(lasheight_classify.CLASSIFY_ABOVE_HEIGHT, - self.tr("classify above height"), None, None, 100.0)) - self.addParametersPointOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasheight")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - self.addParametersIgnoreClass1Commands(commands) - self.addParametersIgnoreClass2Commands(commands) - if self.getParameterValue(lasheight_classify.REPLACE_Z): - commands.append("-replace_z") - classify = self.getParameterValue(lasheight_classify.CLASSIFY_BELOW) - if (classify != 0): - commands.append("-classify_below") - commands.append(str(self.getParameterValue(lasheight_classify.CLASSIFY_BELOW_HEIGHT))) - commands.append(str(classify)) - classify = self.getParameterValue(lasheight_classify.CLASSIFY_BETWEEN1) - if (classify != 0): - commands.append("-classify_between") - commands.append(str(self.getParameterValue(lasheight_classify.CLASSIFY_BETWEEN1_HEIGHT_FROM))) - commands.append(str(self.getParameterValue(lasheight_classify.CLASSIFY_BETWEEN1_HEIGHT_TO))) - commands.append(str(classify)) - classify = self.getParameterValue(lasheight_classify.CLASSIFY_BETWEEN2) - if (classify != 0): - commands.append("-classify_between") - commands.append(str(self.getParameterValue(lasheight_classify.CLASSIFY_BETWEEN2_HEIGHT_FROM))) - commands.append(str(self.getParameterValue(lasheight_classify.CLASSIFY_BETWEEN2_HEIGHT_TO))) - commands.append(str(classify)) - classify = self.getParameterValue(lasheight_classify.CLASSIFY_ABOVE) - if (classify != 0): - commands.append("-classify_above") - commands.append(str(self.getParameterValue(lasheight_classify.CLASSIFY_ABOVE_HEIGHT))) - commands.append(str(classify)) - self.addParametersPointOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasindex.py b/python/plugins/processing/algs/lidar/lastools/lasindex.py deleted file mode 100644 index ef7d347b9885..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasindex.py +++ /dev/null @@ -1,67 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasindex.py - --------------------- - Date : September 2013 and May 2016 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Martin Isenburg' -__date__ = 'September 2013' -__copyright__ = '(C) 2013, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean - - -class lasindex(LAStoolsAlgorithm): - - MOBILE_OR_TERRESTRIAL = "MOBILE_OR_TERRESTRIAL" - APPEND_LAX = "APPEND_LAX" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasindex') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParameter(ParameterBoolean(lasindex.APPEND_LAX, - self.tr("append *.lax file to *.laz file"), False)) - self.addParameter(ParameterBoolean(lasindex.MOBILE_OR_TERRESTRIAL, - self.tr("is mobile or terrestrial LiDAR (not airborne)"), False)) - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - if (LAStoolsUtils.hasWine()): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasindex.exe")] - else: - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasindex")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - if self.getParameterValue(lasindex.APPEND_LAX): - commands.append("-append") - if self.getParameterValue(lasindex.MOBILE_OR_TERRESTRIAL): - commands.append("-tile_size") - commands.append("10") - commands.append("-maximum") - commands.append("-100") - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasindexPro.py b/python/plugins/processing/algs/lidar/lastools/lasindexPro.py deleted file mode 100644 index 8af2cda756ce..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasindexPro.py +++ /dev/null @@ -1,69 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasindexPro.py - --------------------- - Date : October 2014 and May 2016 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean - - -class lasindexPro(LAStoolsAlgorithm): - - MOBILE_OR_TERRESTRIAL = "MOBILE_OR_TERRESTRIAL" - APPEND_LAX = "APPEND_LAX" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasindexPro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParameter(ParameterBoolean(lasindexPro.APPEND_LAX, - self.tr("append *.lax file to *.laz file"), False)) - self.addParameter(ParameterBoolean(lasindexPro.MOBILE_OR_TERRESTRIAL, - self.tr("is mobile or terrestrial LiDAR (not airborne)"), False)) - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - if (LAStoolsUtils.hasWine()): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasindex.exe")] - else: - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasindex")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - if self.getParameterValue(lasindexPro.APPEND_LAX): - commands.append("-append") - if self.getParameterValue(lasindexPro.MOBILE_OR_TERRESTRIAL): - commands.append("-tile_size") - commands.append("10") - commands.append("-maximum") - commands.append("-100") - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasinfo.py b/python/plugins/processing/algs/lidar/lastools/lasinfo.py deleted file mode 100644 index 3feccd6468d2..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasinfo.py +++ /dev/null @@ -1,116 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasinfo.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com - --------------------- - Date : September 2013 and May 2016 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterBoolean -from processing.core.outputs import OutputFile -from processing.core.parameters import ParameterNumber - - -class lasinfo(LAStoolsAlgorithm): - - COMPUTE_DENSITY = "COMPUTE_DENSITY" - REPAIR_BB = "REPAIR_BB" - REPAIR_COUNTERS = "REPAIR_COUNTERS" - HISTO1 = "HISTO1" - HISTO2 = "HISTO2" - HISTO3 = "HISTO3" - HISTOGRAM = ["---", "x", "y", "z", "intensity", "classification", "scan_angle", "user_data", "point_source", "gps_time", "X", "Y", "Z"] - HISTO1_BIN = "HISTO1_BIN" - HISTO2_BIN = "HISTO2_BIN" - HISTO3_BIN = "HISTO3_BIN" - OUTPUT = "OUTPUT" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasinfo') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParameter(ParameterBoolean(lasinfo.COMPUTE_DENSITY, - self.tr("compute density"), False)) - self.addParameter(ParameterBoolean(lasinfo.REPAIR_BB, - self.tr("repair bounding box"), False)) - self.addParameter(ParameterBoolean(lasinfo.REPAIR_COUNTERS, - self.tr("repair counters"), False)) - self.addParameter(ParameterSelection(lasinfo.HISTO1, - self.tr("histogram"), lasinfo.HISTOGRAM, 0)) - self.addParameter(ParameterNumber(lasinfo.HISTO1_BIN, - self.tr("bin size"), 0, None, 1.0)) - self.addParameter(ParameterSelection(lasinfo.HISTO2, - self.tr("histogram"), lasinfo.HISTOGRAM, 0)) - self.addParameter(ParameterNumber(lasinfo.HISTO2_BIN, - self.tr("bin size"), 0, None, 1.0)) - self.addParameter(ParameterSelection(lasinfo.HISTO3, - self.tr("histogram"), lasinfo.HISTOGRAM, 0)) - self.addParameter(ParameterNumber(lasinfo.HISTO3_BIN, - self.tr("bin size"), 0, None, 1.0)) - self.addOutput(OutputFile(lasinfo.OUTPUT, - self.tr("Output ASCII file"))) - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - if (LAStoolsUtils.hasWine()): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasinfo.exe")] - else: - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasinfo")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - if self.getParameterValue(lasinfo.COMPUTE_DENSITY): - commands.append("-cd") - if self.getParameterValue(lasinfo.REPAIR_BB): - commands.append("-repair_bb") - if self.getParameterValue(lasinfo.REPAIR_COUNTERS): - commands.append("-repair_counters") - histo = self.getParameterValue(lasinfo.HISTO1) - if histo != 0: - commands.append("-histo") - commands.append(lasinfo.HISTOGRAM[histo]) - commands.append(str(self.getParameterValue(lasinfo.HISTO1_BIN))) - histo = self.getParameterValue(lasinfo.HISTO2) - if histo != 0: - commands.append("-histo") - commands.append(lasinfo.HISTOGRAM[histo]) - commands.append(str(self.getParameterValue(lasinfo.HISTO2_BIN))) - histo = self.getParameterValue(lasinfo.HISTO3) - if histo != 0: - commands.append("-histo") - commands.append(lasinfo.HISTOGRAM[histo]) - commands.append(str(self.getParameterValue(lasinfo.HISTO3_BIN))) - commands.append("-o") - commands.append(self.getOutputValue(lasinfo.OUTPUT)) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasinfoPro.py b/python/plugins/processing/algs/lidar/lastools/lasinfoPro.py deleted file mode 100644 index eb904e0de4a7..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasinfoPro.py +++ /dev/null @@ -1,113 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasinfoPro.py - --------------------- - Date : October 2014 and May 2016 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterNumber - - -class lasinfoPro(LAStoolsAlgorithm): - - COMPUTE_DENSITY = "COMPUTE_DENSITY" - REPAIR_BB = "REPAIR_BB" - REPAIR_COUNTERS = "REPAIR_COUNTERS" - HISTO1 = "HISTO1" - HISTO2 = "HISTO2" - HISTO3 = "HISTO3" - HISTOGRAM = ["---", "x", "y", "z", "intensity", "classification", "scan_angle", "user_data", "point_source", "gps_time", "X", "Y", "Z"] - HISTO1_BIN = "HISTO1_BIN" - HISTO2_BIN = "HISTO2_BIN" - HISTO3_BIN = "HISTO3_BIN" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasinfoPro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParameter(ParameterBoolean(lasinfoPro.COMPUTE_DENSITY, - self.tr("compute density"), False)) - self.addParameter(ParameterBoolean(lasinfoPro.REPAIR_BB, - self.tr("repair bounding box"), False)) - self.addParameter(ParameterBoolean(lasinfoPro.REPAIR_COUNTERS, - self.tr("repair counters"), False)) - self.addParameter(ParameterSelection(lasinfoPro.HISTO1, - self.tr("histogram"), lasinfoPro.HISTOGRAM, 0)) - self.addParameter(ParameterNumber(lasinfoPro.HISTO1_BIN, - self.tr("bin size"), 0, None, 1.0)) - self.addParameter(ParameterSelection(lasinfoPro.HISTO2, - self.tr("histogram"), lasinfoPro.HISTOGRAM, 0)) - self.addParameter(ParameterNumber(lasinfoPro.HISTO2_BIN, - self.tr("bin size"), 0, None, 1.0)) - self.addParameter(ParameterSelection(lasinfoPro.HISTO3, - self.tr("histogram"), lasinfoPro.HISTOGRAM, 0)) - self.addParameter(ParameterNumber(lasinfoPro.HISTO3_BIN, - self.tr("bin size"), 0, None, 1.0)) - self.addParametersOutputDirectoryGUI() - self.addParametersOutputAppendixGUI() - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - if (LAStoolsUtils.hasWine()): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasinfo.exe")] - else: - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasinfo")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - if self.getParameterValue(lasinfoPro.COMPUTE_DENSITY): - commands.append("-cd") - if self.getParameterValue(lasinfoPro.REPAIR_BB): - commands.append("-repair_bb") - if self.getParameterValue(lasinfoPro.REPAIR_COUNTERS): - commands.append("-repair_counters") - histo = self.getParameterValue(lasinfoPro.HISTO1) - if histo != 0: - commands.append("-histo") - commands.append(lasinfoPro.HISTOGRAM[histo]) - commands.append(str(self.getParameterValue(lasinfoPro.HISTO1_BIN))) - histo = self.getParameterValue(lasinfoPro.HISTO2) - if histo != 0: - commands.append("-histo") - commands.append(lasinfoPro.HISTOGRAM[histo]) - commands.append(str(self.getParameterValue(lasinfoPro.HISTO2_BIN))) - histo = self.getParameterValue(lasinfoPro.HISTO3) - if histo != 0: - commands.append("-histo") - commands.append(lasinfoPro.HISTOGRAM[histo]) - commands.append(str(self.getParameterValue(lasinfoPro.HISTO3_BIN))) - self.addParametersOutputDirectoryCommands(commands) - self.addParametersOutputAppendixCommands(commands) - commands.append("-otxt") - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasmerge.py b/python/plugins/processing/algs/lidar/lastools/lasmerge.py deleted file mode 100644 index 5a660a836a52..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasmerge.py +++ /dev/null @@ -1,96 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasmerge.py - --------------------- - Date : September 2013 and May 2016 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Martin Isenburg' -__date__ = 'September 2013' -__copyright__ = '(C) 2013, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterFile - - -class lasmerge(LAStoolsAlgorithm): - - FILE2 = "FILE2" - FILE3 = "FILE3" - FILE4 = "FILE4" - FILE5 = "FILE5" - FILE6 = "FILE6" - FILE7 = "FILE7" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasmerge') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersFilesAreFlightlinesGUI() - self.addParametersApplyFileSourceIdGUI() - self.addParametersPointInputGUI() - self.addParameter(ParameterFile(lasmerge.FILE2, self.tr("2nd file"))) - self.addParameter(ParameterFile(lasmerge.FILE3, self.tr("3rd file"))) - self.addParameter(ParameterFile(lasmerge.FILE4, self.tr("4th file"))) - self.addParameter(ParameterFile(lasmerge.FILE5, self.tr("5th file"))) - self.addParameter(ParameterFile(lasmerge.FILE6, self.tr("6th file"))) - self.addParameter(ParameterFile(lasmerge.FILE7, self.tr("7th file"))) - self.addParametersPointOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - if (LAStoolsUtils.hasWine()): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasmerge.exe")] - else: - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasmerge")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - file2 = self.getParameterValue(lasmerge.FILE2) - if file2 is not None: - commands.append("-i") - commands.append(file2) - file3 = self.getParameterValue(lasmerge.FILE3) - if file3 is not None: - commands.append("-i") - commands.append(file3) - file4 = self.getParameterValue(lasmerge.FILE4) - if file4 is not None: - commands.append("-i") - commands.append(file4) - file5 = self.getParameterValue(lasmerge.FILE5) - if file5 is not None: - commands.append("-i") - commands.append(file5) - file6 = self.getParameterValue(lasmerge.FILE6) - if file6 is not None: - commands.append("-i") - commands.append(file6) - file7 = self.getParameterValue(lasmerge.FILE7) - if file7 is not None: - commands.append("-i") - commands.append(file7) - self.addParametersFilesAreFlightlinesCommands(commands) - self.addParametersApplyFileSourceIdCommands(commands) - self.addParametersPointOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasmergePro.py b/python/plugins/processing/algs/lidar/lastools/lasmergePro.py deleted file mode 100644 index ce1490b7cd0b..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasmergePro.py +++ /dev/null @@ -1,55 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasmergePro.py - --------------------- - Date : October 2014 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - - -class lasmergePro(LAStoolsAlgorithm): - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasmergePro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParametersFilesAreFlightlinesGUI() - self.addParametersApplyFileSourceIdGUI() - self.addParametersPointOutputGUI() - self.addParametersAdditionalGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - if (LAStoolsUtils.hasWine()): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasmerge.exe")] - else: - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasmerge")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - self.addParametersFilesAreFlightlinesCommands(commands) - self.addParametersApplyFileSourceIdCommands(commands) - self.addParametersPointOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasnoise.py b/python/plugins/processing/algs/lidar/lastools/lasnoise.py deleted file mode 100644 index b5a496880ec1..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasnoise.py +++ /dev/null @@ -1,91 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasnoise.py - --------------------- - Date : September 2013 and May 2016 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'September 2013' -__copyright__ = '(C) 2013, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterSelection - - -class lasnoise(LAStoolsAlgorithm): - - ISOLATED = "ISOLATED" - STEP_XY = "STEP_XY" - STEP_Z = "STEP_Z" - OPERATION = "OPERATION" - OPERATIONS = ["classify", "remove"] - CLASSIFY_AS = "CLASSIFY_AS" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasnoise') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParametersIgnoreClass1GUI() - self.addParametersIgnoreClass2GUI() - self.addParameter(ParameterNumber(lasnoise.ISOLATED, - self.tr("isolated if surrounding cells have only"), 0, None, 5)) - self.addParameter(ParameterNumber(lasnoise.STEP_XY, - self.tr("resolution of isolation grid in xy"), 0, None, 4.0)) - self.addParameter(ParameterNumber(lasnoise.STEP_Z, - self.tr("resolution of isolation grid in z"), 0, None, 4.0)) - self.addParameter(ParameterSelection(lasnoise.OPERATION, - self.tr("what to do with isolated points"), lasnoise.OPERATIONS, 0)) - self.addParameter(ParameterNumber(lasnoise.CLASSIFY_AS, - self.tr("classify as"), 0, None, 7)) - self.addParametersPointOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasnoise")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - self.addParametersIgnoreClass1Commands(commands) - self.addParametersIgnoreClass2Commands(commands) - isolated = self.getParameterValue(lasnoise.ISOLATED) - commands.append("-isolated") - commands.append(str(isolated)) - step_xy = self.getParameterValue(lasnoise.STEP_XY) - commands.append("-step_xy") - commands.append(str(step_xy)) - step_z = self.getParameterValue(lasnoise.STEP_Z) - commands.append("-step_z") - commands.append(str(step_z)) - operation = self.getParameterValue(lasnoise.OPERATION) - if operation != 0: - commands.append("-remove_noise") - else: - commands.append("-classify_as") - classify_as = self.getParameterValue(lasnoise.CLASSIFY_AS) - commands.append(str(classify_as)) - self.addParametersPointOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasnoisePro.py b/python/plugins/processing/algs/lidar/lastools/lasnoisePro.py deleted file mode 100644 index 510bb3030513..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasnoisePro.py +++ /dev/null @@ -1,97 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasnoisePro.py - --------------------- - Date : October 2014 and May 2016 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterSelection - - -class lasnoisePro(LAStoolsAlgorithm): - - ISOLATED = "ISOLATED" - STEP_XY = "STEP_XY" - STEP_Z = "STEP_Z" - OPERATION = "OPERATION" - OPERATIONS = ["classify", "remove"] - CLASSIFY_AS = "CLASSIFY_AS" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasnoisePro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParametersIgnoreClass1GUI() - self.addParametersIgnoreClass2GUI() - self.addParameter(ParameterNumber(lasnoisePro.ISOLATED, - self.tr("isolated if surrounding cells have only"), 0, None, 5)) - self.addParameter(ParameterNumber(lasnoisePro.STEP_XY, - self.tr("resolution of isolation grid in xy"), 0, None, 4.0)) - self.addParameter(ParameterNumber(lasnoisePro.STEP_Z, - self.tr("resolution of isolation grid in z"), 0, None, 4.0)) - self.addParameter(ParameterSelection(lasnoisePro.OPERATION, - self.tr("what to do with isolated points"), lasnoisePro.OPERATIONS, 0)) - self.addParameter(ParameterNumber(lasnoisePro.CLASSIFY_AS, - self.tr("classify as"), 0, None, 7)) - self.addParametersOutputDirectoryGUI() - self.addParametersOutputAppendixGUI() - self.addParametersPointOutputFormatGUI() - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasnoise")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - self.addParametersIgnoreClass1Commands(commands) - self.addParametersIgnoreClass2Commands(commands) - isolated = self.getParameterValue(lasnoisePro.ISOLATED) - commands.append("-isolated") - commands.append(str(isolated)) - step_xy = self.getParameterValue(lasnoisePro.STEP_XY) - commands.append("-step_xy") - commands.append(str(step_xy)) - step_z = self.getParameterValue(lasnoisePro.STEP_Z) - commands.append("-step_z") - commands.append(str(step_z)) - operation = self.getParameterValue(lasnoisePro.OPERATION) - if operation != 0: - commands.append("-remove_noise") - else: - commands.append("-classify_as") - classify_as = self.getParameterValue(lasnoisePro.CLASSIFY_AS) - commands.append(str(classify_as)) - self.addParametersOutputDirectoryCommands(commands) - self.addParametersOutputAppendixCommands(commands) - self.addParametersPointOutputFormatCommands(commands) - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasoverage.py b/python/plugins/processing/algs/lidar/lastools/lasoverage.py deleted file mode 100644 index 3ddbe82050dc..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasoverage.py +++ /dev/null @@ -1,75 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasoverage.py - --------------------- - Date : September 2013 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'September 2013' -__copyright__ = '(C) 2013, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterSelection - - -class lasoverage(LAStoolsAlgorithm): - - CHECK_STEP = "CHECK_STEP" - OPERATION = "OPERATION" - OPERATIONS = ["classify as overlap", "flag as withheld", "remove from output"] - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasoverage') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParametersHorizontalFeetGUI() - self.addParametersFilesAreFlightlinesGUI() - self.addParameter(ParameterNumber(lasoverage.CHECK_STEP, - self.tr("size of grid used for scan angle check"), 0, None, 1.0)) - self.addParameter(ParameterSelection(lasoverage.OPERATION, - self.tr("mode of operation"), lasoverage.OPERATIONS, 0)) - self.addParametersPointOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasoverage")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - self.addParametersHorizontalFeetCommands(commands) - self.addParametersFilesAreFlightlinesCommands(commands) - step = self.getParameterValue(lasoverage.CHECK_STEP) - if step != 1.0: - commands.append("-step") - commands.append(str(step)) - operation = self.getParameterValue(lasoverage.OPERATION) - if operation == 1: - commands.append("-flag_as_withheld") - elif operation == 2: - commands.append("-remove_overage") - self.addParametersPointOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasoveragePro.py b/python/plugins/processing/algs/lidar/lastools/lasoveragePro.py deleted file mode 100644 index 08c4abebfccb..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasoveragePro.py +++ /dev/null @@ -1,81 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasoveragePro.py - --------------------- - Date : October 2014 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterSelection - - -class lasoveragePro(LAStoolsAlgorithm): - - CHECK_STEP = "CHECK_STEP" - OPERATION = "OPERATION" - OPERATIONS = ["classify as overlap", "flag as withheld", "remove from output"] - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasoveragePro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParametersHorizontalFeetGUI() - self.addParametersFilesAreFlightlinesGUI() - self.addParameter(ParameterNumber(lasoveragePro.CHECK_STEP, - self.tr("size of grid used for scan angle check"), 0, None, 1.0)) - self.addParameter(ParameterSelection(lasoveragePro.OPERATION, - self.tr("mode of operation"), lasoveragePro.OPERATIONS, 0)) - self.addParametersOutputDirectoryGUI() - self.addParametersOutputAppendixGUI() - self.addParametersPointOutputFormatGUI() - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasoverage")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - self.addParametersHorizontalFeetCommands(commands) - self.addParametersFilesAreFlightlinesCommands(commands) - step = self.getParameterValue(lasoveragePro.CHECK_STEP) - if step != 1.0: - commands.append("-step") - commands.append(str(step)) - operation = self.getParameterValue(lasoveragePro.OPERATION) - if operation == 1: - commands.append("-flag_as_withheld") - elif operation == 2: - commands.append("-remove_overage") - self.addParametersOutputDirectoryCommands(commands) - self.addParametersOutputAppendixCommands(commands) - self.addParametersPointOutputFormatCommands(commands) - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasoverlap.py b/python/plugins/processing/algs/lidar/lastools/lasoverlap.py deleted file mode 100644 index 50e57d1f6ea8..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasoverlap.py +++ /dev/null @@ -1,90 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasoverlap.py - --------------------- - Date : September 2013 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'September 2013' -__copyright__ = '(C) 2013, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterSelection - - -class lasoverlap(LAStoolsAlgorithm): - - CHECK_STEP = "CHECK_STEP" - ATTRIBUTE = "ATTRIBUTE" - OPERATION = "OPERATION" - ATTRIBUTES = ["elevation", "intensity", "number_of_returns", "scan_angle_abs", "density"] - OPERATIONS = ["lowest", "highest", "average"] - CREATE_OVERLAP_RASTER = "CREATE_OVERLAP_RASTER" - CREATE_DIFFERENCE_RASTER = "CREATE_DIFFERENCE_RASTER" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasoverlap') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParametersFilter1ReturnClassFlagsGUI() - self.addParameter(ParameterNumber(lasoverlap.CHECK_STEP, - self.tr("size of grid used for overlap check"), 0, None, 2.0)) - self.addParameter(ParameterSelection(lasoverlap.ATTRIBUTE, - self.tr("attribute to check"), lasoverlap.ATTRIBUTES, 0)) - self.addParameter(ParameterSelection(lasoverlap.OPERATION, - self.tr("operation on attribute per cell"), lasoverlap.OPERATIONS, 0)) - self.addParameter(ParameterBoolean(lasoverlap.CREATE_OVERLAP_RASTER, - self.tr("create overlap raster"), True)) - self.addParameter(ParameterBoolean(lasoverlap.CREATE_DIFFERENCE_RASTER, - self.tr("create difference raster"), True)) - self.addParametersRasterOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasoverlap")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - self.addParametersFilter1ReturnClassFlagsCommands(commands) - step = self.getParameterValue(lasoverlap.CHECK_STEP) - if step != 0.0: - commands.append("-step") - commands.append(str(step)) - commands.append("-values") - attribute = self.getParameterValue(lasoverlap.ATTRIBUTE) - if attribute != 0: - commands.append("-" + lasoverlap.ATTRIBUTES[attribute]) - operation = self.getParameterValue(lasoverlap.OPERATION) - if operation != 0: - commands.append("-" + lasoverlap.OPERATIONS[operation]) - if not self.getParameterValue(lasoverlap.CREATE_OVERLAP_RASTER): - commands.append("-no_over") - if not self.getParameterValue(lasoverlap.CREATE_DIFFERENCE_RASTER): - commands.append("-no_diff") - self.addParametersRasterOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasoverlapPro.py b/python/plugins/processing/algs/lidar/lastools/lasoverlapPro.py deleted file mode 100644 index a308a53a4e8c..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasoverlapPro.py +++ /dev/null @@ -1,100 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasoverlapPro.py - --------------------- - Date : October 2014 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterSelection - - -class lasoverlapPro(LAStoolsAlgorithm): - - CHECK_STEP = "CHECK_STEP" - ATTRIBUTE = "ATTRIBUTE" - OPERATION = "OPERATION" - ATTRIBUTES = ["elevation", "intensity", "number_of_returns", "scan_angle_abs", "density"] - OPERATIONS = ["lowest", "highest", "average"] - CREATE_OVERLAP_RASTER = "CREATE_OVERLAP_RASTER" - CREATE_DIFFERENCE_RASTER = "CREATE_DIFFERENCE_RASTER" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasoverlapPro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParametersFilesAreFlightlinesGUI() - self.addParametersFilter1ReturnClassFlagsGUI() - self.addParameter(ParameterNumber(lasoverlapPro.CHECK_STEP, - self.tr("size of grid used for overlap check"), 0, None, 2.0)) - self.addParameter(ParameterSelection(lasoverlapPro.ATTRIBUTE, - self.tr("attribute to check"), lasoverlapPro.ATTRIBUTES, 0)) - self.addParameter(ParameterSelection(lasoverlapPro.OPERATION, - self.tr("operation on attribute per cell"), lasoverlapPro.OPERATIONS, 0)) - self.addParameter(ParameterBoolean(lasoverlapPro.CREATE_OVERLAP_RASTER, - self.tr("create overlap raster"), True)) - self.addParameter(ParameterBoolean(lasoverlapPro.CREATE_DIFFERENCE_RASTER, - self.tr("create difference raster"), True)) - self.addParametersOutputDirectoryGUI() - self.addParametersOutputAppendixGUI() - self.addParametersRasterOutputFormatGUI() - self.addParametersRasterOutputGUI() - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasoverlap")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - self.addParametersFilesAreFlightlinesCommands(commands) - self.addParametersFilter1ReturnClassFlagsCommands(commands) - step = self.getParameterValue(lasoverlapPro.CHECK_STEP) - if step != 0.0: - commands.append("-step") - commands.append(str(step)) - commands.append("-values") - attribute = self.getParameterValue(lasoverlapPro.ATTRIBUTE) - if attribute != 0: - commands.append("-" + lasoverlapPro.ATTRIBUTES[attribute]) - operation = self.getParameterValue(lasoverlapPro.OPERATION) - if operation != 0: - commands.append("-" + lasoverlapPro.OPERATIONS[operation]) - if not self.getParameterValue(lasoverlapPro.CREATE_OVERLAP_RASTER): - commands.append("-no_over") - if not self.getParameterValue(lasoverlapPro.CREATE_DIFFERENCE_RASTER): - commands.append("-no_diff") - self.addParametersOutputDirectoryCommands(commands) - self.addParametersOutputAppendixCommands(commands) - self.addParametersRasterOutputFormatCommands(commands) - self.addParametersRasterOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasprecision.py b/python/plugins/processing/algs/lidar/lastools/lasprecision.py deleted file mode 100644 index 4be0ef67cd39..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasprecision.py +++ /dev/null @@ -1,59 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasprecision.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com - --------------------- - Date : September 2013 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.outputs import OutputFile - - -class lasprecision(LAStoolsAlgorithm): - - OUTPUT = "OUTPUT" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasprecision') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addOutput(OutputFile(lasprecision.OUTPUT, self.tr("Output ASCII file"))) - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasprecision")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - commands.append("-o") - commands.append(self.getOutputValue(lasprecision.OUTPUT)) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/laspublish.py b/python/plugins/processing/algs/lidar/lastools/laspublish.py deleted file mode 100644 index 04c714b1ec1a..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/laspublish.py +++ /dev/null @@ -1,125 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - laspublish.py - --------------------- - Date : May 2016 - Copyright : (C) 2016 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from __future__ import absolute_import -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Martin Isenburg' -__date__ = 'May 2016' -__copyright__ = '(C) 2016, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterString -from processing.core.parameters import ParameterFile - - -class laspublish(LAStoolsAlgorithm): - - MODE = "MODE" - MODES = ["3D only", "3D + download map", "download map only"] - DIR = "DIR" - SHOW_SKYBOX = "SHOW_SKYBOX" - USE_EDL = "USE_EDL" - MATERIAL = "MATERIAL" - MATERIALS = ["elevation", "intensity", "return_number", "point_source", "rgb"] - COPY_OR_MOVE = "COPY_OR_MOVE" - COPY_OR_MOVE_OPTIONS = ["copy into portal dir", "move into portal dir", "neither"] - PORTAL_DIRECTORY = "PORTAL_DIRECTORY" - PORTAL_HTML_PAGE = "PORTAL_HTML_PAGE" - OVERWRITE_EXISTING = "OVERWRITE_EXISTING" - PORTAL_TITLE = "PORTAL_TITLE" - PORTAL_DESCRIPTION = "PORTAL_DESCRIPTION" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('laspublish') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParameter(ParameterSelection(laspublish.MODE, - self.tr("type of portal"), laspublish.MODES, 1)) - self.addParameter(ParameterBoolean(laspublish.USE_EDL, - self.tr("use Eye Dome Lighting (EDL)"), True)) - self.addParameter(ParameterBoolean(laspublish.SHOW_SKYBOX, - self.tr("show Skybox"), True)) - self.addParameter(ParameterSelection(laspublish.MATERIAL, - self.tr("default material colors on start-up"), laspublish.MATERIALS, 0)) - self.addParameter(ParameterFile(laspublish.PORTAL_DIRECTORY, - self.tr("portal output directory"), True, False)) - self.addParameter(ParameterSelection(laspublish.COPY_OR_MOVE, - self.tr("copy or move source LiDAR files into portal (only for download portals)"), laspublish.COPY_OR_MOVE_OPTIONS, 2)) - self.addParameter(ParameterBoolean(laspublish.OVERWRITE_EXISTING, - self.tr("overwrite existing files"), True)) - self.addParameter(ParameterString(laspublish.PORTAL_HTML_PAGE, - self.tr("portal HTML page"), "portal.html", False)) - self.addParameter(ParameterString(laspublish.PORTAL_TITLE, - self.tr("portal title"), "My LiDAR Portal")) - self.addParameter(ParameterString(laspublish.PORTAL_DESCRIPTION, - self.tr("portal description"))) - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "laspublish")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - mode = self.getParameterValue(laspublish.MODE) - if (mode == 0): - commands.append("-only_3D") - elif (mode == 2): - commands.append("-only_2D") - material = self.getParameterValue(laspublish.MATERIAL) - commands.append("-" + laspublish.MATERIALS[material]) - if not self.getParameterValue(laspublish.USE_EDL): - commands.append("-no_edl") - if not self.getParameterValue(laspublish.SHOW_SKYBOX): - commands.append("-no_skybox") - portal_directory = self.getParameterValue(laspublish.PORTAL_DIRECTORY) - if portal_directory != "": - commands.append("-odir") - commands.append('"' + portal_directory + '"') - copy_or_move = self.getParameterValue(laspublish.COPY_OR_MOVE) - if (copy_or_move == 0): - commands.append("-copy_source_files") - elif (copy_or_move == 1): - commands.append("-move_source_files") - commands.append("-really_move") - if self.getParameterValue(laspublish.OVERWRITE_EXISTING): - commands.append("-overwrite") - portal_html_page = self.getParameterValue(laspublish.PORTAL_HTML_PAGE) - if portal_html_page != "": - commands.append("-o") - commands.append('"' + portal_html_page + '"') - title = self.getParameterValue(laspublish.PORTAL_TITLE) - if title != "": - commands.append("-title") - commands.append('"' + title + '"') - description = self.getParameterValue(laspublish.PORTAL_DESCRIPTION) - if description != "": - commands.append("-description") - commands.append('"' + description + '"') - commands.append("-olaz") - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/laspublishPro.py b/python/plugins/processing/algs/lidar/lastools/laspublishPro.py deleted file mode 100644 index 208294954082..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/laspublishPro.py +++ /dev/null @@ -1,125 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - laspublishPro.py - --------------------- - Date : May 2016 - Copyright : (C) 2016 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from __future__ import absolute_import -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Martin Isenburg' -__date__ = 'May 2016' -__copyright__ = '(C) 2016, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterString -from processing.core.parameters import ParameterFile - - -class laspublishPro(LAStoolsAlgorithm): - - MODE = "MODE" - MODES = ["3D only", "3D + download map", "download map only"] - DIR = "DIR" - SHOW_SKYBOX = "SHOW_SKYBOX" - USE_EDL = "USE_EDL" - MATERIAL = "MATERIAL" - MATERIALS = ["elevation", "intensity", "return_number", "point_source", "rgb"] - COPY_OR_MOVE = "COPY_OR_MOVE" - COPY_OR_MOVE_OPTIONS = ["copy into portal dir", "move into portal dir", "neither"] - PORTAL_DIRECTORY = "PORTAL_DIRECTORY" - PORTAL_HTML_PAGE = "PORTAL_HTML_PAGE" - OVERWRITE_EXISTING = "OVERWRITE_EXISTING" - PORTAL_TITLE = "PORTAL_TITLE" - PORTAL_DESCRIPTION = "PORTAL_DESCRIPTION" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('laspublishPro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersVerboseGUI() - self.addParametersPointInputFolderGUI() - self.addParameter(ParameterSelection(laspublishPro.MODE, - self.tr("type of portal"), laspublishPro.MODES, 1)) - self.addParameter(ParameterBoolean(laspublishPro.USE_EDL, - self.tr("use Eye Dome Lighting (EDL)"), True)) - self.addParameter(ParameterBoolean(laspublishPro.SHOW_SKYBOX, - self.tr("show Skybox"), True)) - self.addParameter(ParameterSelection(laspublishPro.MATERIAL, - self.tr("default material colors on start-up"), laspublishPro.MATERIALS, 0)) - self.addParameter(ParameterFile(laspublishPro.PORTAL_DIRECTORY, - self.tr("portal output directory"), True, False)) - self.addParameter(ParameterSelection(laspublishPro.COPY_OR_MOVE, - self.tr("copy or move source LiDAR files into portal (only for download portals)"), laspublishPro.COPY_OR_MOVE_OPTIONS, 2)) - self.addParameter(ParameterBoolean(laspublishPro.OVERWRITE_EXISTING, - self.tr("overwrite existing files"), True)) - self.addParameter(ParameterString(laspublishPro.PORTAL_HTML_PAGE, - self.tr("portal HTML page"), "portal.html", False)) - self.addParameter(ParameterString(laspublishPro.PORTAL_TITLE, - self.tr("portal title"), "My LiDAR Portal")) - self.addParameter(ParameterString(laspublishPro.PORTAL_DESCRIPTION, - self.tr("portal description"))) - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "laspublish")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - mode = self.getParameterValue(laspublishPro.MODE) - if (mode == 0): - commands.append("-only_3D") - elif (mode == 2): - commands.append("-only_2D") - material = self.getParameterValue(laspublishPro.MATERIAL) - commands.append("-" + laspublishPro.MATERIALS[material]) - if not self.getParameterValue(laspublishPro.USE_EDL): - commands.append("-no_edl") - if not self.getParameterValue(laspublishPro.SHOW_SKYBOX): - commands.append("-no_skybox") - portal_directory = self.getParameterValue(laspublishPro.PORTAL_DIRECTORY) - if portal_directory != "": - commands.append("-odir") - commands.append('"' + portal_directory + '"') - copy_or_move = self.getParameterValue(laspublishPro.COPY_OR_MOVE) - if (copy_or_move == 0): - commands.append("-copy_source_files") - elif (copy_or_move == 1): - commands.append("-move_source_files") - commands.append("-really_move") - if self.getParameterValue(laspublishPro.OVERWRITE_EXISTING): - commands.append("-overwrite") - portal_html_page = self.getParameterValue(laspublishPro.PORTAL_HTML_PAGE) - if portal_html_page != "": - commands.append("-o") - commands.append('"' + portal_html_page + '"') - title = self.getParameterValue(laspublishPro.PORTAL_TITLE) - if title != "": - commands.append("-title") - commands.append('"' + title + '"') - description = self.getParameterValue(laspublishPro.PORTAL_DESCRIPTION) - if description != "": - commands.append("-description") - commands.append('"' + description + '"') - commands.append("-olaz") - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasquery.py b/python/plugins/processing/algs/lidar/lastools/lasquery.py deleted file mode 100644 index 2b778849a162..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasquery.py +++ /dev/null @@ -1,80 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasinfo.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com - --------------------- - Date : March 2014 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'March 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from processing.core.parameters import ParameterExtent -from .LAStoolsAlgorithm import LAStoolsAlgorithm -from qgis.core import QgsMapLayer, QgsProject - - -class lasquery(LAStoolsAlgorithm): - - AOI = "AOI" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasquery') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParameter(ParameterExtent(self.AOI, self.tr('area of interest'), optional=False)) - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasview")] - self.addParametersVerboseCommands(commands) - - # get area-of-interest - aoi = str(self.getParameterValue(self.AOI)) - aoiCoords = aoi.split(',') - - # get layers - layers = QgsProject.instance().mapLayers() - - # loop over layers - for name, layer in list(layers.items()): - layerType = layer.type() - if layerType == QgsMapLayer.VectorLayer: - shp_file_name = layer.source() - file_name = shp_file_name[:-4] + ".laz" - commands.append('-i') - commands.append(file_name) - - commands.append("-files_are_flightlines") - commands.append('-inside') - commands.append(aoiCoords[0]) - commands.append(aoiCoords[2]) - commands.append(aoiCoords[1]) - commands.append(aoiCoords[3]) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lassort.py b/python/plugins/processing/algs/lidar/lastools/lassort.py deleted file mode 100644 index 35f88c1a60de..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lassort.py +++ /dev/null @@ -1,63 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lassort.py - --------------------- - Date : September 2013 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Martin Isenburg' -__date__ = 'September 2013' -__copyright__ = '(C) 2013, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean - - -class lassort(LAStoolsAlgorithm): - - BY_GPS_TIME = "BY_GPS_TIME" - BY_POINT_SOURCE_ID = "BY_POINT_SOURCE_ID" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lassort') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParameter(ParameterBoolean(lassort.BY_GPS_TIME, - self.tr("sort by GPS time"), False)) - self.addParameter(ParameterBoolean(lassort.BY_POINT_SOURCE_ID, - self.tr("sort by point source ID"), False)) - self.addParametersPointOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lassort")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - if self.getParameterValue(lassort.BY_GPS_TIME): - commands.append("-gps_time") - if self.getParameterValue(lassort.BY_POINT_SOURCE_ID): - commands.append("-point_source") - self.addParametersPointOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lassortPro.py b/python/plugins/processing/algs/lidar/lastools/lassortPro.py deleted file mode 100644 index 6529b218a2ec..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lassortPro.py +++ /dev/null @@ -1,69 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lassortPro.py - --------------------- - Date : October 2014 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean - - -class lassortPro(LAStoolsAlgorithm): - - BY_GPS_TIME = "BY_GPS_TIME" - BY_POINT_SOURCE_ID = "BY_POINT_SOURCE_ID" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lassortPro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParameter(ParameterBoolean(lassortPro.BY_GPS_TIME, - self.tr("sort by GPS time"), False)) - self.addParameter(ParameterBoolean(lassortPro.BY_POINT_SOURCE_ID, - self.tr("sort by point source ID"), False)) - self.addParametersOutputDirectoryGUI() - self.addParametersOutputAppendixGUI() - self.addParametersPointOutputFormatGUI() - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lassort")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - if self.getParameterValue(lassortPro.BY_GPS_TIME): - commands.append("-gps_time") - if self.getParameterValue(lassortPro.BY_POINT_SOURCE_ID): - commands.append("-point_source") - self.addParametersOutputDirectoryCommands(commands) - self.addParametersOutputAppendixCommands(commands) - self.addParametersPointOutputFormatCommands(commands) - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lassplit.py b/python/plugins/processing/algs/lidar/lastools/lassplit.py deleted file mode 100644 index 3f7f193e90dd..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lassplit.py +++ /dev/null @@ -1,78 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lassplit.py - --------------------- - Date : March 2014 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'March 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterSelection - - -class lassplit(LAStoolsAlgorithm): - - DIGITS = "DIGITS" - OPERATION = "OPERATION" - OPERATIONS = ["by_flightline", "by_classification", "by_gps_time_interval", "by_intensity_interval", "by_x_interval", "by_y_interval", "by_z_interval", "by_scan_angle_interval", "by_user_data_interval", "every_x_points", "recover_flightlines"] - INTERVAL = "INTERVAL" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lassplit') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParameter(ParameterNumber(lassplit.DIGITS, - self.tr("number of digits for file names"), 0, None, 5)) - self.addParameter(ParameterSelection(lassplit.OPERATION, - self.tr("how to split"), lassplit.OPERATIONS, 0)) - self.addParameter(ParameterNumber(lassplit.INTERVAL, - self.tr("interval or number"), 0, None, 5)) - self.addParametersPointOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lassplit")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - digits = self.getParameterValue(lassplit.DIGITS) - if digits != 5: - commands.append("-digits") - commands.append(str(digits)) - operation = self.getParameterValue(lassplit.OPERATION) - if operation != 0: - if operation == 9: - commands.append("-split") - else: - commands.append("-" + lassplit.OPERATIONS[operation]) - if operation > 1 and operation < 10: - interval = self.getParameterValue(lassplit.INTERVAL) - commands.append(str(interval)) - self.addParametersPointOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasthin.py b/python/plugins/processing/algs/lidar/lastools/lasthin.py deleted file mode 100644 index 0277bbf88b76..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasthin.py +++ /dev/null @@ -1,93 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasthin.py - --------------------- - Date : September 2013 and May 2016 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'September 2013' -__copyright__ = '(C) 2013, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterSelection - - -class lasthin(LAStoolsAlgorithm): - - THIN_STEP = "THIN_STEP" - OPERATION = "OPERATION" - OPERATIONS = ["lowest", "random", "highest", "central", "adaptive", "contours"] - THRESHOLD_OR_INTERVAL = "THRESHOLD_OR_INTERVAL" - WITHHELD = "WITHHELD" - CLASSIFY_AS = "CLASSIFY_AS" - CLASSIFY_AS_CLASS = "CLASSIFY_AS_CLASS" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasthin') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParametersIgnoreClass1GUI() - self.addParametersIgnoreClass2GUI() - self.addParameter(ParameterNumber(lasthin.THIN_STEP, - self.tr("size of grid used for thinning"), 0, None, 1.0)) - self.addParameter(ParameterSelection(lasthin.OPERATION, - self.tr("keep particular point per cell"), lasthin.OPERATIONS, 0)) - self.addParameter(ParameterNumber(lasthin.THRESHOLD_OR_INTERVAL, - self.tr("vertical threshold or contour intervals (only for 'adaptive' or 'contours' thinning)"), 0, None, 0.1)) - self.addParameter(ParameterBoolean(lasthin.WITHHELD, - self.tr("mark thinned-away points as withheld"), False)) - self.addParameter(ParameterBoolean(lasthin.CLASSIFY_AS, - self.tr("classify surviving points as class"), False)) - self.addParameter(ParameterNumber(lasthin.CLASSIFY_AS_CLASS, - self.tr("class"), 0, None, 8)) - self.addParametersPointOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasthin")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - self.addParametersIgnoreClass1Commands(commands) - self.addParametersIgnoreClass2Commands(commands) - step = self.getParameterValue(lasthin.THIN_STEP) - if step != 0.0: - commands.append("-step") - commands.append(str(step)) - operation = self.getParameterValue(lasthin.OPERATION) - if operation != 0: - commands.append("-" + self.OPERATIONS[operation]) - if (operation >= 4): - commands.append(str(self.getParameterValue(lasthin.THRESHOLD_OR_INTERVAL))) - if self.getParameterValue(lasthin.WITHHELD): - commands.append("-withheld") - if self.getParameterValue(lasthin.CLASSIFY_AS): - commands.append("-classify_as") - commands.append(str(self.getParameterValue(lasthin.CLASSIFY_AS_CLASS))) - self.addParametersPointOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasthinPro.py b/python/plugins/processing/algs/lidar/lastools/lasthinPro.py deleted file mode 100644 index 1dc42d9fd156..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasthinPro.py +++ /dev/null @@ -1,99 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasthinPro.py - --------------------- - Date : October 2014 and May 2016 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterSelection - - -class lasthinPro(LAStoolsAlgorithm): - - THIN_STEP = "THIN_STEP" - OPERATION = "OPERATION" - OPERATIONS = ["lowest", "random", "highest", "central", "adaptive", "contours"] - THRESHOLD_OR_INTERVAL = "THRESHOLD_OR_INTERVAL" - WITHHELD = "WITHHELD" - CLASSIFY_AS = "CLASSIFY_AS" - CLASSIFY_AS_CLASS = "CLASSIFY_AS_CLASS" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasthinPro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParametersIgnoreClass1GUI() - self.addParametersIgnoreClass2GUI() - self.addParameter(ParameterNumber(lasthinPro.THIN_STEP, - self.tr("size of grid used for thinning"), 0, None, 1.0)) - self.addParameter(ParameterSelection(lasthinPro.OPERATION, - self.tr("keep particular point per cell"), lasthinPro.OPERATIONS, 0)) - self.addParameter(ParameterNumber(lasthinPro.THRESHOLD_OR_INTERVAL, - self.tr("vertical threshold or contour intervals (only for 'adaptive' or 'contours' thinning)"), 0, None, 0.1)) - self.addParameter(ParameterBoolean(lasthinPro.WITHHELD, - self.tr("mark thinned-away points as withheld"), False)) - self.addParameter(ParameterBoolean(lasthinPro.CLASSIFY_AS, - self.tr("classify surviving points as class"), False)) - self.addParameter(ParameterNumber(lasthinPro.CLASSIFY_AS_CLASS, - self.tr("class"), 0, None, 8)) - self.addParametersOutputDirectoryGUI() - self.addParametersOutputAppendixGUI() - self.addParametersPointOutputFormatGUI() - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasthin")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - self.addParametersIgnoreClass1Commands(commands) - self.addParametersIgnoreClass2Commands(commands) - step = self.getParameterValue(lasthinPro.THIN_STEP) - if step != 0.0: - commands.append("-step") - commands.append(str(step)) - operation = self.getParameterValue(lasthinPro.OPERATION) - if (operation != 0): - commands.append("-" + self.OPERATIONS[operation]) - if (operation >= 4): - commands.append(str(self.getParameterValue(lasthinPro.THRESHOLD_OR_INTERVAL))) - if self.getParameterValue(lasthinPro.WITHHELD): - commands.append("-withheld") - if self.getParameterValue(lasthinPro.CLASSIFY_AS): - commands.append("-classify_as") - commands.append(str(self.getParameterValue(lasthinPro.CLASSIFY_AS_CLASS))) - self.addParametersOutputDirectoryCommands(commands) - self.addParametersOutputAppendixCommands(commands) - self.addParametersPointOutputFormatCommands(commands) - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lastile.py b/python/plugins/processing/algs/lidar/lastools/lastile.py deleted file mode 100644 index 01b05ae7096a..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lastile.py +++ /dev/null @@ -1,80 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lastile.py - --------------------- - Date : September 2013 and May 2016 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'September 2013' -__copyright__ = '(C) 2013, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterNumber - - -class lastile(LAStoolsAlgorithm): - - TILE_SIZE = "TILE_SIZE" - BUFFER = "BUFFER" - REVERSIBLE = "REVERSIBLE" - FLAG_AS_WITHHELD = "FLAG_AS_WITHHELD" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lastile') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParameter(ParameterNumber(lastile.TILE_SIZE, - self.tr("tile size (side length of square tile)"), - 0.0, None, 1000.0)) - self.addParameter(ParameterNumber(lastile.BUFFER, - self.tr("buffer around each tile"), - 0.0, None, 25.0)) - self.addParameter(ParameterBoolean(lastile.FLAG_AS_WITHHELD, - self.tr("flag buffer points as 'withheld' for easier removal later"), True)) - self.addParameter(ParameterBoolean(lastile.REVERSIBLE, - self.tr("make tiling reversible (advanced, usually not needed)"), False)) - self.addParametersPointOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lastile")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - tile_size = self.getParameterValue(lastile.TILE_SIZE) - commands.append("-tile_size") - commands.append(str(tile_size)) - buffer = self.getParameterValue(lastile.BUFFER) - if buffer != 0.0: - commands.append("-buffer") - commands.append(str(buffer)) - if self.getParameterValue(lastile.FLAG_AS_WITHHELD): - commands.append("-flag_as_withheld") - if self.getParameterValue(lastile.REVERSIBLE): - commands.append("-reversible") - self.addParametersPointOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lastilePro.py b/python/plugins/processing/algs/lidar/lastools/lastilePro.py deleted file mode 100644 index 02f374cf91e5..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lastilePro.py +++ /dev/null @@ -1,94 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lastilePro.py - --------------------- - Date : April 2014 and May 2016 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'April 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterString - - -class lastilePro(LAStoolsAlgorithm): - - TILE_SIZE = "TILE_SIZE" - BUFFER = "BUFFER" - FLAG_AS_WITHHELD = "FLAG_AS_WITHHELD" - EXTRA_PASS = "EXTRA_PASS" - BASE_NAME = "BASE_NAME" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lastilePro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParametersFilesAreFlightlinesGUI() - self.addParametersApplyFileSourceIdGUI() - self.addParameter(ParameterNumber(lastilePro.TILE_SIZE, - self.tr("tile size (side length of square tile)"), - 0.0, None, 1000.0)) - self.addParameter(ParameterNumber(lastilePro.BUFFER, - self.tr("buffer around each tile (avoids edge artifacts)"), - 0.0, None, 25.0)) - self.addParameter(ParameterBoolean(lastilePro.FLAG_AS_WITHHELD, - self.tr("flag buffer points as 'withheld' for easier removal later"), True)) - self.addParameter(ParameterBoolean(lastilePro.EXTRA_PASS, - self.tr("more than 2000 tiles"), False)) - self.addParametersOutputDirectoryGUI() - self.addParameter(ParameterString(lastilePro.BASE_NAME, - self.tr("tile base name (using sydney.laz creates sydney_274000_4714000.laz)"))) - self.addParametersPointOutputFormatGUI() - self.addParametersAdditionalGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lastile")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - self.addParametersFilesAreFlightlinesCommands(commands) - self.addParametersApplyFileSourceIdCommands(commands) - tile_size = self.getParameterValue(lastilePro.TILE_SIZE) - commands.append("-tile_size") - commands.append(str(tile_size)) - buffer = self.getParameterValue(lastilePro.BUFFER) - if buffer != 0.0: - commands.append("-buffer") - commands.append(str(buffer)) - if self.getParameterValue(lastilePro.FLAG_AS_WITHHELD): - commands.append("-flag_as_withheld") - if self.getParameterValue(lastilePro.EXTRA_PASS): - commands.append("-extra_pass") - self.addParametersOutputDirectoryCommands(commands) - base_name = self.getParameterValue(lastilePro.BASE_NAME) - if base_name is not None: - commands.append("-o") - commands.append('"' + base_name + '"') - self.addParametersPointOutputFormatCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasvalidate.py b/python/plugins/processing/algs/lidar/lastools/lasvalidate.py deleted file mode 100644 index 20c72ce660b5..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasvalidate.py +++ /dev/null @@ -1,60 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasvalidate.py - --------------------- - Date : September 2013 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Martin Isenburg' -__date__ = 'September 2013' -__copyright__ = '(C) 2013, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean -from processing.core.outputs import OutputFile - - -class lasvalidate(LAStoolsAlgorithm): - - ONE_REPORT_PER_FILE = "ONE_REPORT_PER_FILE" - OUTPUT = "OUTPUT" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasvalidate') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersPointInputGUI() - self.addParameter(ParameterBoolean(lasvalidate.ONE_REPORT_PER_FILE, - self.tr("save report to '*_LVS.xml'"), False)) - self.addOutput(OutputFile(lasvalidate.OUTPUT, self.tr("Output XML file"))) - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasvalidate")] - self.addParametersPointInputCommands(commands) - if self.getParameterValue(lasvalidate.ONE_REPORT_PER_FILE): - commands.append("-oxml") - else: - commands.append("-o") - commands.append(self.getOutputValue(lasvalidate.OUTPUT)) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasvalidatePro.py b/python/plugins/processing/algs/lidar/lastools/lasvalidatePro.py deleted file mode 100644 index 8ce82ead98eb..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasvalidatePro.py +++ /dev/null @@ -1,60 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasvalidatePro.py - --------------------- - Date : October 2014 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean -from processing.core.outputs import OutputFile - - -class lasvalidatePro(LAStoolsAlgorithm): - - ONE_REPORT_PER_FILE = "ONE_REPORT_PER_FILE" - OUTPUT = "OUTPUT" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasvalidatePro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParameter(ParameterBoolean(lasvalidatePro.ONE_REPORT_PER_FILE, - self.tr("generate one '*_LVS.xml' report per file"), False)) - self.addOutput(OutputFile(lasvalidatePro.OUTPUT, self.tr("Output XML file"))) - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasvalidate")] - self.addParametersPointInputFolderCommands(commands) - if self.getParameterValue(lasvalidatePro.ONE_REPORT_PER_FILE): - commands.append("-oxml") - else: - commands.append("-o") - commands.append(self.getOutputValue(lasvalidatePro.OUTPUT)) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasview.py b/python/plugins/processing/algs/lidar/lastools/lasview.py deleted file mode 100644 index 59e3e1fc2379..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasview.py +++ /dev/null @@ -1,81 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasview.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com - --------------------- - Date : September 2013 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from __future__ import print_function -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'September 2013' -__copyright__ = '(C) 2013, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterNumber - - -class lasview(LAStoolsAlgorithm): - - POINTS = "POINTS" - - SIZE = "SIZE" - SIZES = ["1024 768", "800 600", "1200 900", "1200 400", "1550 900", "1550 1150"] - - COLORING = "COLORING" - COLORINGS = ["default", "classification", "elevation1", "elevation2", "intensity", "return", "flightline", "rgb"] - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasview') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParameter(ParameterNumber(lasview.POINTS, - self.tr("max number of points sampled"), 100000, 20000000, 5000000)) - self.addParameter(ParameterSelection(lasview.COLORING, - self.tr("color by"), lasview.COLORINGS, 0)) - self.addParameter(ParameterSelection(lasview.SIZE, - self.tr("window size (x y) in pixels"), lasview.SIZES, 0)) - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasview")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - points = self.getParameterValue(lasview.POINTS) - commands.append("-points " + str(points)) - coloring = self.getParameterValue(lasview.COLORING) - if coloring != 0: - commands.append("-color_by_" + lasview.COLORINGS[coloring]) - size = self.getParameterValue(lasview.SIZE) - if size != 0: - commands.append("-win " + lasview.SIZES[size]) - self.addParametersAdditionalCommands(commands) - - # fix_print_with_import - print(commands) - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/lasviewPro.py b/python/plugins/processing/algs/lidar/lastools/lasviewPro.py deleted file mode 100644 index 8facdfa34bf5..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/lasviewPro.py +++ /dev/null @@ -1,76 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - lasviewPro.py - --------------------- - Date : October 2014 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterSelection -from processing.core.parameters import ParameterNumber - - -class lasviewPro(LAStoolsAlgorithm): - - POINTS = "POINTS" - - SIZE = "SIZE" - SIZES = ["1024 768", "800 600", "1200 900", "1200 400", "1550 900", "1550 1150"] - - COLORING = "COLORING" - COLORINGS = ["default", "classification", "elevation1", "elevation2", "intensity", "return", "flightline", "rgb"] - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('lasviewPro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParametersFilesAreFlightlinesGUI() - self.addParameter(ParameterNumber(lasviewPro.POINTS, - self.tr("max number of points sampled"), 100000, 20000000, 5000000)) - self.addParameter(ParameterSelection(lasviewPro.COLORING, - self.tr("color by"), lasviewPro.COLORINGS, 0)) - self.addParameter(ParameterSelection(lasviewPro.SIZE, - self.tr("window size (x y) in pixels"), lasviewPro.SIZES, 0)) - self.addParametersAdditionalGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "lasview")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - self.addParametersFilesAreFlightlinesCommands(commands) - points = self.getParameterValue(lasviewPro.POINTS) - commands.append("-points " + str(points)) - self.addParametersAdditionalCommands(commands) - coloring = self.getParameterValue(lasviewPro.COLORING) - if coloring != 0: - commands.append("-color_by_" + lasviewPro.COLORINGS[coloring]) - size = self.getParameterValue(lasviewPro.SIZE) - if size != 0: - commands.append("-win " + lasviewPro.SIZES[size]) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/laszip.py b/python/plugins/processing/algs/lidar/lastools/laszip.py deleted file mode 100644 index 6048431fa12b..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/laszip.py +++ /dev/null @@ -1,71 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - laszip.py - --------------------- - Date : September 2013 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Martin Isenburg' -__date__ = 'September 2013' -__copyright__ = '(C) 2013, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean - - -class laszip(LAStoolsAlgorithm): - - REPORT_SIZE = "REPORT_SIZE" - CREATE_LAX = "CREATE_LAX" - APPEND_LAX = "APPEND_LAX" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('laszip') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParametersPointInputGUI() - self.addParameter(ParameterBoolean(laszip.REPORT_SIZE, - self.tr("only report size"), False)) - self.addParameter(ParameterBoolean(laszip.CREATE_LAX, - self.tr("create spatial indexing file (*.lax)"), False)) - self.addParameter(ParameterBoolean(laszip.APPEND_LAX, - self.tr("append *.lax into *.laz file"), False)) - self.addParametersPointOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - if (LAStoolsUtils.hasWine()): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "laszip.exe")] - else: - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "laszip")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputCommands(commands) - if self.getParameterValue(laszip.REPORT_SIZE): - commands.append("-size") - if self.getParameterValue(laszip.CREATE_LAX): - commands.append("-lax") - if self.getParameterValue(laszip.APPEND_LAX): - commands.append("-append") - self.addParametersPointOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/laszipPro.py b/python/plugins/processing/algs/lidar/lastools/laszipPro.py deleted file mode 100644 index 9a9a12b682eb..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/laszipPro.py +++ /dev/null @@ -1,78 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - laszipPro.py - --------------------- - Date : October 2014 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterBoolean - - -class laszipPro(LAStoolsAlgorithm): - - REPORT_SIZE = "REPORT_SIZE" - CREATE_LAX = "CREATE_LAX" - APPEND_LAX = "APPEND_LAX" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('laszipPro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersPointInputFolderGUI() - self.addParameter(ParameterBoolean(laszipPro.REPORT_SIZE, - self.tr("only report size"), False)) - self.addParameter(ParameterBoolean(laszipPro.CREATE_LAX, - self.tr("create spatial indexing file (*.lax)"), False)) - self.addParameter(ParameterBoolean(laszipPro.APPEND_LAX, - self.tr("append *.lax into *.laz file"), False)) - self.addParametersOutputDirectoryGUI() - self.addParametersOutputAppendixGUI() - self.addParametersPointOutputFormatGUI() - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - if (LAStoolsUtils.hasWine()): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "laszip.exe")] - else: - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "laszip")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - if self.getParameterValue(laszipPro.REPORT_SIZE): - commands.append("-size") - if self.getParameterValue(laszipPro.CREATE_LAX): - commands.append("-lax") - if self.getParameterValue(laszipPro.APPEND_LAX): - commands.append("-append") - self.addParametersCoresCommands(commands) - self.addParametersOutputDirectoryCommands(commands) - self.addParametersOutputAppendixCommands(commands) - self.addParametersPointOutputFormatCommands(commands) - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/shp2las.py b/python/plugins/processing/algs/lidar/lastools/shp2las.py deleted file mode 100644 index 038fd0698999..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/shp2las.py +++ /dev/null @@ -1,69 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - shp2las.py - --------------------- - Date : September 2013 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'September 2013' -__copyright__ = '(C) 2013, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterFile - - -class shp2las(LAStoolsAlgorithm): - - INPUT = "INPUT" - SCALE_FACTOR_XY = "SCALE_FACTOR_XY" - SCALE_FACTOR_Z = "SCALE_FACTOR_Z" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('shp2las') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParameter(ParameterFile(shp2las.INPUT, - self.tr("Input SHP file"))) - self.addParameter(ParameterNumber(shp2las.SCALE_FACTOR_XY, - self.tr("resolution of x and y coordinate"), 0, None, 0.01)) - self.addParameter(ParameterNumber(shp2las.SCALE_FACTOR_Z, - self.tr("resolution of z coordinate"), 0, None, 0.01)) - self.addParametersPointOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "shp2las")] - self.addParametersVerboseCommands(commands) - commands.append("-i") - commands.append(self.getParameterValue(shp2las.INPUT)) - scale_factor_xy = self.getParameterValue(shp2las.SCALE_FACTOR_XY) - scale_factor_z = self.getParameterValue(shp2las.SCALE_FACTOR_Z) - if scale_factor_xy != 0.01 or scale_factor_z != 0.01: - commands.append("-set_scale_factor") - commands.append(str(scale_factor_xy) + " " + str(scale_factor_xy) + " " + str(scale_factor_z)) - self.addParametersPointOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/txt2las.py b/python/plugins/processing/algs/lidar/lastools/txt2las.py deleted file mode 100644 index 7dd9e1637462..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/txt2las.py +++ /dev/null @@ -1,121 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - txt2las.py - --------------------- - Date : September 2013 and May 2016 - Copyright : (C) 2013 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'September 2013' -__copyright__ = '(C) 2013, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterString -from processing.core.parameters import ParameterFile -from processing.core.parameters import ParameterSelection - - -class txt2las(LAStoolsAlgorithm): - - INPUT = "INPUT" - PARSE = "PARSE" - SKIP = "SKIP" - SCALE_FACTOR_XY = "SCALE_FACTOR_XY" - SCALE_FACTOR_Z = "SCALE_FACTOR_Z" - - STATE_PLANES = ["---", "AK_10", "AK_2", "AK_3", "AK_4", "AK_5", "AK_6", "AK_7", "AK_8", "AK_9", "AL_E", "AL_W", "AR_N", "AR_S", "AZ_C", "AZ_E", "AZ_W", "CA_I", "CA_II", "CA_III", "CA_IV", "CA_V", "CA_VI", "CA_VII", "CO_C", "CO_N", "CO_S", "CT", "DE", "FL_E", "FL_N", "FL_W", "GA_E", "GA_W", "HI_1", "HI_2", "HI_3", "HI_4", "HI_5", "IA_N", "IA_S", "ID_C", "ID_E", "ID_W", "IL_E", "IL_W", "IN_E", "IN_W", "KS_N", "KS_S", "KY_N", "KY_S", "LA_N", "LA_S", "MA_I", "MA_M", "MD", "ME_E", "ME_W", "MI_C", "MI_N", "MI_S", "MN_C", "MN_N", "MN_S", "MO_C", "MO_E", "MO_W", "MS_E", "MS_W", "MT_C", "MT_N", "MT_S", "NC", "ND_N", "ND_S", "NE_N", "NE_S", "NH", "NJ", "NM_C", "NM_E", "NM_W", "NV_C", "NV_E", "NV_W", "NY_C", "NY_E", "NY_LI", "NY_W", "OH_N", "OH_S", "OK_N", "OK_S", "OR_N", "OR_S", "PA_N", "PA_S", "PR", "RI", "SC_N", "SC_S", "SD_N", "SD_S", "St.Croix", "TN", "TX_C", "TX_N", "TX_NC", "TX_S", "TX_SC", "UT_C", "UT_N", "UT_S", "VA_N", "VA_S", "VT", "WA_N", "WA_S", "WI_C", "WI_N", "WI_S", "WV_N", "WV_S", "WY_E", "WY_EC", "WY_W", "WY_WC"] - - UTM_ZONES = ["---", "1 (north)", "2 (north)", "3 (north)", "4 (north)", "5 (north)", "6 (north)", "7 (north)", "8 (north)", "9 (north)", "10 (north)", "11 (north)", "12 (north)", "13 (north)", "14 (north)", "15 (north)", "16 (north)", "17 (north)", "18 (north)", "19 (north)", "20 (north)", "21 (north)", "22 (north)", "23 (north)", "24 (north)", "25 (north)", "26 (north)", "27 (north)", "28 (north)", "29 (north)", "30 (north)", "31 (north)", "32 (north)", "33 (north)", "34 (north)", "35 (north)", "36 (north)", "37 (north)", "38 (north)", "39 (north)", "40 (north)", "41 (north)", "42 (north)", "43 (north)", "44 (north)", "45 (north)", "46 (north)", "47 (north)", "48 (north)", "49 (north)", "50 (north)", "51 (north)", "52 (north)", "53 (north)", "54 (north)", "55 (north)", "56 (north)", "57 (north)", "58 (north)", "59 (north)", "60 (north)", "1 (south)", "2 (south)", "3 (south)", "4 (south)", "5 (south)", "6 (south)", "7 (south)", "8 (south)", "9 (south)", "10 (south)", "11 (south)", "12 (south)", "13 (south)", "14 (south)", "15 (south)", "16 (south)", "17 (south)", "18 (south)", "19 (south)", "20 (south)", "21 (south)", "22 (south)", "23 (south)", "24 (south)", "25 (south)", "26 (south)", "27 (south)", "28 (south)", "29 (south)", "30 (south)", "31 (south)", "32 (south)", "33 (south)", "34 (south)", "35 (south)", "36 (south)", "37 (south)", "38 (south)", "39 (south)", "40 (south)", "41 (south)", "42 (south)", "43 (south)", "44 (south)", "45 (south)", "46 (south)", "47 (south)", "48 (south)", "49 (south)", "50 (south)", "51 (south)", "52 (south)", "53 (south)", "54 (south)", "55 (south)", "56 (south)", "57 (south)", "58 (south)", "59 (south)", "60 (south)"] - - PROJECTIONS = ["---", "utm", "sp83", "sp27", "longlat", "latlong", "ecef"] - - PROJECTION = "PROJECTION" - UTM = "UTM" - SP = "SP" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('txt2las') - self.group, self.i18n_group = self.trAlgorithm('LAStools') - self.addParametersVerboseGUI() - self.addParameter(ParameterFile(txt2las.INPUT, - self.tr("Input ASCII file"))) - self.addParameter(ParameterString(txt2las.PARSE, - self.tr("parse lines as", "xyz"))) - self.addParameter(ParameterNumber(txt2las.SKIP, - self.tr("skip the first n lines"), 0, None, 0)) - self.addParameter(ParameterNumber(txt2las.SCALE_FACTOR_XY, - self.tr("resolution of x and y coordinate"), 0, None, 0.01)) - self.addParameter(ParameterNumber(txt2las.SCALE_FACTOR_Z, - self.tr("resolution of z coordinate"), 0, None, 0.01)) - self.addParameter(ParameterSelection(txt2las.PROJECTION, - self.tr("projection"), txt2las.PROJECTIONS, 0)) - self.addParameter(ParameterSelection(txt2las.UTM, - self.tr("utm zone"), txt2las.UTM_ZONES, 0)) - self.addParameter(ParameterSelection(txt2las.SP, - self.tr("state plane code"), txt2las.STATE_PLANES, 0)) - self.addParametersPointOutputGUI() - self.addParametersAdditionalGUI() - - def processAlgorithm(self, feedback): - if (LAStoolsUtils.hasWine()): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "txt2las.exe")] - else: - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "txt2las")] - self.addParametersVerboseCommands(commands) - commands.append("-i") - commands.append(self.getParameterValue(txt2las.INPUT)) - parse_string = self.getParameterValue(txt2las.PARSE) - if parse_string != "xyz": - commands.append("-parse") - commands.append(parse_string) - skip = self.getParameterValue(txt2las.SKIP) - if parse_string != 0: - commands.append("-skip") - commands.append(str(skip)) - scale_factor_xy = self.getParameterValue(txt2las.SCALE_FACTOR_XY) - scale_factor_z = self.getParameterValue(txt2las.SCALE_FACTOR_Z) - if scale_factor_xy != 0.01 or scale_factor_z != 0.01: - commands.append("-set_scale") - commands.append(str(scale_factor_xy) + " " + str(scale_factor_xy) + " " + str(scale_factor_z)) - projection = self.getParameterValue(txt2las.PROJECTION) - if projection != 0: - if projection == 1: - utm_zone = self.getParameterValue(txt2las.UTM) - if utm_zone != 0: - commands.append("-" + txt2las.PROJECTIONS[projection]) - if utm_zone > 60: - commands.append(str(utm_zone - 60) + "M") - else: - commands.append(str(utm_zone) + "N") - elif projection < 4: - sp_code = self.getParameterValue(txt2las.SP) - if sp_code != 0: - commands.append("-" + txt2las.PROJECTIONS[projection]) - commands.append(txt2las.STATE_PLANES[sp_code]) - else: - commands.append("-" + txt2las.PROJECTIONS[projection]) - self.addParametersPointOutputCommands(commands) - self.addParametersAdditionalCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/lidar/lastools/txt2lasPro.py b/python/plugins/processing/algs/lidar/lastools/txt2lasPro.py deleted file mode 100644 index 57380821f840..000000000000 --- a/python/plugins/processing/algs/lidar/lastools/txt2lasPro.py +++ /dev/null @@ -1,123 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - txt2lasPro.py - --------------------- - Date : October 2014 and May 2016 - Copyright : (C) 2014 by Martin Isenburg - Email : martin near rapidlasso point com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import str - -__author__ = 'Martin Isenburg' -__date__ = 'October 2014' -__copyright__ = '(C) 2014, Martin Isenburg' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from .LAStoolsUtils import LAStoolsUtils -from .LAStoolsAlgorithm import LAStoolsAlgorithm - -from processing.core.parameters import ParameterNumber -from processing.core.parameters import ParameterString -from processing.core.parameters import ParameterSelection - - -class txt2lasPro(LAStoolsAlgorithm): - - PARSE = "PARSE" - SKIP = "SKIP" - SCALE_FACTOR_XY = "SCALE_FACTOR_XY" - SCALE_FACTOR_Z = "SCALE_FACTOR_Z" - - STATE_PLANES = ["---", "AK_10", "AK_2", "AK_3", "AK_4", "AK_5", "AK_6", "AK_7", "AK_8", "AK_9", "AL_E", "AL_W", "AR_N", "AR_S", "AZ_C", "AZ_E", "AZ_W", "CA_I", "CA_II", "CA_III", "CA_IV", "CA_V", "CA_VI", "CA_VII", "CO_C", "CO_N", "CO_S", "CT", "DE", "FL_E", "FL_N", "FL_W", "GA_E", "GA_W", "HI_1", "HI_2", "HI_3", "HI_4", "HI_5", "IA_N", "IA_S", "ID_C", "ID_E", "ID_W", "IL_E", "IL_W", "IN_E", "IN_W", "KS_N", "KS_S", "KY_N", "KY_S", "LA_N", "LA_S", "MA_I", "MA_M", "MD", "ME_E", "ME_W", "MI_C", "MI_N", "MI_S", "MN_C", "MN_N", "MN_S", "MO_C", "MO_E", "MO_W", "MS_E", "MS_W", "MT_C", "MT_N", "MT_S", "NC", "ND_N", "ND_S", "NE_N", "NE_S", "NH", "NJ", "NM_C", "NM_E", "NM_W", "NV_C", "NV_E", "NV_W", "NY_C", "NY_E", "NY_LI", "NY_W", "OH_N", "OH_S", "OK_N", "OK_S", "OR_N", "OR_S", "PA_N", "PA_S", "PR", "RI", "SC_N", "SC_S", "SD_N", "SD_S", "St.Croix", "TN", "TX_C", "TX_N", "TX_NC", "TX_S", "TX_SC", "UT_C", "UT_N", "UT_S", "VA_N", "VA_S", "VT", "WA_N", "WA_S", "WI_C", "WI_N", "WI_S", "WV_N", "WV_S", "WY_E", "WY_EC", "WY_W", "WY_WC"] - - UTM_ZONES = ["---", "1 (north)", "2 (north)", "3 (north)", "4 (north)", "5 (north)", "6 (north)", "7 (north)", "8 (north)", "9 (north)", "10 (north)", "11 (north)", "12 (north)", "13 (north)", "14 (north)", "15 (north)", "16 (north)", "17 (north)", "18 (north)", "19 (north)", "20 (north)", "21 (north)", "22 (north)", "23 (north)", "24 (north)", "25 (north)", "26 (north)", "27 (north)", "28 (north)", "29 (north)", "30 (north)", "31 (north)", "32 (north)", "33 (north)", "34 (north)", "35 (north)", "36 (north)", "37 (north)", "38 (north)", "39 (north)", "40 (north)", "41 (north)", "42 (north)", "43 (north)", "44 (north)", "45 (north)", "46 (north)", "47 (north)", "48 (north)", "49 (north)", "50 (north)", "51 (north)", "52 (north)", "53 (north)", "54 (north)", "55 (north)", "56 (north)", "57 (north)", "58 (north)", "59 (north)", "60 (north)", "1 (south)", "2 (south)", "3 (south)", "4 (south)", "5 (south)", "6 (south)", "7 (south)", "8 (south)", "9 (south)", "10 (south)", "11 (south)", "12 (south)", "13 (south)", "14 (south)", "15 (south)", "16 (south)", "17 (south)", "18 (south)", "19 (south)", "20 (south)", "21 (south)", "22 (south)", "23 (south)", "24 (south)", "25 (south)", "26 (south)", "27 (south)", "28 (south)", "29 (south)", "30 (south)", "31 (south)", "32 (south)", "33 (south)", "34 (south)", "35 (south)", "36 (south)", "37 (south)", "38 (south)", "39 (south)", "40 (south)", "41 (south)", "42 (south)", "43 (south)", "44 (south)", "45 (south)", "46 (south)", "47 (south)", "48 (south)", "49 (south)", "50 (south)", "51 (south)", "52 (south)", "53 (south)", "54 (south)", "55 (south)", "56 (south)", "57 (south)", "58 (south)", "59 (south)", "60 (south)"] - - PROJECTIONS = ["---", "utm", "sp83", "sp27", "longlat", "latlong", "ecef"] - - PROJECTION = "PROJECTION" - UTM = "UTM" - SP = "SP" - - def defineCharacteristics(self): - self.name, self.i18n_name = self.trAlgorithm('txt2lasPro') - self.group, self.i18n_group = self.trAlgorithm('LAStools Production') - self.addParametersGenericInputFolderGUI("*.txt") - self.addParameter(ParameterString(txt2lasPro.PARSE, - self.tr("parse lines as"), "xyz")) - self.addParameter(ParameterNumber(txt2lasPro.SKIP, - self.tr("skip the first n lines"), 0, None, 0)) - self.addParameter(ParameterNumber(txt2lasPro.SCALE_FACTOR_XY, - self.tr("resolution of x and y coordinate"), 0, None, 0.01)) - self.addParameter(ParameterNumber(txt2lasPro.SCALE_FACTOR_Z, - self.tr("resolution of z coordinate"), 0, None, 0.01)) - self.addParameter(ParameterSelection(txt2lasPro.PROJECTION, - self.tr("projection"), txt2lasPro.PROJECTIONS, 0)) - self.addParameter(ParameterSelection(txt2lasPro.UTM, - self.tr("utm zone"), txt2lasPro.UTM_ZONES, 0)) - self.addParameter(ParameterSelection(txt2lasPro.SP, - self.tr("state plane code"), txt2lasPro.STATE_PLANES, 0)) - self.addParametersOutputDirectoryGUI() - self.addParametersOutputAppendixGUI() - self.addParametersPointOutputFormatGUI() - self.addParametersAdditionalGUI() - self.addParametersCoresGUI() - self.addParametersVerboseGUI() - - def processAlgorithm(self, feedback): - if (LAStoolsUtils.hasWine()): - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "txt2las.exe")] - else: - commands = [os.path.join(LAStoolsUtils.LAStoolsPath(), "bin", "txt2las")] - self.addParametersVerboseCommands(commands) - self.addParametersPointInputFolderCommands(commands) - parse_string = self.getParameterValue(txt2lasPro.PARSE) - if parse_string != "xyz": - commands.append("-parse") - commands.append(parse_string) - skip = self.getParameterValue(txt2lasPro.SKIP) - if parse_string != 0: - commands.append("-skip") - commands.append(str(skip)) - scale_factor_xy = self.getParameterValue(txt2lasPro.SCALE_FACTOR_XY) - scale_factor_z = self.getParameterValue(txt2lasPro.SCALE_FACTOR_Z) - if scale_factor_xy != 0.01 or scale_factor_z != 0.01: - commands.append("-set_scale") - commands.append(str(scale_factor_xy) + " " + str(scale_factor_xy) + " " + str(scale_factor_z)) - projection = self.getParameterValue(txt2lasPro.PROJECTION) - if projection != 0: - if projection == 1: - utm_zone = self.getParameterValue(txt2lasPro.UTM) - if utm_zone != 0: - commands.append("-" + txt2lasPro.PROJECTIONS[projection]) - if utm_zone > 60: - commands.append(str(utm_zone - 60) + "M") - else: - commands.append(str(utm_zone) + "N") - elif projection < 4: - sp_code = self.getParameterValue(txt2lasPro.SP) - if sp_code != 0: - commands.append("-" + txt2lasPro.PROJECTIONS[projection]) - commands.append(txt2lasPro.STATE_PLANES[sp_code]) - else: - commands.append("-" + txt2lasPro.PROJECTIONS[projection]) - self.addParametersOutputDirectoryCommands(commands) - self.addParametersOutputAppendixCommands(commands) - self.addParametersPointOutputFormatCommands(commands) - self.addParametersAdditionalCommands(commands) - self.addParametersCoresCommands(commands) - - LAStoolsUtils.runLAStools(commands, feedback) diff --git a/python/plugins/processing/algs/otb/CMakeLists.txt b/python/plugins/processing/algs/otb/CMakeLists.txt deleted file mode 100644 index 3b781189acff..000000000000 --- a/python/plugins/processing/algs/otb/CMakeLists.txt +++ /dev/null @@ -1,28 +0,0 @@ -FILE(GLOB PY_FILES *.py) -FILE(GLOB HELPER_FILES helper/*.py) -FILE(GLOB DESCR_FILES description/5.0.0/*.xml) -FiLE(GLOB HELP_FILES description/5.0.0/doc/*.html) - -FILE(GLOB DESCR_FILES description/5.4.0/*.xml) -FiLE(GLOB HELP_FILES description/5.4.0/doc/*.html) - -FILE(GLOB DESCR_FILES description/5.6.0/*.xml) -FiLE(GLOB HELP_FILES description/5.6.0/doc/*.html) - -FILE(GLOB DESCR_FILES description/5.8.0/*.xml) -FiLE(GLOB HELP_FILES description/5.8.0/doc/*.html) - -PLUGIN_INSTALL(processing ./algs/otb ${PY_FILES}) -PLUGIN_INSTALL(processing ./algs/otb/helper ${HELPER_FILES}) - -PLUGIN_INSTALL(processing ./algs/otb/description/5.0.0 ${DESCR_FILES}) -PLUGIN_INSTALL(processing ./algs/otb/description/5.0.0/doc ${HELP_FILES}) - -PLUGIN_INSTALL(processing ./algs/otb/description/5.4.0 ${DESCR_FILES}) -PLUGIN_INSTALL(processing ./algs/otb/description/5.4.0/doc ${HELP_FILES}) - -PLUGIN_INSTALL(processing ./algs/otb/description/5.6.0 ${DESCR_FILES}) -PLUGIN_INSTALL(processing ./algs/otb/description/5.6.0/doc ${HELP_FILES}) - -PLUGIN_INSTALL(processing ./algs/otb/description/5.8.0 ${DESCR_FILES}) -PLUGIN_INSTALL(processing ./algs/otb/description/5.8.0/doc ${HELP_FILES}) diff --git a/python/plugins/processing/algs/otb/OTBAlgorithm.py b/python/plugins/processing/algs/otb/OTBAlgorithm.py deleted file mode 100644 index 408446ef386e..000000000000 --- a/python/plugins/processing/algs/otb/OTBAlgorithm.py +++ /dev/null @@ -1,354 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - OTBAlgorithm.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - (C) 2013 by CS Systemes d'information (CS SI) - Email : volayaf at gmail dot com - otb at c-s dot fr (CS SI) - Contributors : Victor Olaya - Julien Malik (CS SI) - Changing the way to load algorithms first version - Oscar Picas (CS SI) - Changing the way to load algorithms - Alexia Mondot (CS SI) - Add hdf5 support -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from builtins import next -from future import standard_library -standard_library.install_aliases() -from builtins import map -from builtins import str - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -import re -from qgis.PyQt.QtCore import QCoreApplication -from qgis.PyQt.QtGui import QIcon -from processing.core.GeoAlgorithm import GeoAlgorithm -from processing.core.parameters import ParameterMultipleInput -from processing.core.parameters import ParameterRaster -from processing.core.parameters import ParameterVector -from processing.core.parameters import ParameterBoolean -from processing.core.parameters import ParameterSelection -from processing.core.ProcessingLog import ProcessingLog -from processing.core.parameters import getParameterFromString -from processing.core.outputs import getOutputFromString -from . import OTBUtils -from processing.core.parameters import ParameterExtent -from processing.tools.system import getTempFilename -import xml.etree.ElementTree as ET -import traceback - -pluginPath = os.path.normpath(os.path.join( - os.path.split(os.path.dirname(__file__))[0], os.pardir)) - - -class OTBAlgorithm(GeoAlgorithm): - - REGION_OF_INTEREST = "ROI" - - def __init__(self, descriptionfile): - GeoAlgorithm.__init__(self) - self.roiFile = None - self.descriptionFile = descriptionfile - self.defineCharacteristicsFromFile() - self.numExportedLayers = 0 - self.hasROI = None - self._icon = None - - def __str__(self): - return("Algo : " + self.name + " from app : " + self.cliName + " in : " + self.group) - - def getCopy(self): - newone = OTBAlgorithm(self.descriptionFile) - newone.provider = self.provider - return newone - - def getIcon(self): - if self._icon is None: - self._icon = QIcon(os.path.join(pluginPath, 'images', 'otb.png')) - return self._icon - - def help(self): - version = OTBUtils.getInstalledVersion() - folder = OTBUtils.compatibleDescriptionPath(version) - if folder is None: - return False, None - folder = os.path.join(folder, 'doc') - helpfile = os.path.join(str(folder), self.appkey + ".html") - if os.path.exists(helpfile): - return False, helpfile - else: - return False, None - - def adapt_list_to_string(self, c_list): - a_list = c_list[1:] - if a_list[0] in ["ParameterVector", "ParameterMultipleInput"]: - if c_list[0] == "ParameterType_InputImageList": - a_list[3] = 3 - elif c_list[0] == "ParameterType_InputFilenameList": - a_list[3] = 4 - else: - a_list[3] = -1 - - a_list[1] = "-%s" % a_list[1] - b_list = [";".join(x) if isinstance(x, list) else str(x) for x in a_list] - res = "|".join(b_list) - return res - - def get_list_from_node(self, myet): - all_params = [] - for parameter in myet.iter('parameter'): - rebuild = [] - par_type = parameter.find('parameter_type').text - key = parameter.find('key').text - name = parameter.find('name').text - source_par_type = parameter.find('parameter_type').attrib['source_parameter_type'] - rebuild.append(source_par_type) - rebuild.append(par_type) - rebuild.append(key) - rebuild.append(name) - for each in parameter[4:]: - if each.tag not in ["hidden"]: - if len(list(each)) == 0: - rebuild.append(each.text) - else: - rebuild.append([item.text for item in each.iter('choice')]) - all_params.append(rebuild) - return all_params - - def defineCharacteristicsFromFile(self): - with open(self.descriptionFile) as content: - dom_model = ET.fromstring(content.read()) - - self.appkey = dom_model.find('key').text - self.cliName = dom_model.find('exec').text - self.name = dom_model.find('longname').text - self.i18n_name = QCoreApplication.translate("OTBAlgorithm", self.name) - self.group = dom_model.find('group').text - self.i18n_group = QCoreApplication.translate("OTBAlgorithm", self.group) - - rebu = None - the_result = None - - try: - rebu = self.get_list_from_node(dom_model) - the_result = list(map(self.adapt_list_to_string, rebu)) - except Exception as e: - ProcessingLog.addToLog(ProcessingLog.LOG_ERROR, - self.tr('Could not open OTB algorithm: %s\n%s' % (self.descriptionFile, traceback.format_exc()))) - raise e - - for line in the_result: - try: - if line.startswith("Parameter") or line.startswith("*Parameter"): - if line.startswith("*Parameter"): - param = getParameterFromString(line[1:]) - param.isAdvanced = True - else: - param = getParameterFromString(line) - # Hack for initializing the elevation parameters from Processing configuration - if param.name == "-elev.dem.path" or param.name == "-elev.dem" or "elev.dem" in param.name: - param.default = OTBUtils.otbSRTMPath() - elif param.name == "-elev.dem.geoid" or param.name == "-elev.geoid" or "elev.geoid" in param.name: - param.default = OTBUtils.otbGeoidPath() - self.addParameter(param) - elif line.startswith("Extent"): - self.addParameter(ParameterExtent(self.REGION_OF_INTEREST, "Region of interest", "0,1,0,1")) - self.hasROI = True - else: - self.addOutput(getOutputFromString(line)) - except Exception as e: - ProcessingLog.addToLog(ProcessingLog.LOG_ERROR, - self.tr('Could not open OTB algorithm: %s\n%s' % (self.descriptionFile, line))) - raise e - - def processAlgorithm(self, feedback): - currentOs = os.name - - path = OTBUtils.otbPath() - - commands = [] - commands.append(os.path.join(path, self.cliName)) - - self.roiVectors = {} - self.roiRasters = {} - for param in self.parameters: - # get the given input(s) - if param.name in ["-il", "-in"]: - newparams = "" - listeParameters = param.value.split(";") - for inputParameter in listeParameters: - # if HDF5 file - if "HDF5" in inputParameter: - if currentOs == "posix": - data = inputParameter[6:] - else: - data = inputParameter[5:] - dataset = data - - #on windows, there isn't " - #if data[-1] == '"': - if currentOs == "posix": - data = data[:data.index('"')] - else: - data = data[:data.index('://')] - #try : - if currentOs == "posix": - dataset.index('"') - dataset = os.path.basename(data) + dataset[dataset.index('"'):] - #except ValueError : - else: - #dataset = os.path.basename( data ) + '"' + dataset[dataset.index('://'):] - dataset = dataset[dataset.index('://'):] - - #get index of the subdataset with gdal - if currentOs == "posix": - commandgdal = "gdalinfo " + data + " | grep '" + dataset + "$'" - else: - commandgdal = "gdalinfo " + data + " | findstr \"" + dataset + "$\"" - resultGDAL = os.popen(commandgdal).readlines() - indexSubdataset = -1 - if resultGDAL: - indexSubdatasetString = re.search("SUBDATASET_(\d+)_", resultGDAL[0]) - if indexSubdatasetString: - #match between () - indexSubdataset = indexSubdatasetString.group(1) - else: - indexSubdataset = -1 - else: - #print "Error : no match of ", dataset, "$ in gdalinfo " + data - indexSubdataset = -1 - - if not indexSubdataset == -1: - indexSubdataset = int(indexSubdataset) - 1 - newParam = "\'" + data + "?&sdataidx=" + str(indexSubdataset) + "\'" - - else: - newParam = inputParameter - - newparams += newParam - # no hdf5 - else: - newparams += inputParameter - newparams += ";" - if newparams[-1] == ";": - newparams = newparams[:-1] - param.value = newparams - - if param.value is None or param.value == "": - continue - if isinstance(param, ParameterVector): - commands.append(param.name) - if self.hasROI: - roiFile = getTempFilename('shp') - commands.append(roiFile) - self.roiVectors[param.value] = roiFile - else: - commands.append("\"" + param.value + "\"") - elif isinstance(param, ParameterRaster): - commands.append(param.name) - if self.hasROI: - roiFile = getTempFilename('tif') - commands.append(roiFile) - self.roiRasters[param.value] = roiFile - else: - commands.append("\"" + param.value + "\"") - elif isinstance(param, ParameterMultipleInput): - commands.append(param.name) - files = str(param.value).split(";") - paramvalue = " ".join(["\"" + f + " \"" for f in files]) - commands.append(paramvalue) - elif isinstance(param, ParameterSelection): - commands.append(param.name) - idx = int(param.value) - commands.append(str(param.options[idx][1])) - elif isinstance(param, ParameterBoolean): - if param.value: - commands.append(param.name) - commands.append(str(param.value).lower()) - elif isinstance(param, ParameterExtent): - self.roiValues = param.value.split(",") - else: - commands.append(param.name) - commands.append(str(param.value)) - - for out in self.outputs: - commands.append(out.name) - commands.append('"' + out.value + '"') - for roiInput, roiFile in list(self.roiRasters.items()): - startX, startY = float(self.roiValues[0]), float(self.roiValues[1]) - sizeX = float(self.roiValues[2]) - startX - sizeY = float(self.roiValues[3]) - startY - helperCommands = [ - "otbcli_ExtractROI", - "-in", roiInput, - "-out", roiFile, - "-startx", str(startX), - "-starty", str(startY), - "-sizex", str(sizeX), - "-sizey", str(sizeY) - ] - ProcessingLog.addToLog(ProcessingLog.LOG_INFO, helperCommands) - feedback.pushCommandInfo(helperCommands) - OTBUtils.executeOtb(helperCommands, feedback) - - if self.roiRasters: - supportRaster = next(iter(list(self.roiRasters.values()))) - for roiInput, roiFile in list(self.roiVectors.items()): - helperCommands = [ - "otbcli_VectorDataExtractROIApplication", - "-vd.in", roiInput, - "-io.in", supportRaster, - "-io.out", roiFile, - "-elev.dem.path", OTBUtils.otbSRTMPath()] - ProcessingLog.addToLog(ProcessingLog.LOG_INFO, helperCommands) - feedback.pushCommandInfo(helperCommands) - OTBUtils.executeOtb(helperCommands, feedback) - - loglines = [] - loglines.append(self.tr('OTB execution command')) - for line in commands: - loglines.append(line) - feedback.pushCommandInfo(line) - - ProcessingLog.addToLog(ProcessingLog.LOG_INFO, loglines) - import processing.algs.otb.OTBSpecific_XMLLoading - module = processing.algs.otb.OTBSpecific_XMLLoading - - found = False - if 'adapt%s' % self.appkey in dir(module): - found = True - commands = getattr(module, 'adapt%s' % self.appkey)(commands) - else: - the_key = 'adapt%s' % self.appkey - if '-' in the_key: - base_key = the_key.split("-")[0] - if base_key in dir(module): - found = True - commands = getattr(module, base_key)(commands) - - if not found: - ProcessingLog.addToLog(ProcessingLog.LOG_INFO, - self.tr("Adapter for %s not found") % the_key) - - #frames = inspect.getouterframes(inspect.currentframe())[1:] - #for a_frame in frames: - # frame,filename,line_number,function_name,lines,index = a_frame - # ProcessingLog.addToLog(ProcessingLog.LOG_INFO, "%s %s %s %s %s %s" % (frame,filename,line_number,function_name,lines,index)) - - OTBUtils.executeOtb(commands, feedback) diff --git a/python/plugins/processing/algs/otb/OTBAlgorithmProvider.py b/python/plugins/processing/algs/otb/OTBAlgorithmProvider.py deleted file mode 100644 index a3be7a47bede..000000000000 --- a/python/plugins/processing/algs/otb/OTBAlgorithmProvider.py +++ /dev/null @@ -1,109 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - OTBAlgorithmProvider.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - (C) 2013 by CS Systemes d'information - Email : volayaf at gmail dot com - otb at c-s dot fr - Contributors : Victor Olaya - Julien Malik - Changing the way to load algorithms : loading from xml - Oscar Picas - Changing the way to load algorithms : loading from xml -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from builtins import str - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -from qgis.PyQt.QtGui import QIcon -from processing.core.AlgorithmProvider import AlgorithmProvider -from processing.core.ProcessingConfig import ProcessingConfig, Setting -from . import OTBUtils -from .OTBAlgorithm import OTBAlgorithm -from processing.core.ProcessingLog import ProcessingLog - -pluginPath = os.path.normpath(os.path.join( - os.path.split(os.path.dirname(__file__))[0], os.pardir)) - - -class OTBAlgorithmProvider(AlgorithmProvider): - - def __init__(self): - super().__init__() - self.activate = True - - def name(self): - version = OTBUtils.getInstalledVersion() - return "Orfeo ToolBox ({})".format(version) if version is not None else "Orfeo ToolBox" - - def id(self): - return "otb" - - def icon(self): - return QIcon(os.path.join(pluginPath, 'images', 'otb.png')) - - def _loadAlgorithms(self): - self.algs = [] - - version = OTBUtils.getInstalledVersion(True) - if version is None: - return - - folder = OTBUtils.compatibleDescriptionPath(version) - if folder is None: - ProcessingLog.addToLog(ProcessingLog.LOG_ERROR, - self.tr('Problem with OTB installation: installed OTB version (%s) is not supported') % version) - return - - for descriptionFile in os.listdir(folder): - if descriptionFile.endswith("xml"): - try: - alg = OTBAlgorithm(os.path.join(folder, descriptionFile)) - - if alg.name.strip() != "": - self.algs.append(alg) - else: - ProcessingLog.addToLog(ProcessingLog.LOG_ERROR, - self.tr("Could not open OTB algorithm: %s") % descriptionFile) - except Exception as e: - ProcessingLog.addToLog(ProcessingLog.LOG_ERROR, - self.tr("Could not open OTB algorithm: %s\n%s") % (descriptionFile, str(e))) - - def initializeSettings(self): - AlgorithmProvider.initializeSettings(self) - ProcessingConfig.addSetting(Setting(self.name(), - OTBUtils.OTB_FOLDER, - self.tr("OTB command line tools folder"), OTBUtils.findOtbPath(), - valuetype=Setting.FOLDER)) - ProcessingConfig.addSetting(Setting(self.name(), - OTBUtils.OTB_LIB_FOLDER, - self.tr("OTB applications folder"), OTBUtils.findOtbLibPath(), - valuetype=Setting.FOLDER)) - ProcessingConfig.addSetting(Setting(self.name(), - OTBUtils.OTB_SRTM_FOLDER, - self.tr("SRTM tiles folder"), OTBUtils.otbSRTMPath(), - valuetype=Setting.FOLDER)) - ProcessingConfig.addSetting(Setting(self.name(), - OTBUtils.OTB_GEOID_FILE, - self.tr("Geoid file"), OTBUtils.otbGeoidPath(), - valuetype=Setting.FOLDER)) - - def unload(self): - AlgorithmProvider.unload(self) - ProcessingConfig.removeSetting(OTBUtils.OTB_FOLDER) - ProcessingConfig.removeSetting(OTBUtils.OTB_LIB_FOLDER) diff --git a/python/plugins/processing/algs/otb/OTBSpecific_XMLLoading.py b/python/plugins/processing/algs/otb/OTBSpecific_XMLLoading.py deleted file mode 100644 index b9018d2f103f..000000000000 --- a/python/plugins/processing/algs/otb/OTBSpecific_XMLLoading.py +++ /dev/null @@ -1,358 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - OTBSpecific_XMLLoading.py - ------------------------- - Date : 11-12-13 - Copyright : (C) 2013 by CS Systemes d'information (CS SI) - Email : otb at c-s dot fr (CS SI) - Contributors : Julien Malik (CS SI) - creation of otbspecific - Oscar Picas (CS SI) - - Alexia Mondot (CS SI) - split otbspecific into 2 files - add functions -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** - -When an OTB algorithms is run, this file allows adapting user parameter to fit the otbapplication. - -Most of the following functions are like follows : - adaptNameOfTheOTBApplication(commands_list) -The command list is a list of all parameters of the given algorithm with all user values. -""" -from builtins import str -from builtins import map - - -__author__ = 'Julien Malik, Oscar Picas, Alexia Mondot' -__date__ = 'December 2013' -__copyright__ = '(C) 2013, CS Systemes d\'information (CS SI)' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' -__version__ = "3.8" - -import os - -try: - import processing # NOQA -except ImportError as e: - raise Exception("Processing must be installed and available in PYTHONPATH") - -from processing.core.ProcessingConfig import ProcessingConfig - -from . import OTBUtils - - -def adaptBinaryMorphologicalOperation(commands_list): - val = commands_list[commands_list.index("-filter") + 1] - - def replace_dilate(param, value): - if ".dilate" in str(param): - return param.replace("dilate", value) - else: - return param - - import functools - com_list = list(map(functools.partial(replace_dilate, value=val), commands_list)) - - val = com_list[com_list.index("-structype.ball.xradius") + 1] - - pos = com_list.index("-structype.ball.xradius") + 2 - - com_list.insert(pos, '-structype.ball.yradius') - com_list.insert(pos + 1, val) - - return com_list - - -def adaptEdgeExtraction(commands_list): - """ - Add filter.touzi.yradius as the same value as filter.touzi.xradius - """ - val = commands_list[commands_list.index("-filter") + 1] - if val == 'touzi': - bval = commands_list[commands_list.index("-filter.touzi.xradius") + 1] - pos = commands_list.index("-filter.touzi.xradius") + 2 - commands_list.insert(pos, "-filter.touzi.yradius") - commands_list.insert(pos + 1, bval) - return commands_list - - -def adaptGrayScaleMorphologicalOperation(commands_list): - """ - Add structype.ball.yradius as the same value as structype.ball.xradius (as it is a ball) - """ - val = commands_list[commands_list.index("-structype.ball.xradius") + 1] - pos = commands_list.index("-structype.ball.xradius") + 2 - commands_list.insert(pos, "-structype.ball.yradius") - commands_list.insert(pos + 1, val) - return commands_list - - -def adaptSplitImage(commands_list): - """ - Ran by default, the extension of output file is .file. Replace it with ".tif" - If no extension given, put ".tif" at the end of the filename. - """ - commands_list2 = [] - for item in commands_list: - if ".file" in item: - item = item.replace(".file", ".tif") - if item == "-out": - index = commands_list.index(item) - if "." not in os.path.basename(commands_list[index + 1]): - commands_list[index + 1] = commands_list[index + 1][:-1] + ".tif" + commands_list[index + 1][-1] - commands_list2.append(item) - return commands_list2 - - -def adaptLSMSVectorization(commands_list): - """ - Ran by default, the extension of output file is .file. Replace it with ".shp" - If no extension given, put ".shp" at the end of the filename. - """ - commands_list2 = [] - for item in commands_list: - if ".file" in item: - item = item.replace(".file", ".shp") - if item == "-out": - index = commands_list.index(item) - if "." not in os.path.basename(commands_list[index + 1]): - commands_list[index + 1] = commands_list[index + 1][:-1] + ".shp" + commands_list[index + 1][-1] - commands_list2.append(item) - - return commands_list2 - - -def adaptComputeImagesStatistics(commands_list): - """ - Ran by default, the extension of output file is .file. Replace it with ".xml" - If no extension given, put ".shp" at the end of the filename. - """ - commands_list2 = [] - for item in commands_list: - if ".file" in item: - item = item.replace(".file", ".xml") - commands_list2.append(item) - if item == "-out": - index = commands_list.index(item) - if "." not in os.path.basename(commands_list[index + 1]): - commands_list[index + 1] = commands_list[index + 1][:-1] + ".xml" + commands_list[index + 1][-1] - - return commands_list2 - - -def adaptKmzExport(commands_list): - """ - Ran by default, the extension of output file is .file. Replace it with ".kmz" - If no extension given, put ".kmz" at the end of the filename. - Check geoid file, srtm folder and given elevation and manage arguments. - """ - adaptGeoidSrtm(commands_list) - commands_list2 = [] - for item in commands_list: - if ".file" in item: - item = item.replace(".file", ".kmz") - if item == "-out": - index = commands_list.index(item) - if "." not in os.path.basename(commands_list[index + 1]): - commands_list[index + 1] = commands_list[index + 1][:-1] + ".kmz" + commands_list[index + 1][-1] - - commands_list2.append(item) - return commands_list2 - - -def adaptColorMapping(commands_list): - """ - The output of this algorithm must be in uint8. - """ - indexInput = commands_list.index("-out") - commands_list[indexInput + 1] = commands_list[indexInput + 1] + '" "uint8"' - return commands_list - - -def adaptStereoFramework(commands_list): - """ - Remove parameter and user value instead of giving None. - Check geoid file, srtm folder and given elevation and manage arguments. - """ - commands_list2 = commands_list - adaptGeoidSrtm(commands_list2) - for item in commands_list: - if "None" in item: - index = commands_list2.index(item) - argumentToRemove = commands_list2[index - 1] - commands_list2.remove(item) - commands_list2.remove(argumentToRemove) - return commands_list2 - - -def adaptComputeConfusionMatrix(commands_list): - """ - Ran by default, the extension of output file is .file. Replace it with ".csv" - If no extension given, put ".csv" at the end of the filename. - """ - commands_list2 = [] - for item in commands_list: - if ".file" in item: - item = item.replace(".file", ".csv") - if item == "-out": - index = commands_list.index(item) - if "." not in os.path.basename(commands_list[index + 1]): - commands_list[index + 1] = commands_list[index + 1][:-1] + ".csv" + commands_list[index + 1][-1] - - commands_list2.append(item) - return commands_list2 - - -def adaptRadiometricIndices(commands_list): - """ - Replace indice nickname by its corresponding entry in the following dictionary : - indices = {"ndvi" : "Vegetation:NDVI", "tndvi" : "Vegetation:TNDVI", "rvi" : "Vegetation:RVI", "savi" : "Vegetation:SAVI", - "tsavi" : "Vegetation:TSAVI", "msavi" : "Vegetation:MSAVI", "msavi2" : "Vegetation:MSAVI2", "gemi" : "Vegetation:GEMI", - "ipvi" : "Vegetation:IPVI", - "ndwi" : "Water:NDWI", "ndwi2" : "Water:NDWI2", "mndwi" :"Water:MNDWI" , "ndpi" : "Water:NDPI", - "ndti" : "Water:NDTI", - "ri" : "Soil:RI", "ci" : "Soil:CI", "bi" : "Soil:BI", "bi2" : "Soil:BI2"} - """ -# "laindvilog" : , "lairefl" : , "laindviformo" : , - indices = {"ndvi": "Vegetation:NDVI", "tndvi": "Vegetation:TNDVI", "rvi": "Vegetation:RVI", "savi": "Vegetation:SAVI", - "tsavi": "Vegetation:TSAVI", "msavi": "Vegetation:MSAVI", "msavi2": "Vegetation:MSAVI2", "gemi": "Vegetation:GEMI", - "ipvi": "Vegetation:IPVI", - "ndwi": "Water:NDWI", "ndwi2": "Water:NDWI2", "mndwi": "Water:MNDWI", "ndpi": "Water:NDPI", - "ndti": "Water:NDTI", - "ri": "Soil:RI", "ci": "Soil:CI", "bi": "Soil:BI", "bi2": "Soil:BI2"} - for item in commands_list: - if item in indices: - commands_list[commands_list.index(item)] = indices[item] - return commands_list - - -def adaptDisparityMapToElevationMap(commands_list): - """ - Check geoid file, srtm folder and given elevation and manage arguments. - """ - adaptGeoidSrtm(commands_list) - return commands_list - - -def adaptConnectedComponentSegmentation(commands_list): - """ - Remove parameter and user value instead of giving None. - """ - commands_list2 = commands_list - adaptGeoidSrtm(commands_list2) - for item in commands_list: - if "None" in item: - index = commands_list2.index(item) - argumentToRemove = commands_list2[index - 1] - commands_list2.remove(item) - commands_list2.remove(argumentToRemove) - #commands_list2.append(item) - return commands_list2 - - -def adaptSuperimpose(commands_list): - """ - Check geoid file, srtm folder and given elevation and manage arguments. - """ - adaptGeoidSrtm(commands_list) - return commands_list - - -def adaptOrthoRectification(commands_list): - """ - Check geoid file, srtm folder and given elevation and manage arguments. - """ - adaptGeoidSrtm(commands_list) - return commands_list - - -def adaptExtractROI(commands_list): - """ - Check geoid file, srtm folder and given elevation and manage arguments. - """ - adaptGeoidSrtm(commands_list) - return commands_list - - -def adaptTrainImagesClassifier(commands_list): - """ - Check geoid file, srtm folder and given elevation and manage arguments. - """ - adaptGeoidSrtm(commands_list) - return commands_list - - -def adaptGeoidSrtm(commands_list): - """ - Check geoid file, srtm folder and given elevation and manage arguments. - """ - srtm, geoid = ckeckGeoidSrtmSettings() - - if srtm: - if commands_list[0].endswith("ExtractROI"): - commands_list.append("-mode.fit.elev.dem") - commands_list.append(srtm) - else: - commands_list.append("-elev.dem") - commands_list.append(srtm) - if geoid: - if commands_list[0].endswith("ExtractROI"): - commands_list.append("-mode.fit.elev.geoid") - commands_list.append(geoid) - else: - commands_list.append("-elev.geoid") - commands_list.append(geoid) - - -def adaptComputePolylineFeatureFromImage(commands_list): - """ - Remove parameter and user value instead of giving None. - Check geoid file, srtm folder and given elevation and manage arguments. - """ - commands_list2 = commands_list - adaptGeoidSrtm(commands_list2) - for item in commands_list: - if "None" in item: - index = commands_list2.index(item) - argumentToRemove = commands_list2[index - 1] - commands_list2.remove(item) - commands_list2.remove(argumentToRemove) - # commands_list2.append(item) - return commands_list2 - - -def adaptComputeOGRLayersFeaturesStatistics(commands_list): - """ - Remove parameter and user value instead of giving None. - Check geoid file, srtm folder and given elevation and manage arguments. - """ - commands_list2 = commands_list - adaptGeoidSrtm(commands_list2) - for item in commands_list: - if "None" in item: - index = commands_list2.index(item) - argumentToRemove = commands_list2[index - 1] - commands_list2.remove(item) - commands_list2.remove(argumentToRemove) - # commands_list2.append(item) - return commands_list2 - - -def ckeckGeoidSrtmSettings(): - folder = ProcessingConfig.getSetting(OTBUtils.OTB_SRTM_FOLDER) - if folder is None: - folder = "" - - filepath = ProcessingConfig.getSetting(OTBUtils.OTB_GEOID_FILE) - if filepath is None: - filepath = "" - - return folder, filepath diff --git a/python/plugins/processing/algs/otb/OTBUtils.py b/python/plugins/processing/algs/otb/OTBUtils.py deleted file mode 100644 index ef79e3fb7339..000000000000 --- a/python/plugins/processing/algs/otb/OTBUtils.py +++ /dev/null @@ -1,293 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - OTBUtils.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - (C) 2013 by CS Systemes d'information (CS SI) - Email : volayaf at gmail dot com - otb at c-s dot fr (CS SI) - Contributors : Victor Olaya - Julien Malik, Oscar Picas (CS SI) - add functions to manage xml tree - Alexia Mondot (CS SI) - add a trick for OTBApplication SplitImages -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from builtins import str - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import os -import re -import time -from qgis.PyQt.QtCore import QCoreApplication -from qgis.core import (QgsApplication, - QgsProcessingFeedback) -import subprocess -from processing.core.ProcessingConfig import ProcessingConfig -from processing.core.ProcessingLog import ProcessingLog -from processing.tools.system import isMac, isWindows -import logging -import xml.etree.ElementTree as ET -import traceback - - -OTB_FOLDER = "OTB_FOLDER" -OTB_LIB_FOLDER = "OTB_LIB_FOLDER" -OTB_SRTM_FOLDER = "OTB_SRTM_FOLDER" -OTB_GEOID_FILE = "OTB_GEOID_FILE" - - -def findOtbPath(): - folder = "" - #try to configure the path automatically - if isMac(): - testfolder = os.path.join(str(QgsApplication.prefixPath()), "bin") - if os.path.exists(os.path.join(testfolder, "otbcli")): - folder = testfolder - else: - testfolder = "/usr/local/bin" - if os.path.exists(os.path.join(testfolder, "otbcli")): - folder = testfolder - elif isWindows(): - testfolder = os.path.join(os.path.dirname(QgsApplication.prefixPath()), - os.pardir, "bin") - if os.path.exists(os.path.join(testfolder, "otbcli.bat")): - folder = testfolder - else: - testfolder = "/usr/bin" - if os.path.exists(os.path.join(testfolder, "otbcli")): - folder = testfolder - return folder - - -def otbPath(): - folder = ProcessingConfig.getSetting(OTB_FOLDER) - if folder is None: - folder = "" - return folder - - -def findOtbLibPath(): - folder = "" - #try to configure the path automatically - if isMac(): - testfolder = os.path.join(str(QgsApplication.prefixPath()), "lib/otb/applications") - if os.path.exists(testfolder): - folder = testfolder - else: - testfolder = "/usr/local/lib/otb/applications" - if os.path.exists(testfolder): - folder = testfolder - elif isWindows(): - testfolder = os.path.join(os.path.dirname(QgsApplication.prefixPath()), "orfeotoolbox", "applications") - if os.path.exists(testfolder): - folder = testfolder - else: - testfolder = "/usr/lib/otb/applications" - if os.path.exists(testfolder): - folder = testfolder - return folder - - -def otbLibPath(): - return ProcessingConfig.getSetting(OTB_LIB_FOLDER) or '' - - -def otbSRTMPath(): - return ProcessingConfig.getSetting(OTB_SRTM_FOLDER) or '' - - -def otbGeoidPath(): - return ProcessingConfig.getSetting(OTB_GEOID_FILE) or '' - - -def otbDescriptionPath(): - return os.path.join(os.path.dirname(__file__), "description") - -_installedVersion = None -_installedVersionFound = False - - -def getInstalledVersion(runOtb=False): - global _installedVersion - global _installedVersionFound - - if _installedVersionFound and not runOtb: - return _installedVersion - - if otbPath() is None or otbLibPath() is None: - _installedVersionFound = False - return None - commands = [os.path.join(otbPath(), "otbcli_Smoothing")] - feedback = QgsProcessingFeedback() - out = executeOtb(commands, feedback, False) - for line in out: - if "version" in line: - _installedVersionFound = True - _installedVersion = line.split("version")[-1].strip() - break - return _installedVersion - - -def compatibleDescriptionPath(version): - supportedVersions = {"5.0.0": "5.0.0", - "5.4.0": "5.4.0", - "5.6.0": "5.6.0", - "5.8.0": "5.8.0"} - if version is None: - return None - if version not in supportedVersions: - lastVersion = sorted(supportedVersions.keys())[-1] - if version > lastVersion: - version = lastVersion - else: - return None - - return os.path.join(otbDescriptionPath(), supportedVersions[version]) - - -def executeOtb(commands, feedback, addToLog=True): - loglines = [] - loglines.append(tr("OTB execution console output")) - os.putenv('ITK_AUTOLOAD_PATH', otbLibPath()) - fused_command = ''.join(['"%s" ' % re.sub(r'^"|"$', '', c) for c in commands]) - with subprocess.Popen(fused_command, shell=True, stdout=subprocess.PIPE, stdin=subprocess.DEVNULL, stderr=subprocess.STDOUT, universal_newlines=True) as proc: - if isMac(): # This trick avoids having an uninterrupted system call exception if OTB is not installed - time.sleep(1) - for line in iter(proc.stdout.readline, ""): - if "[*" in line: - idx = line.find("[*") - perc = int(line[idx - 4:idx - 2].strip(" ")) - if perc != 0: - feedback.setProgress(perc) - else: - loglines.append(line) - feedback.pushConsoleInfo(line) - - if addToLog: - ProcessingLog.addToLog(ProcessingLog.LOG_INFO, loglines) - - return loglines - - -def tr(string, context=''): - if context == '': - context = 'OTBUtils' - return QCoreApplication.translate(context, string) - - -def get_choices_of(doc, parameter): - choices = [] - try: - t5 = [item for item in doc.findall('.//parameter') if item.find('key').text == parameter] - choices = [item.text for item in t5[0].findall('options/choices/choice')] - except: - logger = logging.getLogger('OTBGenerator') - logger.warning(traceback.format_exc()) - return choices - - -def remove_dependent_choices(doc, parameter, choice): - choices = get_choices_of(doc, parameter) - choices.remove(choice) - for a_choice in choices: - t4 = [item for item in doc.findall('.//parameter') if '.%s' % a_choice in item.find('key').text] - for t5 in t4: - doc.remove(t5) - - -def renameValueField(doc, textitem, field, newValue): - t4 = [item for item in doc.findall('.//parameter') if item.find('key').text == textitem] - for t5 in t4: - t5.find(field).text = newValue - - -def remove_independent_choices(doc, parameter, choice): - choices = [] - choices.append(choice) - for a_choice in choices: - t4 = [item for item in doc.findall('.//parameter') if '.%s' % a_choice in item.find('key').text] - for t5 in t4: - doc.remove(t5) - - -def remove_parameter_by_key(doc, parameter): - t4 = [item for item in doc.findall('.//parameter') if item.find('key').text == parameter] - for t5 in t4: - doc.remove(t5) - - -def remove_other_choices(doc, parameter, choice): - t5 = [item for item in doc.findall('.//parameter') if item.find('key').text == parameter] - if len(t5) > 0: - choices = [item for item in t5[0].findall('options/choices/choice') if item.text != choice] - choice_root = t5[0].findall('options/choices')[0] - for a_choice in choices: - choice_root.remove(a_choice) - - -def remove_choice(doc, parameter, choice): - t5 = [item for item in doc.findall('.//parameter') if item.find('key').text == parameter] - if len(t5) > 0: - choices = [item for item in t5[0].findall('options/choices/choice') if item.text == choice] - choice_root = t5[0].findall('options/choices')[0] - for a_choice in choices: - choice_root.remove(a_choice) - - -def split_by_choice(doc, parameter): - """ - splits the given doc into several docs according to the given parameter - returns a dictionary of documents - """ - result = {} - choices = get_choices_of(doc, parameter) - import copy - for choice in choices: - #creates a new copy of the document - working_copy = copy.deepcopy(doc) - remove_dependent_choices(working_copy, parameter, choice) - #remove all other choices except the current one - remove_other_choices(working_copy, parameter, choice) - #set a new name according to the choice - old_app_name = working_copy.find('key').text - working_copy.find('key').text = '%s-%s' % (old_app_name, choice) - working_copy.find('longname').text = '%s (%s)' % (old_app_name, choice) - #add it to the dictionary - result[choice] = working_copy - return result - - -def remove_parameter_by_criteria(doc, criteria): - t4 = [item for item in doc.findall('./parameter') if criteria(item)] - for t5 in t4: - doc.getroot().remove(t5) - - -def defaultWrite(available_app, original_dom_document): - with open("description/%s.xml" % available_app, "w") as fh: - the_root = original_dom_document - ET.ElementTree(the_root).write(fh) - - -def defaultSplit(available_app, original_dom_document, parameter): - the_root = original_dom_document - split = split_by_choice(the_root, parameter) - the_list = [] - for key in split: - defaultWrite('%s-%s' % (available_app, key), split[key]) - the_list.append(split[key]) - return the_list diff --git a/python/plugins/processing/algs/otb/__init__.py b/python/plugins/processing/algs/otb/__init__.py deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/python/plugins/processing/algs/otb/description/5.0.0/BandMath.xml b/python/plugins/processing/algs/otb/description/5.0.0/BandMath.xml deleted file mode 100644 index ca4a9359939d..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/BandMath.xml +++ /dev/null @@ -1,41 +0,0 @@ - - BandMath - otbcli_BandMath - Band Math - Miscellaneous - Perform a mathematical operation on monoband images - - ParameterMultipleInput - il - Input image list - Image list to perform computation on. - - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterString - exp - Expression - The mathematical expression to apply. -Use im1b1 for the first band, im1b2 for the second one... - - - False - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/BandMathX.xml b/python/plugins/processing/algs/otb/description/5.0.0/BandMathX.xml deleted file mode 100644 index 6c7f348d6e34..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/BandMathX.xml +++ /dev/null @@ -1,55 +0,0 @@ - - BandMathX - otbcli_BandMathX - Band Math X - Miscellaneous - This application performs mathematical operations on multiband images. -Mathematical formula interpretation is done via muParserX library : http://articles.beltoforion.de/article.php?a=muparserx - - ParameterMultipleInput - il - Input image list - Image list to perform computation on. - - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterString - exp - Expressions - Mathematical expression to apply. - - - True - - - ParameterFile - incontext - Import context - A txt file containing user's constants and expressions. - - True - - - OutputFile - outcontext - Export context - A txt file where to save user's constants and expressions. - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/BinaryMorphologicalOperation-closing.xml b/python/plugins/processing/algs/otb/description/5.0.0/BinaryMorphologicalOperation-closing.xml deleted file mode 100644 index 9593d1af4d02..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/BinaryMorphologicalOperation-closing.xml +++ /dev/null @@ -1,72 +0,0 @@ - - BinaryMorphologicalOperation-closing - otbcli_BinaryMorphologicalOperation - BinaryMorphologicalOperation (closing) - Feature Extraction - Performs morphological operations on an input image channel - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - closing - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/BinaryMorphologicalOperation-dilate.xml b/python/plugins/processing/algs/otb/description/5.0.0/BinaryMorphologicalOperation-dilate.xml deleted file mode 100644 index 1e608f06251f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/BinaryMorphologicalOperation-dilate.xml +++ /dev/null @@ -1,90 +0,0 @@ - - BinaryMorphologicalOperation-dilate - otbcli_BinaryMorphologicalOperation - BinaryMorphologicalOperation (dilate) - Feature Extraction - Performs morphological operations on an input image channel - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - dilate - - - 0 - - - ParameterNumber - filter.dilate.foreval - Foreground Value - The Foreground Value - - - 1 - - - ParameterNumber - filter.dilate.backval - Background Value - The Background Value - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/BinaryMorphologicalOperation-erode.xml b/python/plugins/processing/algs/otb/description/5.0.0/BinaryMorphologicalOperation-erode.xml deleted file mode 100644 index e7ef87a93fcb..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/BinaryMorphologicalOperation-erode.xml +++ /dev/null @@ -1,72 +0,0 @@ - - BinaryMorphologicalOperation-erode - otbcli_BinaryMorphologicalOperation - BinaryMorphologicalOperation (erode) - Feature Extraction - Performs morphological operations on an input image channel - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - erode - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/BinaryMorphologicalOperation-opening.xml b/python/plugins/processing/algs/otb/description/5.0.0/BinaryMorphologicalOperation-opening.xml deleted file mode 100644 index 92d8668cdb51..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/BinaryMorphologicalOperation-opening.xml +++ /dev/null @@ -1,72 +0,0 @@ - - BinaryMorphologicalOperation-opening - otbcli_BinaryMorphologicalOperation - BinaryMorphologicalOperation (opening) - Feature Extraction - Performs morphological operations on an input image channel - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - opening - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/ClassificationMapRegularization.xml b/python/plugins/processing/algs/otb/description/5.0.0/ClassificationMapRegularization.xml deleted file mode 100644 index 300c48fac7da..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/ClassificationMapRegularization.xml +++ /dev/null @@ -1,64 +0,0 @@ - - ClassificationMapRegularization - otbcli_ClassificationMapRegularization - Classification Map Regularization - Learning - Filters the input labeled image using Majority Voting in a ball shaped neighborhood. - - ParameterRaster - io.in - Input classification image - The input labeled image to regularize. - False - - - OutputRaster - io.out - Output regularized image - The output regularized labeled image. - - - - ParameterNumber - ip.radius - Structuring element radius (in pixels) - The radius of the ball shaped structuring element (expressed in pixels). By default, 'ip.radius = 1 pixel'. - - - 1 - - - ParameterBoolean - ip.suvbool - Multiple majority: Undecided(X)/Original - Pixels with more than 1 majority class are marked as Undecided if this parameter is checked (true), or keep their Original labels otherwise (false). Please note that the Undecided value must be different from existing labels in the input labeled image. By default, 'ip.suvbool = false'. - True - - - ParameterNumber - ip.nodatalabel - Label for the NoData class - Label for the NoData class. Such input pixels keep their NoData label in the output image. By default, 'ip.nodatalabel = 0'. - - - 0 - - - ParameterNumber - ip.undecidedlabel - Label for the Undecided class - Label for the Undecided class. By default, 'ip.undecidedlabel = 0'. - - - 0 - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/ColorMapping-continuous.xml b/python/plugins/processing/algs/otb/description/5.0.0/ColorMapping-continuous.xml deleted file mode 100644 index acf6d0e55b28..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/ColorMapping-continuous.xml +++ /dev/null @@ -1,98 +0,0 @@ - - ColorMapping-continuous - otbcli_ColorMapping - ColorMapping (continuous) - Image Manipulation - Maps an input label image to 8-bits RGB using look-up tables. - - ParameterRaster - in - Input Image - Input image filename - False - - - OutputRaster - out - Output Image - Output image filename - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterSelection - op - Operation - Selection of the operation to execute (default is : label to color). - - - labeltocolor - - - 0 - - - ParameterSelection - method - Color mapping method - Selection of color mapping methods and their parameters. - - - continuous - - - 0 - - - ParameterSelection - method.continuous.lut - Look-up tables - Available look-up tables. - - - red - green - blue - grey - hot - cool - spring - summer - autumn - winter - copper - jet - hsv - overunder - relief - - - 0 - - - ParameterNumber - method.continuous.min - Mapping range lower value - Set the lower input value of the mapping range. - - - 0 - - - ParameterNumber - method.continuous.max - Mapping range higher value - Set the higher input value of the mapping range. - - - 255 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/ColorMapping-custom.xml b/python/plugins/processing/algs/otb/description/5.0.0/ColorMapping-custom.xml deleted file mode 100644 index 6257536c0886..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/ColorMapping-custom.xml +++ /dev/null @@ -1,65 +0,0 @@ - - ColorMapping-custom - otbcli_ColorMapping - ColorMapping (custom) - Image Manipulation - Maps an input label image to 8-bits RGB using look-up tables. - - ParameterRaster - in - Input Image - Input image filename - False - - - OutputRaster - out - Output Image - Output image filename - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterSelection - op - Operation - Selection of the operation to execute (default is : label to color). - - - labeltocolor - - - 0 - - - ParameterSelection - method - Color mapping method - Selection of color mapping methods and their parameters. - - - custom - - - 0 - - - ParameterFile - method.custom.lut - Look-up table file - An ASCII file containing the look-up table -with one color per line -(for instance the line '1 255 0 0' means that all pixels with label 1 will be replaced by RGB color 255 0 0) -Lines beginning with a # are ignored - - False - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/ColorMapping-image.xml b/python/plugins/processing/algs/otb/description/5.0.0/ColorMapping-image.xml deleted file mode 100644 index 60ef8c5ae9ad..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/ColorMapping-image.xml +++ /dev/null @@ -1,88 +0,0 @@ - - ColorMapping-image - otbcli_ColorMapping - ColorMapping (image) - Image Manipulation - Maps an input label image to 8-bits RGB using look-up tables. - - ParameterRaster - in - Input Image - Input image filename - False - - - OutputRaster - out - Output Image - Output image filename - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterSelection - op - Operation - Selection of the operation to execute (default is : label to color). - - - labeltocolor - - - 0 - - - ParameterSelection - method - Color mapping method - Selection of color mapping methods and their parameters. - - - image - - - 0 - - - ParameterRaster - method.image.in - Support Image - Support image filename. For each label, the LUT is calculated from the mean pixel value in the support image, over the corresponding labeled areas. First of all, the support image is normalized with extrema rejection - False - - - ParameterNumber - method.image.nodatavalue - NoData value - NoData value for each channel of the support image, which will not be handled in the LUT estimation. If NOT checked, ALL the pixel values of the support image will be handled in the LUT estimation. - - - 0 - - - ParameterNumber - method.image.low - lower quantile - lower quantile for image normalization - - - 2 - - - ParameterNumber - method.image.up - upper quantile - upper quantile for image normalization - - - 2 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/ColorMapping-optimal.xml b/python/plugins/processing/algs/otb/description/5.0.0/ColorMapping-optimal.xml deleted file mode 100644 index d1a2922fcdcc..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/ColorMapping-optimal.xml +++ /dev/null @@ -1,63 +0,0 @@ - - ColorMapping-optimal - otbcli_ColorMapping - ColorMapping (optimal) - Image Manipulation - Maps an input label image to 8-bits RGB using look-up tables. - - ParameterRaster - in - Input Image - Input image filename - False - - - OutputRaster - out - Output Image - Output image filename - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterSelection - op - Operation - Selection of the operation to execute (default is : label to color). - - - labeltocolor - - - 0 - - - ParameterSelection - method - Color mapping method - Selection of color mapping methods and their parameters. - - - optimal - - - 0 - - - ParameterNumber - method.optimal.background - Background label - Value of the background label - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/CompareImages.xml b/python/plugins/processing/algs/otb/description/5.0.0/CompareImages.xml deleted file mode 100644 index 62a7197e3d14..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/CompareImages.xml +++ /dev/null @@ -1,75 +0,0 @@ - - CompareImages - otbcli_CompareImages - Images comparison - Miscellaneous - Estimator between 2 images. - - ParameterRaster - ref.in - Reference image - Image used as reference in the comparison - False - - - ParameterNumber - ref.channel - Reference image channel - Used channel for the reference image - - - 1 - - - ParameterRaster - meas.in - Measured image - Image used as measured in the comparison - False - - - ParameterNumber - meas.channel - Measured image channel - Used channel for the measured image - - - 1 - - - ParameterNumber - roi.startx - Start X - ROI start x position. - - - 0 - - - ParameterNumber - roi.starty - Start Y - ROI start y position. - - - 0 - - - ParameterNumber - roi.sizex - Size X - Size along x in pixels. - - - 0 - - - ParameterNumber - roi.sizey - Size Y - Size along y in pixels. - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/ComputeConfusionMatrix-raster.xml b/python/plugins/processing/algs/otb/description/5.0.0/ComputeConfusionMatrix-raster.xml deleted file mode 100644 index 92cba17e860e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/ComputeConfusionMatrix-raster.xml +++ /dev/null @@ -1,57 +0,0 @@ - - ComputeConfusionMatrix-raster - otbcli_ComputeConfusionMatrix - ComputeConfusionMatrix (raster) - Learning - Computes the confusion matrix of a classification - - ParameterRaster - in - Input Image - The input classification image. - False - - - OutputFile - out - Matrix output - Filename to store the output matrix (csv format) - - - ParameterSelection - ref - Ground truth - Choice of ground truth format - - - raster - - - 0 - - - ParameterRaster - ref.raster.in - Input reference image - Input image containing the ground truth labels - False - - - ParameterNumber - nodatalabel - Value for nodata pixels - Label for the NoData class. Such input pixels will be discarded from the ground truth and from the input classification map. By default, 'nodatalabel = 0'. - - - 0 - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/ComputeConfusionMatrix-vector.xml b/python/plugins/processing/algs/otb/description/5.0.0/ComputeConfusionMatrix-vector.xml deleted file mode 100644 index 46531907de2a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/ComputeConfusionMatrix-vector.xml +++ /dev/null @@ -1,67 +0,0 @@ - - ComputeConfusionMatrix-vector - otbcli_ComputeConfusionMatrix - ComputeConfusionMatrix (vector) - Learning - Computes the confusion matrix of a classification - - ParameterRaster - in - Input Image - The input classification image. - False - - - OutputFile - out - Matrix output - Filename to store the output matrix (csv format) - - - ParameterSelection - ref - Ground truth - Choice of ground truth format - - - vector - - - 0 - - - ParameterFile - ref.vector.in - Input reference vector data - Input vector data of the ground truth - - False - - - ParameterString - ref.vector.field - Field name - Field name containing the label values - Class - - True - - - ParameterNumber - nodatalabel - Value for nodata pixels - Label for the NoData class. Such input pixels will be discarded from the ground truth and from the input classification map. By default, 'nodatalabel = 0'. - - - 0 - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/ComputeImagesStatistics.xml b/python/plugins/processing/algs/otb/description/5.0.0/ComputeImagesStatistics.xml deleted file mode 100644 index 60a2805afe98..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/ComputeImagesStatistics.xml +++ /dev/null @@ -1,30 +0,0 @@ - - ComputeImagesStatistics - otbcli_ComputeImagesStatistics - Compute Images second order statistics - Learning - Computes global mean and standard deviation for each band from a set of images and optionally saves the results in an XML file. - - ParameterMultipleInput - il - Input images - List of input images filenames. - - False - - - ParameterNumber - bv - Background Value - Background value to ignore in statistics computation. - - - 0.0 - - - OutputFile - out - Output XML file - XML filename where the statistics are saved for future reuse. - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/ComputeModulusAndPhase-OneEntry.xml b/python/plugins/processing/algs/otb/description/5.0.0/ComputeModulusAndPhase-OneEntry.xml deleted file mode 100644 index bdc2f5229eff..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/ComputeModulusAndPhase-OneEntry.xml +++ /dev/null @@ -1,49 +0,0 @@ - - ComputeModulusAndPhase-one-OneEntry - otbcli_ComputeModulusAndPhase - ComputeModulusAndPhase-one (OneEntry) - Miscellaneous - This application computes the modulus and the phase of a complex SAR data. - - ParameterSelection - nbinput - Number Of inputs - Choice about the number of input files used to store the real and imaginary part of the SAR image - - - one - - - 0 - - - ParameterRaster - nbinput.one.in - Input image - Image file with SAR data. - False - - - OutputRaster - mod - Modulus - Modulus of the input: sqrt(real*real + imag*imag). - - - - OutputRaster - pha - Phase - Phase of the input: atan2(imag, real). - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/ComputeModulusAndPhase-TwoEntries.xml b/python/plugins/processing/algs/otb/description/5.0.0/ComputeModulusAndPhase-TwoEntries.xml deleted file mode 100644 index 336924e5605d..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/ComputeModulusAndPhase-TwoEntries.xml +++ /dev/null @@ -1,56 +0,0 @@ - - ComputeModulusAndPhase-two-TwoEntries - otbcli_ComputeModulusAndPhase - ComputeModulusAndPhase-two (TwoEntries) - Miscellaneous - This application computes the modulus and the phase of a complex SAR data. - - ParameterSelection - nbinput - Number Of inputs - Choice about the number of input files used to store the real and imaginary part of the SAR image - - - two - - - 0 - - - ParameterRaster - nbinput.two.re - Real part input - Image file with real part of the SAR data. - False - - - ParameterRaster - nbinput.two.im - Imaginary part input - Image file with imaginary part of the SAR data. - False - - - OutputRaster - mod - Modulus - Modulus of the input: sqrt(real*real + imag*imag). - - - - OutputRaster - pha - Phase - Phase of the input: atan2(imag, real). - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/ComputeOGRLayersFeaturesStatistics.xml b/python/plugins/processing/algs/otb/description/5.0.0/ComputeOGRLayersFeaturesStatistics.xml deleted file mode 100644 index 9e977f02f11f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/ComputeOGRLayersFeaturesStatistics.xml +++ /dev/null @@ -1,31 +0,0 @@ - - ComputeOGRLayersFeaturesStatistics - otbcli_ComputeOGRLayersFeaturesStatistics - ComputeOGRLayersFeaturesStatistics - Segmentation - Compute statistics of the features in a set of OGR Layers - - ParameterFile - inshp - Name of the input shapefile - Name of the input shapefile - - False - - - ParameterFile - outstats - XML file containing mean and variance of each feature. - XML file containing mean and variance of each feature. - - False - - - ParameterString - feat - List of features to consider for statistics. - List of features to consider for statistics. - - - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/ComputePolylineFeatureFromImage.xml b/python/plugins/processing/algs/otb/description/5.0.0/ComputePolylineFeatureFromImage.xml deleted file mode 100644 index c9fa538ff42b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/ComputePolylineFeatureFromImage.xml +++ /dev/null @@ -1,56 +0,0 @@ - - ComputePolylineFeatureFromImage - otbcli_ComputePolylineFeatureFromImage - Compute Polyline Feature From Image - Feature Extraction - This application compute for each studied polyline, contained in the input VectorData, the chosen descriptors. - - ParameterRaster - in - Input Image - An image to compute the descriptors on. - False - - - ParameterVector - vd - Vector Data - Vector data containing the polylines where the features will be computed. - - False - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - - - ParameterString - expr - Feature expression - The feature formula (b1 < 0.3) where b1 is the standard name of input image first band - - - False - - - ParameterString - field - Feature name - The field name corresponding to the feature codename (NONDVI, ROADSA...) - - - False - - - OutputVector - out - Output Vector Data - The output vector data containing polylines with a new field - - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/ConcatenateImages.xml b/python/plugins/processing/algs/otb/description/5.0.0/ConcatenateImages.xml deleted file mode 100644 index eea11d897702..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/ConcatenateImages.xml +++ /dev/null @@ -1,31 +0,0 @@ - - ConcatenateImages - otbcli_ConcatenateImages - Images Concatenation - Image Manipulation - Concatenate a list of images of the same size into a single multi-channel one. - - ParameterMultipleInput - il - Input images list - The list of images to concatenate - - False - - - OutputRaster - out - Output Image - The concatenated output image - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/ConcatenateVectorData.xml b/python/plugins/processing/algs/otb/description/5.0.0/ConcatenateVectorData.xml deleted file mode 100644 index 9b95a36fee95..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/ConcatenateVectorData.xml +++ /dev/null @@ -1,22 +0,0 @@ - - ConcatenateVectorData - otbcli_ConcatenateVectorData - Concatenate - Vector Data Manipulation - Concatenate VectorDatas - - ParameterMultipleInput - vd - Input VectorDatas to concatenate - VectorData files to be concatenated in an unique VectorData - - False - - - OutputVector - out - Concatenated VectorData - Output conctenated VectorData - - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/ConnectedComponentSegmentation.xml b/python/plugins/processing/algs/otb/description/5.0.0/ConnectedComponentSegmentation.xml deleted file mode 100644 index 69292833ace9..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/ConnectedComponentSegmentation.xml +++ /dev/null @@ -1,66 +0,0 @@ - - ConnectedComponentSegmentation - otbcli_ConnectedComponentSegmentation - Connected Component Segmentation - Segmentation - Connected component segmentation and object based image filtering of the input image according to user-defined criterions. - - ParameterRaster - in - Input Image - The image to segment. - False - - - OutputVector - out - Output Shape - The segmentation shape. - - - - ParameterString - mask - Mask expression - Mask mathematical expression (only if support image is given) - - - True - - - ParameterString - expr - Connected Component Expression - Formula used for connected component segmentation - - - False - - - ParameterNumber - minsize - Minimum Object Size - Min object size (area in pixel) - - - 2 - - - ParameterString - obia - OBIA Expression - OBIA mathematical expression - - - True - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/Convert.xml b/python/plugins/processing/algs/otb/description/5.0.0/Convert.xml deleted file mode 100644 index 5d527e1bf4ed..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/Convert.xml +++ /dev/null @@ -1,78 +0,0 @@ - - Convert - otbcli_Convert - Image Conversion - Image Manipulation - Convert an image to a different format, eventually rescaling the data and/or changing the pixel type. - - ParameterRaster - in - Input image - Input image - False - - - ParameterSelection - type - Rescale type - Transfer function for the rescaling - - - none - linear - log2 - - - 0 - - - ParameterNumber - type.linear.gamma - Gamma correction factor - Gamma correction factor - - - 1 - - - ParameterRaster - mask - Input mask - The masked pixels won't be used to adapt the dynamic (the mask must have the same dimensions as the input image) - True - - - ParameterNumber - hcp.high - High Cut Quantile - Quantiles to cut from histogram high values before computing min/max rescaling (in percent, 2 by default) - - - 2 - - - ParameterNumber - hcp.low - Low Cut Quantile - Quantiles to cut from histogram low values before computing min/max rescaling (in percent, 2 by default) - - - 2 - - - OutputRaster - out - Output Image - Output image - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/DEMConvert.xml b/python/plugins/processing/algs/otb/description/5.0.0/DEMConvert.xml deleted file mode 100644 index 8e017ebe1336..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/DEMConvert.xml +++ /dev/null @@ -1,20 +0,0 @@ - - DEMConvert - otbcli_DEMConvert - DEM Conversion - Image Manipulation - Converts a geo-referenced DEM image into a general raster file compatible with OTB DEM handling. - - ParameterRaster - in - Input geo-referenced DEM - Input geo-referenced DEM to convert to general raster format. - False - - - OutputFile - out - Prefix of the output files - will be used to get the prefix (name withtout extensions) of the files to write. Three files - prefix.geom, prefix.omd and prefix.ras - will be generated. - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/Despeckle-frost.xml b/python/plugins/processing/algs/otb/description/5.0.0/Despeckle-frost.xml deleted file mode 100644 index 88fcada626b4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/Despeckle-frost.xml +++ /dev/null @@ -1,60 +0,0 @@ - - Despeckle-frost - otbcli_Despeckle - Despeckle (frost) - Image Filtering - Perform speckle noise reduction on SAR image. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterSelection - filter - speckle filtering method - - - - frost - - - 0 - - - ParameterNumber - filter.frost.rad - Radius - Radius for frost filter - - - 1 - - - ParameterNumber - filter.frost.deramp - deramp - Decrease factor declaration - - - 0.1 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/Despeckle-lee.xml b/python/plugins/processing/algs/otb/description/5.0.0/Despeckle-lee.xml deleted file mode 100644 index 8eb044d5e2b6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/Despeckle-lee.xml +++ /dev/null @@ -1,60 +0,0 @@ - - Despeckle-lee - otbcli_Despeckle - Despeckle (lee) - Image Filtering - Perform speckle noise reduction on SAR image. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterSelection - filter - speckle filtering method - - - - lee - - - 0 - - - ParameterNumber - filter.lee.rad - Radius - Radius for lee filter - - - 1 - - - ParameterNumber - filter.lee.nblooks - nb looks - Nb looks for lee filter - - - 1 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/DimensionalityReduction-ica.xml b/python/plugins/processing/algs/otb/description/5.0.0/DimensionalityReduction-ica.xml deleted file mode 100644 index 5b98c8c73da6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/DimensionalityReduction-ica.xml +++ /dev/null @@ -1,80 +0,0 @@ - - DimensionalityReduction-ica - otbcli_DimensionalityReduction - DimensionalityReduction (ica) - Image Filtering - Perform Dimension reduction of the input image. - - ParameterRaster - in - Input Image - The input image to apply dimensionality reduction. - False - - - OutputRaster - out - Output Image - output image. Components are ordered by decreasing eigenvalues. - - - - OutputRaster - outinv - Inverse Output Image - reconstruct output image. - - - - ParameterSelection - method - Algorithm - Selection of the reduction dimension method. - - - ica - - - 0 - - - ParameterNumber - method.ica.iter - number of iterations - - - - 20 - - - ParameterNumber - method.ica.mu - Give the increment weight of W in [0, 1] - - - - 1 - - - ParameterNumber - nbcomp - Number of Components. - Number of relevant components kept. By default all components are kept. - - - 0 - - - ParameterBoolean - normalize - Normalize. - center AND reduce data before Dimensionality reduction. - True - - - OutputFile - outmatrix - Transformation matrix output (text format) - Filename to store the transformation matrix (csv format) - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/DimensionalityReduction-maf.xml b/python/plugins/processing/algs/otb/description/5.0.0/DimensionalityReduction-maf.xml deleted file mode 100644 index 10aac2d61074..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/DimensionalityReduction-maf.xml +++ /dev/null @@ -1,55 +0,0 @@ - - DimensionalityReduction-maf - otbcli_DimensionalityReduction - DimensionalityReduction (maf) - Image Filtering - Perform Dimension reduction of the input image. - - ParameterRaster - in - Input Image - The input image to apply dimensionality reduction. - False - - - OutputRaster - out - Output Image - output image. Components are ordered by decreasing eigenvalues. - - - - ParameterSelection - method - Algorithm - Selection of the reduction dimension method. - - - maf - - - 0 - - - ParameterNumber - nbcomp - Number of Components. - Number of relevant components kept. By default all components are kept. - - - 0 - - - ParameterBoolean - normalize - Normalize. - center AND reduce data before Dimensionality reduction. - True - - - OutputFile - outmatrix - Transformation matrix output (text format) - Filename to store the transformation matrix (csv format) - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/DimensionalityReduction-napca.xml b/python/plugins/processing/algs/otb/description/5.0.0/DimensionalityReduction-napca.xml deleted file mode 100644 index b9b6be641f0f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/DimensionalityReduction-napca.xml +++ /dev/null @@ -1,80 +0,0 @@ - - DimensionalityReduction-napca - otbcli_DimensionalityReduction - DimensionalityReduction (napca) - Image Filtering - Perform Dimension reduction of the input image. - - ParameterRaster - in - Input Image - The input image to apply dimensionality reduction. - False - - - OutputRaster - out - Output Image - output image. Components are ordered by decreasing eigenvalues. - - - - OutputRaster - outinv - Inverse Output Image - reconstruct output image. - - - - ParameterSelection - method - Algorithm - Selection of the reduction dimension method. - - - napca - - - 0 - - - ParameterNumber - method.napca.radiusx - Set the x radius of the sliding window. - - - - 1 - - - ParameterNumber - method.napca.radiusy - Set the y radius of the sliding window. - - - - 1 - - - ParameterNumber - nbcomp - Number of Components. - Number of relevant components kept. By default all components are kept. - - - 0 - - - ParameterBoolean - normalize - Normalize. - center AND reduce data before Dimensionality reduction. - True - - - OutputFile - outmatrix - Transformation matrix output (text format) - Filename to store the transformation matrix (csv format) - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/DimensionalityReduction-pca.xml b/python/plugins/processing/algs/otb/description/5.0.0/DimensionalityReduction-pca.xml deleted file mode 100644 index e4cc02a54862..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/DimensionalityReduction-pca.xml +++ /dev/null @@ -1,62 +0,0 @@ - - DimensionalityReduction-pca - otbcli_DimensionalityReduction - DimensionalityReduction (pca) - Image Filtering - Perform Dimension reduction of the input image. - - ParameterRaster - in - Input Image - The input image to apply dimensionality reduction. - False - - - OutputRaster - out - Output Image - output image. Components are ordered by decreasing eigenvalues. - - - - OutputRaster - outinv - Inverse Output Image - reconstruct output image. - - - - ParameterSelection - method - Algorithm - Selection of the reduction dimension method. - - - pca - - - 0 - - - ParameterNumber - nbcomp - Number of Components. - Number of relevant components kept. By default all components are kept. - - - 0 - - - ParameterBoolean - normalize - Normalize. - center AND reduce data before Dimensionality reduction. - True - - - OutputFile - outmatrix - Transformation matrix output (text format) - Filename to store the transformation matrix (csv format) - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/EdgeExtraction-gradient.xml b/python/plugins/processing/algs/otb/description/5.0.0/EdgeExtraction-gradient.xml deleted file mode 100644 index 4c367d9593e2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/EdgeExtraction-gradient.xml +++ /dev/null @@ -1,51 +0,0 @@ - - EdgeExtraction-gradient - otbcli_EdgeExtraction - EdgeExtraction (gradient) - Feature Extraction - Computes edge features on every pixel of the input image selected channel - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterSelection - filter - Edge feature - Choice of edge feature - - - gradient - - - 0 - - - OutputRaster - out - Feature Output Image - Output image containing the edge features. - - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/EdgeExtraction-sobel.xml b/python/plugins/processing/algs/otb/description/5.0.0/EdgeExtraction-sobel.xml deleted file mode 100644 index 80d9e1158b86..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/EdgeExtraction-sobel.xml +++ /dev/null @@ -1,51 +0,0 @@ - - EdgeExtraction-sobel - otbcli_EdgeExtraction - EdgeExtraction (sobel) - Feature Extraction - Computes edge features on every pixel of the input image selected channel - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterSelection - filter - Edge feature - Choice of edge feature - - - sobel - - - 0 - - - OutputRaster - out - Feature Output Image - Output image containing the edge features. - - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/EdgeExtraction-touzi.xml b/python/plugins/processing/algs/otb/description/5.0.0/EdgeExtraction-touzi.xml deleted file mode 100644 index 4238d64bbf3a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/EdgeExtraction-touzi.xml +++ /dev/null @@ -1,60 +0,0 @@ - - EdgeExtraction-touzi - otbcli_EdgeExtraction - EdgeExtraction (touzi) - Feature Extraction - Computes edge features on every pixel of the input image selected channel - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterSelection - filter - Edge feature - Choice of edge feature - - - touzi - - - 0 - - - ParameterNumber - filter.touzi.xradius - The Radius - The Radius - - - 1 - - - OutputRaster - out - Feature Output Image - Output image containing the edge features. - - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/ExtractROI-fit.xml b/python/plugins/processing/algs/otb/description/5.0.0/ExtractROI-fit.xml deleted file mode 100644 index ec498b2d2741..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/ExtractROI-fit.xml +++ /dev/null @@ -1,58 +0,0 @@ - - ExtractROI-fit - otbcli_ExtractROI - ExtractROI (fit) - Image Manipulation - Extract a ROI defined by the user. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterSelection - mode - Extraction mode - - - - fit - - - 0 - - - ParameterRaster - mode.fit.ref - Reference image - Reference image to define the ROI - False - - - ParameterNumber - mode.fit.elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/ExtractROI-standard.xml b/python/plugins/processing/algs/otb/description/5.0.0/ExtractROI-standard.xml deleted file mode 100644 index 4eb65f3044fc..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/ExtractROI-standard.xml +++ /dev/null @@ -1,78 +0,0 @@ - - ExtractROI-standard - otbcli_ExtractROI - ExtractROI (standard) - Image Manipulation - Extract a ROI defined by the user. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterSelection - mode - Extraction mode - - - - standard - - - 0 - - - ParameterNumber - startx - Start X - ROI start x position. - - - 0 - - - ParameterNumber - starty - Start Y - ROI start y position. - - - 0 - - - ParameterNumber - sizex - Size X - size along x in pixels. - - - 0 - - - ParameterNumber - sizey - Size Y - size along y in pixels. - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/FusionOfClassifications-dempstershafer.xml b/python/plugins/processing/algs/otb/description/5.0.0/FusionOfClassifications-dempstershafer.xml deleted file mode 100644 index 407da26e5db9..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/FusionOfClassifications-dempstershafer.xml +++ /dev/null @@ -1,75 +0,0 @@ - - FusionOfClassifications-dempstershafer - otbcli_FusionOfClassifications - FusionOfClassifications (dempstershafer) - Learning - Fuses several classifications maps of the same image on the basis of class labels. - - ParameterMultipleInput - il - Input classifications - List of input classification maps to fuse. Labels in each classification image must represent the same class. - - False - - - ParameterSelection - method - Fusion method - Selection of the fusion method and its parameters. - - - dempstershafer - - - 0 - - - ParameterMultipleInput - method.dempstershafer.cmfl - Confusion Matrices - A list of confusion matrix files (*.CSV format) to define the masses of belief and the class labels. Each file should be formatted the following way: the first line, beginning with a '#' symbol, should be a list of the class labels present in the corresponding input classification image, organized in the same order as the confusion matrix rows/columns. - - False - - - ParameterSelection - method.dempstershafer.mob - Mass of belief measurement - Type of confusion matrix measurement used to compute the masses of belief of each classifier. - - - precision - recall - accuracy - kappa - - - 0 - - - ParameterNumber - nodatalabel - Label for the NoData class - Label for the NoData class. Such input pixels keep their NoData label in the output image and are not handled in the fusion process. By default, 'nodatalabel = 0'. - - - 0 - - - ParameterNumber - undecidedlabel - Label for the Undecided class - Label for the Undecided class. Pixels with more than 1 fused class are marked as Undecided. Please note that the Undecided value must be different from existing labels in the input classifications. By default, 'undecidedlabel = 0'. - - - 0 - - - OutputRaster - out - The output classification image - The output classification image resulting from the fusion of the input classification images. - - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/FusionOfClassifications-majorityvoting.xml b/python/plugins/processing/algs/otb/description/5.0.0/FusionOfClassifications-majorityvoting.xml deleted file mode 100644 index 22a743bdd711..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/FusionOfClassifications-majorityvoting.xml +++ /dev/null @@ -1,52 +0,0 @@ - - FusionOfClassifications-majorityvoting - otbcli_FusionOfClassifications - FusionOfClassifications (majorityvoting) - Learning - Fuses several classifications maps of the same image on the basis of class labels. - - ParameterMultipleInput - il - Input classifications - List of input classification maps to fuse. Labels in each classification image must represent the same class. - - False - - - ParameterSelection - method - Fusion method - Selection of the fusion method and its parameters. - - - majorityvoting - - - 0 - - - ParameterNumber - nodatalabel - Label for the NoData class - Label for the NoData class. Such input pixels keep their NoData label in the output image and are not handled in the fusion process. By default, 'nodatalabel = 0'. - - - 0 - - - ParameterNumber - undecidedlabel - Label for the Undecided class - Label for the Undecided class. Pixels with more than 1 fused class are marked as Undecided. Please note that the Undecided value must be different from existing labels in the input classifications. By default, 'undecidedlabel = 0'. - - - 0 - - - OutputRaster - out - The output classification image - The output classification image resulting from the fusion of the input classification images. - - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/GrayScaleMorphologicalOperation-closing.xml b/python/plugins/processing/algs/otb/description/5.0.0/GrayScaleMorphologicalOperation-closing.xml deleted file mode 100644 index 00e204da52b0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/GrayScaleMorphologicalOperation-closing.xml +++ /dev/null @@ -1,72 +0,0 @@ - - GrayScaleMorphologicalOperation-closing - otbcli_GrayScaleMorphologicalOperation - GrayScaleMorphologicalOperation (closing) - Feature Extraction - Performs morphological operations on a grayscale input image - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - closing - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/GrayScaleMorphologicalOperation-dilate.xml b/python/plugins/processing/algs/otb/description/5.0.0/GrayScaleMorphologicalOperation-dilate.xml deleted file mode 100644 index c811ad2b1191..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/GrayScaleMorphologicalOperation-dilate.xml +++ /dev/null @@ -1,72 +0,0 @@ - - GrayScaleMorphologicalOperation-dilate - otbcli_GrayScaleMorphologicalOperation - GrayScaleMorphologicalOperation (dilate) - Feature Extraction - Performs morphological operations on a grayscale input image - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - dilate - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/GrayScaleMorphologicalOperation-erode.xml b/python/plugins/processing/algs/otb/description/5.0.0/GrayScaleMorphologicalOperation-erode.xml deleted file mode 100644 index 8e3602090f16..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/GrayScaleMorphologicalOperation-erode.xml +++ /dev/null @@ -1,72 +0,0 @@ - - GrayScaleMorphologicalOperation-erode - otbcli_GrayScaleMorphologicalOperation - GrayScaleMorphologicalOperation (erode) - Feature Extraction - Performs morphological operations on a grayscale input image - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - erode - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/GrayScaleMorphologicalOperation-opening.xml b/python/plugins/processing/algs/otb/description/5.0.0/GrayScaleMorphologicalOperation-opening.xml deleted file mode 100644 index 8e50230edd52..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/GrayScaleMorphologicalOperation-opening.xml +++ /dev/null @@ -1,72 +0,0 @@ - - GrayScaleMorphologicalOperation-opening - otbcli_GrayScaleMorphologicalOperation - GrayScaleMorphologicalOperation (opening) - Feature Extraction - Performs morphological operations on a grayscale input image - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - opening - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/HaralickTextureExtraction.xml b/python/plugins/processing/algs/otb/description/5.0.0/HaralickTextureExtraction.xml deleted file mode 100644 index 9ce1ceac932b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/HaralickTextureExtraction.xml +++ /dev/null @@ -1,116 +0,0 @@ - - HaralickTextureExtraction - otbcli_HaralickTextureExtraction - Haralick Texture Extraction - Feature Extraction - Computes textures on every pixel of the input image selected channel - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterNumber - parameters.xrad - X Radius - X Radius - - - 2 - - - ParameterNumber - parameters.yrad - Y Radius - Y Radius - - - 2 - - - ParameterNumber - parameters.xoff - X Offset - X Offset - - - 1 - - - ParameterNumber - parameters.yoff - Y Offset - Y Offset - - - 1 - - - ParameterNumber - parameters.min - Image Minimum - Image Minimum - - - 0 - - - ParameterNumber - parameters.max - Image Maximum - Image Maximum - - - 255 - - - ParameterNumber - parameters.nbbin - Histogram number of bin - Histogram number of bin - - - 8 - - - ParameterSelection - texture - Texture Set Selection - Choice of The Texture Set - - - simple - advanced - higher - - - 0 - - - OutputRaster - out - Output Image - Output image containing the selected texture features. - - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/HooverCompareSegmentation.xml b/python/plugins/processing/algs/otb/description/5.0.0/HooverCompareSegmentation.xml deleted file mode 100644 index 7c772ecbc39f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/HooverCompareSegmentation.xml +++ /dev/null @@ -1,89 +0,0 @@ - - HooverCompareSegmentation - otbcli_HooverCompareSegmentation - Hoover compare segmentation - Segmentation - Compare two segmentations with Hoover metrics - - ParameterRaster - ingt - Input ground truth - A partial ground truth segmentation image. - False - - - ParameterRaster - inms - Input machine segmentation - A machine segmentation image. - False - - - ParameterNumber - bg - Background label - Label value of the background in the input segmentations - - - 0 - - - ParameterNumber - th - Overlapping threshold - Overlapping threshold used to find Hoover instances. - - - 0.75 - - - OutputRaster - outgt - Colored ground truth output - The colored ground truth output image. - - - - OutputRaster - outms - Colored machine segmentation output - The colored machine segmentation output image. - - - - ParameterNumber - rc - Correct detection score - Overall score for correct detection (RC) - - - 0.0 - - - ParameterNumber - rf - Over-segmentation score - Overall score for over segmentation (RF) - - - 0.0 - - - ParameterNumber - ra - Under-segmentation score - Overall score for under segmentation (RA) - - - 0.0 - - - ParameterNumber - rm - Missed detection score - Overall score for missed detection (RM) - - - 0.0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/ImageClassifier.xml b/python/plugins/processing/algs/otb/description/5.0.0/ImageClassifier.xml deleted file mode 100644 index 2f4b659566d2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/ImageClassifier.xml +++ /dev/null @@ -1,53 +0,0 @@ - - ImageClassifier - otbcli_ImageClassifier - Image Classification - Learning - Performs a classification of the input image according to a model file. - - ParameterRaster - in - Input Image - The input image to classify. - False - - - ParameterRaster - mask - Input Mask - The mask allows restricting classification of the input image to the area where mask pixel values are greater than 0. - True - - - ParameterFile - model - Model file - A model file (produced by TrainImagesClassifier application, maximal class label = 65535). - - False - - - ParameterFile - imstat - Statistics file - A XML file containing mean and standard deviation to center and reduce samples before classification (produced by ComputeImagesStatistics application). - - True - - - OutputRaster - out - Output Image - Output image containing class labels - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/ImageEnvelope.xml b/python/plugins/processing/algs/otb/description/5.0.0/ImageEnvelope.xml deleted file mode 100644 index 66d4e3ecc61c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/ImageEnvelope.xml +++ /dev/null @@ -1,39 +0,0 @@ - - ImageEnvelope - otbcli_ImageEnvelope - Image Envelope - Geometry - Extracts an image envelope. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputVector - out - Output Vector Data - Vector data file containing the envelope - - - - ParameterNumber - sr - Sampling Rate - Sampling rate for image edges (in pixel) - - - 0 - - - ParameterString - proj - Projection - Projection to be used to compute the envelope (default is WGS84) - - - True - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/KMeansClassification.xml b/python/plugins/processing/algs/otb/description/5.0.0/KMeansClassification.xml deleted file mode 100644 index 94e0f9024781..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/KMeansClassification.xml +++ /dev/null @@ -1,79 +0,0 @@ - - KMeansClassification - otbcli_KMeansClassification - Unsupervised KMeans image classification - Learning - Unsupervised KMeans image classification - - ParameterRaster - in - Input Image - Input image to classify. - False - - - OutputRaster - out - Output Image - Output image containing the class indexes. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterRaster - vm - Validity Mask - Validity mask. Only non-zero pixels will be used to estimate KMeans modes. - True - - - ParameterNumber - ts - Training set size - Size of the training set (in pixels). - - - 100 - - - ParameterNumber - nc - Number of classes - Number of modes, which will be used to generate class membership. - - - 5 - - - ParameterNumber - maxit - Maximum number of iterations - Maximum number of iterations for the learning step. - - - 1000 - - - ParameterNumber - ct - Convergence threshold - Convergence threshold for class centroid (L2 distance, by default 0.0001). - - - 0.0001 - - - OutputFile - outmeans - Centroid filename - Output text file containing centroid positions - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/KmzExport.xml b/python/plugins/processing/algs/otb/description/5.0.0/KmzExport.xml deleted file mode 100644 index a3a736d57f49..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/KmzExport.xml +++ /dev/null @@ -1,52 +0,0 @@ - - KmzExport - otbcli_KmzExport - Image to KMZ Export - Miscellaneous - Export the input image in a KMZ product. - - ParameterRaster - in - Input image - Input image - False - - - OutputFile - out - Output .kmz product - Output Kmz product directory (with .kmz extension) - - - ParameterNumber - tilesize - Tile Size - Size of the tiles in the kmz product, in number of pixels (default = 512). - - - 512 - - - ParameterRaster - logo - Image logo - Path to the image logo to add to the KMZ product. - True - - - ParameterRaster - legend - Image legend - Path to the image legend to add to the KMZ product. - True - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/LSMSSegmentation.xml b/python/plugins/processing/algs/otb/description/5.0.0/LSMSSegmentation.xml deleted file mode 100644 index 14449f66bb82..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/LSMSSegmentation.xml +++ /dev/null @@ -1,88 +0,0 @@ - - LSMSSegmentation - otbcli_LSMSSegmentation - Exact Large-Scale Mean-Shift segmentation, step 2 - Segmentation - Second step of the exact Large-Scale Mean-Shift segmentation workflow. - - ParameterRaster - in - Filtered image - The filtered image (cf. Adaptive MeanShift Smoothing application). - False - - - ParameterRaster - inpos - Spatial image - The spatial image. Spatial input is the displacement map (output of the Adaptive MeanShift Smoothing application). - True - - - OutputRaster - out - Output Image - The output image. The output image is the segmentation of the filtered image. It is recommended to set the pixel type to uint32. - - - - ParameterNumber - ranger - Range radius - Range radius defining the radius (expressed in radiometry unit) in the multi-spectral space. - - - 15 - - - ParameterNumber - spatialr - Spatial radius - Spatial radius of the neighborhood. - - - 5 - - - ParameterNumber - minsize - Minimum Region Size - Minimum Region Size. If, after the segmentation, a region is of size lower than this criterion, the region is deleted. - - - 0 - - - ParameterNumber - tilesizex - Size of tiles in pixel (X-axis) - Size of tiles along the X-axis. - - - 500 - - - ParameterNumber - tilesizey - Size of tiles in pixel (Y-axis) - Size of tiles along the Y-axis. - - - 500 - - - ParameterFile - tmpdir - Directory where to write temporary files - This applications need to write temporary files for each tile. This parameter allows choosing the path where to write those files. If disabled, the current path will be used. - - True - - - ParameterBoolean - cleanup - Temporary files cleaning - If activated, the application will try to clean all temporary files it created - True - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/LSMSSmallRegionsMerging.xml b/python/plugins/processing/algs/otb/description/5.0.0/LSMSSmallRegionsMerging.xml deleted file mode 100644 index 9cb0621e80c3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/LSMSSmallRegionsMerging.xml +++ /dev/null @@ -1,55 +0,0 @@ - - LSMSSmallRegionsMerging - otbcli_LSMSSmallRegionsMerging - Exact Large-Scale Mean-Shift segmentation, step 3 (optional) - Segmentation - Third (optional) step of the exact Large-Scale Mean-Shift segmentation workflow. - - ParameterRaster - in - Input image - The input image. - False - - - ParameterRaster - inseg - Segmented image - The segmented image input. Segmented image input is the segmentation of the input image. - False - - - OutputRaster - out - Output Image - The output image. The output image is the input image where the minimal regions have been merged. - - - - ParameterNumber - minsize - Minimum Region Size - Minimum Region Size. If, after the segmentation, a region is of size lower than this criterion, the region is merged with the "nearest" region (radiometrically). - - - 50 - - - ParameterNumber - tilesizex - Size of tiles in pixel (X-axis) - Size of tiles along the X-axis. - - - 500 - - - ParameterNumber - tilesizey - Size of tiles in pixel (Y-axis) - Size of tiles along the Y-axis. - - - 500 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/LSMSVectorization.xml b/python/plugins/processing/algs/otb/description/5.0.0/LSMSVectorization.xml deleted file mode 100644 index 04a4ab8db638..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/LSMSVectorization.xml +++ /dev/null @@ -1,45 +0,0 @@ - - LSMSVectorization - otbcli_LSMSVectorization - Exact Large-Scale Mean-Shift segmentation, step 4 - Segmentation - Fourth step of the exact Large-Scale Mean-Shift segmentation workflow. - - ParameterRaster - in - Input Image - The input image. - False - - - ParameterRaster - inseg - Segmented image - The segmented image input. Segmented image input is the segmentation of the input image. - False - - - OutputVector - out - Output GIS vector file - The output GIS vector file, representing the vectorized version of the segmented image where the features of the polygons are the radiometric means and variances. - - - ParameterNumber - tilesizex - Size of tiles in pixel (X-axis) - Size of tiles along the X-axis. - - - 500 - - - ParameterNumber - tilesizey - Size of tiles in pixel (Y-axis) - Size of tiles along the Y-axis. - - - 500 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/LineSegmentDetection.xml b/python/plugins/processing/algs/otb/description/5.0.0/LineSegmentDetection.xml deleted file mode 100644 index 20822d5dc351..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/LineSegmentDetection.xml +++ /dev/null @@ -1,28 +0,0 @@ - - LineSegmentDetection - otbcli_LineSegmentDetection - Line segment detection - Feature Extraction - Detect line segments in raster - - ParameterRaster - in - Input Image - Input image on which lines will be detected. - False - - - OutputVector - out - Output Detected lines - Output detected line segments (vector data). - - - - ParameterBoolean - norescale - No rescaling in [0, 255] - By default, the input image amplitude is rescaled between [0,255]. Turn on this parameter to skip rescaling - True - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/LocalStatisticExtraction.xml b/python/plugins/processing/algs/otb/description/5.0.0/LocalStatisticExtraction.xml deleted file mode 100644 index f94caa6580fc..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/LocalStatisticExtraction.xml +++ /dev/null @@ -1,48 +0,0 @@ - - LocalStatisticExtraction - otbcli_LocalStatisticExtraction - Local Statistic Extraction - Feature Extraction - Computes local statistical moments on every pixel in the selected channel of the input image - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterNumber - radius - Neighborhood radius - The computational window radius. - - - 3 - - - OutputRaster - out - Feature Output Image - Output image containing the local statistical moments. - - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/MeanShiftSmoothing.xml b/python/plugins/processing/algs/otb/description/5.0.0/MeanShiftSmoothing.xml deleted file mode 100644 index 641fa82caec2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/MeanShiftSmoothing.xml +++ /dev/null @@ -1,89 +0,0 @@ - - MeanShiftSmoothing - otbcli_MeanShiftSmoothing - Exact Large-Scale Mean-Shift segmentation, step 1 (smoothing) - Image Filtering - Perform mean shift filtering - - ParameterRaster - in - Input Image - The input image. - False - - - OutputRaster - fout - Filtered output - The filtered output image. - - - - OutputRaster - foutpos - Spatial image - The spatial image output. Spatial image output is a displacement map (pixel position after convergence). - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterNumber - spatialr - Spatial radius - Spatial radius of the neighborhood. - - - 5 - - - ParameterNumber - ranger - Range radius - Range radius defining the radius (expressed in radiometry unit) in the multi-spectral space. - - - 15 - - - ParameterNumber - thres - Mode convergence threshold - Algorithm iterative scheme will stop if mean-shift vector is below this threshold or if iteration number reached maximum number of iterations. - - - 0.1 - - - ParameterNumber - maxiter - Maximum number of iterations - Algorithm iterative scheme will stop if convergence hasn't been reached after the maximum number of iterations. - - - 100 - - - ParameterNumber - rangeramp - Range radius coefficient - This coefficient makes dependent the ranger of the colorimetry of the filtered pixel : y = rangeramp*x+ranger. - - - 0 - - - ParameterBoolean - modesearch - Mode search. - If activated pixel iterative convergence is stopped if the path . Be careful, with this option, the result will slightly depend on thread number - True - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/MultivariateAlterationDetector.xml b/python/plugins/processing/algs/otb/description/5.0.0/MultivariateAlterationDetector.xml deleted file mode 100644 index ec433281f34b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/MultivariateAlterationDetector.xml +++ /dev/null @@ -1,37 +0,0 @@ - - MultivariateAlterationDetector - otbcli_MultivariateAlterationDetector - Multivariate alteration detector - Feature Extraction - Multivariate Alteration Detector - - ParameterRaster - in1 - Input Image 1 - Image which describe initial state of the scene. - False - - - ParameterRaster - in2 - Input Image 2 - Image which describe scene after perturbations. - False - - - OutputRaster - out - Change Map - Image of detected changes. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/OGRLayerClassifier.xml b/python/plugins/processing/algs/otb/description/5.0.0/OGRLayerClassifier.xml deleted file mode 100644 index 05be660b57b3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/OGRLayerClassifier.xml +++ /dev/null @@ -1,46 +0,0 @@ - - OGRLayerClassifier - otbcli_OGRLayerClassifier - OGRLayerClassifier - Segmentation - Classify an OGR layer based on a machine learning model and a list of features to consider. - - ParameterFile - inshp - Name of the input shapefile - Name of the input shapefile - - False - - - ParameterFile - instats - XML file containing mean and variance of each feature. - XML file containing mean and variance of each feature. - - False - - - OutputFile - insvm - Input model filename. - Input model filename. - - - ParameterString - feat - Features - Features to be calculated - - - - - ParameterString - cfield - Field containing the predicted class. - Field containing the predicted class - predicted - - False - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/OpticalCalibration.xml b/python/plugins/processing/algs/otb/description/5.0.0/OpticalCalibration.xml deleted file mode 100644 index 1c324aa48369..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/OpticalCalibration.xml +++ /dev/null @@ -1,167 +0,0 @@ - - OpticalCalibration - otbcli_OpticalCalibration - Optical calibration - Calibration - Perform optical calibration TOA/TOC (Top Of Atmosphere/Top Of Canopy). Supported sensors: QuickBird, Ikonos, WorldView2, Formosat, Spot5, Pleiades, Spot6. For other sensors the application also allows to provide calibration parameters manually. - - ParameterRaster - in - Input - Input image filename (values in DN) - False - - - OutputRaster - out - Output - Output calibrated image filename - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterSelection - level - Calibration Level - - - - toa - toatoim - - - 0 - - - ParameterNumber - acqui.minute - Minute - Minute (0-59) - - - 0 - - - ParameterNumber - acqui.hour - Hour - Hour (0-23) - - - 12 - - - ParameterNumber - acqui.day - Day - Day (1-31) - - - 1 - - - ParameterNumber - acqui.month - Month - Month (1-12) - - - 1 - - - ParameterNumber - acqui.year - Year - Year - - - 2000 - - - ParameterNumber - acqui.sun.elev - Sun elevation angle (deg) - Sun elevation angle (in degrees) - - - 90 - - - ParameterNumber - acqui.sun.azim - Sun azimuth angle (deg) - Sun azimuth angle (in degrees) - - - 0 - - - ParameterNumber - acqui.view.elev - Viewing elevation angle (deg) - Viewing elevation angle (in degrees) - - - 90 - - - ParameterNumber - acqui.view.azim - Viewing azimuth angle (deg) - Viewing azimuth angle (in degrees) - - - 0 - - - ParameterFile - acqui.gainbias - Gains | biases - Gains | biases - - True - - - ParameterFile - acqui.solarilluminations - Solar illuminations - Solar illuminations (one value per band) - - True - - - ParameterFile - atmo.rsr - Relative Spectral Response File - Sensor relative spectral response file -By default the application gets this information in the metadata - - True - - - ParameterNumber - atmo.radius - Window radius (adjacency effects) - Window radius for adjacency effects correctionsSetting this parameters will enable the correction ofadjacency effects - - - 2 - - - ParameterNumber - atmo.pixsize - Pixel size (in km) - Pixel size (in km )used tocompute adjacency effects, it doesn't have tomatch the image spacing - - - 1 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/OrthoRectification-epsg.xml b/python/plugins/processing/algs/otb/description/5.0.0/OrthoRectification-epsg.xml deleted file mode 100644 index dc3d34539a83..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/OrthoRectification-epsg.xml +++ /dev/null @@ -1,115 +0,0 @@ - - OrthoRectification-epsg - otbcli_OrthoRectification - OrthoRectification (epsg) - Geometry - This application allows ortho-rectifying optical images from supported sensors. - - - ParameterRaster - io.in - Input Image - The input image to ortho-rectify - False - - - OutputRaster - io.out - Output Image - The ortho-rectified output image - - - - ParameterSelection - map - Output Cartographic Map Projection - Parameters of the output map projection to be used. - - - epsg - - - 0 - - - ParameterNumber - map.epsg.code - EPSG Code - See www.spatialreference.org to find which EPSG code is associated to your projection - - - 4326 - - - ParameterSelection - outputs.mode - Parameters estimation modes - - - - autosize - autospacing - - - 0 - - - ParameterNumber - outputs.default - Default pixel value - Default value to write when outside of input image. - - - 0 - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows defining how the input image will be interpolated during resampling. - - - bco - nn - linear - - - 0 - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows controlling the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - - - ParameterNumber - opt.ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - - - ParameterNumber - opt.gridspacing - Resampling grid spacing - Resampling is done according to a coordinate mapping deformation grid, whose pixel size is set by this parameter, and expressed in the coordinate system of the output image The closer to the output spacing this parameter is, the more precise will be the ortho-rectified image,but increasing this parameter will reduce processing time. - - - 4 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/OrthoRectification-fit-to-ortho.xml b/python/plugins/processing/algs/otb/description/5.0.0/OrthoRectification-fit-to-ortho.xml deleted file mode 100644 index 07cfdd3f1ffd..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/OrthoRectification-fit-to-ortho.xml +++ /dev/null @@ -1,100 +0,0 @@ - - OrthoRectification-fit-to-ortho - otbcli_OrthoRectification - OrthoRectification (fit-to-ortho) - Geometry - This application allows ortho-rectifying optical images from supported sensors. - - - ParameterRaster - io.in - Input Image - The input image to ortho-rectify - False - - - OutputRaster - io.out - Output Image - The ortho-rectified output image - - - - ParameterSelection - outputs.mode - Parameters estimation modes - - - - orthofit - - - 0 - - - ParameterRaster - outputs.ortho - Model ortho-image - A model ortho-image that can be used to compute size, origin and spacing of the output - True - - - ParameterNumber - outputs.default - Default pixel value - Default value to write when outside of input image. - - - 0 - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows defining how the input image will be interpolated during resampling. - - - bco - nn - linear - - - 0 - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows controlling the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - - - ParameterNumber - opt.ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - - - ParameterNumber - opt.gridspacing - Resampling grid spacing - Resampling is done according to a coordinate mapping deformation grid, whose pixel size is set by this parameter, and expressed in the coordinate system of the output image The closer to the output spacing this parameter is, the more precise will be the ortho-rectified image,but increasing this parameter will reduce processing time. - - - 4 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/OrthoRectification-lambert-WGS84.xml b/python/plugins/processing/algs/otb/description/5.0.0/OrthoRectification-lambert-WGS84.xml deleted file mode 100644 index 3db86bb149f7..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/OrthoRectification-lambert-WGS84.xml +++ /dev/null @@ -1,108 +0,0 @@ - - OrthoRectification-lambert-WGS84 - otbcli_OrthoRectification - OrthoRectification (lambert-WGS84) - Geometry - This application allows ortho-rectifying optical images from supported sensors. - - - ParameterRaster - io.in - Input Image - The input image to ortho-rectify - False - - - OutputRaster - io.out - Output Image - The ortho-rectified output image - - - - ParameterSelection - map - Output Cartographic Map Projection - Parameters of the output map projection to be used. - - - lambert2 - lambert93 - wgs - - - 0 - - - ParameterSelection - outputs.mode - Parameters estimation modes - - - - autosize - autospacing - - - 0 - - - ParameterNumber - outputs.default - Default pixel value - Default value to write when outside of input image. - - - 0 - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows defining how the input image will be interpolated during resampling. - - - bco - nn - linear - - - 0 - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows controlling the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - - - ParameterNumber - opt.ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - - - ParameterNumber - opt.gridspacing - Resampling grid spacing - Resampling is done according to a coordinate mapping deformation grid, whose pixel size is set by this parameter, and expressed in the coordinate system of the output image The closer to the output spacing this parameter is, the more precise will be the ortho-rectified image,but increasing this parameter will reduce processing time. - - - 4 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/OrthoRectification-utm.xml b/python/plugins/processing/algs/otb/description/5.0.0/OrthoRectification-utm.xml deleted file mode 100644 index 621fffccc273..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/OrthoRectification-utm.xml +++ /dev/null @@ -1,122 +0,0 @@ - - OrthoRectification-utm - otbcli_OrthoRectification - OrthoRectification (utm) - Geometry - This application allows ortho-rectifying optical images from supported sensors. - - - ParameterRaster - io.in - Input Image - The input image to ortho-rectify - False - - - OutputRaster - io.out - Output Image - The ortho-rectified output image - - - - ParameterSelection - map - Output Cartographic Map Projection - Parameters of the output map projection to be used. - - - utm - - - 0 - - - ParameterNumber - map.utm.zone - Zone number - The zone number ranges from 1 to 60 and allows defining the transverse mercator projection (along with the hemisphere) - - - 31 - - - ParameterBoolean - map.utm.northhem - Northern Hemisphere - The transverse mercator projections are defined by their zone number as well as the hemisphere. Activate this parameter if your image is in the northern hemisphere. - True - - - ParameterSelection - outputs.mode - Parameters estimation modes - - - - autosize - autospacing - - - 0 - - - ParameterNumber - outputs.default - Default pixel value - Default value to write when outside of input image. - - - 0 - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows defining how the input image will be interpolated during resampling. - - - bco - nn - linear - - - 0 - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows controlling the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - - - ParameterNumber - opt.ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - - - ParameterNumber - opt.gridspacing - Resampling grid spacing - Resampling is done according to a coordinate mapping deformation grid, whose pixel size is set by this parameter, and expressed in the coordinate system of the output image The closer to the output spacing this parameter is, the more precise will be the ortho-rectified image,but increasing this parameter will reduce processing time. - - - 4 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/Pansharpening-bayes.xml b/python/plugins/processing/algs/otb/description/5.0.0/Pansharpening-bayes.xml deleted file mode 100644 index 50c390f607bc..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/Pansharpening-bayes.xml +++ /dev/null @@ -1,67 +0,0 @@ - - Pansharpening-bayes - otbcli_Pansharpening - Pansharpening (bayes) - Geometry - Perform P+XS pansharpening - - ParameterRaster - inp - Input PAN Image - Input panchromatic image. - False - - - ParameterRaster - inxs - Input XS Image - Input XS image. - False - - - OutputRaster - out - Output image - Output image. - - - - ParameterSelection - method - Algorithm - Selection of the pan-sharpening method. - - - bayes - - - 0 - - - ParameterNumber - method.bayes.lambda - Weight - Set the weighting value. - - - 0.9999 - - - ParameterNumber - method.bayes.s - S coefficient - Set the S coefficient. - - - 1 - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/Pansharpening-lmvm.xml b/python/plugins/processing/algs/otb/description/5.0.0/Pansharpening-lmvm.xml deleted file mode 100644 index 821e2928a183..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/Pansharpening-lmvm.xml +++ /dev/null @@ -1,67 +0,0 @@ - - Pansharpening-lmvm - otbcli_Pansharpening - Pansharpening (lmvm) - Geometry - Perform P+XS pansharpening - - ParameterRaster - inp - Input PAN Image - Input panchromatic image. - False - - - ParameterRaster - inxs - Input XS Image - Input XS image. - False - - - OutputRaster - out - Output image - Output image. - - - - ParameterSelection - method - Algorithm - Selection of the pan-sharpening method. - - - lmvm - - - 0 - - - ParameterNumber - method.lmvm.radiusx - X radius - Set the x radius of the sliding window. - - - 3 - - - ParameterNumber - method.lmvm.radiusy - Y radius - Set the y radius of the sliding window. - - - 3 - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/Pansharpening-rcs.xml b/python/plugins/processing/algs/otb/description/5.0.0/Pansharpening-rcs.xml deleted file mode 100644 index c37f6f900ff8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/Pansharpening-rcs.xml +++ /dev/null @@ -1,49 +0,0 @@ - - Pansharpening-rcs - otbcli_Pansharpening - Pansharpening (rcs) - Geometry - Perform P+XS pansharpening - - ParameterRaster - inp - Input PAN Image - Input panchromatic image. - False - - - ParameterRaster - inxs - Input XS Image - Input XS image. - False - - - OutputRaster - out - Output image - Output image. - - - - ParameterSelection - method - Algorithm - Selection of the pan-sharpening method. - - - rcs - - - 0 - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/RadiometricIndices.xml b/python/plugins/processing/algs/otb/description/5.0.0/RadiometricIndices.xml deleted file mode 100644 index 504495fac13c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/RadiometricIndices.xml +++ /dev/null @@ -1,124 +0,0 @@ - - RadiometricIndices - otbcli_RadiometricIndices - Radiometric Indices - Feature Extraction - Compute radiometric indices. - - ParameterRaster - in - Input Image - Input image - False - - - OutputRaster - out - Output Image - Radiometric indices output image - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterNumber - channels.blue - Blue Channel - Blue channel index - - - 1 - - - ParameterNumber - channels.green - Green Channel - Green channel index - - - 1 - - - ParameterNumber - channels.red - Red Channel - Red channel index - - - 1 - - - ParameterNumber - channels.nir - NIR Channel - NIR channel index - - - 1 - - - ParameterNumber - channels.mir - Mir Channel - Mir channel index - - - 1 - - - ParameterSelection - list - Available Radiometric Indices - List of available radiometric indices with their relevant channels in brackets: - Vegetation:NDVI - Normalized difference vegetation index (Red, NIR) - Vegetation:TNDVI - Transformed normalized difference vegetation index (Red, NIR) - Vegetation:RVI - Ratio vegetation index (Red, NIR) - Vegetation:SAVI - Soil adjusted vegetation index (Red, NIR) - Vegetation:TSAVI - Transformed soil adjusted vegetation index (Red, NIR) - Vegetation:MSAVI - Modified soil adjusted vegetation index (Red, NIR) - Vegetation:MSAVI2 - Modified soil adjusted vegetation index 2 (Red, NIR) - Vegetation:GEMI - Global environment monitoring index (Red, NIR) - Vegetation:IPVI - Infrared percentage vegetation index (Red, NIR) - - Water:NDWI - Normalized difference water index (Gao 1996) (NIR, MIR) - Water:NDWI2 - Normalized difference water index (Mc Feeters 1996) (Green, NIR) - Water:MNDWI - Modified normalized difference water index (Xu 2006) (Green, MIR) - Water:NDPI - Normalized difference pond index (Lacaux et al.) (MIR, Green) - Water:NDTI - Normalized difference turbidity index (Lacaux et al.) (Red, Green) - - Soil:RI - Redness index (Red, Green) - Soil:CI - Color index (Red, Green) - Soil:BI - Brightness index (Red, Green) - Soil:BI2 - Brightness index 2 (NIR, Red, Green) - - - ndvi - tndvi - rvi - savi - tsavi - msavi - msavi2 - gemi - ipvi - ndwi - ndwi2 - mndwi - ndpi - ndti - ri - ci - bi - bi2 - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/Rasterization-image.xml b/python/plugins/processing/algs/otb/description/5.0.0/Rasterization-image.xml deleted file mode 100644 index 9799f180c68e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/Rasterization-image.xml +++ /dev/null @@ -1,78 +0,0 @@ - - Rasterization-image - otbcli_Rasterization - Rasterization (image) - Vector Data Manipulation - Rasterize a vector dataset. - - ParameterFile - in - Input vector dataset - The input vector dataset to be rasterized - - False - - - OutputRaster - out - Ouptut image - An output image containing the rasterized vector dataset - - - - ParameterRaster - im - Input reference image - A reference image from which to import output grid and projection reference system information. - True - - - ParameterNumber - background - Background value - Default value for pixels not belonging to any geometry - - - 0 - - - ParameterSelection - mode - Rasterization mode - Choice of rasterization modes - - - binary - attribute - - - 0 - - - ParameterNumber - mode.binary.foreground - Foreground value - Value for pixels inside a geometry - - - 255 - - - ParameterString - mode.attribute.field - The attribute field to burn - Name of the attribute field to burn - DN - - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/Rasterization-manual.xml b/python/plugins/processing/algs/otb/description/5.0.0/Rasterization-manual.xml deleted file mode 100644 index 229e44131c1d..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/Rasterization-manual.xml +++ /dev/null @@ -1,134 +0,0 @@ - - Rasterization-manual - otbcli_Rasterization - Rasterization (manual) - Vector Data Manipulation - Rasterize a vector dataset. - - ParameterFile - in - Input vector dataset - The input vector dataset to be rasterized - - False - - - OutputRaster - out - Ouptut image - An output image containing the rasterized vector dataset - - - - ParameterNumber - szx - Output size x - Output size along x axis (useless if support image is given) - - - 0 - - - ParameterNumber - szy - Output size y - Output size along y axis (useless if support image is given) - - - 0 - - - ParameterNumber - epsg - Output EPSG code - EPSG code for the output projection reference system (EPSG 4326 for WGS84, 32631 for UTM31N...,useless if support image is given) - - - 0 - - - ParameterNumber - orx - Output Upper-left x - Output upper-left corner x coordinate (useless if support image is given) - - - 0.0 - - - ParameterNumber - ory - Output Upper-left y - Output upper-left corner y coordinate (useless if support image is given) - - - 0.0 - - - ParameterNumber - spx - Spacing (GSD) x - Spacing (ground sampling distance) along x axis (useless if support image is given) - - - 0.0 - - - ParameterNumber - spy - Spacing (GSD) y - Spacing (ground sampling distance) along y axis (useless if support image is given) - - - 0.0 - - - ParameterNumber - background - Background value - Default value for pixels not belonging to any geometry - - - 0 - - - ParameterSelection - mode - Rasterization mode - Choice of rasterization modes - - - binary - attribute - - - 0 - - - ParameterNumber - mode.binary.foreground - Foreground value - Value for pixels inside a geometry - - - 255 - - - ParameterString - mode.attribute.field - The attribute field to burn - Name of the attribute field to burn - DN - - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/ReadImageInfo.xml b/python/plugins/processing/algs/otb/description/5.0.0/ReadImageInfo.xml deleted file mode 100644 index 475dc9625f34..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/ReadImageInfo.xml +++ /dev/null @@ -1,57 +0,0 @@ - - ReadImageInfo - otbcli_ReadImageInfo - Read image information - Image Manipulation - Get information about the image - - ParameterRaster - in - Input Image - Input image to analyse - False - - - ParameterBoolean - keywordlist - Display the OSSIM keywordlist - Output the OSSIM keyword list. It contains metadata information (sensor model, geometry ). Information is stored in a keyword list (pairs of key/value) - True - - - ParameterString - gcp.ids - GCPs Id - GCPs identifier - - - False - - - ParameterString - gcp.info - GCPs Info - GCPs Information - - - False - - - ParameterString - gcp.imcoord - GCPs Image Coordinates - GCPs Image coordinates - - - False - - - ParameterString - gcp.geocoord - GCPs Geographic Coordinates - GCPs Geographic Coordinates - - - False - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/Rescale.xml b/python/plugins/processing/algs/otb/description/5.0.0/Rescale.xml deleted file mode 100644 index af6d67ffbf6f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/Rescale.xml +++ /dev/null @@ -1,48 +0,0 @@ - - Rescale - otbcli_Rescale - Rescale Image - Image Manipulation - Rescale the image between two given values. - - ParameterRaster - in - Input Image - The image to scale. - False - - - OutputRaster - out - Output Image - The rescaled image filename. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterNumber - outmin - Output min value - Minimum value of the output image. - - - 0 - - - ParameterNumber - outmax - Output max value - Maximum value of the output image. - - - 255 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/RigidTransformResample-id.xml b/python/plugins/processing/algs/otb/description/5.0.0/RigidTransformResample-id.xml deleted file mode 100644 index 07b6b02c6574..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/RigidTransformResample-id.xml +++ /dev/null @@ -1,83 +0,0 @@ - - RigidTransformResample-id - otbcli_RigidTransformResample - RigidTransformResample (id) - Geometry - Resample an image with a rigid transform - - ParameterRaster - in - Input image - The input image to translate. - False - - - OutputRaster - out - Output image - The transformed output image. - - - - ParameterSelection - transform.type - Type of transformation - Type of transformation. Available transformations are spatial scaling, translation and rotation with scaling factor - - - id - - - 0 - - - ParameterNumber - transform.type.id.scalex - X scaling - Scaling factor between the output X spacing and the input X spacing - - - 1 - - - ParameterNumber - transform.type.id.scaley - Y scaling - Scaling factor between the output Y spacing and the input Y spacing - - - 1 - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows defining how the input image will be interpolated during resampling. - - - nn - linear - bco - - - 2 - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows controlling the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - - - ParameterNumber - ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/RigidTransformResample-rotation.xml b/python/plugins/processing/algs/otb/description/5.0.0/RigidTransformResample-rotation.xml deleted file mode 100644 index c34ea458b0ed..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/RigidTransformResample-rotation.xml +++ /dev/null @@ -1,92 +0,0 @@ - - RigidTransformResample-rotation - otbcli_RigidTransformResample - RigidTransformResample (rotation) - Geometry - Resample an image with a rigid transform - - ParameterRaster - in - Input image - The input image to translate. - False - - - OutputRaster - out - Output image - The transformed output image. - - - - ParameterSelection - transform.type - Type of transformation - Type of transformation. Available transformations are spatial scaling, translation and rotation with scaling factor - - - rotation - - - 0 - - - ParameterNumber - transform.type.rotation.angle - Rotation angle - The rotation angle in degree (values between -180 and 180) - - - 0 - - - ParameterNumber - transform.type.rotation.scalex - X scaling - Scale factor between the X spacing of the rotated output image and the X spacing of the unrotated image - - - 1 - - - ParameterNumber - transform.type.rotation.scaley - Y scaling - Scale factor between the Y spacing of the rotated output image and the Y spacing of the unrotated image - - - 1 - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows defining how the input image will be interpolated during resampling. - - - nn - linear - bco - - - 2 - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows controlling the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - - - ParameterNumber - ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/RigidTransformResample-translation.xml b/python/plugins/processing/algs/otb/description/5.0.0/RigidTransformResample-translation.xml deleted file mode 100644 index 992f869dc224..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/RigidTransformResample-translation.xml +++ /dev/null @@ -1,101 +0,0 @@ - - RigidTransformResample-translation - otbcli_RigidTransformResample - RigidTransformResample (translation) - Geometry - Resample an image with a rigid transform - - ParameterRaster - in - Input image - The input image to translate. - False - - - OutputRaster - out - Output image - The transformed output image. - - - - ParameterSelection - transform.type - Type of transformation - Type of transformation. Available transformations are spatial scaling, translation and rotation with scaling factor - - - translation - - - 0 - - - ParameterNumber - transform.type.translation.tx - The X translation (in physical units) - The translation value along X axis (in physical units). - - - 0 - - - ParameterNumber - transform.type.translation.ty - The Y translation (in physical units) - The translation value along Y axis (in physical units) - - - 0 - - - ParameterNumber - transform.type.translation.scalex - X scaling - Scaling factor between the output X spacing and the input X spacing - - - 1 - - - ParameterNumber - transform.type.translation.scaley - Y scaling - Scaling factor between the output Y spacing and the input Y spacing - - - 1 - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows defining how the input image will be interpolated during resampling. - - - nn - linear - bco - - - 2 - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows controlling the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - - - ParameterNumber - ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/SFSTextureExtraction.xml b/python/plugins/processing/algs/otb/description/5.0.0/SFSTextureExtraction.xml deleted file mode 100644 index 34c9ec322c22..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/SFSTextureExtraction.xml +++ /dev/null @@ -1,84 +0,0 @@ - - SFSTextureExtraction - otbcli_SFSTextureExtraction - SFS Texture Extraction - Feature Extraction - Computes Structural Feature Set textures on every pixel of the input image selected channel - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterNumber - parameters.spethre - Spectral Threshold - Spectral Threshold - - - 50 - - - ParameterNumber - parameters.spathre - Spatial Threshold - Spatial Threshold - - - 100 - - - ParameterNumber - parameters.nbdir - Number of Direction - Number of Direction - - - 20 - - - ParameterNumber - parameters.alpha - Alpha - Alpha - - - 1 - - - ParameterNumber - parameters.maxcons - Ratio Maximum Consideration Number - Ratio Maximum Consideration Number - - - 5 - - - OutputRaster - out - Feature Output Image - Output image containing the SFS texture features. - - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/SOMClassification.xml b/python/plugins/processing/algs/otb/description/5.0.0/SOMClassification.xml deleted file mode 100644 index 9c1daa046cdb..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/SOMClassification.xml +++ /dev/null @@ -1,143 +0,0 @@ - - SOMClassification - otbcli_SOMClassification - SOM Classification - Learning - SOM image classification. - - ParameterRaster - in - InputImage - Input image to classify. - False - - - OutputRaster - out - OutputImage - Output classified image (each pixel contains the index of its corresponding vector in the SOM). - - - - ParameterRaster - vm - ValidityMask - Validity mask (only pixels corresponding to a mask value greater than 0 will be used for learning) - True - - - ParameterNumber - tp - TrainingProbability - Probability for a sample to be selected in the training set - - - 1 - - - ParameterNumber - ts - TrainingSetSize - Maximum training set size (in pixels) - - - 0 - - - OutputRaster - som - SOM Map - Output image containing the Self-Organizing Map - - - - ParameterNumber - sx - SizeX - X size of the SOM map - - - 32 - - - ParameterNumber - sy - SizeY - Y size of the SOM map - - - 32 - - - ParameterNumber - nx - NeighborhoodX - X size of the initial neighborhood in the SOM map - - - 10 - - - ParameterNumber - ny - NeighborhoodY - Y size of the initial neighborhood in the SOM map - - - 10 - - - ParameterNumber - ni - NumberIteration - Number of iterations for SOM learning - - - 5 - - - ParameterNumber - bi - BetaInit - Initial learning coefficient - - - 1 - - - ParameterNumber - bf - BetaFinal - Final learning coefficient - - - 0.1 - - - ParameterNumber - iv - InitialValue - Maximum initial neuron weight - - - 0 - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/Segmentation-cc.xml b/python/plugins/processing/algs/otb/description/5.0.0/Segmentation-cc.xml deleted file mode 100644 index 812478dff54f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/Segmentation-cc.xml +++ /dev/null @@ -1,152 +0,0 @@ - - Segmentation-cc - otbcli_Segmentation - Segmentation (cc) - Segmentation - Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported. - - ParameterRaster - in - Input Image - The input image to segment - False - - - ParameterSelection - filter - Segmentation algorithm - Choice of segmentation algorithm (mean-shift by default) - - - cc - - - 0 - - - ParameterString - filter.cc.expr - Condition - User defined connection condition, written as a mathematical expression. Available variables are p(i)b(i), intensity_p(i) and distance (example of expression : distance < 10 ) - - - False - - - ParameterSelection - mode - Processing mode - Choice of processing mode, either raster or large-scale. - - - vector - - - 0 - - - OutputVector - mode.vector.out - Output vector file - The output vector file or database (name can be anything understood by OGR) - - - ParameterSelection - mode.vector.outmode - Writing mode for the output vector file - This allows setting the writing behaviour for the output vector file. Please note that the actual behaviour depends on the file format. - - - ulco - ovw - ulovw - ulu - - - 0 - - - ParameterRaster - mode.vector.inmask - Mask Image - Only pixels whose mask value is strictly positive will be segmented. - True - - - ParameterBoolean - mode.vector.neighbor - 8-neighbor connectivity - Activate 8-Neighborhood connectivity (default is 4). - True - - - ParameterBoolean - mode.vector.stitch - Stitch polygons - Scan polygons on each side of tiles and stitch polygons which connect by more than one pixel. - True - - - ParameterNumber - mode.vector.minsize - Minimum object size - Objects whose size is below the minimum object size (area in pixels) will be ignored during vectorization. - - - 1 - - - ParameterNumber - mode.vector.simplify - Simplify polygons - Simplify polygons according to a given tolerance (in pixel). This option allows reducing the size of the output file or database. - - - 0.1 - - - ParameterString - mode.vector.layername - Layer name - Name of the layer in the vector file or database (default is Layer). - layer - - False - - - ParameterString - mode.vector.fieldname - Geometry index field name - Name of the field holding the geometry index in the output vector file or database. - DN - - False - - - ParameterNumber - mode.vector.tilesize - Tiles size - User defined tiles size for tile-based segmentation. Optimal tile size is selected according to available RAM if null. - - - 1024 - - - ParameterNumber - mode.vector.startlabel - Starting geometry index - Starting value of the geometry index field - - - 1 - - - ParameterString - mode.vector.ogroptions - OGR options for layer creation - A list of layer creation options in the form KEY=VALUE that will be passed directly to OGR without any validity checking. Options may depend on the file format, and can be found in OGR documentation. - - - True - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/Segmentation-edison.xml b/python/plugins/processing/algs/otb/description/5.0.0/Segmentation-edison.xml deleted file mode 100644 index 15a2c322ac76..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/Segmentation-edison.xml +++ /dev/null @@ -1,180 +0,0 @@ - - Segmentation-edison - otbcli_Segmentation - Segmentation (edison) - Segmentation - Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported. - - ParameterRaster - in - Input Image - The input image to segment - False - - - ParameterSelection - filter - Segmentation algorithm - Choice of segmentation algorithm (mean-shift by default) - - - edison - - - 0 - - - ParameterNumber - filter.edison.spatialr - Spatial radius - Spatial radius defining neighborhood. - - - 5 - - - ParameterNumber - filter.edison.ranger - Range radius - Range radius defining the radius (expressed in radiometry unit) in the multi-spectral space. - - - 15 - - - ParameterNumber - filter.edison.minsize - Minimum region size - Minimum size of a region in segmentation. Smaller clusters will be merged to the neighboring cluster with the closest radiometry. - - - 100 - - - ParameterNumber - filter.edison.scale - Scale factor - Scaling of the image before processing. This is useful for images with narrow decimal ranges (like [0,1] for instance). - - - 1 - - - ParameterSelection - mode - Processing mode - Choice of processing mode, either raster or large-scale. - - - vector - - - 0 - - - OutputVector - mode.vector.out - Output vector file - The output vector file or database (name can be anything understood by OGR) - - - - ParameterSelection - mode.vector.outmode - Writing mode for the output vector file - This allows setting the writing behaviour for the output vector file. Please note that the actual behaviour depends on the file format. - - - ulco - ovw - ulovw - ulu - - - 0 - - - ParameterRaster - mode.vector.inmask - Mask Image - Only pixels whose mask value is strictly positive will be segmented. - True - - - ParameterBoolean - mode.vector.neighbor - 8-neighbor connectivity - Activate 8-Neighborhood connectivity (default is 4). - True - - - ParameterBoolean - mode.vector.stitch - Stitch polygons - Scan polygons on each side of tiles and stitch polygons which connect by more than one pixel. - True - - - ParameterNumber - mode.vector.minsize - Minimum object size - Objects whose size is below the minimum object size (area in pixels) will be ignored during vectorization. - - - 1 - - - ParameterNumber - mode.vector.simplify - Simplify polygons - Simplify polygons according to a given tolerance (in pixel). This option allows reducing the size of the output file or database. - - - 0.1 - - - ParameterString - mode.vector.layername - Layer name - Name of the layer in the vector file or database (default is Layer). - layer - - False - - - ParameterString - mode.vector.fieldname - Geometry index field name - Name of the field holding the geometry index in the output vector file or database. - DN - - False - - - ParameterNumber - mode.vector.tilesize - Tiles size - User defined tiles size for tile-based segmentation. Optimal tile size is selected according to available RAM if null. - - - 1024 - - - ParameterNumber - mode.vector.startlabel - Starting geometry index - Starting value of the geometry index field - - - 1 - - - ParameterString - mode.vector.ogroptions - OGR options for layer creation - A list of layer creation options in the form KEY=VALUE that will be passed directly to OGR without any validity checking. Options may depend on the file format, and can be found in OGR documentation. - - - True - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/Segmentation-meanshift.xml b/python/plugins/processing/algs/otb/description/5.0.0/Segmentation-meanshift.xml deleted file mode 100644 index 8efe19171e5c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/Segmentation-meanshift.xml +++ /dev/null @@ -1,188 +0,0 @@ - - Segmentation-meanshift - otbcli_Segmentation - Segmentation (meanshift) - Segmentation - Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported. - - ParameterRaster - in - Input Image - The input image to segment - False - - - ParameterSelection - filter - Segmentation algorithm - Choice of segmentation algorithm (mean-shift by default) - - - meanshift - - - 0 - - - ParameterNumber - filter.meanshift.spatialr - Spatial radius - Spatial radius of the neighborhood. - - - 5 - - - ParameterNumber - filter.meanshift.ranger - Range radius - Range radius defining the radius (expressed in radiometry unit) in the multispectral space. - - - 15 - - - ParameterNumber - filter.meanshift.thres - Mode convergence threshold - Algorithm iterative scheme will stop if mean-shift vector is below this threshold or if iteration number reached maximum number of iterations. - - - 0.1 - - - ParameterNumber - filter.meanshift.maxiter - Maximum number of iterations - Algorithm iterative scheme will stop if convergence hasn't been reached after the maximum number of iterations. - - - 100 - - - ParameterNumber - filter.meanshift.minsize - Minimum region size - Minimum size of a region (in pixel unit) in segmentation. Smaller clusters will be merged to the neighboring cluster with the closest radiometry. If set to 0 no pruning is done. - - - 100 - - - ParameterSelection - mode - Processing mode - Choice of processing mode, either raster or large-scale. - - - vector - - - 0 - - - OutputVector - mode.vector.out - Output vector file - The output vector file or database (name can be anything understood by OGR) - - - ParameterSelection - mode.vector.outmode - Writing mode for the output vector file - This allows setting the writing behaviour for the output vector file. Please note that the actual behaviour depends on the file format. - - - ulco - ovw - ulovw - ulu - - - 0 - - - ParameterRaster - mode.vector.inmask - Mask Image - Only pixels whose mask value is strictly positive will be segmented. - True - - - ParameterBoolean - mode.vector.neighbor - 8-neighbor connectivity - Activate 8-Neighborhood connectivity (default is 4). - True - - - ParameterBoolean - mode.vector.stitch - Stitch polygons - Scan polygons on each side of tiles and stitch polygons which connect by more than one pixel. - True - - - ParameterNumber - mode.vector.minsize - Minimum object size - Objects whose size is below the minimum object size (area in pixels) will be ignored during vectorization. - - - 1 - - - ParameterNumber - mode.vector.simplify - Simplify polygons - Simplify polygons according to a given tolerance (in pixel). This option allows reducing the size of the output file or database. - - - 0.1 - - - ParameterString - mode.vector.layername - Layer name - Name of the layer in the vector file or database (default is Layer). - layer - - False - - - ParameterString - mode.vector.fieldname - Geometry index field name - Name of the field holding the geometry index in the output vector file or database. - DN - - False - - - ParameterNumber - mode.vector.tilesize - Tiles size - User defined tiles size for tile-based segmentation. Optimal tile size is selected according to available RAM if null. - - - 1024 - - - ParameterNumber - mode.vector.startlabel - Starting geometry index - Starting value of the geometry index field - - - 1 - - - ParameterString - mode.vector.ogroptions - OGR options for layer creation - A list of layer creation options in the form KEY=VALUE that will be passed directly to OGR without any validity checking. Options may depend on the file format, and can be found in OGR documentation. - - - True - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/Segmentation-mprofiles.xml b/python/plugins/processing/algs/otb/description/5.0.0/Segmentation-mprofiles.xml deleted file mode 100644 index ebefdeae4527..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/Segmentation-mprofiles.xml +++ /dev/null @@ -1,179 +0,0 @@ - - Segmentation-mprofiles - otbcli_Segmentation - Segmentation (mprofiles) - Segmentation - Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported. - - ParameterRaster - in - Input Image - The input image to segment - False - - - ParameterSelection - filter - Segmentation algorithm - Choice of segmentation algorithm (mean-shift by default) - - - mprofiles - - - 0 - - - ParameterNumber - filter.mprofiles.size - Profile Size - Size of the profiles - - - 5 - - - ParameterNumber - filter.mprofiles.start - Initial radius - Initial radius of the structuring element (in pixels) - - - 1 - - - ParameterNumber - filter.mprofiles.step - Radius step. - Radius step along the profile (in pixels) - - - 1 - - - ParameterNumber - filter.mprofiles.sigma - Threshold of the final decision rule - Profiles values under the threshold will be ignored. - - - 1 - - - ParameterSelection - mode - Processing mode - Choice of processing mode, either raster or large-scale. - - - vector - - - 0 - - - OutputVector - mode.vector.out - Output vector file - The output vector file or database (name can be anything understood by OGR) - - - ParameterSelection - mode.vector.outmode - Writing mode for the output vector file - This allows setting the writing behaviour for the output vector file. Please note that the actual behaviour depends on the file format. - - - ulco - ovw - ulovw - ulu - - - 0 - - - ParameterRaster - mode.vector.inmask - Mask Image - Only pixels whose mask value is strictly positive will be segmented. - True - - - ParameterBoolean - mode.vector.neighbor - 8-neighbor connectivity - Activate 8-Neighborhood connectivity (default is 4). - True - - - ParameterBoolean - mode.vector.stitch - Stitch polygons - Scan polygons on each side of tiles and stitch polygons which connect by more than one pixel. - True - - - ParameterNumber - mode.vector.minsize - Minimum object size - Objects whose size is below the minimum object size (area in pixels) will be ignored during vectorization. - - - 1 - - - ParameterNumber - mode.vector.simplify - Simplify polygons - Simplify polygons according to a given tolerance (in pixel). This option allows reducing the size of the output file or database. - - - 0.1 - - - ParameterString - mode.vector.layername - Layer name - Name of the layer in the vector file or database (default is Layer). - layer - - False - - - ParameterString - mode.vector.fieldname - Geometry index field name - Name of the field holding the geometry index in the output vector file or database. - DN - - False - - - ParameterNumber - mode.vector.tilesize - Tiles size - User defined tiles size for tile-based segmentation. Optimal tile size is selected according to available RAM if null. - - - 1024 - - - ParameterNumber - mode.vector.startlabel - Starting geometry index - Starting value of the geometry index field - - - 1 - - - ParameterString - mode.vector.ogroptions - OGR options for layer creation - A list of layer creation options in the form KEY=VALUE that will be passed directly to OGR without any validity checking. Options may depend on the file format, and can be found in OGR documentation. - - - True - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/Segmentation-watershed.xml b/python/plugins/processing/algs/otb/description/5.0.0/Segmentation-watershed.xml deleted file mode 100644 index f4d43a5d7303..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/Segmentation-watershed.xml +++ /dev/null @@ -1,161 +0,0 @@ - - Segmentation-watershed - otbcli_Segmentation - Segmentation (watershed) - Segmentation - Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported. - - ParameterRaster - in - Input Image - The input image to segment - False - - - ParameterSelection - filter - Segmentation algorithm - Choice of segmentation algorithm (mean-shift by default) - - - watershed - - - 0 - - - ParameterNumber - filter.watershed.threshold - Depth Threshold - Depth threshold Units in percentage of the maximum depth in the image. - - - 0.01 - - - ParameterNumber - filter.watershed.level - Flood Level - flood level for generating the merge tree from the initial segmentation (between 0 and 1) - - - 0.1 - - - ParameterSelection - mode - Processing mode - Choice of processing mode, either raster or large-scale. - - - vector - - - 0 - - - OutputVector - mode.vector.out - Output vector file - The output vector file or database (name can be anything understood by OGR) - - - ParameterSelection - mode.vector.outmode - Writing mode for the output vector file - This allows setting the writing behaviour for the output vector file. Please note that the actual behaviour depends on the file format. - - - ulco - ovw - ulovw - ulu - - - 0 - - - ParameterRaster - mode.vector.inmask - Mask Image - Only pixels whose mask value is strictly positive will be segmented. - True - - - ParameterBoolean - mode.vector.neighbor - 8-neighbor connectivity - Activate 8-Neighborhood connectivity (default is 4). - True - - - ParameterBoolean - mode.vector.stitch - Stitch polygons - Scan polygons on each side of tiles and stitch polygons which connect by more than one pixel. - True - - - ParameterNumber - mode.vector.minsize - Minimum object size - Objects whose size is below the minimum object size (area in pixels) will be ignored during vectorization. - - - 1 - - - ParameterNumber - mode.vector.simplify - Simplify polygons - Simplify polygons according to a given tolerance (in pixel). This option allows reducing the size of the output file or database. - - - 0.1 - - - ParameterString - mode.vector.layername - Layer name - Name of the layer in the vector file or database (default is Layer). - layer - - False - - - ParameterString - mode.vector.fieldname - Geometry index field name - Name of the field holding the geometry index in the output vector file or database. - DN - - False - - - ParameterNumber - mode.vector.tilesize - Tiles size - User defined tiles size for tile-based segmentation. Optimal tile size is selected according to available RAM if null. - - - 1024 - - - ParameterNumber - mode.vector.startlabel - Starting geometry index - Starting value of the geometry index field - - - 1 - - - ParameterString - mode.vector.ogroptions - OGR options for layer creation - A list of layer creation options in the form KEY=VALUE that will be passed directly to OGR without any validity checking. Options may depend on the file format, and can be found in OGR documentation. - - - True - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/Smoothing-anidif.xml b/python/plugins/processing/algs/otb/description/5.0.0/Smoothing-anidif.xml deleted file mode 100644 index 3f1d1c62d699..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/Smoothing-anidif.xml +++ /dev/null @@ -1,69 +0,0 @@ - - Smoothing-anidif - otbcli_Smoothing - Smoothing (anidif) - Image Filtering - Apply a smoothing filter to an image - - ParameterRaster - in - Input Image - Input image to smooth. - False - - - OutputRaster - out - Output Image - Output smoothed image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterSelection - type - Smoothing Type - Smoothing kernel to apply - - - anidif - - - 2 - - - ParameterNumber - type.anidif.timestep - Time Step - Diffusion equation time step - - - 0.125 - - - ParameterNumber - type.anidif.nbiter - Nb Iterations - Controls the sensitivity of the conductance term - - - 10 - - - ParameterNumber - type.anidif.conductance - Conductance - - - - 1 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/Smoothing-gaussian.xml b/python/plugins/processing/algs/otb/description/5.0.0/Smoothing-gaussian.xml deleted file mode 100644 index a07675ba03e5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/Smoothing-gaussian.xml +++ /dev/null @@ -1,51 +0,0 @@ - - Smoothing-gaussian - otbcli_Smoothing - Smoothing (gaussian) - Image Filtering - Apply a smoothing filter to an image - - ParameterRaster - in - Input Image - Input image to smooth. - False - - - OutputRaster - out - Output Image - Output smoothed image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterSelection - type - Smoothing Type - Smoothing kernel to apply - - - gaussian - - - 2 - - - ParameterNumber - type.gaussian.radius - Radius - Gaussian radius (in pixels) - - - 2 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/Smoothing-mean.xml b/python/plugins/processing/algs/otb/description/5.0.0/Smoothing-mean.xml deleted file mode 100644 index d093be754662..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/Smoothing-mean.xml +++ /dev/null @@ -1,51 +0,0 @@ - - Smoothing-mean - otbcli_Smoothing - Smoothing (mean) - Image Filtering - Apply a smoothing filter to an image - - ParameterRaster - in - Input Image - Input image to smooth. - False - - - OutputRaster - out - Output Image - Output smoothed image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - - ParameterSelection - type - Smoothing Type - Smoothing kernel to apply - - - mean - - - 2 - - - ParameterNumber - type.mean.radius - Radius - Mean radius (in pixels) - - - 2 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/SplitImage.xml b/python/plugins/processing/algs/otb/description/5.0.0/SplitImage.xml deleted file mode 100644 index 20eb786f32ea..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/SplitImage.xml +++ /dev/null @@ -1,29 +0,0 @@ - - SplitImage - otbcli_SplitImage - Split Image - Image Manipulation - Rescale the image between two given values. - - ParameterRaster - in - Input Image - Input image to be split into individual images. - False - - - OutputFile - out - Output Image - The base filename of the split images. - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/StereoFramework.xml b/python/plugins/processing/algs/otb/description/5.0.0/StereoFramework.xml deleted file mode 100644 index 4a0ef1f51d1a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/StereoFramework.xml +++ /dev/null @@ -1,315 +0,0 @@ - - StereoFramework - otbcli_StereoFramework - Stereo Framework - Stereo - Compute the ground elevation based on one or multiple stereo pair(s) - - ParameterMultipleInput - input.il - Input images list - The list of images. - - False - - - ParameterString - input.co - Couples list - List of index of couples im image list. Couples must be separated by a comma. (index start at 0). for example : 0 1,1 2 will process a first couple composed of the first and the second image in image list, then the first and the third image -. note that images are handled by pairs. if left empty couples are created from input index i.e. a first couple will be composed of the first and second image, a second couple with third and fourth image etc. (in this case image list must be even). - - - True - - - ParameterNumber - input.channel - Image channel used for the block matching - Used channel for block matching (used for all images) - - - 1 - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - - - ParameterNumber - output.res - Output resolution - Spatial sampling distance of the output elevation : the cell size (in m) - - - 1 - - - ParameterNumber - output.nodata - NoData value - DSM empty cells are filled with this value (optional -32768 by default) - - - -32768 - - - ParameterSelection - output.fusionmethod - Method to fuse measures in each DSM cell - This parameter allows choosing the method used to fuse elevation measurements in each output DSM cell - - - max - min - mean - acc - - - 0 - - - OutputRaster - output.out - Output DSM - Output elevation image - - - - ParameterSelection - output.mode - Parameters estimation modes - - - - fit - user - - - 0 - - - ParameterNumber - output.mode.user.ulx - Upper Left X - Cartographic X coordinate of upper-left corner (meters for cartographic projections, degrees for geographic ones) - - - 0.0 - - - ParameterNumber - output.mode.user.uly - Upper Left Y - Cartographic Y coordinate of the upper-left corner (meters for cartographic projections, degrees for geographic ones) - - - 0.0 - - - ParameterNumber - output.mode.user.sizex - Size X - Size of projected image along X (in pixels) - - - 0 - - - ParameterNumber - output.mode.user.sizey - Size Y - Size of projected image along Y (in pixels) - - - 0 - - - ParameterNumber - output.mode.user.spacingx - Pixel Size X - Size of each pixel along X axis (meters for cartographic projections, degrees for geographic ones) - - - 0.0 - - - ParameterNumber - output.mode.user.spacingy - Pixel Size Y - Size of each pixel along Y axis (meters for cartographic projections, degrees for geographic ones) - - - 0.0 - - - ParameterSelection - map - Output Cartographic Map Projection - Parameters of the output map projection to be used. - - - utm - lambert2 - lambert93 - wgs - epsg - - - 3 - - - ParameterNumber - map.utm.zone - Zone number - The zone number ranges from 1 to 60 and allows defining the transverse mercator projection (along with the hemisphere) - - - 31 - - - ParameterBoolean - map.utm.northhem - Northern Hemisphere - The transverse mercator projections are defined by their zone number as well as the hemisphere. Activate this parameter if your image is in the northern hemisphere. - True - - - ParameterNumber - map.epsg.code - EPSG Code - See www.spatialreference.org to find which EPSG code is associated to your projection - - - 4326 - - - ParameterNumber - stereorect.fwdgridstep - Step of the displacement grid (in pixels) - Stereo-rectification displacement grid only varies slowly. Therefore, it is recommended to use a coarser grid (higher step value) in case of large images - - - 16 - - - ParameterNumber - stereorect.invgridssrate - Sub-sampling rate for epipolar grid inversion - Grid inversion is an heavy process that implies spline regression on control points. To avoid eating to much memory, this parameter allows sub-sampling first the field to invert. - - - 10 - - - ParameterSelection - bm.metric - Block-matching metric - - - - ssdmean - ssd - ncc - lp - - - 0 - - - ParameterNumber - bm.metric.lp.p - p value - Value of the p parameter in Lp pseudo-norm (must be positive) - - - 1 - - - ParameterNumber - bm.radius - Radius of blocks for matching filter (in pixels) - The radius of blocks in Block-Matching (in pixels) - - - 2 - - - ParameterNumber - bm.minhoffset - Minimum altitude offset (in meters) - Minimum altitude below the selected elevation source (in meters) - - - -20 - - - ParameterNumber - bm.maxhoffset - Maximum altitude offset (in meters) - Maximum altitude above the selected elevation source (in meters) - - - 20 - - - ParameterBoolean - postproc.bij - Use bijection consistency in block matching strategy - use bijection consistency. Right to Left correlation is computed to validate Left to Right disparities. If bijection is not found pixel is rejected. - True - - - ParameterBoolean - postproc.med - Use median disparities filtering - disparities output can be filtered using median post filtering (disabled by default). - True - - - ParameterNumber - postproc.metrict - Correlation metric threshold - Use block matching metric output to discard pixels with low correlation value (disabled by default, float value) - - - 0.6 - - - ParameterRaster - mask.left - Input left mask - Mask for left input image - True - - - ParameterRaster - mask.right - Input right mask - Mask for right input image - True - - - ParameterNumber - mask.variancet - Discard pixels with low local variance - This parameter allows discarding pixels whose local variance is too small (the size of the neighborhood is given by the radius parameter) - - - 50 - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/Superimpose.xml b/python/plugins/processing/algs/otb/description/5.0.0/Superimpose.xml deleted file mode 100644 index 61bb75dd7097..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/Superimpose.xml +++ /dev/null @@ -1,91 +0,0 @@ - - Superimpose - otbcli_Superimpose - Superimpose sensor - Geometry - Using available image metadata, project one image onto another one - - ParameterRaster - inr - Reference input - The input reference image. - False - - - ParameterRaster - inm - The image to reproject - The image to reproject into the geometry of the reference input. - False - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - - - ParameterNumber - lms - Spacing of the deformation field - Generate a coarser deformation field with the given spacing - - - 4 - - - OutputRaster - out - Output image - Output reprojected image. - - - - ParameterSelection - mode - Mode - Superimposition mode - - - default - phr - - - 0 - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows defining how the input image will be interpolated during resampling. - - - bco - nn - linear - - - 0 - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows controlling the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/TileFusion.xml b/python/plugins/processing/algs/otb/description/5.0.0/TileFusion.xml deleted file mode 100644 index 1a9ca2292207..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/TileFusion.xml +++ /dev/null @@ -1,40 +0,0 @@ - - TileFusion - otbcli_TileFusion - Image Tile Fusion - Image Manipulation - Fusion of an image made of several tile files. - - ParameterMultipleInput - il - Input Tile Images - Input tiles to concatenate (in lexicographic order : (0,0) (1,0) (0,1) (1,1)). - - False - - - ParameterNumber - cols - Number of tile columns - Number of columns in the tile array - - - 0 - - - ParameterNumber - rows - Number of tile rows - Number of rows in the tile array - - - 0 - - - OutputRaster - out - Output Image - Output entire image - - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-ann.xml b/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-ann.xml deleted file mode 100644 index f59f858a6b37..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-ann.xml +++ /dev/null @@ -1,247 +0,0 @@ - - TrainImagesClassifier-ann - otbcli_TrainImagesClassifier - TrainImagesClassifier (ann) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - ann - - - 0 - - - ParameterSelection - classifier.ann.t - Train Method Type - Type of training method for the multilayer perceptron (MLP) neural network. - - - reg - back - - - 0 - - - ParameterString - classifier.ann.sizes - Number of neurons in each intermediate layer - The number of neurons in each intermediate layer (excluding input and output layers). - - - False - - - ParameterSelection - classifier.ann.f - Neuron activation function type - Neuron activation function. - - - ident - sig - gau - - - 1 - - - ParameterNumber - classifier.ann.a - Alpha parameter of the activation function - Alpha parameter of the activation function (used only with sigmoid and gaussian functions). - - - 1 - - - ParameterNumber - classifier.ann.b - Beta parameter of the activation function - Beta parameter of the activation function (used only with sigmoid and gaussian functions). - - - 1 - - - ParameterNumber - classifier.ann.bpdw - Strength of the weight gradient term in the BACKPROP method - Strength of the weight gradient term in the BACKPROP method. The recommended value is about 0.1. - - - 0.1 - - - ParameterNumber - classifier.ann.bpms - Strength of the momentum term (the difference between weights on the 2 previous iterations) - Strength of the momentum term (the difference between weights on the 2 previous iterations). This parameter provides some inertia to smooth the random fluctuations of the weights. It can vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough. - - - 0.1 - - - ParameterNumber - classifier.ann.rdw - Initial value Delta_0 of update-values Delta_{ij} in RPROP method - Initial value Delta_0 of update-values Delta_{ij} in RPROP method (default = 0.1). - - - 0.1 - - - ParameterNumber - classifier.ann.rdwm - Update-values lower limit Delta_{min} in RPROP method - Update-values lower limit Delta_{min} in RPROP method. It must be positive (default = 1e-7). - - - 1e-07 - - - ParameterSelection - classifier.ann.term - Termination criteria - Termination criteria. - - - iter - eps - all - - - 2 - - - ParameterNumber - classifier.ann.eps - Epsilon value used in the Termination criteria - Epsilon value used in the Termination criteria. - - - 0.01 - - - ParameterNumber - classifier.ann.iter - Maximum number of iterations used in the Termination criteria - Maximum number of iterations used in the Termination criteria. - - - 1000 - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-bayes.xml b/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-bayes.xml deleted file mode 100644 index e55a44bdb363..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-bayes.xml +++ /dev/null @@ -1,125 +0,0 @@ - - TrainImagesClassifier-bayes - otbcli_TrainImagesClassifier - TrainImagesClassifier (bayes) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - bayes - - - 0 - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-boost.xml b/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-boost.xml deleted file mode 100644 index 32388c6fadfa..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-boost.xml +++ /dev/null @@ -1,167 +0,0 @@ - - TrainImagesClassifier-boost - otbcli_TrainImagesClassifier - TrainImagesClassifier (boost) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - boost - - - 0 - - - ParameterSelection - classifier.boost.t - Boost Type - Type of Boosting algorithm. - - - discrete - real - logit - gentle - - - 1 - - - ParameterNumber - classifier.boost.w - Weak count - The number of weak classifiers. - - - 100 - - - ParameterNumber - classifier.boost.r - Weight Trim Rate - A threshold between 0 and 1 used to save computational time. Samples with summary weight <= (1 - weight_trim_rate) do not participate in the next iteration of training. Set this parameter to 0 to turn off this functionality. - - - 0.95 - - - ParameterNumber - classifier.boost.m - Maximum depth of the tree - Maximum depth of the tree. - - - 1 - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-dt.xml b/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-dt.xml deleted file mode 100644 index 9750df1566f3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-dt.xml +++ /dev/null @@ -1,184 +0,0 @@ - - TrainImagesClassifier-dt - otbcli_TrainImagesClassifier - TrainImagesClassifier (dt) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - dt - - - 0 - - - ParameterNumber - classifier.dt.max - Maximum depth of the tree - The training algorithm attempts to split each node while its depth is smaller than the maximum possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or if the tree is pruned. - - - 65535 - - - ParameterNumber - classifier.dt.min - Minimum number of samples in each node - If all absolute differences between an estimated value in a node and the values of the train samples in this node are smaller than this regression accuracy parameter, then the node will not be split. - - - 10 - - - ParameterNumber - classifier.dt.ra - Termination criteria for regression tree - - - - 0.01 - - - ParameterNumber - classifier.dt.cat - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split. - - - 10 - - - ParameterNumber - classifier.dt.f - K-fold cross-validations - If cv_folds > 1, then it prunes a tree with K-fold cross-validation where K is equal to cv_folds. - - - 10 - - - ParameterBoolean - classifier.dt.r - Set Use1seRule flag to false - If true, then a pruning will be harsher. This will make a tree more compact and more resistant to the training data noise but a bit less accurate. - True - - - ParameterBoolean - classifier.dt.t - Set TruncatePrunedTree flag to false - If true, then pruned branches are physically removed from the tree. - True - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-gbt.xml b/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-gbt.xml deleted file mode 100644 index bce83894df1f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-gbt.xml +++ /dev/null @@ -1,161 +0,0 @@ - - TrainImagesClassifier-gbt - otbcli_TrainImagesClassifier - TrainImagesClassifier (gbt) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - gbt - - - 0 - - - ParameterNumber - classifier.gbt.w - Number of boosting algorithm iterations - Number "w" of boosting algorithm iterations, with w*K being the total number of trees in the GBT model, where K is the output number of classes. - - - 200 - - - ParameterNumber - classifier.gbt.s - Regularization parameter - Regularization parameter. - - - 0.01 - - - ParameterNumber - classifier.gbt.p - Portion of the whole training set used for each algorithm iteration - Portion of the whole training set used for each algorithm iteration. The subset is generated randomly. - - - 0.8 - - - ParameterNumber - classifier.gbt.max - Maximum depth of the tree - The training algorithm attempts to split each node while its depth is smaller than the maximum possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or if the tree is pruned. - - - 3 - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-knn.xml b/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-knn.xml deleted file mode 100644 index c47ce4b5676f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-knn.xml +++ /dev/null @@ -1,134 +0,0 @@ - - TrainImagesClassifier-knn - otbcli_TrainImagesClassifier - TrainImagesClassifier (knn) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - knn - - - 0 - - - ParameterNumber - classifier.knn.k - Number of Neighbors - The number of neighbors to use. - - - 32 - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-libsvm.xml b/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-libsvm.xml deleted file mode 100644 index 38ec6a5a636b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-libsvm.xml +++ /dev/null @@ -1,156 +0,0 @@ - - TrainImagesClassifier-libsvm - otbcli_TrainImagesClassifier - TrainImagesClassifier (libsvm) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - libsvm - - - 0 - - - ParameterSelection - classifier.libsvm.k - SVM Kernel Type - SVM Kernel Type. - - - linear - rbf - poly - sigmoid - - - 0 - - - ParameterNumber - classifier.libsvm.c - Cost parameter C - SVM models have a cost parameter C (1 by default) to control the trade-off between training errors and forcing rigid margins. - - - 1 - - - ParameterBoolean - classifier.libsvm.opt - Parameters optimization - SVM parameters optimization flag. - True - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-rf.xml b/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-rf.xml deleted file mode 100644 index 8701f8f870d1..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-rf.xml +++ /dev/null @@ -1,188 +0,0 @@ - - TrainImagesClassifier-rf - otbcli_TrainImagesClassifier - TrainImagesClassifier (rf) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - rf - - - 0 - - - ParameterNumber - classifier.rf.max - Maximum depth of the tree - The depth of the tree. A low value will likely underfit and conversely a high value will likely overfit. The optimal value can be obtained using cross validation or other suitable methods. - - - 5 - - - ParameterNumber - classifier.rf.min - Minimum number of samples in each node - If the number of samples in a node is smaller than this parameter, then the node will not be split. A reasonable value is a small percentage of the total data e.g. 1 percent. - - - 10 - - - ParameterNumber - classifier.rf.ra - Termination Criteria for regression tree - If all absolute differences between an estimated value in a node and the values of the train samples in this node are smaller than this regression accuracy parameter, then the node will not be split. - - - 0 - - - ParameterNumber - classifier.rf.cat - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split. - - - 10 - - - ParameterNumber - classifier.rf.var - Size of the randomly selected subset of features at each tree node - The size of the subset of features, randomly selected at each tree node, that are used to find the best split(s). If you set it to 0, then the size will be set to the square root of the total number of features. - - - 0 - - - ParameterNumber - classifier.rf.nbtrees - Maximum number of trees in the forest - The maximum number of trees in the forest. Typically, the more trees you have, the better the accuracy. However, the improvement in accuracy generally diminishes and reaches an asymptote for a certain number of trees. Also to keep in mind, increasing the number of trees increases the prediction time linearly. - - - 100 - - - ParameterNumber - classifier.rf.acc - Sufficient accuracy (OOB error) - Sufficient accuracy (OOB error). - - - 0.01 - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-svm.xml b/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-svm.xml deleted file mode 100644 index fd2a015ca6d5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/TrainImagesClassifier-svm.xml +++ /dev/null @@ -1,209 +0,0 @@ - - TrainImagesClassifier-svm - otbcli_TrainImagesClassifier - TrainImagesClassifier (svm) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - svm - - - 0 - - - ParameterSelection - classifier.svm.m - SVM Model Type - Type of SVM formulation. - - - csvc - nusvc - oneclass - - - 0 - - - ParameterSelection - classifier.svm.k - SVM Kernel Type - SVM Kernel Type. - - - linear - rbf - poly - sigmoid - - - 0 - - - ParameterNumber - classifier.svm.c - Cost parameter C - SVM models have a cost parameter C (1 by default) to control the trade-off between training errors and forcing rigid margins. - - - 1 - - - ParameterNumber - classifier.svm.nu - Parameter nu of a SVM optimization problem (NU_SVC / ONE_CLASS) - Parameter nu of a SVM optimization problem. - - - 0 - - - ParameterNumber - classifier.svm.coef0 - Parameter coef0 of a kernel function (POLY / SIGMOID) - Parameter coef0 of a kernel function (POLY / SIGMOID). - - - 0 - - - ParameterNumber - classifier.svm.gamma - Parameter gamma of a kernel function (POLY / RBF / SIGMOID) - Parameter gamma of a kernel function (POLY / RBF / SIGMOID). - - - 1 - - - ParameterNumber - classifier.svm.degree - Parameter degree of a kernel function (POLY) - Parameter degree of a kernel function (POLY). - - - 1 - - - ParameterBoolean - classifier.svm.opt - Parameters optimization - SVM parameters optimization flag. --If set to True, then the optimal SVM parameters will be estimated. Parameters are considered optimal by OpenCV when the cross-validation estimate of the test set error is minimal. Finally, the SVM training process is computed 10 times with these optimal parameters over subsets corresponding to 1/10th of the training samples using the k-fold cross-validation (with k = 10). --If set to False, the SVM classification process will be computed once with the currently set input SVM parameters over the training samples. --Thus, even with identical input SVM parameters and a similar random seed, the output SVM models will be different according to the method used (optimized or not) because the samples are not identically processed within OpenCV. - True - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/TrainOGRLayersClassifier.xml b/python/plugins/processing/algs/otb/description/5.0.0/TrainOGRLayersClassifier.xml deleted file mode 100644 index b9f301930d23..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/TrainOGRLayersClassifier.xml +++ /dev/null @@ -1,46 +0,0 @@ - - TrainOGRLayersClassifier - otbcli_TrainOGRLayersClassifier - TrainOGRLayersClassifier - Segmentation - Train a SVM classifier based on labeled geometries and a list of features to consider. - - ParameterFile - inshp - Name of the input shapefile - Name of the input shapefile - - False - - - ParameterFile - instats - XML file containing mean and variance of each feature. - XML file containing mean and variance of each feature. - - False - - - OutputFile - outsvm - Output model filename. - Output model filename. - - - ParameterString - feat - List of features to consider for classification. - List of features to consider for classification. - - - - - ParameterString - cfield - Field containing the class id for supervision - Field containing the class id for supervision. Only geometries with this field available will be taken into account. - class - - False - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/VectorDataExtractROI.xml b/python/plugins/processing/algs/otb/description/5.0.0/VectorDataExtractROI.xml deleted file mode 100644 index e53bbce82031..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/VectorDataExtractROI.xml +++ /dev/null @@ -1,38 +0,0 @@ - - VectorDataExtractROI - otbcli_VectorDataExtractROI - VectorData Extract ROI - Vector Data Manipulation - Perform an extract ROI on the input vector data according to the input image extent - - ParameterVector - io.vd - Input Vector data - Input vector data - - False - - - ParameterRaster - io.in - Support image - Support image that specifies the extracted region - False - - - OutputVector - io.out - Output Vector data - Output extracted vector data - - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/VectorDataReprojection-image.xml b/python/plugins/processing/algs/otb/description/5.0.0/VectorDataReprojection-image.xml deleted file mode 100644 index e095895298a8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/VectorDataReprojection-image.xml +++ /dev/null @@ -1,57 +0,0 @@ - - VectorDataReprojection-image - otbcli_VectorDataReprojection - VectorDataReprojection (image) - Vector Data Manipulation - This application allows reprojecting a vector data using support image projection reference, or a user specified map projection - - - ParameterFile - in.vd - Input vector data - The input vector data to reproject - - False - - - ParameterRaster - in.kwl - Use image keywords list - Optional input image to fill vector data with image kwl. - True - - - OutputFile - out.vd - Output vector data - The reprojected vector data - - - ParameterSelection - out.proj - Output Projection choice - - - - image - - - 0 - - - ParameterRaster - out.proj.image.in - Image used to get projection map - Projection map will be found using image metadata - False - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/VectorDataReprojection-user.xml b/python/plugins/processing/algs/otb/description/5.0.0/VectorDataReprojection-user.xml deleted file mode 100644 index b4e919df1050..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/VectorDataReprojection-user.xml +++ /dev/null @@ -1,91 +0,0 @@ - - VectorDataReprojection-user - otbcli_VectorDataReprojection - VectorDataReprojection (user) - Vector Data Manipulation - This application allows reprojecting a vector data using support image projection reference, or a user specified map projection - - - ParameterFile - in.vd - Input vector data - The input vector data to reproject - - False - - - ParameterRaster - in.kwl - Use image keywords list - Optional input image to fill vector data with image kwl. - True - - - OutputFile - out.vd - Output vector data - The reprojected vector data - - - ParameterSelection - out.proj - Output Projection choice - - - - user - - - 0 - - - ParameterSelection - out.proj.user.map - Output Cartographic Map Projection - Parameters of the output map projection to be used. - - - utm - lambert2 - lambert93 - wgs - epsg - - - 0 - - - ParameterNumber - out.proj.user.map.utm.zone - Zone number - The zone number ranges from 1 to 60 and allows defining the transverse mercator projection (along with the hemisphere) - - - 31 - - - ParameterBoolean - out.proj.user.map.utm.northhem - Northern Hemisphere - The transverse mercator projections are defined by their zone number as well as the hemisphere. Activate this parameter if your image is in the northern hemisphere. - True - - - ParameterNumber - out.proj.user.map.epsg.code - EPSG Code - See www.spatialreference.org to find which EPSG code is associated to your projection - - - 4326 - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/VectorDataTransform.xml b/python/plugins/processing/algs/otb/description/5.0.0/VectorDataTransform.xml deleted file mode 100644 index 509877afbcb2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/VectorDataTransform.xml +++ /dev/null @@ -1,83 +0,0 @@ - - VectorDataTransform - otbcli_VectorDataTransform - Vector Data Transformation - Vector Data Manipulation - Apply a transform to each vertex of the input VectorData - - ParameterVector - vd - Input Vector data - Input vector data to transform - - False - - - OutputVector - out - Output Vector data - Output transformed vector data - - - - ParameterRaster - in - Support image - Image needed as a support to the vector data - False - - - ParameterNumber - transform.tx - Translation X - Translation in the X direction (in pixels) - - - 0 - - - ParameterNumber - transform.ty - Translation Y - Translation in the Y direction (in pixels) - - - 0 - - - ParameterNumber - transform.ro - Rotation Angle - Angle of the rotation to apply in degrees - - - 0 - - - ParameterNumber - transform.centerx - Center X - X coordinate of the rotation center (in physical units) - - - 0 - - - ParameterNumber - transform.centery - Center Y - Y coordinate of the rotation center (in physical units) - - - 0 - - - ParameterNumber - transform.scale - Scale - The scale to apply - - - 1 - - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/BandMath.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/BandMath.html deleted file mode 100644 index c6c9657d6e99..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/BandMath.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

BandMath

Brief Description

Perform a mathematical operation on monoband images

Tags

Util

Long Description

This application performs a mathematical operation on monoband images. Mathematical formula interpretation is done via MuParser libraries http://muparser.sourceforge.net/.For MuParser version prior to v2 use 'and' and 'or' logical operators, and ternary operator 'if(; ; )'.For MuParser version superior to 2.0 uses '&&' and '||' logical operators, and C++ like ternary if-then-else operator.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/BandMathX.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/BandMathX.html deleted file mode 100644 index 35403f160703..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/BandMathX.html +++ /dev/null @@ -1,98 +0,0 @@ - - -

BandMathX

Brief Description

This application performs mathematical operations on multiband images. -Mathematical formula interpretation is done via muParserX library : http://articles.beltoforion.de/article.php?a=muparserx

Tags

Util

Long Description

The goal of this documentation is to give the user some hints about the syntax used in this application. -The syntax is mainly constrained by the muparserx library, which can be considered as the core of the application. - - -- Fundamentals: - -The default prefix name for variables related to the ith input is 'im(i+1)'(note the indexing from 1 to N, for N inputs). -The following list summaries the available variables for input #0 (and so on for every input): - -im1 --> a pixel from first input, made of n components (n bands) -im1bj --> jth component of a pixel from first input (first band is indexed by 1) -im1bjNkxp --> a neighbourhood ('N') of pixels of the jth component from first input, of size kxp -im1PhyX and im1PhyY --> spacing of first input in X and Y directions (horizontal and vertical) -im1bjMean im1bjMin im1bjMax im1bjSum im1bjVar --> mean, min, max, sum, variance of jth band from first input (global statistics) - -Moreover, we also have the following generic variables: -idxX and idxY --> indices of the current pixel - -Always keep in mind that this application only addresses mathematically well-defined formulas. -For instance, it is not possible to add vectors of different dimensions (this implies the addition of a row vector with a column vector), -or add a scalar to a vector or a matrix, or divide two vectors, and so on... -Thus, it is important to remember that a pixel of n components is always represented as a row vector. - -Example : - - im1 + im2 (1) - -represents the addition of pixels from first and second inputs. This expression is consistent only if -both inputs have the same number of bands. -Note that it is also possible to use the following expressions to obtain the same result: - - im1b1 + im2b1 - im1b2 + im2b2 (2) - .... - -Nevertheless, the first expression is by far much pleaseant. We call this new functionality the 'batch mode' -(performing the same operation in a band-to-band fashion). - - -- Operations involving neighborhoods of pixels: - -Another new fonctionnality is the possibility to perform operations that involve neighborhoods of pixels. -Variable related to such neighborhoods are always defined following the pattern imIbJNKxP, where: -- I is an number identifying the image input (remember, input #0 = im1, and so on) -- J is an number identifying the band (remember, first band is indexed by 1) -- KxP are two numbers that represent the size of the neighborhood (first one is related to the horizontal direction) -All neighborhood are centred, thus K and P must be odd numbers. -Many operators come with this new functionality: dotpr, mean var median min max... -For instance, if im1 represents the pixel of 3 bands image: - - im1 - mean(im1b1N5x5,im1b2N5x5,im1b3N5x5) (3) - -could represent a high pass filter (Note that by implying three neighborhoods, the operator mean returns a row vector of three components. -It is a typical behaviour for many operators of this application). - - -- Operators: - -In addition to the previous operators, other operators are available: -- existing operators/functions from muParserX, that were not originally defined for vectors and -matrices (for instance cos, sin, ...). These new operators/ functions keep the original names to which we added the prefix 'v' for vector (vcos, vsin, ...). -- mult, div and pow operators, that perform element-wise multiplication, division or exponentiation of vector/matrices (for instance im1 div im2) -- mlt, dv and pw operators, that perform multiplication, division or exponentiation of vector/matrices by a scalar (for instance im1 dv 2.0) -- bands, which is a very useful operator. It allows selecting specific bands from an image, and/or to rearrange them in a new vector; -for instance bands(im1,{1,2,1,1}) produces a vector of 4 components made of band 1, band 2, band 1 and band 1 values from the first input. -Note that curly brackets must be used in order to select the desired band indices. -... and so on. - - -- Application itself: - -The application takes the following parameters : -- Setting the list of inputs can be done with the 'il' parameter. -- Setting expressions can be done with the 'exp' parameter (see also limitations section below). -- Setting constants can be done with the 'incontext' parameter. User must provide a txt file with a specific syntax: #type name value -An example of such a file is given below: - -#F expo 1.1 -#M kernel1 { 0.1 , 0.2 , 0.3; 0.4 , 0.5 , 0.6; 0.7 , 0.8 , 0.9; 1 , 1.1 , 1.2; 1.3 , 1.4 , 1.5 } - -As we can see, #I/#F allows the definition of an integer/float constant, whereas #M allows the definition of a vector/matrix. -In the latter case, elements of a row must be separated by commas, and rows must be separated by semicolons. -It is also possible to define expressions within the same txt file, with the pattern #E expr. For instance (two expressions; see also limitations section below): - -#E $dotpr(kernel1,im1b1N3x5); im2b1^expo$ - -- The 'outcontext' parameter allows saving user's constants and expressions (context). -- Setting the output image can be done with the 'out' parameter (multi-outputs is not implemented yet). - - -Finally, we strongly recommend that the reader takes a look at the cookbook, where additional information can be found (http://www.orfeo-toolbox.org/packages/OTBCookBook.pdf). -

Parameters

Limitations

The application is currently unable to produce one output image per expression, contrary to otbBandMathXImageFilter. -Separating expressions by semi-colons (; ) will concatenate their results into a unique multiband output image.

Authors

OTB-Team

See Also

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/BinaryMorphologicalOperation-closing.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/BinaryMorphologicalOperation-closing.html deleted file mode 100644 index 97a2b19d7065..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/BinaryMorphologicalOperation-closing.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BinaryMorphologicalOperation

Brief Description

Performs morphological operations on an input image channel

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs binary morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkBinaryDilateImageFilter, itkBinaryErodeImageFilter, itkBinaryMorphologicalOpeningImageFilter and itkBinaryMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/BinaryMorphologicalOperation-dilate.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/BinaryMorphologicalOperation-dilate.html deleted file mode 100644 index 97a2b19d7065..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/BinaryMorphologicalOperation-dilate.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BinaryMorphologicalOperation

Brief Description

Performs morphological operations on an input image channel

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs binary morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkBinaryDilateImageFilter, itkBinaryErodeImageFilter, itkBinaryMorphologicalOpeningImageFilter and itkBinaryMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/BinaryMorphologicalOperation-erode.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/BinaryMorphologicalOperation-erode.html deleted file mode 100644 index 97a2b19d7065..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/BinaryMorphologicalOperation-erode.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BinaryMorphologicalOperation

Brief Description

Performs morphological operations on an input image channel

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs binary morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkBinaryDilateImageFilter, itkBinaryErodeImageFilter, itkBinaryMorphologicalOpeningImageFilter and itkBinaryMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/BinaryMorphologicalOperation-opening.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/BinaryMorphologicalOperation-opening.html deleted file mode 100644 index 97a2b19d7065..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/BinaryMorphologicalOperation-opening.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BinaryMorphologicalOperation

Brief Description

Performs morphological operations on an input image channel

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs binary morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkBinaryDilateImageFilter, itkBinaryErodeImageFilter, itkBinaryMorphologicalOpeningImageFilter and itkBinaryMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/BinaryMorphologicalOperation.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/BinaryMorphologicalOperation.html deleted file mode 100644 index 97a2b19d7065..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/BinaryMorphologicalOperation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BinaryMorphologicalOperation

Brief Description

Performs morphological operations on an input image channel

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs binary morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkBinaryDilateImageFilter, itkBinaryErodeImageFilter, itkBinaryMorphologicalOpeningImageFilter and itkBinaryMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/BlockMatching.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/BlockMatching.html deleted file mode 100644 index 3c9f7a6b13f5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/BlockMatching.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BlockMatching

Brief Description

Performs block-matching to estimate pixel-wise disparities between two images

Tags

Stereo

Long Description

This application allows performing block-matching to estimate pixel-wise disparities between two images. The application allows choosing the block-matching method to use. It also allows inputting masks (related to the left and right input image) of pixels for which the disparity should be investigated. Additionally, two criteria can be optionally used to disable disparity investigation for some pixel: a no-data value, and a threshold on the local variance. This allows speeding up computation by avoiding to investigate disparities that will not be reliable anyway. For efficiency reasons, if the optimal metric values image is desired, it will be concatenated to the output image (which will then have three bands : horizontal disparity, vertical disparity and metric value). One can split these images afterward.

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbStereoRectificationGridGenerator

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/BundleToPerfectSensor.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/BundleToPerfectSensor.html deleted file mode 100644 index 8dcdd8566369..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/BundleToPerfectSensor.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BundleToPerfectSensor

Brief Description

Perform P+XS pansharpening

Tags

Geometry,Pansharpening

Long Description

This application performs P+XS pansharpening. The default mode use Pan and XS sensor models to estimate the transformation to superimpose XS over Pan before the fusion ("default mode"). The application provides also a PHR mode for Pleiades images which does not use sensor models as Pan and XS products are already coregistered but only estimate an affine transformation to superimpose XS over the Pan.Note that this option is automatically activated in case Pleiades images are detected as input.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ClassificationMapRegularization.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ClassificationMapRegularization.html deleted file mode 100644 index a974324ba3f4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ClassificationMapRegularization.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

ClassificationMapRegularization

Brief Description

Filters the input labeled image using Majority Voting in a ball shaped neighbordhood.

Tags

Learning,Image Analysis

Long Description

This application filters the input labeled image (with a maximal class label = 65535) using Majority Voting in a ball shaped neighbordhood. Majority Voting takes the more representative value of all the pixels identified by the ball shaped structuring element and then sets the center pixel to this majority label value. - -NoData is the label of the NOT classified pixels in the input image. These input pixels keep their NoData label in the output image. - -Pixels with more than 1 majority class are marked as Undecided if the parameter 'ip.suvbool == true', or keep their Original labels otherwise.

Parameters

Limitations

The input image must be a single band labeled image (with a maximal class label = 65535). The structuring element radius must have a minimum value equal to 1 pixel. Please note that the Undecided value must be different from existing labels in the input labeled image.

Authors

OTB-Team

See Also

Documentation of the ClassificationMapRegularization application.

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ColorMapping-continuous.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ColorMapping-continuous.html deleted file mode 100644 index ee53ade0eade..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ColorMapping-continuous.html +++ /dev/null @@ -1,13 +0,0 @@ - - -

ColorMapping

Brief Description

Maps an input label image to 8-bits RGB using look-up tables.

Tags

Utilities,Image Manipulation,Image MetaData,Learning

Long Description

This application allows mapping a label image to a 8-bits RGB image (in both ways) using different methods. - -The custom method allows using a custom look-up table. The look-up table is loaded from a text file where each line describes an entry. The typical use of this method is to colorise a classification map. - -The continuous method allows mapping a range of values in a scalar input image to a colored image using continuous look-up table, in order to enhance image interpretation. Several look-up tables can been chosen with different color ranges. --The optimal method computes an optimal look-up table. When processing a segmentation label image (label to color), the color difference between adjacent segmented regions is maximized. When processing an unknown color image (color to label), all the present colors are mapped to a continuous label list. - - The support image method uses a color support image to associate an average color to each region.

Parameters

Limitations

The segmentation optimal method does not support streaming, and thus large images. The operation color to label is not implemented for the methods continuous LUT and support image LUT. - ColorMapping using support image is not threaded.

Authors

OTB-Team

See Also

ImageSVMClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ColorMapping-custom.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ColorMapping-custom.html deleted file mode 100644 index ee53ade0eade..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ColorMapping-custom.html +++ /dev/null @@ -1,13 +0,0 @@ - - -

ColorMapping

Brief Description

Maps an input label image to 8-bits RGB using look-up tables.

Tags

Utilities,Image Manipulation,Image MetaData,Learning

Long Description

This application allows mapping a label image to a 8-bits RGB image (in both ways) using different methods. - -The custom method allows using a custom look-up table. The look-up table is loaded from a text file where each line describes an entry. The typical use of this method is to colorise a classification map. - -The continuous method allows mapping a range of values in a scalar input image to a colored image using continuous look-up table, in order to enhance image interpretation. Several look-up tables can been chosen with different color ranges. --The optimal method computes an optimal look-up table. When processing a segmentation label image (label to color), the color difference between adjacent segmented regions is maximized. When processing an unknown color image (color to label), all the present colors are mapped to a continuous label list. - - The support image method uses a color support image to associate an average color to each region.

Parameters

Limitations

The segmentation optimal method does not support streaming, and thus large images. The operation color to label is not implemented for the methods continuous LUT and support image LUT. - ColorMapping using support image is not threaded.

Authors

OTB-Team

See Also

ImageSVMClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ColorMapping-image.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ColorMapping-image.html deleted file mode 100644 index ee53ade0eade..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ColorMapping-image.html +++ /dev/null @@ -1,13 +0,0 @@ - - -

ColorMapping

Brief Description

Maps an input label image to 8-bits RGB using look-up tables.

Tags

Utilities,Image Manipulation,Image MetaData,Learning

Long Description

This application allows mapping a label image to a 8-bits RGB image (in both ways) using different methods. - -The custom method allows using a custom look-up table. The look-up table is loaded from a text file where each line describes an entry. The typical use of this method is to colorise a classification map. - -The continuous method allows mapping a range of values in a scalar input image to a colored image using continuous look-up table, in order to enhance image interpretation. Several look-up tables can been chosen with different color ranges. --The optimal method computes an optimal look-up table. When processing a segmentation label image (label to color), the color difference between adjacent segmented regions is maximized. When processing an unknown color image (color to label), all the present colors are mapped to a continuous label list. - - The support image method uses a color support image to associate an average color to each region.

Parameters

Limitations

The segmentation optimal method does not support streaming, and thus large images. The operation color to label is not implemented for the methods continuous LUT and support image LUT. - ColorMapping using support image is not threaded.

Authors

OTB-Team

See Also

ImageSVMClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ColorMapping-optimal.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ColorMapping-optimal.html deleted file mode 100644 index ee53ade0eade..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ColorMapping-optimal.html +++ /dev/null @@ -1,13 +0,0 @@ - - -

ColorMapping

Brief Description

Maps an input label image to 8-bits RGB using look-up tables.

Tags

Utilities,Image Manipulation,Image MetaData,Learning

Long Description

This application allows mapping a label image to a 8-bits RGB image (in both ways) using different methods. - -The custom method allows using a custom look-up table. The look-up table is loaded from a text file where each line describes an entry. The typical use of this method is to colorise a classification map. - -The continuous method allows mapping a range of values in a scalar input image to a colored image using continuous look-up table, in order to enhance image interpretation. Several look-up tables can been chosen with different color ranges. --The optimal method computes an optimal look-up table. When processing a segmentation label image (label to color), the color difference between adjacent segmented regions is maximized. When processing an unknown color image (color to label), all the present colors are mapped to a continuous label list. - - The support image method uses a color support image to associate an average color to each region.

Parameters

Limitations

The segmentation optimal method does not support streaming, and thus large images. The operation color to label is not implemented for the methods continuous LUT and support image LUT. - ColorMapping using support image is not threaded.

Authors

OTB-Team

See Also

ImageSVMClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ColorMapping.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ColorMapping.html deleted file mode 100644 index ee53ade0eade..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ColorMapping.html +++ /dev/null @@ -1,13 +0,0 @@ - - -

ColorMapping

Brief Description

Maps an input label image to 8-bits RGB using look-up tables.

Tags

Utilities,Image Manipulation,Image MetaData,Learning

Long Description

This application allows mapping a label image to a 8-bits RGB image (in both ways) using different methods. - -The custom method allows using a custom look-up table. The look-up table is loaded from a text file where each line describes an entry. The typical use of this method is to colorise a classification map. - -The continuous method allows mapping a range of values in a scalar input image to a colored image using continuous look-up table, in order to enhance image interpretation. Several look-up tables can been chosen with different color ranges. --The optimal method computes an optimal look-up table. When processing a segmentation label image (label to color), the color difference between adjacent segmented regions is maximized. When processing an unknown color image (color to label), all the present colors are mapped to a continuous label list. - - The support image method uses a color support image to associate an average color to each region.

Parameters

Limitations

The segmentation optimal method does not support streaming, and thus large images. The operation color to label is not implemented for the methods continuous LUT and support image LUT. - ColorMapping using support image is not threaded.

Authors

OTB-Team

See Also

ImageSVMClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/CompareImages.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/CompareImages.html deleted file mode 100644 index 89de722c4732..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/CompareImages.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

CompareImages

Brief Description

Estimator between 2 images.

Tags

Statistics

Long Description

This application computes MSE (Mean Squared Error), MAE (Mean Absolute Error) and PSNR (Peak Signal to Noise Ratio) between the channel of two images (reference and measurement). The user has to set the used channel and can specify a ROI.

Parameters

Limitations

None

Authors

OTB-Team

See Also

BandMath application, ImageStatistics

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ComputeConfusionMatrix-raster.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ComputeConfusionMatrix-raster.html deleted file mode 100644 index 8a6c9eed9ad3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ComputeConfusionMatrix-raster.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputeConfusionMatrix

Brief Description

Computes the confusion matrix of a classification

Tags

Learning

Long Description

This application computes the confusion matrix of a classification map relatively to a ground truth. This ground truth can be given as a raster or a vector data. Only reference and produced pixels with values different from NoData are handled in the calculation of the confusion matrix. The confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the output file, the reference and produced class labels are ordered according to the rows/columns of the confusion matrix.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ComputeConfusionMatrix-vector.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ComputeConfusionMatrix-vector.html deleted file mode 100644 index 8a6c9eed9ad3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ComputeConfusionMatrix-vector.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputeConfusionMatrix

Brief Description

Computes the confusion matrix of a classification

Tags

Learning

Long Description

This application computes the confusion matrix of a classification map relatively to a ground truth. This ground truth can be given as a raster or a vector data. Only reference and produced pixels with values different from NoData are handled in the calculation of the confusion matrix. The confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the output file, the reference and produced class labels are ordered according to the rows/columns of the confusion matrix.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ComputeConfusionMatrix.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ComputeConfusionMatrix.html deleted file mode 100644 index 8a6c9eed9ad3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ComputeConfusionMatrix.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputeConfusionMatrix

Brief Description

Computes the confusion matrix of a classification

Tags

Learning

Long Description

This application computes the confusion matrix of a classification map relatively to a ground truth. This ground truth can be given as a raster or a vector data. Only reference and produced pixels with values different from NoData are handled in the calculation of the confusion matrix. The confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the output file, the reference and produced class labels are ordered according to the rows/columns of the confusion matrix.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ComputeImagesStatistics.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ComputeImagesStatistics.html deleted file mode 100644 index 05cb9575bdbb..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ComputeImagesStatistics.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputeImagesStatistics

Brief Description

Computes global mean and standard deviation for each band from a set of images and optionally saves the results in an XML file.

Tags

Learning,Image Analysis

Long Description

This application computes a global mean and standard deviation for each band of a set of images and optionally saves the results in an XML file. The output XML is intended to be used an input for the TrainImagesClassifier application to normalize samples before learning.

Parameters

Limitations

Each image of the set must contain the same bands as the others (i.e. same types, in the same order).

Authors

OTB-Team

See Also

Documentation of the TrainImagesClassifier application.

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ComputeOGRLayersFeaturesStatistics.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ComputeOGRLayersFeaturesStatistics.html deleted file mode 100644 index 42c4651f14e3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ComputeOGRLayersFeaturesStatistics.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputeOGRLayersFeaturesStatistics

Brief Description

Compute statistics of the features in a set of OGR Layers

Tags

Segmentation

Long Description

Compute statistics (mean and standard deviation) of the features in a set of OGR Layers, and write them in an XML file. This XML file can then be used by the training application.

Parameters

Limitations

Experimental. For now only shapefiles are supported.

Authors

David Youssefi during internship at CNES

See Also

OGRLayerClassifier,TrainOGRLayersClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ComputePolylineFeatureFromImage.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ComputePolylineFeatureFromImage.html deleted file mode 100644 index 9e0e8444c576..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ComputePolylineFeatureFromImage.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputePolylineFeatureFromImage

Brief Description

This application compute for each studied polyline, contained in the input VectorData, the chosen descriptors.

Tags

Feature Extraction

Long Description

The first step in the classifier fusion based validation is to compute, for each studied polyline, the chosen descriptors.

Parameters

Limitations

Since it does not rely on streaming process, take care of the size of input image before launching application.

Authors

OTB-Team

See Also

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ConcatenateImages.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ConcatenateImages.html deleted file mode 100644 index f5d2ac5e2c28..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ConcatenateImages.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ConcatenateImages

Brief Description

Concatenate a list of images of the same size into a single multi-channel one.

Tags

Image Manipulation,Concatenation,Multi-channel

Long Description

This application performs images channels concatenation. It will walk the input image list (single or multi-channel) and generates a single multi-channel image. The channel order is the one of the list.

Parameters

Limitations

All input images must have the same size.

Authors

OTB-Team

See Also

Rescale application, Convert

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ConcatenateVectorData.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ConcatenateVectorData.html deleted file mode 100644 index 1760c34db99c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ConcatenateVectorData.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ConcatenateVectorData

Brief Description

Concatenate VectorDatas

Tags

Vector Data Manipulation

Long Description

This application concatenates a list of VectorData to produce a unique VectorData as output.Note that the VectorDatas must be of the same type (Storing polygons only, lines only, or points only)

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ConnectedComponentSegmentation.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ConnectedComponentSegmentation.html deleted file mode 100644 index d7ee4d6c0d5d..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ConnectedComponentSegmentation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ConnectedComponentSegmentation

Brief Description

Connected component segmentation and object based image filtering of the input image according to user-defined criterions.

Tags

Image Analysis,Segmentation

Long Description

This application allows performing a masking, connected components segmentation and object based image filtering. First and optionally, a mask can be built based on user-defined criterions to select pixels of the image which will be segmented. Then a connected component segmentation is performed with a user defined criterion to decide whether two neighbouring pixels belong to the same segment or not. After this segmentation step, an object based image filtering is applied using another user-defined criterion reasoning on segment properties, like shape or radiometric attributes. Criterions are mathematical expressions analysed by the MuParser library (http://muparser.sourceforge.net/). For instance, expression "((b1>80) and intensity>95)" will merge two neighbouring pixel in a single segment if their intensity is more than 95 and their value in the first image band is more than 80. See parameters documentation for a list of available attributes. The output of the object based image filtering is vectorized and can be written in shapefile or KML format. If the input image is in raw geometry, resulting polygons will be transformed to WGS84 using sensor modelling before writing, to ensure consistency with GIS softwares. For this purpose, a Digital Elevation Model can be provided to the application. The whole processing is done on a per-tile basis for large images, so this application can handle images of arbitrary size.

Parameters

Limitations

Due to the tiling scheme in case of large images, some segments can be arbitrarily split across multiple tiles.

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/Convert.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/Convert.html deleted file mode 100644 index d639181f4282..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/Convert.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

Convert

Brief Description

Convert an image to a different format, eventually rescaling the data and/or changing the pixel type.

Tags

Conversion,Image Dynamic,Image Manipulation

Long Description

This application performs an image pixel type conversion (short, ushort, uchar, int, uint, float and double types are handled). The output image is written in the specified format (ie. that corresponds to the given extension). - The conversion can include a rescale using the image 2 percent minimum and maximum values. The rescale can be linear or log2.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Rescale

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ConvertCartoToGeoPoint.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ConvertCartoToGeoPoint.html deleted file mode 100644 index 7d5c59ef0e7d..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ConvertCartoToGeoPoint.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ConvertCartoToGeoPoint

Brief Description

Convert cartographic coordinates to geographic one.

Tags

Coordinates,Geometry

Long Description

This application computes the geographic coordinates from a cartographic one. User has to give the X and Y coordinate and the cartographic projection (UTM/LAMBERT/LAMBERT2/LAMBERT93/SINUS/ECKERT4/TRANSMERCATOR/MOLLWEID/SVY21).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ConvertSensorToGeoPoint.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ConvertSensorToGeoPoint.html deleted file mode 100644 index 2f7c862990d8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ConvertSensorToGeoPoint.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ConvertSensorToGeoPoint

Brief Description

Sensor to geographic coordinates conversion.

Tags

Geometry

Long Description

This Application converts a sensor point of an input image to a geographic point using the Forward Sensor Model of the input image.

Parameters

Limitations

None

Authors

OTB-Team

See Also

ConvertCartoToGeoPoint application, otbObtainUTMZoneFromGeoPoint

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/CookBook.css b/python/plugins/processing/algs/otb/description/5.0.0/doc/CookBook.css deleted file mode 100644 index 7c5ca0b5b37c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/CookBook.css +++ /dev/null @@ -1,200 +0,0 @@ - -/* start css.sty */ -.cmmi-10{font-style: italic;} -.ptmr7t-{font-family: monospace;} -.ptmr7t-{font-family: monospace;} -.ptmr7t-{font-family: monospace;} -.phvr7t-x-x-223{font-size:223%; font-family: sans-serif;} -.phvr7t-x-x-223{ font-family: sans-serif;} -.phvr7t-x-x-223{ font-family: sans-serif;} -.ptmri7t-{font-style: italic;} -.phvr7t-x-x-155{font-size:155%; font-family: sans-serif;} -.phvr7t-x-x-155{ font-family: sans-serif;} -.phvr7t-x-x-155{ font-family: sans-serif;} -.ptmr7t-x-x-120{font-size:120%;font-family: monospace;} -.ptmr7t-x-x-120{font-family: monospace;} -.ptmr7t-x-x-120{font-family: monospace;} -.pcrr7tn-x-x-120{font-size:120%;font-family: monospace;} -.zptmcmr-x-x-90{font-size:90%; font-style: italic;} -.zptmcmrm-x-x-90{font-size:90%;} -.zptmcmrm-x-x-70{font-size:70%;} -.zpzccmry-x-x-90{font-size:90%; font-weight: bold; font-style: italic;} -.ptmb7t-x-x-90{font-size:90%; font-weight: bold;} -.ptmb7t-x-x-90{ font-weight: bold;} -.ptmri7t-x-x-120{font-size:120%;font-style: italic;} -.ptmri7t-x-x-90{font-size:90%;font-style: italic;} -.ptmr7t-x-x-90{font-size:90%;font-family: monospace;} -.ptmr7t-x-x-90{font-family: monospace;} -.ptmr7t-x-x-90{font-family: monospace;} -.phvr7t-x-x-81{font-size:81%; font-family: sans-serif;} -.phvr7t-x-x-81{ font-family: sans-serif;} -.phvr7t-x-x-81{ font-family: sans-serif;} -.ptmb7t-{ font-weight: bold;} -.ptmb7t-{ font-weight: bold;} -.pcrr7tn-{font-family: monospace;} -.pcrr7tn-x-x-90{font-size:90%;font-family: monospace;} -.zpzccmry-{ font-weight: bold; font-style: italic;} -.zptmcmr-{ font-style: italic;} -.zptmcmr-x-x-74{font-size:74%; font-style: italic;} -.zptmcmrm-x-x-74{font-size:74%;} -.zpzccmry-x-x-74{font-size:74%; font-weight: bold; font-style: italic;} -.pcrr7tn-x-x-70{font-size:70%;font-family: monospace;} -.phvro7t-x-x-81{font-size:81%;font-family:sans-serif; font-style:oblique;} -.pcrro7t-x-x-70{font-size:70%;font-family: monospace; font-style: oblique;} -.pcrb7t-x-x-70{font-size:70%; font-family: monospace; font-weight: bold;} -.phvb7t-x-x-81{font-size:81%;font-family: sans-serif; font-weight: bold;} -.cmsy-9{font-size:90%;} -p.noindent { text-indent: 0em } -td p.noindent { text-indent: 0em; margin-top:0em; } -p.nopar { text-indent: 0em; } -p.indent{ text-indent: 1.5em } -@media print {div.crosslinks {visibility:hidden;}} -a img { border-top: 0; border-left: 0; border-right: 0; } -center { margin-top:1em; margin-bottom:1em; } -td center { margin-top:0em; margin-bottom:0em; } -.Canvas { position:relative; } -img.math{vertical-align:middle;} -li p.indent { text-indent: 0em } -li p:first-child{ margin-top:0em; } -li p:last-child, li div:last-child { margin-bottom:0.5em; } -li p~ul:last-child, li p~ol:last-child{ margin-bottom:0.5em; } -.enumerate1 {list-style-type:decimal;} -.enumerate2 {list-style-type:lower-alpha;} -.enumerate3 {list-style-type:lower-roman;} -.enumerate4 {list-style-type:upper-alpha;} -div.newtheorem { margin-bottom: 2em; margin-top: 2em;} -.obeylines-h,.obeylines-v {white-space: nowrap; } -div.obeylines-v p { margin-top:0; margin-bottom:0; } -.overline{ text-decoration:overline; } -.overline img{ border-top: 1px solid black; } -td.displaylines {text-align:center; white-space:nowrap;} -.centerline {text-align:center;} -.rightline {text-align:right;} -div.verbatim {font-family: monospace; white-space: nowrap; text-align:left; clear:both; } -.fbox {padding-left:3.0pt; padding-right:3.0pt; text-indent:0pt; border:solid black 0.4pt; } -div.fbox {display:table} -div.center div.fbox {text-align:center; clear:both; padding-left:3.0pt; padding-right:3.0pt; text-indent:0pt; border:solid black 0.4pt; } -div.minipage{width:100%;} -div.center, div.center div.center {text-align: center; margin-left:1em; margin-right:1em;} -div.center div {text-align: left;} -div.flushright, div.flushright div.flushright {text-align: right;} -div.flushright div {text-align: left;} -div.flushleft {text-align: left;} -.underline{ text-decoration:underline; } -.underline img{ border-bottom: 1px solid black; margin-bottom:1pt; } -.framebox-c, .framebox-l, .framebox-r { padding-left:3.0pt; padding-right:3.0pt; text-indent:0pt; border:solid black 0.4pt; } -.framebox-c {text-align:center;} -.framebox-l {text-align:left;} -.framebox-r {text-align:right;} -span.thank-mark{ vertical-align: super } -span.footnote-mark sup.textsuperscript, span.footnote-mark a sup.textsuperscript{ font-size:80%; } -div.tabular, div.center div.tabular {text-align: center; margin-top:0.5em; margin-bottom:0.5em; } -table.tabular td p{margin-top:0em;} -table.tabular {margin-left: auto; margin-right: auto;} -td p:first-child{ margin-top:0em; } -td p:last-child{ margin-bottom:0em; } -div.td00{ margin-left:0pt; margin-right:0pt; } -div.td01{ margin-left:0pt; margin-right:5pt; } -div.td10{ margin-left:5pt; margin-right:0pt; } -div.td11{ margin-left:5pt; margin-right:5pt; } -table[rules] {border-left:solid black 0.4pt; border-right:solid black 0.4pt; } -td.td00{ padding-left:0pt; padding-right:0pt; } -td.td01{ padding-left:0pt; padding-right:5pt; } -td.td10{ padding-left:5pt; padding-right:0pt; } -td.td11{ padding-left:5pt; padding-right:5pt; } -table[rules] {border-left:solid black 0.4pt; border-right:solid black 0.4pt; } -.hline hr, .cline hr{ height : 1px; margin:0px; } -.tabbing-right {text-align:right;} -span.TEX {letter-spacing: -0.125em; } -span.TEX span.E{ position:relative;top:0.5ex;left:-0.0417em;} -a span.TEX span.E {text-decoration: none; } -span.LATEX span.A{ position:relative; top:-0.5ex; left:-0.4em; font-size:85%;} -span.LATEX span.TEX{ position:relative; left: -0.4em; } -div.float, div.figure {margin-left: auto; margin-right: auto;} -div.float img {text-align:center;} -div.figure img {text-align:center;} -.marginpar {width:20%; float:right; text-align:left; margin-left:auto; margin-top:0.5em; font-size:85%; text-decoration:underline;} -.marginpar p{margin-top:0.4em; margin-bottom:0.4em;} -table.equation {width:100%;} -.equation td{text-align:center; } -td.equation { margin-top:1em; margin-bottom:1em; } -td.equation-label { width:5%; text-align:center; } -td.eqnarray4 { width:5%; white-space: normal; } -td.eqnarray2 { width:5%; } -table.eqnarray-star, table.eqnarray {width:100%;} -div.eqnarray{text-align:center;} -div.array {text-align:center;} -div.pmatrix {text-align:center;} -table.pmatrix {width:100%;} -span.pmatrix img{vertical-align:middle;} -div.pmatrix {text-align:center;} -table.pmatrix {width:100%;} -span.bar-css {text-decoration:overline;} -img.cdots{vertical-align:middle;} -.partToc a, .partToc, .likepartToc a, .likepartToc {line-height: 200%; font-weight:bold; font-size:110%;} -.chapterToc a, .chapterToc, .likechapterToc a, .likechapterToc, .appendixToc a, .appendixToc {line-height: 200%; font-weight:bold;} -.index-item, .index-subitem, .index-subsubitem {display:block} -div.caption {text-indent:-2em; margin-left:3em; margin-right:1em; text-align:left;} -div.caption span.id{font-weight: bold; white-space: nowrap; } -h1.partHead{text-align: center} -p.bibitem { text-indent: -2em; margin-left: 2em; margin-top:0.6em; margin-bottom:0.6em; } -p.bibitem-p { text-indent: 0em; margin-left: 2em; margin-top:0.6em; margin-bottom:0.6em; } -.paragraphHead, .likeparagraphHead { margin-top:2em; font-weight: bold;} -.subparagraphHead, .likesubparagraphHead { font-weight: bold;} -.quote {margin-bottom:0.25em; margin-top:0.25em; margin-left:1em; margin-right:1em; text-align:justify;} -.verse{white-space:nowrap; margin-left:2em} -div.maketitle {text-align:center;} -h2.titleHead{text-align:center;} -div.maketitle{ margin-bottom: 2em; } -div.author, div.date {text-align:center;} -div.thanks{text-align:left; margin-left:10%; font-size:85%; font-style:italic; } -div.author{white-space: nowrap;} -.quotation {margin-bottom:0.25em; margin-top:0.25em; margin-left:1em; } -h1.partHead{text-align: center} - .chapterToc, .likechapterToc {margin-left:0em;} - .chapterToc ~ .likesectionToc, .chapterToc ~ .sectionToc, .likechapterToc ~ .likesectionToc, .likechapterToc ~ .sectionToc {margin-left:2em;} - .chapterToc ~ .likesectionToc ~ .likesubsectionToc, .chapterToc ~ .likesectionToc ~ .subsectionToc, .chapterToc ~ .sectionToc ~ .likesubsectionToc, .chapterToc ~ .sectionToc ~ .subsectionToc, .likechapterToc ~ .likesectionToc ~ .likesubsectionToc, .likechapterToc ~ .likesectionToc ~ .subsectionToc, .likechapterToc ~ .sectionToc ~ .likesubsectionToc, .likechapterToc ~ .sectionToc ~ .subsectionToc {margin-left:4em;} -.chapterToc ~ .likesectionToc ~ .likesubsectionToc ~ .likesubsubsectionToc, .chapterToc ~ .likesectionToc ~ .likesubsectionToc ~ .subsubsectionToc, .chapterToc ~ .likesectionToc ~ .subsectionToc ~ .likesubsubsectionToc, .chapterToc ~ .likesectionToc ~ .subsectionToc ~ .subsubsectionToc, .chapterToc ~ .sectionToc ~ .likesubsectionToc ~ .likesubsubsectionToc, .chapterToc ~ .sectionToc ~ .likesubsectionToc ~ .subsubsectionToc, .chapterToc ~ .sectionToc ~ .subsectionToc ~ .likesubsubsectionToc, .chapterToc ~ .sectionToc ~ .subsectionToc ~ .subsubsectionToc, .likechapterToc ~ .likesectionToc ~ .likesubsectionToc ~ .likesubsubsectionToc, .likechapterToc ~ .likesectionToc ~ .likesubsectionToc ~ .subsubsectionToc, .likechapterToc ~ .likesectionToc ~ .subsectionToc ~ .likesubsubsectionToc, .likechapterToc ~ .likesectionToc ~ .subsectionToc ~ .subsubsectionToc, .likechapterToc ~ .sectionToc ~ .likesubsectionToc ~ .likesubsubsectionToc, .likechapterToc ~ .sectionToc ~ .likesubsectionToc ~ .subsubsectionToc, .likechapterToc ~ .sectionToc ~ .subsectionToc ~ .likesubsubsectionToc .likechapterToc ~ .sectionToc ~ .subsectionToc ~ .subsubsectionToc {margin-left:6em;} - .likesectionToc , .sectionToc {margin-left:0em;} - .likesectionToc ~ .likesubsectionToc, .likesectionToc ~ .subsectionToc, .sectionToc ~ .likesubsectionToc, .sectionToc ~ .subsectionToc {margin-left:2em;} -.likesectionToc ~ .likesubsectionToc ~ .likesubsubsectionToc, .likesectionToc ~ .likesubsectionToc ~ .subsubsectionToc, .likesectionToc ~ .subsectionToc ~ .likesubsubsectionToc, .likesectionToc ~ .subsectionToc ~ .subsubsectionToc, .sectionToc ~ .likesubsectionToc ~ .likesubsubsectionToc, .sectionToc ~ .likesubsectionToc ~ .subsubsectionToc, .sectionToc ~ .subsectionToc ~ .likesubsubsectionToc, .sectionToc ~ .subsectionToc ~ .subsubsectionToc {margin-left:4em;} - .likesubsectionToc, .subsectionToc {margin-left:0em;} - .likesubsectionToc ~ .subsubsectionToc, .subsectionToc ~ .subsubsectionToc, {margin-left:2em;} -table[rules] {border-left:solid black 0.4pt; border-right:solid black 0.4pt; } -div.longtable{text-align:center;} -.figure img.graphics {margin-left:10%;} -.picins-dr, .picins-rd { float:right; padding: 2px; margin-left:5px; margin-bottom:3px; border: 1px dashed black; } -.picins-dl, .picins-ld, .picins-d { float:left; padding: 2px; margin-right:5px; margin-bottom:3px; border: 1px dashed black; } -.picins-fr, .picins-rf { float:right; padding: 2px; margin-left:5px; margin-bottom:3px; border: 1px dashed black; } -.picins-fl, .picins-lf, .picins-f { float:left; padding: 2px; margin-right:5px; margin-bottom:3px; border: 1px solid black; } -.picins-sr, .picins-rs { float:right; padding: 2px; margin-left:5px; margin-bottom:3px; border-left: 1px solid black; border-top: 1px solid black; border-right: 4px solid black; border-bottom: 4px solid black; } -.picins-sl, .picins-ls, .picins-s { float:left; padding: 2px; margin-right:5px; margin-bottom:3px; border-left: 1px solid black; border-top: 1px solid black; border-right: 4px solid black; border-bottom: 4px solid black; } -.picins-xr, .picins-rx { float:right; padding: 2px; margin-left:5px; margin-bottom:3px; border-left: 1px solid black; border-top: 1px solid black; border-right: 1px solid black; border-bottom: 1px solid black; } -.picins-xl, .picins-lx, .picins-x { float:left; padding: 2px; margin-right:5px; margin-bottom:3px; border-left: 1px solid black; border-top: 1px solid black; border-right: 1px solid black; border-bottom: 1px solid black; } -.picins-r { float:right; padding: 2px; margin-left:5px; margin-bottom:3px; } -.picins-l, .picins- { float:left; padding: 2px; margin-right:5px; margin-bottom:3px; } -.floatingfigure-r { float:right; text-align:left; margin-top:0.5em; margin-bottom:0.5em; margin-left:0em;} -.floatingfigure-l { float:left; text-align:left; margin-top:0.5em; margin-bottom:0.5em; margin-right:0em; } -.caption span.id{font-weight: bold;} -.equation td{text-align:center; } -.equation-star td{text-align:center; } -table.equation-star { width:100%; } -table.equation { width:100%; } -table.align, table.alignat, table.xalignat, table.xxalignat, table.flalign {width:100%; margin-left:5%; white-space: nowrap;} -table.align-star, table.alignat-star, table.xalignat-star, table.flalign-star {margin-left:auto; margin-right:auto; white-space: nowrap;} -td.align-label { width:5%; text-align:center; } -td.align-odd { text-align:right; padding-right:0.3em;} -td.align-even { text-align:left; padding-right:0.6em;} -table.multline, table.multline-star {width:100%;} -td.gather {text-align:center; } -table.gather {width:100%;} -div.gather-star {text-align:center;} -div.verbatiminput {font-family: monospace; white-space: nowrap; } -.alltt P { margin-bottom : 0em; margin-top : 0em; } -.alltt { margin-bottom : 1em; margin-top : 1em; } -.lstlisting .label{margin-right:0.5em; } -div.lstlisting{font-family: monospace; white-space: nowrap; margin-top:0.5em; margin-bottom:0.5em; } -div.lstinputlisting{ font-family: monospace; white-space: nowrap; } -.lstinputlisting .label{margin-right:0.5em;} -/* end css.sty */ - diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/DEMConvert.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/DEMConvert.html deleted file mode 100644 index e51b5030bbfd..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/DEMConvert.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DEMConvert

Brief Description

Converts a geo-referenced DEM image into a general raster file compatible with OTB DEM handling.

Tags

Image Manipulation

Long Description

In order to be understood by the Orfeo ToolBox and the underlying OSSIM library, a geo-referenced Digital Elevation Model image can be converted into a general raster image, which consists in 3 files with the following extensions: .ras, .geom and .omd. Once converted, you have to place these files in a separate directory, and you can then use this directory to set the "DEM Directory" parameter of a DEM based OTB application or filter.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/DSFuzzyModelEstimation.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/DSFuzzyModelEstimation.html deleted file mode 100644 index 5879d221eac5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/DSFuzzyModelEstimation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DSFuzzyModelEstimation

Brief Description

Estimate feature fuzzy model parameters using 2 vector data (ground truth samples and wrong samples).

Tags

Feature Extraction

Long Description

Estimate feature fuzzy model parameters using 2 vector data (ground truth samples and wrong samples).

Parameters

Limitations

None.

Authors

OTB-Team

See Also

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/Despeckle-frost.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/Despeckle-frost.html deleted file mode 100644 index 66987867075c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/Despeckle-frost.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Despeckle

Brief Description

Perform speckle noise reduction on SAR image.

Tags

SAR,Image Filtering

Long Description

This application reduce speckle noise. Two methods are available: Lee and Frost.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/Despeckle-lee.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/Despeckle-lee.html deleted file mode 100644 index 66987867075c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/Despeckle-lee.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Despeckle

Brief Description

Perform speckle noise reduction on SAR image.

Tags

SAR,Image Filtering

Long Description

This application reduce speckle noise. Two methods are available: Lee and Frost.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/Despeckle.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/Despeckle.html deleted file mode 100644 index 66987867075c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/Despeckle.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Despeckle

Brief Description

Perform speckle noise reduction on SAR image.

Tags

SAR,Image Filtering

Long Description

This application reduce speckle noise. Two methods are available: Lee and Frost.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/DimensionalityReduction-ica.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/DimensionalityReduction-ica.html deleted file mode 100644 index 487251cf0c4b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/DimensionalityReduction-ica.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DimensionalityReduction

Brief Description

Perform Dimension reduction of the input image.

Tags

Dimensionality Reduction,Image Filtering

Long Description

Performs dimensionality reduction on input image. PCA,NA-PCA,MAF,ICA methods are available. It is also possible to compute the inverse transform to reconstruct the image. It is also possible to optionnaly export the transformation matrix to a text file.

Parameters

Limitations

This application does not provide the inverse transform and the transformation matrix export for the MAF.

Authors

OTB-Team

See Also

"Kernel maximum autocorrelation factor and minimum noise fraction transformations," IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 612-624, (2011)

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/DimensionalityReduction-maf.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/DimensionalityReduction-maf.html deleted file mode 100644 index 487251cf0c4b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/DimensionalityReduction-maf.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DimensionalityReduction

Brief Description

Perform Dimension reduction of the input image.

Tags

Dimensionality Reduction,Image Filtering

Long Description

Performs dimensionality reduction on input image. PCA,NA-PCA,MAF,ICA methods are available. It is also possible to compute the inverse transform to reconstruct the image. It is also possible to optionnaly export the transformation matrix to a text file.

Parameters

Limitations

This application does not provide the inverse transform and the transformation matrix export for the MAF.

Authors

OTB-Team

See Also

"Kernel maximum autocorrelation factor and minimum noise fraction transformations," IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 612-624, (2011)

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/DimensionalityReduction-napca.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/DimensionalityReduction-napca.html deleted file mode 100644 index 487251cf0c4b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/DimensionalityReduction-napca.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DimensionalityReduction

Brief Description

Perform Dimension reduction of the input image.

Tags

Dimensionality Reduction,Image Filtering

Long Description

Performs dimensionality reduction on input image. PCA,NA-PCA,MAF,ICA methods are available. It is also possible to compute the inverse transform to reconstruct the image. It is also possible to optionnaly export the transformation matrix to a text file.

Parameters

Limitations

This application does not provide the inverse transform and the transformation matrix export for the MAF.

Authors

OTB-Team

See Also

"Kernel maximum autocorrelation factor and minimum noise fraction transformations," IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 612-624, (2011)

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/DimensionalityReduction-pca.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/DimensionalityReduction-pca.html deleted file mode 100644 index 487251cf0c4b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/DimensionalityReduction-pca.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DimensionalityReduction

Brief Description

Perform Dimension reduction of the input image.

Tags

Dimensionality Reduction,Image Filtering

Long Description

Performs dimensionality reduction on input image. PCA,NA-PCA,MAF,ICA methods are available. It is also possible to compute the inverse transform to reconstruct the image. It is also possible to optionnaly export the transformation matrix to a text file.

Parameters

Limitations

This application does not provide the inverse transform and the transformation matrix export for the MAF.

Authors

OTB-Team

See Also

"Kernel maximum autocorrelation factor and minimum noise fraction transformations," IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 612-624, (2011)

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/DimensionalityReduction.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/DimensionalityReduction.html deleted file mode 100644 index 487251cf0c4b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/DimensionalityReduction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DimensionalityReduction

Brief Description

Perform Dimension reduction of the input image.

Tags

Dimensionality Reduction,Image Filtering

Long Description

Performs dimensionality reduction on input image. PCA,NA-PCA,MAF,ICA methods are available. It is also possible to compute the inverse transform to reconstruct the image. It is also possible to optionnaly export the transformation matrix to a text file.

Parameters

Limitations

This application does not provide the inverse transform and the transformation matrix export for the MAF.

Authors

OTB-Team

See Also

"Kernel maximum autocorrelation factor and minimum noise fraction transformations," IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 612-624, (2011)

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/DisparityMapToElevationMap.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/DisparityMapToElevationMap.html deleted file mode 100644 index 1b44612fe5f1..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/DisparityMapToElevationMap.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DisparityMapToElevationMap

Brief Description

Projects a disparity map into a regular elevation map

Tags

Stereo

Long Description

This application uses a disparity map computed from a stereo image pair to produce an elevation map on the ground area covered by the stereo pair. The needed inputs are : the disparity map, the stereo pair (in original geometry) and the epipolar deformation grids. These grids have to link the original geometry (stereo pair) and the epipolar geometry (disparity map).

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbStereoRectificationGridGenerator otbBlockMatching

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/DownloadSRTMTiles.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/DownloadSRTMTiles.html deleted file mode 100644 index 3c6871d7a5eb..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/DownloadSRTMTiles.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DownloadSRTMTiles

Brief Description

Download or list SRTM tiles related to a set of images

Tags

Utilities,Image Manipulation

Long Description

This application allows selecting the appropriate SRTM tiles that covers a list of images. It builds a list of the required tiles. Two modes are available: the first one download those tiles from the USGS SRTM3 website (http://dds.cr.usgs.gov/srtm/version2_1/SRTM3/), the second one list those tiles in a local directory. In both cases, you need to indicate the directory in which directory tiles will be download or the location of local SRTM files.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/EdgeExtraction-gradient.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/EdgeExtraction-gradient.html deleted file mode 100644 index aa436906807a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/EdgeExtraction-gradient.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

EdgeExtraction

Brief Description

Computes edge features on every pixel of the input image selected channel

Tags

Edge,Feature Extraction

Long Description

This application computes edge features on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otb class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/EdgeExtraction-sobel.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/EdgeExtraction-sobel.html deleted file mode 100644 index aa436906807a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/EdgeExtraction-sobel.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

EdgeExtraction

Brief Description

Computes edge features on every pixel of the input image selected channel

Tags

Edge,Feature Extraction

Long Description

This application computes edge features on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otb class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/EdgeExtraction-touzi.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/EdgeExtraction-touzi.html deleted file mode 100644 index aa436906807a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/EdgeExtraction-touzi.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

EdgeExtraction

Brief Description

Computes edge features on every pixel of the input image selected channel

Tags

Edge,Feature Extraction

Long Description

This application computes edge features on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otb class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/EdgeExtraction.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/EdgeExtraction.html deleted file mode 100644 index aa436906807a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/EdgeExtraction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

EdgeExtraction

Brief Description

Computes edge features on every pixel of the input image selected channel

Tags

Edge,Feature Extraction

Long Description

This application computes edge features on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otb class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ExtractROI-fit.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ExtractROI-fit.html deleted file mode 100644 index ec344b83a617..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ExtractROI-fit.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ExtractROI

Brief Description

Extract a ROI defined by the user.

Tags

Image Manipulation

Long Description

This application extracts a Region Of Interest with user defined size, or reference image.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ExtractROI-standard.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ExtractROI-standard.html deleted file mode 100644 index ec344b83a617..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ExtractROI-standard.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ExtractROI

Brief Description

Extract a ROI defined by the user.

Tags

Image Manipulation

Long Description

This application extracts a Region Of Interest with user defined size, or reference image.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ExtractROI.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ExtractROI.html deleted file mode 100644 index ec344b83a617..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ExtractROI.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ExtractROI

Brief Description

Extract a ROI defined by the user.

Tags

Image Manipulation

Long Description

This application extracts a Region Of Interest with user defined size, or reference image.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/FineRegistration.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/FineRegistration.html deleted file mode 100644 index 0c8330415a83..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/FineRegistration.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

FineRegistration

Brief Description

Estimate disparity map between two images.

Tags

Stereo

Long Description

Estimate disparity map between two images. Output image contain x offset, y offset and metric value.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/FusionOfClassifications-dempstershafer.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/FusionOfClassifications-dempstershafer.html deleted file mode 100644 index c9723c035cd0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/FusionOfClassifications-dempstershafer.html +++ /dev/null @@ -1,9 +0,0 @@ - - -

FusionOfClassifications

Brief Description

Fuses several classifications maps of the same image on the basis of class labels.

Tags

Learning,Image Analysis

Long Description

This application allows fusing several classification maps and produces a single more robust classification map. Fusion is done either by mean of Majority Voting, or with the Dempster Shafer combination method on class labels. - -MAJORITY VOTING: for each pixel, the class with the highest number of votes is selected. - -DEMPSTER SHAFER: for each pixel, the class label for which the Belief Function is maximal is selected. This Belief Function is calculated by mean of the Dempster Shafer combination of Masses of Belief, and indicates the belief that each input classification map presents for each label value. Moreover, the Masses of Belief are based on the input confusion matrices of each classification map, either by using the PRECISION or RECALL rates, or the OVERALL ACCURACY, or the KAPPA coefficient. Thus, each input classification map needs to be associated with its corresponding input confusion matrix file for the Dempster Shafer fusion. --Input pixels with the NODATA label are not handled in the fusion of classification maps. Moreover, pixels for which all the input classifiers are set to NODATA keep this value in the output fused image. --In case of number of votes equality, the UNDECIDED label is attributed to the pixel.

Parameters

Limitations

None

Authors

OTB-Team

See Also

ImageClassifier application

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/FusionOfClassifications-majorityvoting.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/FusionOfClassifications-majorityvoting.html deleted file mode 100644 index c9723c035cd0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/FusionOfClassifications-majorityvoting.html +++ /dev/null @@ -1,9 +0,0 @@ - - -

FusionOfClassifications

Brief Description

Fuses several classifications maps of the same image on the basis of class labels.

Tags

Learning,Image Analysis

Long Description

This application allows fusing several classification maps and produces a single more robust classification map. Fusion is done either by mean of Majority Voting, or with the Dempster Shafer combination method on class labels. - -MAJORITY VOTING: for each pixel, the class with the highest number of votes is selected. - -DEMPSTER SHAFER: for each pixel, the class label for which the Belief Function is maximal is selected. This Belief Function is calculated by mean of the Dempster Shafer combination of Masses of Belief, and indicates the belief that each input classification map presents for each label value. Moreover, the Masses of Belief are based on the input confusion matrices of each classification map, either by using the PRECISION or RECALL rates, or the OVERALL ACCURACY, or the KAPPA coefficient. Thus, each input classification map needs to be associated with its corresponding input confusion matrix file for the Dempster Shafer fusion. --Input pixels with the NODATA label are not handled in the fusion of classification maps. Moreover, pixels for which all the input classifiers are set to NODATA keep this value in the output fused image. --In case of number of votes equality, the UNDECIDED label is attributed to the pixel.

Parameters

Limitations

None

Authors

OTB-Team

See Also

ImageClassifier application

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/FusionOfClassifications.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/FusionOfClassifications.html deleted file mode 100644 index c9723c035cd0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/FusionOfClassifications.html +++ /dev/null @@ -1,9 +0,0 @@ - - -

FusionOfClassifications

Brief Description

Fuses several classifications maps of the same image on the basis of class labels.

Tags

Learning,Image Analysis

Long Description

This application allows fusing several classification maps and produces a single more robust classification map. Fusion is done either by mean of Majority Voting, or with the Dempster Shafer combination method on class labels. - -MAJORITY VOTING: for each pixel, the class with the highest number of votes is selected. - -DEMPSTER SHAFER: for each pixel, the class label for which the Belief Function is maximal is selected. This Belief Function is calculated by mean of the Dempster Shafer combination of Masses of Belief, and indicates the belief that each input classification map presents for each label value. Moreover, the Masses of Belief are based on the input confusion matrices of each classification map, either by using the PRECISION or RECALL rates, or the OVERALL ACCURACY, or the KAPPA coefficient. Thus, each input classification map needs to be associated with its corresponding input confusion matrix file for the Dempster Shafer fusion. --Input pixels with the NODATA label are not handled in the fusion of classification maps. Moreover, pixels for which all the input classifiers are set to NODATA keep this value in the output fused image. --In case of number of votes equality, the UNDECIDED label is attributed to the pixel.

Parameters

Limitations

None

Authors

OTB-Team

See Also

ImageClassifier application

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/GeneratePlyFile.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/GeneratePlyFile.html deleted file mode 100644 index 9cefcc9d5b87..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/GeneratePlyFile.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GeneratePlyFile

Brief Description

Generate a 3D Ply file from a DEM and a color image.

Tags

Geometry

Long Description

Generate a 3D Ply file from a DEM and a color image.

Parameters

Limitations

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/GenerateRPCSensorModel.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/GenerateRPCSensorModel.html deleted file mode 100644 index 63c73dfbd0a2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/GenerateRPCSensorModel.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GenerateRPCSensorModel

Brief Description

Generate a RPC sensor model from a list of Ground Control Points.

Tags

Geometry

Long Description

This application generates a RPC sensor model from a list of Ground Control Points. At least 20 points are required for estimation wihtout elevation support, and 40 points for estimation with elevation support. Elevation support will be automatically deactivated if an insufficient amount of points is provided. The application can optionnaly output a file containing accuracy statistics for each point, and a vector file containing segments represening points residues. The map projection parameter allows defining a map projection in which the accuracy is evaluated.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OrthoRectication,HomologousPointsExtraction,RefineSensorModel

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/GrayScaleMorphologicalOperation-closing.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/GrayScaleMorphologicalOperation-closing.html deleted file mode 100644 index e3081bbf311b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/GrayScaleMorphologicalOperation-closing.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GrayScaleMorphologicalOperation

Brief Description

Performs morphological operations on a grayscale input image

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs grayscale morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkGrayscaleDilateImageFilter, itkGrayscaleErodeImageFilter, itkGrayscaleMorphologicalOpeningImageFilter and itkGrayscaleMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/GrayScaleMorphologicalOperation-dilate.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/GrayScaleMorphologicalOperation-dilate.html deleted file mode 100644 index e3081bbf311b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/GrayScaleMorphologicalOperation-dilate.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GrayScaleMorphologicalOperation

Brief Description

Performs morphological operations on a grayscale input image

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs grayscale morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkGrayscaleDilateImageFilter, itkGrayscaleErodeImageFilter, itkGrayscaleMorphologicalOpeningImageFilter and itkGrayscaleMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/GrayScaleMorphologicalOperation-erode.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/GrayScaleMorphologicalOperation-erode.html deleted file mode 100644 index e3081bbf311b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/GrayScaleMorphologicalOperation-erode.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GrayScaleMorphologicalOperation

Brief Description

Performs morphological operations on a grayscale input image

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs grayscale morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkGrayscaleDilateImageFilter, itkGrayscaleErodeImageFilter, itkGrayscaleMorphologicalOpeningImageFilter and itkGrayscaleMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/GrayScaleMorphologicalOperation-opening.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/GrayScaleMorphologicalOperation-opening.html deleted file mode 100644 index e3081bbf311b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/GrayScaleMorphologicalOperation-opening.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GrayScaleMorphologicalOperation

Brief Description

Performs morphological operations on a grayscale input image

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs grayscale morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkGrayscaleDilateImageFilter, itkGrayscaleErodeImageFilter, itkGrayscaleMorphologicalOpeningImageFilter and itkGrayscaleMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/GrayScaleMorphologicalOperation.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/GrayScaleMorphologicalOperation.html deleted file mode 100644 index e3081bbf311b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/GrayScaleMorphologicalOperation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GrayScaleMorphologicalOperation

Brief Description

Performs morphological operations on a grayscale input image

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs grayscale morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkGrayscaleDilateImageFilter, itkGrayscaleErodeImageFilter, itkGrayscaleMorphologicalOpeningImageFilter and itkGrayscaleMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/GridBasedImageResampling.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/GridBasedImageResampling.html deleted file mode 100644 index 19116df65ec7..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/GridBasedImageResampling.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GridBasedImageResampling

Brief Description

Resamples an image according to a resampling grid

Tags

Geometry

Long Description

This application allows performing image resampling from an input resampling grid.

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbStereorecificationGridGeneration

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/HaralickTextureExtraction.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/HaralickTextureExtraction.html deleted file mode 100644 index ef966758bb63..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/HaralickTextureExtraction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

HaralickTextureExtraction

Brief Description

Computes textures on every pixel of the input image selected channel

Tags

Textures,Feature Extraction

Long Description

This application computes Haralick, advanced and higher order textures on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbScalarImageToTexturesFilter, otbScalarImageToAdvancedTexturesFilter and otbScalarImageToHigherOrderTexturesFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/HomologousPointsExtraction.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/HomologousPointsExtraction.html deleted file mode 100644 index 3dd23b1e41ae..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/HomologousPointsExtraction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

HomologousPointsExtraction

Brief Description

Allows computing homologous points between images using keypoints

Tags

Feature Extraction

Long Description

This application allows computing homologous points between images using keypoints. SIFT or SURF keypoints can be used and the band on which keypoints are computed can be set independently for both images. The application offers two modes : the first is the full mode where keypoints are extracted from the full extent of both images (please note that in this mode large image file are not supported). The second mode, called geobins, allows setting-up spatial binning to get fewer points spread accross the entire image. In this mode, the corresponding spatial bin in the second image is estimated using geographical transform or sensor modelling, and is padded according to the user defined precision. Last, in both modes the application can filter matches whose colocalisation in first image exceed this precision. The elevation parameters are to deal more precisely with sensor modelling in case of sensor geometry data. The outvector option allows creating a vector file with segments corresponding to the localisation error between the matches. It can be useful to assess the precision of a registration for instance. The vector file is always reprojected to EPSG:4326 to allow display in a GIS. This is done via reprojection or by applying the image sensor models.

Parameters

Limitations

Full mode does not handle large images.

Authors

OTB-Team

See Also

RefineSensorModel

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/HooverCompareSegmentation.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/HooverCompareSegmentation.html deleted file mode 100644 index 7fdbe9a5043f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/HooverCompareSegmentation.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

HooverCompareSegmentation

Brief Description

Compare two segmentations with Hoover metrics

Tags

Segmentation

Long Description

This application compares a machine segmentation (MS) with a partial ground truth segmentation (GT). The Hoover metrics are used to estimate scores for correct detection, over-segmentation, under-segmentation and missed detection. - The application can output the overall Hoover scores along with coloredimages of the MS and GT segmentation showing the state of each region (correct detection, over-segmentation, under-segmentation, missed) - The Hoover metrics are described in : Hoover et al., "An experimental comparison of range image segmentation algorithms", IEEE PAMI vol. 18, no. 7, July 1996.

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbHooverMatrixFilter, otbHooverInstanceFilter, otbLabelMapToAttributeImageFilter

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/HyperspectralUnmixing.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/HyperspectralUnmixing.html deleted file mode 100644 index 3670df595b2e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/HyperspectralUnmixing.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

HyperspectralUnmixing

Brief Description

Estimate abundance maps from an hyperspectral image and a set of endmembers.

Tags

Hyperspectral

Long Description

The application applies a linear unmixing algorithm to an hyperspectral data cube. This method supposes that the mixture between materials in the scene is macroscopic and simulates a linear mixing model of spectra. -The Linear Mixing Model (LMM) acknowledges that reflectance spectrum associated with each pixel is a linear combination of pure materials in the recovery area, commonly known as endmembers. Endmembers can be estimated using the VertexComponentAnalysis application. -The application allows estimating the abundance maps with several algorithms : Unconstrained Least Square (ucls), Fully Constrained Least Square (fcls), Image Space Reconstruction Algorithm (isra) and Non-negative constrained Least Square (ncls) and Minimum Dispersion Constrained Non Negative Matrix Factorization (MDMDNMF). -

Parameters

Limitations

None

Authors

OTB-Team

See Also

VertexComponentAnalysis

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ImageClassifier.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ImageClassifier.html deleted file mode 100644 index 978a0bb2c21b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ImageClassifier.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ImageClassifier

Brief Description

Performs a classification of the input image according to a model file.

Tags

Learning

Long Description

This application performs an image classification based on a model file produced by the TrainImagesClassifier application. Pixels of the output image will contain the class labels decided by the classifier (maximal class label = 65535). The input pixels can be optionally centered and reduced according to the statistics file produced by the ComputeImagesStatistics application. An optional input mask can be provided, in which case only input image pixels whose corresponding mask value is greater than 0 will be classified. The remaining of pixels will be given the label 0 in the output image.

Parameters

Limitations

The input image must have the same type, order and number of bands than the images used to produce the statistics file and the SVM model file. If a statistics file was used during training by the TrainImagesClassifier, it is mandatory to use the same statistics file for classification. If an input mask is used, its size must match the input image size.

Authors

OTB-Team

See Also

TrainImagesClassifier, ValidateImagesClassifier, ComputeImagesStatistics

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ImageEnvelope.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ImageEnvelope.html deleted file mode 100644 index 6b0e00023b41..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ImageEnvelope.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ImageEnvelope

Brief Description

Extracts an image envelope.

Tags

Geometry

Long Description

Build a vector data containing the polygon of the image envelope.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/KMeansClassification.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/KMeansClassification.html deleted file mode 100644 index 47414f7b94d3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/KMeansClassification.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

KMeansClassification

Brief Description

Unsupervised KMeans image classification

Tags

Segmentation,Learning

Long Description

Performs unsupervised KMeans image classification.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/KmzExport.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/KmzExport.html deleted file mode 100644 index 98c54781ec2e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/KmzExport.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

KmzExport

Brief Description

Export the input image in a KMZ product.

Tags

KMZ,Export

Long Description

This application exports the input image in a kmz product that can be display in the Google Earth software. The user can set the size of the product size, a logo and a legend to the product. Furthemore, to obtain a product that fits the relief, a DEM can be used.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Conversion

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/LSMSSegmentation.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/LSMSSegmentation.html deleted file mode 100644 index 6d07dcf15518..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/LSMSSegmentation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

LSMSSegmentation

Brief Description

Second step of the exact Large-Scale Mean-Shift segmentation workflow.

Tags

Segmentation,LSMS

Long Description

This application performs the second step of the exact Large-Scale Mean-Shift segmentation workflow (LSMS). Filtered range image and spatial image should be created with the MeanShiftSmoothing application, with modesearch parameter disabled. If spatial image is not set, the application will only process the range image and spatial radius parameter will not be taken into account. This application will produce a labeled image where neighbor pixels whose range distance is below range radius (and optionally spatial distance below spatial radius) will be grouped together into the same cluster. For large images one can use the nbtilesx and nbtilesy parameters for tile-wise processing, with the guarantees of identical results. Please note that this application will generate a lot of temporary files (as many as the number of tiles), and will therefore require twice the size of the final result in term of disk space. The cleanup option (activated by default) allows removing all temporary file as soon as they are not needed anymore (if cleanup is activated, tmpdir set and tmpdir does not exists before running the application, it will be removed as well during cleanup). The tmpdir option allows defining a directory where to write the temporary files. Please also note that the output image type should be set to uint32 to ensure that there are enough labels available.

Parameters

Limitations

This application is part of the Large-Scale Mean-Shift segmentation workflow (LSMS) and may not be suited for any other purpose.

Authors

David Youssefi

See Also

MeanShiftSmoothing, LSMSSmallRegionsMerging, LSMSVectorization

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/LSMSSmallRegionsMerging.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/LSMSSmallRegionsMerging.html deleted file mode 100644 index b293794cb450..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/LSMSSmallRegionsMerging.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

LSMSSmallRegionsMerging

Brief Description

Third (optional) step of the exact Large-Scale Mean-Shift segmentation workflow.

Tags

Segmentation,LSMS

Long Description

This application performs the third step of the exact Large-Scale Mean-Shift segmentation workflow (LSMS). Given a segmentation result (label image) and the original image, it will merge regions whose size in pixels is lower than minsize parameter with the adjacent regions with the adjacent region with closest radiometry and acceptable size. Small regions will be processed by size: first all regions of area, which is equal to 1 pixel will be merged with adjacent region, then all regions of area equal to 2 pixels, until regions of area minsize. For large images one can use the nbtilesx and nbtilesy parameters for tile-wise processing, with the guarantees of identical results.

Parameters

Limitations

This application is part of the Large-Scale Mean-Shift segmentation workflow (LSMS) and may not be suited for any other purpose.

Authors

David Youssefi

See Also

LSMSSegmentation, LSMSVectorization, MeanShiftSmoothing

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/LSMSVectorization.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/LSMSVectorization.html deleted file mode 100644 index 5414a039181e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/LSMSVectorization.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

LSMSVectorization

Brief Description

Fourth step of the exact Large-Scale Mean-Shift segmentation workflow.

Tags

Segmentation,LSMS

Long Description

This application performs the fourth step of the exact Large-Scale Mean-Shift segmentation workflow (LSMS). Given a segmentation result (label image), that may have been processed for small regions merging or not, it will convert it to a GIS vector file containing one polygon per segment. Each polygon contains additional fields: mean and variance of each channels from input image (in parameter), segmentation image label, number of pixels in the polygon. For large images one can use the nbtilesx and nbtilesy parameters for tile-wise processing, with the guarantees of identical results.

Parameters

Limitations

This application is part of the Large-Scale Mean-Shift segmentation workflow (LSMS) and may not be suited for any other purpose.

Authors

David Youssefi

See Also

MeanShiftSmoothing, LSMSSegmentation, LSMSSmallRegionsMerging

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/LineSegmentDetection.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/LineSegmentDetection.html deleted file mode 100644 index ca90896346a1..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/LineSegmentDetection.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

LineSegmentDetection

Brief Description

Detect line segments in raster

Tags

Feature Extraction

Long Description

This application detects locally straight contours in a image. It is based on Burns, Hanson, and Riseman method and use an a contrario validation approach (Desolneux, Moisan, and Morel). The algorithm was published by Rafael Gromponevon Gioi, Jérémie Jakubowicz, Jean-Michel Morel and Gregory Randall. - The given approach computes gradient and level lines of the image and detects aligned points in line support region. The application allows exporting the detected lines in a vector data.

Parameters

Limitations

None

Authors

OTB-Team

See Also

On Line demonstration of the LSD algorithm is available here: http://www.ipol.im/pub/algo/gjmr_line_segment_detector/ -

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/LocalStatisticExtraction.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/LocalStatisticExtraction.html deleted file mode 100644 index 6ff7bc151aa1..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/LocalStatisticExtraction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

LocalStatisticExtraction

Brief Description

Computes local statistical moments on every pixel in the selected channel of the input image

Tags

Statistics,Feature Extraction

Long Description

This application computes the 4 local statistical moments on every pixel in the selected channel of the input image, over a specified neighborhood. The output image is multi band with one statistical moment (feature) per band. Thus, the 4 output features are the Mean, the Variance, the Skewness and the Kurtosis. They are provided in this exact order in the output image.

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbRadiometricMomentsImageFunction class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/MeanShiftSmoothing.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/MeanShiftSmoothing.html deleted file mode 100644 index 491953882a83..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/MeanShiftSmoothing.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

MeanShiftSmoothing

Brief Description

Perform mean shift filtering

Tags

Image Filtering,LSMS

Long Description

This application performs mean shift fitlering (multi-threaded).

Parameters

Limitations

With mode search option, the result will slightly depend on thread number.

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/MultiResolutionPyramid.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/MultiResolutionPyramid.html deleted file mode 100644 index 1d565caa2d42..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/MultiResolutionPyramid.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

MultiResolutionPyramid

Brief Description

Build a multi-resolution pyramid of the image.

Tags

Conversion,Image Manipulation,Image MultiResolution,Util

Long Description

This application builds a multi-resolution pyramid of the input image. User can specified the number of levels of the pyramid and the subsampling factor. To speed up the process, you can use the fast scheme option

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/MultivariateAlterationDetector.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/MultivariateAlterationDetector.html deleted file mode 100644 index 9ddd73e4da91..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/MultivariateAlterationDetector.html +++ /dev/null @@ -1,21 +0,0 @@ - - -

MultivariateAlterationDetector

Brief Description

Multivariate Alteration Detector

Tags

Feature Extraction

Long Description

This application detects change between two given images.

Parameters

Limitations

None

Authors

OTB-Team

See Also

This filter implements the Multivariate Alteration Detector, based on the following work: - A. A. Nielsen and K. Conradsen, Multivariate alteration detection (mad) in multispectral, bi-temporal image data: a new approach to change detection studies, Remote Sens. Environ., vol. 64, pp. 1-19, (1998) - - Multivariate Alteration Detector takes two images as inputs and produce a set of N change maps as a VectorImage (where N is the maximum of number of bands in first and second image) with the following properties: - - Change maps are differences of a pair of linear combinations of bands from image 1 and bands from image 2 chosen to maximize the correlation. - - Each change map is orthogonal to the others. - - This is a statistical method which can handle different modalities and even different bands and number of bands between images. - - If numbers of bands in image 1 and 2 are equal, then change maps are sorted by increasing correlation. If number of bands is different, the change maps are sorted by decreasing correlation. - - The GetV1() and GetV2() methods allow retrieving the linear combinations used to generate the Mad change maps as a vnl_matrix of double, and the GetRho() method allows retrieving the correlation associated to each Mad change maps as a vnl_vector. - - This filter has been implemented from the Matlab code kindly made available by the authors here: - http://www2.imm.dtu.dk/~aa/software.html - - Both cases (same and different number of bands) have been validated by comparing the output image to the output produced by the Matlab code, and the reference images for testing have been generated from the Matlab code using Octave.

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/OGRLayerClassifier.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/OGRLayerClassifier.html deleted file mode 100644 index 9b8b14bf2ff0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/OGRLayerClassifier.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

OGRLayerClassifier

Brief Description

Classify an OGR layer based on a machine learning model and a list of features to consider.

Tags

Segmentation

Long Description

This application will apply a trained machine learning model on the selected feature to get a classification of each geometry contained in an OGR layer. The list of feature must match the list used for training. The predicted label is written in the user defined field for each geometry.

Parameters

Limitations

Experimental. Only shapefiles are supported for now.

Authors

David Youssefi during internship at CNES

See Also

ComputeOGRLayersFeaturesStatistics,TrainOGRLayersClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/OSMDownloader.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/OSMDownloader.html deleted file mode 100644 index a5db79920284..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/OSMDownloader.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

OSMDownloader

Brief Description

Generate a vector data from OSM on the input image extend

Tags

Image MetaData

Long Description

Generate a vector data from Open Street Map data. A DEM could be use. By default, the entire layer is downloaded, an image can be use as support for the OSM data. The application can provide also available classes in layers . This application required an Internet access. Informations about the OSM project : http://www.openstreetmap.fr/

Parameters

Limitations

None

Authors

OTB-Team

See Also

Convertion

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ObtainUTMZoneFromGeoPoint.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ObtainUTMZoneFromGeoPoint.html deleted file mode 100644 index eb416fdd3d84..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ObtainUTMZoneFromGeoPoint.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ObtainUTMZoneFromGeoPoint

Brief Description

UTM zone determination from a geographic point.

Tags

Coordinates

Long Description

This application returns the UTM zone of an input geographic point.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

Obtain a UTM Zone \ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/OpticalCalibration.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/OpticalCalibration.html deleted file mode 100644 index c021f157bfca..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/OpticalCalibration.html +++ /dev/null @@ -1,60 +0,0 @@ - - -

OpticalCalibration

Brief Description

Perform optical calibration TOA/TOC (Top Of Atmosphere/Top Of Canopy). Supported sensors: QuickBird, Ikonos, WorldView2, Formosat, Spot5, Pleiades, Spot6. For other sensors the application also allows to provide calibration parameters manually.

Tags

Calibration

Long Description

The application allows converting pixel values from DN (for Digital Numbers) to reflectance. Calibrated values are called surface reflectivity and its values lie in the range [0, 1]. -The first level is called Top Of Atmosphere (TOA) reflectivity. It takes into account the sensor gain, sensor spectral response and the solar illuminations. -The second level is called Top Of Canopy (TOC) reflectivity. In addition to sensor gain and solar illuminations, it takes into account the optical thickness of the atmosphere, the atmospheric pressure, the water vapor amount, the ozone amount, as well as the composition and amount of aerosol gasses. -It is also possible to indicate an AERONET file which contains atmospheric parameters (version 1 and version 2 of Aeronet file are supported. Note that computing TOC reflectivity will internally compute first TOA and then TOC reflectance. - --------------------------- - -If the sensor is not supported by the metadata interface factory of OTB, users still have the possibility to give the needed parameters to the application. -For TOA conversion, these parameters are : -- day and month of acquisition, or flux normalization coefficient; -- sun elevation angle; -- gains and biases, one pair of values for each band (passed by a file); -- solar illuminations, one value for each band (passed by a file). - -For the conversion from DN (for Digital Numbers) to spectral radiance (or 'TOA radiance') L, the following formula is used : - -(1) L(b) = DN(b)/gain(b)+bias(b) (in W/m2/steradians/micrometers) with b being a band ID. - -These values are provided by the user thanks to a simple txt file with two lines, one for the gains and one for the biases. -Each value must be separated with colons (:), with eventual spaces. Blank lines are not allowed. If a line begins with the '#' symbol, then it is considered as comments. -Note that sometimes, the values provided by certain metadata files assume the formula L(b) = gain(b)*DC(b)+bias(b). -In this case, be sure to provide the inverse gain values so that the application can correctly interpret them. - -In order to convert TOA radiance to TOA reflectance, the following formula is used : - -(2) R(b) = (pi*L(b)*d*d) / (ESUN(b)*cos(θ)) (no dimension) where : - -- L(b) is the spectral radiance for band b -- pi is the famous mathematical constant (3.14159...) -- d is the earth-sun distance (in astronomical units) and depends on the acquisition's day and month -- ESUN(b) is the mean TOA solar irradiance (or solar illumination) in W/m²/micrometers -- θ is the solar zenith angle in degrees. -Note that the application asks for the solar elevation angle, and will perfom the conversion to the zenith angle itself (ze. angle = 90° - el. angle). -Note also that ESUN(b) not only depends on the band b, but also on the spectral sensitivity of the sensor in this particular band. In other words, the influence of spectral sensitivities is included within the ESUN different values. -These values are provided by the user thanks to a txt file following the same convention as before. -Instead of providing the date of acquisition, the user can also provide a flux normalization coefficient 'fn'. The formula used instead will be the following : - -(3) R(b) = (pi*L(b)) / (ESUN(b)*fn*fn*cos(θ)) - -Whatever the formula used (2 or 3), the user should pay attention to the interpretation of the parameters he will provide to the application, by taking into account the original formula that the metadata files assumes. - -Below, we give two examples of txt files containing information about gains/biases and solar illuminations : - -- gainbias.txt : -# Gain values for each band. Each value must be separated with colons (:), with eventual spaces. Blank lines not allowed. -10.4416 : 9.529 : 8.5175 : 14.0063 -# Bias values for each band. -0.0 : 0.0 : 0.0 : 0.0 - -- solarillumination.txt : -# Solar illumination values in watt/m2/micron ('micron' means actually 'for each band'). -# Each value must be separated with colons (:), with eventual spaces. Blank lines not allowed. -1540.494123 : 1826.087443 : 1982.671954 : 1094.747446 - -Finally, the 'Logs' tab provides useful messages that can help the user in knowing the process different status.

Parameters

Limitations

None

Authors

OTB-Team

See Also

The OTB CookBook

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/OrthoRectification-epsg.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/OrthoRectification-epsg.html deleted file mode 100644 index 74289aacb312..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/OrthoRectification-epsg.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

OrthoRectification

Brief Description

This application allows ortho-rectifying optical images from supported sensors. -

Tags

Geometry

Long Description

An inverse sensor model is built from the input image metadata to convert geographical to raw geometry coordinates. This inverse sensor model is then combined with the chosen map projection to build a global coordinate mapping grid. Last, this grid is used to resample using the chosen interpolation algorithm. A Digital Elevation Model can be specified to account for terrain deformations. -In case of SPOT5 images, the sensor model can be approximated by an RPC model in order to speed-up computation.

Parameters

Limitations

Supported sensors are Pleiades, SPOT5 (TIF format), Ikonos, Quickbird, Worldview2, GeoEye.

Authors

OTB-Team

See Also

Ortho-rectification chapter from the OTB Software Guide

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/OrthoRectification-fit-to-ortho.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/OrthoRectification-fit-to-ortho.html deleted file mode 100644 index 74289aacb312..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/OrthoRectification-fit-to-ortho.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

OrthoRectification

Brief Description

This application allows ortho-rectifying optical images from supported sensors. -

Tags

Geometry

Long Description

An inverse sensor model is built from the input image metadata to convert geographical to raw geometry coordinates. This inverse sensor model is then combined with the chosen map projection to build a global coordinate mapping grid. Last, this grid is used to resample using the chosen interpolation algorithm. A Digital Elevation Model can be specified to account for terrain deformations. -In case of SPOT5 images, the sensor model can be approximated by an RPC model in order to speed-up computation.

Parameters

Limitations

Supported sensors are Pleiades, SPOT5 (TIF format), Ikonos, Quickbird, Worldview2, GeoEye.

Authors

OTB-Team

See Also

Ortho-rectification chapter from the OTB Software Guide

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/OrthoRectification-lambert-WGS84.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/OrthoRectification-lambert-WGS84.html deleted file mode 100644 index 74289aacb312..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/OrthoRectification-lambert-WGS84.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

OrthoRectification

Brief Description

This application allows ortho-rectifying optical images from supported sensors. -

Tags

Geometry

Long Description

An inverse sensor model is built from the input image metadata to convert geographical to raw geometry coordinates. This inverse sensor model is then combined with the chosen map projection to build a global coordinate mapping grid. Last, this grid is used to resample using the chosen interpolation algorithm. A Digital Elevation Model can be specified to account for terrain deformations. -In case of SPOT5 images, the sensor model can be approximated by an RPC model in order to speed-up computation.

Parameters

Limitations

Supported sensors are Pleiades, SPOT5 (TIF format), Ikonos, Quickbird, Worldview2, GeoEye.

Authors

OTB-Team

See Also

Ortho-rectification chapter from the OTB Software Guide

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/OrthoRectification-utm.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/OrthoRectification-utm.html deleted file mode 100644 index 74289aacb312..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/OrthoRectification-utm.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

OrthoRectification

Brief Description

This application allows ortho-rectifying optical images from supported sensors. -

Tags

Geometry

Long Description

An inverse sensor model is built from the input image metadata to convert geographical to raw geometry coordinates. This inverse sensor model is then combined with the chosen map projection to build a global coordinate mapping grid. Last, this grid is used to resample using the chosen interpolation algorithm. A Digital Elevation Model can be specified to account for terrain deformations. -In case of SPOT5 images, the sensor model can be approximated by an RPC model in order to speed-up computation.

Parameters

Limitations

Supported sensors are Pleiades, SPOT5 (TIF format), Ikonos, Quickbird, Worldview2, GeoEye.

Authors

OTB-Team

See Also

Ortho-rectification chapter from the OTB Software Guide

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/OrthoRectification.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/OrthoRectification.html deleted file mode 100644 index 74289aacb312..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/OrthoRectification.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

OrthoRectification

Brief Description

This application allows ortho-rectifying optical images from supported sensors. -

Tags

Geometry

Long Description

An inverse sensor model is built from the input image metadata to convert geographical to raw geometry coordinates. This inverse sensor model is then combined with the chosen map projection to build a global coordinate mapping grid. Last, this grid is used to resample using the chosen interpolation algorithm. A Digital Elevation Model can be specified to account for terrain deformations. -In case of SPOT5 images, the sensor model can be approximated by an RPC model in order to speed-up computation.

Parameters

Limitations

Supported sensors are Pleiades, SPOT5 (TIF format), Ikonos, Quickbird, Worldview2, GeoEye.

Authors

OTB-Team

See Also

Ortho-rectification chapter from the OTB Software Guide

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/Pansharpening-bayes.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/Pansharpening-bayes.html deleted file mode 100644 index dd25169010ad..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/Pansharpening-bayes.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Pansharpening

Brief Description

Perform P+XS pansharpening

Tags

Geometry,Pansharpening

Long Description

This application performs P+XS pansharpening. Pansharpening is a process of merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution color image. Algorithms available in the applications are: RCS, bayesian fusion and Local Mean and Variance Matching(LMVM).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/Pansharpening-lmvm.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/Pansharpening-lmvm.html deleted file mode 100644 index dd25169010ad..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/Pansharpening-lmvm.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Pansharpening

Brief Description

Perform P+XS pansharpening

Tags

Geometry,Pansharpening

Long Description

This application performs P+XS pansharpening. Pansharpening is a process of merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution color image. Algorithms available in the applications are: RCS, bayesian fusion and Local Mean and Variance Matching(LMVM).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/Pansharpening-rcs.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/Pansharpening-rcs.html deleted file mode 100644 index dd25169010ad..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/Pansharpening-rcs.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Pansharpening

Brief Description

Perform P+XS pansharpening

Tags

Geometry,Pansharpening

Long Description

This application performs P+XS pansharpening. Pansharpening is a process of merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution color image. Algorithms available in the applications are: RCS, bayesian fusion and Local Mean and Variance Matching(LMVM).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/Pansharpening.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/Pansharpening.html deleted file mode 100644 index dd25169010ad..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/Pansharpening.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Pansharpening

Brief Description

Perform P+XS pansharpening

Tags

Geometry,Pansharpening

Long Description

This application performs P+XS pansharpening. Pansharpening is a process of merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution color image. Algorithms available in the applications are: RCS, bayesian fusion and Local Mean and Variance Matching(LMVM).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/PixelValue.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/PixelValue.html deleted file mode 100644 index 53b7cab54fcf..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/PixelValue.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

PixelValue

Brief Description

Get the value of a pixel.

Tags

Utilities,Coordinates,Raster

Long Description

Get the value of a pixel. -Pay attention, index starts at 0.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/Quicklook.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/Quicklook.html deleted file mode 100644 index c84a0abaf342..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/Quicklook.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

Quicklook

Brief Description

Generates a subsampled version of an image extract

Tags

Image Manipulation

Long Description

Generates a subsampled version of an extract of an image defined by ROIStart and ROISize. - This extract is subsampled using the ratio OR the output image Size.

Parameters

Limitations

This application does not provide yet the optimal way to decode coarser level of resolution from JPEG2000 images (like in Monteverdi). -Trying to subsampled huge JPEG200 image with the application will lead to poor performances for now.

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/RadiometricIndices.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/RadiometricIndices.html deleted file mode 100644 index 9686e915d862..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/RadiometricIndices.html +++ /dev/null @@ -1,25 +0,0 @@ - - -

RadiometricIndices

Brief Description

Compute radiometric indices.

Tags

Radiometric Indices,Feature Extraction

Long Description

This application computes radiometric indices using the relevant channels of the input image. The output is a multi band image into which each channel is one of the selected indices.

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbVegetationIndicesFunctor, otbWaterIndicesFunctor and otbSoilIndicesFunctor classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/Rasterization-image.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/Rasterization-image.html deleted file mode 100644 index aa8b6cba79b4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/Rasterization-image.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

Rasterization

Brief Description

Rasterize a vector dataset.

Tags

Vector Data Manipulation

Long Description

This application allows reprojecting and rasterizing a vector dataset. The grid of the rasterized output can be set by using a reference image, or by setting all parmeters (origin, size, spacing) by hand. In the latter case, at least the spacing (ground sampling distance) is needed (other parameters are computed automatically). The rasterized output can also be in a different projection reference system than the input dataset. - There are two rasterize mode available in the application. The first is the binary mode: it allows rendering all pixels belonging to a geometry of the input dataset in the foreground color, while rendering the other in background color. The second one allows rendering pixels belonging to a geometry woth respect to an attribute of this geometry. The field of the attribute to render can be set by the user. In the second mode, the background value is still used for unassociated pixels.

Parameters

Limitations

None

Authors

OTB-Team

See Also

For now, support of input dataset with multiple layers having different projection reference system is limited.

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/Rasterization-manual.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/Rasterization-manual.html deleted file mode 100644 index aa8b6cba79b4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/Rasterization-manual.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

Rasterization

Brief Description

Rasterize a vector dataset.

Tags

Vector Data Manipulation

Long Description

This application allows reprojecting and rasterizing a vector dataset. The grid of the rasterized output can be set by using a reference image, or by setting all parmeters (origin, size, spacing) by hand. In the latter case, at least the spacing (ground sampling distance) is needed (other parameters are computed automatically). The rasterized output can also be in a different projection reference system than the input dataset. - There are two rasterize mode available in the application. The first is the binary mode: it allows rendering all pixels belonging to a geometry of the input dataset in the foreground color, while rendering the other in background color. The second one allows rendering pixels belonging to a geometry woth respect to an attribute of this geometry. The field of the attribute to render can be set by the user. In the second mode, the background value is still used for unassociated pixels.

Parameters

Limitations

None

Authors

OTB-Team

See Also

For now, support of input dataset with multiple layers having different projection reference system is limited.

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/Rasterization.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/Rasterization.html deleted file mode 100644 index aa8b6cba79b4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/Rasterization.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

Rasterization

Brief Description

Rasterize a vector dataset.

Tags

Vector Data Manipulation

Long Description

This application allows reprojecting and rasterizing a vector dataset. The grid of the rasterized output can be set by using a reference image, or by setting all parmeters (origin, size, spacing) by hand. In the latter case, at least the spacing (ground sampling distance) is needed (other parameters are computed automatically). The rasterized output can also be in a different projection reference system than the input dataset. - There are two rasterize mode available in the application. The first is the binary mode: it allows rendering all pixels belonging to a geometry of the input dataset in the foreground color, while rendering the other in background color. The second one allows rendering pixels belonging to a geometry woth respect to an attribute of this geometry. The field of the attribute to render can be set by the user. In the second mode, the background value is still used for unassociated pixels.

Parameters

Limitations

None

Authors

OTB-Team

See Also

For now, support of input dataset with multiple layers having different projection reference system is limited.

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/ReadImageInfo.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/ReadImageInfo.html deleted file mode 100644 index d9d2619105f2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/ReadImageInfo.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ReadImageInfo

Brief Description

Get information about the image

Tags

Utilities,Image Manipulation,Image MetaData

Long Description

Display information about the input image like: image size, origin, spacing, metadata, projections...

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/RefineSensorModel.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/RefineSensorModel.html deleted file mode 100644 index ad8dcf2eb2cf..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/RefineSensorModel.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

RefineSensorModel

Brief Description

Perform least-square fit of a sensor model to a set of tie points

Tags

Geometry

Long Description

This application reads a geom file containing a sensor model and a text file containing a list of ground control point, and performs a least-square fit of the sensor model adjustable parameters to these tie points. It produces an updated geom file as output, as well as an optional ground control points based statistics file and a vector file containing residues. The output geom file can then be used to ortho-rectify the data more accurately. Plaease note that for a proper use of the application, elevation must be correctly set (including DEM and geoid file). The map parameters allows choosing a map projection in which the accuracy will be estimated in meters.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OrthoRectification,HomologousPointsExtraction

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/Rescale.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/Rescale.html deleted file mode 100644 index bb606af15ef8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/Rescale.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Rescale

Brief Description

Rescale the image between two given values.

Tags

Image Manipulation

Long Description

This application scales the given image pixel intensity between two given values. By default min (resp. max) value is set to 0 (resp. 255).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/RigidTransformResample-id.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/RigidTransformResample-id.html deleted file mode 100644 index cb9d45c49209..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/RigidTransformResample-id.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

RigidTransformResample

Brief Description

Resample an image with a rigid transform

Tags

Conversion,Geometry

Long Description

This application performs a parametric transform on the input image. Scaling, translation and rotation with scaling factor are handled. Parameters of the transform is expressed in physical units, thus particular attention must be paid on pixel size (value, and sign). Moreover transform is expressed from input space to output space (on the contrary ITK Transforms are expressed form output space to input space).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Translation

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/RigidTransformResample-rotation.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/RigidTransformResample-rotation.html deleted file mode 100644 index cb9d45c49209..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/RigidTransformResample-rotation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

RigidTransformResample

Brief Description

Resample an image with a rigid transform

Tags

Conversion,Geometry

Long Description

This application performs a parametric transform on the input image. Scaling, translation and rotation with scaling factor are handled. Parameters of the transform is expressed in physical units, thus particular attention must be paid on pixel size (value, and sign). Moreover transform is expressed from input space to output space (on the contrary ITK Transforms are expressed form output space to input space).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Translation

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/RigidTransformResample-translation.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/RigidTransformResample-translation.html deleted file mode 100644 index cb9d45c49209..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/RigidTransformResample-translation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

RigidTransformResample

Brief Description

Resample an image with a rigid transform

Tags

Conversion,Geometry

Long Description

This application performs a parametric transform on the input image. Scaling, translation and rotation with scaling factor are handled. Parameters of the transform is expressed in physical units, thus particular attention must be paid on pixel size (value, and sign). Moreover transform is expressed from input space to output space (on the contrary ITK Transforms are expressed form output space to input space).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Translation

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/RigidTransformResample.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/RigidTransformResample.html deleted file mode 100644 index cb9d45c49209..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/RigidTransformResample.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

RigidTransformResample

Brief Description

Resample an image with a rigid transform

Tags

Conversion,Geometry

Long Description

This application performs a parametric transform on the input image. Scaling, translation and rotation with scaling factor are handled. Parameters of the transform is expressed in physical units, thus particular attention must be paid on pixel size (value, and sign). Moreover transform is expressed from input space to output space (on the contrary ITK Transforms are expressed form output space to input space).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Translation

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/SFSTextureExtraction.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/SFSTextureExtraction.html deleted file mode 100644 index 2ba4a75ca805..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/SFSTextureExtraction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

SFSTextureExtraction

Brief Description

Computes Structural Feature Set textures on every pixel of the input image selected channel

Tags

Textures,Feature Extraction

Long Description

This application computes SFS textures on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbSFSTexturesImageFilter class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/SOMClassification.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/SOMClassification.html deleted file mode 100644 index a6fc8a522dfc..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/SOMClassification.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

SOMClassification

Brief Description

SOM image classification.

Tags

Segmentation,Learning

Long Description

Unsupervised Self Organizing Map image classification.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/SarRadiometricCalibration.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/SarRadiometricCalibration.html deleted file mode 100644 index 52be7031060e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/SarRadiometricCalibration.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

SarRadiometricCalibration

Brief Description

Perform SAR calibration on input complex images

Tags

Calibration,SAR

Long Description

This application performs SAR calibration on input complex images.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/Segmentation-cc.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/Segmentation-cc.html deleted file mode 100644 index 1d2290ebb4b1..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/Segmentation-cc.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

Segmentation

Brief Description

Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported.

Tags

Segmentation

Long Description

This application allows performing various segmentation algorithms on a multispectral image.Available segmentation algorithms are two different versions of Mean-Shift segmentation algorithm (one being multi-threaded), simple pixel based connected components according to a user-defined criterion, and watershed from the gradient of the intensity (norm of spectral bands vector). The application has two different modes that affects the nature of its output. - -In raster mode, the output of the application is a classical image of unique labels identifying the segmented regions. The labeled output can be passed to the ColorMapping application to render regions with contrasted colors. Please note that this mode loads the whole input image into memory, and as such can not handle large images. - -To segment large data, one can use the vector mode. In this case, the output of the application is a vector file or database. The input image is split into tiles (whose size can be set using the tilesize parameter), and each tile is loaded, segmented with the chosen algorithm, vectorized, and written into the output file or database. This piece-wise behavior ensure that memory will never get overloaded, and that images of any size can be processed. There are few more options in the vector mode. The simplify option allows simplifying the geometry (i.e. remove nodes in polygons) according to a user-defined tolerance. The stitch option allows applying to try to stitch together polygons corresponding to segmented region that may have been split by the tiling scheme.

Parameters

Limitations

In raster mode, the application can not handle large input images. Stitching step of vector mode might become slow with very large input images. -MeanShift filter results depends on the number of threads used. -Watershed and multiscale geodesic morphology segmentation will be performed on the amplitude of the input image.

Authors

OTB-Team

See Also

MeanShiftSegmentation

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/Segmentation-meanshift.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/Segmentation-meanshift.html deleted file mode 100644 index 1d2290ebb4b1..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/Segmentation-meanshift.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

Segmentation

Brief Description

Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported.

Tags

Segmentation

Long Description

This application allows performing various segmentation algorithms on a multispectral image.Available segmentation algorithms are two different versions of Mean-Shift segmentation algorithm (one being multi-threaded), simple pixel based connected components according to a user-defined criterion, and watershed from the gradient of the intensity (norm of spectral bands vector). The application has two different modes that affects the nature of its output. - -In raster mode, the output of the application is a classical image of unique labels identifying the segmented regions. The labeled output can be passed to the ColorMapping application to render regions with contrasted colors. Please note that this mode loads the whole input image into memory, and as such can not handle large images. - -To segment large data, one can use the vector mode. In this case, the output of the application is a vector file or database. The input image is split into tiles (whose size can be set using the tilesize parameter), and each tile is loaded, segmented with the chosen algorithm, vectorized, and written into the output file or database. This piece-wise behavior ensure that memory will never get overloaded, and that images of any size can be processed. There are few more options in the vector mode. The simplify option allows simplifying the geometry (i.e. remove nodes in polygons) according to a user-defined tolerance. The stitch option allows applying to try to stitch together polygons corresponding to segmented region that may have been split by the tiling scheme.

Parameters

Limitations

In raster mode, the application can not handle large input images. Stitching step of vector mode might become slow with very large input images. -MeanShift filter results depends on the number of threads used. -Watershed and multiscale geodesic morphology segmentation will be performed on the amplitude of the input image.

Authors

OTB-Team

See Also

MeanShiftSegmentation

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/Segmentation-mprofiles.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/Segmentation-mprofiles.html deleted file mode 100644 index 1d2290ebb4b1..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/Segmentation-mprofiles.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

Segmentation

Brief Description

Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported.

Tags

Segmentation

Long Description

This application allows performing various segmentation algorithms on a multispectral image.Available segmentation algorithms are two different versions of Mean-Shift segmentation algorithm (one being multi-threaded), simple pixel based connected components according to a user-defined criterion, and watershed from the gradient of the intensity (norm of spectral bands vector). The application has two different modes that affects the nature of its output. - -In raster mode, the output of the application is a classical image of unique labels identifying the segmented regions. The labeled output can be passed to the ColorMapping application to render regions with contrasted colors. Please note that this mode loads the whole input image into memory, and as such can not handle large images. - -To segment large data, one can use the vector mode. In this case, the output of the application is a vector file or database. The input image is split into tiles (whose size can be set using the tilesize parameter), and each tile is loaded, segmented with the chosen algorithm, vectorized, and written into the output file or database. This piece-wise behavior ensure that memory will never get overloaded, and that images of any size can be processed. There are few more options in the vector mode. The simplify option allows simplifying the geometry (i.e. remove nodes in polygons) according to a user-defined tolerance. The stitch option allows applying to try to stitch together polygons corresponding to segmented region that may have been split by the tiling scheme.

Parameters

Limitations

In raster mode, the application can not handle large input images. Stitching step of vector mode might become slow with very large input images. -MeanShift filter results depends on the number of threads used. -Watershed and multiscale geodesic morphology segmentation will be performed on the amplitude of the input image.

Authors

OTB-Team

See Also

MeanShiftSegmentation

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/Segmentation-watershed.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/Segmentation-watershed.html deleted file mode 100644 index 1d2290ebb4b1..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/Segmentation-watershed.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

Segmentation

Brief Description

Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported.

Tags

Segmentation

Long Description

This application allows performing various segmentation algorithms on a multispectral image.Available segmentation algorithms are two different versions of Mean-Shift segmentation algorithm (one being multi-threaded), simple pixel based connected components according to a user-defined criterion, and watershed from the gradient of the intensity (norm of spectral bands vector). The application has two different modes that affects the nature of its output. - -In raster mode, the output of the application is a classical image of unique labels identifying the segmented regions. The labeled output can be passed to the ColorMapping application to render regions with contrasted colors. Please note that this mode loads the whole input image into memory, and as such can not handle large images. - -To segment large data, one can use the vector mode. In this case, the output of the application is a vector file or database. The input image is split into tiles (whose size can be set using the tilesize parameter), and each tile is loaded, segmented with the chosen algorithm, vectorized, and written into the output file or database. This piece-wise behavior ensure that memory will never get overloaded, and that images of any size can be processed. There are few more options in the vector mode. The simplify option allows simplifying the geometry (i.e. remove nodes in polygons) according to a user-defined tolerance. The stitch option allows applying to try to stitch together polygons corresponding to segmented region that may have been split by the tiling scheme.

Parameters

Limitations

In raster mode, the application can not handle large input images. Stitching step of vector mode might become slow with very large input images. -MeanShift filter results depends on the number of threads used. -Watershed and multiscale geodesic morphology segmentation will be performed on the amplitude of the input image.

Authors

OTB-Team

See Also

MeanShiftSegmentation

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/Segmentation.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/Segmentation.html deleted file mode 100644 index 1d2290ebb4b1..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/Segmentation.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

Segmentation

Brief Description

Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported.

Tags

Segmentation

Long Description

This application allows performing various segmentation algorithms on a multispectral image.Available segmentation algorithms are two different versions of Mean-Shift segmentation algorithm (one being multi-threaded), simple pixel based connected components according to a user-defined criterion, and watershed from the gradient of the intensity (norm of spectral bands vector). The application has two different modes that affects the nature of its output. - -In raster mode, the output of the application is a classical image of unique labels identifying the segmented regions. The labeled output can be passed to the ColorMapping application to render regions with contrasted colors. Please note that this mode loads the whole input image into memory, and as such can not handle large images. - -To segment large data, one can use the vector mode. In this case, the output of the application is a vector file or database. The input image is split into tiles (whose size can be set using the tilesize parameter), and each tile is loaded, segmented with the chosen algorithm, vectorized, and written into the output file or database. This piece-wise behavior ensure that memory will never get overloaded, and that images of any size can be processed. There are few more options in the vector mode. The simplify option allows simplifying the geometry (i.e. remove nodes in polygons) according to a user-defined tolerance. The stitch option allows applying to try to stitch together polygons corresponding to segmented region that may have been split by the tiling scheme.

Parameters

Limitations

In raster mode, the application can not handle large input images. Stitching step of vector mode might become slow with very large input images. -MeanShift filter results depends on the number of threads used. -Watershed and multiscale geodesic morphology segmentation will be performed on the amplitude of the input image.

Authors

OTB-Team

See Also

MeanShiftSegmentation

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/Smoothing-anidif.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/Smoothing-anidif.html deleted file mode 100644 index c8bcf1ce16d6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/Smoothing-anidif.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Smoothing

Brief Description

Apply a smoothing filter to an image

Tags

Image Filtering

Long Description

This application applies smoothing filter to an image. Either gaussian, mean, or anisotropic diffusion are available.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/Smoothing-gaussian.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/Smoothing-gaussian.html deleted file mode 100644 index c8bcf1ce16d6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/Smoothing-gaussian.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Smoothing

Brief Description

Apply a smoothing filter to an image

Tags

Image Filtering

Long Description

This application applies smoothing filter to an image. Either gaussian, mean, or anisotropic diffusion are available.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/Smoothing-mean.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/Smoothing-mean.html deleted file mode 100644 index c8bcf1ce16d6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/Smoothing-mean.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Smoothing

Brief Description

Apply a smoothing filter to an image

Tags

Image Filtering

Long Description

This application applies smoothing filter to an image. Either gaussian, mean, or anisotropic diffusion are available.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/Smoothing.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/Smoothing.html deleted file mode 100644 index c8bcf1ce16d6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/Smoothing.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Smoothing

Brief Description

Apply a smoothing filter to an image

Tags

Image Filtering

Long Description

This application applies smoothing filter to an image. Either gaussian, mean, or anisotropic diffusion are available.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/SplitImage.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/SplitImage.html deleted file mode 100644 index b589865061db..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/SplitImage.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

SplitImage

Brief Description

Split a N multiband image into N images

Tags

Image Manipulation

Long Description

This application splits a N-bands image into N mono-band images. The output images filename will be generated from the output parameter. Thus if the input image has 2 channels, and the user has set an output outimage.tif, the generated images will be outimage_0.tif and outimage_1.tif

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/StereoFramework.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/StereoFramework.html deleted file mode 100644 index f7a26707877c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/StereoFramework.html +++ /dev/null @@ -1,16 +0,0 @@ - - -

StereoFramework

Brief Description

Compute the ground elevation based on one or multiple stereo pair(s)

Tags

Stereo

Long Description

Compute the ground elevation with a stereo block matching algorithm between one or mulitple stereo pair in sensor geometry. The output is projected in desired geographic or cartographic map projection (UTM by default). The pipeline is made of the following steps: -for each sensor pair : - - compute the epipolar displacement grids from the stereo pair (direct and inverse) - - resample the stereo pair into epipolar geometry using BCO interpolation - - create masks for each epipolar image : remove black borders and resample input masks - - compute horizontal disparities with a block matching algorithm - - refine disparities to sub-pixel precision with a dichotomy algorithm - - apply an optional median filter - - filter disparities based on the correlation score and exploration bounds - - translate disparities in sensor geometry - convert disparity to 3D Map. -Then fuse all 3D maps to produce DSM.

Parameters

Limitations

Authors

OTB-Team

See Also

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/StereoRectificationGridGenerator.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/StereoRectificationGridGenerator.html deleted file mode 100644 index 56bafc0fa3a4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/StereoRectificationGridGenerator.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

StereoRectificationGridGenerator

Brief Description

Generates two deformation fields to stereo-rectify (i.e. resample in epipolar geometry) a pair of stereo images up to the sensor model precision

Tags

Stereo

Long Description

This application generates a pair of deformation grid to stereo-rectify a pair of stereo images according to sensor modelling and a mean elevation hypothesis. The deformation grids can be passed to the StereoRectificationGridGenerator application for actual resampling in epipolar geometry.

Parameters

Limitations

Generation of the deformation grid is not streamable, pay attention to this fact when setting the grid step.

Authors

OTB-Team

See Also

otbGridBasedImageResampling

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/Superimpose.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/Superimpose.html deleted file mode 100644 index 61419fbf7be8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/Superimpose.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Superimpose

Brief Description

Using available image metadata, project one image onto another one

Tags

Geometry,Superimposition

Long Description

This application performs the projection of an image into the geometry of another one.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/TestApplication.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/TestApplication.html deleted file mode 100644 index aac6ba570cde..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/TestApplication.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TestApplication

Brief Description

This application helps developers to test parameters types

Tags

Test

Long Description

The purpose of this application is to test parameters types.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/TileFusion.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/TileFusion.html deleted file mode 100644 index ff003aa4becc..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/TileFusion.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TileFusion

Brief Description

Fusion of an image made of several tile files.

Tags

Image Manipulation

Long Description

Concatenate several tile files into a single image file.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-ann.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-ann.html deleted file mode 100644 index f925fbfcec80..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-ann.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-bayes.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-bayes.html deleted file mode 100644 index f925fbfcec80..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-bayes.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-boost.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-boost.html deleted file mode 100644 index f925fbfcec80..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-boost.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-dt.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-dt.html deleted file mode 100644 index f925fbfcec80..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-dt.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-gbt.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-gbt.html deleted file mode 100644 index f925fbfcec80..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-gbt.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-knn.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-knn.html deleted file mode 100644 index f925fbfcec80..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-knn.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-libsvm.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-libsvm.html deleted file mode 100644 index f925fbfcec80..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-libsvm.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-rf.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-rf.html deleted file mode 100644 index f925fbfcec80..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-rf.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-svm.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-svm.html deleted file mode 100644 index f925fbfcec80..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier-svm.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier.html deleted file mode 100644 index f925fbfcec80..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainImagesClassifier.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainOGRLayersClassifier.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainOGRLayersClassifier.html deleted file mode 100644 index 551a2cd45440..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/TrainOGRLayersClassifier.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TrainOGRLayersClassifier

Brief Description

Train a SVM classifier based on labeled geometries and a list of features to consider.

Tags

Segmentation

Long Description

This application trains a SVM classifier based on labeled geometries and a list of features to consider for classification.

Parameters

Limitations

Experimental. For now only shapefiles are supported. Tuning of SVM classifier is not available.

Authors

David Youssefi during internship at CNES

See Also

OGRLayerClassifier,ComputeOGRLayersFeaturesStatistics

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataDSValidation.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataDSValidation.html deleted file mode 100644 index e2cd2032ac0a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataDSValidation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

VectorDataDSValidation

Brief Description

Vector data validation based on the fusion of features using Dempster-Shafer evidence theory framework.

Tags

Feature Extraction

Long Description

This application validates or unvalidate the studied samples using the Dempster-Shafer theory.

Parameters

Limitations

None.

Authors

OTB-Team

See Also

http://en.wikipedia.org/wiki/Dempster-Shafer_theory

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataExtractROI.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataExtractROI.html deleted file mode 100644 index 5acd2390b3b6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataExtractROI.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

VectorDataExtractROI

Brief Description

Perform an extract ROI on the input vector data according to the input image extent

Tags

Vector Data Manipulation

Long Description

This application extracts the vector data features belonging to a region specified by the support image envelope. Any features intersecting the support region is copied to output. The output geometries are NOT cropped.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataReprojection-image.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataReprojection-image.html deleted file mode 100644 index a95ad11d7db0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataReprojection-image.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

VectorDataReprojection

Brief Description

This application allows reprojecting a vector data using support image projection reference, or a user specified map projection -

Tags

Geometry,Vector Data Manipulation,Coordinates

Long Description

This application allows reprojecting a vector data using support image projection reference, or a user given map projection. - If given, image keywordlist can be added to reprojected vectordata.

Parameters

Limitations

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataReprojection-user.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataReprojection-user.html deleted file mode 100644 index a95ad11d7db0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataReprojection-user.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

VectorDataReprojection

Brief Description

This application allows reprojecting a vector data using support image projection reference, or a user specified map projection -

Tags

Geometry,Vector Data Manipulation,Coordinates

Long Description

This application allows reprojecting a vector data using support image projection reference, or a user given map projection. - If given, image keywordlist can be added to reprojected vectordata.

Parameters

Limitations

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataReprojection.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataReprojection.html deleted file mode 100644 index a95ad11d7db0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataReprojection.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

VectorDataReprojection

Brief Description

This application allows reprojecting a vector data using support image projection reference, or a user specified map projection -

Tags

Geometry,Vector Data Manipulation,Coordinates

Long Description

This application allows reprojecting a vector data using support image projection reference, or a user given map projection. - If given, image keywordlist can be added to reprojected vectordata.

Parameters

Limitations

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataSetField.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataSetField.html deleted file mode 100644 index 34a074002ed3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataSetField.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

VectorDataSetField

Brief Description

Set a field in vector data.

Tags

Vector Data Manipulation

Long Description

Set a specified field to a specified value on all features of a vector data.

Parameters

Limitations

Doesn't work with KML files yet

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataTransform.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataTransform.html deleted file mode 100644 index ad5cda0a1e68..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/VectorDataTransform.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

VectorDataTransform

Brief Description

Apply a transform to each vertex of the input VectorData

Tags

Vector Data Manipulation

Long Description

This application performs a transformation of an input vector data transforming each vertex in the vector data. The applied transformation manages translation, rotation and scale, and can be centered or not.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.0.0/doc/VertexComponentAnalysis.html b/python/plugins/processing/algs/otb/description/5.0.0/doc/VertexComponentAnalysis.html deleted file mode 100644 index 345c7725e11f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.0.0/doc/VertexComponentAnalysis.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

VertexComponentAnalysis

Brief Description

Find endmembers in hyperspectral images with Vertex Component Analysis

Tags

Hyperspectral,Dimensionality Reduction

Long Description

Applies the Vertex Component Analysis to an hyperspectral image to extract endmembers

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/BandMath.xml b/python/plugins/processing/algs/otb/description/5.4.0/BandMath.xml deleted file mode 100644 index 8b42b40b3d94..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/BandMath.xml +++ /dev/null @@ -1,43 +0,0 @@ - - BandMath - otbcli_BandMath - Band Math - Miscellaneous - Perform a mathematical operation on monoband images - - ParameterMultipleInput - il - Input image list - Image list to perform computation on. - - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterString - exp - Expression - The mathematical expression to apply. -Use im1b1 for the first band, im1b2 for the second one... - - - False - - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/BinaryMorphologicalOperation-closing.xml b/python/plugins/processing/algs/otb/description/5.4.0/BinaryMorphologicalOperation-closing.xml deleted file mode 100644 index 2961f167e085..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/BinaryMorphologicalOperation-closing.xml +++ /dev/null @@ -1,77 +0,0 @@ - - BinaryMorphologicalOperation-closing - otbcli_BinaryMorphologicalOperation - BinaryMorphologicalOperation (closing) - Feature Extraction - Performs morphological operations on an input image channel - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - closing - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/BinaryMorphologicalOperation-dilate.xml b/python/plugins/processing/algs/otb/description/5.4.0/BinaryMorphologicalOperation-dilate.xml deleted file mode 100644 index 23477a328fa3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/BinaryMorphologicalOperation-dilate.xml +++ /dev/null @@ -1,97 +0,0 @@ - - BinaryMorphologicalOperation-dilate - otbcli_BinaryMorphologicalOperation - BinaryMorphologicalOperation (dilate) - Feature Extraction - Performs morphological operations on an input image channel - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - dilate - - - 0 - False - - - ParameterNumber - filter.dilate.foreval - Foreground Value - The Foreground Value - - - 1 - False - - - ParameterNumber - filter.dilate.backval - Background Value - The Background Value - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/BinaryMorphologicalOperation-erode.xml b/python/plugins/processing/algs/otb/description/5.4.0/BinaryMorphologicalOperation-erode.xml deleted file mode 100644 index c25c24f0e54c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/BinaryMorphologicalOperation-erode.xml +++ /dev/null @@ -1,77 +0,0 @@ - - BinaryMorphologicalOperation-erode - otbcli_BinaryMorphologicalOperation - BinaryMorphologicalOperation (erode) - Feature Extraction - Performs morphological operations on an input image channel - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - erode - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/BinaryMorphologicalOperation-opening.xml b/python/plugins/processing/algs/otb/description/5.4.0/BinaryMorphologicalOperation-opening.xml deleted file mode 100644 index 9af9fcb74cb0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/BinaryMorphologicalOperation-opening.xml +++ /dev/null @@ -1,77 +0,0 @@ - - BinaryMorphologicalOperation-opening - otbcli_BinaryMorphologicalOperation - BinaryMorphologicalOperation (opening) - Feature Extraction - Performs morphological operations on an input image channel - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - opening - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/ClassificationMapRegularization.xml b/python/plugins/processing/algs/otb/description/5.4.0/ClassificationMapRegularization.xml deleted file mode 100644 index 549ddcd791cb..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/ClassificationMapRegularization.xml +++ /dev/null @@ -1,69 +0,0 @@ - - ClassificationMapRegularization - otbcli_ClassificationMapRegularization - Classification Map Regularization - Learning - Filters the input labeled image using Majority Voting in a ball shaped neighbordhood. - - ParameterRaster - io.in - Input classification image - The input labeled image to regularize. - False - - - OutputRaster - io.out - Output regularized image - The output regularized labeled image. - - - - ParameterNumber - ip.radius - Structuring element radius (in pixels) - The radius of the ball shaped structuring element (expressed in pixels). By default, 'ip.radius = 1 pixel'. - - - 1 - False - - - ParameterBoolean - ip.suvbool - Multiple majority: Undecided(X)/Original - Pixels with more than 1 majority class are marked as Undecided if this parameter is checked (true), or keep their Original labels otherwise (false). Please note that the Undecided value must be different from existing labels in the input labeled image. By default, 'ip.suvbool = false'. - True - True - - - ParameterNumber - ip.nodatalabel - Label for the NoData class - Label for the NoData class. Such input pixels keep their NoData label in the output image. By default, 'ip.nodatalabel = 0'. - - - 0 - False - - - ParameterNumber - ip.undecidedlabel - Label for the Undecided class - Label for the Undecided class. By default, 'ip.undecidedlabel = 0'. - - - 0 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/ColorMapping-continuous.xml b/python/plugins/processing/algs/otb/description/5.4.0/ColorMapping-continuous.xml deleted file mode 100644 index 82ef3bd7a488..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/ColorMapping-continuous.xml +++ /dev/null @@ -1,104 +0,0 @@ - - ColorMapping-continuous - otbcli_ColorMapping - ColorMapping (continuous) - Image Manipulation - Maps an input label image to 8-bits RGB using look-up tables. - - ParameterRaster - in - Input Image - Input image filename - False - - - OutputRaster - out - Output Image - Output image filename - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - op - Operation - Selection of the operation to execute (default is : label to color). - - - labeltocolor - - - 0 - False - - - ParameterSelection - method - Color mapping method - Selection of color mapping methods and their parameters. - - - continuous - - - 0 - False - - - ParameterSelection - method.continuous.lut - Look-up tables - Available look-up tables. - - - red - green - blue - grey - hot - cool - spring - summer - autumn - winter - copper - jet - hsv - overunder - relief - - - 0 - False - - - ParameterNumber - method.continuous.min - Mapping range lower value - Set the lower input value of the mapping range. - - - 0 - False - - - ParameterNumber - method.continuous.max - Mapping range higher value - Set the higher input value of the mapping range. - - - 255 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/ColorMapping-custom.xml b/python/plugins/processing/algs/otb/description/5.4.0/ColorMapping-custom.xml deleted file mode 100644 index 7e9c7e0b515b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/ColorMapping-custom.xml +++ /dev/null @@ -1,68 +0,0 @@ - - ColorMapping-custom - otbcli_ColorMapping - ColorMapping (custom) - Image Manipulation - Maps an input label image to 8-bits RGB using look-up tables. - - ParameterRaster - in - Input Image - Input image filename - False - - - OutputRaster - out - Output Image - Output image filename - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - op - Operation - Selection of the operation to execute (default is : label to color). - - - labeltocolor - - - 0 - False - - - ParameterSelection - method - Color mapping method - Selection of color mapping methods and their parameters. - - - custom - - - 0 - False - - - ParameterFile - method.custom.lut - Look-up table file - An ASCII file containing the look-up table -with one color per line -(for instance the line '1 255 0 0' means that all pixels with label 1 will be replaced by RGB color 255 0 0) -Lines beginning with a # are ignored - - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/ColorMapping-image.xml b/python/plugins/processing/algs/otb/description/5.4.0/ColorMapping-image.xml deleted file mode 100644 index 3d52f5ffa05e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/ColorMapping-image.xml +++ /dev/null @@ -1,94 +0,0 @@ - - ColorMapping-image - otbcli_ColorMapping - ColorMapping (image) - Image Manipulation - Maps an input label image to 8-bits RGB using look-up tables. - - ParameterRaster - in - Input Image - Input image filename - False - - - OutputRaster - out - Output Image - Output image filename - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - op - Operation - Selection of the operation to execute (default is : label to color). - - - labeltocolor - - - 0 - False - - - ParameterSelection - method - Color mapping method - Selection of color mapping methods and their parameters. - - - image - - - 0 - False - - - ParameterRaster - method.image.in - Support Image - Support image filename. For each label, the LUT is calculated from the mean pixel value in the support image, over the corresponding labeled areas. First of all, the support image is normalized with extrema rejection - False - - - ParameterNumber - method.image.nodatavalue - NoData value - NoData value for each channel of the support image, which will not be handled in the LUT estimation. If NOT checked, ALL the pixel values of the support image will be handled in the LUT estimation. - - - 0 - True - - - ParameterNumber - method.image.low - lower quantile - lower quantile for image normalization - - - 2 - True - - - ParameterNumber - method.image.up - upper quantile - upper quantile for image normalization - - - 2 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/ColorMapping-optimal.xml b/python/plugins/processing/algs/otb/description/5.4.0/ColorMapping-optimal.xml deleted file mode 100644 index 473d2916c1ab..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/ColorMapping-optimal.xml +++ /dev/null @@ -1,67 +0,0 @@ - - ColorMapping-optimal - otbcli_ColorMapping - ColorMapping (optimal) - Image Manipulation - Maps an input label image to 8-bits RGB using look-up tables. - - ParameterRaster - in - Input Image - Input image filename - False - - - OutputRaster - out - Output Image - Output image filename - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - op - Operation - Selection of the operation to execute (default is : label to color). - - - labeltocolor - - - 0 - False - - - ParameterSelection - method - Color mapping method - Selection of color mapping methods and their parameters. - - - optimal - - - 0 - False - - - ParameterNumber - method.optimal.background - Background label - Value of the background label - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/CompareImages.xml b/python/plugins/processing/algs/otb/description/5.4.0/CompareImages.xml deleted file mode 100644 index 1ad8ea4f9ef8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/CompareImages.xml +++ /dev/null @@ -1,81 +0,0 @@ - - CompareImages - otbcli_CompareImages - Images comparaison - Miscellaneous - Estimator between 2 images. - - ParameterRaster - ref.in - Reference image - Image used as reference in the comparison - False - - - ParameterNumber - ref.channel - Reference image channel - Used channel for the reference image - - - 1 - False - - - ParameterRaster - meas.in - Measured image - Image used as measured in the comparison - False - - - ParameterNumber - meas.channel - Measured image channel - Used channel for the measured image - - - 1 - False - - - ParameterNumber - roi.startx - Start X - ROI start x position. - - - 0 - False - - - ParameterNumber - roi.starty - Start Y - ROI start y position. - - - 0 - False - - - ParameterNumber - roi.sizex - Size X - Size along x in pixels. - - - 0 - False - - - ParameterNumber - roi.sizey - Size Y - Size along y in pixels. - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/ComputeConfusionMatrix-raster.xml b/python/plugins/processing/algs/otb/description/5.4.0/ComputeConfusionMatrix-raster.xml deleted file mode 100644 index 5bf8a8210eb5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/ComputeConfusionMatrix-raster.xml +++ /dev/null @@ -1,60 +0,0 @@ - - ComputeConfusionMatrix-raster - otbcli_ComputeConfusionMatrix - ComputeConfusionMatrix (raster) - Learning - Computes the confusion matrix of a classification - - ParameterRaster - in - Input Image - The input classification image. - False - - - OutputFile - out - Matrix output - Filename to store the output matrix (csv format) - - - ParameterSelection - ref - Ground truth - Choice of ground truth format - - - raster - - - 0 - False - - - ParameterRaster - ref.raster.in - Input reference image - Input image containing the ground truth labels - False - - - ParameterNumber - nodatalabel - Value for nodata pixels - Label for the NoData class. Such input pixels will be discarded from the ground truth and from the input classification map. By default, 'nodatalabel = 0'. - - - 0 - True - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/ComputeConfusionMatrix-vector.xml b/python/plugins/processing/algs/otb/description/5.4.0/ComputeConfusionMatrix-vector.xml deleted file mode 100644 index 991e23b3f835..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/ComputeConfusionMatrix-vector.xml +++ /dev/null @@ -1,71 +0,0 @@ - - ComputeConfusionMatrix-vector - otbcli_ComputeConfusionMatrix - ComputeConfusionMatrix (vector) - Learning - Computes the confusion matrix of a classification - - ParameterRaster - in - Input Image - The input classification image. - False - - - OutputFile - out - Matrix output - Filename to store the output matrix (csv format) - - - ParameterSelection - ref - Ground truth - Choice of ground truth format - - - vector - - - 0 - False - - - ParameterFile - ref.vector.in - Input reference vector data - Input vector data of the ground truth - - False - - - ParameterString - ref.vector.field - Field name - Field name containing the label values - Class - - True - - - - ParameterNumber - nodatalabel - Value for nodata pixels - Label for the NoData class. Such input pixels will be discarded from the ground truth and from the input classification map. By default, 'nodatalabel = 0'. - - - 0 - True - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/ComputeImagesStatistics.xml b/python/plugins/processing/algs/otb/description/5.4.0/ComputeImagesStatistics.xml deleted file mode 100644 index b4430a982ca6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/ComputeImagesStatistics.xml +++ /dev/null @@ -1,31 +0,0 @@ - - ComputeImagesStatistics - otbcli_ComputeImagesStatistics - Compute Images second order statistics - Learning - Computes global mean and standard deviation for each band from a set of images and optionally saves the results in an XML file. - - ParameterMultipleInput - il - Input images - List of input images filenames. - - False - - - ParameterNumber - bv - Background Value - Background value to ignore in statistics computation. - - - 0.0 - True - - - OutputFile - out - Output XML file - XML filename where the statistics are saved for future reuse. - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/ComputeOGRLayersFeaturesStatistics.xml b/python/plugins/processing/algs/otb/description/5.4.0/ComputeOGRLayersFeaturesStatistics.xml deleted file mode 100644 index b7dd4dea07c3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/ComputeOGRLayersFeaturesStatistics.xml +++ /dev/null @@ -1,30 +0,0 @@ - - ComputeOGRLayersFeaturesStatistics - otbcli_ComputeOGRLayersFeaturesStatistics - ComputeOGRLayersFeaturesStatistics - Segmentation - Compute statistics of the features in a set of OGR Layers - - ParameterVector - inshp - Name of the input shapefile - Name of the input shapefile - - False - - - OutputFile - outstats - XML file containing mean and variance of each feature. - XML file containing mean and variance of each feature. - - - ParameterString - feat - List of features to consider for statistics. - List of features to consider for statistics. - - - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/ComputePolylineFeatureFromImage.xml b/python/plugins/processing/algs/otb/description/5.4.0/ComputePolylineFeatureFromImage.xml deleted file mode 100644 index 2841a27ed1d8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/ComputePolylineFeatureFromImage.xml +++ /dev/null @@ -1,60 +0,0 @@ - - ComputePolylineFeatureFromImage - otbcli_ComputePolylineFeatureFromImage - Compute Polyline Feature From Image - Feature Extraction - This application compute for each studied polyline, contained in the input VectorData, the chosen descriptors. - - ParameterRaster - in - Input Image - An image to compute the descriptors on. - False - - - ParameterVector - vd - Vector Data - Vector data containing the polylines where the features will be computed. - - False - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterString - expr - Feature expression - The feature formula (b1 < 0.3) where b1 is the standard name of input image first band - - - False - - - - ParameterString - field - Feature name - The field name corresponding to the feature codename (NONDVI, ROADSA...) - - - False - - - - OutputVector - out - Output Vector Data - The output vector data containing polylines with a new field - - - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/ConcatenateImages.xml b/python/plugins/processing/algs/otb/description/5.4.0/ConcatenateImages.xml deleted file mode 100644 index 4f7c9f4aa70f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/ConcatenateImages.xml +++ /dev/null @@ -1,32 +0,0 @@ - - ConcatenateImages - otbcli_ConcatenateImages - Images Concatenation - Image Manipulation - Concatenate a list of images of the same size into a single multi-channel one. - - ParameterMultipleInput - il - Input images list - The list of images to concatenate - - False - - - OutputRaster - out - Output Image - The concatenated output image - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/ConcatenateVectorData.xml b/python/plugins/processing/algs/otb/description/5.4.0/ConcatenateVectorData.xml deleted file mode 100644 index 6aa638a7ccf3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/ConcatenateVectorData.xml +++ /dev/null @@ -1,23 +0,0 @@ - - ConcatenateVectorData - otbcli_ConcatenateVectorData - Concatenate - Vector Data Manipulation - Concatenate VectorDatas - - ParameterMultipleInput - vd - Input VectorDatas to concatenate - VectorData files to be concatenated in an unique VectorData - - False - - - OutputVector - out - Concatenated VectorData - Output conctenated VectorData - - - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/ConnectedComponentSegmentation.xml b/python/plugins/processing/algs/otb/description/5.4.0/ConnectedComponentSegmentation.xml deleted file mode 100644 index 33b63d060d6a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/ConnectedComponentSegmentation.xml +++ /dev/null @@ -1,72 +0,0 @@ - - ConnectedComponentSegmentation - otbcli_ConnectedComponentSegmentation - Connected Component Segmentation - Segmentation - Connected component segmentation and object based image filtering of the input image according to user-defined criterions. - - ParameterRaster - in - Input Image - The image to segment. - False - - - OutputVector - out - Output Shape - The segmentation shape. - - - - - ParameterString - mask - Mask expression - Mask mathematical expression (only if support image is given) - - - True - - - - ParameterString - expr - Connected Component Expression - Formula used for connected component segmentation - - - False - - - - ParameterNumber - minsize - Minimum Object Size - Min object size (area in pixel) - - - 2 - True - - - ParameterString - obia - OBIA Expression - OBIA mathematical expression - - - True - - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/Convert.xml b/python/plugins/processing/algs/otb/description/5.4.0/Convert.xml deleted file mode 100644 index b5e626721b40..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/Convert.xml +++ /dev/null @@ -1,83 +0,0 @@ - - Convert - otbcli_Convert - Image Conversion - Image Manipulation - Convert an image to a different format, eventually rescaling the data and/or changing the pixel type. - - ParameterRaster - in - Input image - Input image - False - - - ParameterSelection - type - Rescale type - Transfer function for the rescaling - - - none - linear - log2 - - - 0 - False - - - ParameterNumber - type.linear.gamma - Gamma correction factor - Gamma correction factor - - - 1 - True - - - ParameterRaster - mask - Input mask - The masked pixels won't be used to adapt the dynamic (the mask must have the same dimensions as the input image) - True - - - ParameterNumber - hcp.high - High Cut Quantile - Quantiles to cut from histogram high values before computing min/max rescaling (in percent, 2 by default) - - - 2 - True - - - ParameterNumber - hcp.low - Low Cut Quantile - Quantiles to cut from histogram low values before computing min/max rescaling (in percent, 2 by default) - - - 2 - True - - - OutputRaster - out - Output Image - Output image - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/DEMConvert.xml b/python/plugins/processing/algs/otb/description/5.4.0/DEMConvert.xml deleted file mode 100644 index 8e017ebe1336..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/DEMConvert.xml +++ /dev/null @@ -1,20 +0,0 @@ - - DEMConvert - otbcli_DEMConvert - DEM Conversion - Image Manipulation - Converts a geo-referenced DEM image into a general raster file compatible with OTB DEM handling. - - ParameterRaster - in - Input geo-referenced DEM - Input geo-referenced DEM to convert to general raster format. - False - - - OutputFile - out - Prefix of the output files - will be used to get the prefix (name withtout extensions) of the files to write. Three files - prefix.geom, prefix.omd and prefix.ras - will be generated. - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/Despeckle-frost.xml b/python/plugins/processing/algs/otb/description/5.4.0/Despeckle-frost.xml deleted file mode 100644 index e7e3d54aebcf..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/Despeckle-frost.xml +++ /dev/null @@ -1,64 +0,0 @@ - - Despeckle-frost - otbcli_Despeckle - Despeckle (frost) - Image Filtering - Perform speckle noise reduction on SAR image. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - filter - speckle filtering method - - - - frost - - - 0 - False - - - ParameterNumber - filter.frost.rad - Radius - Radius for frost filter - - - 1 - False - - - ParameterNumber - filter.frost.deramp - deramp - Decrease factor declaration - - - 0.1 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/Despeckle-gammamap.xml b/python/plugins/processing/algs/otb/description/5.4.0/Despeckle-gammamap.xml deleted file mode 100644 index 25609700b661..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/Despeckle-gammamap.xml +++ /dev/null @@ -1,64 +0,0 @@ - - Despeckle-gammamap - otbcli_Despeckle - Despeckle (gammamap) - Image Filtering - Perform speckle noise reduction on SAR image. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - filter - speckle filtering method - - - - gammamap - - - 0 - False - - - ParameterNumber - filter.gammamap.rad - Radius - Radius for GammaMAP filter - - - 1 - False - - - ParameterNumber - filter.gammamap.nblooks - nb looks - Nb looks for GammaMAP filter - - - 1 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/Despeckle-kuan.xml b/python/plugins/processing/algs/otb/description/5.4.0/Despeckle-kuan.xml deleted file mode 100644 index ac47ace38d3b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/Despeckle-kuan.xml +++ /dev/null @@ -1,64 +0,0 @@ - - Despeckle-kuan - otbcli_Despeckle - Despeckle (kuan) - Image Filtering - Perform speckle noise reduction on SAR image. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - filter - speckle filtering method - - - - kuan - - - 0 - False - - - ParameterNumber - filter.kuan.rad - Radius - Radius for Kuan filter - - - 0 - False - - - ParameterNumber - filter.kuan.nblooks - nb looks - Nb looks for Kuan filter - - - 0.0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/Despeckle-lee.xml b/python/plugins/processing/algs/otb/description/5.4.0/Despeckle-lee.xml deleted file mode 100644 index 99dad8b3254c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/Despeckle-lee.xml +++ /dev/null @@ -1,64 +0,0 @@ - - Despeckle-lee - otbcli_Despeckle - Despeckle (lee) - Image Filtering - Perform speckle noise reduction on SAR image. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - filter - speckle filtering method - - - - lee - - - 0 - False - - - ParameterNumber - filter.lee.rad - Radius - Radius for lee filter - - - 1 - False - - - ParameterNumber - filter.lee.nblooks - nb looks - Nb looks for lee filter - - - 1 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/DimensionalityReduction-ica.xml b/python/plugins/processing/algs/otb/description/5.4.0/DimensionalityReduction-ica.xml deleted file mode 100644 index 6b4fbdfd3953..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/DimensionalityReduction-ica.xml +++ /dev/null @@ -1,85 +0,0 @@ - - DimensionalityReduction-ica - otbcli_DimensionalityReduction - DimensionalityReduction (ica) - Image Filtering - Perform Dimension reduction of the input image. - - ParameterRaster - in - Input Image - The input image to apply dimensionality reduction. - False - - - OutputRaster - out - Output Image - output image. Components are ordered by decreasing eigenvalues. - - - - OutputRaster - outinv - Inverse Output Image - reconstruct output image. - - - - ParameterSelection - method - Algorithm - Selection of the reduction dimension method. - - - ica - - - 0 - False - - - ParameterNumber - method.ica.iter - number of iterations - - - - 20 - True - - - ParameterNumber - method.ica.mu - Give the increment weight of W in [0, 1] - - - - 1 - True - - - ParameterNumber - nbcomp - Number of Components. - Number of relevant components kept. By default all components are kept. - - - 0 - True - - - ParameterBoolean - normalize - Normalize. - center AND reduce data before Dimensionality reduction. - True - True - - - OutputFile - outmatrix - Transformation matrix output (text format) - Filename to store the transformation matrix (csv format) - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/DimensionalityReduction-maf.xml b/python/plugins/processing/algs/otb/description/5.4.0/DimensionalityReduction-maf.xml deleted file mode 100644 index 78b403bab5d4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/DimensionalityReduction-maf.xml +++ /dev/null @@ -1,58 +0,0 @@ - - DimensionalityReduction-maf - otbcli_DimensionalityReduction - DimensionalityReduction (maf) - Image Filtering - Perform Dimension reduction of the input image. - - ParameterRaster - in - Input Image - The input image to apply dimensionality reduction. - False - - - OutputRaster - out - Output Image - output image. Components are ordered by decreasing eigenvalues. - - - - ParameterSelection - method - Algorithm - Selection of the reduction dimension method. - - - maf - - - 0 - False - - - ParameterNumber - nbcomp - Number of Components. - Number of relevant components kept. By default all components are kept. - - - 0 - True - - - ParameterBoolean - normalize - Normalize. - center AND reduce data before Dimensionality reduction. - True - True - - - OutputFile - outmatrix - Transformation matrix output (text format) - Filename to store the transformation matrix (csv format) - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/DimensionalityReduction-napca.xml b/python/plugins/processing/algs/otb/description/5.4.0/DimensionalityReduction-napca.xml deleted file mode 100644 index 6917a53ab9c4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/DimensionalityReduction-napca.xml +++ /dev/null @@ -1,85 +0,0 @@ - - DimensionalityReduction-napca - otbcli_DimensionalityReduction - DimensionalityReduction (napca) - Image Filtering - Perform Dimension reduction of the input image. - - ParameterRaster - in - Input Image - The input image to apply dimensionality reduction. - False - - - OutputRaster - out - Output Image - output image. Components are ordered by decreasing eigenvalues. - - - - OutputRaster - outinv - Inverse Output Image - reconstruct output image. - - - - ParameterSelection - method - Algorithm - Selection of the reduction dimension method. - - - napca - - - 0 - False - - - ParameterNumber - method.napca.radiusx - Set the x radius of the sliding window. - - - - 1 - False - - - ParameterNumber - method.napca.radiusy - Set the y radius of the sliding window. - - - - 1 - False - - - ParameterNumber - nbcomp - Number of Components. - Number of relevant components kept. By default all components are kept. - - - 0 - True - - - ParameterBoolean - normalize - Normalize. - center AND reduce data before Dimensionality reduction. - True - True - - - OutputFile - outmatrix - Transformation matrix output (text format) - Filename to store the transformation matrix (csv format) - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/DimensionalityReduction-pca.xml b/python/plugins/processing/algs/otb/description/5.4.0/DimensionalityReduction-pca.xml deleted file mode 100644 index c1c5439b39ca..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/DimensionalityReduction-pca.xml +++ /dev/null @@ -1,65 +0,0 @@ - - DimensionalityReduction-pca - otbcli_DimensionalityReduction - DimensionalityReduction (pca) - Image Filtering - Perform Dimension reduction of the input image. - - ParameterRaster - in - Input Image - The input image to apply dimensionality reduction. - False - - - OutputRaster - out - Output Image - output image. Components are ordered by decreasing eigenvalues. - - - - OutputRaster - outinv - Inverse Output Image - reconstruct output image. - - - - ParameterSelection - method - Algorithm - Selection of the reduction dimension method. - - - pca - - - 0 - False - - - ParameterNumber - nbcomp - Number of Components. - Number of relevant components kept. By default all components are kept. - - - 0 - True - - - ParameterBoolean - normalize - Normalize. - center AND reduce data before Dimensionality reduction. - True - True - - - OutputFile - outmatrix - Transformation matrix output (text format) - Filename to store the transformation matrix (csv format) - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/EdgeExtraction-gradient.xml b/python/plugins/processing/algs/otb/description/5.4.0/EdgeExtraction-gradient.xml deleted file mode 100644 index 6bf5b003761a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/EdgeExtraction-gradient.xml +++ /dev/null @@ -1,54 +0,0 @@ - - EdgeExtraction-gradient - otbcli_EdgeExtraction - EdgeExtraction (gradient) - Feature Extraction - Computes edge features on every pixel of the input image selected channel - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - filter - Edge feature - Choice of edge feature - - - gradient - - - 0 - False - - - OutputRaster - out - Feature Output Image - Output image containing the edge features. - - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/EdgeExtraction-sobel.xml b/python/plugins/processing/algs/otb/description/5.4.0/EdgeExtraction-sobel.xml deleted file mode 100644 index e322268eb1fa..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/EdgeExtraction-sobel.xml +++ /dev/null @@ -1,54 +0,0 @@ - - EdgeExtraction-sobel - otbcli_EdgeExtraction - EdgeExtraction (sobel) - Feature Extraction - Computes edge features on every pixel of the input image selected channel - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - filter - Edge feature - Choice of edge feature - - - sobel - - - 0 - False - - - OutputRaster - out - Feature Output Image - Output image containing the edge features. - - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/EdgeExtraction-touzi.xml b/python/plugins/processing/algs/otb/description/5.4.0/EdgeExtraction-touzi.xml deleted file mode 100644 index ea043b256958..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/EdgeExtraction-touzi.xml +++ /dev/null @@ -1,64 +0,0 @@ - - EdgeExtraction-touzi - otbcli_EdgeExtraction - EdgeExtraction (touzi) - Feature Extraction - Computes edge features on every pixel of the input image selected channel - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - filter - Edge feature - Choice of edge feature - - - touzi - - - 0 - False - - - ParameterNumber - filter.touzi.xradius - The Radius - The Radius - - - 1 - False - - - OutputRaster - out - Feature Output Image - Output image containing the edge features. - - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/ExtractROI-fit.xml b/python/plugins/processing/algs/otb/description/5.4.0/ExtractROI-fit.xml deleted file mode 100644 index 973c0a19da11..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/ExtractROI-fit.xml +++ /dev/null @@ -1,61 +0,0 @@ - - ExtractROI-fit - otbcli_ExtractROI - ExtractROI (fit) - Image Manipulation - Extract a ROI defined by the user. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - mode - Extraction mode - - - - fit - - - 0 - False - - - ParameterRaster - mode.fit.ref - Reference image - Reference image to define the ROI - False - - - ParameterNumber - mode.fit.elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/ExtractROI-standard.xml b/python/plugins/processing/algs/otb/description/5.4.0/ExtractROI-standard.xml deleted file mode 100644 index e898dbf6b6cc..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/ExtractROI-standard.xml +++ /dev/null @@ -1,84 +0,0 @@ - - ExtractROI-standard - otbcli_ExtractROI - ExtractROI (standard) - Image Manipulation - Extract a ROI defined by the user. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - mode - Extraction mode - - - - standard - - - 0 - False - - - ParameterNumber - startx - Start X - ROI start x position. - - - 0 - False - - - ParameterNumber - starty - Start Y - ROI start y position. - - - 0 - False - - - ParameterNumber - sizex - Size X - size along x in pixels. - - - 0 - False - - - ParameterNumber - sizey - Size Y - size along y in pixels. - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/FusionOfClassifications-dempstershafer.xml b/python/plugins/processing/algs/otb/description/5.4.0/FusionOfClassifications-dempstershafer.xml deleted file mode 100644 index 96d4a0cbe02c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/FusionOfClassifications-dempstershafer.xml +++ /dev/null @@ -1,79 +0,0 @@ - - FusionOfClassifications-dempstershafer - otbcli_FusionOfClassifications - FusionOfClassifications (dempstershafer) - Learning - Fuses several classifications maps of the same image on the basis of class labels. - - ParameterMultipleInput - il - Input classifications - List of input classification maps to fuse. Labels in each classification image must represent the same class. - - False - - - ParameterSelection - method - Fusion method - Selection of the fusion method and its parameters. - - - dempstershafer - - - 0 - False - - - ParameterMultipleInput - method.dempstershafer.cmfl - Confusion Matrices - A list of confusion matrix files (*.CSV format) to define the masses of belief and the class labels. Each file should be formatted the following way: the first line, beginning with a '#' symbol, should be a list of the class labels present in the corresponding input classification image, organized in the same order as the confusion matrix rows/columns. - - False - - - ParameterSelection - method.dempstershafer.mob - Mass of belief measurement - Type of confusion matrix measurement used to compute the masses of belief of each classifier. - - - precision - recall - accuracy - kappa - - - 0 - False - - - ParameterNumber - nodatalabel - Label for the NoData class - Label for the NoData class. Such input pixels keep their NoData label in the output image and are not handled in the fusion process. By default, 'nodatalabel = 0'. - - - 0 - False - - - ParameterNumber - undecidedlabel - Label for the Undecided class - Label for the Undecided class. Pixels with more than 1 fused class are marked as Undecided. Please note that the Undecided value must be different from existing labels in the input classifications. By default, 'undecidedlabel = 0'. - - - 0 - False - - - OutputRaster - out - The output classification image - The output classification image resulting from the fusion of the input classification images. - - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/FusionOfClassifications-majorityvoting.xml b/python/plugins/processing/algs/otb/description/5.4.0/FusionOfClassifications-majorityvoting.xml deleted file mode 100644 index abd3f7cb1289..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/FusionOfClassifications-majorityvoting.xml +++ /dev/null @@ -1,55 +0,0 @@ - - FusionOfClassifications-majorityvoting - otbcli_FusionOfClassifications - FusionOfClassifications (majorityvoting) - Learning - Fuses several classifications maps of the same image on the basis of class labels. - - ParameterMultipleInput - il - Input classifications - List of input classification maps to fuse. Labels in each classification image must represent the same class. - - False - - - ParameterSelection - method - Fusion method - Selection of the fusion method and its parameters. - - - majorityvoting - - - 0 - False - - - ParameterNumber - nodatalabel - Label for the NoData class - Label for the NoData class. Such input pixels keep their NoData label in the output image and are not handled in the fusion process. By default, 'nodatalabel = 0'. - - - 0 - False - - - ParameterNumber - undecidedlabel - Label for the Undecided class - Label for the Undecided class. Pixels with more than 1 fused class are marked as Undecided. Please note that the Undecided value must be different from existing labels in the input classifications. By default, 'undecidedlabel = 0'. - - - 0 - False - - - OutputRaster - out - The output classification image - The output classification image resulting from the fusion of the input classification images. - - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/GrayScaleMorphologicalOperation-closing.xml b/python/plugins/processing/algs/otb/description/5.4.0/GrayScaleMorphologicalOperation-closing.xml deleted file mode 100644 index 5d5e5f146bc8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/GrayScaleMorphologicalOperation-closing.xml +++ /dev/null @@ -1,77 +0,0 @@ - - GrayScaleMorphologicalOperation-closing - otbcli_GrayScaleMorphologicalOperation - GrayScaleMorphologicalOperation (closing) - Feature Extraction - Performs morphological operations on a grayscale input image - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - closing - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/GrayScaleMorphologicalOperation-dilate.xml b/python/plugins/processing/algs/otb/description/5.4.0/GrayScaleMorphologicalOperation-dilate.xml deleted file mode 100644 index 7302c31336de..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/GrayScaleMorphologicalOperation-dilate.xml +++ /dev/null @@ -1,77 +0,0 @@ - - GrayScaleMorphologicalOperation-dilate - otbcli_GrayScaleMorphologicalOperation - GrayScaleMorphologicalOperation (dilate) - Feature Extraction - Performs morphological operations on a grayscale input image - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - dilate - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/GrayScaleMorphologicalOperation-erode.xml b/python/plugins/processing/algs/otb/description/5.4.0/GrayScaleMorphologicalOperation-erode.xml deleted file mode 100644 index 7da86e36fea2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/GrayScaleMorphologicalOperation-erode.xml +++ /dev/null @@ -1,77 +0,0 @@ - - GrayScaleMorphologicalOperation-erode - otbcli_GrayScaleMorphologicalOperation - GrayScaleMorphologicalOperation (erode) - Feature Extraction - Performs morphological operations on a grayscale input image - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - erode - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/GrayScaleMorphologicalOperation-opening.xml b/python/plugins/processing/algs/otb/description/5.4.0/GrayScaleMorphologicalOperation-opening.xml deleted file mode 100644 index e9781f67cab4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/GrayScaleMorphologicalOperation-opening.xml +++ /dev/null @@ -1,77 +0,0 @@ - - GrayScaleMorphologicalOperation-opening - otbcli_GrayScaleMorphologicalOperation - GrayScaleMorphologicalOperation (opening) - Feature Extraction - Performs morphological operations on a grayscale input image - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - opening - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/HaralickTextureExtraction.xml b/python/plugins/processing/algs/otb/description/5.4.0/HaralickTextureExtraction.xml deleted file mode 100644 index 12a02eeacf11..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/HaralickTextureExtraction.xml +++ /dev/null @@ -1,126 +0,0 @@ - - HaralickTextureExtraction - otbcli_HaralickTextureExtraction - Haralick Texture Extraction - Feature Extraction - Computes textures on every pixel of the input image selected channel - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterNumber - parameters.xrad - X Radius - X Radius - - - 2 - False - - - ParameterNumber - parameters.yrad - Y Radius - Y Radius - - - 2 - False - - - ParameterNumber - parameters.xoff - X Offset - X Offset - - - 1 - False - - - ParameterNumber - parameters.yoff - Y Offset - Y Offset - - - 1 - False - - - ParameterNumber - parameters.min - Image Minimum - Image Minimum - - - 0 - False - - - ParameterNumber - parameters.max - Image Maximum - Image Maximum - - - 255 - False - - - ParameterNumber - parameters.nbbin - Histogram number of bin - Histogram number of bin - - - 8 - False - - - ParameterSelection - texture - Texture Set Selection - Choice of The Texture Set - - - simple - advanced - higher - - - 0 - False - - - OutputRaster - out - Output Image - Output image containing the selected texture features. - - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/HooverCompareSegmentation.xml b/python/plugins/processing/algs/otb/description/5.4.0/HooverCompareSegmentation.xml deleted file mode 100644 index 2646745b3f7e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/HooverCompareSegmentation.xml +++ /dev/null @@ -1,95 +0,0 @@ - - HooverCompareSegmentation - otbcli_HooverCompareSegmentation - Hoover compare segmentation - Segmentation - Compare two segmentations with Hoover metrics - - ParameterRaster - ingt - Input ground truth - A partial ground truth segmentation image. - False - - - ParameterRaster - inms - Input machine segmentation - A machine segmentation image. - False - - - ParameterNumber - bg - Background label - Label value of the background in the input segmentations - - - 0 - False - - - ParameterNumber - th - Overlapping threshold - Overlapping threshold used to find Hoover instances. - - - 0.75 - False - - - OutputRaster - outgt - Colored ground truth output - The colored ground truth output image. - - - - OutputRaster - outms - Colored machine segmentation output - The colored machine segmentation output image. - - - - ParameterNumber - rc - Correct detection score - Overall score for correct detection (RC) - - - 0.0 - False - - - ParameterNumber - rf - Over-segmentation score - Overall score for over segmentation (RF) - - - 0.0 - False - - - ParameterNumber - ra - Under-segmentation score - Overall score for under segmentation (RA) - - - 0.0 - False - - - ParameterNumber - rm - Missed detection score - Overall score for missed detection (RM) - - - 0.0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/ImageClassifier.xml b/python/plugins/processing/algs/otb/description/5.4.0/ImageClassifier.xml deleted file mode 100644 index 555b0eca3e43..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/ImageClassifier.xml +++ /dev/null @@ -1,72 +0,0 @@ - - ImageClassifier - otbcli_ImageClassifier - Image Classification - Learning - Performs a classification of the input image according to a model file. - - ParameterRaster - in - Input Image - The input image to classify. - False - - - ParameterRaster - mask - Input Mask - The mask allows restricting classification of the input image to the area where mask pixel values are greater than 0. - True - - - ParameterFile - model - Model file - A model file (produced by TrainImagesClassifier application, maximal class label = 65535). - - False - - - ParameterFile - imstat - Statistics file - A XML file containing mean and standard deviation to center and reduce samples before classification (produced by ComputeImagesStatistics application). - - True - - - OutputRaster - out - Output Image - Output image containing class labels - - - - OutputRaster - confmap - Confidence map - Confidence map of the produced classification. The confidence index depends on the model : - - LibSVM : difference between the two highest probabilities (needs a model with probability estimates, so that classes probabilities can be computed for each sample) - - OpenCV - * Boost : sum of votes - * DecisionTree : (not supported) - * GradientBoostedTree : (not supported) - * KNearestNeighbors : number of neighbors with the same label - * NeuralNetwork : difference between the two highest responses - * NormalBayes : (not supported) - * RandomForest : Confidence (proportion of votes for the majority class). Margin (normalized difference of the votes of the 2 majority classes) is not available for now. - * SVM : distance to margin (only works for 2-class models) - - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/ImageEnvelope.xml b/python/plugins/processing/algs/otb/description/5.4.0/ImageEnvelope.xml deleted file mode 100644 index 937f73bbe933..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/ImageEnvelope.xml +++ /dev/null @@ -1,42 +0,0 @@ - - ImageEnvelope - otbcli_ImageEnvelope - Image Envelope - Geometry - Extracts an image envelope. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputVector - out - Output Vector Data - Vector data file containing the envelope - - - - - ParameterNumber - sr - Sampling Rate - Sampling rate for image edges (in pixel) - - - 0 - True - - - ParameterString - proj - Projection - Projection to be used to compute the envelope (default is WGS84) - - - True - - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/KMeansClassification.xml b/python/plugins/processing/algs/otb/description/5.4.0/KMeansClassification.xml deleted file mode 100644 index 9cac46c41172..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/KMeansClassification.xml +++ /dev/null @@ -1,84 +0,0 @@ - - KMeansClassification - otbcli_KMeansClassification - Unsupervised KMeans image classification - Learning - Unsupervised KMeans image classification - - ParameterRaster - in - Input Image - Input image to classify. - False - - - OutputRaster - out - Output Image - Output image containing the class indexes. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterRaster - vm - Validity Mask - Validity mask. Only non-zero pixels will be used to estimate KMeans modes. - True - - - ParameterNumber - ts - Training set size - Size of the training set (in pixels). - - - 100 - True - - - ParameterNumber - nc - Number of classes - Number of modes, which will be used to generate class membership. - - - 5 - False - - - ParameterNumber - maxit - Maximum number of iterations - Maximum number of iterations for the learning step. - - - 1000 - True - - - ParameterNumber - ct - Convergence threshold - Convergence threshold for class centroid (L2 distance, by default 0.0001). - - - 0.0001 - True - - - OutputFile - outmeans - Centroid filename - Output text file containing centroid positions - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/KmzExport.xml b/python/plugins/processing/algs/otb/description/5.4.0/KmzExport.xml deleted file mode 100644 index 57469ba47a5c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/KmzExport.xml +++ /dev/null @@ -1,54 +0,0 @@ - - KmzExport - otbcli_KmzExport - Image to KMZ Export - Miscellaneous - Export the input image in a KMZ product. - - ParameterRaster - in - Input image - Input image - False - - - OutputFile - out - Output .kmz product - Output Kmz product directory (with .kmz extension) - - - ParameterNumber - tilesize - Tile Size - Size of the tiles in the kmz product, in number of pixels (default = 512). - - - 512 - True - - - ParameterRaster - logo - Image logo - Path to the image logo to add to the KMZ product. - True - - - ParameterRaster - legend - Image legend - Path to the image legend to add to the KMZ product. - True - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/LSMSSegmentation.xml b/python/plugins/processing/algs/otb/description/5.4.0/LSMSSegmentation.xml deleted file mode 100644 index 2eef025b37a0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/LSMSSegmentation.xml +++ /dev/null @@ -1,94 +0,0 @@ - - LSMSSegmentation - otbcli_LSMSSegmentation - Exact Large-Scale Mean-Shift segmentation, step 2 - Segmentation - Second step of the exact Large-Scale Mean-Shift segmentation workflow. - - ParameterRaster - in - Filtered image - The filtered image (cf. Adaptive MeanShift Smoothing application). - False - - - ParameterRaster - inpos - Spatial image - The spatial image. Spatial input is the displacement map (output of the Adaptive MeanShift Smoothing application). - True - - - OutputRaster - out - Output Image - The output image. The output image is the segmentation of the filtered image. It is recommended to set the pixel type to uint32. - - - - ParameterNumber - ranger - Range radius - Range radius defining the radius (expressed in radiometry unit) in the multi-spectral space. - - - 15 - True - - - ParameterNumber - spatialr - Spatial radius - Spatial radius of the neighborhood. - - - 5 - True - - - ParameterNumber - minsize - Minimum Region Size - Minimum Region Size. If, after the segmentation, a region is of size lower than this criterion, the region is deleted. - - - 0 - True - - - ParameterNumber - tilesizex - Size of tiles in pixel (X-axis) - Size of tiles along the X-axis. - - - 500 - False - - - ParameterNumber - tilesizey - Size of tiles in pixel (Y-axis) - Size of tiles along the Y-axis. - - - 500 - False - - - ParameterFile - tmpdir - Directory where to write temporary files - This applications need to write temporary files for each tile. This parameter allows choosing the path where to write those files. If disabled, the current path will be used. - - True - - - ParameterBoolean - cleanup - Temporary files cleaning - If activated, the application will try to clean all temporary files it created - True - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/LSMSSmallRegionsMerging.xml b/python/plugins/processing/algs/otb/description/5.4.0/LSMSSmallRegionsMerging.xml deleted file mode 100644 index c3ccd89c0e4a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/LSMSSmallRegionsMerging.xml +++ /dev/null @@ -1,58 +0,0 @@ - - LSMSSmallRegionsMerging - otbcli_LSMSSmallRegionsMerging - Exact Large-Scale Mean-Shift segmentation, step 3 (optional) - Segmentation - Third (optional) step of the exact Large-Scale Mean-Shift segmentation workflow. - - ParameterRaster - in - Input image - The input image. - False - - - ParameterRaster - inseg - Segmented image - The segmented image input. Segmented image input is the segmentation of the input image. - False - - - OutputRaster - out - Output Image - The output image. The output image is the input image where the minimal regions have been merged. - - - - ParameterNumber - minsize - Minimum Region Size - Minimum Region Size. If, after the segmentation, a region is of size lower than this criterion, the region is merged with the "nearest" region (radiometrically). - - - 50 - True - - - ParameterNumber - tilesizex - Size of tiles in pixel (X-axis) - Size of tiles along the X-axis. - - - 500 - False - - - ParameterNumber - tilesizey - Size of tiles in pixel (Y-axis) - Size of tiles along the Y-axis. - - - 500 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/LSMSVectorization.xml b/python/plugins/processing/algs/otb/description/5.4.0/LSMSVectorization.xml deleted file mode 100644 index 8987a219f834..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/LSMSVectorization.xml +++ /dev/null @@ -1,47 +0,0 @@ - - LSMSVectorization - otbcli_LSMSVectorization - Exact Large-Scale Mean-Shift segmentation, step 4 - Segmentation - Fourth step of the exact Large-Scale Mean-Shift segmentation workflow. - - ParameterRaster - in - Input Image - The input image. - False - - - ParameterRaster - inseg - Segmented image - The segmented image input. Segmented image input is the segmentation of the input image. - False - - - OutputVector - out - Output GIS vector file - The output GIS vector file, representing the vectorized version of the segmented image where the features of the polygons are the radiometric means and variances. - - - ParameterNumber - tilesizex - Size of tiles in pixel (X-axis) - Size of tiles along the X-axis. - - - 500 - False - - - ParameterNumber - tilesizey - Size of tiles in pixel (Y-axis) - Size of tiles along the Y-axis. - - - 500 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/LineSegmentDetection.xml b/python/plugins/processing/algs/otb/description/5.4.0/LineSegmentDetection.xml deleted file mode 100644 index 850f4f8a0a9f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/LineSegmentDetection.xml +++ /dev/null @@ -1,30 +0,0 @@ - - LineSegmentDetection - otbcli_LineSegmentDetection - Line segment detection - Feature Extraction - Detect line segments in raster - - ParameterRaster - in - Input Image - Input image on which lines will be detected. - False - - - OutputVector - out - Output Detected lines - Output detected line segments (vector data). - - - - - ParameterBoolean - norescale - No rescaling in [0, 255] - By default, the input image amplitude is rescaled between [0,255]. Turn on this parameter to skip rescaling - True - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/LocalStatisticExtraction.xml b/python/plugins/processing/algs/otb/description/5.4.0/LocalStatisticExtraction.xml deleted file mode 100644 index 663bd63bd3e9..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/LocalStatisticExtraction.xml +++ /dev/null @@ -1,51 +0,0 @@ - - LocalStatisticExtraction - otbcli_LocalStatisticExtraction - Local Statistic Extraction - Feature Extraction - Computes local statistical moments on every pixel in the selected channel of the input image - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterNumber - radius - Neighborhood radius - The computational window radius. - - - 3 - False - - - OutputRaster - out - Feature Output Image - Output image containing the local statistical moments. - - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/MeanShiftSmoothing.xml b/python/plugins/processing/algs/otb/description/5.4.0/MeanShiftSmoothing.xml deleted file mode 100644 index 22fb7dd68bf4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/MeanShiftSmoothing.xml +++ /dev/null @@ -1,96 +0,0 @@ - - MeanShiftSmoothing - otbcli_MeanShiftSmoothing - Exact Large-Scale Mean-Shift segmentation, step 1 (smoothing) - Image Filtering - Perform mean shift filtering - - ParameterRaster - in - Input Image - The input image. - False - - - OutputRaster - fout - Filtered output - The filtered output image. - - - - OutputRaster - foutpos - Spatial image - The spatial image output. Spatial image output is a displacement map (pixel position after convergence). - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterNumber - spatialr - Spatial radius - Spatial radius of the neighborhood. - - - 5 - True - - - ParameterNumber - ranger - Range radius - Range radius defining the radius (expressed in radiometry unit) in the multi-spectral space. - - - 15 - True - - - ParameterNumber - thres - Mode convergence threshold - Algorithm iterative scheme will stop if mean-shift vector is below this threshold or if iteration number reached maximum number of iterations. - - - 0.1 - True - - - ParameterNumber - maxiter - Maximum number of iterations - Algorithm iterative scheme will stop if convergence hasn't been reached after the maximum number of iterations. - - - 100 - True - - - ParameterNumber - rangeramp - Range radius coefficient - This coefficient makes dependent the ranger of the colorimetry of the filtered pixel : y = rangeramp*x+ranger. - - - 0 - True - - - ParameterBoolean - modesearch - Mode search. - If activated pixel iterative convergence is stopped if the path . Be careful, with this option, the result will slightly depend on thread number - True - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/MultivariateAlterationDetector.xml b/python/plugins/processing/algs/otb/description/5.4.0/MultivariateAlterationDetector.xml deleted file mode 100644 index 3fa140a5e78d..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/MultivariateAlterationDetector.xml +++ /dev/null @@ -1,38 +0,0 @@ - - MultivariateAlterationDetector - otbcli_MultivariateAlterationDetector - Multivariate alteration detector - Feature Extraction - Multivariate Alteration Detector - - ParameterRaster - in1 - Input Image 1 - Image which describe initial state of the scene. - False - - - ParameterRaster - in2 - Input Image 2 - Image which describe scene after perturbations. - False - - - OutputRaster - out - Change Map - Image of detected changes. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/OGRLayerClassifier.xml b/python/plugins/processing/algs/otb/description/5.4.0/OGRLayerClassifier.xml deleted file mode 100644 index 9e199b2d6060..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/OGRLayerClassifier.xml +++ /dev/null @@ -1,48 +0,0 @@ - - OGRLayerClassifier - otbcli_OGRLayerClassifier - OGRLayerClassifier - Segmentation - Classify an OGR layer based on a machine learning model and a list of features to consider. - - ParameterVector - inshp - Name of the input shapefile - Name of the input shapefile - - False - - - ParameterFile - instats - XML file containing mean and variance of each feature. - XML file containing mean and variance of each feature. - - False - - - OutputFile - insvm - Input model filename. - Input model filename. - - - ParameterString - feat - Features - Features to be calculated - - - False - - - ParameterString - cfield - Field containing the predicted class. - Field containing the predicted class - predicted - - False - - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/OrthoRectification-epsg.xml b/python/plugins/processing/algs/otb/description/5.4.0/OrthoRectification-epsg.xml deleted file mode 100644 index 1ca3123425f6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/OrthoRectification-epsg.xml +++ /dev/null @@ -1,124 +0,0 @@ - - OrthoRectification-epsg - otbcli_OrthoRectification - OrthoRectification (epsg) - Geometry - This application allows ortho-rectification of optical images from supported sensors. - - - ParameterRaster - io.in - Input Image - The input image to ortho-rectify - False - - - OutputRaster - io.out - Output Image - The ortho-rectified output image - - - - ParameterSelection - map - Output Cartographic Map Projection - Parameters of the output map projection to be used. - - - epsg - - - 0 - False - - - ParameterNumber - map.epsg.code - EPSG Code - See www.spatialreference.org to find which EPSG code is associated to your projection - - - 4326 - False - - - ParameterSelection - outputs.mode - Parameters estimation modes - - - - autosize - autospacing - - - 0 - False - - - ParameterNumber - outputs.default - Default pixel value - Default value to write when outside of input image. - - - 0 - True - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows one to define how the input image will be interpolated during resampling. - - - bco - nn - linear - - - 0 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows one to control the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artefacts. - - - 2 - False - - - ParameterNumber - opt.ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - True - - - ParameterNumber - opt.gridspacing - Resampling grid spacing - Resampling is done according to a coordinate mapping deformation grid, whose pixel size is set by this parameter, and expressed in the coordinate system of the output image The closer to the output spacing this parameter is, the more precise will be the ortho-rectified image,but increasing this parameter will reduce processing time. - - - 4 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/OrthoRectification-fit-to-ortho.xml b/python/plugins/processing/algs/otb/description/5.4.0/OrthoRectification-fit-to-ortho.xml deleted file mode 100644 index 5d7ce55ef3c1..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/OrthoRectification-fit-to-ortho.xml +++ /dev/null @@ -1,107 +0,0 @@ - - OrthoRectification-fit-to-ortho - otbcli_OrthoRectification - OrthoRectification (fit-to-ortho) - Geometry - This application allows ortho-rectification of optical images from supported sensors. - - - ParameterRaster - io.in - Input Image - The input image to ortho-rectify - False - - - OutputRaster - io.out - Output Image - The ortho-rectified output image - - - - ParameterSelection - outputs.mode - Parameters estimation modes - - - - orthofit - - - 0 - False - - - ParameterRaster - outputs.ortho - Model ortho-image - A model ortho-image that can be used to compute size, origin and spacing of the output - True - - - ParameterNumber - outputs.default - Default pixel value - Default value to write when outside of input image. - - - 0 - True - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows one to define how the input image will be interpolated during resampling. - - - bco - nn - linear - - - 0 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows one to control the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artefacts. - - - 2 - False - - - ParameterNumber - opt.ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - True - - - ParameterNumber - opt.gridspacing - Resampling grid spacing - Resampling is done according to a coordinate mapping deformation grid, whose pixel size is set by this parameter, and expressed in the coordinate system of the output image The closer to the output spacing this parameter is, the more precise will be the ortho-rectified image,but increasing this parameter will reduce processing time. - - - 4 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/OrthoRectification-lambert-WGS84.xml b/python/plugins/processing/algs/otb/description/5.4.0/OrthoRectification-lambert-WGS84.xml deleted file mode 100644 index 30f0bd09a7c1..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/OrthoRectification-lambert-WGS84.xml +++ /dev/null @@ -1,116 +0,0 @@ - - OrthoRectification-lambert-WGS84 - otbcli_OrthoRectification - OrthoRectification (lambert-WGS84) - Geometry - This application allows ortho-rectification of optical images from supported sensors. - - - ParameterRaster - io.in - Input Image - The input image to ortho-rectify - False - - - OutputRaster - io.out - Output Image - The ortho-rectified output image - - - - ParameterSelection - map - Output Cartographic Map Projection - Parameters of the output map projection to be used. - - - lambert2 - lambert93 - wgs - - - 0 - False - - - ParameterSelection - outputs.mode - Parameters estimation modes - - - - autosize - autospacing - - - 0 - False - - - ParameterNumber - outputs.default - Default pixel value - Default value to write when outside of input image. - - - 0 - True - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows one to define how the input image will be interpolated during resampling. - - - bco - nn - linear - - - 0 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows one to control the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artefacts. - - - 2 - False - - - ParameterNumber - opt.ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - True - - - ParameterNumber - opt.gridspacing - Resampling grid spacing - Resampling is done according to a coordinate mapping deformation grid, whose pixel size is set by this parameter, and expressed in the coordinate system of the output image The closer to the output spacing this parameter is, the more precise will be the ortho-rectified image,but increasing this parameter will reduce processing time. - - - 4 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/OrthoRectification-utm.xml b/python/plugins/processing/algs/otb/description/5.4.0/OrthoRectification-utm.xml deleted file mode 100644 index 4f9a84fe044d..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/OrthoRectification-utm.xml +++ /dev/null @@ -1,132 +0,0 @@ - - OrthoRectification-utm - otbcli_OrthoRectification - OrthoRectification (utm) - Geometry - This application allows ortho-rectification of optical images from supported sensors. - - - ParameterRaster - io.in - Input Image - The input image to ortho-rectify - False - - - OutputRaster - io.out - Output Image - The ortho-rectified output image - - - - ParameterSelection - map - Output Cartographic Map Projection - Parameters of the output map projection to be used. - - - utm - - - 0 - False - - - ParameterNumber - map.utm.zone - Zone number - The zone number ranges from 1 to 60 and allows defining the transverse mercator projection (along with the hemisphere) - - - 31 - False - - - ParameterBoolean - map.utm.northhem - Northern Hemisphere - The transverse mercator projections are defined by their zone number as well as the hemisphere. Activate this parameter if your image is in the northern hemisphere. - True - True - - - ParameterSelection - outputs.mode - Parameters estimation modes - - - - autosize - autospacing - - - 0 - False - - - ParameterNumber - outputs.default - Default pixel value - Default value to write when outside of input image. - - - 0 - True - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows one to define how the input image will be interpolated during resampling. - - - bco - nn - linear - - - 0 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows one to control the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artefacts. - - - 2 - False - - - ParameterNumber - opt.ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - True - - - ParameterNumber - opt.gridspacing - Resampling grid spacing - Resampling is done according to a coordinate mapping deformation grid, whose pixel size is set by this parameter, and expressed in the coordinate system of the output image The closer to the output spacing this parameter is, the more precise will be the ortho-rectified image,but increasing this parameter will reduce processing time. - - - 4 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/Pansharpening-bayes.xml b/python/plugins/processing/algs/otb/description/5.4.0/Pansharpening-bayes.xml deleted file mode 100644 index 9b45d08e9e73..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/Pansharpening-bayes.xml +++ /dev/null @@ -1,71 +0,0 @@ - - Pansharpening-bayes - otbcli_Pansharpening - Pansharpening (bayes) - Geometry - Perform P+XS pansharpening - - ParameterRaster - inp - Input PAN Image - Input panchromatic image. - False - - - ParameterRaster - inxs - Input XS Image - Input XS image. - False - - - OutputRaster - out - Output image - Output image. - - - - ParameterSelection - method - Algorithm - Selection of the pan-sharpening method. - - - bayes - - - 0 - False - - - ParameterNumber - method.bayes.lambda - Weight - Set the weighting value. - - - 0.9999 - False - - - ParameterNumber - method.bayes.s - S coefficient - Set the S coefficient. - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/Pansharpening-lmvm.xml b/python/plugins/processing/algs/otb/description/5.4.0/Pansharpening-lmvm.xml deleted file mode 100644 index fd6d171f58b9..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/Pansharpening-lmvm.xml +++ /dev/null @@ -1,71 +0,0 @@ - - Pansharpening-lmvm - otbcli_Pansharpening - Pansharpening (lmvm) - Geometry - Perform P+XS pansharpening - - ParameterRaster - inp - Input PAN Image - Input panchromatic image. - False - - - ParameterRaster - inxs - Input XS Image - Input XS image. - False - - - OutputRaster - out - Output image - Output image. - - - - ParameterSelection - method - Algorithm - Selection of the pan-sharpening method. - - - lmvm - - - 0 - False - - - ParameterNumber - method.lmvm.radiusx - X radius - Set the x radius of the sliding window. - - - 3 - False - - - ParameterNumber - method.lmvm.radiusy - Y radius - Set the y radius of the sliding window. - - - 3 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/Pansharpening-rcs.xml b/python/plugins/processing/algs/otb/description/5.4.0/Pansharpening-rcs.xml deleted file mode 100644 index d8b9c1bc8480..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/Pansharpening-rcs.xml +++ /dev/null @@ -1,51 +0,0 @@ - - Pansharpening-rcs - otbcli_Pansharpening - Pansharpening (rcs) - Geometry - Perform P+XS pansharpening - - ParameterRaster - inp - Input PAN Image - Input panchromatic image. - False - - - ParameterRaster - inxs - Input XS Image - Input XS image. - False - - - OutputRaster - out - Output image - Output image. - - - - ParameterSelection - method - Algorithm - Selection of the pan-sharpening method. - - - rcs - - - 0 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/RadiometricIndices.xml b/python/plugins/processing/algs/otb/description/5.4.0/RadiometricIndices.xml deleted file mode 100644 index 41aa91db8123..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/RadiometricIndices.xml +++ /dev/null @@ -1,131 +0,0 @@ - - RadiometricIndices - otbcli_RadiometricIndices - Radiometric Indices - Feature Extraction - Compute radiometric indices. - - ParameterRaster - in - Input Image - Input image - False - - - OutputRaster - out - Output Image - Radiometric indices output image - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterNumber - channels.blue - Blue Channel - Blue channel index - - - 1 - False - - - ParameterNumber - channels.green - Green Channel - Green channel index - - - 1 - False - - - ParameterNumber - channels.red - Red Channel - Red channel index - - - 1 - False - - - ParameterNumber - channels.nir - NIR Channel - NIR channel index - - - 1 - False - - - ParameterNumber - channels.mir - Mir Channel - Mir channel index - - - 1 - False - - - ParameterSelection - list - Available Radiometric Indices - List of available radiometric indices with their relevant channels in brackets: - Vegetation:NDVI - Normalized difference vegetation index (Red, NIR) - Vegetation:TNDVI - Transformed normalized difference vegetation index (Red, NIR) - Vegetation:RVI - Ratio vegetation index (Red, NIR) - Vegetation:SAVI - Soil adjusted vegetation index (Red, NIR) - Vegetation:TSAVI - Transformed soil adjusted vegetation index (Red, NIR) - Vegetation:MSAVI - Modified soil adjusted vegetation index (Red, NIR) - Vegetation:MSAVI2 - Modified soil adjusted vegetation index 2 (Red, NIR) - Vegetation:GEMI - Global environment monitoring index (Red, NIR) - Vegetation:IPVI - Infrared percentage vegetation index (Red, NIR) - - Water:NDWI - Normalized difference water index (Gao 1996) (NIR, MIR) - Water:NDWI2 - Normalized difference water index (Mc Feeters 1996) (Green, NIR) - Water:MNDWI - Modified normalized difference water index (Xu 2006) (Green, MIR) - Water:NDPI - Normalized difference pond index (Lacaux et al.) (MIR, Green) - Water:NDTI - Normalized difference turbidity index (Lacaux et al.) (Red, Green) - - Soil:RI - Redness index (Red, Green) - Soil:CI - Color index (Red, Green) - Soil:BI - Brightness index (Red, Green) - Soil:BI2 - Brightness index 2 (NIR, Red, Green) - - - ndvi - tndvi - rvi - savi - tsavi - msavi - msavi2 - gemi - ipvi - ndwi - ndwi2 - mndwi - ndpi - ndti - ri - ci - bi - bi2 - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/Rasterization-image.xml b/python/plugins/processing/algs/otb/description/5.4.0/Rasterization-image.xml deleted file mode 100644 index b7cf4262711d..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/Rasterization-image.xml +++ /dev/null @@ -1,83 +0,0 @@ - - Rasterization-image - otbcli_Rasterization - Rasterization (image) - Vector Data Manipulation - Rasterize a vector dataset. - - ParameterVector - in - Input vector dataset - The input vector dataset to be rasterized - - False - - - OutputRaster - out - Output image - An output image containing the rasterized vector dataset - - - - ParameterRaster - im - Input reference image - A reference image from which to import output grid and projection reference system information. - True - - - ParameterNumber - background - Background value - Default value for pixels not belonging to any geometry - - - 0 - False - - - ParameterSelection - mode - Rasterization mode - Choice of rasterization modes - - - binary - attribute - - - 0 - False - - - ParameterNumber - mode.binary.foreground - Foreground value - Value for pixels inside a geometry - - - 255 - False - - - ParameterString - mode.attribute.field - The attribute field to burn - Name of the attribute field to burn - DN - - False - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/Rasterization-manual.xml b/python/plugins/processing/algs/otb/description/5.4.0/Rasterization-manual.xml deleted file mode 100644 index 97e0d288fd3a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/Rasterization-manual.xml +++ /dev/null @@ -1,146 +0,0 @@ - - Rasterization-manual - otbcli_Rasterization - Rasterization (manual) - Vector Data Manipulation - Rasterize a vector dataset. - - ParameterVector - in - Input vector dataset - The input vector dataset to be rasterized - - False - - - OutputRaster - out - Output image - An output image containing the rasterized vector dataset - - - - ParameterNumber - szx - Output size x - Output size along x axis (useless if support image is given) - - - 0 - True - - - ParameterNumber - szy - Output size y - Output size along y axis (useless if support image is given) - - - 0 - True - - - ParameterNumber - epsg - Output EPSG code - EPSG code for the output projection reference system (EPSG 4326 for WGS84, 32631 for UTM31N...,useless if support image is given) - - - 0 - True - - - ParameterNumber - orx - Output Upper-left x - Output upper-left corner x coordinate (useless if support image is given) - - - 0.0 - True - - - ParameterNumber - ory - Output Upper-left y - Output upper-left corner y coordinate (useless if support image is given) - - - 0.0 - True - - - ParameterNumber - spx - Spacing (GSD) x - Spacing (ground sampling distance) along x axis (useless if support image is given) - - - 0.0 - True - - - ParameterNumber - spy - Spacing (GSD) y - Spacing (ground sampling distance) along y axis (useless if support image is given) - - - 0.0 - True - - - ParameterNumber - background - Background value - Default value for pixels not belonging to any geometry - - - 0 - False - - - ParameterSelection - mode - Rasterization mode - Choice of rasterization modes - - - binary - attribute - - - 0 - False - - - ParameterNumber - mode.binary.foreground - Foreground value - Value for pixels inside a geometry - - - 255 - False - - - ParameterString - mode.attribute.field - The attribute field to burn - Name of the attribute field to burn - DN - - False - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/ReadImageInfo.xml b/python/plugins/processing/algs/otb/description/5.4.0/ReadImageInfo.xml deleted file mode 100644 index 9ddf48cfedd7..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/ReadImageInfo.xml +++ /dev/null @@ -1,62 +0,0 @@ - - ReadImageInfo - otbcli_ReadImageInfo - Read image information - Image Manipulation - Get information about the image - - ParameterRaster - in - Input Image - Input image to analyse - False - - - ParameterBoolean - keywordlist - Display the OSSIM keywordlist - Output the OSSIM keyword list. It contains metadata information (sensor model, geometry ). Information is stored in keyword list (pairs of key/value) - True - True - - - ParameterString - gcp.ids - GCPs Id - GCPs identifier - - - False - - - - ParameterString - gcp.info - GCPs Info - GCPs Information - - - False - - - - ParameterString - gcp.imcoord - GCPs Image Coordinates - GCPs Image coordinates - - - False - - - - ParameterString - gcp.geocoord - GCPs Geographic Coordinates - GCPs Geographic Coordinates - - - False - - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/Rescale.xml b/python/plugins/processing/algs/otb/description/5.4.0/Rescale.xml deleted file mode 100644 index 6ae441121fea..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/Rescale.xml +++ /dev/null @@ -1,51 +0,0 @@ - - Rescale - otbcli_Rescale - Rescale Image - Image Manipulation - Rescale the image between two given values. - - ParameterRaster - in - Input Image - The image to scale. - False - - - OutputRaster - out - Output Image - The rescaled image filename. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterNumber - outmin - Output min value - Minimum value of the output image. - - - 0 - True - - - ParameterNumber - outmax - Output max value - Maximum value of the output image. - - - 255 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/RigidTransformResample-id.xml b/python/plugins/processing/algs/otb/description/5.4.0/RigidTransformResample-id.xml deleted file mode 100644 index 034f79e8052d..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/RigidTransformResample-id.xml +++ /dev/null @@ -1,89 +0,0 @@ - - RigidTransformResample-id - otbcli_RigidTransformResample - RigidTransformResample (id) - Geometry - Resample an image with a rigid transform - - ParameterRaster - in - Input image - The input image to translate. - False - - - OutputRaster - out - Output image - The transformed output image. - - - - ParameterSelection - transform.type - Type of transformation - Type of transformation. Available transformations are spatial scaling, translation and rotation with scaling factor - - - id - - - 0 - False - - - ParameterNumber - transform.type.id.scalex - X scaling - Scaling factor between the output X spacing and the input X spacing - - - 1 - False - - - ParameterNumber - transform.type.id.scaley - Y scaling - Scaling factor between the output Y spacing and the input Y spacing - - - 1 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows one to define how the input image will be interpolated during resampling. - - - nn - linear - bco - - - 2 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows controlling the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artefacts. - - - 2 - False - - - ParameterNumber - ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/RigidTransformResample-rotation.xml b/python/plugins/processing/algs/otb/description/5.4.0/RigidTransformResample-rotation.xml deleted file mode 100644 index 69130b640956..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/RigidTransformResample-rotation.xml +++ /dev/null @@ -1,99 +0,0 @@ - - RigidTransformResample-rotation - otbcli_RigidTransformResample - RigidTransformResample (rotation) - Geometry - Resample an image with a rigid transform - - ParameterRaster - in - Input image - The input image to translate. - False - - - OutputRaster - out - Output image - The transformed output image. - - - - ParameterSelection - transform.type - Type of transformation - Type of transformation. Available transformations are spatial scaling, translation and rotation with scaling factor - - - rotation - - - 0 - False - - - ParameterNumber - transform.type.rotation.angle - Rotation angle - The rotation angle in degree (values between -180 and 180) - - - 0 - False - - - ParameterNumber - transform.type.rotation.scalex - X scaling - Scale factor between the X spacing of the rotated output image and the X spacing of the unrotated image - - - 1 - False - - - ParameterNumber - transform.type.rotation.scaley - Y scaling - Scale factor between the Y spacing of the rotated output image and the Y spacing of the unrotated image - - - 1 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows one to define how the input image will be interpolated during resampling. - - - nn - linear - bco - - - 2 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows controlling the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artefacts. - - - 2 - False - - - ParameterNumber - ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/RigidTransformResample-translation.xml b/python/plugins/processing/algs/otb/description/5.4.0/RigidTransformResample-translation.xml deleted file mode 100644 index f723ac8487a3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/RigidTransformResample-translation.xml +++ /dev/null @@ -1,109 +0,0 @@ - - RigidTransformResample-translation - otbcli_RigidTransformResample - RigidTransformResample (translation) - Geometry - Resample an image with a rigid transform - - ParameterRaster - in - Input image - The input image to translate. - False - - - OutputRaster - out - Output image - The transformed output image. - - - - ParameterSelection - transform.type - Type of transformation - Type of transformation. Available transformations are spatial scaling, translation and rotation with scaling factor - - - translation - - - 0 - False - - - ParameterNumber - transform.type.translation.tx - The X translation (in physical units) - The translation value along X axis (in physical units). - - - 0 - False - - - ParameterNumber - transform.type.translation.ty - The Y translation (in physical units) - The translation value along Y axis (in physical units) - - - 0 - False - - - ParameterNumber - transform.type.translation.scalex - X scaling - Scaling factor between the output X spacing and the input X spacing - - - 1 - False - - - ParameterNumber - transform.type.translation.scaley - Y scaling - Scaling factor between the output Y spacing and the input Y spacing - - - 1 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows one to define how the input image will be interpolated during resampling. - - - nn - linear - bco - - - 2 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows controlling the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artefacts. - - - 2 - False - - - ParameterNumber - ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/SFSTextureExtraction.xml b/python/plugins/processing/algs/otb/description/5.4.0/SFSTextureExtraction.xml deleted file mode 100644 index d4c6b1e2abd0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/SFSTextureExtraction.xml +++ /dev/null @@ -1,91 +0,0 @@ - - SFSTextureExtraction - otbcli_SFSTextureExtraction - SFS Texture Extraction - Feature Extraction - Computes Structural Feature Set textures on every pixel of the input image selected channel - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterNumber - parameters.spethre - Spectral Threshold - Spectral Threshold - - - 50 - False - - - ParameterNumber - parameters.spathre - Spatial Threshold - Spatial Threshold - - - 100 - False - - - ParameterNumber - parameters.nbdir - Number of Direction - Number of Direction - - - 20 - False - - - ParameterNumber - parameters.alpha - Alpha - Alpha - - - 1 - False - - - ParameterNumber - parameters.maxcons - Ratio Maximum Consideration Number - Ratio Maximum Consideration Number - - - 5 - False - - - OutputRaster - out - Feature Output Image - Output image containing the SFS texture features. - - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/SOMClassification.xml b/python/plugins/processing/algs/otb/description/5.4.0/SOMClassification.xml deleted file mode 100644 index c6ebd5d63651..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/SOMClassification.xml +++ /dev/null @@ -1,155 +0,0 @@ - - SOMClassification - otbcli_SOMClassification - SOM Classification - Learning - SOM image classification. - - ParameterRaster - in - InputImage - Input image to classify. - False - - - OutputRaster - out - OutputImage - Output classified image (each pixel contains the index of its corresponding vector in the SOM). - - - - ParameterRaster - vm - ValidityMask - Validity mask (only pixels corresponding to a mask value greater than 0 will be used for learning) - True - - - ParameterNumber - tp - TrainingProbability - Probability for a sample to be selected in the training set - - - 1 - True - - - ParameterNumber - ts - TrainingSetSize - Maximum training set size (in pixels) - - - 0 - True - - - OutputRaster - som - SOM Map - Output image containing the Self-Organizing Map - - - - ParameterNumber - sx - SizeX - X size of the SOM map - - - 32 - True - - - ParameterNumber - sy - SizeY - Y size of the SOM map - - - 32 - True - - - ParameterNumber - nx - NeighborhoodX - X size of the initial neighborhood in the SOM map - - - 10 - True - - - ParameterNumber - ny - NeighborhoodY - Y size of the initial neighborhood in the SOM map - - - 10 - False - - - ParameterNumber - ni - NumberIteration - Number of iterations for SOM learning - - - 5 - True - - - ParameterNumber - bi - BetaInit - Initial learning coefficient - - - 1 - True - - - ParameterNumber - bf - BetaFinal - Final learning coefficient - - - 0.1 - True - - - ParameterNumber - iv - InitialValue - Maximum initial neuron weight - - - 0 - True - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/Segmentation-cc.xml b/python/plugins/processing/algs/otb/description/5.4.0/Segmentation-cc.xml deleted file mode 100644 index fcde12fa02fb..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/Segmentation-cc.xml +++ /dev/null @@ -1,165 +0,0 @@ - - Segmentation-cc - otbcli_Segmentation - Segmentation (cc) - Segmentation - Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported. - - ParameterRaster - in - Input Image - The input image to segment - False - - - ParameterSelection - filter - Segmentation algorithm - Choice of segmentation algorithm (mean-shift by default) - - - cc - - - 0 - False - - - ParameterString - filter.cc.expr - Condition - User defined connection condition, written as a mathematical expression. Available variables are p(i)b(i), intensity_p(i) and distance (example of expression : distance < 10 ) - - - False - - - - ParameterSelection - mode - Processing mode - Choice of processing mode, either raster or large-scale. - - - vector - - - 0 - False - - - OutputVector - mode.vector.out - Output vector file - The output vector file or database (name can be anything understood by OGR) - - - ParameterSelection - mode.vector.outmode - Writing mode for the output vector file - This allows one to set the writing behaviour for the output vector file. Please note that the actual behaviour depends on the file format. - - - ulco - ovw - ulovw - ulu - - - 0 - False - - - ParameterRaster - mode.vector.inmask - Mask Image - Only pixels whose mask value is strictly positive will be segmented. - True - - - ParameterBoolean - mode.vector.neighbor - 8-neighbor connectivity - Activate 8-Neighborhood connectivity (default is 4). - True - True - - - ParameterBoolean - mode.vector.stitch - Stitch polygons - Scan polygons on each side of tiles and stitch polygons which connect by more than one pixel. - True - True - - - ParameterNumber - mode.vector.minsize - Minimum object size - Objects whose size is below the minimum object size (area in pixels) will be ignored during vectorization. - - - 1 - True - - - ParameterNumber - mode.vector.simplify - Simplify polygons - Simplify polygons according to a given tolerance (in pixel). This option allows reducing the size of the output file or database. - - - 0.1 - True - - - ParameterString - mode.vector.layername - Layer name - Name of the layer in the vector file or database (default is Layer). - layer - - False - - - - ParameterString - mode.vector.fieldname - Geometry index field name - Name of the field holding the geometry index in the output vector file or database. - DN - - False - - - - ParameterNumber - mode.vector.tilesize - Tiles size - User defined tiles size for tile-based segmentation. Optimal tile size is selected according to available RAM if null. - - - 1024 - False - - - ParameterNumber - mode.vector.startlabel - Starting geometry index - Starting value of the geometry index field - - - 1 - False - - - ParameterString - mode.vector.ogroptions - OGR options for layer creation - A list of layer creation options in the form KEY=VALUE that will be passed directly to OGR without any validity checking. Options may depend on the file format, and can be found in OGR documentation. - - - True - - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/Segmentation-meanshift.xml b/python/plugins/processing/algs/otb/description/5.4.0/Segmentation-meanshift.xml deleted file mode 100644 index 6ad6efcaec22..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/Segmentation-meanshift.xml +++ /dev/null @@ -1,205 +0,0 @@ - - Segmentation-meanshift - otbcli_Segmentation - Segmentation (meanshift) - Segmentation - Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported. - - ParameterRaster - in - Input Image - The input image to segment - False - - - ParameterSelection - filter - Segmentation algorithm - Choice of segmentation algorithm (mean-shift by default) - - - meanshift - - - 0 - False - - - ParameterNumber - filter.meanshift.spatialr - Spatial radius - Spatial radius of the neighborhood. - - - 5 - False - - - ParameterNumber - filter.meanshift.ranger - Range radius - Range radius defining the radius (expressed in radiometry unit) in the multispectral space. - - - 15 - False - - - ParameterNumber - filter.meanshift.thres - Mode convergence threshold - Algorithm iterative scheme will stop if mean-shift vector is below this threshold or if iteration number reached maximum number of iterations. - - - 0.1 - False - - - ParameterNumber - filter.meanshift.maxiter - Maximum number of iterations - Algorithm iterative scheme will stop if convergence hasn't been reached after the maximum number of iterations. - - - 100 - False - - - ParameterNumber - filter.meanshift.minsize - Minimum region size - Minimum size of a region (in pixel unit) in segmentation. Smaller clusters will be merged to the neighboring cluster with the closest radiometry. If set to 0 no pruning is done. - - - 100 - False - - - ParameterSelection - mode - Processing mode - Choice of processing mode, either raster or large-scale. - - - vector - - - 0 - False - - - OutputVector - mode.vector.out - Output vector file - The output vector file or database (name can be anything understood by OGR) - - - ParameterSelection - mode.vector.outmode - Writing mode for the output vector file - This allows one to set the writing behaviour for the output vector file. Please note that the actual behaviour depends on the file format. - - - ulco - ovw - ulovw - ulu - - - 0 - False - - - ParameterRaster - mode.vector.inmask - Mask Image - Only pixels whose mask value is strictly positive will be segmented. - True - - - ParameterBoolean - mode.vector.neighbor - 8-neighbor connectivity - Activate 8-Neighborhood connectivity (default is 4). - True - True - - - ParameterBoolean - mode.vector.stitch - Stitch polygons - Scan polygons on each side of tiles and stitch polygons which connect by more than one pixel. - True - True - - - ParameterNumber - mode.vector.minsize - Minimum object size - Objects whose size is below the minimum object size (area in pixels) will be ignored during vectorization. - - - 1 - True - - - ParameterNumber - mode.vector.simplify - Simplify polygons - Simplify polygons according to a given tolerance (in pixel). This option allows reducing the size of the output file or database. - - - 0.1 - True - - - ParameterString - mode.vector.layername - Layer name - Name of the layer in the vector file or database (default is Layer). - layer - - False - - - - ParameterString - mode.vector.fieldname - Geometry index field name - Name of the field holding the geometry index in the output vector file or database. - DN - - False - - - - ParameterNumber - mode.vector.tilesize - Tiles size - User defined tiles size for tile-based segmentation. Optimal tile size is selected according to available RAM if null. - - - 1024 - False - - - ParameterNumber - mode.vector.startlabel - Starting geometry index - Starting value of the geometry index field - - - 1 - False - - - ParameterString - mode.vector.ogroptions - OGR options for layer creation - A list of layer creation options in the form KEY=VALUE that will be passed directly to OGR without any validity checking. Options may depend on the file format, and can be found in OGR documentation. - - - True - - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/Segmentation-mprofiles.xml b/python/plugins/processing/algs/otb/description/5.4.0/Segmentation-mprofiles.xml deleted file mode 100644 index 24a262ab039a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/Segmentation-mprofiles.xml +++ /dev/null @@ -1,195 +0,0 @@ - - Segmentation-mprofiles - otbcli_Segmentation - Segmentation (mprofiles) - Segmentation - Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported. - - ParameterRaster - in - Input Image - The input image to segment - False - - - ParameterSelection - filter - Segmentation algorithm - Choice of segmentation algorithm (mean-shift by default) - - - mprofiles - - - 0 - False - - - ParameterNumber - filter.mprofiles.size - Profile Size - Size of the profiles - - - 5 - False - - - ParameterNumber - filter.mprofiles.start - Initial radius - Initial radius of the structuring element (in pixels) - - - 1 - False - - - ParameterNumber - filter.mprofiles.step - Radius step. - Radius step along the profile (in pixels) - - - 1 - False - - - ParameterNumber - filter.mprofiles.sigma - Threshold of the final decision rule - Profiles values under the threshold will be ignored. - - - 1 - False - - - ParameterSelection - mode - Processing mode - Choice of processing mode, either raster or large-scale. - - - vector - - - 0 - False - - - OutputVector - mode.vector.out - Output vector file - The output vector file or database (name can be anything understood by OGR) - - - ParameterSelection - mode.vector.outmode - Writing mode for the output vector file - This allows one to set the writing behaviour for the output vector file. Please note that the actual behaviour depends on the file format. - - - ulco - ovw - ulovw - ulu - - - 0 - False - - - ParameterRaster - mode.vector.inmask - Mask Image - Only pixels whose mask value is strictly positive will be segmented. - True - - - ParameterBoolean - mode.vector.neighbor - 8-neighbor connectivity - Activate 8-Neighborhood connectivity (default is 4). - True - True - - - ParameterBoolean - mode.vector.stitch - Stitch polygons - Scan polygons on each side of tiles and stitch polygons which connect by more than one pixel. - True - True - - - ParameterNumber - mode.vector.minsize - Minimum object size - Objects whose size is below the minimum object size (area in pixels) will be ignored during vectorization. - - - 1 - True - - - ParameterNumber - mode.vector.simplify - Simplify polygons - Simplify polygons according to a given tolerance (in pixel). This option allows reducing the size of the output file or database. - - - 0.1 - True - - - ParameterString - mode.vector.layername - Layer name - Name of the layer in the vector file or database (default is Layer). - layer - - False - - - - ParameterString - mode.vector.fieldname - Geometry index field name - Name of the field holding the geometry index in the output vector file or database. - DN - - False - - - - ParameterNumber - mode.vector.tilesize - Tiles size - User defined tiles size for tile-based segmentation. Optimal tile size is selected according to available RAM if null. - - - 1024 - False - - - ParameterNumber - mode.vector.startlabel - Starting geometry index - Starting value of the geometry index field - - - 1 - False - - - ParameterString - mode.vector.ogroptions - OGR options for layer creation - A list of layer creation options in the form KEY=VALUE that will be passed directly to OGR without any validity checking. Options may depend on the file format, and can be found in OGR documentation. - - - True - - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/Segmentation-watershed.xml b/python/plugins/processing/algs/otb/description/5.4.0/Segmentation-watershed.xml deleted file mode 100644 index f16a66c61370..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/Segmentation-watershed.xml +++ /dev/null @@ -1,175 +0,0 @@ - - Segmentation-watershed - otbcli_Segmentation - Segmentation (watershed) - Segmentation - Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported. - - ParameterRaster - in - Input Image - The input image to segment - False - - - ParameterSelection - filter - Segmentation algorithm - Choice of segmentation algorithm (mean-shift by default) - - - watershed - - - 0 - False - - - ParameterNumber - filter.watershed.threshold - Depth Threshold - Depth threshold Units in percentage of the maximum depth in the image. - - - 0.01 - False - - - ParameterNumber - filter.watershed.level - Flood Level - flood level for generating the merge tree from the initial segmentation (between 0 and 1) - - - 0.1 - False - - - ParameterSelection - mode - Processing mode - Choice of processing mode, either raster or large-scale. - - - vector - - - 0 - False - - - OutputVector - mode.vector.out - Output vector file - The output vector file or database (name can be anything understood by OGR) - - - ParameterSelection - mode.vector.outmode - Writing mode for the output vector file - This allows one to set the writing behaviour for the output vector file. Please note that the actual behaviour depends on the file format. - - - ulco - ovw - ulovw - ulu - - - 0 - False - - - ParameterRaster - mode.vector.inmask - Mask Image - Only pixels whose mask value is strictly positive will be segmented. - True - - - ParameterBoolean - mode.vector.neighbor - 8-neighbor connectivity - Activate 8-Neighborhood connectivity (default is 4). - True - True - - - ParameterBoolean - mode.vector.stitch - Stitch polygons - Scan polygons on each side of tiles and stitch polygons which connect by more than one pixel. - True - True - - - ParameterNumber - mode.vector.minsize - Minimum object size - Objects whose size is below the minimum object size (area in pixels) will be ignored during vectorization. - - - 1 - True - - - ParameterNumber - mode.vector.simplify - Simplify polygons - Simplify polygons according to a given tolerance (in pixel). This option allows reducing the size of the output file or database. - - - 0.1 - True - - - ParameterString - mode.vector.layername - Layer name - Name of the layer in the vector file or database (default is Layer). - layer - - False - - - - ParameterString - mode.vector.fieldname - Geometry index field name - Name of the field holding the geometry index in the output vector file or database. - DN - - False - - - - ParameterNumber - mode.vector.tilesize - Tiles size - User defined tiles size for tile-based segmentation. Optimal tile size is selected according to available RAM if null. - - - 1024 - False - - - ParameterNumber - mode.vector.startlabel - Starting geometry index - Starting value of the geometry index field - - - 1 - False - - - ParameterString - mode.vector.ogroptions - OGR options for layer creation - A list of layer creation options in the form KEY=VALUE that will be passed directly to OGR without any validity checking. Options may depend on the file format, and can be found in OGR documentation. - - - True - - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/Smoothing-anidif.xml b/python/plugins/processing/algs/otb/description/5.4.0/Smoothing-anidif.xml deleted file mode 100644 index 84f43f070f40..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/Smoothing-anidif.xml +++ /dev/null @@ -1,74 +0,0 @@ - - Smoothing-anidif - otbcli_Smoothing - Smoothing (anidif) - Image Filtering - Apply a smoothing filter to an image - - ParameterRaster - in - Input Image - Input image to smooth. - False - - - OutputRaster - out - Output Image - Output smoothed image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - type - Smoothing Type - Smoothing kernel to apply - - - anidif - - - 2 - False - - - ParameterNumber - type.anidif.timestep - Time Step - Diffusion equation time step - - - 0.125 - False - - - ParameterNumber - type.anidif.nbiter - Nb Iterations - Controls the sensitivity of the conductance term - - - 10 - False - - - ParameterNumber - type.anidif.conductance - Conductance - - - - 1 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/Smoothing-gaussian.xml b/python/plugins/processing/algs/otb/description/5.4.0/Smoothing-gaussian.xml deleted file mode 100644 index 49f7cc1fc551..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/Smoothing-gaussian.xml +++ /dev/null @@ -1,54 +0,0 @@ - - Smoothing-gaussian - otbcli_Smoothing - Smoothing (gaussian) - Image Filtering - Apply a smoothing filter to an image - - ParameterRaster - in - Input Image - Input image to smooth. - False - - - OutputRaster - out - Output Image - Output smoothed image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - type - Smoothing Type - Smoothing kernel to apply - - - gaussian - - - 2 - False - - - ParameterNumber - type.gaussian.radius - Radius - Gaussian radius (in pixels) - - - 2 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/Smoothing-mean.xml b/python/plugins/processing/algs/otb/description/5.4.0/Smoothing-mean.xml deleted file mode 100644 index 8e010db17ede..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/Smoothing-mean.xml +++ /dev/null @@ -1,54 +0,0 @@ - - Smoothing-mean - otbcli_Smoothing - Smoothing (mean) - Image Filtering - Apply a smoothing filter to an image - - ParameterRaster - in - Input Image - Input image to smooth. - False - - - OutputRaster - out - Output Image - Output smoothed image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - type - Smoothing Type - Smoothing kernel to apply - - - mean - - - 2 - False - - - ParameterNumber - type.mean.radius - Radius - Mean radius (in pixels) - - - 2 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/StereoFramework.xml b/python/plugins/processing/algs/otb/description/5.4.0/StereoFramework.xml deleted file mode 100644 index c43dc02aa5c4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/StereoFramework.xml +++ /dev/null @@ -1,344 +0,0 @@ - - StereoFramework - otbcli_StereoFramework - Stereo Framework - Stereo - Compute the ground elevation based on one or multiple stereo pair(s) - - ParameterMultipleInput - input.il - Input images list - The list of images. - - False - - - ParameterString - input.co - Couples list - List of index of couples im image list. Couples must be separated by a comma. (index start at 0). for example : 0 1,1 2 will process a first couple composed of the first and the second image in image list, then the first and the third image -. note that images are handled by pairs. if left empty couples are created from input index i.e. a first couple will be composed of the first and second image, a second couple with third and fourth image etc. (in this case image list must be even). - - - True - - - - ParameterNumber - input.channel - Image channel used for the block matching - Used channel for block matching (used for all images) - - - 1 - False - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - output.res - Output resolution - Spatial sampling distance of the output elevation : the cell size (in m) - - - 1 - False - - - ParameterNumber - output.nodata - NoData value - DSM empty cells are filled with this value (optional -32768 by default) - - - -32768 - True - - - ParameterSelection - output.fusionmethod - Method to fuse measures in each DSM cell - This parameter allows one to choose the method used to fuse elevation measurements in each output DSM cell - - - max - min - mean - acc - - - 0 - False - - - OutputRaster - output.out - Output DSM - Output elevation image - - - - ParameterSelection - output.mode - Parameters estimation modes - - - - fit - user - - - 0 - False - - - ParameterNumber - output.mode.user.ulx - Upper Left X - Cartographic X coordinate of upper-left corner (meters for cartographic projections, degrees for geographic ones) - - - 0.0 - False - - - ParameterNumber - output.mode.user.uly - Upper Left Y - Cartographic Y coordinate of the upper-left corner (meters for cartographic projections, degrees for geographic ones) - - - 0.0 - False - - - ParameterNumber - output.mode.user.sizex - Size X - Size of projected image along X (in pixels) - - - 0 - False - - - ParameterNumber - output.mode.user.sizey - Size Y - Size of projected image along Y (in pixels) - - - 0 - False - - - ParameterNumber - output.mode.user.spacingx - Pixel Size X - Size of each pixel along X axis (meters for cartographic projections, degrees for geographic ones) - - - 0.0 - False - - - ParameterNumber - output.mode.user.spacingy - Pixel Size Y - Size of each pixel along Y axis (meters for cartographic projections, degrees for geographic ones) - - - 0.0 - False - - - ParameterSelection - map - Output Cartographic Map Projection - Parameters of the output map projection to be used. - - - utm - lambert2 - lambert93 - wgs - epsg - - - 3 - False - - - ParameterNumber - map.utm.zone - Zone number - The zone number ranges from 1 to 60 and allows defining the transverse mercator projection (along with the hemisphere) - - - 31 - False - - - ParameterBoolean - map.utm.northhem - Northern Hemisphere - The transverse mercator projections are defined by their zone number as well as the hemisphere. Activate this parameter if your image is in the northern hemisphere. - True - True - - - ParameterNumber - map.epsg.code - EPSG Code - See www.spatialreference.org to find which EPSG code is associated to your projection - - - 4326 - False - - - ParameterNumber - stereorect.fwdgridstep - Step of the displacement grid (in pixels) - Stereo-rectification displacement grid only varies slowly. Therefore, it is recommended to use a coarser grid (higher step value) in case of large images - - - 16 - True - - - ParameterNumber - stereorect.invgridssrate - Sub-sampling rate for epipolar grid inversion - Grid inversion is an heavy process that implies spline regression on control points. To avoid eating to much memory, this parameter allows one to first sub-sample the field to invert. - - - 10 - True - - - ParameterSelection - bm.metric - Block-matching metric - - - - ssdmean - ssd - ncc - lp - - - 0 - False - - - ParameterNumber - bm.metric.lp.p - p value - Value of the p parameter in Lp pseudo-norm (must be positive) - - - 1 - False - - - ParameterNumber - bm.radius - Radius of blocks for matching filter (in pixels) - The radius of blocks in Block-Matching (in pixels) - - - 2 - True - - - ParameterNumber - bm.minhoffset - Minimum altitude offset (in meters) - Minimum altitude below the selected elevation source (in meters) - - - -20 - False - - - ParameterNumber - bm.maxhoffset - Maximum altitude offset (in meters) - Maximum altitude above the selected elevation source (in meters) - - - 20 - False - - - ParameterBoolean - postproc.bij - Use bijection consistency in block matching strategy - use bijection consistency. Right to Left correlation is computed to validate Left to Right disparities. If bijection is not found pixel is rejected. - True - True - - - ParameterBoolean - postproc.med - Use median disparities filtering - disparities output can be filtered using median post filtering (disabled by default). - True - True - - - ParameterNumber - postproc.metrict - Correlation metric threshold - Use block matching metric output to discard pixels with low correlation value (disabled by default, float value) - - - 0.6 - True - - - ParameterRaster - mask.left - Input left mask - Mask for left input image - True - - - ParameterRaster - mask.right - Input right mask - Mask for right input image - True - - - ParameterNumber - mask.variancet - Discard pixels with low local variance - This parameter allows one to discard pixels whose local variance is too small (the size of the neighborhood is given by the radius parameter) - - - 50 - True - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/Superimpose.xml b/python/plugins/processing/algs/otb/description/5.4.0/Superimpose.xml deleted file mode 100644 index 00453d389689..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/Superimpose.xml +++ /dev/null @@ -1,97 +0,0 @@ - - Superimpose - otbcli_Superimpose - Superimpose sensor - Geometry - Using available image metadata, project one image onto another one - - ParameterRaster - inr - Reference input - The input reference image. - False - - - ParameterRaster - inm - The image to reproject - The image to reproject into the geometry of the reference input. - False - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - lms - Spacing of the deformation field - Generate a coarser deformation field with the given spacing - - - 4 - True - - - OutputRaster - out - Output image - Output reprojected image. - - - - ParameterSelection - mode - Mode - Superimposition mode - - - default - phr - - - 0 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows defining how the input image will be interpolated during resampling. - - - bco - nn - linear - - - 0 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows controling the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artefacts. - - - 2 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/TileFusion.xml b/python/plugins/processing/algs/otb/description/5.4.0/TileFusion.xml deleted file mode 100644 index b157b2caa49a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/TileFusion.xml +++ /dev/null @@ -1,42 +0,0 @@ - - TileFusion - otbcli_TileFusion - Image Tile Fusion - Image Manipulation - Fusion of an image made of several tile files. - - ParameterMultipleInput - il - Input Tile Images - Input tiles to concatenate (in lexicographic order : (0,0) (1,0) (0,1) (1,1)). - - False - - - ParameterNumber - cols - Number of tile columns - Number of columns in the tile array - - - 0 - False - - - ParameterNumber - rows - Number of tile rows - Number of rows in the tile array - - - 0 - False - - - OutputRaster - out - Output Image - Output entire image - - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-ann.xml b/python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-ann.xml deleted file mode 100644 index 0381ea96df49..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-ann.xml +++ /dev/null @@ -1,268 +0,0 @@ - - TrainImagesClassifier-ann - otbcli_TrainImagesClassifier - TrainImagesClassifier (ann) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - False - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - False - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - ann - - - 0 - False - - - ParameterSelection - classifier.ann.t - Train Method Type - Type of training method for the multilayer perceptron (MLP) neural network. - - - reg - back - - - 0 - False - - - ParameterString - classifier.ann.sizes - Number of neurons in each intermediate layer - The number of neurons in each intermediate layer (excluding input and output layers). - - - False - - - - ParameterSelection - classifier.ann.f - Neuron activation function type - Neuron activation function. - - - ident - sig - gau - - - 1 - False - - - ParameterNumber - classifier.ann.a - Alpha parameter of the activation function - Alpha parameter of the activation function (used only with sigmoid and gaussian functions). - - - 1 - False - - - ParameterNumber - classifier.ann.b - Beta parameter of the activation function - Beta parameter of the activation function (used only with sigmoid and gaussian functions). - - - 1 - False - - - ParameterNumber - classifier.ann.bpdw - Strength of the weight gradient term in the BACKPROP method - Strength of the weight gradient term in the BACKPROP method. The recommended value is about 0.1. - - - 0.1 - False - - - ParameterNumber - classifier.ann.bpms - Strength of the momentum term (the difference between weights on the 2 previous iterations) - Strength of the momentum term (the difference between weights on the 2 previous iterations). This parameter provides some inertia to smooth the random fluctuations of the weights. It can vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough. - - - 0.1 - False - - - ParameterNumber - classifier.ann.rdw - Initial value Delta_0 of update-values Delta_{ij} in RPROP method - Initial value Delta_0 of update-values Delta_{ij} in RPROP method (default = 0.1). - - - 0.1 - False - - - ParameterNumber - classifier.ann.rdwm - Update-values lower limit Delta_{min} in RPROP method - Update-values lower limit Delta_{min} in RPROP method. It must be positive (default = 1e-7). - - - 1e-07 - False - - - ParameterSelection - classifier.ann.term - Termination criteria - Termination criteria. - - - iter - eps - all - - - 2 - False - - - ParameterNumber - classifier.ann.eps - Epsilon value used in the Termination criteria - Epsilon value used in the Termination criteria. - - - 0.01 - False - - - ParameterNumber - classifier.ann.iter - Maximum number of iterations used in the Termination criteria - Maximum number of iterations used in the Termination criteria. - - - 1000 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-bayes.xml b/python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-bayes.xml deleted file mode 100644 index d1182d51be1b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-bayes.xml +++ /dev/null @@ -1,134 +0,0 @@ - - TrainImagesClassifier-bayes - otbcli_TrainImagesClassifier - TrainImagesClassifier (bayes) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - False - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - False - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - bayes - - - 0 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-boost.xml b/python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-boost.xml deleted file mode 100644 index 2838d3a69ad5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-boost.xml +++ /dev/null @@ -1,180 +0,0 @@ - - TrainImagesClassifier-boost - otbcli_TrainImagesClassifier - TrainImagesClassifier (boost) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - False - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - False - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - boost - - - 0 - False - - - ParameterSelection - classifier.boost.t - Boost Type - Type of Boosting algorithm. - - - discrete - real - logit - gentle - - - 1 - False - - - ParameterNumber - classifier.boost.w - Weak count - The number of weak classifiers. - - - 100 - False - - - ParameterNumber - classifier.boost.r - Weight Trim Rate - A threshold between 0 and 1 used to save computational time. Samples with summary weight <= (1 - weight_trim_rate) do not participate in the next iteration of training. Set this parameter to 0 to turn off this functionality. - - - 0.95 - False - - - ParameterNumber - classifier.boost.m - Maximum depth of the tree - Maximum depth of the tree. - - - 1 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-dt.xml b/python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-dt.xml deleted file mode 100644 index 697cf10f34ac..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-dt.xml +++ /dev/null @@ -1,200 +0,0 @@ - - TrainImagesClassifier-dt - otbcli_TrainImagesClassifier - TrainImagesClassifier (dt) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - False - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - False - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - dt - - - 0 - False - - - ParameterNumber - classifier.dt.max - Maximum depth of the tree - The training algorithm attempts to split each node while its depth is smaller than the maximum possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or if the tree is pruned. - - - 65535 - False - - - ParameterNumber - classifier.dt.min - Minimum number of samples in each node - If all absolute differences between an estimated value in a node and the values of the train samples in this node are smaller than this regression accuracy parameter, then the node will not be split. - - - 10 - False - - - ParameterNumber - classifier.dt.ra - Termination criteria for regression tree - - - - 0.01 - False - - - ParameterNumber - classifier.dt.cat - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split. - - - 10 - False - - - ParameterNumber - classifier.dt.f - K-fold cross-validations - If cv_folds > 1, then it prunes a tree with K-fold cross-validation where K is equal to cv_folds. - - - 10 - False - - - ParameterBoolean - classifier.dt.r - Set Use1seRule flag to false - If true, then a pruning will be harsher. This will make a tree more compact and more resistant to the training data noise but a bit less accurate. - True - True - - - ParameterBoolean - classifier.dt.t - Set TruncatePrunedTree flag to false - If true, then pruned branches are physically removed from the tree. - True - True - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-gbt.xml b/python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-gbt.xml deleted file mode 100644 index 456efc81582d..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-gbt.xml +++ /dev/null @@ -1,174 +0,0 @@ - - TrainImagesClassifier-gbt - otbcli_TrainImagesClassifier - TrainImagesClassifier (gbt) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - False - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - False - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - gbt - - - 0 - False - - - ParameterNumber - classifier.gbt.w - Number of boosting algorithm iterations - Number "w" of boosting algorithm iterations, with w*K being the total number of trees in the GBT model, where K is the output number of classes. - - - 200 - False - - - ParameterNumber - classifier.gbt.s - Regularization parameter - Regularization parameter. - - - 0.01 - False - - - ParameterNumber - classifier.gbt.p - Portion of the whole training set used for each algorithm iteration - Portion of the whole training set used for each algorithm iteration. The subset is generated randomly. - - - 0.8 - False - - - ParameterNumber - classifier.gbt.max - Maximum depth of the tree - The training algorithm attempts to split each node while its depth is smaller than the maximum possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or if the tree is pruned. - - - 3 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-knn.xml b/python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-knn.xml deleted file mode 100644 index 0b6ec7b41889..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-knn.xml +++ /dev/null @@ -1,144 +0,0 @@ - - TrainImagesClassifier-knn - otbcli_TrainImagesClassifier - TrainImagesClassifier (knn) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - False - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - False - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - knn - - - 0 - False - - - ParameterNumber - classifier.knn.k - Number of Neighbors - The number of neighbors to use. - - - 32 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-rf.xml b/python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-rf.xml deleted file mode 100644 index 437405a26e79..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/TrainImagesClassifier-rf.xml +++ /dev/null @@ -1,204 +0,0 @@ - - TrainImagesClassifier-rf - otbcli_TrainImagesClassifier - TrainImagesClassifier (rf) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - False - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - False - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - rf - - - 0 - False - - - ParameterNumber - classifier.rf.max - Maximum depth of the tree - The depth of the tree. A low value will likely underfit and conversely a high value will likely overfit. The optimal value can be obtained using cross validation or other suitable methods. - - - 5 - False - - - ParameterNumber - classifier.rf.min - Minimum number of samples in each node - If the number of samples in a node is smaller than this parameter, then the node will not be split. A reasonable value is a small percentage of the total data e.g. 1 percent. - - - 10 - False - - - ParameterNumber - classifier.rf.ra - Termination Criteria for regression tree - If all absolute differences between an estimated value in a node and the values of the train samples in this node are smaller than this regression accuracy parameter, then the node will not be split. - - - 0 - False - - - ParameterNumber - classifier.rf.cat - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split. - - - 10 - False - - - ParameterNumber - classifier.rf.var - Size of the randomly selected subset of features at each tree node - The size of the subset of features, randomly selected at each tree node, that are used to find the best split(s). If you set it to 0, then the size will be set to the square root of the total number of features. - - - 0 - False - - - ParameterNumber - classifier.rf.nbtrees - Maximum number of trees in the forest - The maximum number of trees in the forest. Typically, the more trees you have, the better the accuracy. However, the improvement in accuracy generally diminishes and reaches an asymptote for a certain number of trees. Also to keep in mind, increasing the number of trees increases the prediction time linearly. - - - 100 - False - - - ParameterNumber - classifier.rf.acc - Sufficient accuracy (OOB error) - Sufficient accuracy (OOB error). - - - 0.01 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/TrainOGRLayersClassifier.xml b/python/plugins/processing/algs/otb/description/5.4.0/TrainOGRLayersClassifier.xml deleted file mode 100644 index 78e135a40aa8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/TrainOGRLayersClassifier.xml +++ /dev/null @@ -1,48 +0,0 @@ - - TrainOGRLayersClassifier - otbcli_TrainOGRLayersClassifier - TrainOGRLayersClassifier - Segmentation - Train a SVM classifier based on labeled geometries and a list of features to consider. - - ParameterVector - inshp - Name of the input shapefile - Name of the input shapefile - - False - - - ParameterFile - instats - XML file containing mean and variance of each feature. - XML file containing mean and variance of each feature. - - False - - - OutputFile - outsvm - Output model filename. - Output model filename. - - - ParameterString - feat - List of features to consider for classification. - List of features to consider for classification. - - - False - - - ParameterString - cfield - Field containing the class id for supervision - Field containing the class id for supervision. Only geometries with this field available will be taken into account. - class - - False - - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/VectorDataExtractROI.xml b/python/plugins/processing/algs/otb/description/5.4.0/VectorDataExtractROI.xml deleted file mode 100644 index 8efb7cca5dee..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/VectorDataExtractROI.xml +++ /dev/null @@ -1,40 +0,0 @@ - - VectorDataExtractROI - otbcli_VectorDataExtractROI - VectorData Extract ROI - Vector Data Manipulation - Perform an extract ROI on the input vector data according to the input image extent - - ParameterVector - io.vd - Input Vector data - Input vector data - - False - - - ParameterRaster - io.in - Support image - Support image that specifies the extracted region - False - - - OutputVector - io.out - Output Vector data - Output extracted vector data - - - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/VectorDataReprojection-image.xml b/python/plugins/processing/algs/otb/description/5.4.0/VectorDataReprojection-image.xml deleted file mode 100644 index 8a3948907e85..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/VectorDataReprojection-image.xml +++ /dev/null @@ -1,59 +0,0 @@ - - VectorDataReprojection-image - otbcli_VectorDataReprojection - VectorDataReprojection (image) - Vector Data Manipulation - Reproject a vector data using support image projection reference, or a user specified map projection - - - ParameterFile - in.vd - Input vector data - The input vector data to reproject - - False - - - ParameterRaster - in.kwl - Use image keywords list - Optional input image to fill vector data with image kwl. - True - - - OutputFile - out.vd - Output vector data - The reprojected vector data - - - ParameterSelection - out.proj - Output Projection choice - - - - image - - - 0 - False - - - ParameterRaster - out.proj.image.in - Image used to get projection map - Projection map will be found using image metadata - False - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/VectorDataReprojection-user.xml b/python/plugins/processing/algs/otb/description/5.4.0/VectorDataReprojection-user.xml deleted file mode 100644 index 0392ba55b214..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/VectorDataReprojection-user.xml +++ /dev/null @@ -1,97 +0,0 @@ - - VectorDataReprojection-user - otbcli_VectorDataReprojection - VectorDataReprojection (user) - Vector Data Manipulation - Reproject a vector data using support image projection reference, or a user specified map projection - - - ParameterFile - in.vd - Input vector data - The input vector data to reproject - - False - - - ParameterRaster - in.kwl - Use image keywords list - Optional input image to fill vector data with image kwl. - True - - - OutputFile - out.vd - Output vector data - The reprojected vector data - - - ParameterSelection - out.proj - Output Projection choice - - - - user - - - 0 - False - - - ParameterSelection - out.proj.user.map - Output Cartographic Map Projection - Parameters of the output map projection to be used. - - - utm - lambert2 - lambert93 - wgs - epsg - - - 0 - False - - - ParameterNumber - out.proj.user.map.utm.zone - Zone number - The zone number ranges from 1 to 60 and allows defining the transverse mercator projection (along with the hemisphere) - - - 31 - False - - - ParameterBoolean - out.proj.user.map.utm.northhem - Northern Hemisphere - The transverse mercator projections are defined by their zone number as well as the hemisphere. Activate this parameter if your image is in the northern hemisphere. - True - True - - - ParameterNumber - out.proj.user.map.epsg.code - EPSG Code - See www.spatialreference.org to find which EPSG code is associated to your projection - - - 4326 - False - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/VectorDataTransform.xml b/python/plugins/processing/algs/otb/description/5.4.0/VectorDataTransform.xml deleted file mode 100644 index 60f107e94a54..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/VectorDataTransform.xml +++ /dev/null @@ -1,90 +0,0 @@ - - VectorDataTransform - otbcli_VectorDataTransform - Vector Data Transformation - Vector Data Manipulation - Apply a transform to each vertex of the input VectorData - - ParameterVector - vd - Input Vector data - Input vector data to transform - - False - - - OutputVector - out - Output Vector data - Output transformed vector data - - - - - ParameterRaster - in - Support image - Image needed as a support to the vector data - False - - - ParameterNumber - transform.tx - Translation X - Translation in the X direction (in pixels) - - - 0 - False - - - ParameterNumber - transform.ty - Translation Y - Translation in the Y direction (in pixels) - - - 0 - False - - - ParameterNumber - transform.ro - Rotation Angle - Angle of the rotation to apply in degrees - - - 0 - False - - - ParameterNumber - transform.centerx - Center X - X coordinate of the rotation center (in physical units) - - - 0 - False - - - ParameterNumber - transform.centery - Center Y - Y coordinate of the rotation center (in physical units) - - - 0 - False - - - ParameterNumber - transform.scale - Scale - The scale to apply - - - 1 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/BandMath.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/BandMath.html deleted file mode 100644 index c6c9657d6e99..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/BandMath.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

BandMath

Brief Description

Perform a mathematical operation on monoband images

Tags

Util

Long Description

This application performs a mathematical operation on monoband images. Mathematical formula interpretation is done via MuParser libraries http://muparser.sourceforge.net/.For MuParser version prior to v2 use 'and' and 'or' logical operators, and ternary operator 'if(; ; )'.For MuParser version superior to 2.0 uses '&&' and '||' logical operators, and C++ like ternary if-then-else operator.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/BinaryMorphologicalOperation-closing.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/BinaryMorphologicalOperation-closing.html deleted file mode 100644 index 97a2b19d7065..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/BinaryMorphologicalOperation-closing.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BinaryMorphologicalOperation

Brief Description

Performs morphological operations on an input image channel

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs binary morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkBinaryDilateImageFilter, itkBinaryErodeImageFilter, itkBinaryMorphologicalOpeningImageFilter and itkBinaryMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/BinaryMorphologicalOperation-dilate.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/BinaryMorphologicalOperation-dilate.html deleted file mode 100644 index 97a2b19d7065..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/BinaryMorphologicalOperation-dilate.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BinaryMorphologicalOperation

Brief Description

Performs morphological operations on an input image channel

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs binary morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkBinaryDilateImageFilter, itkBinaryErodeImageFilter, itkBinaryMorphologicalOpeningImageFilter and itkBinaryMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/BinaryMorphologicalOperation-erode.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/BinaryMorphologicalOperation-erode.html deleted file mode 100644 index 97a2b19d7065..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/BinaryMorphologicalOperation-erode.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BinaryMorphologicalOperation

Brief Description

Performs morphological operations on an input image channel

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs binary morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkBinaryDilateImageFilter, itkBinaryErodeImageFilter, itkBinaryMorphologicalOpeningImageFilter and itkBinaryMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/BinaryMorphologicalOperation-opening.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/BinaryMorphologicalOperation-opening.html deleted file mode 100644 index 97a2b19d7065..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/BinaryMorphologicalOperation-opening.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BinaryMorphologicalOperation

Brief Description

Performs morphological operations on an input image channel

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs binary morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkBinaryDilateImageFilter, itkBinaryErodeImageFilter, itkBinaryMorphologicalOpeningImageFilter and itkBinaryMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/BinaryMorphologicalOperation.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/BinaryMorphologicalOperation.html deleted file mode 100644 index 97a2b19d7065..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/BinaryMorphologicalOperation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BinaryMorphologicalOperation

Brief Description

Performs morphological operations on an input image channel

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs binary morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkBinaryDilateImageFilter, itkBinaryErodeImageFilter, itkBinaryMorphologicalOpeningImageFilter and itkBinaryMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/BlockMatching.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/BlockMatching.html deleted file mode 100644 index 4d5caf2e62c5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/BlockMatching.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BlockMatching

Brief Description

Performs block-matching to estimate pixel-wise disparities between two images

Tags

Stereo

Long Description

This application allows one to performs block-matching to estimate pixel-wise disparities between two images. One must chose block-matching method and input masks (related to the left and right input image) of pixels for which the disparity should be investigated. Additionally, two criteria can be optionally used to disable disparity investigation for some pixel: a no-data value, and a threshold on the local variance. This allows one to speed-up computation by avoiding to investigate disparities that will not be reliable anyway. For efficiency reasons, if the optimal metric values image is desired, it will be concatenated to the output image (which will then have three bands : horizontal disparity, vertical disparity and metric value). One can split these images afterward.

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbStereoRectificationGridGenerator

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/BundleToPerfectSensor.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/BundleToPerfectSensor.html deleted file mode 100644 index 8dcdd8566369..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/BundleToPerfectSensor.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BundleToPerfectSensor

Brief Description

Perform P+XS pansharpening

Tags

Geometry,Pansharpening

Long Description

This application performs P+XS pansharpening. The default mode use Pan and XS sensor models to estimate the transformation to superimpose XS over Pan before the fusion ("default mode"). The application provides also a PHR mode for Pleiades images which does not use sensor models as Pan and XS products are already coregistered but only estimate an affine transformation to superimpose XS over the Pan.Note that this option is automatically activated in case Pleiades images are detected as input.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ClassificationMapRegularization.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ClassificationMapRegularization.html deleted file mode 100644 index a974324ba3f4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ClassificationMapRegularization.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

ClassificationMapRegularization

Brief Description

Filters the input labeled image using Majority Voting in a ball shaped neighbordhood.

Tags

Learning,Image Analysis

Long Description

This application filters the input labeled image (with a maximal class label = 65535) using Majority Voting in a ball shaped neighbordhood. Majority Voting takes the more representative value of all the pixels identified by the ball shaped structuring element and then sets the center pixel to this majority label value. - -NoData is the label of the NOT classified pixels in the input image. These input pixels keep their NoData label in the output image. - -Pixels with more than 1 majority class are marked as Undecided if the parameter 'ip.suvbool == true', or keep their Original labels otherwise.

Parameters

Limitations

The input image must be a single band labeled image (with a maximal class label = 65535). The structuring element radius must have a minimum value equal to 1 pixel. Please note that the Undecided value must be different from existing labels in the input labeled image.

Authors

OTB-Team

See Also

Documentation of the ClassificationMapRegularization application.

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ColorMapping-continuous.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ColorMapping-continuous.html deleted file mode 100644 index 6ce8d15474ab..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ColorMapping-continuous.html +++ /dev/null @@ -1,13 +0,0 @@ - - -

ColorMapping

Brief Description

Maps an input label image to 8-bits RGB using look-up tables.

Tags

Utilities,Image Manipulation,Image MetaData,Learning

Long Description

This application allows one to map a label image to a 8-bits RGB image (in both ways) using different methods. - -The custom method allows one to use a custom look-up table. The look-up table is loaded from a text file where each line describes an entry. The typical use of this method is to colorise a classification map. - -The continuous method allows mapping a range of values in a scalar input image to a colored image using continuous look-up table, in order to enhance image interpretation. Several look-up tables can been chosen with different color ranges. --The optimal method computes an optimal look-up table. When processing a segmentation label image (label to color), the color difference between adjacent segmented regions is maximized. When processing an unknown color image (color to label), all the present colors are mapped to a continuous label list. - - The support image method uses a color support image to associate an average color to each region.

Parameters

Limitations

The segmentation optimal method does not support streaming, and thus large images. The operation color to label is not implemented for the methods continuous LUT and support image LUT. - ColorMapping using support image is not threaded.

Authors

OTB-Team

See Also

ImageSVMClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ColorMapping-custom.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ColorMapping-custom.html deleted file mode 100644 index 6ce8d15474ab..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ColorMapping-custom.html +++ /dev/null @@ -1,13 +0,0 @@ - - -

ColorMapping

Brief Description

Maps an input label image to 8-bits RGB using look-up tables.

Tags

Utilities,Image Manipulation,Image MetaData,Learning

Long Description

This application allows one to map a label image to a 8-bits RGB image (in both ways) using different methods. - -The custom method allows one to use a custom look-up table. The look-up table is loaded from a text file where each line describes an entry. The typical use of this method is to colorise a classification map. - -The continuous method allows mapping a range of values in a scalar input image to a colored image using continuous look-up table, in order to enhance image interpretation. Several look-up tables can been chosen with different color ranges. --The optimal method computes an optimal look-up table. When processing a segmentation label image (label to color), the color difference between adjacent segmented regions is maximized. When processing an unknown color image (color to label), all the present colors are mapped to a continuous label list. - - The support image method uses a color support image to associate an average color to each region.

Parameters

Limitations

The segmentation optimal method does not support streaming, and thus large images. The operation color to label is not implemented for the methods continuous LUT and support image LUT. - ColorMapping using support image is not threaded.

Authors

OTB-Team

See Also

ImageSVMClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ColorMapping-image.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ColorMapping-image.html deleted file mode 100644 index 6ce8d15474ab..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ColorMapping-image.html +++ /dev/null @@ -1,13 +0,0 @@ - - -

ColorMapping

Brief Description

Maps an input label image to 8-bits RGB using look-up tables.

Tags

Utilities,Image Manipulation,Image MetaData,Learning

Long Description

This application allows one to map a label image to a 8-bits RGB image (in both ways) using different methods. - -The custom method allows one to use a custom look-up table. The look-up table is loaded from a text file where each line describes an entry. The typical use of this method is to colorise a classification map. - -The continuous method allows mapping a range of values in a scalar input image to a colored image using continuous look-up table, in order to enhance image interpretation. Several look-up tables can been chosen with different color ranges. --The optimal method computes an optimal look-up table. When processing a segmentation label image (label to color), the color difference between adjacent segmented regions is maximized. When processing an unknown color image (color to label), all the present colors are mapped to a continuous label list. - - The support image method uses a color support image to associate an average color to each region.

Parameters

Limitations

The segmentation optimal method does not support streaming, and thus large images. The operation color to label is not implemented for the methods continuous LUT and support image LUT. - ColorMapping using support image is not threaded.

Authors

OTB-Team

See Also

ImageSVMClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ColorMapping-optimal.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ColorMapping-optimal.html deleted file mode 100644 index 6ce8d15474ab..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ColorMapping-optimal.html +++ /dev/null @@ -1,13 +0,0 @@ - - -

ColorMapping

Brief Description

Maps an input label image to 8-bits RGB using look-up tables.

Tags

Utilities,Image Manipulation,Image MetaData,Learning

Long Description

This application allows one to map a label image to a 8-bits RGB image (in both ways) using different methods. - -The custom method allows one to use a custom look-up table. The look-up table is loaded from a text file where each line describes an entry. The typical use of this method is to colorise a classification map. - -The continuous method allows mapping a range of values in a scalar input image to a colored image using continuous look-up table, in order to enhance image interpretation. Several look-up tables can been chosen with different color ranges. --The optimal method computes an optimal look-up table. When processing a segmentation label image (label to color), the color difference between adjacent segmented regions is maximized. When processing an unknown color image (color to label), all the present colors are mapped to a continuous label list. - - The support image method uses a color support image to associate an average color to each region.

Parameters

Limitations

The segmentation optimal method does not support streaming, and thus large images. The operation color to label is not implemented for the methods continuous LUT and support image LUT. - ColorMapping using support image is not threaded.

Authors

OTB-Team

See Also

ImageSVMClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ColorMapping.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ColorMapping.html deleted file mode 100644 index 6ce8d15474ab..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ColorMapping.html +++ /dev/null @@ -1,13 +0,0 @@ - - -

ColorMapping

Brief Description

Maps an input label image to 8-bits RGB using look-up tables.

Tags

Utilities,Image Manipulation,Image MetaData,Learning

Long Description

This application allows one to map a label image to a 8-bits RGB image (in both ways) using different methods. - -The custom method allows one to use a custom look-up table. The look-up table is loaded from a text file where each line describes an entry. The typical use of this method is to colorise a classification map. - -The continuous method allows mapping a range of values in a scalar input image to a colored image using continuous look-up table, in order to enhance image interpretation. Several look-up tables can been chosen with different color ranges. --The optimal method computes an optimal look-up table. When processing a segmentation label image (label to color), the color difference between adjacent segmented regions is maximized. When processing an unknown color image (color to label), all the present colors are mapped to a continuous label list. - - The support image method uses a color support image to associate an average color to each region.

Parameters

Limitations

The segmentation optimal method does not support streaming, and thus large images. The operation color to label is not implemented for the methods continuous LUT and support image LUT. - ColorMapping using support image is not threaded.

Authors

OTB-Team

See Also

ImageSVMClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/CompareImages.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/CompareImages.html deleted file mode 100644 index 89de722c4732..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/CompareImages.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

CompareImages

Brief Description

Estimator between 2 images.

Tags

Statistics

Long Description

This application computes MSE (Mean Squared Error), MAE (Mean Absolute Error) and PSNR (Peak Signal to Noise Ratio) between the channel of two images (reference and measurement). The user has to set the used channel and can specify a ROI.

Parameters

Limitations

None

Authors

OTB-Team

See Also

BandMath application, ImageStatistics

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ComputeConfusionMatrix-raster.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ComputeConfusionMatrix-raster.html deleted file mode 100644 index 8a6c9eed9ad3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ComputeConfusionMatrix-raster.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputeConfusionMatrix

Brief Description

Computes the confusion matrix of a classification

Tags

Learning

Long Description

This application computes the confusion matrix of a classification map relatively to a ground truth. This ground truth can be given as a raster or a vector data. Only reference and produced pixels with values different from NoData are handled in the calculation of the confusion matrix. The confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the output file, the reference and produced class labels are ordered according to the rows/columns of the confusion matrix.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ComputeConfusionMatrix-vector.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ComputeConfusionMatrix-vector.html deleted file mode 100644 index 8a6c9eed9ad3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ComputeConfusionMatrix-vector.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputeConfusionMatrix

Brief Description

Computes the confusion matrix of a classification

Tags

Learning

Long Description

This application computes the confusion matrix of a classification map relatively to a ground truth. This ground truth can be given as a raster or a vector data. Only reference and produced pixels with values different from NoData are handled in the calculation of the confusion matrix. The confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the output file, the reference and produced class labels are ordered according to the rows/columns of the confusion matrix.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ComputeConfusionMatrix.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ComputeConfusionMatrix.html deleted file mode 100644 index 8a6c9eed9ad3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ComputeConfusionMatrix.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputeConfusionMatrix

Brief Description

Computes the confusion matrix of a classification

Tags

Learning

Long Description

This application computes the confusion matrix of a classification map relatively to a ground truth. This ground truth can be given as a raster or a vector data. Only reference and produced pixels with values different from NoData are handled in the calculation of the confusion matrix. The confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the output file, the reference and produced class labels are ordered according to the rows/columns of the confusion matrix.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ComputeImagesStatistics.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ComputeImagesStatistics.html deleted file mode 100644 index 05cb9575bdbb..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ComputeImagesStatistics.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputeImagesStatistics

Brief Description

Computes global mean and standard deviation for each band from a set of images and optionally saves the results in an XML file.

Tags

Learning,Image Analysis

Long Description

This application computes a global mean and standard deviation for each band of a set of images and optionally saves the results in an XML file. The output XML is intended to be used an input for the TrainImagesClassifier application to normalize samples before learning.

Parameters

Limitations

Each image of the set must contain the same bands as the others (i.e. same types, in the same order).

Authors

OTB-Team

See Also

Documentation of the TrainImagesClassifier application.

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ComputeOGRLayersFeaturesStatistics.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ComputeOGRLayersFeaturesStatistics.html deleted file mode 100644 index 42c4651f14e3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ComputeOGRLayersFeaturesStatistics.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputeOGRLayersFeaturesStatistics

Brief Description

Compute statistics of the features in a set of OGR Layers

Tags

Segmentation

Long Description

Compute statistics (mean and standard deviation) of the features in a set of OGR Layers, and write them in an XML file. This XML file can then be used by the training application.

Parameters

Limitations

Experimental. For now only shapefiles are supported.

Authors

David Youssefi during internship at CNES

See Also

OGRLayerClassifier,TrainOGRLayersClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ComputePolylineFeatureFromImage.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ComputePolylineFeatureFromImage.html deleted file mode 100644 index 646191e845f4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ComputePolylineFeatureFromImage.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputePolylineFeatureFromImage

Brief Description

This application compute for each studied polyline, contained in the input VectorData, the chosen descriptors.

Tags

Feature Extraction

Long Description

The first step in the classifier fusion based validation is to compute, for each studied polyline, the chosen descriptors.

Parameters

Limitations

Since it does not rely on streaming process, take care of the size of input image before launching application.

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ConcatenateImages.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ConcatenateImages.html deleted file mode 100644 index f5d2ac5e2c28..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ConcatenateImages.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ConcatenateImages

Brief Description

Concatenate a list of images of the same size into a single multi-channel one.

Tags

Image Manipulation,Concatenation,Multi-channel

Long Description

This application performs images channels concatenation. It will walk the input image list (single or multi-channel) and generates a single multi-channel image. The channel order is the one of the list.

Parameters

Limitations

All input images must have the same size.

Authors

OTB-Team

See Also

Rescale application, Convert

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ConcatenateVectorData.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ConcatenateVectorData.html deleted file mode 100644 index 1760c34db99c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ConcatenateVectorData.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ConcatenateVectorData

Brief Description

Concatenate VectorDatas

Tags

Vector Data Manipulation

Long Description

This application concatenates a list of VectorData to produce a unique VectorData as output.Note that the VectorDatas must be of the same type (Storing polygons only, lines only, or points only)

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ConnectedComponentSegmentation.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ConnectedComponentSegmentation.html deleted file mode 100644 index 86198d1547a7..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ConnectedComponentSegmentation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ConnectedComponentSegmentation

Brief Description

Connected component segmentation and object based image filtering of the input image according to user-defined criterions.

Tags

Image Analysis,Segmentation

Long Description

This application allows one to perform a masking, connected components segmentation and object based image filtering. First and optionally, a mask can be built based on user-defined criterions to select pixels of the image which will be segmented. Then a connected component segmentation is performed with a user defined criterion to decide whether two neighbouring pixels belong to the same segment or not. After this segmentation step, an object based image filtering is applied using another user-defined criterion reasoning on segment properties, like shape or radiometric attributes. Criterions are mathematical expressions analysed by the MuParser library (http://muparser.sourceforge.net/). For instance, expression "((b1>80) and intensity>95)" will merge two neighbouring pixel in a single segment if their intensity is more than 95 and their value in the first image band is more than 80. See parameters documentation for a list of available attributes. The output of the object based image filtering is vectorized and can be written in shapefile or KML format. If the input image is in raw geometry, resulting polygons will be transformed to WGS84 using sensor modelling before writing, to ensure consistency with GIS software. For this purpose, a Digital Elevation Model can be provided to the application. The whole processing is done on a per-tile basis for large images, so this application can handle images of arbitrary size.

Parameters

Limitations

Due to the tiling scheme in case of large images, some segments can be arbitrarily split across multiple tiles.

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Convert.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Convert.html deleted file mode 100644 index d639181f4282..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Convert.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

Convert

Brief Description

Convert an image to a different format, eventually rescaling the data and/or changing the pixel type.

Tags

Conversion,Image Dynamic,Image Manipulation

Long Description

This application performs an image pixel type conversion (short, ushort, uchar, int, uint, float and double types are handled). The output image is written in the specified format (ie. that corresponds to the given extension). - The conversion can include a rescale using the image 2 percent minimum and maximum values. The rescale can be linear or log2.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Rescale

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ConvertCartoToGeoPoint.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ConvertCartoToGeoPoint.html deleted file mode 100644 index 7d5c59ef0e7d..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ConvertCartoToGeoPoint.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ConvertCartoToGeoPoint

Brief Description

Convert cartographic coordinates to geographic one.

Tags

Coordinates,Geometry

Long Description

This application computes the geographic coordinates from a cartographic one. User has to give the X and Y coordinate and the cartographic projection (UTM/LAMBERT/LAMBERT2/LAMBERT93/SINUS/ECKERT4/TRANSMERCATOR/MOLLWEID/SVY21).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ConvertSensorToGeoPoint.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ConvertSensorToGeoPoint.html deleted file mode 100644 index 2f7c862990d8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ConvertSensorToGeoPoint.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ConvertSensorToGeoPoint

Brief Description

Sensor to geographic coordinates conversion.

Tags

Geometry

Long Description

This Application converts a sensor point of an input image to a geographic point using the Forward Sensor Model of the input image.

Parameters

Limitations

None

Authors

OTB-Team

See Also

ConvertCartoToGeoPoint application, otbObtainUTMZoneFromGeoPoint

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/DEMConvert.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/DEMConvert.html deleted file mode 100644 index e51b5030bbfd..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/DEMConvert.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DEMConvert

Brief Description

Converts a geo-referenced DEM image into a general raster file compatible with OTB DEM handling.

Tags

Image Manipulation

Long Description

In order to be understood by the Orfeo ToolBox and the underlying OSSIM library, a geo-referenced Digital Elevation Model image can be converted into a general raster image, which consists in 3 files with the following extensions: .ras, .geom and .omd. Once converted, you have to place these files in a separate directory, and you can then use this directory to set the "DEM Directory" parameter of a DEM based OTB application or filter.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/DSFuzzyModelEstimation.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/DSFuzzyModelEstimation.html deleted file mode 100644 index e5d507ba025b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/DSFuzzyModelEstimation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DSFuzzyModelEstimation

Brief Description

Estimate feature fuzzy model parameters using 2 vector data (ground truth samples and wrong samples).

Tags

Feature Extraction

Long Description

Estimate feature fuzzy model parameters using 2 vector data (ground truth samples and wrong samples).

Parameters

Limitations

None.

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Despeckle-frost.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Despeckle-frost.html deleted file mode 100644 index 7ec3875fb9d8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Despeckle-frost.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Despeckle

Brief Description

Perform speckle noise reduction on SAR image.

Tags

SAR,Image Filtering

Long Description

This application reduce speckle noise. Four methods are available: Lee, Frost, GammaMAP and Kuan.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Despeckle-gammamap.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Despeckle-gammamap.html deleted file mode 100644 index 7ec3875fb9d8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Despeckle-gammamap.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Despeckle

Brief Description

Perform speckle noise reduction on SAR image.

Tags

SAR,Image Filtering

Long Description

This application reduce speckle noise. Four methods are available: Lee, Frost, GammaMAP and Kuan.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Despeckle-kuan.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Despeckle-kuan.html deleted file mode 100644 index 7ec3875fb9d8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Despeckle-kuan.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Despeckle

Brief Description

Perform speckle noise reduction on SAR image.

Tags

SAR,Image Filtering

Long Description

This application reduce speckle noise. Four methods are available: Lee, Frost, GammaMAP and Kuan.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Despeckle-lee.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Despeckle-lee.html deleted file mode 100644 index 7ec3875fb9d8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Despeckle-lee.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Despeckle

Brief Description

Perform speckle noise reduction on SAR image.

Tags

SAR,Image Filtering

Long Description

This application reduce speckle noise. Four methods are available: Lee, Frost, GammaMAP and Kuan.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Despeckle.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Despeckle.html deleted file mode 100644 index 7ec3875fb9d8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Despeckle.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Despeckle

Brief Description

Perform speckle noise reduction on SAR image.

Tags

SAR,Image Filtering

Long Description

This application reduce speckle noise. Four methods are available: Lee, Frost, GammaMAP and Kuan.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/DimensionalityReduction-ica.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/DimensionalityReduction-ica.html deleted file mode 100644 index 80fc079f1fc5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/DimensionalityReduction-ica.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DimensionalityReduction

Brief Description

Perform Dimension reduction of the input image.

Tags

Dimensionality Reduction,Image Filtering

Long Description

Performs dimensionality reduction on input image. PCA,NA-PCA,MAF,ICA methods are available. It is also possible to compute the inverse transform to reconstruct the image. It is also possible to optionnaly export the transformation matrix to a text file.

Parameters

Limitations

This application does not provide the inverse transform and the transformation matrix export for the MAF.

Authors

OTB-Team

See Also

"Kernel maximum autocorrelation factor and minimum noise fraction transformations," IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 612-624, (2011)

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/DimensionalityReduction-maf.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/DimensionalityReduction-maf.html deleted file mode 100644 index 80fc079f1fc5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/DimensionalityReduction-maf.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DimensionalityReduction

Brief Description

Perform Dimension reduction of the input image.

Tags

Dimensionality Reduction,Image Filtering

Long Description

Performs dimensionality reduction on input image. PCA,NA-PCA,MAF,ICA methods are available. It is also possible to compute the inverse transform to reconstruct the image. It is also possible to optionnaly export the transformation matrix to a text file.

Parameters

Limitations

This application does not provide the inverse transform and the transformation matrix export for the MAF.

Authors

OTB-Team

See Also

"Kernel maximum autocorrelation factor and minimum noise fraction transformations," IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 612-624, (2011)

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/DimensionalityReduction-napca.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/DimensionalityReduction-napca.html deleted file mode 100644 index 80fc079f1fc5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/DimensionalityReduction-napca.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DimensionalityReduction

Brief Description

Perform Dimension reduction of the input image.

Tags

Dimensionality Reduction,Image Filtering

Long Description

Performs dimensionality reduction on input image. PCA,NA-PCA,MAF,ICA methods are available. It is also possible to compute the inverse transform to reconstruct the image. It is also possible to optionnaly export the transformation matrix to a text file.

Parameters

Limitations

This application does not provide the inverse transform and the transformation matrix export for the MAF.

Authors

OTB-Team

See Also

"Kernel maximum autocorrelation factor and minimum noise fraction transformations," IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 612-624, (2011)

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/DimensionalityReduction-pca.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/DimensionalityReduction-pca.html deleted file mode 100644 index 80fc079f1fc5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/DimensionalityReduction-pca.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DimensionalityReduction

Brief Description

Perform Dimension reduction of the input image.

Tags

Dimensionality Reduction,Image Filtering

Long Description

Performs dimensionality reduction on input image. PCA,NA-PCA,MAF,ICA methods are available. It is also possible to compute the inverse transform to reconstruct the image. It is also possible to optionnaly export the transformation matrix to a text file.

Parameters

Limitations

This application does not provide the inverse transform and the transformation matrix export for the MAF.

Authors

OTB-Team

See Also

"Kernel maximum autocorrelation factor and minimum noise fraction transformations," IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 612-624, (2011)

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/DimensionalityReduction.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/DimensionalityReduction.html deleted file mode 100644 index 80fc079f1fc5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/DimensionalityReduction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DimensionalityReduction

Brief Description

Perform Dimension reduction of the input image.

Tags

Dimensionality Reduction,Image Filtering

Long Description

Performs dimensionality reduction on input image. PCA,NA-PCA,MAF,ICA methods are available. It is also possible to compute the inverse transform to reconstruct the image. It is also possible to optionnaly export the transformation matrix to a text file.

Parameters

Limitations

This application does not provide the inverse transform and the transformation matrix export for the MAF.

Authors

OTB-Team

See Also

"Kernel maximum autocorrelation factor and minimum noise fraction transformations," IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 612-624, (2011)

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/DisparityMapToElevationMap.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/DisparityMapToElevationMap.html deleted file mode 100644 index 303ac0e39a48..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/DisparityMapToElevationMap.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DisparityMapToElevationMap

Brief Description

Projects a disparity map into a regular elevation map

Tags

Stereo

Long Description

This application uses a disparity map computed from a stereo image pair to produce an elevation map on the ground area covered by the stereo pair. The needed inputs are : the disparity map, the stereo pair (in original geometry) and the epipolar deformation grids. These grids have to link the original geometry (stereo pair) and the epipolar geometry (disparity map).

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbStereoRectificationGridGenerator otbBlockMatching

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/DownloadSRTMTiles.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/DownloadSRTMTiles.html deleted file mode 100644 index 3c6871d7a5eb..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/DownloadSRTMTiles.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DownloadSRTMTiles

Brief Description

Download or list SRTM tiles related to a set of images

Tags

Utilities,Image Manipulation

Long Description

This application allows selecting the appropriate SRTM tiles that covers a list of images. It builds a list of the required tiles. Two modes are available: the first one download those tiles from the USGS SRTM3 website (http://dds.cr.usgs.gov/srtm/version2_1/SRTM3/), the second one list those tiles in a local directory. In both cases, you need to indicate the directory in which directory tiles will be download or the location of local SRTM files.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/EdgeExtraction-gradient.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/EdgeExtraction-gradient.html deleted file mode 100644 index aa436906807a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/EdgeExtraction-gradient.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

EdgeExtraction

Brief Description

Computes edge features on every pixel of the input image selected channel

Tags

Edge,Feature Extraction

Long Description

This application computes edge features on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otb class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/EdgeExtraction-sobel.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/EdgeExtraction-sobel.html deleted file mode 100644 index aa436906807a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/EdgeExtraction-sobel.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

EdgeExtraction

Brief Description

Computes edge features on every pixel of the input image selected channel

Tags

Edge,Feature Extraction

Long Description

This application computes edge features on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otb class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/EdgeExtraction-touzi.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/EdgeExtraction-touzi.html deleted file mode 100644 index aa436906807a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/EdgeExtraction-touzi.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

EdgeExtraction

Brief Description

Computes edge features on every pixel of the input image selected channel

Tags

Edge,Feature Extraction

Long Description

This application computes edge features on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otb class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/EdgeExtraction.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/EdgeExtraction.html deleted file mode 100644 index aa436906807a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/EdgeExtraction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

EdgeExtraction

Brief Description

Computes edge features on every pixel of the input image selected channel

Tags

Edge,Feature Extraction

Long Description

This application computes edge features on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otb class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ExtractROI-fit.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ExtractROI-fit.html deleted file mode 100644 index 8b36b6da38c8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ExtractROI-fit.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ExtractROI

Brief Description

Extract a ROI defined by the user.

Tags

Image Manipulation

Long Description

This application extracts a Region Of Interest with user defined size, or reference image.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ExtractROI-standard.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ExtractROI-standard.html deleted file mode 100644 index 8b36b6da38c8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ExtractROI-standard.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ExtractROI

Brief Description

Extract a ROI defined by the user.

Tags

Image Manipulation

Long Description

This application extracts a Region Of Interest with user defined size, or reference image.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ExtractROI.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ExtractROI.html deleted file mode 100644 index 8b36b6da38c8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ExtractROI.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ExtractROI

Brief Description

Extract a ROI defined by the user.

Tags

Image Manipulation

Long Description

This application extracts a Region Of Interest with user defined size, or reference image.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/FineRegistration.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/FineRegistration.html deleted file mode 100644 index 0c8330415a83..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/FineRegistration.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

FineRegistration

Brief Description

Estimate disparity map between two images.

Tags

Stereo

Long Description

Estimate disparity map between two images. Output image contain x offset, y offset and metric value.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/FusionOfClassifications-dempstershafer.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/FusionOfClassifications-dempstershafer.html deleted file mode 100644 index b5b88b5fc6ff..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/FusionOfClassifications-dempstershafer.html +++ /dev/null @@ -1,9 +0,0 @@ - - -

FusionOfClassifications

Brief Description

Fuses several classifications maps of the same image on the basis of class labels.

Tags

Learning,Image Analysis

Long Description

This application allows you to fuse several classification maps and produces a single more robust classification map. Fusion is done either by mean of Majority Voting, or with the Dempster Shafer combination method on class labels. - -MAJORITY VOTING: for each pixel, the class with the highest number of votes is selected. - -DEMPSTER SHAFER: for each pixel, the class label for which the Belief Function is maximal is selected. This Belief Function is calculated by mean of the Dempster Shafer combination of Masses of Belief, and indicates the belief that each input classification map presents for each label value. Moreover, the Masses of Belief are based on the input confusion matrices of each classification map, either by using the PRECISION or RECALL rates, or the OVERALL ACCURACY, or the KAPPA coefficient. Thus, each input classification map needs to be associated with its corresponding input confusion matrix file for the Dempster Shafer fusion. --Input pixels with the NODATA label are not handled in the fusion of classification maps. Moreover, pixels for which all the input classifiers are set to NODATA keep this value in the output fused image. --In case of number of votes equality, the UNDECIDED label is attributed to the pixel.

Parameters

Limitations

None

Authors

OTB-Team

See Also

ImageClassifier application

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/FusionOfClassifications-majorityvoting.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/FusionOfClassifications-majorityvoting.html deleted file mode 100644 index b5b88b5fc6ff..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/FusionOfClassifications-majorityvoting.html +++ /dev/null @@ -1,9 +0,0 @@ - - -

FusionOfClassifications

Brief Description

Fuses several classifications maps of the same image on the basis of class labels.

Tags

Learning,Image Analysis

Long Description

This application allows you to fuse several classification maps and produces a single more robust classification map. Fusion is done either by mean of Majority Voting, or with the Dempster Shafer combination method on class labels. - -MAJORITY VOTING: for each pixel, the class with the highest number of votes is selected. - -DEMPSTER SHAFER: for each pixel, the class label for which the Belief Function is maximal is selected. This Belief Function is calculated by mean of the Dempster Shafer combination of Masses of Belief, and indicates the belief that each input classification map presents for each label value. Moreover, the Masses of Belief are based on the input confusion matrices of each classification map, either by using the PRECISION or RECALL rates, or the OVERALL ACCURACY, or the KAPPA coefficient. Thus, each input classification map needs to be associated with its corresponding input confusion matrix file for the Dempster Shafer fusion. --Input pixels with the NODATA label are not handled in the fusion of classification maps. Moreover, pixels for which all the input classifiers are set to NODATA keep this value in the output fused image. --In case of number of votes equality, the UNDECIDED label is attributed to the pixel.

Parameters

Limitations

None

Authors

OTB-Team

See Also

ImageClassifier application

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/FusionOfClassifications.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/FusionOfClassifications.html deleted file mode 100644 index b5b88b5fc6ff..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/FusionOfClassifications.html +++ /dev/null @@ -1,9 +0,0 @@ - - -

FusionOfClassifications

Brief Description

Fuses several classifications maps of the same image on the basis of class labels.

Tags

Learning,Image Analysis

Long Description

This application allows you to fuse several classification maps and produces a single more robust classification map. Fusion is done either by mean of Majority Voting, or with the Dempster Shafer combination method on class labels. - -MAJORITY VOTING: for each pixel, the class with the highest number of votes is selected. - -DEMPSTER SHAFER: for each pixel, the class label for which the Belief Function is maximal is selected. This Belief Function is calculated by mean of the Dempster Shafer combination of Masses of Belief, and indicates the belief that each input classification map presents for each label value. Moreover, the Masses of Belief are based on the input confusion matrices of each classification map, either by using the PRECISION or RECALL rates, or the OVERALL ACCURACY, or the KAPPA coefficient. Thus, each input classification map needs to be associated with its corresponding input confusion matrix file for the Dempster Shafer fusion. --Input pixels with the NODATA label are not handled in the fusion of classification maps. Moreover, pixels for which all the input classifiers are set to NODATA keep this value in the output fused image. --In case of number of votes equality, the UNDECIDED label is attributed to the pixel.

Parameters

Limitations

None

Authors

OTB-Team

See Also

ImageClassifier application

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/GeneratePlyFile.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/GeneratePlyFile.html deleted file mode 100644 index 9cefcc9d5b87..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/GeneratePlyFile.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GeneratePlyFile

Brief Description

Generate a 3D Ply file from a DEM and a color image.

Tags

Geometry

Long Description

Generate a 3D Ply file from a DEM and a color image.

Parameters

Limitations

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/GenerateRPCSensorModel.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/GenerateRPCSensorModel.html deleted file mode 100644 index 63c73dfbd0a2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/GenerateRPCSensorModel.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GenerateRPCSensorModel

Brief Description

Generate a RPC sensor model from a list of Ground Control Points.

Tags

Geometry

Long Description

This application generates a RPC sensor model from a list of Ground Control Points. At least 20 points are required for estimation wihtout elevation support, and 40 points for estimation with elevation support. Elevation support will be automatically deactivated if an insufficient amount of points is provided. The application can optionnaly output a file containing accuracy statistics for each point, and a vector file containing segments represening points residues. The map projection parameter allows defining a map projection in which the accuracy is evaluated.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OrthoRectication,HomologousPointsExtraction,RefineSensorModel

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/GrayScaleMorphologicalOperation-closing.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/GrayScaleMorphologicalOperation-closing.html deleted file mode 100644 index e3081bbf311b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/GrayScaleMorphologicalOperation-closing.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GrayScaleMorphologicalOperation

Brief Description

Performs morphological operations on a grayscale input image

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs grayscale morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkGrayscaleDilateImageFilter, itkGrayscaleErodeImageFilter, itkGrayscaleMorphologicalOpeningImageFilter and itkGrayscaleMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/GrayScaleMorphologicalOperation-dilate.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/GrayScaleMorphologicalOperation-dilate.html deleted file mode 100644 index e3081bbf311b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/GrayScaleMorphologicalOperation-dilate.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GrayScaleMorphologicalOperation

Brief Description

Performs morphological operations on a grayscale input image

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs grayscale morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkGrayscaleDilateImageFilter, itkGrayscaleErodeImageFilter, itkGrayscaleMorphologicalOpeningImageFilter and itkGrayscaleMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/GrayScaleMorphologicalOperation-erode.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/GrayScaleMorphologicalOperation-erode.html deleted file mode 100644 index e3081bbf311b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/GrayScaleMorphologicalOperation-erode.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GrayScaleMorphologicalOperation

Brief Description

Performs morphological operations on a grayscale input image

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs grayscale morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkGrayscaleDilateImageFilter, itkGrayscaleErodeImageFilter, itkGrayscaleMorphologicalOpeningImageFilter and itkGrayscaleMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/GrayScaleMorphologicalOperation-opening.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/GrayScaleMorphologicalOperation-opening.html deleted file mode 100644 index e3081bbf311b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/GrayScaleMorphologicalOperation-opening.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GrayScaleMorphologicalOperation

Brief Description

Performs morphological operations on a grayscale input image

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs grayscale morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkGrayscaleDilateImageFilter, itkGrayscaleErodeImageFilter, itkGrayscaleMorphologicalOpeningImageFilter and itkGrayscaleMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/GrayScaleMorphologicalOperation.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/GrayScaleMorphologicalOperation.html deleted file mode 100644 index e3081bbf311b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/GrayScaleMorphologicalOperation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GrayScaleMorphologicalOperation

Brief Description

Performs morphological operations on a grayscale input image

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs grayscale morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkGrayscaleDilateImageFilter, itkGrayscaleErodeImageFilter, itkGrayscaleMorphologicalOpeningImageFilter and itkGrayscaleMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/GridBasedImageResampling.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/GridBasedImageResampling.html deleted file mode 100644 index db4219381fd5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/GridBasedImageResampling.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GridBasedImageResampling

Brief Description

Resamples an image according to a resampling grid

Tags

Geometry

Long Description

This application allows performing image resampling from an input resampling grid.

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbStereorecificationGridGeneration

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/HaralickTextureExtraction.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/HaralickTextureExtraction.html deleted file mode 100644 index ef966758bb63..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/HaralickTextureExtraction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

HaralickTextureExtraction

Brief Description

Computes textures on every pixel of the input image selected channel

Tags

Textures,Feature Extraction

Long Description

This application computes Haralick, advanced and higher order textures on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbScalarImageToTexturesFilter, otbScalarImageToAdvancedTexturesFilter and otbScalarImageToHigherOrderTexturesFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/HomologousPointsExtraction.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/HomologousPointsExtraction.html deleted file mode 100644 index 39ef77e1dc46..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/HomologousPointsExtraction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

HomologousPointsExtraction

Brief Description

Compute homologous points between images using keypoints

Tags

Feature Extraction

Long Description

This application allows computing homologous points between images using keypoints. SIFT or SURF keypoints can be used and the band on which keypoints are computed can be set independantly for both images. The application offers two modes : the first is the full mode where keypoints are extracted from the full extent of both images (please note that in this mode large image file are not supported). The second mode, called geobins, allows one to set-up spatial binning to get fewer points spread across the entire image. In this mode, the corresponding spatial bin in the second image is estimated using geographical transform or sensor modelling, and is padded according to the user defined precision. Last, in both modes the application can filter matches whose colocalisation in first image exceed this precision. The elevation parameters are to deal more precisely with sensor modelling in case of sensor geometry data. The outvector option allows creating a vector file with segments corresponding to the localisation error between the matches. It can be useful to assess the precision of a registration for instance. The vector file is always reprojected to EPSG:4326 to allow display in a GIS. This is done via reprojection or by applying the image sensor models.

Parameters

Limitations

Full mode does not handle large images.

Authors

OTB-Team

See Also

RefineSensorModel

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/HooverCompareSegmentation.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/HooverCompareSegmentation.html deleted file mode 100644 index 7fdbe9a5043f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/HooverCompareSegmentation.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

HooverCompareSegmentation

Brief Description

Compare two segmentations with Hoover metrics

Tags

Segmentation

Long Description

This application compares a machine segmentation (MS) with a partial ground truth segmentation (GT). The Hoover metrics are used to estimate scores for correct detection, over-segmentation, under-segmentation and missed detection. - The application can output the overall Hoover scores along with coloredimages of the MS and GT segmentation showing the state of each region (correct detection, over-segmentation, under-segmentation, missed) - The Hoover metrics are described in : Hoover et al., "An experimental comparison of range image segmentation algorithms", IEEE PAMI vol. 18, no. 7, July 1996.

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbHooverMatrixFilter, otbHooverInstanceFilter, otbLabelMapToAttributeImageFilter

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/HyperspectralUnmixing.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/HyperspectralUnmixing.html deleted file mode 100644 index 17e18c1055e8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/HyperspectralUnmixing.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

HyperspectralUnmixing

Brief Description

Estimate abundance maps from an hyperspectral image and a set of endmembers.

Tags

Hyperspectral

Long Description

The application applies a linear unmixing algorithm to an hyperspectral data cube. This method supposes that the mixture between materials in the scene is macroscopic and simulates a linear mixing model of spectra. -The Linear Mixing Model (LMM) acknowledges that reflectance spectrum associated with each pixel is a linear combination of pure materials in the recovery area, commonly known as endmembers. Endmembers can be estimated using the VertexComponentAnalysis application. -The application allows one to estimate the abundance maps with several algorithms : Unconstrained Least Square (ucls), Fully Constrained Least Square (fcls), Image Space Reconstruction Algorithm (isra) and Non-negative constrained Least Square (ncls) and Minimum Dispersion Constrained Non Negative Matrix Factorization (MDMDNMF). -

Parameters

Limitations

None

Authors

OTB-Team

See Also

VertexComponentAnalysis

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ImageClassifier.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ImageClassifier.html deleted file mode 100644 index 609883188384..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ImageClassifier.html +++ /dev/null @@ -1,16 +0,0 @@ - - -

ImageClassifier

Brief Description

Performs a classification of the input image according to a model file.

Tags

Learning

Long Description

This application performs an image classification based on a model file produced by the TrainImagesClassifier application. Pixels of the output image will contain the class labels decided by the classifier (maximal class label = 65535). The input pixels can be optionally centered and reduced according to the statistics file produced by the ComputeImagesStatistics application. An optional input mask can be provided, in which case only input image pixels whose corresponding mask value is greater than 0 will be classified. The remaining of pixels will be given the label 0 in the output image.

Parameters

Limitations

The input image must have the same type, order and number of bands than the images used to produce the statistics file and the SVM model file. If a statistics file was used during training by the TrainImagesClassifier, it is mandatory to use the same statistics file for classification. If an input mask is used, its size must match the input image size.

Authors

OTB-Team

See Also

TrainImagesClassifier, ValidateImagesClassifier, ComputeImagesStatistics

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ImageEnvelope.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ImageEnvelope.html deleted file mode 100644 index 6b0e00023b41..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ImageEnvelope.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ImageEnvelope

Brief Description

Extracts an image envelope.

Tags

Geometry

Long Description

Build a vector data containing the polygon of the image envelope.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/KMeansClassification.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/KMeansClassification.html deleted file mode 100644 index 47414f7b94d3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/KMeansClassification.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

KMeansClassification

Brief Description

Unsupervised KMeans image classification

Tags

Segmentation,Learning

Long Description

Performs unsupervised KMeans image classification.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/KmzExport.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/KmzExport.html deleted file mode 100644 index 98c54781ec2e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/KmzExport.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

KmzExport

Brief Description

Export the input image in a KMZ product.

Tags

KMZ,Export

Long Description

This application exports the input image in a kmz product that can be display in the Google Earth software. The user can set the size of the product size, a logo and a legend to the product. Furthemore, to obtain a product that fits the relief, a DEM can be used.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Conversion

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/LSMSSegmentation.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/LSMSSegmentation.html deleted file mode 100644 index 6d07dcf15518..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/LSMSSegmentation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

LSMSSegmentation

Brief Description

Second step of the exact Large-Scale Mean-Shift segmentation workflow.

Tags

Segmentation,LSMS

Long Description

This application performs the second step of the exact Large-Scale Mean-Shift segmentation workflow (LSMS). Filtered range image and spatial image should be created with the MeanShiftSmoothing application, with modesearch parameter disabled. If spatial image is not set, the application will only process the range image and spatial radius parameter will not be taken into account. This application will produce a labeled image where neighbor pixels whose range distance is below range radius (and optionally spatial distance below spatial radius) will be grouped together into the same cluster. For large images one can use the nbtilesx and nbtilesy parameters for tile-wise processing, with the guarantees of identical results. Please note that this application will generate a lot of temporary files (as many as the number of tiles), and will therefore require twice the size of the final result in term of disk space. The cleanup option (activated by default) allows removing all temporary file as soon as they are not needed anymore (if cleanup is activated, tmpdir set and tmpdir does not exists before running the application, it will be removed as well during cleanup). The tmpdir option allows defining a directory where to write the temporary files. Please also note that the output image type should be set to uint32 to ensure that there are enough labels available.

Parameters

Limitations

This application is part of the Large-Scale Mean-Shift segmentation workflow (LSMS) and may not be suited for any other purpose.

Authors

David Youssefi

See Also

MeanShiftSmoothing, LSMSSmallRegionsMerging, LSMSVectorization

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/LSMSSmallRegionsMerging.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/LSMSSmallRegionsMerging.html deleted file mode 100644 index b293794cb450..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/LSMSSmallRegionsMerging.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

LSMSSmallRegionsMerging

Brief Description

Third (optional) step of the exact Large-Scale Mean-Shift segmentation workflow.

Tags

Segmentation,LSMS

Long Description

This application performs the third step of the exact Large-Scale Mean-Shift segmentation workflow (LSMS). Given a segmentation result (label image) and the original image, it will merge regions whose size in pixels is lower than minsize parameter with the adjacent regions with the adjacent region with closest radiometry and acceptable size. Small regions will be processed by size: first all regions of area, which is equal to 1 pixel will be merged with adjacent region, then all regions of area equal to 2 pixels, until regions of area minsize. For large images one can use the nbtilesx and nbtilesy parameters for tile-wise processing, with the guarantees of identical results.

Parameters

Limitations

This application is part of the Large-Scale Mean-Shift segmentation workflow (LSMS) and may not be suited for any other purpose.

Authors

David Youssefi

See Also

LSMSSegmentation, LSMSVectorization, MeanShiftSmoothing

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/LSMSVectorization.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/LSMSVectorization.html deleted file mode 100644 index 5414a039181e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/LSMSVectorization.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

LSMSVectorization

Brief Description

Fourth step of the exact Large-Scale Mean-Shift segmentation workflow.

Tags

Segmentation,LSMS

Long Description

This application performs the fourth step of the exact Large-Scale Mean-Shift segmentation workflow (LSMS). Given a segmentation result (label image), that may have been processed for small regions merging or not, it will convert it to a GIS vector file containing one polygon per segment. Each polygon contains additional fields: mean and variance of each channels from input image (in parameter), segmentation image label, number of pixels in the polygon. For large images one can use the nbtilesx and nbtilesy parameters for tile-wise processing, with the guarantees of identical results.

Parameters

Limitations

This application is part of the Large-Scale Mean-Shift segmentation workflow (LSMS) and may not be suited for any other purpose.

Authors

David Youssefi

See Also

MeanShiftSmoothing, LSMSSegmentation, LSMSSmallRegionsMerging

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/LineSegmentDetection.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/LineSegmentDetection.html deleted file mode 100644 index ca90896346a1..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/LineSegmentDetection.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

LineSegmentDetection

Brief Description

Detect line segments in raster

Tags

Feature Extraction

Long Description

This application detects locally straight contours in a image. It is based on Burns, Hanson, and Riseman method and use an a contrario validation approach (Desolneux, Moisan, and Morel). The algorithm was published by Rafael Gromponevon Gioi, Jérémie Jakubowicz, Jean-Michel Morel and Gregory Randall. - The given approach computes gradient and level lines of the image and detects aligned points in line support region. The application allows exporting the detected lines in a vector data.

Parameters

Limitations

None

Authors

OTB-Team

See Also

On Line demonstration of the LSD algorithm is available here: http://www.ipol.im/pub/algo/gjmr_line_segment_detector/ -

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/LocalStatisticExtraction.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/LocalStatisticExtraction.html deleted file mode 100644 index 6ff7bc151aa1..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/LocalStatisticExtraction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

LocalStatisticExtraction

Brief Description

Computes local statistical moments on every pixel in the selected channel of the input image

Tags

Statistics,Feature Extraction

Long Description

This application computes the 4 local statistical moments on every pixel in the selected channel of the input image, over a specified neighborhood. The output image is multi band with one statistical moment (feature) per band. Thus, the 4 output features are the Mean, the Variance, the Skewness and the Kurtosis. They are provided in this exact order in the output image.

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbRadiometricMomentsImageFunction class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ManageNoData.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ManageNoData.html deleted file mode 100644 index ab86a07b1275..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ManageNoData.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ManageNoData

Brief Description

Manage No-Data

Tags

Conversion,Image Dynamic,Image Manipulation

Long Description

This application has two modes. The first allows building a mask of no-data pixels from the no-data flags read from the image file. The second allows updating the change the no-data value of an image (pixels value and metadata). This last mode also allows replacing NaN in images with a proper no-data value. To do so, one should activate the NaN is no-data option.

Parameters

Limitations

None

Authors

OTB-Team

See Also

BanMath

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/MeanShiftSmoothing.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/MeanShiftSmoothing.html deleted file mode 100644 index 491953882a83..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/MeanShiftSmoothing.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

MeanShiftSmoothing

Brief Description

Perform mean shift filtering

Tags

Image Filtering,LSMS

Long Description

This application performs mean shift fitlering (multi-threaded).

Parameters

Limitations

With mode search option, the result will slightly depend on thread number.

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/MultiResolutionPyramid.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/MultiResolutionPyramid.html deleted file mode 100644 index 35f527bb0353..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/MultiResolutionPyramid.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

MultiResolutionPyramid

Brief Description

Build a multi-resolution pyramid of the image.

Tags

Conversion,Image Manipulation,Image MultiResolution,Util

Long Description

This application builds a multi-resolution pyramid of the input image. User can specified the number of levels of the pyramid and the subsampling factor. To speed up the process, you can use the fast scheme option

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/MultivariateAlterationDetector.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/MultivariateAlterationDetector.html deleted file mode 100644 index 2505ba3cba15..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/MultivariateAlterationDetector.html +++ /dev/null @@ -1,21 +0,0 @@ - - -

MultivariateAlterationDetector

Brief Description

Multivariate Alteration Detector

Tags

Feature Extraction

Long Description

This application detects change between two given images.

Parameters

Limitations

None

Authors

OTB-Team

See Also

This filter implements the Multivariate Alteration Detector, based on the following work: - A. A. Nielsen and K. Conradsen, Multivariate alteration detection (mad) in multispectral, bi-temporal image data: a new approach to change detection studies, Remote Sens. Environ., vol. 64, pp. 1-19, (1998) - - Multivariate Alteration Detector takes two images as inputs and produce a set of N change maps as a VectorImage (where N is the maximum of number of bands in first and second image) with the following properties: - - Change maps are differences of a pair of linear combinations of bands from image 1 and bands from image 2 chosen to maximize the correlation. - - Each change map is orthogonal to the others. - - This is a statistical method which can handle different modalities and even different bands and number of bands between images. - - If numbers of bands in image 1 and 2 are equal, then change maps are sorted by increasing correlation. If number of bands is different, the change maps are sorted by decreasing correlation. - - The GetV1() and GetV2() methods allow retrieving the linear combinations used to generate the Mad change maps as a vnl_matrix of double, and the GetRho() method allows retrieving the correlation associated to each Mad change maps as a vnl_vector. - - This filter has been implemented from the Matlab code kindly made available by the authors here: - http://www2.imm.dtu.dk/~aa/software.html - - Both cases (same and different number of bands) have been validated by comparing the output image to the output produced by the Matlab code, and the reference images for testing have been generated from the Matlab code using Octave.

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/OGRLayerClassifier.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/OGRLayerClassifier.html deleted file mode 100644 index 9b8b14bf2ff0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/OGRLayerClassifier.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

OGRLayerClassifier

Brief Description

Classify an OGR layer based on a machine learning model and a list of features to consider.

Tags

Segmentation

Long Description

This application will apply a trained machine learning model on the selected feature to get a classification of each geometry contained in an OGR layer. The list of feature must match the list used for training. The predicted label is written in the user defined field for each geometry.

Parameters

Limitations

Experimental. Only shapefiles are supported for now.

Authors

David Youssefi during internship at CNES

See Also

ComputeOGRLayersFeaturesStatistics,TrainOGRLayersClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/OSMDownloader.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/OSMDownloader.html deleted file mode 100644 index e675bf1e6653..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/OSMDownloader.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

OSMDownloader

Brief Description

Generate a vector data from OSM on the input image extend

Tags

Image MetaData

Long Description

Generate a vector data from Open Street Map data. A DEM could be use. By default, the entire layer is downloaded, an image can be use as support for the OSM data. The application can provide also available classes in layers . This application required an Internet access. Information about the OSM project : http://www.openstreetmap.fr/

Parameters

Limitations

None

Authors

OTB-Team

See Also

Conversion

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ObtainUTMZoneFromGeoPoint.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ObtainUTMZoneFromGeoPoint.html deleted file mode 100644 index eb416fdd3d84..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ObtainUTMZoneFromGeoPoint.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ObtainUTMZoneFromGeoPoint

Brief Description

UTM zone determination from a geographic point.

Tags

Coordinates

Long Description

This application returns the UTM zone of an input geographic point.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

Obtain a UTM Zone \ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/OrthoRectification-epsg.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/OrthoRectification-epsg.html deleted file mode 100644 index c90885e2c5c0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/OrthoRectification-epsg.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

OrthoRectification

Brief Description

This application allows ortho-rectification of optical images from supported sensors. -

Tags

Geometry

Long Description

An inverse sensor model is built from the input image metadata to convert geographical to raw geometry coordinates. This inverse sensor model is then combined with the chosen map projection to build a global coordinate mapping grid. Last, this grid is used to resample using the chosen interpolation algorithm. A Digital Elevation Model can be specified to account for terrain deformations. -In case of SPOT5 images, the sensor model can be approximated by an RPC model in order to speed-up computation.

Parameters

Limitations

Supported sensors are Pleiades, SPOT5 (TIF format), Ikonos, Quickbird, Worldview2, GeoEye.

Authors

OTB-Team

See Also

Ortho-rectification chapter from the OTB Software Guide

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/OrthoRectification-fit-to-ortho.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/OrthoRectification-fit-to-ortho.html deleted file mode 100644 index c90885e2c5c0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/OrthoRectification-fit-to-ortho.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

OrthoRectification

Brief Description

This application allows ortho-rectification of optical images from supported sensors. -

Tags

Geometry

Long Description

An inverse sensor model is built from the input image metadata to convert geographical to raw geometry coordinates. This inverse sensor model is then combined with the chosen map projection to build a global coordinate mapping grid. Last, this grid is used to resample using the chosen interpolation algorithm. A Digital Elevation Model can be specified to account for terrain deformations. -In case of SPOT5 images, the sensor model can be approximated by an RPC model in order to speed-up computation.

Parameters

Limitations

Supported sensors are Pleiades, SPOT5 (TIF format), Ikonos, Quickbird, Worldview2, GeoEye.

Authors

OTB-Team

See Also

Ortho-rectification chapter from the OTB Software Guide

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/OrthoRectification-lambert-WGS84.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/OrthoRectification-lambert-WGS84.html deleted file mode 100644 index c90885e2c5c0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/OrthoRectification-lambert-WGS84.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

OrthoRectification

Brief Description

This application allows ortho-rectification of optical images from supported sensors. -

Tags

Geometry

Long Description

An inverse sensor model is built from the input image metadata to convert geographical to raw geometry coordinates. This inverse sensor model is then combined with the chosen map projection to build a global coordinate mapping grid. Last, this grid is used to resample using the chosen interpolation algorithm. A Digital Elevation Model can be specified to account for terrain deformations. -In case of SPOT5 images, the sensor model can be approximated by an RPC model in order to speed-up computation.

Parameters

Limitations

Supported sensors are Pleiades, SPOT5 (TIF format), Ikonos, Quickbird, Worldview2, GeoEye.

Authors

OTB-Team

See Also

Ortho-rectification chapter from the OTB Software Guide

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/OrthoRectification-utm.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/OrthoRectification-utm.html deleted file mode 100644 index c90885e2c5c0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/OrthoRectification-utm.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

OrthoRectification

Brief Description

This application allows ortho-rectification of optical images from supported sensors. -

Tags

Geometry

Long Description

An inverse sensor model is built from the input image metadata to convert geographical to raw geometry coordinates. This inverse sensor model is then combined with the chosen map projection to build a global coordinate mapping grid. Last, this grid is used to resample using the chosen interpolation algorithm. A Digital Elevation Model can be specified to account for terrain deformations. -In case of SPOT5 images, the sensor model can be approximated by an RPC model in order to speed-up computation.

Parameters

Limitations

Supported sensors are Pleiades, SPOT5 (TIF format), Ikonos, Quickbird, Worldview2, GeoEye.

Authors

OTB-Team

See Also

Ortho-rectification chapter from the OTB Software Guide

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/OrthoRectification.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/OrthoRectification.html deleted file mode 100644 index c90885e2c5c0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/OrthoRectification.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

OrthoRectification

Brief Description

This application allows ortho-rectification of optical images from supported sensors. -

Tags

Geometry

Long Description

An inverse sensor model is built from the input image metadata to convert geographical to raw geometry coordinates. This inverse sensor model is then combined with the chosen map projection to build a global coordinate mapping grid. Last, this grid is used to resample using the chosen interpolation algorithm. A Digital Elevation Model can be specified to account for terrain deformations. -In case of SPOT5 images, the sensor model can be approximated by an RPC model in order to speed-up computation.

Parameters

Limitations

Supported sensors are Pleiades, SPOT5 (TIF format), Ikonos, Quickbird, Worldview2, GeoEye.

Authors

OTB-Team

See Also

Ortho-rectification chapter from the OTB Software Guide

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Pansharpening-bayes.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Pansharpening-bayes.html deleted file mode 100644 index dd25169010ad..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Pansharpening-bayes.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Pansharpening

Brief Description

Perform P+XS pansharpening

Tags

Geometry,Pansharpening

Long Description

This application performs P+XS pansharpening. Pansharpening is a process of merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution color image. Algorithms available in the applications are: RCS, bayesian fusion and Local Mean and Variance Matching(LMVM).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Pansharpening-lmvm.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Pansharpening-lmvm.html deleted file mode 100644 index dd25169010ad..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Pansharpening-lmvm.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Pansharpening

Brief Description

Perform P+XS pansharpening

Tags

Geometry,Pansharpening

Long Description

This application performs P+XS pansharpening. Pansharpening is a process of merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution color image. Algorithms available in the applications are: RCS, bayesian fusion and Local Mean and Variance Matching(LMVM).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Pansharpening-rcs.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Pansharpening-rcs.html deleted file mode 100644 index dd25169010ad..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Pansharpening-rcs.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Pansharpening

Brief Description

Perform P+XS pansharpening

Tags

Geometry,Pansharpening

Long Description

This application performs P+XS pansharpening. Pansharpening is a process of merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution color image. Algorithms available in the applications are: RCS, bayesian fusion and Local Mean and Variance Matching(LMVM).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Pansharpening.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Pansharpening.html deleted file mode 100644 index dd25169010ad..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Pansharpening.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Pansharpening

Brief Description

Perform P+XS pansharpening

Tags

Geometry,Pansharpening

Long Description

This application performs P+XS pansharpening. Pansharpening is a process of merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution color image. Algorithms available in the applications are: RCS, bayesian fusion and Local Mean and Variance Matching(LMVM).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/PixelValue.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/PixelValue.html deleted file mode 100644 index 53b7cab54fcf..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/PixelValue.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

PixelValue

Brief Description

Get the value of a pixel.

Tags

Utilities,Coordinates,Raster

Long Description

Get the value of a pixel. -Pay attention, index starts at 0.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/PolygonClassStatistics.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/PolygonClassStatistics.html deleted file mode 100644 index 5c21f9f73f34..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/PolygonClassStatistics.html +++ /dev/null @@ -1,12 +0,0 @@ - - -

PolygonClassStatistics

Brief Description

Computes statistics on a training polygon set.

Tags

Learning

Long Description

The application processes a set of geometries intended for training (they should have a field giving the associated class). The geometries are analysed against a support image to compute statistics : - - number of samples per class - - number of samples per geometry -An optional raster mask can be used to discard samples. Different types of geometry are supported : polygons, lines, points. The behaviour is different for each type of geometry : - - polygon: select pixels whose center is inside the polygon - - lines : select pixels intersecting the line - - points : select closest pixel to the point -

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/PredictRegression.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/PredictRegression.html deleted file mode 100644 index d86079ee3811..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/PredictRegression.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

PredictRegression

Brief Description

Performs a prediction of the input image according to a regression model file.

Tags

Learning

Long Description

This application predict output values from an input image, based on a regression model file produced by the TrainRegression application. Pixels of the output image will contain the predicted values fromthe regression model (single band). The input pixels can be optionally centered and reduced according to the statistics file produced by the ComputeImagesStatistics application. An optional input mask can be provided, in which case only input image pixels whose corresponding mask value is greater than 0 will be processed. The remaining of pixels will be given the value 0 in the output image.

Parameters

Limitations

The input image must contain the feature bands used for the model training (without the predicted value). If a statistics file was used during training by the TrainRegression, it is mandatory to use the same statistics file for prediction. If an input mask is used, its size must match the input image size.

Authors

OTB-Team

See Also

TrainRegression, ComputeImagesStatistics

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Quicklook.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Quicklook.html deleted file mode 100644 index c84a0abaf342..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Quicklook.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

Quicklook

Brief Description

Generates a subsampled version of an image extract

Tags

Image Manipulation

Long Description

Generates a subsampled version of an extract of an image defined by ROIStart and ROISize. - This extract is subsampled using the ratio OR the output image Size.

Parameters

Limitations

This application does not provide yet the optimal way to decode coarser level of resolution from JPEG2000 images (like in Monteverdi). -Trying to subsampled huge JPEG200 image with the application will lead to poor performances for now.

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/RadiometricIndices.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/RadiometricIndices.html deleted file mode 100644 index 9686e915d862..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/RadiometricIndices.html +++ /dev/null @@ -1,25 +0,0 @@ - - -

RadiometricIndices

Brief Description

Compute radiometric indices.

Tags

Radiometric Indices,Feature Extraction

Long Description

This application computes radiometric indices using the relevant channels of the input image. The output is a multi band image into which each channel is one of the selected indices.

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbVegetationIndicesFunctor, otbWaterIndicesFunctor and otbSoilIndicesFunctor classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Rasterization-image.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Rasterization-image.html deleted file mode 100644 index 3569e88c5c6b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Rasterization-image.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

Rasterization

Brief Description

Rasterize a vector dataset.

Tags

Vector Data Manipulation

Long Description

This application allows reprojecting and rasterize a vector dataset. The grid of the rasterized output can be set by using a reference image, or by setting all parmeters (origin, size, spacing) by hand. In the latter case, at least the spacing (ground sampling distance) is needed (other parameters are computed automatically). The rasterized output can also be in a different projection reference system than the input dataset. - There are two rasterize mode available in the application. The first is the binary mode: it allows rendering all pixels belonging to a geometry of the input dataset in the foreground color, while rendering the other in background color. The second one allows rendering pixels belonging to a geometry woth respect to an attribute of this geometry. The field of the attribute to render can be set by the user. In the second mode, the background value is still used for unassociated pixels.

Parameters

Limitations

None

Authors

OTB-Team

See Also

For now, support of input dataset with multiple layers having different projection reference system is limited.

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Rasterization-manual.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Rasterization-manual.html deleted file mode 100644 index 3569e88c5c6b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Rasterization-manual.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

Rasterization

Brief Description

Rasterize a vector dataset.

Tags

Vector Data Manipulation

Long Description

This application allows reprojecting and rasterize a vector dataset. The grid of the rasterized output can be set by using a reference image, or by setting all parmeters (origin, size, spacing) by hand. In the latter case, at least the spacing (ground sampling distance) is needed (other parameters are computed automatically). The rasterized output can also be in a different projection reference system than the input dataset. - There are two rasterize mode available in the application. The first is the binary mode: it allows rendering all pixels belonging to a geometry of the input dataset in the foreground color, while rendering the other in background color. The second one allows rendering pixels belonging to a geometry woth respect to an attribute of this geometry. The field of the attribute to render can be set by the user. In the second mode, the background value is still used for unassociated pixels.

Parameters

Limitations

None

Authors

OTB-Team

See Also

For now, support of input dataset with multiple layers having different projection reference system is limited.

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Rasterization.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Rasterization.html deleted file mode 100644 index 3569e88c5c6b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Rasterization.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

Rasterization

Brief Description

Rasterize a vector dataset.

Tags

Vector Data Manipulation

Long Description

This application allows reprojecting and rasterize a vector dataset. The grid of the rasterized output can be set by using a reference image, or by setting all parmeters (origin, size, spacing) by hand. In the latter case, at least the spacing (ground sampling distance) is needed (other parameters are computed automatically). The rasterized output can also be in a different projection reference system than the input dataset. - There are two rasterize mode available in the application. The first is the binary mode: it allows rendering all pixels belonging to a geometry of the input dataset in the foreground color, while rendering the other in background color. The second one allows rendering pixels belonging to a geometry woth respect to an attribute of this geometry. The field of the attribute to render can be set by the user. In the second mode, the background value is still used for unassociated pixels.

Parameters

Limitations

None

Authors

OTB-Team

See Also

For now, support of input dataset with multiple layers having different projection reference system is limited.

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/ReadImageInfo.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/ReadImageInfo.html deleted file mode 100644 index 876e143d7608..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/ReadImageInfo.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ReadImageInfo

Brief Description

Get information about the image

Tags

Utilities,Image Manipulation,Image MetaData

Long Description

Display information about the input image like: image size, origin, spacing, metadata, projections...

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/RefineSensorModel.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/RefineSensorModel.html deleted file mode 100644 index 868feaeff787..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/RefineSensorModel.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

RefineSensorModel

Brief Description

Perform least-square fit of a sensor model to a set of tie points

Tags

Geometry

Long Description

This application reads a geom file containing a sensor model and a text file containing a list of ground control point, and performs a least-square fit of the sensor model adjustable parameters to these tie points. It produces an updated geom file as output, as well as an optional ground control points based statistics file and a vector file containing residues. The output geom file can then be used to ortho-rectify the data more accurately. Plaease note that for a proper use of the application, elevation must be correctly set (including DEM and geoid file). The map parameters allows one to choose a map projection in which the accuracy will be estimated in meters.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OrthoRectification,HomologousPointsExtraction

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Rescale.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Rescale.html deleted file mode 100644 index bb606af15ef8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Rescale.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Rescale

Brief Description

Rescale the image between two given values.

Tags

Image Manipulation

Long Description

This application scales the given image pixel intensity between two given values. By default min (resp. max) value is set to 0 (resp. 255).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/RigidTransformResample-id.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/RigidTransformResample-id.html deleted file mode 100644 index 2b3101772c0c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/RigidTransformResample-id.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

RigidTransformResample

Brief Description

Resample an image with a rigid transform

Tags

Conversion,Geometry

Long Description

This application performs a parametric transform on the input image. Scaling, translation and rotation with scaling factor are handled. Parameters of the transform is expressed in physical units, thus particular attention must be paid on pixel size (value, and sign). Moreover transform is expressed from input space to output space (on the contrary ITK Transforms are expressed form output space to input space).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Translation

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/RigidTransformResample-rotation.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/RigidTransformResample-rotation.html deleted file mode 100644 index 2b3101772c0c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/RigidTransformResample-rotation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

RigidTransformResample

Brief Description

Resample an image with a rigid transform

Tags

Conversion,Geometry

Long Description

This application performs a parametric transform on the input image. Scaling, translation and rotation with scaling factor are handled. Parameters of the transform is expressed in physical units, thus particular attention must be paid on pixel size (value, and sign). Moreover transform is expressed from input space to output space (on the contrary ITK Transforms are expressed form output space to input space).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Translation

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/RigidTransformResample-translation.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/RigidTransformResample-translation.html deleted file mode 100644 index 2b3101772c0c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/RigidTransformResample-translation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

RigidTransformResample

Brief Description

Resample an image with a rigid transform

Tags

Conversion,Geometry

Long Description

This application performs a parametric transform on the input image. Scaling, translation and rotation with scaling factor are handled. Parameters of the transform is expressed in physical units, thus particular attention must be paid on pixel size (value, and sign). Moreover transform is expressed from input space to output space (on the contrary ITK Transforms are expressed form output space to input space).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Translation

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/RigidTransformResample.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/RigidTransformResample.html deleted file mode 100644 index 2b3101772c0c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/RigidTransformResample.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

RigidTransformResample

Brief Description

Resample an image with a rigid transform

Tags

Conversion,Geometry

Long Description

This application performs a parametric transform on the input image. Scaling, translation and rotation with scaling factor are handled. Parameters of the transform is expressed in physical units, thus particular attention must be paid on pixel size (value, and sign). Moreover transform is expressed from input space to output space (on the contrary ITK Transforms are expressed form output space to input space).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Translation

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/SARCalibration.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/SARCalibration.html deleted file mode 100644 index bd06f4869601..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/SARCalibration.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

SARCalibration

Brief Description

Perform radiometric calibration of SAR images. Following sensors are supported: TerraSAR-X, Sentinel1 and Radarsat-2.Both Single Look Complex(SLC) and detected products are supported as input. -

Tags

Calibration,SAR

Long Description

The objective of SAR calibration is to provide imagery in which the pixel values can be directly related to the radar backscatter of the scene. This application allows computing Sigma Naught (Radiometric Calibration) for TerraSAR-X, Sentinel1 L1 and Radarsat-2 sensors. Metadata are automatically retrieved from image products.The application supports complex and non-complex images (SLC or detected products). -

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/SARDecompositions.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/SARDecompositions.html deleted file mode 100644 index e1a43aadade2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/SARDecompositions.html +++ /dev/null @@ -1,15 +0,0 @@ - - -

SARDecompositions

Brief Description

From one-band complex images (each one related to an element of the Sinclair matrix), returns the selected decomposition.

Tags

SAR

Long Description

From one-band complex images (HH, HV, VH, VV), returns the selected decomposition. - -All the decompositions implemented are intended for the mono-static case (transmitter and receiver are co-located). -There are two kinds of decomposition : coherent ones and incoherent ones. -In the coherent case, only the Pauli decomposition is available. -In the incoherent case, there the decompositions available : Huynen, Barnes, and H-alpha-A. -User must provide three one-band complex images HH, HV or VH, and VV (mono-static case <=> HV = VH). -Incoherent decompositions consist in averaging 3x3 complex coherency/covariance matrices; the user must provide the size of the averaging window, thanks to the parameter inco.kernelsize. -

Parameters

Limitations

Some decompositions output real images, while this application outputs complex images for general purpose. -Users should pay attention to extract the real part of the results provided by this application. -

Authors

OTB-Team

See Also

SARPolarMatrixConvert, SARPolarSynth

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/SARPolarMatrixConvert.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/SARPolarMatrixConvert.html deleted file mode 100644 index 6f50b0d56481..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/SARPolarMatrixConvert.html +++ /dev/null @@ -1,32 +0,0 @@ - - -

SARPolarMatrixConvert

Brief Description

This applications allows converting classical polarimetric matrices to each other.

Tags

SAR

Long Description

This application allows converting classical polarimetric matrices to each other. -For instance, it is possible to get the coherency matrix from the Sinclar one, or the Mueller matrix from the coherency one. -The filters used in this application never handle matrices, but images where each band is related to their elements. -As most of the time SAR polarimetry handles symetric/hermitian matrices, only the relevant elements are stored, so that the images representing them have a minimal number of bands. -For instance, the coherency matrix size is 3x3 in the monostatic case, and 4x4 in the bistatic case : it will thus be stored in a 6-band or a 10-band complex image (the diagonal and the upper elements of the matrix). - -The Sinclair matrix is a special case : it is always represented as 3 or 4 one-band complex images (for mono- or bistatic case). -The available conversions are listed below: - ---- Monostatic case --- -1 msinclairtocoherency --> Sinclair matrix to coherency matrix (input : 3 x 1 complex channel (HH, HV or VH, VV) | output : 6 complex channels) -2 msinclairtocovariance --> Sinclair matrix to covariance matrix (input : 3 x 1 complex channel (HH, HV or VH, VV) | output : 6 complex channels) -3 msinclairtocircovariance --> Sinclair matrix to circular covariance matrix (input : 3 x 1 complex channel (HH, HV or VH, VV) | output : 6 complex channels) -4 mcoherencytomueller --> Coherency matrix to Mueller matrix (input : 6 complex channels | 16 real channels) -5 mcovariancetocoherencydegree --> Covariance matrix to coherency degree (input : 6 complex channels | 3 complex channels) -6 mcovariancetocoherency --> Covariance matrix to coherency matrix (input : 6 complex channels | 6 complex channels) -7 mlinearcovariancetocircularcovariance --> Covariance matrix to circular covariance matrix (input : 6 complex channels | output : 6 complex channels) - ---- Bistatic case --- -8 bsinclairtocoherency --> Sinclair matrix to coherency matrix (input : 4 x 1 complex channel (HH, HV, VH, VV) | 10 complex channels) -9 bsinclairtocovariance --> Sinclair matrix to covariance matrix (input : 4 x 1 complex channel (HH, HV, VH, VV) | output : 10 complex channels) -10 bsinclairtocircovariance --> Sinclair matrix to circular covariance matrix (input : 4 x 1 complex channel (HH, HV, VH, VV) | output : 10 complex channels) - ---- Both cases --- -11 sinclairtomueller --> Sinclair matrix to Mueller matrix (input : 4 x 1 complex channel (HH, HV, VH, VV) | output : 16 real channels) -12 muellertomcovariance --> Mueller matrix to covariance matrix (input : 16 real channels | output : 6 complex channels) -13 muellertopoldegandpower --> Mueller matrix to polarization degree and power (input : 16 real channels | output : 4 real channels) -

Parameters

Limitations

None

Authors

OTB-Team

See Also

SARPolarSynth, SARDecompositions

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/SARPolarSynth.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/SARPolarSynth.html deleted file mode 100644 index 3173baec19e6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/SARPolarSynth.html +++ /dev/null @@ -1,32 +0,0 @@ - - -

SARPolarSynth

Brief Description

Gives, for each pixel, the power that would have been received by a SAR system with a basis different from the classical (H,V) one (polarimetric synthetis).

Tags

SAR

Long Description

This application gives, for each pixel, the power that would have been received by a SAR system with a basis different from the classical (H,V) one (polarimetric synthetis). -The new basis A and B are indicated through two Jones vectors, defined by the user thanks to orientation (psi) and ellipticity (khi) parameters. -These parameters are namely psii, khii, psir and khir. The suffixes (i) and (r) refer to the transmiting antenna and the receiving antenna respectively. -Orientations and ellipticities are given in degrees, and are between -90°/90° and -45°/45° respectively. - -Four polarization architectures can be processed : -1) HH_HV_VH_VV : full polarization, general bistatic case. -2) HH_HV_VV or HH_VH_VV : full polarization, monostatic case (transmitter and receiver are co-located). -3) HH_HV : dual polarization. -4) VH_VV : dual polarization. -The application takes a complex vector image as input, where each band correspond to a particular emission/reception polarization scheme. -User must comply with the band order given above, since the bands are used to build the Sinclair matrix. - -In order to determine the architecture, the application first relies on the number of bands of the input image. -1) Architecture HH_HV_VH_VV is the only one with four bands, there is no possible confusion. -2) Concerning HH_HV_VV and HH_VH_VV architectures, both correspond to a three channels image. But they are processed in the same way, as the Sinclair matrix is symetric in the monostatic case. -3) Finally, the two last architectures (dual polarizations), can't be distinguished only by the number of bands of the input image. - User must then use the parameters emissionh and emissionv to indicate the architecture of the system : emissionh=1 and emissionv=0 --> HH_HV, emissionh=0 and emissionv=1 --> VH_VV. -Note : if the architecture is HH_HV, khii and psii are automatically set to 0°/0°; if the architecture is VH_VV, khii and psii are automatically set to 0°/90°. - -It is also possible to force the calculation to co-polar or cross-polar modes. -In the co-polar case, values for psir and khir will be ignored and forced to psii and khii; same as the cross-polar mode, where khir and psir will be forced to psii+90° and -khii. - -Finally, the result of the polarimetric synthetis is expressed in the power domain, through a one-band scalar image. -Note: this application doesn't take into account the terms which do not depend on the polarization of the antennas. -The parameter gain can be used for this purpose. - -More details can be found in the OTB CookBook (SAR processing chapter).

Parameters

Limitations

None

Authors

OTB-Team

See Also

SARDecompositions, SARPolarMatrixConvert

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/SFSTextureExtraction.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/SFSTextureExtraction.html deleted file mode 100644 index faf6fea0fd80..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/SFSTextureExtraction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

SFSTextureExtraction

Brief Description

Computes Structural Feature Set textures on every pixel of the input image selected channel

Tags

Textures,Feature Extraction

Long Description

This application computes SFS textures on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbSFSTexturesImageFilter class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/SOMClassification.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/SOMClassification.html deleted file mode 100644 index a6fc8a522dfc..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/SOMClassification.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

SOMClassification

Brief Description

SOM image classification.

Tags

Segmentation,Learning

Long Description

Unsupervised Self Organizing Map image classification.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/SarRadiometricCalibration.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/SarRadiometricCalibration.html deleted file mode 100644 index f7375f6957b3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/SarRadiometricCalibration.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

SarRadiometricCalibration

Brief Description

Perform radiometric calibration of SAR images. Following sensors are supported: TerraSAR-X, Sentinel1 and Radarsat-2.Both Single Look Complex(SLC) and detected products are supported as input. -

Tags

Calibration,SAR

Long Description

The objective of SAR calibration is to provide imagery in which the pixel values can be directly related to the radar backscatter of the scene. This application allows computing Sigma Naught (Radiometric Calibration) for TerraSAR-X, Sentinel1 L1 and Radarsat-2 sensors. Metadata are automatically retrieved from image products.The application supports complex and non-complex images (SLC or detected products). -

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Segmentation-cc.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Segmentation-cc.html deleted file mode 100644 index b88a1efd82c1..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Segmentation-cc.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

Segmentation

Brief Description

Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported.

Tags

Segmentation

Long Description

This application allows one to perform various segmentation algorithms on a multispectral image.Available segmentation algorithms are two different versions of Mean-Shift segmentation algorithm (one being multi-threaded), simple pixel based connected components according to a user-defined criterion, and watershed from the gradient of the intensity (norm of spectral bands vector). The application has two different modes that affects the nature of its output. - -In raster mode, the output of the application is a classical image of unique labels identifying the segmented regions. The labeled output can be passed to the ColorMapping application to render regions with contrasted colours. Please note that this mode loads the whole input image into memory, and as such can not handle large images. - -To segment large data, one can use the vector mode. In this case, the output of the application is a vector file or database. The input image is split into tiles (whose size can be set using the tilesize parameter), and each tile is loaded, segmented with the chosen algorithm, vectorized, and written into the output file or database. This piece-wise behavior ensure that memory will never get overloaded, and that images of any size can be processed. There are few more options in the vector mode. The simplify option allows simplifying the geometry (i.e. remove nodes in polygons) according to a user-defined tolerance. The stitch option tries to stitch together the polygons corresponding to segmented region that may have been split by the tiling scheme.

Parameters

Limitations

In raster mode, the application can not handle large input images. Stitching step of vector mode might become slow with very large input images. -MeanShift filter results depends on the number of threads used. -Watershed and multiscale geodesic morphology segmentation will be performed on the amplitude of the input image.

Authors

OTB-Team

See Also

MeanShiftSegmentation

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Segmentation-meanshift.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Segmentation-meanshift.html deleted file mode 100644 index b88a1efd82c1..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Segmentation-meanshift.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

Segmentation

Brief Description

Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported.

Tags

Segmentation

Long Description

This application allows one to perform various segmentation algorithms on a multispectral image.Available segmentation algorithms are two different versions of Mean-Shift segmentation algorithm (one being multi-threaded), simple pixel based connected components according to a user-defined criterion, and watershed from the gradient of the intensity (norm of spectral bands vector). The application has two different modes that affects the nature of its output. - -In raster mode, the output of the application is a classical image of unique labels identifying the segmented regions. The labeled output can be passed to the ColorMapping application to render regions with contrasted colours. Please note that this mode loads the whole input image into memory, and as such can not handle large images. - -To segment large data, one can use the vector mode. In this case, the output of the application is a vector file or database. The input image is split into tiles (whose size can be set using the tilesize parameter), and each tile is loaded, segmented with the chosen algorithm, vectorized, and written into the output file or database. This piece-wise behavior ensure that memory will never get overloaded, and that images of any size can be processed. There are few more options in the vector mode. The simplify option allows simplifying the geometry (i.e. remove nodes in polygons) according to a user-defined tolerance. The stitch option tries to stitch together the polygons corresponding to segmented region that may have been split by the tiling scheme.

Parameters

Limitations

In raster mode, the application can not handle large input images. Stitching step of vector mode might become slow with very large input images. -MeanShift filter results depends on the number of threads used. -Watershed and multiscale geodesic morphology segmentation will be performed on the amplitude of the input image.

Authors

OTB-Team

See Also

MeanShiftSegmentation

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Segmentation-mprofiles.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Segmentation-mprofiles.html deleted file mode 100644 index b88a1efd82c1..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Segmentation-mprofiles.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

Segmentation

Brief Description

Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported.

Tags

Segmentation

Long Description

This application allows one to perform various segmentation algorithms on a multispectral image.Available segmentation algorithms are two different versions of Mean-Shift segmentation algorithm (one being multi-threaded), simple pixel based connected components according to a user-defined criterion, and watershed from the gradient of the intensity (norm of spectral bands vector). The application has two different modes that affects the nature of its output. - -In raster mode, the output of the application is a classical image of unique labels identifying the segmented regions. The labeled output can be passed to the ColorMapping application to render regions with contrasted colours. Please note that this mode loads the whole input image into memory, and as such can not handle large images. - -To segment large data, one can use the vector mode. In this case, the output of the application is a vector file or database. The input image is split into tiles (whose size can be set using the tilesize parameter), and each tile is loaded, segmented with the chosen algorithm, vectorized, and written into the output file or database. This piece-wise behavior ensure that memory will never get overloaded, and that images of any size can be processed. There are few more options in the vector mode. The simplify option allows simplifying the geometry (i.e. remove nodes in polygons) according to a user-defined tolerance. The stitch option tries to stitch together the polygons corresponding to segmented region that may have been split by the tiling scheme.

Parameters

Limitations

In raster mode, the application can not handle large input images. Stitching step of vector mode might become slow with very large input images. -MeanShift filter results depends on the number of threads used. -Watershed and multiscale geodesic morphology segmentation will be performed on the amplitude of the input image.

Authors

OTB-Team

See Also

MeanShiftSegmentation

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Segmentation-watershed.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Segmentation-watershed.html deleted file mode 100644 index b88a1efd82c1..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Segmentation-watershed.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

Segmentation

Brief Description

Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported.

Tags

Segmentation

Long Description

This application allows one to perform various segmentation algorithms on a multispectral image.Available segmentation algorithms are two different versions of Mean-Shift segmentation algorithm (one being multi-threaded), simple pixel based connected components according to a user-defined criterion, and watershed from the gradient of the intensity (norm of spectral bands vector). The application has two different modes that affects the nature of its output. - -In raster mode, the output of the application is a classical image of unique labels identifying the segmented regions. The labeled output can be passed to the ColorMapping application to render regions with contrasted colours. Please note that this mode loads the whole input image into memory, and as such can not handle large images. - -To segment large data, one can use the vector mode. In this case, the output of the application is a vector file or database. The input image is split into tiles (whose size can be set using the tilesize parameter), and each tile is loaded, segmented with the chosen algorithm, vectorized, and written into the output file or database. This piece-wise behavior ensure that memory will never get overloaded, and that images of any size can be processed. There are few more options in the vector mode. The simplify option allows simplifying the geometry (i.e. remove nodes in polygons) according to a user-defined tolerance. The stitch option tries to stitch together the polygons corresponding to segmented region that may have been split by the tiling scheme.

Parameters

Limitations

In raster mode, the application can not handle large input images. Stitching step of vector mode might become slow with very large input images. -MeanShift filter results depends on the number of threads used. -Watershed and multiscale geodesic morphology segmentation will be performed on the amplitude of the input image.

Authors

OTB-Team

See Also

MeanShiftSegmentation

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Segmentation.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Segmentation.html deleted file mode 100644 index b88a1efd82c1..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Segmentation.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

Segmentation

Brief Description

Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported.

Tags

Segmentation

Long Description

This application allows one to perform various segmentation algorithms on a multispectral image.Available segmentation algorithms are two different versions of Mean-Shift segmentation algorithm (one being multi-threaded), simple pixel based connected components according to a user-defined criterion, and watershed from the gradient of the intensity (norm of spectral bands vector). The application has two different modes that affects the nature of its output. - -In raster mode, the output of the application is a classical image of unique labels identifying the segmented regions. The labeled output can be passed to the ColorMapping application to render regions with contrasted colours. Please note that this mode loads the whole input image into memory, and as such can not handle large images. - -To segment large data, one can use the vector mode. In this case, the output of the application is a vector file or database. The input image is split into tiles (whose size can be set using the tilesize parameter), and each tile is loaded, segmented with the chosen algorithm, vectorized, and written into the output file or database. This piece-wise behavior ensure that memory will never get overloaded, and that images of any size can be processed. There are few more options in the vector mode. The simplify option allows simplifying the geometry (i.e. remove nodes in polygons) according to a user-defined tolerance. The stitch option tries to stitch together the polygons corresponding to segmented region that may have been split by the tiling scheme.

Parameters

Limitations

In raster mode, the application can not handle large input images. Stitching step of vector mode might become slow with very large input images. -MeanShift filter results depends on the number of threads used. -Watershed and multiscale geodesic morphology segmentation will be performed on the amplitude of the input image.

Authors

OTB-Team

See Also

MeanShiftSegmentation

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Smoothing-anidif.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Smoothing-anidif.html deleted file mode 100644 index c8bcf1ce16d6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Smoothing-anidif.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Smoothing

Brief Description

Apply a smoothing filter to an image

Tags

Image Filtering

Long Description

This application applies smoothing filter to an image. Either gaussian, mean, or anisotropic diffusion are available.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Smoothing-gaussian.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Smoothing-gaussian.html deleted file mode 100644 index c8bcf1ce16d6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Smoothing-gaussian.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Smoothing

Brief Description

Apply a smoothing filter to an image

Tags

Image Filtering

Long Description

This application applies smoothing filter to an image. Either gaussian, mean, or anisotropic diffusion are available.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Smoothing-mean.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Smoothing-mean.html deleted file mode 100644 index c8bcf1ce16d6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Smoothing-mean.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Smoothing

Brief Description

Apply a smoothing filter to an image

Tags

Image Filtering

Long Description

This application applies smoothing filter to an image. Either gaussian, mean, or anisotropic diffusion are available.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Smoothing.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Smoothing.html deleted file mode 100644 index c8bcf1ce16d6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Smoothing.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Smoothing

Brief Description

Apply a smoothing filter to an image

Tags

Image Filtering

Long Description

This application applies smoothing filter to an image. Either gaussian, mean, or anisotropic diffusion are available.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/SplitImage.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/SplitImage.html deleted file mode 100644 index b589865061db..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/SplitImage.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

SplitImage

Brief Description

Split a N multiband image into N images

Tags

Image Manipulation

Long Description

This application splits a N-bands image into N mono-band images. The output images filename will be generated from the output parameter. Thus if the input image has 2 channels, and the user has set an output outimage.tif, the generated images will be outimage_0.tif and outimage_1.tif

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/StereoFramework.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/StereoFramework.html deleted file mode 100644 index 157150ba4866..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/StereoFramework.html +++ /dev/null @@ -1,16 +0,0 @@ - - -

StereoFramework

Brief Description

Compute the ground elevation based on one or multiple stereo pair(s)

Tags

Stereo

Long Description

Compute the ground elevation with a stereo block matching algorithm between one or multiple stereo pair in sensor geometry. The output is projected in desired geographic or cartographic map projection (UTM by default). The pipeline is made of the following steps: -for each sensor pair : - - compute the epipolar displacement grids from the stereo pair (direct and inverse) - - resample the stereo pair into epipolar geometry using BCO interpolation - - create masks for each epipolar image : remove black borders and resample input masks - - compute horizontal disparities with a block matching algorithm - - refine disparities to sub-pixel precision with a dichotomy algorithm - - apply an optional median filter - - filter disparities based on the correlation score and exploration bounds - - translate disparities in sensor geometry - convert disparity to 3D Map. -Then fuse all 3D maps to produce DSM.

Parameters

Limitations

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/StereoRectificationGridGenerator.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/StereoRectificationGridGenerator.html deleted file mode 100644 index 56bafc0fa3a4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/StereoRectificationGridGenerator.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

StereoRectificationGridGenerator

Brief Description

Generates two deformation fields to stereo-rectify (i.e. resample in epipolar geometry) a pair of stereo images up to the sensor model precision

Tags

Stereo

Long Description

This application generates a pair of deformation grid to stereo-rectify a pair of stereo images according to sensor modelling and a mean elevation hypothesis. The deformation grids can be passed to the StereoRectificationGridGenerator application for actual resampling in epipolar geometry.

Parameters

Limitations

Generation of the deformation grid is not streamable, pay attention to this fact when setting the grid step.

Authors

OTB-Team

See Also

otbGridBasedImageResampling

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/Superimpose.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/Superimpose.html deleted file mode 100644 index 3dc2f37a031a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/Superimpose.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Superimpose

Brief Description

Using available image metadata, project one image onto another one

Tags

Geometry,Superimposition

Long Description

This application performs the projection of an image into the geometry of another one.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/TestApplication.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/TestApplication.html deleted file mode 100644 index aac6ba570cde..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/TestApplication.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TestApplication

Brief Description

This application helps developers to test parameters types

Tags

Test

Long Description

The purpose of this application is to test parameters types.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/TileFusion.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/TileFusion.html deleted file mode 100644 index ff003aa4becc..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/TileFusion.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TileFusion

Brief Description

Fusion of an image made of several tile files.

Tags

Image Manipulation

Long Description

Concatenate several tile files into a single image file.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-ann.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-ann.html deleted file mode 100644 index 425c1e3c34e2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-ann.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-bayes.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-bayes.html deleted file mode 100644 index 425c1e3c34e2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-bayes.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-boost.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-boost.html deleted file mode 100644 index 425c1e3c34e2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-boost.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-dt.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-dt.html deleted file mode 100644 index 425c1e3c34e2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-dt.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-gbt.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-gbt.html deleted file mode 100644 index 425c1e3c34e2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-gbt.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-knn.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-knn.html deleted file mode 100644 index 425c1e3c34e2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-knn.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-rf.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-rf.html deleted file mode 100644 index 425c1e3c34e2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier-rf.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier.html deleted file mode 100644 index 425c1e3c34e2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainImagesClassifier.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainOGRLayersClassifier.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainOGRLayersClassifier.html deleted file mode 100644 index 551a2cd45440..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainOGRLayersClassifier.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TrainOGRLayersClassifier

Brief Description

Train a SVM classifier based on labeled geometries and a list of features to consider.

Tags

Segmentation

Long Description

This application trains a SVM classifier based on labeled geometries and a list of features to consider for classification.

Parameters

Limitations

Experimental. For now only shapefiles are supported. Tuning of SVM classifier is not available.

Authors

David Youssefi during internship at CNES

See Also

OGRLayerClassifier,ComputeOGRLayersFeaturesStatistics

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainRegression.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainRegression.html deleted file mode 100644 index 0e2c801d7af2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/TrainRegression.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

TrainRegression

Brief Description

Train a classifier from multiple images to perform regression.

Tags

Learning

Long Description

This application trains a classifier from multiple input images or a csv file, in order to perform regression. Predictors are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The output value for each predictor is assumed to be the last band (or the last column for CSV files). Training and validation predictor lists are built such that their size is inferior to maximum bounds given by the user, and the proportion corresponds to the balance parameter. Several classifier parameters can be set depending on the chosen classifier. In the validation process, the mean square error is computed - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataDSValidation.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataDSValidation.html deleted file mode 100644 index e2cd2032ac0a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataDSValidation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

VectorDataDSValidation

Brief Description

Vector data validation based on the fusion of features using Dempster-Shafer evidence theory framework.

Tags

Feature Extraction

Long Description

This application validates or unvalidate the studied samples using the Dempster-Shafer theory.

Parameters

Limitations

None.

Authors

OTB-Team

See Also

http://en.wikipedia.org/wiki/Dempster-Shafer_theory

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataExtractROI.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataExtractROI.html deleted file mode 100644 index 5acd2390b3b6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataExtractROI.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

VectorDataExtractROI

Brief Description

Perform an extract ROI on the input vector data according to the input image extent

Tags

Vector Data Manipulation

Long Description

This application extracts the vector data features belonging to a region specified by the support image envelope. Any features intersecting the support region is copied to output. The output geometries are NOT cropped.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataReprojection-image.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataReprojection-image.html deleted file mode 100644 index 6a546974cf23..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataReprojection-image.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

VectorDataReprojection

Brief Description

Reproject a vector data using support image projection reference, or a user specified map projection -

Tags

Geometry,Vector Data Manipulation,Coordinates

Long Description

This application allows reprojecting a vector data using support image projection reference, or a user given map projection. - If given, image keywordlist can be added to reprojected vectordata.

Parameters

Limitations

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataReprojection-user.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataReprojection-user.html deleted file mode 100644 index 6a546974cf23..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataReprojection-user.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

VectorDataReprojection

Brief Description

Reproject a vector data using support image projection reference, or a user specified map projection -

Tags

Geometry,Vector Data Manipulation,Coordinates

Long Description

This application allows reprojecting a vector data using support image projection reference, or a user given map projection. - If given, image keywordlist can be added to reprojected vectordata.

Parameters

Limitations

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataReprojection.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataReprojection.html deleted file mode 100644 index 6a546974cf23..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataReprojection.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

VectorDataReprojection

Brief Description

Reproject a vector data using support image projection reference, or a user specified map projection -

Tags

Geometry,Vector Data Manipulation,Coordinates

Long Description

This application allows reprojecting a vector data using support image projection reference, or a user given map projection. - If given, image keywordlist can be added to reprojected vectordata.

Parameters

Limitations

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataSetField.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataSetField.html deleted file mode 100644 index 34a074002ed3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataSetField.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

VectorDataSetField

Brief Description

Set a field in vector data.

Tags

Vector Data Manipulation

Long Description

Set a specified field to a specified value on all features of a vector data.

Parameters

Limitations

Doesn't work with KML files yet

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataTransform.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataTransform.html deleted file mode 100644 index ad5cda0a1e68..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/VectorDataTransform.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

VectorDataTransform

Brief Description

Apply a transform to each vertex of the input VectorData

Tags

Vector Data Manipulation

Long Description

This application performs a transformation of an input vector data transforming each vertex in the vector data. The applied transformation manages translation, rotation and scale, and can be centered or not.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.4.0/doc/VertexComponentAnalysis.html b/python/plugins/processing/algs/otb/description/5.4.0/doc/VertexComponentAnalysis.html deleted file mode 100644 index 345c7725e11f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.4.0/doc/VertexComponentAnalysis.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

VertexComponentAnalysis

Brief Description

Find endmembers in hyperspectral images with Vertex Component Analysis

Tags

Hyperspectral,Dimensionality Reduction

Long Description

Applies the Vertex Component Analysis to an hyperspectral image to extract endmembers

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/BandMath.xml b/python/plugins/processing/algs/otb/description/5.6.0/BandMath.xml deleted file mode 100644 index 62e199221bc2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/BandMath.xml +++ /dev/null @@ -1,42 +0,0 @@ - - BandMath - otbcli_BandMath - Band Math - Miscellaneous - Perform a mathematical operation on monoband images - - ParameterMultipleInput - il - Input image list - Image list to perform computation on. - - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterString - exp - Expression - The mathematical expression to apply. -Use im1b1 for the first band, im1b2 for the second one... - - - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/BinaryMorphologicalOperation-closing.xml b/python/plugins/processing/algs/otb/description/5.6.0/BinaryMorphologicalOperation-closing.xml deleted file mode 100644 index 2961f167e085..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/BinaryMorphologicalOperation-closing.xml +++ /dev/null @@ -1,77 +0,0 @@ - - BinaryMorphologicalOperation-closing - otbcli_BinaryMorphologicalOperation - BinaryMorphologicalOperation (closing) - Feature Extraction - Performs morphological operations on an input image channel - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - closing - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/BinaryMorphologicalOperation-dilate.xml b/python/plugins/processing/algs/otb/description/5.6.0/BinaryMorphologicalOperation-dilate.xml deleted file mode 100644 index 23477a328fa3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/BinaryMorphologicalOperation-dilate.xml +++ /dev/null @@ -1,97 +0,0 @@ - - BinaryMorphologicalOperation-dilate - otbcli_BinaryMorphologicalOperation - BinaryMorphologicalOperation (dilate) - Feature Extraction - Performs morphological operations on an input image channel - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - dilate - - - 0 - False - - - ParameterNumber - filter.dilate.foreval - Foreground Value - The Foreground Value - - - 1 - False - - - ParameterNumber - filter.dilate.backval - Background Value - The Background Value - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/BinaryMorphologicalOperation-erode.xml b/python/plugins/processing/algs/otb/description/5.6.0/BinaryMorphologicalOperation-erode.xml deleted file mode 100644 index c25c24f0e54c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/BinaryMorphologicalOperation-erode.xml +++ /dev/null @@ -1,77 +0,0 @@ - - BinaryMorphologicalOperation-erode - otbcli_BinaryMorphologicalOperation - BinaryMorphologicalOperation (erode) - Feature Extraction - Performs morphological operations on an input image channel - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - erode - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/BinaryMorphologicalOperation-opening.xml b/python/plugins/processing/algs/otb/description/5.6.0/BinaryMorphologicalOperation-opening.xml deleted file mode 100644 index 9af9fcb74cb0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/BinaryMorphologicalOperation-opening.xml +++ /dev/null @@ -1,77 +0,0 @@ - - BinaryMorphologicalOperation-opening - otbcli_BinaryMorphologicalOperation - BinaryMorphologicalOperation (opening) - Feature Extraction - Performs morphological operations on an input image channel - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - opening - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/ClassificationMapRegularization.xml b/python/plugins/processing/algs/otb/description/5.6.0/ClassificationMapRegularization.xml deleted file mode 100644 index 549ddcd791cb..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/ClassificationMapRegularization.xml +++ /dev/null @@ -1,69 +0,0 @@ - - ClassificationMapRegularization - otbcli_ClassificationMapRegularization - Classification Map Regularization - Learning - Filters the input labeled image using Majority Voting in a ball shaped neighbordhood. - - ParameterRaster - io.in - Input classification image - The input labeled image to regularize. - False - - - OutputRaster - io.out - Output regularized image - The output regularized labeled image. - - - - ParameterNumber - ip.radius - Structuring element radius (in pixels) - The radius of the ball shaped structuring element (expressed in pixels). By default, 'ip.radius = 1 pixel'. - - - 1 - False - - - ParameterBoolean - ip.suvbool - Multiple majority: Undecided(X)/Original - Pixels with more than 1 majority class are marked as Undecided if this parameter is checked (true), or keep their Original labels otherwise (false). Please note that the Undecided value must be different from existing labels in the input labeled image. By default, 'ip.suvbool = false'. - True - True - - - ParameterNumber - ip.nodatalabel - Label for the NoData class - Label for the NoData class. Such input pixels keep their NoData label in the output image. By default, 'ip.nodatalabel = 0'. - - - 0 - False - - - ParameterNumber - ip.undecidedlabel - Label for the Undecided class - Label for the Undecided class. By default, 'ip.undecidedlabel = 0'. - - - 0 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/ColorMapping-continuous.xml b/python/plugins/processing/algs/otb/description/5.6.0/ColorMapping-continuous.xml deleted file mode 100644 index 82ef3bd7a488..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/ColorMapping-continuous.xml +++ /dev/null @@ -1,104 +0,0 @@ - - ColorMapping-continuous - otbcli_ColorMapping - ColorMapping (continuous) - Image Manipulation - Maps an input label image to 8-bits RGB using look-up tables. - - ParameterRaster - in - Input Image - Input image filename - False - - - OutputRaster - out - Output Image - Output image filename - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - op - Operation - Selection of the operation to execute (default is : label to color). - - - labeltocolor - - - 0 - False - - - ParameterSelection - method - Color mapping method - Selection of color mapping methods and their parameters. - - - continuous - - - 0 - False - - - ParameterSelection - method.continuous.lut - Look-up tables - Available look-up tables. - - - red - green - blue - grey - hot - cool - spring - summer - autumn - winter - copper - jet - hsv - overunder - relief - - - 0 - False - - - ParameterNumber - method.continuous.min - Mapping range lower value - Set the lower input value of the mapping range. - - - 0 - False - - - ParameterNumber - method.continuous.max - Mapping range higher value - Set the higher input value of the mapping range. - - - 255 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/ColorMapping-custom.xml b/python/plugins/processing/algs/otb/description/5.6.0/ColorMapping-custom.xml deleted file mode 100644 index 7e9c7e0b515b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/ColorMapping-custom.xml +++ /dev/null @@ -1,68 +0,0 @@ - - ColorMapping-custom - otbcli_ColorMapping - ColorMapping (custom) - Image Manipulation - Maps an input label image to 8-bits RGB using look-up tables. - - ParameterRaster - in - Input Image - Input image filename - False - - - OutputRaster - out - Output Image - Output image filename - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - op - Operation - Selection of the operation to execute (default is : label to color). - - - labeltocolor - - - 0 - False - - - ParameterSelection - method - Color mapping method - Selection of color mapping methods and their parameters. - - - custom - - - 0 - False - - - ParameterFile - method.custom.lut - Look-up table file - An ASCII file containing the look-up table -with one color per line -(for instance the line '1 255 0 0' means that all pixels with label 1 will be replaced by RGB color 255 0 0) -Lines beginning with a # are ignored - - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/ColorMapping-image.xml b/python/plugins/processing/algs/otb/description/5.6.0/ColorMapping-image.xml deleted file mode 100644 index 3d52f5ffa05e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/ColorMapping-image.xml +++ /dev/null @@ -1,94 +0,0 @@ - - ColorMapping-image - otbcli_ColorMapping - ColorMapping (image) - Image Manipulation - Maps an input label image to 8-bits RGB using look-up tables. - - ParameterRaster - in - Input Image - Input image filename - False - - - OutputRaster - out - Output Image - Output image filename - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - op - Operation - Selection of the operation to execute (default is : label to color). - - - labeltocolor - - - 0 - False - - - ParameterSelection - method - Color mapping method - Selection of color mapping methods and their parameters. - - - image - - - 0 - False - - - ParameterRaster - method.image.in - Support Image - Support image filename. For each label, the LUT is calculated from the mean pixel value in the support image, over the corresponding labeled areas. First of all, the support image is normalized with extrema rejection - False - - - ParameterNumber - method.image.nodatavalue - NoData value - NoData value for each channel of the support image, which will not be handled in the LUT estimation. If NOT checked, ALL the pixel values of the support image will be handled in the LUT estimation. - - - 0 - True - - - ParameterNumber - method.image.low - lower quantile - lower quantile for image normalization - - - 2 - True - - - ParameterNumber - method.image.up - upper quantile - upper quantile for image normalization - - - 2 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/ColorMapping-optimal.xml b/python/plugins/processing/algs/otb/description/5.6.0/ColorMapping-optimal.xml deleted file mode 100644 index 473d2916c1ab..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/ColorMapping-optimal.xml +++ /dev/null @@ -1,67 +0,0 @@ - - ColorMapping-optimal - otbcli_ColorMapping - ColorMapping (optimal) - Image Manipulation - Maps an input label image to 8-bits RGB using look-up tables. - - ParameterRaster - in - Input Image - Input image filename - False - - - OutputRaster - out - Output Image - Output image filename - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - op - Operation - Selection of the operation to execute (default is : label to color). - - - labeltocolor - - - 0 - False - - - ParameterSelection - method - Color mapping method - Selection of color mapping methods and their parameters. - - - optimal - - - 0 - False - - - ParameterNumber - method.optimal.background - Background label - Value of the background label - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/CompareImages.xml b/python/plugins/processing/algs/otb/description/5.6.0/CompareImages.xml deleted file mode 100644 index 1f43f049bee5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/CompareImages.xml +++ /dev/null @@ -1,91 +0,0 @@ - - CompareImages - otbcli_CompareImages - Image comparison - Miscellaneous - Estimator between 2 images. - - ParameterRaster - ref.in - Reference image - Image used as reference in the comparison - False - - - ParameterNumber - ref.channel - Reference image channel - Used channel for the reference image - - - 1 - False - - - ParameterRaster - meas.in - Measured image - Image used as measured in the comparison - False - - - ParameterNumber - meas.channel - Measured image channel - Used channel for the measured image - - - 1 - False - - - ParameterNumber - roi.startx - Start X - ROI start x position. - - - 0 - False - - - ParameterNumber - roi.starty - Start Y - ROI start y position. - - - 0 - False - - - ParameterNumber - roi.sizex - Size X - Size along x in pixels. - - - 0 - False - - - ParameterNumber - roi.sizey - Size Y - Size along y in pixels. - - - 0 - False - - - ParameterNumber - count - count - Nb of pixels which are different - - - 0.0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/ComputeConfusionMatrix-raster.xml b/python/plugins/processing/algs/otb/description/5.6.0/ComputeConfusionMatrix-raster.xml deleted file mode 100644 index 5bf8a8210eb5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/ComputeConfusionMatrix-raster.xml +++ /dev/null @@ -1,60 +0,0 @@ - - ComputeConfusionMatrix-raster - otbcli_ComputeConfusionMatrix - ComputeConfusionMatrix (raster) - Learning - Computes the confusion matrix of a classification - - ParameterRaster - in - Input Image - The input classification image. - False - - - OutputFile - out - Matrix output - Filename to store the output matrix (csv format) - - - ParameterSelection - ref - Ground truth - Choice of ground truth format - - - raster - - - 0 - False - - - ParameterRaster - ref.raster.in - Input reference image - Input image containing the ground truth labels - False - - - ParameterNumber - nodatalabel - Value for nodata pixels - Label for the NoData class. Such input pixels will be discarded from the ground truth and from the input classification map. By default, 'nodatalabel = 0'. - - - 0 - True - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/ComputeConfusionMatrix-vector.xml b/python/plugins/processing/algs/otb/description/5.6.0/ComputeConfusionMatrix-vector.xml deleted file mode 100644 index 39c49febcf87..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/ComputeConfusionMatrix-vector.xml +++ /dev/null @@ -1,70 +0,0 @@ - - ComputeConfusionMatrix-vector - otbcli_ComputeConfusionMatrix - ComputeConfusionMatrix (vector) - Learning - Computes the confusion matrix of a classification - - ParameterRaster - in - Input Image - The input classification image. - False - - - OutputFile - out - Matrix output - Filename to store the output matrix (csv format) - - - ParameterSelection - ref - Ground truth - Choice of ground truth format - - - vector - - - 0 - False - - - ParameterFile - ref.vector.in - Input reference vector data - Input vector data of the ground truth - - False - - - ParameterString - ref.vector.field - Field name - Field name containing the label values - Class - - True - - - ParameterNumber - nodatalabel - Value for nodata pixels - Label for the NoData class. Such input pixels will be discarded from the ground truth and from the input classification map. By default, 'nodatalabel = 0'. - - - 0 - True - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/ComputeImagesStatistics.xml b/python/plugins/processing/algs/otb/description/5.6.0/ComputeImagesStatistics.xml deleted file mode 100644 index b4430a982ca6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/ComputeImagesStatistics.xml +++ /dev/null @@ -1,31 +0,0 @@ - - ComputeImagesStatistics - otbcli_ComputeImagesStatistics - Compute Images second order statistics - Learning - Computes global mean and standard deviation for each band from a set of images and optionally saves the results in an XML file. - - ParameterMultipleInput - il - Input images - List of input images filenames. - - False - - - ParameterNumber - bv - Background Value - Background value to ignore in statistics computation. - - - 0.0 - True - - - OutputFile - out - Output XML file - XML filename where the statistics are saved for future reuse. - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/ComputeOGRLayersFeaturesStatistics.xml b/python/plugins/processing/algs/otb/description/5.6.0/ComputeOGRLayersFeaturesStatistics.xml deleted file mode 100644 index b7dd4dea07c3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/ComputeOGRLayersFeaturesStatistics.xml +++ /dev/null @@ -1,30 +0,0 @@ - - ComputeOGRLayersFeaturesStatistics - otbcli_ComputeOGRLayersFeaturesStatistics - ComputeOGRLayersFeaturesStatistics - Segmentation - Compute statistics of the features in a set of OGR Layers - - ParameterVector - inshp - Name of the input shapefile - Name of the input shapefile - - False - - - OutputFile - outstats - XML file containing mean and variance of each feature. - XML file containing mean and variance of each feature. - - - ParameterString - feat - List of features to consider for statistics. - List of features to consider for statistics. - - - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/ComputePolylineFeatureFromImage.xml b/python/plugins/processing/algs/otb/description/5.6.0/ComputePolylineFeatureFromImage.xml deleted file mode 100644 index a8a72a432205..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/ComputePolylineFeatureFromImage.xml +++ /dev/null @@ -1,57 +0,0 @@ - - ComputePolylineFeatureFromImage - otbcli_ComputePolylineFeatureFromImage - Compute Polyline Feature From Image - Feature Extraction - This application compute for each studied polyline, contained in the input VectorData, the chosen descriptors. - - ParameterRaster - in - Input Image - An image to compute the descriptors on. - False - - - ParameterVector - vd - Vector Data - Vector data containing the polylines where the features will be computed. - - False - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterString - expr - Feature expression - The feature formula (b1 < 0.3) where b1 is the standard name of input image first band - - - False - - - ParameterString - field - Feature name - The field name corresponding to the feature codename (NONDVI, ROADSA...) - - - False - - - OutputVector - out - Output Vector Data - The output vector data containing polylines with a new field - - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/ConcatenateImages.xml b/python/plugins/processing/algs/otb/description/5.6.0/ConcatenateImages.xml deleted file mode 100644 index 4f7c9f4aa70f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/ConcatenateImages.xml +++ /dev/null @@ -1,32 +0,0 @@ - - ConcatenateImages - otbcli_ConcatenateImages - Images Concatenation - Image Manipulation - Concatenate a list of images of the same size into a single multi-channel one. - - ParameterMultipleInput - il - Input images list - The list of images to concatenate - - False - - - OutputRaster - out - Output Image - The concatenated output image - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/ConcatenateVectorData.xml b/python/plugins/processing/algs/otb/description/5.6.0/ConcatenateVectorData.xml deleted file mode 100644 index 9b95a36fee95..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/ConcatenateVectorData.xml +++ /dev/null @@ -1,22 +0,0 @@ - - ConcatenateVectorData - otbcli_ConcatenateVectorData - Concatenate - Vector Data Manipulation - Concatenate VectorDatas - - ParameterMultipleInput - vd - Input VectorDatas to concatenate - VectorData files to be concatenated in an unique VectorData - - False - - - OutputVector - out - Concatenated VectorData - Output conctenated VectorData - - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/ConnectedComponentSegmentation.xml b/python/plugins/processing/algs/otb/description/5.6.0/ConnectedComponentSegmentation.xml deleted file mode 100644 index c36dad59db8c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/ConnectedComponentSegmentation.xml +++ /dev/null @@ -1,68 +0,0 @@ - - ConnectedComponentSegmentation - otbcli_ConnectedComponentSegmentation - Connected Component Segmentation - Segmentation - Connected component segmentation and object based image filtering of the input image according to user-defined criterions. - - ParameterRaster - in - Input Image - The image to segment. - False - - - OutputVector - out - Output Shape - The segmentation shape. - - - - ParameterString - mask - Mask expression - Mask mathematical expression (only if support image is given) - - - True - - - ParameterString - expr - Connected Component Expression - Formula used for connected component segmentation - - - False - - - ParameterNumber - minsize - Minimum Object Size - Min object size (area in pixel) - - - 2 - True - - - ParameterString - obia - OBIA Expression - OBIA mathematical expression - - - True - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/Convert.xml b/python/plugins/processing/algs/otb/description/5.6.0/Convert.xml deleted file mode 100644 index b5e626721b40..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/Convert.xml +++ /dev/null @@ -1,83 +0,0 @@ - - Convert - otbcli_Convert - Image Conversion - Image Manipulation - Convert an image to a different format, eventually rescaling the data and/or changing the pixel type. - - ParameterRaster - in - Input image - Input image - False - - - ParameterSelection - type - Rescale type - Transfer function for the rescaling - - - none - linear - log2 - - - 0 - False - - - ParameterNumber - type.linear.gamma - Gamma correction factor - Gamma correction factor - - - 1 - True - - - ParameterRaster - mask - Input mask - The masked pixels won't be used to adapt the dynamic (the mask must have the same dimensions as the input image) - True - - - ParameterNumber - hcp.high - High Cut Quantile - Quantiles to cut from histogram high values before computing min/max rescaling (in percent, 2 by default) - - - 2 - True - - - ParameterNumber - hcp.low - Low Cut Quantile - Quantiles to cut from histogram low values before computing min/max rescaling (in percent, 2 by default) - - - 2 - True - - - OutputRaster - out - Output Image - Output image - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/DEMConvert.xml b/python/plugins/processing/algs/otb/description/5.6.0/DEMConvert.xml deleted file mode 100644 index 8e017ebe1336..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/DEMConvert.xml +++ /dev/null @@ -1,20 +0,0 @@ - - DEMConvert - otbcli_DEMConvert - DEM Conversion - Image Manipulation - Converts a geo-referenced DEM image into a general raster file compatible with OTB DEM handling. - - ParameterRaster - in - Input geo-referenced DEM - Input geo-referenced DEM to convert to general raster format. - False - - - OutputFile - out - Prefix of the output files - will be used to get the prefix (name withtout extensions) of the files to write. Three files - prefix.geom, prefix.omd and prefix.ras - will be generated. - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/Despeckle-frost.xml b/python/plugins/processing/algs/otb/description/5.6.0/Despeckle-frost.xml deleted file mode 100644 index e7e3d54aebcf..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/Despeckle-frost.xml +++ /dev/null @@ -1,64 +0,0 @@ - - Despeckle-frost - otbcli_Despeckle - Despeckle (frost) - Image Filtering - Perform speckle noise reduction on SAR image. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - filter - speckle filtering method - - - - frost - - - 0 - False - - - ParameterNumber - filter.frost.rad - Radius - Radius for frost filter - - - 1 - False - - - ParameterNumber - filter.frost.deramp - deramp - Decrease factor declaration - - - 0.1 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/Despeckle-gammamap.xml b/python/plugins/processing/algs/otb/description/5.6.0/Despeckle-gammamap.xml deleted file mode 100644 index 25609700b661..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/Despeckle-gammamap.xml +++ /dev/null @@ -1,64 +0,0 @@ - - Despeckle-gammamap - otbcli_Despeckle - Despeckle (gammamap) - Image Filtering - Perform speckle noise reduction on SAR image. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - filter - speckle filtering method - - - - gammamap - - - 0 - False - - - ParameterNumber - filter.gammamap.rad - Radius - Radius for GammaMAP filter - - - 1 - False - - - ParameterNumber - filter.gammamap.nblooks - nb looks - Nb looks for GammaMAP filter - - - 1 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/Despeckle-kuan.xml b/python/plugins/processing/algs/otb/description/5.6.0/Despeckle-kuan.xml deleted file mode 100644 index ac47ace38d3b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/Despeckle-kuan.xml +++ /dev/null @@ -1,64 +0,0 @@ - - Despeckle-kuan - otbcli_Despeckle - Despeckle (kuan) - Image Filtering - Perform speckle noise reduction on SAR image. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - filter - speckle filtering method - - - - kuan - - - 0 - False - - - ParameterNumber - filter.kuan.rad - Radius - Radius for Kuan filter - - - 0 - False - - - ParameterNumber - filter.kuan.nblooks - nb looks - Nb looks for Kuan filter - - - 0.0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/Despeckle-lee.xml b/python/plugins/processing/algs/otb/description/5.6.0/Despeckle-lee.xml deleted file mode 100644 index 99dad8b3254c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/Despeckle-lee.xml +++ /dev/null @@ -1,64 +0,0 @@ - - Despeckle-lee - otbcli_Despeckle - Despeckle (lee) - Image Filtering - Perform speckle noise reduction on SAR image. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - filter - speckle filtering method - - - - lee - - - 0 - False - - - ParameterNumber - filter.lee.rad - Radius - Radius for lee filter - - - 1 - False - - - ParameterNumber - filter.lee.nblooks - nb looks - Nb looks for lee filter - - - 1 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/DimensionalityReduction-ica.xml b/python/plugins/processing/algs/otb/description/5.6.0/DimensionalityReduction-ica.xml deleted file mode 100644 index 6b4fbdfd3953..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/DimensionalityReduction-ica.xml +++ /dev/null @@ -1,85 +0,0 @@ - - DimensionalityReduction-ica - otbcli_DimensionalityReduction - DimensionalityReduction (ica) - Image Filtering - Perform Dimension reduction of the input image. - - ParameterRaster - in - Input Image - The input image to apply dimensionality reduction. - False - - - OutputRaster - out - Output Image - output image. Components are ordered by decreasing eigenvalues. - - - - OutputRaster - outinv - Inverse Output Image - reconstruct output image. - - - - ParameterSelection - method - Algorithm - Selection of the reduction dimension method. - - - ica - - - 0 - False - - - ParameterNumber - method.ica.iter - number of iterations - - - - 20 - True - - - ParameterNumber - method.ica.mu - Give the increment weight of W in [0, 1] - - - - 1 - True - - - ParameterNumber - nbcomp - Number of Components. - Number of relevant components kept. By default all components are kept. - - - 0 - True - - - ParameterBoolean - normalize - Normalize. - center AND reduce data before Dimensionality reduction. - True - True - - - OutputFile - outmatrix - Transformation matrix output (text format) - Filename to store the transformation matrix (csv format) - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/DimensionalityReduction-maf.xml b/python/plugins/processing/algs/otb/description/5.6.0/DimensionalityReduction-maf.xml deleted file mode 100644 index 78b403bab5d4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/DimensionalityReduction-maf.xml +++ /dev/null @@ -1,58 +0,0 @@ - - DimensionalityReduction-maf - otbcli_DimensionalityReduction - DimensionalityReduction (maf) - Image Filtering - Perform Dimension reduction of the input image. - - ParameterRaster - in - Input Image - The input image to apply dimensionality reduction. - False - - - OutputRaster - out - Output Image - output image. Components are ordered by decreasing eigenvalues. - - - - ParameterSelection - method - Algorithm - Selection of the reduction dimension method. - - - maf - - - 0 - False - - - ParameterNumber - nbcomp - Number of Components. - Number of relevant components kept. By default all components are kept. - - - 0 - True - - - ParameterBoolean - normalize - Normalize. - center AND reduce data before Dimensionality reduction. - True - True - - - OutputFile - outmatrix - Transformation matrix output (text format) - Filename to store the transformation matrix (csv format) - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/DimensionalityReduction-napca.xml b/python/plugins/processing/algs/otb/description/5.6.0/DimensionalityReduction-napca.xml deleted file mode 100644 index 6917a53ab9c4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/DimensionalityReduction-napca.xml +++ /dev/null @@ -1,85 +0,0 @@ - - DimensionalityReduction-napca - otbcli_DimensionalityReduction - DimensionalityReduction (napca) - Image Filtering - Perform Dimension reduction of the input image. - - ParameterRaster - in - Input Image - The input image to apply dimensionality reduction. - False - - - OutputRaster - out - Output Image - output image. Components are ordered by decreasing eigenvalues. - - - - OutputRaster - outinv - Inverse Output Image - reconstruct output image. - - - - ParameterSelection - method - Algorithm - Selection of the reduction dimension method. - - - napca - - - 0 - False - - - ParameterNumber - method.napca.radiusx - Set the x radius of the sliding window. - - - - 1 - False - - - ParameterNumber - method.napca.radiusy - Set the y radius of the sliding window. - - - - 1 - False - - - ParameterNumber - nbcomp - Number of Components. - Number of relevant components kept. By default all components are kept. - - - 0 - True - - - ParameterBoolean - normalize - Normalize. - center AND reduce data before Dimensionality reduction. - True - True - - - OutputFile - outmatrix - Transformation matrix output (text format) - Filename to store the transformation matrix (csv format) - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/DimensionalityReduction-pca.xml b/python/plugins/processing/algs/otb/description/5.6.0/DimensionalityReduction-pca.xml deleted file mode 100644 index c1c5439b39ca..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/DimensionalityReduction-pca.xml +++ /dev/null @@ -1,65 +0,0 @@ - - DimensionalityReduction-pca - otbcli_DimensionalityReduction - DimensionalityReduction (pca) - Image Filtering - Perform Dimension reduction of the input image. - - ParameterRaster - in - Input Image - The input image to apply dimensionality reduction. - False - - - OutputRaster - out - Output Image - output image. Components are ordered by decreasing eigenvalues. - - - - OutputRaster - outinv - Inverse Output Image - reconstruct output image. - - - - ParameterSelection - method - Algorithm - Selection of the reduction dimension method. - - - pca - - - 0 - False - - - ParameterNumber - nbcomp - Number of Components. - Number of relevant components kept. By default all components are kept. - - - 0 - True - - - ParameterBoolean - normalize - Normalize. - center AND reduce data before Dimensionality reduction. - True - True - - - OutputFile - outmatrix - Transformation matrix output (text format) - Filename to store the transformation matrix (csv format) - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/EdgeExtraction-gradient.xml b/python/plugins/processing/algs/otb/description/5.6.0/EdgeExtraction-gradient.xml deleted file mode 100644 index 6bf5b003761a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/EdgeExtraction-gradient.xml +++ /dev/null @@ -1,54 +0,0 @@ - - EdgeExtraction-gradient - otbcli_EdgeExtraction - EdgeExtraction (gradient) - Feature Extraction - Computes edge features on every pixel of the input image selected channel - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - filter - Edge feature - Choice of edge feature - - - gradient - - - 0 - False - - - OutputRaster - out - Feature Output Image - Output image containing the edge features. - - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/EdgeExtraction-sobel.xml b/python/plugins/processing/algs/otb/description/5.6.0/EdgeExtraction-sobel.xml deleted file mode 100644 index e322268eb1fa..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/EdgeExtraction-sobel.xml +++ /dev/null @@ -1,54 +0,0 @@ - - EdgeExtraction-sobel - otbcli_EdgeExtraction - EdgeExtraction (sobel) - Feature Extraction - Computes edge features on every pixel of the input image selected channel - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - filter - Edge feature - Choice of edge feature - - - sobel - - - 0 - False - - - OutputRaster - out - Feature Output Image - Output image containing the edge features. - - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/EdgeExtraction-touzi.xml b/python/plugins/processing/algs/otb/description/5.6.0/EdgeExtraction-touzi.xml deleted file mode 100644 index ea043b256958..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/EdgeExtraction-touzi.xml +++ /dev/null @@ -1,64 +0,0 @@ - - EdgeExtraction-touzi - otbcli_EdgeExtraction - EdgeExtraction (touzi) - Feature Extraction - Computes edge features on every pixel of the input image selected channel - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - filter - Edge feature - Choice of edge feature - - - touzi - - - 0 - False - - - ParameterNumber - filter.touzi.xradius - The Radius - The Radius - - - 1 - False - - - OutputRaster - out - Feature Output Image - Output image containing the edge features. - - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/ExtractROI-fit.xml b/python/plugins/processing/algs/otb/description/5.6.0/ExtractROI-fit.xml deleted file mode 100644 index 973c0a19da11..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/ExtractROI-fit.xml +++ /dev/null @@ -1,61 +0,0 @@ - - ExtractROI-fit - otbcli_ExtractROI - ExtractROI (fit) - Image Manipulation - Extract a ROI defined by the user. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - mode - Extraction mode - - - - fit - - - 0 - False - - - ParameterRaster - mode.fit.ref - Reference image - Reference image to define the ROI - False - - - ParameterNumber - mode.fit.elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/ExtractROI-standard.xml b/python/plugins/processing/algs/otb/description/5.6.0/ExtractROI-standard.xml deleted file mode 100644 index e898dbf6b6cc..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/ExtractROI-standard.xml +++ /dev/null @@ -1,84 +0,0 @@ - - ExtractROI-standard - otbcli_ExtractROI - ExtractROI (standard) - Image Manipulation - Extract a ROI defined by the user. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - mode - Extraction mode - - - - standard - - - 0 - False - - - ParameterNumber - startx - Start X - ROI start x position. - - - 0 - False - - - ParameterNumber - starty - Start Y - ROI start y position. - - - 0 - False - - - ParameterNumber - sizex - Size X - size along x in pixels. - - - 0 - False - - - ParameterNumber - sizey - Size Y - size along y in pixels. - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/FusionOfClassifications-dempstershafer.xml b/python/plugins/processing/algs/otb/description/5.6.0/FusionOfClassifications-dempstershafer.xml deleted file mode 100644 index 96d4a0cbe02c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/FusionOfClassifications-dempstershafer.xml +++ /dev/null @@ -1,79 +0,0 @@ - - FusionOfClassifications-dempstershafer - otbcli_FusionOfClassifications - FusionOfClassifications (dempstershafer) - Learning - Fuses several classifications maps of the same image on the basis of class labels. - - ParameterMultipleInput - il - Input classifications - List of input classification maps to fuse. Labels in each classification image must represent the same class. - - False - - - ParameterSelection - method - Fusion method - Selection of the fusion method and its parameters. - - - dempstershafer - - - 0 - False - - - ParameterMultipleInput - method.dempstershafer.cmfl - Confusion Matrices - A list of confusion matrix files (*.CSV format) to define the masses of belief and the class labels. Each file should be formatted the following way: the first line, beginning with a '#' symbol, should be a list of the class labels present in the corresponding input classification image, organized in the same order as the confusion matrix rows/columns. - - False - - - ParameterSelection - method.dempstershafer.mob - Mass of belief measurement - Type of confusion matrix measurement used to compute the masses of belief of each classifier. - - - precision - recall - accuracy - kappa - - - 0 - False - - - ParameterNumber - nodatalabel - Label for the NoData class - Label for the NoData class. Such input pixels keep their NoData label in the output image and are not handled in the fusion process. By default, 'nodatalabel = 0'. - - - 0 - False - - - ParameterNumber - undecidedlabel - Label for the Undecided class - Label for the Undecided class. Pixels with more than 1 fused class are marked as Undecided. Please note that the Undecided value must be different from existing labels in the input classifications. By default, 'undecidedlabel = 0'. - - - 0 - False - - - OutputRaster - out - The output classification image - The output classification image resulting from the fusion of the input classification images. - - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/FusionOfClassifications-majorityvoting.xml b/python/plugins/processing/algs/otb/description/5.6.0/FusionOfClassifications-majorityvoting.xml deleted file mode 100644 index abd3f7cb1289..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/FusionOfClassifications-majorityvoting.xml +++ /dev/null @@ -1,55 +0,0 @@ - - FusionOfClassifications-majorityvoting - otbcli_FusionOfClassifications - FusionOfClassifications (majorityvoting) - Learning - Fuses several classifications maps of the same image on the basis of class labels. - - ParameterMultipleInput - il - Input classifications - List of input classification maps to fuse. Labels in each classification image must represent the same class. - - False - - - ParameterSelection - method - Fusion method - Selection of the fusion method and its parameters. - - - majorityvoting - - - 0 - False - - - ParameterNumber - nodatalabel - Label for the NoData class - Label for the NoData class. Such input pixels keep their NoData label in the output image and are not handled in the fusion process. By default, 'nodatalabel = 0'. - - - 0 - False - - - ParameterNumber - undecidedlabel - Label for the Undecided class - Label for the Undecided class. Pixels with more than 1 fused class are marked as Undecided. Please note that the Undecided value must be different from existing labels in the input classifications. By default, 'undecidedlabel = 0'. - - - 0 - False - - - OutputRaster - out - The output classification image - The output classification image resulting from the fusion of the input classification images. - - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/GrayScaleMorphologicalOperation-closing.xml b/python/plugins/processing/algs/otb/description/5.6.0/GrayScaleMorphologicalOperation-closing.xml deleted file mode 100644 index 5d5e5f146bc8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/GrayScaleMorphologicalOperation-closing.xml +++ /dev/null @@ -1,77 +0,0 @@ - - GrayScaleMorphologicalOperation-closing - otbcli_GrayScaleMorphologicalOperation - GrayScaleMorphologicalOperation (closing) - Feature Extraction - Performs morphological operations on a grayscale input image - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - closing - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/GrayScaleMorphologicalOperation-dilate.xml b/python/plugins/processing/algs/otb/description/5.6.0/GrayScaleMorphologicalOperation-dilate.xml deleted file mode 100644 index 7302c31336de..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/GrayScaleMorphologicalOperation-dilate.xml +++ /dev/null @@ -1,77 +0,0 @@ - - GrayScaleMorphologicalOperation-dilate - otbcli_GrayScaleMorphologicalOperation - GrayScaleMorphologicalOperation (dilate) - Feature Extraction - Performs morphological operations on a grayscale input image - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - dilate - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/GrayScaleMorphologicalOperation-erode.xml b/python/plugins/processing/algs/otb/description/5.6.0/GrayScaleMorphologicalOperation-erode.xml deleted file mode 100644 index 7da86e36fea2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/GrayScaleMorphologicalOperation-erode.xml +++ /dev/null @@ -1,77 +0,0 @@ - - GrayScaleMorphologicalOperation-erode - otbcli_GrayScaleMorphologicalOperation - GrayScaleMorphologicalOperation (erode) - Feature Extraction - Performs morphological operations on a grayscale input image - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - erode - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/GrayScaleMorphologicalOperation-opening.xml b/python/plugins/processing/algs/otb/description/5.6.0/GrayScaleMorphologicalOperation-opening.xml deleted file mode 100644 index e9781f67cab4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/GrayScaleMorphologicalOperation-opening.xml +++ /dev/null @@ -1,77 +0,0 @@ - - GrayScaleMorphologicalOperation-opening - otbcli_GrayScaleMorphologicalOperation - GrayScaleMorphologicalOperation (opening) - Feature Extraction - Performs morphological operations on a grayscale input image - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - opening - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/HaralickTextureExtraction.xml b/python/plugins/processing/algs/otb/description/5.6.0/HaralickTextureExtraction.xml deleted file mode 100644 index 12a02eeacf11..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/HaralickTextureExtraction.xml +++ /dev/null @@ -1,126 +0,0 @@ - - HaralickTextureExtraction - otbcli_HaralickTextureExtraction - Haralick Texture Extraction - Feature Extraction - Computes textures on every pixel of the input image selected channel - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterNumber - parameters.xrad - X Radius - X Radius - - - 2 - False - - - ParameterNumber - parameters.yrad - Y Radius - Y Radius - - - 2 - False - - - ParameterNumber - parameters.xoff - X Offset - X Offset - - - 1 - False - - - ParameterNumber - parameters.yoff - Y Offset - Y Offset - - - 1 - False - - - ParameterNumber - parameters.min - Image Minimum - Image Minimum - - - 0 - False - - - ParameterNumber - parameters.max - Image Maximum - Image Maximum - - - 255 - False - - - ParameterNumber - parameters.nbbin - Histogram number of bin - Histogram number of bin - - - 8 - False - - - ParameterSelection - texture - Texture Set Selection - Choice of The Texture Set - - - simple - advanced - higher - - - 0 - False - - - OutputRaster - out - Output Image - Output image containing the selected texture features. - - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/HooverCompareSegmentation.xml b/python/plugins/processing/algs/otb/description/5.6.0/HooverCompareSegmentation.xml deleted file mode 100644 index 2646745b3f7e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/HooverCompareSegmentation.xml +++ /dev/null @@ -1,95 +0,0 @@ - - HooverCompareSegmentation - otbcli_HooverCompareSegmentation - Hoover compare segmentation - Segmentation - Compare two segmentations with Hoover metrics - - ParameterRaster - ingt - Input ground truth - A partial ground truth segmentation image. - False - - - ParameterRaster - inms - Input machine segmentation - A machine segmentation image. - False - - - ParameterNumber - bg - Background label - Label value of the background in the input segmentations - - - 0 - False - - - ParameterNumber - th - Overlapping threshold - Overlapping threshold used to find Hoover instances. - - - 0.75 - False - - - OutputRaster - outgt - Colored ground truth output - The colored ground truth output image. - - - - OutputRaster - outms - Colored machine segmentation output - The colored machine segmentation output image. - - - - ParameterNumber - rc - Correct detection score - Overall score for correct detection (RC) - - - 0.0 - False - - - ParameterNumber - rf - Over-segmentation score - Overall score for over segmentation (RF) - - - 0.0 - False - - - ParameterNumber - ra - Under-segmentation score - Overall score for under segmentation (RA) - - - 0.0 - False - - - ParameterNumber - rm - Missed detection score - Overall score for missed detection (RM) - - - 0.0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/ImageClassifier.xml b/python/plugins/processing/algs/otb/description/5.6.0/ImageClassifier.xml deleted file mode 100644 index 555b0eca3e43..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/ImageClassifier.xml +++ /dev/null @@ -1,72 +0,0 @@ - - ImageClassifier - otbcli_ImageClassifier - Image Classification - Learning - Performs a classification of the input image according to a model file. - - ParameterRaster - in - Input Image - The input image to classify. - False - - - ParameterRaster - mask - Input Mask - The mask allows restricting classification of the input image to the area where mask pixel values are greater than 0. - True - - - ParameterFile - model - Model file - A model file (produced by TrainImagesClassifier application, maximal class label = 65535). - - False - - - ParameterFile - imstat - Statistics file - A XML file containing mean and standard deviation to center and reduce samples before classification (produced by ComputeImagesStatistics application). - - True - - - OutputRaster - out - Output Image - Output image containing class labels - - - - OutputRaster - confmap - Confidence map - Confidence map of the produced classification. The confidence index depends on the model : - - LibSVM : difference between the two highest probabilities (needs a model with probability estimates, so that classes probabilities can be computed for each sample) - - OpenCV - * Boost : sum of votes - * DecisionTree : (not supported) - * GradientBoostedTree : (not supported) - * KNearestNeighbors : number of neighbors with the same label - * NeuralNetwork : difference between the two highest responses - * NormalBayes : (not supported) - * RandomForest : Confidence (proportion of votes for the majority class). Margin (normalized difference of the votes of the 2 majority classes) is not available for now. - * SVM : distance to margin (only works for 2-class models) - - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/ImageEnvelope.xml b/python/plugins/processing/algs/otb/description/5.6.0/ImageEnvelope.xml deleted file mode 100644 index fef99dd96131..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/ImageEnvelope.xml +++ /dev/null @@ -1,40 +0,0 @@ - - ImageEnvelope - otbcli_ImageEnvelope - Image Envelope - Geometry - Extracts an image envelope. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputVector - out - Output Vector Data - Vector data file containing the envelope - - - - ParameterNumber - sr - Sampling Rate - Sampling rate for image edges (in pixel) - - - 0 - True - - - ParameterString - proj - Projection - Projection to be used to compute the envelope (default is WGS84) - - - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/KMeansClassification.xml b/python/plugins/processing/algs/otb/description/5.6.0/KMeansClassification.xml deleted file mode 100644 index 9cac46c41172..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/KMeansClassification.xml +++ /dev/null @@ -1,84 +0,0 @@ - - KMeansClassification - otbcli_KMeansClassification - Unsupervised KMeans image classification - Learning - Unsupervised KMeans image classification - - ParameterRaster - in - Input Image - Input image to classify. - False - - - OutputRaster - out - Output Image - Output image containing the class indexes. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterRaster - vm - Validity Mask - Validity mask. Only non-zero pixels will be used to estimate KMeans modes. - True - - - ParameterNumber - ts - Training set size - Size of the training set (in pixels). - - - 100 - True - - - ParameterNumber - nc - Number of classes - Number of modes, which will be used to generate class membership. - - - 5 - False - - - ParameterNumber - maxit - Maximum number of iterations - Maximum number of iterations for the learning step. - - - 1000 - True - - - ParameterNumber - ct - Convergence threshold - Convergence threshold for class centroid (L2 distance, by default 0.0001). - - - 0.0001 - True - - - OutputFile - outmeans - Centroid filename - Output text file containing centroid positions - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/KmzExport.xml b/python/plugins/processing/algs/otb/description/5.6.0/KmzExport.xml deleted file mode 100644 index 57469ba47a5c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/KmzExport.xml +++ /dev/null @@ -1,54 +0,0 @@ - - KmzExport - otbcli_KmzExport - Image to KMZ Export - Miscellaneous - Export the input image in a KMZ product. - - ParameterRaster - in - Input image - Input image - False - - - OutputFile - out - Output .kmz product - Output Kmz product directory (with .kmz extension) - - - ParameterNumber - tilesize - Tile Size - Size of the tiles in the kmz product, in number of pixels (default = 512). - - - 512 - True - - - ParameterRaster - logo - Image logo - Path to the image logo to add to the KMZ product. - True - - - ParameterRaster - legend - Image legend - Path to the image legend to add to the KMZ product. - True - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/LSMSSegmentation.xml b/python/plugins/processing/algs/otb/description/5.6.0/LSMSSegmentation.xml deleted file mode 100644 index ceb358d940ea..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/LSMSSegmentation.xml +++ /dev/null @@ -1,94 +0,0 @@ - - LSMSSegmentation - otbcli_LSMSSegmentation - Exact Large-Scale Mean-Shift segmentation, step 2 - Segmentation - Second step of the exact Large-Scale Mean-Shift segmentation workflow. - - ParameterRaster - in - Filtered image - The filtered image (cf. Adaptive MeanShift Smoothing application). - False - - - ParameterRaster - inpos - Spatial image - The spatial image. Spatial input is the displacement map (output of the Adaptive MeanShift Smoothing application). - True - - - OutputRaster - out - Output Image - The output image. The output image is the segmentation of the filtered image. It is recommended to set the pixel type to uint32. - - - - ParameterNumber - spatialr - Spatial radius - Spatial radius of the neighborhood. - - - 5 - True - - - ParameterNumber - ranger - Range radius - Range radius defining the radius (expressed in radiometry unit) in the multi-spectral space. - - - 15 - True - - - ParameterNumber - minsize - Minimum Region Size - Minimum Region Size. If, after the segmentation, a region is of size lower than this criterion, the region is deleted. - - - 0 - True - - - ParameterNumber - tilesizex - Size of tiles in pixel (X-axis) - Size of tiles along the X-axis. - - - 500 - False - - - ParameterNumber - tilesizey - Size of tiles in pixel (Y-axis) - Size of tiles along the Y-axis. - - - 500 - False - - - ParameterFile - tmpdir - Directory where to write temporary files - This applications need to write temporary files for each tile. This parameter allows choosing the path where to write those files. If disabled, the current path will be used. - - True - - - ParameterBoolean - cleanup - Temporary files cleaning - If activated, the application will try to clean all temporary files it created - True - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/LSMSSmallRegionsMerging.xml b/python/plugins/processing/algs/otb/description/5.6.0/LSMSSmallRegionsMerging.xml deleted file mode 100644 index c3ccd89c0e4a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/LSMSSmallRegionsMerging.xml +++ /dev/null @@ -1,58 +0,0 @@ - - LSMSSmallRegionsMerging - otbcli_LSMSSmallRegionsMerging - Exact Large-Scale Mean-Shift segmentation, step 3 (optional) - Segmentation - Third (optional) step of the exact Large-Scale Mean-Shift segmentation workflow. - - ParameterRaster - in - Input image - The input image. - False - - - ParameterRaster - inseg - Segmented image - The segmented image input. Segmented image input is the segmentation of the input image. - False - - - OutputRaster - out - Output Image - The output image. The output image is the input image where the minimal regions have been merged. - - - - ParameterNumber - minsize - Minimum Region Size - Minimum Region Size. If, after the segmentation, a region is of size lower than this criterion, the region is merged with the "nearest" region (radiometrically). - - - 50 - True - - - ParameterNumber - tilesizex - Size of tiles in pixel (X-axis) - Size of tiles along the X-axis. - - - 500 - False - - - ParameterNumber - tilesizey - Size of tiles in pixel (Y-axis) - Size of tiles along the Y-axis. - - - 500 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/LSMSVectorization.xml b/python/plugins/processing/algs/otb/description/5.6.0/LSMSVectorization.xml deleted file mode 100644 index 8987a219f834..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/LSMSVectorization.xml +++ /dev/null @@ -1,47 +0,0 @@ - - LSMSVectorization - otbcli_LSMSVectorization - Exact Large-Scale Mean-Shift segmentation, step 4 - Segmentation - Fourth step of the exact Large-Scale Mean-Shift segmentation workflow. - - ParameterRaster - in - Input Image - The input image. - False - - - ParameterRaster - inseg - Segmented image - The segmented image input. Segmented image input is the segmentation of the input image. - False - - - OutputVector - out - Output GIS vector file - The output GIS vector file, representing the vectorized version of the segmented image where the features of the polygons are the radiometric means and variances. - - - ParameterNumber - tilesizex - Size of tiles in pixel (X-axis) - Size of tiles along the X-axis. - - - 500 - False - - - ParameterNumber - tilesizey - Size of tiles in pixel (Y-axis) - Size of tiles along the Y-axis. - - - 500 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/LineSegmentDetection.xml b/python/plugins/processing/algs/otb/description/5.6.0/LineSegmentDetection.xml deleted file mode 100644 index 6c26134048ba..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/LineSegmentDetection.xml +++ /dev/null @@ -1,29 +0,0 @@ - - LineSegmentDetection - otbcli_LineSegmentDetection - Line segment detection - Feature Extraction - Detect line segments in raster - - ParameterRaster - in - Input Image - Input image on which lines will be detected. - False - - - OutputVector - out - Output Detected lines - Output detected line segments (vector data). - - - - ParameterBoolean - norescale - No rescaling in [0, 255] - By default, the input image amplitude is rescaled between [0,255]. Turn on this parameter to skip rescaling - True - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/LocalStatisticExtraction.xml b/python/plugins/processing/algs/otb/description/5.6.0/LocalStatisticExtraction.xml deleted file mode 100644 index 663bd63bd3e9..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/LocalStatisticExtraction.xml +++ /dev/null @@ -1,51 +0,0 @@ - - LocalStatisticExtraction - otbcli_LocalStatisticExtraction - Local Statistic Extraction - Feature Extraction - Computes local statistical moments on every pixel in the selected channel of the input image - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterNumber - radius - Neighborhood radius - The computational window radius. - - - 3 - False - - - OutputRaster - out - Feature Output Image - Output image containing the local statistical moments. - - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/ManageNoData.xml b/python/plugins/processing/algs/otb/description/5.6.0/ManageNoData.xml deleted file mode 100644 index 01aa54e94416..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/ManageNoData.xml +++ /dev/null @@ -1,101 +0,0 @@ - - ManageNoData - otbcli_ManageNoData - No Data management - Image Manipulation - Manage No-Data - - ParameterRaster - in - Input image - Input image - False - - - OutputRaster - out - Output Image - Output image - - - - ParameterBoolean - usenan - Consider NaN as no-data - If active, the application will consider NaN as no-data values as well - True - True - - - ParameterSelection - mode - No-data handling mode - Allows choosing between different no-data handling options - - - buildmask - changevalue - apply - - - 0 - False - - - ParameterNumber - mode.buildmask.inv - Inside Value - Value given in the output mask to pixels that are not no data pixels - - - 1 - False - - - ParameterNumber - mode.buildmask.outv - Outside Value - Value given in the output mask to pixels that are no data pixels - - - 0 - False - - - ParameterNumber - mode.changevalue.newv - The new no-data value - The new no-data value - - - 0 - False - - - ParameterRaster - mode.apply.mask - Mask image - Mask to be applied on input image (valid pixels have non null values) - False - - - ParameterNumber - mode.apply.ndval - Nodata value used - No Data value used according to the mask image - - - 0 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/MeanShiftSmoothing.xml b/python/plugins/processing/algs/otb/description/5.6.0/MeanShiftSmoothing.xml deleted file mode 100644 index 8b4376573e39..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/MeanShiftSmoothing.xml +++ /dev/null @@ -1,96 +0,0 @@ - - MeanShiftSmoothing - otbcli_MeanShiftSmoothing - Exact Large-Scale Mean-Shift segmentation, step 1 (smoothing) - Image Filtering - Perform mean shift filtering - - ParameterRaster - in - Input Image - The input image. - False - - - OutputRaster - fout - Filtered output - The filtered output image. - - - - OutputRaster - foutpos - Spatial image - The spatial image output. Spatial image output is a displacement map (pixel position after convergence). - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterNumber - spatialr - Spatial radius - Spatial radius of the neighborhood. - - - 5 - True - - - ParameterNumber - ranger - Range radius - Range radius defining the radius (expressed in radiometry unit) in the multi-spectral space. - - - 15 - True - - - ParameterNumber - thres - Mode convergence threshold - Algorithm iterative scheme will stop if mean-shift vector is below this threshold or if iteration number reached maximum number of iterations. - - - 0.1 - True - - - ParameterNumber - maxiter - Maximum number of iterations - Algorithm iterative scheme will stop if convergence hasn't been reached after the maximum number of iterations. - - - 100 - True - - - ParameterNumber - rangeramp - Range radius coefficient - This coefficient makes dependent the ranger of the colorimetry of the filtered pixel : y = rangeramp*x+ranger. - - - 0 - True - - - ParameterBoolean - modesearch - Mode search. - If activated pixel iterative convergence is stopped if the path crosses an already converged pixel. Be careful, with this option, the result will slightly depend on thread number - True - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/MultivariateAlterationDetector.xml b/python/plugins/processing/algs/otb/description/5.6.0/MultivariateAlterationDetector.xml deleted file mode 100644 index 3fa140a5e78d..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/MultivariateAlterationDetector.xml +++ /dev/null @@ -1,38 +0,0 @@ - - MultivariateAlterationDetector - otbcli_MultivariateAlterationDetector - Multivariate alteration detector - Feature Extraction - Multivariate Alteration Detector - - ParameterRaster - in1 - Input Image 1 - Image which describe initial state of the scene. - False - - - ParameterRaster - in2 - Input Image 2 - Image which describe scene after perturbations. - False - - - OutputRaster - out - Change Map - Image of detected changes. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/OGRLayerClassifier.xml b/python/plugins/processing/algs/otb/description/5.6.0/OGRLayerClassifier.xml deleted file mode 100644 index c9a288732461..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/OGRLayerClassifier.xml +++ /dev/null @@ -1,47 +0,0 @@ - - OGRLayerClassifier - otbcli_OGRLayerClassifier - OGRLayerClassifier - Segmentation - Classify an OGR layer based on a machine learning model and a list of features to consider. - - ParameterVector - inshp - Name of the input shapefile - Name of the input shapefile - - False - - - ParameterFile - instats - XML file containing mean and variance of each feature. - XML file containing mean and variance of each feature. - - False - - - OutputFile - insvm - Input model filename. - Input model filename. - - - ParameterString - feat - Features - Features to be calculated - - - False - - - ParameterString - cfield - Field containing the predicted class. - Field containing the predicted class - predicted - - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/OpticalCalibration.xml b/python/plugins/processing/algs/otb/description/5.6.0/OpticalCalibration.xml deleted file mode 100644 index 67cf0b5c0711..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/OpticalCalibration.xml +++ /dev/null @@ -1,180 +0,0 @@ - - OpticalCalibration - otbcli_OpticalCalibration - Optical calibration - Calibration - Perform optical calibration TOA/TOC (Top Of Atmosphere/Top Of Canopy). Supported sensors: QuickBird, Ikonos, WorldView2, Formosat, Spot5, Pleiades, Spot6. For other sensors the application also allows providing calibration parameters manually. - - ParameterRaster - in - Input - Input image filename (values in DN) - False - - - OutputRaster - out - Output - Output calibrated image filename - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - level - Calibration Level - - - - toa - toatoim - - - 0 - False - - - ParameterNumber - acqui.minute - Minute - Minute (0-59) - - - 0 - False - - - ParameterNumber - acqui.hour - Hour - Hour (0-23) - - - 12 - False - - - ParameterNumber - acqui.day - Day - Day (1-31) - - - 1 - False - - - ParameterNumber - acqui.month - Month - Month (1-12) - - - 1 - False - - - ParameterNumber - acqui.year - Year - Year - - - 2000 - False - - - ParameterNumber - acqui.sun.elev - Sun elevation angle (deg) - Sun elevation angle (in degrees) - - - 90 - False - - - ParameterNumber - acqui.sun.azim - Sun azimuth angle (deg) - Sun azimuth angle (in degrees) - - - 0 - False - - - ParameterNumber - acqui.view.elev - Viewing elevation angle (deg) - Viewing elevation angle (in degrees) - - - 90 - False - - - ParameterNumber - acqui.view.azim - Viewing azimuth angle (deg) - Viewing azimuth angle (in degrees) - - - 0 - False - - - ParameterFile - acqui.gainbias - Gains | biases - Gains | biases - - True - - - ParameterFile - acqui.solarilluminations - Solar illuminations - Solar illuminations (one value per band) - - True - - - ParameterFile - atmo.rsr - Relative Spectral Response File - Sensor relative spectral response file -By default the application gets this information in the metadata - - True - - - ParameterNumber - atmo.radius - Window radius (adjacency effects) - Window radius for adjacency effects correctionsSetting this parameters will enable the correction ofadjacency effects - - - 2 - True - - - ParameterNumber - atmo.pixsize - Pixel size (in km) - Pixel size (in km )used tocompute adjacency effects, it doesn't have tomatch the image spacing - - - 1 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/OrthoRectification-epsg.xml b/python/plugins/processing/algs/otb/description/5.6.0/OrthoRectification-epsg.xml deleted file mode 100644 index feac1abc7c86..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/OrthoRectification-epsg.xml +++ /dev/null @@ -1,124 +0,0 @@ - - OrthoRectification-epsg - otbcli_OrthoRectification - OrthoRectification (epsg) - Geometry - This application allows ortho-rectification of optical images from supported sensors. - - - ParameterRaster - io.in - Input Image - The input image to ortho-rectify - False - - - OutputRaster - io.out - Output Image - The ortho-rectified output image - - - - ParameterSelection - map - Output Cartographic Map Projection - Parameters of the output map projection to be used. - - - epsg - - - 0 - False - - - ParameterNumber - map.epsg.code - EPSG Code - See www.spatialreference.org to find which EPSG code is associated to your projection - - - 4326 - False - - - ParameterSelection - outputs.mode - Parameters estimation modes - - - - autosize - autospacing - - - 0 - False - - - ParameterNumber - outputs.default - Default pixel value - Default value to write when outside of input image. - - - 0 - True - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows one to define how the input image will be interpolated during resampling. - - - bco - nn - linear - - - 0 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows one to control the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - False - - - ParameterNumber - opt.ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - True - - - ParameterNumber - opt.gridspacing - Resampling grid spacing - Resampling is done according to a coordinate mapping deformation grid, whose pixel size is set by this parameter, and expressed in the coordinate system of the output image The closer to the output spacing this parameter is, the more precise will be the ortho-rectified image,but increasing this parameter will reduce processing time. - - - 4 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/OrthoRectification-fit-to-ortho.xml b/python/plugins/processing/algs/otb/description/5.6.0/OrthoRectification-fit-to-ortho.xml deleted file mode 100644 index 68d3fd51b22d..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/OrthoRectification-fit-to-ortho.xml +++ /dev/null @@ -1,107 +0,0 @@ - - OrthoRectification-fit-to-ortho - otbcli_OrthoRectification - OrthoRectification (fit-to-ortho) - Geometry - This application allows ortho-rectification of optical images from supported sensors. - - - ParameterRaster - io.in - Input Image - The input image to ortho-rectify - False - - - OutputRaster - io.out - Output Image - The ortho-rectified output image - - - - ParameterSelection - outputs.mode - Parameters estimation modes - - - - orthofit - - - 0 - False - - - ParameterRaster - outputs.ortho - Model ortho-image - A model ortho-image that can be used to compute size, origin and spacing of the output - True - - - ParameterNumber - outputs.default - Default pixel value - Default value to write when outside of input image. - - - 0 - True - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows one to define how the input image will be interpolated during resampling. - - - bco - nn - linear - - - 0 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows one to control the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - False - - - ParameterNumber - opt.ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - True - - - ParameterNumber - opt.gridspacing - Resampling grid spacing - Resampling is done according to a coordinate mapping deformation grid, whose pixel size is set by this parameter, and expressed in the coordinate system of the output image The closer to the output spacing this parameter is, the more precise will be the ortho-rectified image,but increasing this parameter will reduce processing time. - - - 4 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/OrthoRectification-lambert-WGS84.xml b/python/plugins/processing/algs/otb/description/5.6.0/OrthoRectification-lambert-WGS84.xml deleted file mode 100644 index 4ba3128152a4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/OrthoRectification-lambert-WGS84.xml +++ /dev/null @@ -1,116 +0,0 @@ - - OrthoRectification-lambert-WGS84 - otbcli_OrthoRectification - OrthoRectification (lambert-WGS84) - Geometry - This application allows ortho-rectification of optical images from supported sensors. - - - ParameterRaster - io.in - Input Image - The input image to ortho-rectify - False - - - OutputRaster - io.out - Output Image - The ortho-rectified output image - - - - ParameterSelection - map - Output Cartographic Map Projection - Parameters of the output map projection to be used. - - - lambert2 - lambert93 - wgs - - - 0 - False - - - ParameterSelection - outputs.mode - Parameters estimation modes - - - - autosize - autospacing - - - 0 - False - - - ParameterNumber - outputs.default - Default pixel value - Default value to write when outside of input image. - - - 0 - True - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows one to define how the input image will be interpolated during resampling. - - - bco - nn - linear - - - 0 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows one to control the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - False - - - ParameterNumber - opt.ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - True - - - ParameterNumber - opt.gridspacing - Resampling grid spacing - Resampling is done according to a coordinate mapping deformation grid, whose pixel size is set by this parameter, and expressed in the coordinate system of the output image The closer to the output spacing this parameter is, the more precise will be the ortho-rectified image,but increasing this parameter will reduce processing time. - - - 4 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/OrthoRectification-utm.xml b/python/plugins/processing/algs/otb/description/5.6.0/OrthoRectification-utm.xml deleted file mode 100644 index a5a76aa661db..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/OrthoRectification-utm.xml +++ /dev/null @@ -1,132 +0,0 @@ - - OrthoRectification-utm - otbcli_OrthoRectification - OrthoRectification (utm) - Geometry - This application allows ortho-rectification of optical images from supported sensors. - - - ParameterRaster - io.in - Input Image - The input image to ortho-rectify - False - - - OutputRaster - io.out - Output Image - The ortho-rectified output image - - - - ParameterSelection - map - Output Cartographic Map Projection - Parameters of the output map projection to be used. - - - utm - - - 0 - False - - - ParameterNumber - map.utm.zone - Zone number - The zone number ranges from 1 to 60 and allows defining the transverse mercator projection (along with the hemisphere) - - - 31 - False - - - ParameterBoolean - map.utm.northhem - Northern Hemisphere - The transverse mercator projections are defined by their zone number as well as the hemisphere. Activate this parameter if your image is in the northern hemisphere. - True - True - - - ParameterSelection - outputs.mode - Parameters estimation modes - - - - autosize - autospacing - - - 0 - False - - - ParameterNumber - outputs.default - Default pixel value - Default value to write when outside of input image. - - - 0 - True - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows one to define how the input image will be interpolated during resampling. - - - bco - nn - linear - - - 0 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows one to control the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - False - - - ParameterNumber - opt.ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - True - - - ParameterNumber - opt.gridspacing - Resampling grid spacing - Resampling is done according to a coordinate mapping deformation grid, whose pixel size is set by this parameter, and expressed in the coordinate system of the output image The closer to the output spacing this parameter is, the more precise will be the ortho-rectified image,but increasing this parameter will reduce processing time. - - - 4 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/Pansharpening-bayes.xml b/python/plugins/processing/algs/otb/description/5.6.0/Pansharpening-bayes.xml deleted file mode 100644 index 9b45d08e9e73..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/Pansharpening-bayes.xml +++ /dev/null @@ -1,71 +0,0 @@ - - Pansharpening-bayes - otbcli_Pansharpening - Pansharpening (bayes) - Geometry - Perform P+XS pansharpening - - ParameterRaster - inp - Input PAN Image - Input panchromatic image. - False - - - ParameterRaster - inxs - Input XS Image - Input XS image. - False - - - OutputRaster - out - Output image - Output image. - - - - ParameterSelection - method - Algorithm - Selection of the pan-sharpening method. - - - bayes - - - 0 - False - - - ParameterNumber - method.bayes.lambda - Weight - Set the weighting value. - - - 0.9999 - False - - - ParameterNumber - method.bayes.s - S coefficient - Set the S coefficient. - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/Pansharpening-lmvm.xml b/python/plugins/processing/algs/otb/description/5.6.0/Pansharpening-lmvm.xml deleted file mode 100644 index fd6d171f58b9..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/Pansharpening-lmvm.xml +++ /dev/null @@ -1,71 +0,0 @@ - - Pansharpening-lmvm - otbcli_Pansharpening - Pansharpening (lmvm) - Geometry - Perform P+XS pansharpening - - ParameterRaster - inp - Input PAN Image - Input panchromatic image. - False - - - ParameterRaster - inxs - Input XS Image - Input XS image. - False - - - OutputRaster - out - Output image - Output image. - - - - ParameterSelection - method - Algorithm - Selection of the pan-sharpening method. - - - lmvm - - - 0 - False - - - ParameterNumber - method.lmvm.radiusx - X radius - Set the x radius of the sliding window. - - - 3 - False - - - ParameterNumber - method.lmvm.radiusy - Y radius - Set the y radius of the sliding window. - - - 3 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/Pansharpening-rcs.xml b/python/plugins/processing/algs/otb/description/5.6.0/Pansharpening-rcs.xml deleted file mode 100644 index d8b9c1bc8480..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/Pansharpening-rcs.xml +++ /dev/null @@ -1,51 +0,0 @@ - - Pansharpening-rcs - otbcli_Pansharpening - Pansharpening (rcs) - Geometry - Perform P+XS pansharpening - - ParameterRaster - inp - Input PAN Image - Input panchromatic image. - False - - - ParameterRaster - inxs - Input XS Image - Input XS image. - False - - - OutputRaster - out - Output image - Output image. - - - - ParameterSelection - method - Algorithm - Selection of the pan-sharpening method. - - - rcs - - - 0 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/PolygonClassStatistics.xml b/python/plugins/processing/algs/otb/description/5.6.0/PolygonClassStatistics.xml deleted file mode 100644 index 3a5e2f294daa..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/PolygonClassStatistics.xml +++ /dev/null @@ -1,64 +0,0 @@ - - PolygonClassStatistics - otbcli_PolygonClassStatistics - Polygon Class Statistics - Learning - Computes statistics on a training polygon set. - - ParameterRaster - in - InputImage - Support image that will be classified - False - - - ParameterRaster - mask - InputMask - Validity mask (only pixels corresponding to a mask value greater than 0 will be used for statistics) - True - - - ParameterFile - vec - Input vectors - Input geometries to analyse - - False - - - OutputFile - out - Output Statistics - Output file to store statistics (XML format) - - - ParameterString - field - Field Name - Name of the field carrying the class name in the input vectors. - class - - True - - - ParameterNumber - layer - Layer Index - Layer index to read in the input vector file. - - - 0 - True - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/PredictRegression.xml b/python/plugins/processing/algs/otb/description/5.6.0/PredictRegression.xml deleted file mode 100644 index 56ccb8b1d080..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/PredictRegression.xml +++ /dev/null @@ -1,54 +0,0 @@ - - PredictRegression - otbcli_PredictRegression - Predict Regression - Learning - Performs a prediction of the input image according to a regression model file. - - ParameterRaster - in - Input Image - The input image to predict. - False - - - ParameterRaster - mask - Input Mask - The mask allow restricting classification of the input image to the area where mask pixel values are greater than 0. - True - - - ParameterFile - model - Model file - A regression model file (produced by TrainRegression application). - - False - - - ParameterFile - imstat - Statistics file - A XML file containing mean and standard deviation to center and reduce samples before prediction (produced by ComputeImagesStatistics application). If this file containsone more band than the sample size, the last stat of last band will beapplied to expand the output predicted value - - True - - - OutputRaster - out - Output Image - Output image containing predicted values - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/RadiometricIndices.xml b/python/plugins/processing/algs/otb/description/5.6.0/RadiometricIndices.xml deleted file mode 100644 index 41aa91db8123..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/RadiometricIndices.xml +++ /dev/null @@ -1,131 +0,0 @@ - - RadiometricIndices - otbcli_RadiometricIndices - Radiometric Indices - Feature Extraction - Compute radiometric indices. - - ParameterRaster - in - Input Image - Input image - False - - - OutputRaster - out - Output Image - Radiometric indices output image - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterNumber - channels.blue - Blue Channel - Blue channel index - - - 1 - False - - - ParameterNumber - channels.green - Green Channel - Green channel index - - - 1 - False - - - ParameterNumber - channels.red - Red Channel - Red channel index - - - 1 - False - - - ParameterNumber - channels.nir - NIR Channel - NIR channel index - - - 1 - False - - - ParameterNumber - channels.mir - Mir Channel - Mir channel index - - - 1 - False - - - ParameterSelection - list - Available Radiometric Indices - List of available radiometric indices with their relevant channels in brackets: - Vegetation:NDVI - Normalized difference vegetation index (Red, NIR) - Vegetation:TNDVI - Transformed normalized difference vegetation index (Red, NIR) - Vegetation:RVI - Ratio vegetation index (Red, NIR) - Vegetation:SAVI - Soil adjusted vegetation index (Red, NIR) - Vegetation:TSAVI - Transformed soil adjusted vegetation index (Red, NIR) - Vegetation:MSAVI - Modified soil adjusted vegetation index (Red, NIR) - Vegetation:MSAVI2 - Modified soil adjusted vegetation index 2 (Red, NIR) - Vegetation:GEMI - Global environment monitoring index (Red, NIR) - Vegetation:IPVI - Infrared percentage vegetation index (Red, NIR) - - Water:NDWI - Normalized difference water index (Gao 1996) (NIR, MIR) - Water:NDWI2 - Normalized difference water index (Mc Feeters 1996) (Green, NIR) - Water:MNDWI - Modified normalized difference water index (Xu 2006) (Green, MIR) - Water:NDPI - Normalized difference pond index (Lacaux et al.) (MIR, Green) - Water:NDTI - Normalized difference turbidity index (Lacaux et al.) (Red, Green) - - Soil:RI - Redness index (Red, Green) - Soil:CI - Color index (Red, Green) - Soil:BI - Brightness index (Red, Green) - Soil:BI2 - Brightness index 2 (NIR, Red, Green) - - - ndvi - tndvi - rvi - savi - tsavi - msavi - msavi2 - gemi - ipvi - ndwi - ndwi2 - mndwi - ndpi - ndti - ri - ci - bi - bi2 - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/Rasterization-image.xml b/python/plugins/processing/algs/otb/description/5.6.0/Rasterization-image.xml deleted file mode 100644 index 7303ef1d8eae..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/Rasterization-image.xml +++ /dev/null @@ -1,82 +0,0 @@ - - Rasterization-image - otbcli_Rasterization - Rasterization (image) - Vector Data Manipulation - Rasterize a vector dataset. - - ParameterVector - in - Input vector dataset - The input vector dataset to be rasterized - - False - - - OutputRaster - out - Output image - An output image containing the rasterized vector dataset - - - - ParameterRaster - im - Input reference image - A reference image from which to import output grid and projection reference system information. - True - - - ParameterNumber - background - Background value - Default value for pixels not belonging to any geometry - - - 0 - False - - - ParameterSelection - mode - Rasterization mode - Choice of rasterization modes - - - binary - attribute - - - 0 - False - - - ParameterNumber - mode.binary.foreground - Foreground value - Value for pixels inside a geometry - - - 255 - False - - - ParameterString - mode.attribute.field - The attribute field to burn - Name of the attribute field to burn - DN - - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/Rasterization-manual.xml b/python/plugins/processing/algs/otb/description/5.6.0/Rasterization-manual.xml deleted file mode 100644 index c607811994eb..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/Rasterization-manual.xml +++ /dev/null @@ -1,145 +0,0 @@ - - Rasterization-manual - otbcli_Rasterization - Rasterization (manual) - Vector Data Manipulation - Rasterize a vector dataset. - - ParameterVector - in - Input vector dataset - The input vector dataset to be rasterized - - False - - - OutputRaster - out - Output image - An output image containing the rasterized vector dataset - - - - ParameterNumber - szx - Output size x - Output size along x axis (useless if support image is given) - - - 0 - True - - - ParameterNumber - szy - Output size y - Output size along y axis (useless if support image is given) - - - 0 - True - - - ParameterNumber - epsg - Output EPSG code - EPSG code for the output projection reference system (EPSG 4326 for WGS84, 32631 for UTM31N...,useless if support image is given) - - - 0 - True - - - ParameterNumber - orx - Output Upper-left x - Output upper-left corner x coordinate (useless if support image is given) - - - 0.0 - True - - - ParameterNumber - ory - Output Upper-left y - Output upper-left corner y coordinate (useless if support image is given) - - - 0.0 - True - - - ParameterNumber - spx - Spacing (GSD) x - Spacing (ground sampling distance) along x axis (useless if support image is given) - - - 0.0 - True - - - ParameterNumber - spy - Spacing (GSD) y - Spacing (ground sampling distance) along y axis (useless if support image is given) - - - 0.0 - True - - - ParameterNumber - background - Background value - Default value for pixels not belonging to any geometry - - - 0 - False - - - ParameterSelection - mode - Rasterization mode - Choice of rasterization modes - - - binary - attribute - - - 0 - False - - - ParameterNumber - mode.binary.foreground - Foreground value - Value for pixels inside a geometry - - - 255 - False - - - ParameterString - mode.attribute.field - The attribute field to burn - Name of the attribute field to burn - DN - - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/ReadImageInfo.xml b/python/plugins/processing/algs/otb/description/5.6.0/ReadImageInfo.xml deleted file mode 100644 index 0e246b613137..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/ReadImageInfo.xml +++ /dev/null @@ -1,58 +0,0 @@ - - ReadImageInfo - otbcli_ReadImageInfo - Read image information - Image Manipulation - Get information about the image - - ParameterRaster - in - Input Image - Input image to analyse - False - - - ParameterBoolean - keywordlist - Display the OSSIM keywordlist - Output the OSSIM keyword list. It contains metadata information (sensor model, geometry ). Information is stored in keyword list (pairs of key/value) - True - True - - - ParameterString - gcp.ids - GCPs Id - GCPs identifier - - - False - - - ParameterString - gcp.info - GCPs Info - GCPs Information - - - False - - - ParameterString - gcp.imcoord - GCPs Image Coordinates - GCPs Image coordinates - - - False - - - ParameterString - gcp.geocoord - GCPs Geographic Coordinates - GCPs Geographic Coordinates - - - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/Rescale.xml b/python/plugins/processing/algs/otb/description/5.6.0/Rescale.xml deleted file mode 100644 index 6ae441121fea..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/Rescale.xml +++ /dev/null @@ -1,51 +0,0 @@ - - Rescale - otbcli_Rescale - Rescale Image - Image Manipulation - Rescale the image between two given values. - - ParameterRaster - in - Input Image - The image to scale. - False - - - OutputRaster - out - Output Image - The rescaled image filename. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterNumber - outmin - Output min value - Minimum value of the output image. - - - 0 - True - - - ParameterNumber - outmax - Output max value - Maximum value of the output image. - - - 255 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/RigidTransformResample-id.xml b/python/plugins/processing/algs/otb/description/5.6.0/RigidTransformResample-id.xml deleted file mode 100644 index f0b20563d594..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/RigidTransformResample-id.xml +++ /dev/null @@ -1,89 +0,0 @@ - - RigidTransformResample-id - otbcli_RigidTransformResample - RigidTransformResample (id) - Geometry - Resample an image with a rigid transform - - ParameterRaster - in - Input image - The input image to translate. - False - - - OutputRaster - out - Output image - The transformed output image. - - - - ParameterSelection - transform.type - Type of transformation - Type of transformation. Available transformations are spatial scaling, translation and rotation with scaling factor - - - id - - - 0 - False - - - ParameterNumber - transform.type.id.scalex - X scaling - Scaling factor between the output X spacing and the input X spacing - - - 1 - False - - - ParameterNumber - transform.type.id.scaley - Y scaling - Scaling factor between the output Y spacing and the input Y spacing - - - 1 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows one to define how the input image will be interpolated during resampling. - - - nn - linear - bco - - - 2 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows controlling the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - False - - - ParameterNumber - ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/RigidTransformResample-rotation.xml b/python/plugins/processing/algs/otb/description/5.6.0/RigidTransformResample-rotation.xml deleted file mode 100644 index ac2baad02aa2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/RigidTransformResample-rotation.xml +++ /dev/null @@ -1,99 +0,0 @@ - - RigidTransformResample-rotation - otbcli_RigidTransformResample - RigidTransformResample (rotation) - Geometry - Resample an image with a rigid transform - - ParameterRaster - in - Input image - The input image to translate. - False - - - OutputRaster - out - Output image - The transformed output image. - - - - ParameterSelection - transform.type - Type of transformation - Type of transformation. Available transformations are spatial scaling, translation and rotation with scaling factor - - - rotation - - - 0 - False - - - ParameterNumber - transform.type.rotation.angle - Rotation angle - The rotation angle in degree (values between -180 and 180) - - - 0 - False - - - ParameterNumber - transform.type.rotation.scalex - X scaling - Scale factor between the X spacing of the rotated output image and the X spacing of the unrotated image - - - 1 - False - - - ParameterNumber - transform.type.rotation.scaley - Y scaling - Scale factor between the Y spacing of the rotated output image and the Y spacing of the unrotated image - - - 1 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows one to define how the input image will be interpolated during resampling. - - - nn - linear - bco - - - 2 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows controlling the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - False - - - ParameterNumber - ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/RigidTransformResample-translation.xml b/python/plugins/processing/algs/otb/description/5.6.0/RigidTransformResample-translation.xml deleted file mode 100644 index 3d0700a4c494..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/RigidTransformResample-translation.xml +++ /dev/null @@ -1,109 +0,0 @@ - - RigidTransformResample-translation - otbcli_RigidTransformResample - RigidTransformResample (translation) - Geometry - Resample an image with a rigid transform - - ParameterRaster - in - Input image - The input image to translate. - False - - - OutputRaster - out - Output image - The transformed output image. - - - - ParameterSelection - transform.type - Type of transformation - Type of transformation. Available transformations are spatial scaling, translation and rotation with scaling factor - - - translation - - - 0 - False - - - ParameterNumber - transform.type.translation.tx - The X translation (in physical units) - The translation value along X axis (in physical units). - - - 0 - False - - - ParameterNumber - transform.type.translation.ty - The Y translation (in physical units) - The translation value along Y axis (in physical units) - - - 0 - False - - - ParameterNumber - transform.type.translation.scalex - X scaling - Scaling factor between the output X spacing and the input X spacing - - - 1 - False - - - ParameterNumber - transform.type.translation.scaley - Y scaling - Scaling factor between the output Y spacing and the input Y spacing - - - 1 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows one to define how the input image will be interpolated during resampling. - - - nn - linear - bco - - - 2 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows controlling the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - False - - - ParameterNumber - ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/SARCalibration.xml b/python/plugins/processing/algs/otb/description/5.6.0/SARCalibration.xml deleted file mode 100644 index dc3780429458..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/SARCalibration.xml +++ /dev/null @@ -1,56 +0,0 @@ - - SARCalibration - otbcli_SARCalibration - SAR Radiometric calibration - Calibration - Perform radiometric calibration of SAR images. Following sensors are supported: TerraSAR-X, Sentinel1 and Radarsat-2.Both Single Look Complex(SLC) and detected products are supported as input. - - - ParameterRaster - in - Input Image - Input complex image - False - - - OutputRaster - out - Output Image - Output calibrated image. This image contains the backscatter (sigmaNought) of the input image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterBoolean - noise - Disable Noise - Flag to disable noise. For 5.2.0 release, the noise values are only read by TerraSARX product. - True - True - - - ParameterSelection - lut - Lookup table sigma /gamma/ beta/ DN. - Lookup table values are not available with all SAR products. Products that provide lookup table with metadata are: Sentinel1, Radarsat2. - - - sigma - gamma - beta - dn - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/SARDecompositions.xml b/python/plugins/processing/algs/otb/description/5.6.0/SARDecompositions.xml deleted file mode 100644 index 97c97241e9be..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/SARDecompositions.xml +++ /dev/null @@ -1,78 +0,0 @@ - - SARDecompositions - otbcli_SARDecompositions - SARDecompositions - Miscellaneous - From one-band complex images (each one related to an element of the Sinclair matrix), returns the selected decomposition. - - ParameterRaster - inhh - Input Image - Input image (HH) - False - - - ParameterRaster - inhv - Input Image - Input image (HV) - True - - - ParameterRaster - invh - Input Image - Input image (VH) - True - - - ParameterRaster - invv - Input Image - Input image (VV) - False - - - OutputRaster - out - Output Image - Output image - - - - ParameterSelection - decomp - Decompositions - - - - haa - barnes - huynen - pauli - - - 0 - False - - - ParameterNumber - inco.kernelsize - Kernel size for spatial incoherent averaging. - Minute (0-59) - - - 3 - True - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/SARPolarSynth.xml b/python/plugins/processing/algs/otb/description/5.6.0/SARPolarSynth.xml deleted file mode 100644 index d7319cc7469c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/SARPolarSynth.xml +++ /dev/null @@ -1,106 +0,0 @@ - - SARPolarSynth - otbcli_SARPolarSynth - SARPolarSynth - Miscellaneous - Gives, for each pixel, the power that would have been received by a SAR system with a basis different from the classical (H,V) one (polarimetric synthetis). - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - psii - psii - Orientation (transmitting antenna) - - - 0 - False - - - ParameterNumber - khii - khii - Ellipticity (transmitting antenna) - - - 0 - False - - - ParameterNumber - psir - psir - Orientation (receiving antenna) - - - 0 - False - - - ParameterNumber - khir - khir - Ellipticity (receiving antenna) - - - 0 - False - - - ParameterNumber - emissionh - Emission H - This parameter is useful in determining the polarization architecture (dual polarization case). - - - 0 - True - - - ParameterNumber - emissionv - Emission V - This parameter is useful in determining the polarization architecture (dual polarization case). - - - 0 - True - - - ParameterSelection - mode - Forced mode - - - - none - co - cross - - - 0 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/SFSTextureExtraction.xml b/python/plugins/processing/algs/otb/description/5.6.0/SFSTextureExtraction.xml deleted file mode 100644 index d4c6b1e2abd0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/SFSTextureExtraction.xml +++ /dev/null @@ -1,91 +0,0 @@ - - SFSTextureExtraction - otbcli_SFSTextureExtraction - SFS Texture Extraction - Feature Extraction - Computes Structural Feature Set textures on every pixel of the input image selected channel - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterNumber - parameters.spethre - Spectral Threshold - Spectral Threshold - - - 50 - False - - - ParameterNumber - parameters.spathre - Spatial Threshold - Spatial Threshold - - - 100 - False - - - ParameterNumber - parameters.nbdir - Number of Direction - Number of Direction - - - 20 - False - - - ParameterNumber - parameters.alpha - Alpha - Alpha - - - 1 - False - - - ParameterNumber - parameters.maxcons - Ratio Maximum Consideration Number - Ratio Maximum Consideration Number - - - 5 - False - - - OutputRaster - out - Feature Output Image - Output image containing the SFS texture features. - - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/SOMClassification.xml b/python/plugins/processing/algs/otb/description/5.6.0/SOMClassification.xml deleted file mode 100644 index c6ebd5d63651..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/SOMClassification.xml +++ /dev/null @@ -1,155 +0,0 @@ - - SOMClassification - otbcli_SOMClassification - SOM Classification - Learning - SOM image classification. - - ParameterRaster - in - InputImage - Input image to classify. - False - - - OutputRaster - out - OutputImage - Output classified image (each pixel contains the index of its corresponding vector in the SOM). - - - - ParameterRaster - vm - ValidityMask - Validity mask (only pixels corresponding to a mask value greater than 0 will be used for learning) - True - - - ParameterNumber - tp - TrainingProbability - Probability for a sample to be selected in the training set - - - 1 - True - - - ParameterNumber - ts - TrainingSetSize - Maximum training set size (in pixels) - - - 0 - True - - - OutputRaster - som - SOM Map - Output image containing the Self-Organizing Map - - - - ParameterNumber - sx - SizeX - X size of the SOM map - - - 32 - True - - - ParameterNumber - sy - SizeY - Y size of the SOM map - - - 32 - True - - - ParameterNumber - nx - NeighborhoodX - X size of the initial neighborhood in the SOM map - - - 10 - True - - - ParameterNumber - ny - NeighborhoodY - Y size of the initial neighborhood in the SOM map - - - 10 - False - - - ParameterNumber - ni - NumberIteration - Number of iterations for SOM learning - - - 5 - True - - - ParameterNumber - bi - BetaInit - Initial learning coefficient - - - 1 - True - - - ParameterNumber - bf - BetaFinal - Final learning coefficient - - - 0.1 - True - - - ParameterNumber - iv - InitialValue - Maximum initial neuron weight - - - 0 - True - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/SampleExtraction.xml b/python/plugins/processing/algs/otb/description/5.6.0/SampleExtraction.xml deleted file mode 100644 index 4ce6056837e1..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/SampleExtraction.xml +++ /dev/null @@ -1,89 +0,0 @@ - - SampleExtraction - otbcli_SampleExtraction - Sample Extraction - Learning - Extracts samples values from an image. - - ParameterRaster - in - InputImage - Support image - False - - - ParameterFile - vec - Input sampling positions - Vector data file containing samplingpositions. (OGR format) - - False - - - OutputFile - out - Output samples - Output vector data file storing samplevalues (OGR format). If not given, the input vector data file is updated - - - ParameterSelection - outfield - Output field names - Choice between naming method for output fields - - - prefix - list - - - 0 - False - - - ParameterString - outfield.prefix.name - Output field prefix - Prefix used to form the field names thatwill contain the extracted values. - value_ - - False - - - ParameterString - outfield.list.names - Output field names - Full list of output field names. - - - False - - - ParameterString - field - Field Name - Name of the field carrying the classname in the input vectors. This field is copied to output. - class - - True - - - ParameterNumber - layer - Layer Index - Layer index to read in the input vector file. - - - 0 - True - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/SampleSelection.xml b/python/plugins/processing/algs/otb/description/5.6.0/SampleSelection.xml deleted file mode 100644 index 9799de01f3f0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/SampleSelection.xml +++ /dev/null @@ -1,146 +0,0 @@ - - SampleSelection - otbcli_SampleSelection - Sample Selection - Learning - Selects samples from a training vector data set. - - ParameterRaster - in - InputImage - Support image that will be classified - False - - - ParameterRaster - mask - InputMask - Validity mask (only pixels corresponding to a mask value greater than 0 will be used for statistics) - True - - - ParameterFile - vec - Input vectors - Input geometries to analyse - - False - - - OutputFile - out - Output vectors - Output resampled geometries - - - ParameterFile - instats - Input Statistics - Input file storing statistics (XML format) - - False - - - OutputFile - outrates - Output rates - Output rates (CSV formatted) - - - ParameterSelection - sampler - Sampler type - Type of sampling (periodic, pattern based, random) - - - periodic - random - - - 0 - False - - - ParameterNumber - sampler.periodic.jitter - Jitter amplitude - Jitter amplitude added during sample selection (0 = no jitter) - - - 0 - True - - - ParameterSelection - strategy - Sampling strategy - - - - byclass - constant - smallest - all - - - 2 - False - - - ParameterFile - strategy.byclass.in - Number of samples by class - Number of samples by class (CSV format with class name in 1st column and required samples in the 2nd. - - False - - - ParameterNumber - strategy.constant.nb - Number of samples for all classes - Number of samples for all classes - - - 0 - False - - - ParameterString - field - Field Name - Name of the field carrying the class name in the input vectors. - class - - True - - - ParameterNumber - layer - Layer Index - Layer index to read in the input vector file. - - - 0 - True - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/Segmentation-cc.xml b/python/plugins/processing/algs/otb/description/5.6.0/Segmentation-cc.xml deleted file mode 100644 index 539431b77a4f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/Segmentation-cc.xml +++ /dev/null @@ -1,161 +0,0 @@ - - Segmentation-cc - otbcli_Segmentation - Segmentation (cc) - Segmentation - Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported. - - ParameterRaster - in - Input Image - The input image to segment - False - - - ParameterSelection - filter - Segmentation algorithm - Choice of segmentation algorithm (mean-shift by default) - - - cc - - - 0 - False - - - ParameterString - filter.cc.expr - Condition - User defined connection condition, written as a mathematical expression. Available variables are p(i)b(i), intensity_p(i) and distance (example of expression : distance < 10 ) - - - False - - - ParameterSelection - mode - Processing mode - Choice of processing mode, either raster or large-scale. - - - vector - - - 0 - False - - - OutputVector - mode.vector.out - Output vector file - The output vector file or database (name can be anything understood by OGR) - - - ParameterSelection - mode.vector.outmode - Writing mode for the output vector file - This allows one to set the writing behaviour for the output vector file. Please note that the actual behaviour depends on the file format. - - - ulco - ovw - ulovw - ulu - - - 0 - False - - - ParameterRaster - mode.vector.inmask - Mask Image - Only pixels whose mask value is strictly positive will be segmented. - True - - - ParameterBoolean - mode.vector.neighbor - 8-neighbor connectivity - Activate 8-Neighborhood connectivity (default is 4). - True - True - - - ParameterBoolean - mode.vector.stitch - Stitch polygons - Scan polygons on each side of tiles and stitch polygons which connect by more than one pixel. - True - True - - - ParameterNumber - mode.vector.minsize - Minimum object size - Objects whose size is below the minimum object size (area in pixels) will be ignored during vectorization. - - - 1 - True - - - ParameterNumber - mode.vector.simplify - Simplify polygons - Simplify polygons according to a given tolerance (in pixel). This option allows reducing the size of the output file or database. - - - 0.1 - True - - - ParameterString - mode.vector.layername - Layer name - Name of the layer in the vector file or database (default is Layer). - layer - - False - - - ParameterString - mode.vector.fieldname - Geometry index field name - Name of the field holding the geometry index in the output vector file or database. - DN - - False - - - ParameterNumber - mode.vector.tilesize - Tiles size - User defined tiles size for tile-based segmentation. Optimal tile size is selected according to available RAM if null. - - - 1024 - False - - - ParameterNumber - mode.vector.startlabel - Starting geometry index - Starting value of the geometry index field - - - 1 - False - - - ParameterString - mode.vector.ogroptions - OGR options for layer creation - A list of layer creation options in the form KEY=VALUE that will be passed directly to OGR without any validity checking. Options may depend on the file format, and can be found in OGR documentation. - - - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/Segmentation-meanshift.xml b/python/plugins/processing/algs/otb/description/5.6.0/Segmentation-meanshift.xml deleted file mode 100644 index ce1935dd4cf9..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/Segmentation-meanshift.xml +++ /dev/null @@ -1,202 +0,0 @@ - - Segmentation-meanshift - otbcli_Segmentation - Segmentation (meanshift) - Segmentation - Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported. - - ParameterRaster - in - Input Image - The input image to segment - False - - - ParameterSelection - filter - Segmentation algorithm - Choice of segmentation algorithm (mean-shift by default) - - - meanshift - - - 0 - False - - - ParameterNumber - filter.meanshift.spatialr - Spatial radius - Spatial radius of the neighborhood. - - - 5 - False - - - ParameterNumber - filter.meanshift.ranger - Range radius - Range radius defining the radius (expressed in radiometry unit) in the multispectral space. - - - 15 - False - - - ParameterNumber - filter.meanshift.thres - Mode convergence threshold - Algorithm iterative scheme will stop if mean-shift vector is below this threshold or if iteration number reached maximum number of iterations. - - - 0.1 - False - - - ParameterNumber - filter.meanshift.maxiter - Maximum number of iterations - Algorithm iterative scheme will stop if convergence hasn't been reached after the maximum number of iterations. - - - 100 - False - - - ParameterNumber - filter.meanshift.minsize - Minimum region size - Minimum size of a region (in pixel unit) in segmentation. Smaller clusters will be merged to the neighboring cluster with the closest radiometry. If set to 0 no pruning is done. - - - 100 - False - - - ParameterSelection - mode - Processing mode - Choice of processing mode, either raster or large-scale. - - - vector - - - 0 - False - - - OutputVector - mode.vector.out - Output vector file - The output vector file or database (name can be anything understood by OGR) - - - ParameterSelection - mode.vector.outmode - Writing mode for the output vector file - This allows one to set the writing behaviour for the output vector file. Please note that the actual behaviour depends on the file format. - - - ulco - ovw - ulovw - ulu - - - 0 - False - - - ParameterRaster - mode.vector.inmask - Mask Image - Only pixels whose mask value is strictly positive will be segmented. - True - - - ParameterBoolean - mode.vector.neighbor - 8-neighbor connectivity - Activate 8-Neighborhood connectivity (default is 4). - True - True - - - ParameterBoolean - mode.vector.stitch - Stitch polygons - Scan polygons on each side of tiles and stitch polygons which connect by more than one pixel. - True - True - - - ParameterNumber - mode.vector.minsize - Minimum object size - Objects whose size is below the minimum object size (area in pixels) will be ignored during vectorization. - - - 1 - True - - - ParameterNumber - mode.vector.simplify - Simplify polygons - Simplify polygons according to a given tolerance (in pixel). This option allows reducing the size of the output file or database. - - - 0.1 - True - - - ParameterString - mode.vector.layername - Layer name - Name of the layer in the vector file or database (default is Layer). - layer - - False - - - ParameterString - mode.vector.fieldname - Geometry index field name - Name of the field holding the geometry index in the output vector file or database. - DN - - False - - - ParameterNumber - mode.vector.tilesize - Tiles size - User defined tiles size for tile-based segmentation. Optimal tile size is selected according to available RAM if null. - - - 1024 - False - - - ParameterNumber - mode.vector.startlabel - Starting geometry index - Starting value of the geometry index field - - - 1 - False - - - ParameterString - mode.vector.ogroptions - OGR options for layer creation - A list of layer creation options in the form KEY=VALUE that will be passed directly to OGR without any validity checking. Options may depend on the file format, and can be found in OGR documentation. - - - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/Segmentation-mprofiles.xml b/python/plugins/processing/algs/otb/description/5.6.0/Segmentation-mprofiles.xml deleted file mode 100644 index be511f25ee3c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/Segmentation-mprofiles.xml +++ /dev/null @@ -1,192 +0,0 @@ - - Segmentation-mprofiles - otbcli_Segmentation - Segmentation (mprofiles) - Segmentation - Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported. - - ParameterRaster - in - Input Image - The input image to segment - False - - - ParameterSelection - filter - Segmentation algorithm - Choice of segmentation algorithm (mean-shift by default) - - - mprofiles - - - 0 - False - - - ParameterNumber - filter.mprofiles.size - Profile Size - Size of the profiles - - - 5 - False - - - ParameterNumber - filter.mprofiles.start - Initial radius - Initial radius of the structuring element (in pixels) - - - 1 - False - - - ParameterNumber - filter.mprofiles.step - Radius step. - Radius step along the profile (in pixels) - - - 1 - False - - - ParameterNumber - filter.mprofiles.sigma - Threshold of the final decision rule - Profiles values under the threshold will be ignored. - - - 1 - False - - - ParameterSelection - mode - Processing mode - Choice of processing mode, either raster or large-scale. - - - vector - - - 0 - False - - - OutputVector - mode.vector.out - Output vector file - The output vector file or database (name can be anything understood by OGR) - - - ParameterSelection - mode.vector.outmode - Writing mode for the output vector file - This allows one to set the writing behaviour for the output vector file. Please note that the actual behaviour depends on the file format. - - - ulco - ovw - ulovw - ulu - - - 0 - False - - - ParameterRaster - mode.vector.inmask - Mask Image - Only pixels whose mask value is strictly positive will be segmented. - True - - - ParameterBoolean - mode.vector.neighbor - 8-neighbor connectivity - Activate 8-Neighborhood connectivity (default is 4). - True - True - - - ParameterBoolean - mode.vector.stitch - Stitch polygons - Scan polygons on each side of tiles and stitch polygons which connect by more than one pixel. - True - True - - - ParameterNumber - mode.vector.minsize - Minimum object size - Objects whose size is below the minimum object size (area in pixels) will be ignored during vectorization. - - - 1 - True - - - ParameterNumber - mode.vector.simplify - Simplify polygons - Simplify polygons according to a given tolerance (in pixel). This option allows reducing the size of the output file or database. - - - 0.1 - True - - - ParameterString - mode.vector.layername - Layer name - Name of the layer in the vector file or database (default is Layer). - layer - - False - - - ParameterString - mode.vector.fieldname - Geometry index field name - Name of the field holding the geometry index in the output vector file or database. - DN - - False - - - ParameterNumber - mode.vector.tilesize - Tiles size - User defined tiles size for tile-based segmentation. Optimal tile size is selected according to available RAM if null. - - - 1024 - False - - - ParameterNumber - mode.vector.startlabel - Starting geometry index - Starting value of the geometry index field - - - 1 - False - - - ParameterString - mode.vector.ogroptions - OGR options for layer creation - A list of layer creation options in the form KEY=VALUE that will be passed directly to OGR without any validity checking. Options may depend on the file format, and can be found in OGR documentation. - - - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/Segmentation-watershed.xml b/python/plugins/processing/algs/otb/description/5.6.0/Segmentation-watershed.xml deleted file mode 100644 index 20551ccd4161..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/Segmentation-watershed.xml +++ /dev/null @@ -1,172 +0,0 @@ - - Segmentation-watershed - otbcli_Segmentation - Segmentation (watershed) - Segmentation - Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported. - - ParameterRaster - in - Input Image - The input image to segment - False - - - ParameterSelection - filter - Segmentation algorithm - Choice of segmentation algorithm (mean-shift by default) - - - watershed - - - 0 - False - - - ParameterNumber - filter.watershed.threshold - Depth Threshold - Depth threshold Units in percentage of the maximum depth in the image. - - - 0.01 - False - - - ParameterNumber - filter.watershed.level - Flood Level - flood level for generating the merge tree from the initial segmentation (between 0 and 1) - - - 0.1 - False - - - ParameterSelection - mode - Processing mode - Choice of processing mode, either raster or large-scale. - - - vector - - - 0 - False - - - OutputVector - mode.vector.out - Output vector file - The output vector file or database (name can be anything understood by OGR) - - - ParameterSelection - mode.vector.outmode - Writing mode for the output vector file - This allows one to set the writing behaviour for the output vector file. Please note that the actual behaviour depends on the file format. - - - ulco - ovw - ulovw - ulu - - - 0 - False - - - ParameterRaster - mode.vector.inmask - Mask Image - Only pixels whose mask value is strictly positive will be segmented. - True - - - ParameterBoolean - mode.vector.neighbor - 8-neighbor connectivity - Activate 8-Neighborhood connectivity (default is 4). - True - True - - - ParameterBoolean - mode.vector.stitch - Stitch polygons - Scan polygons on each side of tiles and stitch polygons which connect by more than one pixel. - True - True - - - ParameterNumber - mode.vector.minsize - Minimum object size - Objects whose size is below the minimum object size (area in pixels) will be ignored during vectorization. - - - 1 - True - - - ParameterNumber - mode.vector.simplify - Simplify polygons - Simplify polygons according to a given tolerance (in pixel). This option allows reducing the size of the output file or database. - - - 0.1 - True - - - ParameterString - mode.vector.layername - Layer name - Name of the layer in the vector file or database (default is Layer). - layer - - False - - - ParameterString - mode.vector.fieldname - Geometry index field name - Name of the field holding the geometry index in the output vector file or database. - DN - - False - - - ParameterNumber - mode.vector.tilesize - Tiles size - User defined tiles size for tile-based segmentation. Optimal tile size is selected according to available RAM if null. - - - 1024 - False - - - ParameterNumber - mode.vector.startlabel - Starting geometry index - Starting value of the geometry index field - - - 1 - False - - - ParameterString - mode.vector.ogroptions - OGR options for layer creation - A list of layer creation options in the form KEY=VALUE that will be passed directly to OGR without any validity checking. Options may depend on the file format, and can be found in OGR documentation. - - - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/Smoothing-anidif.xml b/python/plugins/processing/algs/otb/description/5.6.0/Smoothing-anidif.xml deleted file mode 100644 index 84f43f070f40..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/Smoothing-anidif.xml +++ /dev/null @@ -1,74 +0,0 @@ - - Smoothing-anidif - otbcli_Smoothing - Smoothing (anidif) - Image Filtering - Apply a smoothing filter to an image - - ParameterRaster - in - Input Image - Input image to smooth. - False - - - OutputRaster - out - Output Image - Output smoothed image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - type - Smoothing Type - Smoothing kernel to apply - - - anidif - - - 2 - False - - - ParameterNumber - type.anidif.timestep - Time Step - Diffusion equation time step - - - 0.125 - False - - - ParameterNumber - type.anidif.nbiter - Nb Iterations - Controls the sensitivity of the conductance term - - - 10 - False - - - ParameterNumber - type.anidif.conductance - Conductance - - - - 1 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/Smoothing-gaussian.xml b/python/plugins/processing/algs/otb/description/5.6.0/Smoothing-gaussian.xml deleted file mode 100644 index 49f7cc1fc551..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/Smoothing-gaussian.xml +++ /dev/null @@ -1,54 +0,0 @@ - - Smoothing-gaussian - otbcli_Smoothing - Smoothing (gaussian) - Image Filtering - Apply a smoothing filter to an image - - ParameterRaster - in - Input Image - Input image to smooth. - False - - - OutputRaster - out - Output Image - Output smoothed image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - type - Smoothing Type - Smoothing kernel to apply - - - gaussian - - - 2 - False - - - ParameterNumber - type.gaussian.radius - Radius - Gaussian radius (in pixels) - - - 2 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/Smoothing-mean.xml b/python/plugins/processing/algs/otb/description/5.6.0/Smoothing-mean.xml deleted file mode 100644 index 8e010db17ede..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/Smoothing-mean.xml +++ /dev/null @@ -1,54 +0,0 @@ - - Smoothing-mean - otbcli_Smoothing - Smoothing (mean) - Image Filtering - Apply a smoothing filter to an image - - ParameterRaster - in - Input Image - Input image to smooth. - False - - - OutputRaster - out - Output Image - Output smoothed image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - type - Smoothing Type - Smoothing kernel to apply - - - mean - - - 2 - False - - - ParameterNumber - type.mean.radius - Radius - Mean radius (in pixels) - - - 2 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/StereoFramework.xml b/python/plugins/processing/algs/otb/description/5.6.0/StereoFramework.xml deleted file mode 100644 index e06cd7fe7c55..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/StereoFramework.xml +++ /dev/null @@ -1,343 +0,0 @@ - - StereoFramework - otbcli_StereoFramework - Stereo Framework - Stereo - Compute the ground elevation based on one or multiple stereo pair(s) - - ParameterMultipleInput - input.il - Input images list - The list of images. - - False - - - ParameterString - input.co - Couples list - List of index of couples im image list. Couples must be separated by a comma. (index start at 0). for example : 0 1,1 2 will process a first couple composed of the first and the second image in image list, then the first and the third image -. note that images are handled by pairs. if left empty couples are created from input index i.e. a first couple will be composed of the first and second image, a second couple with third and fourth image etc. (in this case image list must be even). - - - True - - - ParameterNumber - input.channel - Image channel used for the block matching - Used channel for block matching (used for all images) - - - 1 - False - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - output.res - Output resolution - Spatial sampling distance of the output elevation : the cell size (in m) - - - 1 - False - - - ParameterNumber - output.nodata - NoData value - DSM empty cells are filled with this value (optional -32768 by default) - - - -32768 - True - - - ParameterSelection - output.fusionmethod - Method to fuse measures in each DSM cell - This parameter allows one to choose the method used to fuse elevation measurements in each output DSM cell - - - max - min - mean - acc - - - 0 - False - - - OutputRaster - output.out - Output DSM - Output elevation image - - - - ParameterSelection - output.mode - Parameters estimation modes - - - - fit - user - - - 0 - False - - - ParameterNumber - output.mode.user.ulx - Upper Left X - Cartographic X coordinate of upper-left corner (meters for cartographic projections, degrees for geographic ones) - - - 0.0 - False - - - ParameterNumber - output.mode.user.uly - Upper Left Y - Cartographic Y coordinate of the upper-left corner (meters for cartographic projections, degrees for geographic ones) - - - 0.0 - False - - - ParameterNumber - output.mode.user.sizex - Size X - Size of projected image along X (in pixels) - - - 0 - False - - - ParameterNumber - output.mode.user.sizey - Size Y - Size of projected image along Y (in pixels) - - - 0 - False - - - ParameterNumber - output.mode.user.spacingx - Pixel Size X - Size of each pixel along X axis (meters for cartographic projections, degrees for geographic ones) - - - 0.0 - False - - - ParameterNumber - output.mode.user.spacingy - Pixel Size Y - Size of each pixel along Y axis (meters for cartographic projections, degrees for geographic ones) - - - 0.0 - False - - - ParameterSelection - map - Output Cartographic Map Projection - Parameters of the output map projection to be used. - - - utm - lambert2 - lambert93 - wgs - epsg - - - 3 - False - - - ParameterNumber - map.utm.zone - Zone number - The zone number ranges from 1 to 60 and allows defining the transverse mercator projection (along with the hemisphere) - - - 31 - False - - - ParameterBoolean - map.utm.northhem - Northern Hemisphere - The transverse mercator projections are defined by their zone number as well as the hemisphere. Activate this parameter if your image is in the northern hemisphere. - True - True - - - ParameterNumber - map.epsg.code - EPSG Code - See www.spatialreference.org to find which EPSG code is associated to your projection - - - 4326 - False - - - ParameterNumber - stereorect.fwdgridstep - Step of the displacement grid (in pixels) - Stereo-rectification displacement grid only varies slowly. Therefore, it is recommended to use a coarser grid (higher step value) in case of large images - - - 16 - True - - - ParameterNumber - stereorect.invgridssrate - Sub-sampling rate for epipolar grid inversion - Grid inversion is an heavy process that implies spline regression on control points. To avoid eating to much memory, this parameter allows one to first sub-sample the field to invert. - - - 10 - True - - - ParameterSelection - bm.metric - Block-matching metric - - - - ssdmean - ssd - ncc - lp - - - 0 - False - - - ParameterNumber - bm.metric.lp.p - p value - Value of the p parameter in Lp pseudo-norm (must be positive) - - - 1 - False - - - ParameterNumber - bm.radius - Radius of blocks for matching filter (in pixels) - The radius of blocks in Block-Matching (in pixels) - - - 2 - True - - - ParameterNumber - bm.minhoffset - Minimum altitude offset (in meters) - Minimum altitude below the selected elevation source (in meters) - - - -20 - False - - - ParameterNumber - bm.maxhoffset - Maximum altitude offset (in meters) - Maximum altitude above the selected elevation source (in meters) - - - 20 - False - - - ParameterBoolean - postproc.bij - Use bijection consistency in block matching strategy - use bijection consistency. Right to Left correlation is computed to validate Left to Right disparities. If bijection is not found pixel is rejected. - True - True - - - ParameterBoolean - postproc.med - Use median disparities filtering - disparities output can be filtered using median post filtering (disabled by default). - True - True - - - ParameterNumber - postproc.metrict - Correlation metric threshold - Use block matching metric output to discard pixels with low correlation value (disabled by default, float value) - - - 0.6 - True - - - ParameterRaster - mask.left - Input left mask - Mask for left input image - True - - - ParameterRaster - mask.right - Input right mask - Mask for right input image - True - - - ParameterNumber - mask.variancet - Discard pixels with low local variance - This parameter allows one to discard pixels whose local variance is too small (the size of the neighborhood is given by the radius parameter) - - - 50 - True - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/Superimpose.xml b/python/plugins/processing/algs/otb/description/5.6.0/Superimpose.xml deleted file mode 100644 index fcb4d9fa2a6b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/Superimpose.xml +++ /dev/null @@ -1,97 +0,0 @@ - - Superimpose - otbcli_Superimpose - Superimpose sensor - Geometry - Using available image metadata, project one image onto another one - - ParameterRaster - inr - Reference input - The input reference image. - False - - - ParameterRaster - inm - The image to reproject - The image to reproject into the geometry of the reference input. - False - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - lms - Spacing of the deformation field - Generate a coarser deformation field with the given spacing - - - 4 - True - - - OutputRaster - out - Output image - Output reprojected image. - - - - ParameterSelection - mode - Mode - Superimposition mode - - - default - phr - - - 0 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows defining how the input image will be interpolated during resampling. - - - bco - nn - linear - - - 0 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows controling the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TileFusion.xml b/python/plugins/processing/algs/otb/description/5.6.0/TileFusion.xml deleted file mode 100644 index b157b2caa49a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TileFusion.xml +++ /dev/null @@ -1,42 +0,0 @@ - - TileFusion - otbcli_TileFusion - Image Tile Fusion - Image Manipulation - Fusion of an image made of several tile files. - - ParameterMultipleInput - il - Input Tile Images - Input tiles to concatenate (in lexicographic order : (0,0) (1,0) (0,1) (1,1)). - - False - - - ParameterNumber - cols - Number of tile columns - Number of columns in the tile array - - - 0 - False - - - ParameterNumber - rows - Number of tile rows - Number of rows in the tile array - - - 0 - False - - - OutputRaster - out - Output Image - Output entire image - - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-ann.xml b/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-ann.xml deleted file mode 100644 index 4422c4dc9ea4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-ann.xml +++ /dev/null @@ -1,266 +0,0 @@ - - TrainImagesClassifier-ann - otbcli_TrainImagesClassifier - TrainImagesClassifier (ann) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - False - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - False - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - ann - - - 0 - False - - - ParameterSelection - classifier.ann.t - Train Method Type - Type of training method for the multilayer perceptron (MLP) neural network. - - - reg - back - - - 0 - False - - - ParameterString - classifier.ann.sizes - Number of neurons in each intermediate layer - The number of neurons in each intermediate layer (excluding input and output layers). - - - False - - - ParameterSelection - classifier.ann.f - Neuron activation function type - Neuron activation function. - - - ident - sig - gau - - - 1 - False - - - ParameterNumber - classifier.ann.a - Alpha parameter of the activation function - Alpha parameter of the activation function (used only with sigmoid and gaussian functions). - - - 1 - False - - - ParameterNumber - classifier.ann.b - Beta parameter of the activation function - Beta parameter of the activation function (used only with sigmoid and gaussian functions). - - - 1 - False - - - ParameterNumber - classifier.ann.bpdw - Strength of the weight gradient term in the BACKPROP method - Strength of the weight gradient term in the BACKPROP method. The recommended value is about 0.1. - - - 0.1 - False - - - ParameterNumber - classifier.ann.bpms - Strength of the momentum term (the difference between weights on the 2 previous iterations) - Strength of the momentum term (the difference between weights on the 2 previous iterations). This parameter provides some inertia to smooth the random fluctuations of the weights. It can vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough. - - - 0.1 - False - - - ParameterNumber - classifier.ann.rdw - Initial value Delta_0 of update-values Delta_{ij} in RPROP method - Initial value Delta_0 of update-values Delta_{ij} in RPROP method (default = 0.1). - - - 0.1 - False - - - ParameterNumber - classifier.ann.rdwm - Update-values lower limit Delta_{min} in RPROP method - Update-values lower limit Delta_{min} in RPROP method. It must be positive (default = 1e-7). - - - 1e-07 - False - - - ParameterSelection - classifier.ann.term - Termination criteria - Termination criteria. - - - iter - eps - all - - - 2 - False - - - ParameterNumber - classifier.ann.eps - Epsilon value used in the Termination criteria - Epsilon value used in the Termination criteria. - - - 0.01 - False - - - ParameterNumber - classifier.ann.iter - Maximum number of iterations used in the Termination criteria - Maximum number of iterations used in the Termination criteria. - - - 1000 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-bayes.xml b/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-bayes.xml deleted file mode 100644 index c5051527b855..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-bayes.xml +++ /dev/null @@ -1,133 +0,0 @@ - - TrainImagesClassifier-bayes - otbcli_TrainImagesClassifier - TrainImagesClassifier (bayes) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - False - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - False - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - bayes - - - 0 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-boost.xml b/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-boost.xml deleted file mode 100644 index 72e10d96d4fd..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-boost.xml +++ /dev/null @@ -1,179 +0,0 @@ - - TrainImagesClassifier-boost - otbcli_TrainImagesClassifier - TrainImagesClassifier (boost) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - False - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - False - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - boost - - - 0 - False - - - ParameterSelection - classifier.boost.t - Boost Type - Type of Boosting algorithm. - - - discrete - real - logit - gentle - - - 1 - False - - - ParameterNumber - classifier.boost.w - Weak count - The number of weak classifiers. - - - 100 - False - - - ParameterNumber - classifier.boost.r - Weight Trim Rate - A threshold between 0 and 1 used to save computational time. Samples with summary weight <= (1 - weight_trim_rate) do not participate in the next iteration of training. Set this parameter to 0 to turn off this functionality. - - - 0.95 - False - - - ParameterNumber - classifier.boost.m - Maximum depth of the tree - Maximum depth of the tree. - - - 1 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-dt.xml b/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-dt.xml deleted file mode 100644 index 64cb5079f951..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-dt.xml +++ /dev/null @@ -1,199 +0,0 @@ - - TrainImagesClassifier-dt - otbcli_TrainImagesClassifier - TrainImagesClassifier (dt) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - False - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - False - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - dt - - - 0 - False - - - ParameterNumber - classifier.dt.max - Maximum depth of the tree - The training algorithm attempts to split each node while its depth is smaller than the maximum possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or if the tree is pruned. - - - 65535 - False - - - ParameterNumber - classifier.dt.min - Minimum number of samples in each node - If all absolute differences between an estimated value in a node and the values of the train samples in this node are smaller than this regression accuracy parameter, then the node will not be split. - - - 10 - False - - - ParameterNumber - classifier.dt.ra - Termination criteria for regression tree - - - - 0.01 - False - - - ParameterNumber - classifier.dt.cat - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split. - - - 10 - False - - - ParameterNumber - classifier.dt.f - K-fold cross-validations - If cv_folds > 1, then it prunes a tree with K-fold cross-validation where K is equal to cv_folds. - - - 10 - False - - - ParameterBoolean - classifier.dt.r - Set Use1seRule flag to false - If true, then a pruning will be harsher. This will make a tree more compact and more resistant to the training data noise but a bit less accurate. - True - True - - - ParameterBoolean - classifier.dt.t - Set TruncatePrunedTree flag to false - If true, then pruned branches are physically removed from the tree. - True - True - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-gbt.xml b/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-gbt.xml deleted file mode 100644 index d54f637ff333..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-gbt.xml +++ /dev/null @@ -1,173 +0,0 @@ - - TrainImagesClassifier-gbt - otbcli_TrainImagesClassifier - TrainImagesClassifier (gbt) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - False - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - False - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - gbt - - - 0 - False - - - ParameterNumber - classifier.gbt.w - Number of boosting algorithm iterations - Number "w" of boosting algorithm iterations, with w*K being the total number of trees in the GBT model, where K is the output number of classes. - - - 200 - False - - - ParameterNumber - classifier.gbt.s - Regularization parameter - Regularization parameter. - - - 0.01 - False - - - ParameterNumber - classifier.gbt.p - Portion of the whole training set used for each algorithm iteration - Portion of the whole training set used for each algorithm iteration. The subset is generated randomly. - - - 0.8 - False - - - ParameterNumber - classifier.gbt.max - Maximum depth of the tree - The training algorithm attempts to split each node while its depth is smaller than the maximum possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or if the tree is pruned. - - - 3 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-knn.xml b/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-knn.xml deleted file mode 100644 index a78ca47f1b70..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-knn.xml +++ /dev/null @@ -1,143 +0,0 @@ - - TrainImagesClassifier-knn - otbcli_TrainImagesClassifier - TrainImagesClassifier (knn) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - False - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - False - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - knn - - - 0 - False - - - ParameterNumber - classifier.knn.k - Number of Neighbors - The number of neighbors to use. - - - 32 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-libsvm.xml b/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-libsvm.xml deleted file mode 100644 index 75b57373010b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-libsvm.xml +++ /dev/null @@ -1,190 +0,0 @@ - - TrainImagesClassifier-libsvm - otbcli_TrainImagesClassifier - TrainImagesClassifier (libsvm) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - False - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - False - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - libsvm - - - 0 - False - - - ParameterSelection - classifier.libsvm.k - SVM Kernel Type - SVM Kernel Type. - - - linear - rbf - poly - sigmoid - - - 0 - False - - - ParameterSelection - classifier.libsvm.m - SVM Model Type - Type of SVM formulation. - - - csvc - nusvc - oneclass - - - 0 - False - - - ParameterNumber - classifier.libsvm.c - Cost parameter C - SVM models have a cost parameter C (1 by default) to control the trade-off between training errors and forcing rigid margins. - - - 1 - False - - - ParameterBoolean - classifier.libsvm.opt - Parameters optimization - SVM parameters optimization flag. - True - True - - - ParameterBoolean - classifier.libsvm.prob - Probability estimation - Probability estimation flag. - True - True - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-rf.xml b/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-rf.xml deleted file mode 100644 index 91810de17aad..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TrainImagesClassifier-rf.xml +++ /dev/null @@ -1,203 +0,0 @@ - - TrainImagesClassifier-rf - otbcli_TrainImagesClassifier - TrainImagesClassifier (rf) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - False - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - False - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - rf - - - 0 - False - - - ParameterNumber - classifier.rf.max - Maximum depth of the tree - The depth of the tree. A low value will likely underfit and conversely a high value will likely overfit. The optimal value can be obtained using cross validation or other suitable methods. - - - 5 - False - - - ParameterNumber - classifier.rf.min - Minimum number of samples in each node - If the number of samples in a node is smaller than this parameter, then the node will not be split. A reasonable value is a small percentage of the total data e.g. 1 percent. - - - 10 - False - - - ParameterNumber - classifier.rf.ra - Termination Criteria for regression tree - If all absolute differences between an estimated value in a node and the values of the train samples in this node are smaller than this regression accuracy parameter, then the node will not be split. - - - 0 - False - - - ParameterNumber - classifier.rf.cat - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split. - - - 10 - False - - - ParameterNumber - classifier.rf.var - Size of the randomly selected subset of features at each tree node - The size of the subset of features, randomly selected at each tree node, that are used to find the best split(s). If you set it to 0, then the size will be set to the square root of the total number of features. - - - 0 - False - - - ParameterNumber - classifier.rf.nbtrees - Maximum number of trees in the forest - The maximum number of trees in the forest. Typically, the more trees you have, the better the accuracy. However, the improvement in accuracy generally diminishes and reaches an asymptote for a certain number of trees. Also to keep in mind, increasing the number of trees increases the prediction time linearly. - - - 100 - False - - - ParameterNumber - classifier.rf.acc - Sufficient accuracy (OOB error) - Sufficient accuracy (OOB error). - - - 0.01 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TrainOGRLayersClassifier.xml b/python/plugins/processing/algs/otb/description/5.6.0/TrainOGRLayersClassifier.xml deleted file mode 100644 index ea4270102853..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TrainOGRLayersClassifier.xml +++ /dev/null @@ -1,47 +0,0 @@ - - TrainOGRLayersClassifier - otbcli_TrainOGRLayersClassifier - TrainOGRLayersClassifier (DEPRECATED) - Segmentation - Train a SVM classifier based on labeled geometries and a list of features to consider. - - ParameterVector - inshp - Name of the input shapefile - Name of the input shapefile - - False - - - ParameterFile - instats - XML file containing mean and variance of each feature. - XML file containing mean and variance of each feature. - - False - - - OutputFile - outsvm - Output model filename. - Output model filename. - - - ParameterString - feat - List of features to consider for classification. - List of features to consider for classification. - - - False - - - ParameterString - cfield - Field containing the class id for supervision - Field containing the class id for supervision. Only geometries with this field available will be taken into account. - class - - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-ann.xml b/python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-ann.xml deleted file mode 100644 index c76f6be09573..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-ann.xml +++ /dev/null @@ -1,233 +0,0 @@ - - TrainRegression-ann - otbcli_TrainRegression - TrainRegression (ann) - Learning - Train a classifier from multiple images to perform regression. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. First (n-1) bands should contain the predictor. The last band should contain the output value to predict. - - False - - - ParameterFile - io.csv - Input CSV file - Input CSV file containing the predictors, and the output values in last column. Only used when no input image is given - - True - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.out - Output regression model - Output file containing the model estimated (.txt format). - - - ParameterNumber - io.mse - Mean Square Error - Mean square error computed with the validation predictors - - - 0.0 - False - - - ParameterNumber - sample.mt - Maximum training predictors - Maximum number of training predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation predictors - Maximum number of validation predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - ann - - - 0 - False - - - ParameterSelection - classifier.ann.t - Train Method Type - Type of training method for the multilayer perceptron (MLP) neural network. - - - reg - back - - - 0 - False - - - ParameterString - classifier.ann.sizes - Number of neurons in each intermediate layer - The number of neurons in each intermediate layer (excluding input and output layers). - - - False - - - ParameterSelection - classifier.ann.f - Neuron activation function type - Neuron activation function. - - - ident - sig - gau - - - 1 - False - - - ParameterNumber - classifier.ann.a - Alpha parameter of the activation function - Alpha parameter of the activation function (used only with sigmoid and gaussian functions). - - - 1 - False - - - ParameterNumber - classifier.ann.b - Beta parameter of the activation function - Beta parameter of the activation function (used only with sigmoid and gaussian functions). - - - 1 - False - - - ParameterNumber - classifier.ann.bpdw - Strength of the weight gradient term in the BACKPROP method - Strength of the weight gradient term in the BACKPROP method. The recommended value is about 0.1. - - - 0.1 - False - - - ParameterNumber - classifier.ann.bpms - Strength of the momentum term (the difference between weights on the 2 previous iterations) - Strength of the momentum term (the difference between weights on the 2 previous iterations). This parameter provides some inertia to smooth the random fluctuations of the weights. It can vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough. - - - 0.1 - False - - - ParameterNumber - classifier.ann.rdw - Initial value Delta_0 of update-values Delta_{ij} in RPROP method - Initial value Delta_0 of update-values Delta_{ij} in RPROP method (default = 0.1). - - - 0.1 - False - - - ParameterNumber - classifier.ann.rdwm - Update-values lower limit Delta_{min} in RPROP method - Update-values lower limit Delta_{min} in RPROP method. It must be positive (default = 1e-7). - - - 1e-07 - False - - - ParameterSelection - classifier.ann.term - Termination criteria - Termination criteria. - - - iter - eps - all - - - 2 - False - - - ParameterNumber - classifier.ann.eps - Epsilon value used in the Termination criteria - Epsilon value used in the Termination criteria. - - - 0.01 - False - - - ParameterNumber - classifier.ann.iter - Maximum number of iterations used in the Termination criteria - Maximum number of iterations used in the Termination criteria. - - - 1000 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-dt.xml b/python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-dt.xml deleted file mode 100644 index 3a8bb610ca5e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-dt.xml +++ /dev/null @@ -1,166 +0,0 @@ - - TrainRegression-dt - otbcli_TrainRegression - TrainRegression (dt) - Learning - Train a classifier from multiple images to perform regression. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. First (n-1) bands should contain the predictor. The last band should contain the output value to predict. - - False - - - ParameterFile - io.csv - Input CSV file - Input CSV file containing the predictors, and the output values in last column. Only used when no input image is given - - True - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.out - Output regression model - Output file containing the model estimated (.txt format). - - - ParameterNumber - io.mse - Mean Square Error - Mean square error computed with the validation predictors - - - 0.0 - False - - - ParameterNumber - sample.mt - Maximum training predictors - Maximum number of training predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation predictors - Maximum number of validation predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - dt - - - 0 - False - - - ParameterNumber - classifier.dt.max - Maximum depth of the tree - The training algorithm attempts to split each node while its depth is smaller than the maximum possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or if the tree is pruned. - - - 65535 - False - - - ParameterNumber - classifier.dt.min - Minimum number of samples in each node - If all absolute differences between an estimated value in a node and the values of the train samples in this node are smaller than this regression accuracy parameter, then the node will not be split. - - - 10 - False - - - ParameterNumber - classifier.dt.ra - Termination criteria for regression tree - - - - 0.01 - False - - - ParameterNumber - classifier.dt.cat - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split. - - - 10 - False - - - ParameterNumber - classifier.dt.f - K-fold cross-validations - If cv_folds > 1, then it prunes a tree with K-fold cross-validation where K is equal to cv_folds. - - - 10 - False - - - ParameterBoolean - classifier.dt.r - Set Use1seRule flag to false - If true, then a pruning will be harsher. This will make a tree more compact and more resistant to the training data noise but a bit less accurate. - True - True - - - ParameterBoolean - classifier.dt.t - Set TruncatePrunedTree flag to false - If true, then pruned branches are physically removed from the tree. - True - True - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-gbt.xml b/python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-gbt.xml deleted file mode 100644 index 50d35edbf599..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-gbt.xml +++ /dev/null @@ -1,155 +0,0 @@ - - TrainRegression-gbt - otbcli_TrainRegression - TrainRegression (gbt) - Learning - Train a classifier from multiple images to perform regression. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. First (n-1) bands should contain the predictor. The last band should contain the output value to predict. - - False - - - ParameterFile - io.csv - Input CSV file - Input CSV file containing the predictors, and the output values in last column. Only used when no input image is given - - True - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.out - Output regression model - Output file containing the model estimated (.txt format). - - - ParameterNumber - io.mse - Mean Square Error - Mean square error computed with the validation predictors - - - 0.0 - False - - - ParameterNumber - sample.mt - Maximum training predictors - Maximum number of training predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation predictors - Maximum number of validation predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - gbt - - - 0 - False - - - ParameterSelection - classifier.gbt.t - Loss Function Type - Type of loss functionused for training. - - - sqr - abs - hub - - - 0 - False - - - ParameterNumber - classifier.gbt.w - Number of boosting algorithm iterations - Number "w" of boosting algorithm iterations, with w*K being the total number of trees in the GBT model, where K is the output number of classes. - - - 200 - False - - - ParameterNumber - classifier.gbt.s - Regularization parameter - Regularization parameter. - - - 0.01 - False - - - ParameterNumber - classifier.gbt.p - Portion of the whole training set used for each algorithm iteration - Portion of the whole training set used for each algorithm iteration. The subset is generated randomly. - - - 0.8 - False - - - ParameterNumber - classifier.gbt.max - Maximum depth of the tree - The training algorithm attempts to split each node while its depth is smaller than the maximum possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or if the tree is pruned. - - - 3 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-knn.xml b/python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-knn.xml deleted file mode 100644 index 8482f90aa78b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-knn.xml +++ /dev/null @@ -1,124 +0,0 @@ - - TrainRegression-knn - otbcli_TrainRegression - TrainRegression (knn) - Learning - Train a classifier from multiple images to perform regression. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. First (n-1) bands should contain the predictor. The last band should contain the output value to predict. - - False - - - ParameterFile - io.csv - Input CSV file - Input CSV file containing the predictors, and the output values in last column. Only used when no input image is given - - True - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.out - Output regression model - Output file containing the model estimated (.txt format). - - - ParameterNumber - io.mse - Mean Square Error - Mean square error computed with the validation predictors - - - 0.0 - False - - - ParameterNumber - sample.mt - Maximum training predictors - Maximum number of training predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation predictors - Maximum number of validation predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - knn - - - 0 - False - - - ParameterNumber - classifier.knn.k - Number of Neighbors - The number of neighbors to use. - - - 32 - False - - - ParameterSelection - classifier.knn.rule - Decision rule - Decision rule for regression output - - - mean - median - - - 0 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-libsvm.xml b/python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-libsvm.xml deleted file mode 100644 index b68db7c34911..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-libsvm.xml +++ /dev/null @@ -1,176 +0,0 @@ - - TrainRegression-libsvm - otbcli_TrainRegression - TrainRegression (libsvm) - Learning - Train a classifier from multiple images to perform regression. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. First (n-1) bands should contain the predictor. The last band should contain the output value to predict. - - False - - - ParameterFile - io.csv - Input CSV file - Input CSV file containing the predictors, and the output values in last column. Only used when no input image is given - - True - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.out - Output regression model - Output file containing the model estimated (.txt format). - - - ParameterNumber - io.mse - Mean Square Error - Mean square error computed with the validation predictors - - - 0.0 - False - - - ParameterNumber - sample.mt - Maximum training predictors - Maximum number of training predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation predictors - Maximum number of validation predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - libsvm - - - 0 - False - - - ParameterSelection - classifier.libsvm.k - SVM Kernel Type - SVM Kernel Type. - - - linear - rbf - poly - sigmoid - - - 0 - False - - - ParameterSelection - classifier.libsvm.m - SVM Model Type - Type of SVM formulation. - - - epssvr - nusvr - - - 0 - False - - - ParameterNumber - classifier.libsvm.c - Cost parameter C - SVM models have a cost parameter C (1 by default) to control the trade-off between training errors and forcing rigid margins. - - - 1 - False - - - ParameterBoolean - classifier.libsvm.opt - Parameters optimization - SVM parameters optimization flag. - True - True - - - ParameterBoolean - classifier.libsvm.prob - Probability estimation - Probability estimation flag. - True - True - - - ParameterNumber - classifier.libsvm.eps - Epsilon - - - - 0.001 - False - - - ParameterNumber - classifier.libsvm.nu - Nu - - - - 0.5 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-rf.xml b/python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-rf.xml deleted file mode 100644 index 4a2525bda97c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TrainRegression-rf.xml +++ /dev/null @@ -1,170 +0,0 @@ - - TrainRegression-rf - otbcli_TrainRegression - TrainRegression (rf) - Learning - Train a classifier from multiple images to perform regression. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. First (n-1) bands should contain the predictor. The last band should contain the output value to predict. - - False - - - ParameterFile - io.csv - Input CSV file - Input CSV file containing the predictors, and the output values in last column. Only used when no input image is given - - True - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.out - Output regression model - Output file containing the model estimated (.txt format). - - - ParameterNumber - io.mse - Mean Square Error - Mean square error computed with the validation predictors - - - 0.0 - False - - - ParameterNumber - sample.mt - Maximum training predictors - Maximum number of training predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation predictors - Maximum number of validation predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - rf - - - 0 - False - - - ParameterNumber - classifier.rf.max - Maximum depth of the tree - The depth of the tree. A low value will likely underfit and conversely a high value will likely overfit. The optimal value can be obtained using cross validation or other suitable methods. - - - 5 - False - - - ParameterNumber - classifier.rf.min - Minimum number of samples in each node - If the number of samples in a node is smaller than this parameter, then the node will not be split. A reasonable value is a small percentage of the total data e.g. 1 percent. - - - 10 - False - - - ParameterNumber - classifier.rf.ra - Termination Criteria for regression tree - If all absolute differences between an estimated value in a node and the values of the train samples in this node are smaller than this regression accuracy parameter, then the node will not be split. - - - 0 - False - - - ParameterNumber - classifier.rf.cat - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split. - - - 10 - False - - - ParameterNumber - classifier.rf.var - Size of the randomly selected subset of features at each tree node - The size of the subset of features, randomly selected at each tree node, that are used to find the best split(s). If you set it to 0, then the size will be set to the square root of the total number of features. - - - 0 - False - - - ParameterNumber - classifier.rf.nbtrees - Maximum number of trees in the forest - The maximum number of trees in the forest. Typically, the more trees you have, the better the accuracy. However, the improvement in accuracy generally diminishes and reaches an asymptote for a certain number of trees. Also to keep in mind, increasing the number of trees increases the prediction time linearly. - - - 100 - False - - - ParameterNumber - classifier.rf.acc - Sufficient accuracy (OOB error) - Sufficient accuracy (OOB error). - - - 0.01 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-ann.xml b/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-ann.xml deleted file mode 100644 index 3273e0d7a70d..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-ann.xml +++ /dev/null @@ -1,237 +0,0 @@ - - TrainVectorClassifier-ann - otbcli_TrainVectorClassifier - TrainVectorClassifier (ann) - Learning - Train a classifier based on labeled geometries and a list of features to consider. - - ParameterVector - io.vd - Input Vector Data - Input geometries used for training (note : all geometries from the layer will be used) - - False - - - ParameterFile - io.stats - Input XML image statistics file - XML file containing mean and variance of each feature. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterString - feat - Field names for training features. - List of field names in the input vector data to be used as features for training. - - - False - - - ParameterString - cfield - Field containing the class id for supervision - Field containing the class id for supervision. Only geometries with this field available will be taken into account. - class - - False - - - ParameterNumber - layer - Layer Index - Index of the layer to use in the input vector file. - - - 0 - True - - - ParameterVector - valid.vd - Validation Vector Data - Geometries used for validation (must contain the same fields used for training, all geometries from the layer will be used) - - True - - - ParameterNumber - valid.layer - Layer Index - Index of the layer to use in the validation vector file. - - - 0 - True - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - ann - - - 0 - False - - - ParameterSelection - classifier.ann.t - Train Method Type - Type of training method for the multilayer perceptron (MLP) neural network. - - - reg - back - - - 0 - False - - - ParameterString - classifier.ann.sizes - Number of neurons in each intermediate layer - The number of neurons in each intermediate layer (excluding input and output layers). - - - False - - - ParameterSelection - classifier.ann.f - Neuron activation function type - Neuron activation function. - - - ident - sig - gau - - - 1 - False - - - ParameterNumber - classifier.ann.a - Alpha parameter of the activation function - Alpha parameter of the activation function (used only with sigmoid and gaussian functions). - - - 1 - False - - - ParameterNumber - classifier.ann.b - Beta parameter of the activation function - Beta parameter of the activation function (used only with sigmoid and gaussian functions). - - - 1 - False - - - ParameterNumber - classifier.ann.bpdw - Strength of the weight gradient term in the BACKPROP method - Strength of the weight gradient term in the BACKPROP method. The recommended value is about 0.1. - - - 0.1 - False - - - ParameterNumber - classifier.ann.bpms - Strength of the momentum term (the difference between weights on the 2 previous iterations) - Strength of the momentum term (the difference between weights on the 2 previous iterations). This parameter provides some inertia to smooth the random fluctuations of the weights. It can vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough. - - - 0.1 - False - - - ParameterNumber - classifier.ann.rdw - Initial value Delta_0 of update-values Delta_{ij} in RPROP method - Initial value Delta_0 of update-values Delta_{ij} in RPROP method (default = 0.1). - - - 0.1 - False - - - ParameterNumber - classifier.ann.rdwm - Update-values lower limit Delta_{min} in RPROP method - Update-values lower limit Delta_{min} in RPROP method. It must be positive (default = 1e-7). - - - 1e-07 - False - - - ParameterSelection - classifier.ann.term - Termination criteria - Termination criteria. - - - iter - eps - all - - - 2 - False - - - ParameterNumber - classifier.ann.eps - Epsilon value used in the Termination criteria - Epsilon value used in the Termination criteria. - - - 0.01 - False - - - ParameterNumber - classifier.ann.iter - Maximum number of iterations used in the Termination criteria - Maximum number of iterations used in the Termination criteria. - - - 1000 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-bayes.xml b/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-bayes.xml deleted file mode 100644 index 13bee02e7e97..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-bayes.xml +++ /dev/null @@ -1,104 +0,0 @@ - - TrainVectorClassifier-bayes - otbcli_TrainVectorClassifier - TrainVectorClassifier (bayes) - Learning - Train a classifier based on labeled geometries and a list of features to consider. - - ParameterVector - io.vd - Input Vector Data - Input geometries used for training (note : all geometries from the layer will be used) - - False - - - ParameterFile - io.stats - Input XML image statistics file - XML file containing mean and variance of each feature. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterString - feat - Field names for training features. - List of field names in the input vector data to be used as features for training. - - - False - - - ParameterString - cfield - Field containing the class id for supervision - Field containing the class id for supervision. Only geometries with this field available will be taken into account. - class - - False - - - ParameterNumber - layer - Layer Index - Index of the layer to use in the input vector file. - - - 0 - True - - - ParameterVector - valid.vd - Validation Vector Data - Geometries used for validation (must contain the same fields used for training, all geometries from the layer will be used) - - True - - - ParameterNumber - valid.layer - Layer Index - Index of the layer to use in the validation vector file. - - - 0 - True - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - bayes - - - 0 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-boost.xml b/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-boost.xml deleted file mode 100644 index 1468dda9b312..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-boost.xml +++ /dev/null @@ -1,150 +0,0 @@ - - TrainVectorClassifier-boost - otbcli_TrainVectorClassifier - TrainVectorClassifier (boost) - Learning - Train a classifier based on labeled geometries and a list of features to consider. - - ParameterVector - io.vd - Input Vector Data - Input geometries used for training (note : all geometries from the layer will be used) - - False - - - ParameterFile - io.stats - Input XML image statistics file - XML file containing mean and variance of each feature. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterString - feat - Field names for training features. - List of field names in the input vector data to be used as features for training. - - - False - - - ParameterString - cfield - Field containing the class id for supervision - Field containing the class id for supervision. Only geometries with this field available will be taken into account. - class - - False - - - ParameterNumber - layer - Layer Index - Index of the layer to use in the input vector file. - - - 0 - True - - - ParameterVector - valid.vd - Validation Vector Data - Geometries used for validation (must contain the same fields used for training, all geometries from the layer will be used) - - True - - - ParameterNumber - valid.layer - Layer Index - Index of the layer to use in the validation vector file. - - - 0 - True - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - boost - - - 0 - False - - - ParameterSelection - classifier.boost.t - Boost Type - Type of Boosting algorithm. - - - discrete - real - logit - gentle - - - 1 - False - - - ParameterNumber - classifier.boost.w - Weak count - The number of weak classifiers. - - - 100 - False - - - ParameterNumber - classifier.boost.r - Weight Trim Rate - A threshold between 0 and 1 used to save computational time. Samples with summary weight <= (1 - weight_trim_rate) do not participate in the next iteration of training. Set this parameter to 0 to turn off this functionality. - - - 0.95 - False - - - ParameterNumber - classifier.boost.m - Maximum depth of the tree - Maximum depth of the tree. - - - 1 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-dt.xml b/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-dt.xml deleted file mode 100644 index b64e1e68e64f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-dt.xml +++ /dev/null @@ -1,170 +0,0 @@ - - TrainVectorClassifier-dt - otbcli_TrainVectorClassifier - TrainVectorClassifier (dt) - Learning - Train a classifier based on labeled geometries and a list of features to consider. - - ParameterVector - io.vd - Input Vector Data - Input geometries used for training (note : all geometries from the layer will be used) - - False - - - ParameterFile - io.stats - Input XML image statistics file - XML file containing mean and variance of each feature. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterString - feat - Field names for training features. - List of field names in the input vector data to be used as features for training. - - - False - - - ParameterString - cfield - Field containing the class id for supervision - Field containing the class id for supervision. Only geometries with this field available will be taken into account. - class - - False - - - ParameterNumber - layer - Layer Index - Index of the layer to use in the input vector file. - - - 0 - True - - - ParameterVector - valid.vd - Validation Vector Data - Geometries used for validation (must contain the same fields used for training, all geometries from the layer will be used) - - True - - - ParameterNumber - valid.layer - Layer Index - Index of the layer to use in the validation vector file. - - - 0 - True - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - dt - - - 0 - False - - - ParameterNumber - classifier.dt.max - Maximum depth of the tree - The training algorithm attempts to split each node while its depth is smaller than the maximum possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or if the tree is pruned. - - - 65535 - False - - - ParameterNumber - classifier.dt.min - Minimum number of samples in each node - If all absolute differences between an estimated value in a node and the values of the train samples in this node are smaller than this regression accuracy parameter, then the node will not be split. - - - 10 - False - - - ParameterNumber - classifier.dt.ra - Termination criteria for regression tree - - - - 0.01 - False - - - ParameterNumber - classifier.dt.cat - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split. - - - 10 - False - - - ParameterNumber - classifier.dt.f - K-fold cross-validations - If cv_folds > 1, then it prunes a tree with K-fold cross-validation where K is equal to cv_folds. - - - 10 - False - - - ParameterBoolean - classifier.dt.r - Set Use1seRule flag to false - If true, then a pruning will be harsher. This will make a tree more compact and more resistant to the training data noise but a bit less accurate. - True - True - - - ParameterBoolean - classifier.dt.t - Set TruncatePrunedTree flag to false - If true, then pruned branches are physically removed from the tree. - True - True - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-gbt.xml b/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-gbt.xml deleted file mode 100644 index 92ba41ab21a9..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-gbt.xml +++ /dev/null @@ -1,144 +0,0 @@ - - TrainVectorClassifier-gbt - otbcli_TrainVectorClassifier - TrainVectorClassifier (gbt) - Learning - Train a classifier based on labeled geometries and a list of features to consider. - - ParameterVector - io.vd - Input Vector Data - Input geometries used for training (note : all geometries from the layer will be used) - - False - - - ParameterFile - io.stats - Input XML image statistics file - XML file containing mean and variance of each feature. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterString - feat - Field names for training features. - List of field names in the input vector data to be used as features for training. - - - False - - - ParameterString - cfield - Field containing the class id for supervision - Field containing the class id for supervision. Only geometries with this field available will be taken into account. - class - - False - - - ParameterNumber - layer - Layer Index - Index of the layer to use in the input vector file. - - - 0 - True - - - ParameterVector - valid.vd - Validation Vector Data - Geometries used for validation (must contain the same fields used for training, all geometries from the layer will be used) - - True - - - ParameterNumber - valid.layer - Layer Index - Index of the layer to use in the validation vector file. - - - 0 - True - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - gbt - - - 0 - False - - - ParameterNumber - classifier.gbt.w - Number of boosting algorithm iterations - Number "w" of boosting algorithm iterations, with w*K being the total number of trees in the GBT model, where K is the output number of classes. - - - 200 - False - - - ParameterNumber - classifier.gbt.s - Regularization parameter - Regularization parameter. - - - 0.01 - False - - - ParameterNumber - classifier.gbt.p - Portion of the whole training set used for each algorithm iteration - Portion of the whole training set used for each algorithm iteration. The subset is generated randomly. - - - 0.8 - False - - - ParameterNumber - classifier.gbt.max - Maximum depth of the tree - The training algorithm attempts to split each node while its depth is smaller than the maximum possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or if the tree is pruned. - - - 3 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-knn.xml b/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-knn.xml deleted file mode 100644 index d44ff0e637f9..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-knn.xml +++ /dev/null @@ -1,114 +0,0 @@ - - TrainVectorClassifier-knn - otbcli_TrainVectorClassifier - TrainVectorClassifier (knn) - Learning - Train a classifier based on labeled geometries and a list of features to consider. - - ParameterVector - io.vd - Input Vector Data - Input geometries used for training (note : all geometries from the layer will be used) - - False - - - ParameterFile - io.stats - Input XML image statistics file - XML file containing mean and variance of each feature. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterString - feat - Field names for training features. - List of field names in the input vector data to be used as features for training. - - - False - - - ParameterString - cfield - Field containing the class id for supervision - Field containing the class id for supervision. Only geometries with this field available will be taken into account. - class - - False - - - ParameterNumber - layer - Layer Index - Index of the layer to use in the input vector file. - - - 0 - True - - - ParameterVector - valid.vd - Validation Vector Data - Geometries used for validation (must contain the same fields used for training, all geometries from the layer will be used) - - True - - - ParameterNumber - valid.layer - Layer Index - Index of the layer to use in the validation vector file. - - - 0 - True - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - knn - - - 0 - False - - - ParameterNumber - classifier.knn.k - Number of Neighbors - The number of neighbors to use. - - - 32 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-libsvm.xml b/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-libsvm.xml deleted file mode 100644 index fa2e571c6788..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-libsvm.xml +++ /dev/null @@ -1,161 +0,0 @@ - - TrainVectorClassifier-libsvm - otbcli_TrainVectorClassifier - TrainVectorClassifier (libsvm) - Learning - Train a classifier based on labeled geometries and a list of features to consider. - - ParameterVector - io.vd - Input Vector Data - Input geometries used for training (note : all geometries from the layer will be used) - - False - - - ParameterFile - io.stats - Input XML image statistics file - XML file containing mean and variance of each feature. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterString - feat - Field names for training features. - List of field names in the input vector data to be used as features for training. - - - False - - - ParameterString - cfield - Field containing the class id for supervision - Field containing the class id for supervision. Only geometries with this field available will be taken into account. - class - - False - - - ParameterNumber - layer - Layer Index - Index of the layer to use in the input vector file. - - - 0 - True - - - ParameterVector - valid.vd - Validation Vector Data - Geometries used for validation (must contain the same fields used for training, all geometries from the layer will be used) - - True - - - ParameterNumber - valid.layer - Layer Index - Index of the layer to use in the validation vector file. - - - 0 - True - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - libsvm - - - 0 - False - - - ParameterSelection - classifier.libsvm.k - SVM Kernel Type - SVM Kernel Type. - - - linear - rbf - poly - sigmoid - - - 0 - False - - - ParameterSelection - classifier.libsvm.m - SVM Model Type - Type of SVM formulation. - - - csvc - nusvc - oneclass - - - 0 - False - - - ParameterNumber - classifier.libsvm.c - Cost parameter C - SVM models have a cost parameter C (1 by default) to control the trade-off between training errors and forcing rigid margins. - - - 1 - False - - - ParameterBoolean - classifier.libsvm.opt - Parameters optimization - SVM parameters optimization flag. - True - True - - - ParameterBoolean - classifier.libsvm.prob - Probability estimation - Probability estimation flag. - True - True - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-rf.xml b/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-rf.xml deleted file mode 100644 index b9acbd0ef290..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/TrainVectorClassifier-rf.xml +++ /dev/null @@ -1,174 +0,0 @@ - - TrainVectorClassifier-rf - otbcli_TrainVectorClassifier - TrainVectorClassifier (rf) - Learning - Train a classifier based on labeled geometries and a list of features to consider. - - ParameterVector - io.vd - Input Vector Data - Input geometries used for training (note : all geometries from the layer will be used) - - False - - - ParameterFile - io.stats - Input XML image statistics file - XML file containing mean and variance of each feature. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterString - feat - Field names for training features. - List of field names in the input vector data to be used as features for training. - - - False - - - ParameterString - cfield - Field containing the class id for supervision - Field containing the class id for supervision. Only geometries with this field available will be taken into account. - class - - False - - - ParameterNumber - layer - Layer Index - Index of the layer to use in the input vector file. - - - 0 - True - - - ParameterVector - valid.vd - Validation Vector Data - Geometries used for validation (must contain the same fields used for training, all geometries from the layer will be used) - - True - - - ParameterNumber - valid.layer - Layer Index - Index of the layer to use in the validation vector file. - - - 0 - True - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - rf - - - 0 - False - - - ParameterNumber - classifier.rf.max - Maximum depth of the tree - The depth of the tree. A low value will likely underfit and conversely a high value will likely overfit. The optimal value can be obtained using cross validation or other suitable methods. - - - 5 - False - - - ParameterNumber - classifier.rf.min - Minimum number of samples in each node - If the number of samples in a node is smaller than this parameter, then the node will not be split. A reasonable value is a small percentage of the total data e.g. 1 percent. - - - 10 - False - - - ParameterNumber - classifier.rf.ra - Termination Criteria for regression tree - If all absolute differences between an estimated value in a node and the values of the train samples in this node are smaller than this regression accuracy parameter, then the node will not be split. - - - 0 - False - - - ParameterNumber - classifier.rf.cat - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split. - - - 10 - False - - - ParameterNumber - classifier.rf.var - Size of the randomly selected subset of features at each tree node - The size of the subset of features, randomly selected at each tree node, that are used to find the best split(s). If you set it to 0, then the size will be set to the square root of the total number of features. - - - 0 - False - - - ParameterNumber - classifier.rf.nbtrees - Maximum number of trees in the forest - The maximum number of trees in the forest. Typically, the more trees you have, the better the accuracy. However, the improvement in accuracy generally diminishes and reaches an asymptote for a certain number of trees. Also to keep in mind, increasing the number of trees increases the prediction time linearly. - - - 100 - False - - - ParameterNumber - classifier.rf.acc - Sufficient accuracy (OOB error) - Sufficient accuracy (OOB error). - - - 0.01 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/VectorDataExtractROI.xml b/python/plugins/processing/algs/otb/description/5.6.0/VectorDataExtractROI.xml deleted file mode 100644 index 21e8e2e96dc6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/VectorDataExtractROI.xml +++ /dev/null @@ -1,39 +0,0 @@ - - VectorDataExtractROI - otbcli_VectorDataExtractROI - VectorData Extract ROI - Vector Data Manipulation - Perform an extract ROI on the input vector data according to the input image extent - - ParameterVector - io.vd - Input Vector data - Input vector data - - False - - - ParameterRaster - io.in - Support image - Support image that specifies the extracted region - False - - - OutputVector - io.out - Output Vector data - Output extracted vector data - - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/VectorDataReprojection-image.xml b/python/plugins/processing/algs/otb/description/5.6.0/VectorDataReprojection-image.xml deleted file mode 100644 index 8a3948907e85..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/VectorDataReprojection-image.xml +++ /dev/null @@ -1,59 +0,0 @@ - - VectorDataReprojection-image - otbcli_VectorDataReprojection - VectorDataReprojection (image) - Vector Data Manipulation - Reproject a vector data using support image projection reference, or a user specified map projection - - - ParameterFile - in.vd - Input vector data - The input vector data to reproject - - False - - - ParameterRaster - in.kwl - Use image keywords list - Optional input image to fill vector data with image kwl. - True - - - OutputFile - out.vd - Output vector data - The reprojected vector data - - - ParameterSelection - out.proj - Output Projection choice - - - - image - - - 0 - False - - - ParameterRaster - out.proj.image.in - Image used to get projection map - Projection map will be found using image metadata - False - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/VectorDataReprojection-user.xml b/python/plugins/processing/algs/otb/description/5.6.0/VectorDataReprojection-user.xml deleted file mode 100644 index 0392ba55b214..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/VectorDataReprojection-user.xml +++ /dev/null @@ -1,97 +0,0 @@ - - VectorDataReprojection-user - otbcli_VectorDataReprojection - VectorDataReprojection (user) - Vector Data Manipulation - Reproject a vector data using support image projection reference, or a user specified map projection - - - ParameterFile - in.vd - Input vector data - The input vector data to reproject - - False - - - ParameterRaster - in.kwl - Use image keywords list - Optional input image to fill vector data with image kwl. - True - - - OutputFile - out.vd - Output vector data - The reprojected vector data - - - ParameterSelection - out.proj - Output Projection choice - - - - user - - - 0 - False - - - ParameterSelection - out.proj.user.map - Output Cartographic Map Projection - Parameters of the output map projection to be used. - - - utm - lambert2 - lambert93 - wgs - epsg - - - 0 - False - - - ParameterNumber - out.proj.user.map.utm.zone - Zone number - The zone number ranges from 1 to 60 and allows defining the transverse mercator projection (along with the hemisphere) - - - 31 - False - - - ParameterBoolean - out.proj.user.map.utm.northhem - Northern Hemisphere - The transverse mercator projections are defined by their zone number as well as the hemisphere. Activate this parameter if your image is in the northern hemisphere. - True - True - - - ParameterNumber - out.proj.user.map.epsg.code - EPSG Code - See www.spatialreference.org to find which EPSG code is associated to your projection - - - 4326 - False - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/VectorDataTransform.xml b/python/plugins/processing/algs/otb/description/5.6.0/VectorDataTransform.xml deleted file mode 100644 index e9c974ddfbd6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/VectorDataTransform.xml +++ /dev/null @@ -1,89 +0,0 @@ - - VectorDataTransform - otbcli_VectorDataTransform - Vector Data Transformation - Vector Data Manipulation - Apply a transform to each vertex of the input VectorData - - ParameterVector - vd - Input Vector data - Input vector data to transform - - False - - - OutputVector - out - Output Vector data - Output transformed vector data - - - - ParameterRaster - in - Support image - Image needed as a support to the vector data - False - - - ParameterNumber - transform.tx - Translation X - Translation in the X direction (in pixels) - - - 0 - False - - - ParameterNumber - transform.ty - Translation Y - Translation in the Y direction (in pixels) - - - 0 - False - - - ParameterNumber - transform.ro - Rotation Angle - Angle of the rotation to apply in degrees - - - 0 - False - - - ParameterNumber - transform.centerx - Center X - X coordinate of the rotation center (in physical units) - - - 0 - False - - - ParameterNumber - transform.centery - Center Y - Y coordinate of the rotation center (in physical units) - - - 0 - False - - - ParameterNumber - transform.scale - Scale - The scale to apply - - - 1 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ApplicationExample.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ApplicationExample.html deleted file mode 100644 index f40991c27858..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ApplicationExample.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Example

Brief Description

This application opens an image and save it. Pay attention, it includes Latex snippets in order to generate software guide documentation

Tags

Image Analysis,Test

Long Description

The purpose of this application is to present parameters types, and Application class framework. It is used to generate Software guide documentation for Application chapter example.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/BandMath.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/BandMath.html deleted file mode 100644 index 1667d53f10ae..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/BandMath.html +++ /dev/null @@ -1,10 +0,0 @@ - - -

BandMath

Brief Description

Perform a mathematical operation on monoband images

Tags

Util

Long Description

This application performs a mathematical operation on monoband images.Mathematical formula interpretation is done via MuParser libraries. -For MuParser version superior to 2.0 uses '&&' and '||' logical operators, and ternary operator 'boolean_expression ? if_true : if_false' -For older version of MuParser (prior to v2) use 'and' and 'or' logical operators, and ternary operator 'if(; ; )'. -The list of features and operators is available on MuParser website: http://muparser.sourceforge.net/ -

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/BinaryMorphologicalOperation-closing.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/BinaryMorphologicalOperation-closing.html deleted file mode 100644 index 97a2b19d7065..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/BinaryMorphologicalOperation-closing.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BinaryMorphologicalOperation

Brief Description

Performs morphological operations on an input image channel

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs binary morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkBinaryDilateImageFilter, itkBinaryErodeImageFilter, itkBinaryMorphologicalOpeningImageFilter and itkBinaryMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/BinaryMorphologicalOperation-dilate.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/BinaryMorphologicalOperation-dilate.html deleted file mode 100644 index 97a2b19d7065..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/BinaryMorphologicalOperation-dilate.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BinaryMorphologicalOperation

Brief Description

Performs morphological operations on an input image channel

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs binary morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkBinaryDilateImageFilter, itkBinaryErodeImageFilter, itkBinaryMorphologicalOpeningImageFilter and itkBinaryMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/BinaryMorphologicalOperation-erode.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/BinaryMorphologicalOperation-erode.html deleted file mode 100644 index 97a2b19d7065..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/BinaryMorphologicalOperation-erode.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BinaryMorphologicalOperation

Brief Description

Performs morphological operations on an input image channel

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs binary morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkBinaryDilateImageFilter, itkBinaryErodeImageFilter, itkBinaryMorphologicalOpeningImageFilter and itkBinaryMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/BinaryMorphologicalOperation-opening.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/BinaryMorphologicalOperation-opening.html deleted file mode 100644 index 97a2b19d7065..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/BinaryMorphologicalOperation-opening.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BinaryMorphologicalOperation

Brief Description

Performs morphological operations on an input image channel

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs binary morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkBinaryDilateImageFilter, itkBinaryErodeImageFilter, itkBinaryMorphologicalOpeningImageFilter and itkBinaryMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/BinaryMorphologicalOperation.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/BinaryMorphologicalOperation.html deleted file mode 100644 index 97a2b19d7065..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/BinaryMorphologicalOperation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BinaryMorphologicalOperation

Brief Description

Performs morphological operations on an input image channel

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs binary morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkBinaryDilateImageFilter, itkBinaryErodeImageFilter, itkBinaryMorphologicalOpeningImageFilter and itkBinaryMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/BlockMatching.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/BlockMatching.html deleted file mode 100644 index 4d5caf2e62c5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/BlockMatching.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BlockMatching

Brief Description

Performs block-matching to estimate pixel-wise disparities between two images

Tags

Stereo

Long Description

This application allows one to performs block-matching to estimate pixel-wise disparities between two images. One must chose block-matching method and input masks (related to the left and right input image) of pixels for which the disparity should be investigated. Additionally, two criteria can be optionally used to disable disparity investigation for some pixel: a no-data value, and a threshold on the local variance. This allows one to speed-up computation by avoiding to investigate disparities that will not be reliable anyway. For efficiency reasons, if the optimal metric values image is desired, it will be concatenated to the output image (which will then have three bands : horizontal disparity, vertical disparity and metric value). One can split these images afterward.

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbStereoRectificationGridGenerator

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/BundleToPerfectSensor.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/BundleToPerfectSensor.html deleted file mode 100644 index 8467b5bd4c1d..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/BundleToPerfectSensor.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BundleToPerfectSensor

Brief Description

Perform P+XS pansharpening

Tags

Geometry,Pansharpening

Long Description

This application performs P+XS pansharpening. The default mode use Pan and XS sensor models to estimate the transformation to superimpose XS over Pan before the fusion ("default mode"). The application provides also a PHR mode for Pleiades images which does not use sensor models as Pan and XS products are already coregistered but only estimate an affine transformation to superimpose XS over the Pan.Note that this option is automatically activated in case Pleiades images are detected as input.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ClassificationMapRegularization.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ClassificationMapRegularization.html deleted file mode 100644 index a974324ba3f4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ClassificationMapRegularization.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

ClassificationMapRegularization

Brief Description

Filters the input labeled image using Majority Voting in a ball shaped neighbordhood.

Tags

Learning,Image Analysis

Long Description

This application filters the input labeled image (with a maximal class label = 65535) using Majority Voting in a ball shaped neighbordhood. Majority Voting takes the more representative value of all the pixels identified by the ball shaped structuring element and then sets the center pixel to this majority label value. - -NoData is the label of the NOT classified pixels in the input image. These input pixels keep their NoData label in the output image. - -Pixels with more than 1 majority class are marked as Undecided if the parameter 'ip.suvbool == true', or keep their Original labels otherwise.

Parameters

Limitations

The input image must be a single band labeled image (with a maximal class label = 65535). The structuring element radius must have a minimum value equal to 1 pixel. Please note that the Undecided value must be different from existing labels in the input labeled image.

Authors

OTB-Team

See Also

Documentation of the ClassificationMapRegularization application.

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ColorMapping-continuous.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ColorMapping-continuous.html deleted file mode 100644 index 6ce8d15474ab..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ColorMapping-continuous.html +++ /dev/null @@ -1,13 +0,0 @@ - - -

ColorMapping

Brief Description

Maps an input label image to 8-bits RGB using look-up tables.

Tags

Utilities,Image Manipulation,Image MetaData,Learning

Long Description

This application allows one to map a label image to a 8-bits RGB image (in both ways) using different methods. - -The custom method allows one to use a custom look-up table. The look-up table is loaded from a text file where each line describes an entry. The typical use of this method is to colorise a classification map. - -The continuous method allows mapping a range of values in a scalar input image to a colored image using continuous look-up table, in order to enhance image interpretation. Several look-up tables can been chosen with different color ranges. --The optimal method computes an optimal look-up table. When processing a segmentation label image (label to color), the color difference between adjacent segmented regions is maximized. When processing an unknown color image (color to label), all the present colors are mapped to a continuous label list. - - The support image method uses a color support image to associate an average color to each region.

Parameters

Limitations

The segmentation optimal method does not support streaming, and thus large images. The operation color to label is not implemented for the methods continuous LUT and support image LUT. - ColorMapping using support image is not threaded.

Authors

OTB-Team

See Also

ImageSVMClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ColorMapping-custom.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ColorMapping-custom.html deleted file mode 100644 index 6ce8d15474ab..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ColorMapping-custom.html +++ /dev/null @@ -1,13 +0,0 @@ - - -

ColorMapping

Brief Description

Maps an input label image to 8-bits RGB using look-up tables.

Tags

Utilities,Image Manipulation,Image MetaData,Learning

Long Description

This application allows one to map a label image to a 8-bits RGB image (in both ways) using different methods. - -The custom method allows one to use a custom look-up table. The look-up table is loaded from a text file where each line describes an entry. The typical use of this method is to colorise a classification map. - -The continuous method allows mapping a range of values in a scalar input image to a colored image using continuous look-up table, in order to enhance image interpretation. Several look-up tables can been chosen with different color ranges. --The optimal method computes an optimal look-up table. When processing a segmentation label image (label to color), the color difference between adjacent segmented regions is maximized. When processing an unknown color image (color to label), all the present colors are mapped to a continuous label list. - - The support image method uses a color support image to associate an average color to each region.

Parameters

Limitations

The segmentation optimal method does not support streaming, and thus large images. The operation color to label is not implemented for the methods continuous LUT and support image LUT. - ColorMapping using support image is not threaded.

Authors

OTB-Team

See Also

ImageSVMClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ColorMapping-image.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ColorMapping-image.html deleted file mode 100644 index 6ce8d15474ab..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ColorMapping-image.html +++ /dev/null @@ -1,13 +0,0 @@ - - -

ColorMapping

Brief Description

Maps an input label image to 8-bits RGB using look-up tables.

Tags

Utilities,Image Manipulation,Image MetaData,Learning

Long Description

This application allows one to map a label image to a 8-bits RGB image (in both ways) using different methods. - -The custom method allows one to use a custom look-up table. The look-up table is loaded from a text file where each line describes an entry. The typical use of this method is to colorise a classification map. - -The continuous method allows mapping a range of values in a scalar input image to a colored image using continuous look-up table, in order to enhance image interpretation. Several look-up tables can been chosen with different color ranges. --The optimal method computes an optimal look-up table. When processing a segmentation label image (label to color), the color difference between adjacent segmented regions is maximized. When processing an unknown color image (color to label), all the present colors are mapped to a continuous label list. - - The support image method uses a color support image to associate an average color to each region.

Parameters

Limitations

The segmentation optimal method does not support streaming, and thus large images. The operation color to label is not implemented for the methods continuous LUT and support image LUT. - ColorMapping using support image is not threaded.

Authors

OTB-Team

See Also

ImageSVMClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ColorMapping-optimal.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ColorMapping-optimal.html deleted file mode 100644 index 6ce8d15474ab..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ColorMapping-optimal.html +++ /dev/null @@ -1,13 +0,0 @@ - - -

ColorMapping

Brief Description

Maps an input label image to 8-bits RGB using look-up tables.

Tags

Utilities,Image Manipulation,Image MetaData,Learning

Long Description

This application allows one to map a label image to a 8-bits RGB image (in both ways) using different methods. - -The custom method allows one to use a custom look-up table. The look-up table is loaded from a text file where each line describes an entry. The typical use of this method is to colorise a classification map. - -The continuous method allows mapping a range of values in a scalar input image to a colored image using continuous look-up table, in order to enhance image interpretation. Several look-up tables can been chosen with different color ranges. --The optimal method computes an optimal look-up table. When processing a segmentation label image (label to color), the color difference between adjacent segmented regions is maximized. When processing an unknown color image (color to label), all the present colors are mapped to a continuous label list. - - The support image method uses a color support image to associate an average color to each region.

Parameters

Limitations

The segmentation optimal method does not support streaming, and thus large images. The operation color to label is not implemented for the methods continuous LUT and support image LUT. - ColorMapping using support image is not threaded.

Authors

OTB-Team

See Also

ImageSVMClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ColorMapping.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ColorMapping.html deleted file mode 100644 index 6ce8d15474ab..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ColorMapping.html +++ /dev/null @@ -1,13 +0,0 @@ - - -

ColorMapping

Brief Description

Maps an input label image to 8-bits RGB using look-up tables.

Tags

Utilities,Image Manipulation,Image MetaData,Learning

Long Description

This application allows one to map a label image to a 8-bits RGB image (in both ways) using different methods. - -The custom method allows one to use a custom look-up table. The look-up table is loaded from a text file where each line describes an entry. The typical use of this method is to colorise a classification map. - -The continuous method allows mapping a range of values in a scalar input image to a colored image using continuous look-up table, in order to enhance image interpretation. Several look-up tables can been chosen with different color ranges. --The optimal method computes an optimal look-up table. When processing a segmentation label image (label to color), the color difference between adjacent segmented regions is maximized. When processing an unknown color image (color to label), all the present colors are mapped to a continuous label list. - - The support image method uses a color support image to associate an average color to each region.

Parameters

Limitations

The segmentation optimal method does not support streaming, and thus large images. The operation color to label is not implemented for the methods continuous LUT and support image LUT. - ColorMapping using support image is not threaded.

Authors

OTB-Team

See Also

ImageSVMClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/CompareImages.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/CompareImages.html deleted file mode 100644 index 284fd810a33d..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/CompareImages.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

CompareImages

Brief Description

Estimator between 2 images.

Tags

Statistics

Long Description

This application computes MSE (Mean Squared Error), MAE (Mean Absolute Error) and PSNR (Peak Signal to Noise Ratio) between the channel of two images (reference and measurement). The user has to set the used channel and can specify a ROI.

Parameters

Limitations

None

Authors

OTB-Team

See Also

BandMath application, ImageStatistics

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ComputeConfusionMatrix-raster.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ComputeConfusionMatrix-raster.html deleted file mode 100644 index 8a6c9eed9ad3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ComputeConfusionMatrix-raster.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputeConfusionMatrix

Brief Description

Computes the confusion matrix of a classification

Tags

Learning

Long Description

This application computes the confusion matrix of a classification map relatively to a ground truth. This ground truth can be given as a raster or a vector data. Only reference and produced pixels with values different from NoData are handled in the calculation of the confusion matrix. The confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the output file, the reference and produced class labels are ordered according to the rows/columns of the confusion matrix.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ComputeConfusionMatrix-vector.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ComputeConfusionMatrix-vector.html deleted file mode 100644 index 8a6c9eed9ad3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ComputeConfusionMatrix-vector.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputeConfusionMatrix

Brief Description

Computes the confusion matrix of a classification

Tags

Learning

Long Description

This application computes the confusion matrix of a classification map relatively to a ground truth. This ground truth can be given as a raster or a vector data. Only reference and produced pixels with values different from NoData are handled in the calculation of the confusion matrix. The confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the output file, the reference and produced class labels are ordered according to the rows/columns of the confusion matrix.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ComputeConfusionMatrix.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ComputeConfusionMatrix.html deleted file mode 100644 index 8a6c9eed9ad3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ComputeConfusionMatrix.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputeConfusionMatrix

Brief Description

Computes the confusion matrix of a classification

Tags

Learning

Long Description

This application computes the confusion matrix of a classification map relatively to a ground truth. This ground truth can be given as a raster or a vector data. Only reference and produced pixels with values different from NoData are handled in the calculation of the confusion matrix. The confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the output file, the reference and produced class labels are ordered according to the rows/columns of the confusion matrix.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ComputeImagesStatistics.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ComputeImagesStatistics.html deleted file mode 100644 index 05cb9575bdbb..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ComputeImagesStatistics.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputeImagesStatistics

Brief Description

Computes global mean and standard deviation for each band from a set of images and optionally saves the results in an XML file.

Tags

Learning,Image Analysis

Long Description

This application computes a global mean and standard deviation for each band of a set of images and optionally saves the results in an XML file. The output XML is intended to be used an input for the TrainImagesClassifier application to normalize samples before learning.

Parameters

Limitations

Each image of the set must contain the same bands as the others (i.e. same types, in the same order).

Authors

OTB-Team

See Also

Documentation of the TrainImagesClassifier application.

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ComputeOGRLayersFeaturesStatistics.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ComputeOGRLayersFeaturesStatistics.html deleted file mode 100644 index 42c4651f14e3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ComputeOGRLayersFeaturesStatistics.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputeOGRLayersFeaturesStatistics

Brief Description

Compute statistics of the features in a set of OGR Layers

Tags

Segmentation

Long Description

Compute statistics (mean and standard deviation) of the features in a set of OGR Layers, and write them in an XML file. This XML file can then be used by the training application.

Parameters

Limitations

Experimental. For now only shapefiles are supported.

Authors

David Youssefi during internship at CNES

See Also

OGRLayerClassifier,TrainOGRLayersClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ComputePolylineFeatureFromImage.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ComputePolylineFeatureFromImage.html deleted file mode 100644 index 646191e845f4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ComputePolylineFeatureFromImage.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputePolylineFeatureFromImage

Brief Description

This application compute for each studied polyline, contained in the input VectorData, the chosen descriptors.

Tags

Feature Extraction

Long Description

The first step in the classifier fusion based validation is to compute, for each studied polyline, the chosen descriptors.

Parameters

Limitations

Since it does not rely on streaming process, take care of the size of input image before launching application.

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ConcatenateImages.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ConcatenateImages.html deleted file mode 100644 index f5d2ac5e2c28..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ConcatenateImages.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ConcatenateImages

Brief Description

Concatenate a list of images of the same size into a single multi-channel one.

Tags

Image Manipulation,Concatenation,Multi-channel

Long Description

This application performs images channels concatenation. It will walk the input image list (single or multi-channel) and generates a single multi-channel image. The channel order is the one of the list.

Parameters

Limitations

All input images must have the same size.

Authors

OTB-Team

See Also

Rescale application, Convert

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ConcatenateVectorData.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ConcatenateVectorData.html deleted file mode 100644 index 1760c34db99c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ConcatenateVectorData.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ConcatenateVectorData

Brief Description

Concatenate VectorDatas

Tags

Vector Data Manipulation

Long Description

This application concatenates a list of VectorData to produce a unique VectorData as output.Note that the VectorDatas must be of the same type (Storing polygons only, lines only, or points only)

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ConnectedComponentSegmentation.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ConnectedComponentSegmentation.html deleted file mode 100644 index 86198d1547a7..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ConnectedComponentSegmentation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ConnectedComponentSegmentation

Brief Description

Connected component segmentation and object based image filtering of the input image according to user-defined criterions.

Tags

Image Analysis,Segmentation

Long Description

This application allows one to perform a masking, connected components segmentation and object based image filtering. First and optionally, a mask can be built based on user-defined criterions to select pixels of the image which will be segmented. Then a connected component segmentation is performed with a user defined criterion to decide whether two neighbouring pixels belong to the same segment or not. After this segmentation step, an object based image filtering is applied using another user-defined criterion reasoning on segment properties, like shape or radiometric attributes. Criterions are mathematical expressions analysed by the MuParser library (http://muparser.sourceforge.net/). For instance, expression "((b1>80) and intensity>95)" will merge two neighbouring pixel in a single segment if their intensity is more than 95 and their value in the first image band is more than 80. See parameters documentation for a list of available attributes. The output of the object based image filtering is vectorized and can be written in shapefile or KML format. If the input image is in raw geometry, resulting polygons will be transformed to WGS84 using sensor modelling before writing, to ensure consistency with GIS software. For this purpose, a Digital Elevation Model can be provided to the application. The whole processing is done on a per-tile basis for large images, so this application can handle images of arbitrary size.

Parameters

Limitations

Due to the tiling scheme in case of large images, some segments can be arbitrarily split across multiple tiles.

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Convert.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Convert.html deleted file mode 100644 index d639181f4282..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Convert.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

Convert

Brief Description

Convert an image to a different format, eventually rescaling the data and/or changing the pixel type.

Tags

Conversion,Image Dynamic,Image Manipulation

Long Description

This application performs an image pixel type conversion (short, ushort, uchar, int, uint, float and double types are handled). The output image is written in the specified format (ie. that corresponds to the given extension). - The conversion can include a rescale using the image 2 percent minimum and maximum values. The rescale can be linear or log2.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Rescale

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ConvertCartoToGeoPoint.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ConvertCartoToGeoPoint.html deleted file mode 100644 index 7d5c59ef0e7d..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ConvertCartoToGeoPoint.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ConvertCartoToGeoPoint

Brief Description

Convert cartographic coordinates to geographic one.

Tags

Coordinates,Geometry

Long Description

This application computes the geographic coordinates from a cartographic one. User has to give the X and Y coordinate and the cartographic projection (UTM/LAMBERT/LAMBERT2/LAMBERT93/SINUS/ECKERT4/TRANSMERCATOR/MOLLWEID/SVY21).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ConvertSensorToGeoPoint.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ConvertSensorToGeoPoint.html deleted file mode 100644 index 2f7c862990d8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ConvertSensorToGeoPoint.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ConvertSensorToGeoPoint

Brief Description

Sensor to geographic coordinates conversion.

Tags

Geometry

Long Description

This Application converts a sensor point of an input image to a geographic point using the Forward Sensor Model of the input image.

Parameters

Limitations

None

Authors

OTB-Team

See Also

ConvertCartoToGeoPoint application, otbObtainUTMZoneFromGeoPoint

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/DEMConvert.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/DEMConvert.html deleted file mode 100644 index e51b5030bbfd..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/DEMConvert.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DEMConvert

Brief Description

Converts a geo-referenced DEM image into a general raster file compatible with OTB DEM handling.

Tags

Image Manipulation

Long Description

In order to be understood by the Orfeo ToolBox and the underlying OSSIM library, a geo-referenced Digital Elevation Model image can be converted into a general raster image, which consists in 3 files with the following extensions: .ras, .geom and .omd. Once converted, you have to place these files in a separate directory, and you can then use this directory to set the "DEM Directory" parameter of a DEM based OTB application or filter.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/DSFuzzyModelEstimation.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/DSFuzzyModelEstimation.html deleted file mode 100644 index e5d507ba025b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/DSFuzzyModelEstimation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DSFuzzyModelEstimation

Brief Description

Estimate feature fuzzy model parameters using 2 vector data (ground truth samples and wrong samples).

Tags

Feature Extraction

Long Description

Estimate feature fuzzy model parameters using 2 vector data (ground truth samples and wrong samples).

Parameters

Limitations

None.

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Despeckle-frost.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Despeckle-frost.html deleted file mode 100644 index 7ec3875fb9d8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Despeckle-frost.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Despeckle

Brief Description

Perform speckle noise reduction on SAR image.

Tags

SAR,Image Filtering

Long Description

This application reduce speckle noise. Four methods are available: Lee, Frost, GammaMAP and Kuan.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Despeckle-gammamap.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Despeckle-gammamap.html deleted file mode 100644 index 7ec3875fb9d8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Despeckle-gammamap.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Despeckle

Brief Description

Perform speckle noise reduction on SAR image.

Tags

SAR,Image Filtering

Long Description

This application reduce speckle noise. Four methods are available: Lee, Frost, GammaMAP and Kuan.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Despeckle-kuan.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Despeckle-kuan.html deleted file mode 100644 index 7ec3875fb9d8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Despeckle-kuan.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Despeckle

Brief Description

Perform speckle noise reduction on SAR image.

Tags

SAR,Image Filtering

Long Description

This application reduce speckle noise. Four methods are available: Lee, Frost, GammaMAP and Kuan.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Despeckle-lee.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Despeckle-lee.html deleted file mode 100644 index 7ec3875fb9d8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Despeckle-lee.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Despeckle

Brief Description

Perform speckle noise reduction on SAR image.

Tags

SAR,Image Filtering

Long Description

This application reduce speckle noise. Four methods are available: Lee, Frost, GammaMAP and Kuan.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Despeckle.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Despeckle.html deleted file mode 100644 index 7ec3875fb9d8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Despeckle.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Despeckle

Brief Description

Perform speckle noise reduction on SAR image.

Tags

SAR,Image Filtering

Long Description

This application reduce speckle noise. Four methods are available: Lee, Frost, GammaMAP and Kuan.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/DimensionalityReduction-ica.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/DimensionalityReduction-ica.html deleted file mode 100644 index 80fc079f1fc5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/DimensionalityReduction-ica.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DimensionalityReduction

Brief Description

Perform Dimension reduction of the input image.

Tags

Dimensionality Reduction,Image Filtering

Long Description

Performs dimensionality reduction on input image. PCA,NA-PCA,MAF,ICA methods are available. It is also possible to compute the inverse transform to reconstruct the image. It is also possible to optionnaly export the transformation matrix to a text file.

Parameters

Limitations

This application does not provide the inverse transform and the transformation matrix export for the MAF.

Authors

OTB-Team

See Also

"Kernel maximum autocorrelation factor and minimum noise fraction transformations," IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 612-624, (2011)

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/DimensionalityReduction-maf.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/DimensionalityReduction-maf.html deleted file mode 100644 index 80fc079f1fc5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/DimensionalityReduction-maf.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DimensionalityReduction

Brief Description

Perform Dimension reduction of the input image.

Tags

Dimensionality Reduction,Image Filtering

Long Description

Performs dimensionality reduction on input image. PCA,NA-PCA,MAF,ICA methods are available. It is also possible to compute the inverse transform to reconstruct the image. It is also possible to optionnaly export the transformation matrix to a text file.

Parameters

Limitations

This application does not provide the inverse transform and the transformation matrix export for the MAF.

Authors

OTB-Team

See Also

"Kernel maximum autocorrelation factor and minimum noise fraction transformations," IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 612-624, (2011)

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/DimensionalityReduction-napca.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/DimensionalityReduction-napca.html deleted file mode 100644 index 80fc079f1fc5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/DimensionalityReduction-napca.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DimensionalityReduction

Brief Description

Perform Dimension reduction of the input image.

Tags

Dimensionality Reduction,Image Filtering

Long Description

Performs dimensionality reduction on input image. PCA,NA-PCA,MAF,ICA methods are available. It is also possible to compute the inverse transform to reconstruct the image. It is also possible to optionnaly export the transformation matrix to a text file.

Parameters

Limitations

This application does not provide the inverse transform and the transformation matrix export for the MAF.

Authors

OTB-Team

See Also

"Kernel maximum autocorrelation factor and minimum noise fraction transformations," IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 612-624, (2011)

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/DimensionalityReduction-pca.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/DimensionalityReduction-pca.html deleted file mode 100644 index 80fc079f1fc5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/DimensionalityReduction-pca.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DimensionalityReduction

Brief Description

Perform Dimension reduction of the input image.

Tags

Dimensionality Reduction,Image Filtering

Long Description

Performs dimensionality reduction on input image. PCA,NA-PCA,MAF,ICA methods are available. It is also possible to compute the inverse transform to reconstruct the image. It is also possible to optionnaly export the transformation matrix to a text file.

Parameters

Limitations

This application does not provide the inverse transform and the transformation matrix export for the MAF.

Authors

OTB-Team

See Also

"Kernel maximum autocorrelation factor and minimum noise fraction transformations," IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 612-624, (2011)

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/DimensionalityReduction.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/DimensionalityReduction.html deleted file mode 100644 index 80fc079f1fc5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/DimensionalityReduction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DimensionalityReduction

Brief Description

Perform Dimension reduction of the input image.

Tags

Dimensionality Reduction,Image Filtering

Long Description

Performs dimensionality reduction on input image. PCA,NA-PCA,MAF,ICA methods are available. It is also possible to compute the inverse transform to reconstruct the image. It is also possible to optionnaly export the transformation matrix to a text file.

Parameters

Limitations

This application does not provide the inverse transform and the transformation matrix export for the MAF.

Authors

OTB-Team

See Also

"Kernel maximum autocorrelation factor and minimum noise fraction transformations," IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 612-624, (2011)

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/DisparityMapToElevationMap.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/DisparityMapToElevationMap.html deleted file mode 100644 index 303ac0e39a48..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/DisparityMapToElevationMap.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DisparityMapToElevationMap

Brief Description

Projects a disparity map into a regular elevation map

Tags

Stereo

Long Description

This application uses a disparity map computed from a stereo image pair to produce an elevation map on the ground area covered by the stereo pair. The needed inputs are : the disparity map, the stereo pair (in original geometry) and the epipolar deformation grids. These grids have to link the original geometry (stereo pair) and the epipolar geometry (disparity map).

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbStereoRectificationGridGenerator otbBlockMatching

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/DownloadSRTMTiles.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/DownloadSRTMTiles.html deleted file mode 100644 index 3c6871d7a5eb..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/DownloadSRTMTiles.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DownloadSRTMTiles

Brief Description

Download or list SRTM tiles related to a set of images

Tags

Utilities,Image Manipulation

Long Description

This application allows selecting the appropriate SRTM tiles that covers a list of images. It builds a list of the required tiles. Two modes are available: the first one download those tiles from the USGS SRTM3 website (http://dds.cr.usgs.gov/srtm/version2_1/SRTM3/), the second one list those tiles in a local directory. In both cases, you need to indicate the directory in which directory tiles will be download or the location of local SRTM files.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/EdgeExtraction-gradient.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/EdgeExtraction-gradient.html deleted file mode 100644 index aa436906807a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/EdgeExtraction-gradient.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

EdgeExtraction

Brief Description

Computes edge features on every pixel of the input image selected channel

Tags

Edge,Feature Extraction

Long Description

This application computes edge features on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otb class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/EdgeExtraction-sobel.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/EdgeExtraction-sobel.html deleted file mode 100644 index aa436906807a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/EdgeExtraction-sobel.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

EdgeExtraction

Brief Description

Computes edge features on every pixel of the input image selected channel

Tags

Edge,Feature Extraction

Long Description

This application computes edge features on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otb class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/EdgeExtraction-touzi.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/EdgeExtraction-touzi.html deleted file mode 100644 index aa436906807a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/EdgeExtraction-touzi.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

EdgeExtraction

Brief Description

Computes edge features on every pixel of the input image selected channel

Tags

Edge,Feature Extraction

Long Description

This application computes edge features on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otb class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/EdgeExtraction.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/EdgeExtraction.html deleted file mode 100644 index aa436906807a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/EdgeExtraction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

EdgeExtraction

Brief Description

Computes edge features on every pixel of the input image selected channel

Tags

Edge,Feature Extraction

Long Description

This application computes edge features on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otb class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ExtractROI-fit.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ExtractROI-fit.html deleted file mode 100644 index 8b36b6da38c8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ExtractROI-fit.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ExtractROI

Brief Description

Extract a ROI defined by the user.

Tags

Image Manipulation

Long Description

This application extracts a Region Of Interest with user defined size, or reference image.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ExtractROI-standard.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ExtractROI-standard.html deleted file mode 100644 index 8b36b6da38c8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ExtractROI-standard.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ExtractROI

Brief Description

Extract a ROI defined by the user.

Tags

Image Manipulation

Long Description

This application extracts a Region Of Interest with user defined size, or reference image.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ExtractROI.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ExtractROI.html deleted file mode 100644 index 8b36b6da38c8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ExtractROI.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ExtractROI

Brief Description

Extract a ROI defined by the user.

Tags

Image Manipulation

Long Description

This application extracts a Region Of Interest with user defined size, or reference image.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/FineRegistration.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/FineRegistration.html deleted file mode 100644 index 0c8330415a83..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/FineRegistration.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

FineRegistration

Brief Description

Estimate disparity map between two images.

Tags

Stereo

Long Description

Estimate disparity map between two images. Output image contain x offset, y offset and metric value.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/FusionOfClassifications-dempstershafer.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/FusionOfClassifications-dempstershafer.html deleted file mode 100644 index b5b88b5fc6ff..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/FusionOfClassifications-dempstershafer.html +++ /dev/null @@ -1,9 +0,0 @@ - - -

FusionOfClassifications

Brief Description

Fuses several classifications maps of the same image on the basis of class labels.

Tags

Learning,Image Analysis

Long Description

This application allows you to fuse several classification maps and produces a single more robust classification map. Fusion is done either by mean of Majority Voting, or with the Dempster Shafer combination method on class labels. - -MAJORITY VOTING: for each pixel, the class with the highest number of votes is selected. - -DEMPSTER SHAFER: for each pixel, the class label for which the Belief Function is maximal is selected. This Belief Function is calculated by mean of the Dempster Shafer combination of Masses of Belief, and indicates the belief that each input classification map presents for each label value. Moreover, the Masses of Belief are based on the input confusion matrices of each classification map, either by using the PRECISION or RECALL rates, or the OVERALL ACCURACY, or the KAPPA coefficient. Thus, each input classification map needs to be associated with its corresponding input confusion matrix file for the Dempster Shafer fusion. --Input pixels with the NODATA label are not handled in the fusion of classification maps. Moreover, pixels for which all the input classifiers are set to NODATA keep this value in the output fused image. --In case of number of votes equality, the UNDECIDED label is attributed to the pixel.

Parameters

Limitations

None

Authors

OTB-Team

See Also

ImageClassifier application

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/FusionOfClassifications-majorityvoting.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/FusionOfClassifications-majorityvoting.html deleted file mode 100644 index b5b88b5fc6ff..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/FusionOfClassifications-majorityvoting.html +++ /dev/null @@ -1,9 +0,0 @@ - - -

FusionOfClassifications

Brief Description

Fuses several classifications maps of the same image on the basis of class labels.

Tags

Learning,Image Analysis

Long Description

This application allows you to fuse several classification maps and produces a single more robust classification map. Fusion is done either by mean of Majority Voting, or with the Dempster Shafer combination method on class labels. - -MAJORITY VOTING: for each pixel, the class with the highest number of votes is selected. - -DEMPSTER SHAFER: for each pixel, the class label for which the Belief Function is maximal is selected. This Belief Function is calculated by mean of the Dempster Shafer combination of Masses of Belief, and indicates the belief that each input classification map presents for each label value. Moreover, the Masses of Belief are based on the input confusion matrices of each classification map, either by using the PRECISION or RECALL rates, or the OVERALL ACCURACY, or the KAPPA coefficient. Thus, each input classification map needs to be associated with its corresponding input confusion matrix file for the Dempster Shafer fusion. --Input pixels with the NODATA label are not handled in the fusion of classification maps. Moreover, pixels for which all the input classifiers are set to NODATA keep this value in the output fused image. --In case of number of votes equality, the UNDECIDED label is attributed to the pixel.

Parameters

Limitations

None

Authors

OTB-Team

See Also

ImageClassifier application

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/FusionOfClassifications.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/FusionOfClassifications.html deleted file mode 100644 index b5b88b5fc6ff..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/FusionOfClassifications.html +++ /dev/null @@ -1,9 +0,0 @@ - - -

FusionOfClassifications

Brief Description

Fuses several classifications maps of the same image on the basis of class labels.

Tags

Learning,Image Analysis

Long Description

This application allows you to fuse several classification maps and produces a single more robust classification map. Fusion is done either by mean of Majority Voting, or with the Dempster Shafer combination method on class labels. - -MAJORITY VOTING: for each pixel, the class with the highest number of votes is selected. - -DEMPSTER SHAFER: for each pixel, the class label for which the Belief Function is maximal is selected. This Belief Function is calculated by mean of the Dempster Shafer combination of Masses of Belief, and indicates the belief that each input classification map presents for each label value. Moreover, the Masses of Belief are based on the input confusion matrices of each classification map, either by using the PRECISION or RECALL rates, or the OVERALL ACCURACY, or the KAPPA coefficient. Thus, each input classification map needs to be associated with its corresponding input confusion matrix file for the Dempster Shafer fusion. --Input pixels with the NODATA label are not handled in the fusion of classification maps. Moreover, pixels for which all the input classifiers are set to NODATA keep this value in the output fused image. --In case of number of votes equality, the UNDECIDED label is attributed to the pixel.

Parameters

Limitations

None

Authors

OTB-Team

See Also

ImageClassifier application

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/GeneratePlyFile.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/GeneratePlyFile.html deleted file mode 100644 index 9cefcc9d5b87..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/GeneratePlyFile.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GeneratePlyFile

Brief Description

Generate a 3D Ply file from a DEM and a color image.

Tags

Geometry

Long Description

Generate a 3D Ply file from a DEM and a color image.

Parameters

Limitations

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/GenerateRPCSensorModel.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/GenerateRPCSensorModel.html deleted file mode 100644 index 63c73dfbd0a2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/GenerateRPCSensorModel.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GenerateRPCSensorModel

Brief Description

Generate a RPC sensor model from a list of Ground Control Points.

Tags

Geometry

Long Description

This application generates a RPC sensor model from a list of Ground Control Points. At least 20 points are required for estimation wihtout elevation support, and 40 points for estimation with elevation support. Elevation support will be automatically deactivated if an insufficient amount of points is provided. The application can optionnaly output a file containing accuracy statistics for each point, and a vector file containing segments represening points residues. The map projection parameter allows defining a map projection in which the accuracy is evaluated.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OrthoRectication,HomologousPointsExtraction,RefineSensorModel

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/GrayScaleMorphologicalOperation-closing.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/GrayScaleMorphologicalOperation-closing.html deleted file mode 100644 index e3081bbf311b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/GrayScaleMorphologicalOperation-closing.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GrayScaleMorphologicalOperation

Brief Description

Performs morphological operations on a grayscale input image

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs grayscale morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkGrayscaleDilateImageFilter, itkGrayscaleErodeImageFilter, itkGrayscaleMorphologicalOpeningImageFilter and itkGrayscaleMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/GrayScaleMorphologicalOperation-dilate.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/GrayScaleMorphologicalOperation-dilate.html deleted file mode 100644 index e3081bbf311b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/GrayScaleMorphologicalOperation-dilate.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GrayScaleMorphologicalOperation

Brief Description

Performs morphological operations on a grayscale input image

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs grayscale morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkGrayscaleDilateImageFilter, itkGrayscaleErodeImageFilter, itkGrayscaleMorphologicalOpeningImageFilter and itkGrayscaleMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/GrayScaleMorphologicalOperation-erode.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/GrayScaleMorphologicalOperation-erode.html deleted file mode 100644 index e3081bbf311b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/GrayScaleMorphologicalOperation-erode.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GrayScaleMorphologicalOperation

Brief Description

Performs morphological operations on a grayscale input image

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs grayscale morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkGrayscaleDilateImageFilter, itkGrayscaleErodeImageFilter, itkGrayscaleMorphologicalOpeningImageFilter and itkGrayscaleMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/GrayScaleMorphologicalOperation-opening.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/GrayScaleMorphologicalOperation-opening.html deleted file mode 100644 index e3081bbf311b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/GrayScaleMorphologicalOperation-opening.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GrayScaleMorphologicalOperation

Brief Description

Performs morphological operations on a grayscale input image

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs grayscale morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkGrayscaleDilateImageFilter, itkGrayscaleErodeImageFilter, itkGrayscaleMorphologicalOpeningImageFilter and itkGrayscaleMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/GrayScaleMorphologicalOperation.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/GrayScaleMorphologicalOperation.html deleted file mode 100644 index e3081bbf311b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/GrayScaleMorphologicalOperation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GrayScaleMorphologicalOperation

Brief Description

Performs morphological operations on a grayscale input image

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs grayscale morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkGrayscaleDilateImageFilter, itkGrayscaleErodeImageFilter, itkGrayscaleMorphologicalOpeningImageFilter and itkGrayscaleMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/GridBasedImageResampling.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/GridBasedImageResampling.html deleted file mode 100644 index 5d1cd7f9bb6c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/GridBasedImageResampling.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GridBasedImageResampling

Brief Description

Resamples an image according to a resampling grid

Tags

Geometry

Long Description

This application allows performing image resampling from an input resampling grid.

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbStereorecificationGridGeneration

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/HaralickTextureExtraction.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/HaralickTextureExtraction.html deleted file mode 100644 index ef966758bb63..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/HaralickTextureExtraction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

HaralickTextureExtraction

Brief Description

Computes textures on every pixel of the input image selected channel

Tags

Textures,Feature Extraction

Long Description

This application computes Haralick, advanced and higher order textures on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbScalarImageToTexturesFilter, otbScalarImageToAdvancedTexturesFilter and otbScalarImageToHigherOrderTexturesFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/HomologousPointsExtraction.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/HomologousPointsExtraction.html deleted file mode 100644 index 3e6109cffcb5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/HomologousPointsExtraction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

HomologousPointsExtraction

Brief Description

Compute homologous points between images using keypoints

Tags

Feature Extraction

Long Description

This application allows computing homologous points between images using keypoints. SIFT or SURF keypoints can be used and the band on which keypoints are computed can be set independently for both images. The application offers two modes : the first is the full mode where keypoints are extracted from the full extent of both images (please note that in this mode large image file are not supported). The second mode, called geobins, allows one to set-up spatial binning to get fewer points spread across the entire image. In this mode, the corresponding spatial bin in the second image is estimated using geographical transform or sensor modelling, and is padded according to the user defined precision. Last, in both modes the application can filter matches whose colocalisation in first image exceed this precision. The elevation parameters are to deal more precisely with sensor modelling in case of sensor geometry data. The outvector option allows creating a vector file with segments corresponding to the localisation error between the matches. It can be useful to assess the precision of a registration for instance. The vector file is always reprojected to EPSG:4326 to allow display in a GIS. This is done via reprojection or by applying the image sensor models.

Parameters

Limitations

Full mode does not handle large images.

Authors

OTB-Team

See Also

RefineSensorModel

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/HooverCompareSegmentation.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/HooverCompareSegmentation.html deleted file mode 100644 index 7fdbe9a5043f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/HooverCompareSegmentation.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

HooverCompareSegmentation

Brief Description

Compare two segmentations with Hoover metrics

Tags

Segmentation

Long Description

This application compares a machine segmentation (MS) with a partial ground truth segmentation (GT). The Hoover metrics are used to estimate scores for correct detection, over-segmentation, under-segmentation and missed detection. - The application can output the overall Hoover scores along with coloredimages of the MS and GT segmentation showing the state of each region (correct detection, over-segmentation, under-segmentation, missed) - The Hoover metrics are described in : Hoover et al., "An experimental comparison of range image segmentation algorithms", IEEE PAMI vol. 18, no. 7, July 1996.

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbHooverMatrixFilter, otbHooverInstanceFilter, otbLabelMapToAttributeImageFilter

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/HyperspectralUnmixing.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/HyperspectralUnmixing.html deleted file mode 100644 index 17e18c1055e8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/HyperspectralUnmixing.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

HyperspectralUnmixing

Brief Description

Estimate abundance maps from an hyperspectral image and a set of endmembers.

Tags

Hyperspectral

Long Description

The application applies a linear unmixing algorithm to an hyperspectral data cube. This method supposes that the mixture between materials in the scene is macroscopic and simulates a linear mixing model of spectra. -The Linear Mixing Model (LMM) acknowledges that reflectance spectrum associated with each pixel is a linear combination of pure materials in the recovery area, commonly known as endmembers. Endmembers can be estimated using the VertexComponentAnalysis application. -The application allows one to estimate the abundance maps with several algorithms : Unconstrained Least Square (ucls), Fully Constrained Least Square (fcls), Image Space Reconstruction Algorithm (isra) and Non-negative constrained Least Square (ncls) and Minimum Dispersion Constrained Non Negative Matrix Factorization (MDMDNMF). -

Parameters

Limitations

None

Authors

OTB-Team

See Also

VertexComponentAnalysis

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ImageClassifier.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ImageClassifier.html deleted file mode 100644 index 609883188384..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ImageClassifier.html +++ /dev/null @@ -1,16 +0,0 @@ - - -

ImageClassifier

Brief Description

Performs a classification of the input image according to a model file.

Tags

Learning

Long Description

This application performs an image classification based on a model file produced by the TrainImagesClassifier application. Pixels of the output image will contain the class labels decided by the classifier (maximal class label = 65535). The input pixels can be optionally centered and reduced according to the statistics file produced by the ComputeImagesStatistics application. An optional input mask can be provided, in which case only input image pixels whose corresponding mask value is greater than 0 will be classified. The remaining of pixels will be given the label 0 in the output image.

Parameters

Limitations

The input image must have the same type, order and number of bands than the images used to produce the statistics file and the SVM model file. If a statistics file was used during training by the TrainImagesClassifier, it is mandatory to use the same statistics file for classification. If an input mask is used, its size must match the input image size.

Authors

OTB-Team

See Also

TrainImagesClassifier, ValidateImagesClassifier, ComputeImagesStatistics

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ImageEnvelope.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ImageEnvelope.html deleted file mode 100644 index 6b0e00023b41..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ImageEnvelope.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ImageEnvelope

Brief Description

Extracts an image envelope.

Tags

Geometry

Long Description

Build a vector data containing the polygon of the image envelope.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/KMeansClassification.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/KMeansClassification.html deleted file mode 100644 index 47414f7b94d3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/KMeansClassification.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

KMeansClassification

Brief Description

Unsupervised KMeans image classification

Tags

Segmentation,Learning

Long Description

Performs unsupervised KMeans image classification.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/KmzExport.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/KmzExport.html deleted file mode 100644 index 98c54781ec2e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/KmzExport.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

KmzExport

Brief Description

Export the input image in a KMZ product.

Tags

KMZ,Export

Long Description

This application exports the input image in a kmz product that can be display in the Google Earth software. The user can set the size of the product size, a logo and a legend to the product. Furthemore, to obtain a product that fits the relief, a DEM can be used.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Conversion

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/LSMSSegmentation.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/LSMSSegmentation.html deleted file mode 100644 index ffb7cfde48da..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/LSMSSegmentation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

LSMSSegmentation

Brief Description

Second step of the exact Large-Scale Mean-Shift segmentation workflow.

Tags

Segmentation,LSMS

Long Description

This application performs the second step of the exact Large-Scale Mean-Shift segmentation workflow (LSMS). Filtered range image and spatial image should be created with the MeanShiftSmoothing application, with modesearch parameter disabled. If spatial image is not set, the application will only process the range image and spatial radius parameter will not be taken into account. This application will produce a labeled image where neighbor pixels whose range distance is below range radius (and optionally spatial distance below spatial radius) will be grouped together into the same cluster. For large images one can use the nbtilesx and nbtilesy parameters for tile-wise processing, with the guarantees of identical results. Please note that this application will generate a lot of temporary files (as many as the number of tiles), and will therefore require twice the size of the final result in term of disk space. The cleanup option (activated by default) allows removing all temporary file as soon as they are not needed anymore (if cleanup is activated, tmpdir set and tmpdir does not exists before running the application, it will be removed as well during cleanup). The tmpdir option allows defining a directory where to write the temporary files. Please also note that the output image type should be set to uint32 to ensure that there are enough labels available.

Parameters

Limitations

This application is part of the Large-Scale Mean-Shift segmentation workflow (LSMS) and may not be suited for any other purpose.

Authors

David Youssefi

See Also

MeanShiftSmoothing, LSMSSmallRegionsMerging, LSMSVectorization

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/LSMSSmallRegionsMerging.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/LSMSSmallRegionsMerging.html deleted file mode 100644 index b293794cb450..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/LSMSSmallRegionsMerging.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

LSMSSmallRegionsMerging

Brief Description

Third (optional) step of the exact Large-Scale Mean-Shift segmentation workflow.

Tags

Segmentation,LSMS

Long Description

This application performs the third step of the exact Large-Scale Mean-Shift segmentation workflow (LSMS). Given a segmentation result (label image) and the original image, it will merge regions whose size in pixels is lower than minsize parameter with the adjacent regions with the adjacent region with closest radiometry and acceptable size. Small regions will be processed by size: first all regions of area, which is equal to 1 pixel will be merged with adjacent region, then all regions of area equal to 2 pixels, until regions of area minsize. For large images one can use the nbtilesx and nbtilesy parameters for tile-wise processing, with the guarantees of identical results.

Parameters

Limitations

This application is part of the Large-Scale Mean-Shift segmentation workflow (LSMS) and may not be suited for any other purpose.

Authors

David Youssefi

See Also

LSMSSegmentation, LSMSVectorization, MeanShiftSmoothing

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/LSMSVectorization.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/LSMSVectorization.html deleted file mode 100644 index 5414a039181e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/LSMSVectorization.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

LSMSVectorization

Brief Description

Fourth step of the exact Large-Scale Mean-Shift segmentation workflow.

Tags

Segmentation,LSMS

Long Description

This application performs the fourth step of the exact Large-Scale Mean-Shift segmentation workflow (LSMS). Given a segmentation result (label image), that may have been processed for small regions merging or not, it will convert it to a GIS vector file containing one polygon per segment. Each polygon contains additional fields: mean and variance of each channels from input image (in parameter), segmentation image label, number of pixels in the polygon. For large images one can use the nbtilesx and nbtilesy parameters for tile-wise processing, with the guarantees of identical results.

Parameters

Limitations

This application is part of the Large-Scale Mean-Shift segmentation workflow (LSMS) and may not be suited for any other purpose.

Authors

David Youssefi

See Also

MeanShiftSmoothing, LSMSSegmentation, LSMSSmallRegionsMerging

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/LineSegmentDetection.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/LineSegmentDetection.html deleted file mode 100644 index ca90896346a1..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/LineSegmentDetection.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

LineSegmentDetection

Brief Description

Detect line segments in raster

Tags

Feature Extraction

Long Description

This application detects locally straight contours in a image. It is based on Burns, Hanson, and Riseman method and use an a contrario validation approach (Desolneux, Moisan, and Morel). The algorithm was published by Rafael Gromponevon Gioi, Jérémie Jakubowicz, Jean-Michel Morel and Gregory Randall. - The given approach computes gradient and level lines of the image and detects aligned points in line support region. The application allows exporting the detected lines in a vector data.

Parameters

Limitations

None

Authors

OTB-Team

See Also

On Line demonstration of the LSD algorithm is available here: http://www.ipol.im/pub/algo/gjmr_line_segment_detector/ -

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/LocalStatisticExtraction.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/LocalStatisticExtraction.html deleted file mode 100644 index 6ff7bc151aa1..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/LocalStatisticExtraction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

LocalStatisticExtraction

Brief Description

Computes local statistical moments on every pixel in the selected channel of the input image

Tags

Statistics,Feature Extraction

Long Description

This application computes the 4 local statistical moments on every pixel in the selected channel of the input image, over a specified neighborhood. The output image is multi band with one statistical moment (feature) per band. Thus, the 4 output features are the Mean, the Variance, the Skewness and the Kurtosis. They are provided in this exact order in the output image.

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbRadiometricMomentsImageFunction class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ManageNoData.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ManageNoData.html deleted file mode 100644 index b008bfa00e88..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ManageNoData.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ManageNoData

Brief Description

Manage No-Data

Tags

Conversion,Image Dynamic,Image Manipulation

Long Description

This application has two modes. The first allows building a mask of no-data pixels from the no-data flags read from the image file. The second allows updating the change the no-data value of an image (pixels value and metadata). This last mode also allows replacing NaN in images with a proper no-data value. To do so, one should activate the NaN is no-data option.

Parameters

Limitations

None

Authors

OTB-Team

See Also

BanMath

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/MeanShiftSmoothing.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/MeanShiftSmoothing.html deleted file mode 100644 index c8df67c03328..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/MeanShiftSmoothing.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

MeanShiftSmoothing

Brief Description

Perform mean shift filtering

Tags

Image Filtering,LSMS

Long Description

This application performs mean shift fitlering (multi-threaded).

Parameters

Limitations

With mode search option, the result will slightly depend on thread number.

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/MultiResolutionPyramid.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/MultiResolutionPyramid.html deleted file mode 100644 index 35f527bb0353..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/MultiResolutionPyramid.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

MultiResolutionPyramid

Brief Description

Build a multi-resolution pyramid of the image.

Tags

Conversion,Image Manipulation,Image MultiResolution,Util

Long Description

This application builds a multi-resolution pyramid of the input image. User can specified the number of levels of the pyramid and the subsampling factor. To speed up the process, you can use the fast scheme option

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/MultivariateAlterationDetector.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/MultivariateAlterationDetector.html deleted file mode 100644 index 2505ba3cba15..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/MultivariateAlterationDetector.html +++ /dev/null @@ -1,21 +0,0 @@ - - -

MultivariateAlterationDetector

Brief Description

Multivariate Alteration Detector

Tags

Feature Extraction

Long Description

This application detects change between two given images.

Parameters

Limitations

None

Authors

OTB-Team

See Also

This filter implements the Multivariate Alteration Detector, based on the following work: - A. A. Nielsen and K. Conradsen, Multivariate alteration detection (mad) in multispectral, bi-temporal image data: a new approach to change detection studies, Remote Sens. Environ., vol. 64, pp. 1-19, (1998) - - Multivariate Alteration Detector takes two images as inputs and produce a set of N change maps as a VectorImage (where N is the maximum of number of bands in first and second image) with the following properties: - - Change maps are differences of a pair of linear combinations of bands from image 1 and bands from image 2 chosen to maximize the correlation. - - Each change map is orthogonal to the others. - - This is a statistical method which can handle different modalities and even different bands and number of bands between images. - - If numbers of bands in image 1 and 2 are equal, then change maps are sorted by increasing correlation. If number of bands is different, the change maps are sorted by decreasing correlation. - - The GetV1() and GetV2() methods allow retrieving the linear combinations used to generate the Mad change maps as a vnl_matrix of double, and the GetRho() method allows retrieving the correlation associated to each Mad change maps as a vnl_vector. - - This filter has been implemented from the Matlab code kindly made available by the authors here: - http://www2.imm.dtu.dk/~aa/software.html - - Both cases (same and different number of bands) have been validated by comparing the output image to the output produced by the Matlab code, and the reference images for testing have been generated from the Matlab code using Octave.

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/OGRLayerClassifier.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/OGRLayerClassifier.html deleted file mode 100644 index 9b8b14bf2ff0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/OGRLayerClassifier.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

OGRLayerClassifier

Brief Description

Classify an OGR layer based on a machine learning model and a list of features to consider.

Tags

Segmentation

Long Description

This application will apply a trained machine learning model on the selected feature to get a classification of each geometry contained in an OGR layer. The list of feature must match the list used for training. The predicted label is written in the user defined field for each geometry.

Parameters

Limitations

Experimental. Only shapefiles are supported for now.

Authors

David Youssefi during internship at CNES

See Also

ComputeOGRLayersFeaturesStatistics,TrainOGRLayersClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/OSMDownloader.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/OSMDownloader.html deleted file mode 100644 index e675bf1e6653..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/OSMDownloader.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

OSMDownloader

Brief Description

Generate a vector data from OSM on the input image extend

Tags

Image MetaData

Long Description

Generate a vector data from Open Street Map data. A DEM could be use. By default, the entire layer is downloaded, an image can be use as support for the OSM data. The application can provide also available classes in layers . This application required an Internet access. Information about the OSM project : http://www.openstreetmap.fr/

Parameters

Limitations

None

Authors

OTB-Team

See Also

Conversion

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ObtainUTMZoneFromGeoPoint.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ObtainUTMZoneFromGeoPoint.html deleted file mode 100644 index eb416fdd3d84..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ObtainUTMZoneFromGeoPoint.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ObtainUTMZoneFromGeoPoint

Brief Description

UTM zone determination from a geographic point.

Tags

Coordinates

Long Description

This application returns the UTM zone of an input geographic point.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

Obtain a UTM Zone \ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/OpticalCalibration.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/OpticalCalibration.html deleted file mode 100644 index e64a64b069b5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/OpticalCalibration.html +++ /dev/null @@ -1,60 +0,0 @@ - - -

OpticalCalibration

Brief Description

Perform optical calibration TOA/TOC (Top Of Atmosphere/Top Of Canopy). Supported sensors: QuickBird, Ikonos, WorldView2, Formosat, Spot5, Pleiades, Spot6. For other sensors the application also allows providing calibration parameters manually.

Tags

Calibration

Long Description

The application allows converting pixel values from DN (for Digital Numbers) to reflectance. Calibrated values are called surface reflectivity and its values lie in the range [0, 1]. -The first level is called Top Of Atmosphere (TOA) reflectivity. It takes into account the sensor gain, sensor spectral response and the solar illuminations. -The second level is called Top Of Canopy (TOC) reflectivity. In addition to sensor gain and solar illuminations, it takes into account the optical thickness of the atmosphere, the atmospheric pressure, the water vapor amount, the ozone amount, as well as the composition and amount of aerosol gasses. -It is also possible to indicate an AERONET file which contains atmospheric parameters (version 1 and version 2 of Aeronet file are supported. Note that computing TOC reflectivity will internally compute first TOA and then TOC reflectance. - --------------------------- - -If the sensor is not supported by the metadata interface factory of OTB, users still have the possibility to give the needed parameters to the application. -For TOA conversion, these parameters are : -- day and month of acquisition, or flux normalization coefficient; -- sun elevation angle; -- gains and biases, one pair of values for each band (passed by a file); -- solar illuminations, one value for each band (passed by a file). - -For the conversion from DN (for Digital Numbers) to spectral radiance (or 'TOA radiance') L, the following formula is used : - -(1) L(b) = DN(b)/gain(b)+bias(b) (in W/m2/steradians/micrometers) with b being a band ID. - -These values are provided by the user thanks to a simple txt file with two lines, one for the gains and one for the biases. -Each value must be separated with colons (:), with eventual spaces. Blank lines are not allowed. If a line begins with the '#' symbol, then it is considered as comments. -Note that sometimes, the values provided by certain metadata files assume the formula L(b) = gain(b)*DC(b)+bias(b). -In this case, be sure to provide the inverse gain values so that the application can correctly interpret them. - -In order to convert TOA radiance to TOA reflectance, the following formula is used : - -(2) R(b) = (pi*L(b)*d*d) / (ESUN(b)*cos(θ)) (no dimension) where : - -- L(b) is the spectral radiance for band b -- pi is the famous mathematical constant (3.14159...) -- d is the earth-sun distance (in astronomical units) and depends on the acquisition's day and month -- ESUN(b) is the mean TOA solar irradiance (or solar illumination) in W/m2/micrometers -- θ is the solar zenith angle in degrees. -Note that the application asks for the solar elevation angle, and will perfom the conversion to the zenith angle itself (zenith_angle = 90 - elevation_angle , units : degrees). -Note also that ESUN(b) not only depends on the band b, but also on the spectral sensitivity of the sensor in this particular band. In other words, the influence of spectral sensitivities is included within the ESUN different values. -These values are provided by the user thanks to a txt file following the same convention as before. -Instead of providing the date of acquisition, the user can also provide a flux normalization coefficient 'fn'. The formula used instead will be the following : - -(3) R(b) = (pi*L(b)) / (ESUN(b)*fn*fn*cos(θ)) - -Whatever the formula used (2 or 3), the user should pay attention to the interpretation of the parameters he will provide to the application, by taking into account the original formula that the metadata files assumes. - -Below, we give two examples of txt files containing information about gains/biases and solar illuminations : - -- gainbias.txt : -# Gain values for each band. Each value must be separated with colons (:), with eventual spaces. Blank lines not allowed. -10.4416 : 9.529 : 8.5175 : 14.0063 -# Bias values for each band. -0.0 : 0.0 : 0.0 : 0.0 - -- solarillumination.txt : -# Solar illumination values in watt/m2/micron ('micron' means actually 'for each band'). -# Each value must be separated with colons (:), with eventual spaces. Blank lines not allowed. -1540.494123 : 1826.087443 : 1982.671954 : 1094.747446 - -Finally, the 'Logs' tab provides useful messages that can help the user in knowing the process different status.

Parameters

Limitations

None

Authors

OTB-Team

See Also

The OTB CookBook

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/OrthoRectification-epsg.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/OrthoRectification-epsg.html deleted file mode 100644 index 45127493c53e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/OrthoRectification-epsg.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

OrthoRectification

Brief Description

This application allows ortho-rectification of optical images from supported sensors. -

Tags

Geometry

Long Description

An inverse sensor model is built from the input image metadata to convert geographical to raw geometry coordinates. This inverse sensor model is then combined with the chosen map projection to build a global coordinate mapping grid. Last, this grid is used to resample using the chosen interpolation algorithm. A Digital Elevation Model can be specified to account for terrain deformations. -In case of SPOT5 images, the sensor model can be approximated by an RPC model in order to speed-up computation.

Parameters

Limitations

Supported sensors are Pleiades, SPOT5 (TIF format), Ikonos, Quickbird, Worldview2, GeoEye.

Authors

OTB-Team

See Also

Ortho-rectification chapter from the OTB Software Guide

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/OrthoRectification-fit-to-ortho.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/OrthoRectification-fit-to-ortho.html deleted file mode 100644 index 45127493c53e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/OrthoRectification-fit-to-ortho.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

OrthoRectification

Brief Description

This application allows ortho-rectification of optical images from supported sensors. -

Tags

Geometry

Long Description

An inverse sensor model is built from the input image metadata to convert geographical to raw geometry coordinates. This inverse sensor model is then combined with the chosen map projection to build a global coordinate mapping grid. Last, this grid is used to resample using the chosen interpolation algorithm. A Digital Elevation Model can be specified to account for terrain deformations. -In case of SPOT5 images, the sensor model can be approximated by an RPC model in order to speed-up computation.

Parameters

Limitations

Supported sensors are Pleiades, SPOT5 (TIF format), Ikonos, Quickbird, Worldview2, GeoEye.

Authors

OTB-Team

See Also

Ortho-rectification chapter from the OTB Software Guide

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/OrthoRectification-lambert-WGS84.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/OrthoRectification-lambert-WGS84.html deleted file mode 100644 index 45127493c53e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/OrthoRectification-lambert-WGS84.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

OrthoRectification

Brief Description

This application allows ortho-rectification of optical images from supported sensors. -

Tags

Geometry

Long Description

An inverse sensor model is built from the input image metadata to convert geographical to raw geometry coordinates. This inverse sensor model is then combined with the chosen map projection to build a global coordinate mapping grid. Last, this grid is used to resample using the chosen interpolation algorithm. A Digital Elevation Model can be specified to account for terrain deformations. -In case of SPOT5 images, the sensor model can be approximated by an RPC model in order to speed-up computation.

Parameters

Limitations

Supported sensors are Pleiades, SPOT5 (TIF format), Ikonos, Quickbird, Worldview2, GeoEye.

Authors

OTB-Team

See Also

Ortho-rectification chapter from the OTB Software Guide

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/OrthoRectification-utm.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/OrthoRectification-utm.html deleted file mode 100644 index 45127493c53e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/OrthoRectification-utm.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

OrthoRectification

Brief Description

This application allows ortho-rectification of optical images from supported sensors. -

Tags

Geometry

Long Description

An inverse sensor model is built from the input image metadata to convert geographical to raw geometry coordinates. This inverse sensor model is then combined with the chosen map projection to build a global coordinate mapping grid. Last, this grid is used to resample using the chosen interpolation algorithm. A Digital Elevation Model can be specified to account for terrain deformations. -In case of SPOT5 images, the sensor model can be approximated by an RPC model in order to speed-up computation.

Parameters

Limitations

Supported sensors are Pleiades, SPOT5 (TIF format), Ikonos, Quickbird, Worldview2, GeoEye.

Authors

OTB-Team

See Also

Ortho-rectification chapter from the OTB Software Guide

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/OrthoRectification.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/OrthoRectification.html deleted file mode 100644 index 45127493c53e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/OrthoRectification.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

OrthoRectification

Brief Description

This application allows ortho-rectification of optical images from supported sensors. -

Tags

Geometry

Long Description

An inverse sensor model is built from the input image metadata to convert geographical to raw geometry coordinates. This inverse sensor model is then combined with the chosen map projection to build a global coordinate mapping grid. Last, this grid is used to resample using the chosen interpolation algorithm. A Digital Elevation Model can be specified to account for terrain deformations. -In case of SPOT5 images, the sensor model can be approximated by an RPC model in order to speed-up computation.

Parameters

Limitations

Supported sensors are Pleiades, SPOT5 (TIF format), Ikonos, Quickbird, Worldview2, GeoEye.

Authors

OTB-Team

See Also

Ortho-rectification chapter from the OTB Software Guide

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Pansharpening-bayes.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Pansharpening-bayes.html deleted file mode 100644 index dd25169010ad..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Pansharpening-bayes.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Pansharpening

Brief Description

Perform P+XS pansharpening

Tags

Geometry,Pansharpening

Long Description

This application performs P+XS pansharpening. Pansharpening is a process of merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution color image. Algorithms available in the applications are: RCS, bayesian fusion and Local Mean and Variance Matching(LMVM).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Pansharpening-lmvm.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Pansharpening-lmvm.html deleted file mode 100644 index dd25169010ad..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Pansharpening-lmvm.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Pansharpening

Brief Description

Perform P+XS pansharpening

Tags

Geometry,Pansharpening

Long Description

This application performs P+XS pansharpening. Pansharpening is a process of merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution color image. Algorithms available in the applications are: RCS, bayesian fusion and Local Mean and Variance Matching(LMVM).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Pansharpening-rcs.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Pansharpening-rcs.html deleted file mode 100644 index dd25169010ad..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Pansharpening-rcs.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Pansharpening

Brief Description

Perform P+XS pansharpening

Tags

Geometry,Pansharpening

Long Description

This application performs P+XS pansharpening. Pansharpening is a process of merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution color image. Algorithms available in the applications are: RCS, bayesian fusion and Local Mean and Variance Matching(LMVM).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Pansharpening.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Pansharpening.html deleted file mode 100644 index dd25169010ad..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Pansharpening.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Pansharpening

Brief Description

Perform P+XS pansharpening

Tags

Geometry,Pansharpening

Long Description

This application performs P+XS pansharpening. Pansharpening is a process of merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution color image. Algorithms available in the applications are: RCS, bayesian fusion and Local Mean and Variance Matching(LMVM).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/PixelValue.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/PixelValue.html deleted file mode 100644 index 53b7cab54fcf..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/PixelValue.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

PixelValue

Brief Description

Get the value of a pixel.

Tags

Utilities,Coordinates,Raster

Long Description

Get the value of a pixel. -Pay attention, index starts at 0.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/PolygonClassStatistics.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/PolygonClassStatistics.html deleted file mode 100644 index 5c21f9f73f34..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/PolygonClassStatistics.html +++ /dev/null @@ -1,12 +0,0 @@ - - -

PolygonClassStatistics

Brief Description

Computes statistics on a training polygon set.

Tags

Learning

Long Description

The application processes a set of geometries intended for training (they should have a field giving the associated class). The geometries are analysed against a support image to compute statistics : - - number of samples per class - - number of samples per geometry -An optional raster mask can be used to discard samples. Different types of geometry are supported : polygons, lines, points. The behaviour is different for each type of geometry : - - polygon: select pixels whose center is inside the polygon - - lines : select pixels intersecting the line - - points : select closest pixel to the point -

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/PredictRegression.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/PredictRegression.html deleted file mode 100644 index d86079ee3811..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/PredictRegression.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

PredictRegression

Brief Description

Performs a prediction of the input image according to a regression model file.

Tags

Learning

Long Description

This application predict output values from an input image, based on a regression model file produced by the TrainRegression application. Pixels of the output image will contain the predicted values fromthe regression model (single band). The input pixels can be optionally centered and reduced according to the statistics file produced by the ComputeImagesStatistics application. An optional input mask can be provided, in which case only input image pixels whose corresponding mask value is greater than 0 will be processed. The remaining of pixels will be given the value 0 in the output image.

Parameters

Limitations

The input image must contain the feature bands used for the model training (without the predicted value). If a statistics file was used during training by the TrainRegression, it is mandatory to use the same statistics file for prediction. If an input mask is used, its size must match the input image size.

Authors

OTB-Team

See Also

TrainRegression, ComputeImagesStatistics

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Quicklook.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Quicklook.html deleted file mode 100644 index c84a0abaf342..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Quicklook.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

Quicklook

Brief Description

Generates a subsampled version of an image extract

Tags

Image Manipulation

Long Description

Generates a subsampled version of an extract of an image defined by ROIStart and ROISize. - This extract is subsampled using the ratio OR the output image Size.

Parameters

Limitations

This application does not provide yet the optimal way to decode coarser level of resolution from JPEG2000 images (like in Monteverdi). -Trying to subsampled huge JPEG200 image with the application will lead to poor performances for now.

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/RadiometricIndices.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/RadiometricIndices.html deleted file mode 100644 index 9686e915d862..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/RadiometricIndices.html +++ /dev/null @@ -1,25 +0,0 @@ - - -

RadiometricIndices

Brief Description

Compute radiometric indices.

Tags

Radiometric Indices,Feature Extraction

Long Description

This application computes radiometric indices using the relevant channels of the input image. The output is a multi band image into which each channel is one of the selected indices.

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbVegetationIndicesFunctor, otbWaterIndicesFunctor and otbSoilIndicesFunctor classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Rasterization-image.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Rasterization-image.html deleted file mode 100644 index 3569e88c5c6b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Rasterization-image.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

Rasterization

Brief Description

Rasterize a vector dataset.

Tags

Vector Data Manipulation

Long Description

This application allows reprojecting and rasterize a vector dataset. The grid of the rasterized output can be set by using a reference image, or by setting all parmeters (origin, size, spacing) by hand. In the latter case, at least the spacing (ground sampling distance) is needed (other parameters are computed automatically). The rasterized output can also be in a different projection reference system than the input dataset. - There are two rasterize mode available in the application. The first is the binary mode: it allows rendering all pixels belonging to a geometry of the input dataset in the foreground color, while rendering the other in background color. The second one allows rendering pixels belonging to a geometry woth respect to an attribute of this geometry. The field of the attribute to render can be set by the user. In the second mode, the background value is still used for unassociated pixels.

Parameters

Limitations

None

Authors

OTB-Team

See Also

For now, support of input dataset with multiple layers having different projection reference system is limited.

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Rasterization-manual.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Rasterization-manual.html deleted file mode 100644 index 3569e88c5c6b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Rasterization-manual.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

Rasterization

Brief Description

Rasterize a vector dataset.

Tags

Vector Data Manipulation

Long Description

This application allows reprojecting and rasterize a vector dataset. The grid of the rasterized output can be set by using a reference image, or by setting all parmeters (origin, size, spacing) by hand. In the latter case, at least the spacing (ground sampling distance) is needed (other parameters are computed automatically). The rasterized output can also be in a different projection reference system than the input dataset. - There are two rasterize mode available in the application. The first is the binary mode: it allows rendering all pixels belonging to a geometry of the input dataset in the foreground color, while rendering the other in background color. The second one allows rendering pixels belonging to a geometry woth respect to an attribute of this geometry. The field of the attribute to render can be set by the user. In the second mode, the background value is still used for unassociated pixels.

Parameters

Limitations

None

Authors

OTB-Team

See Also

For now, support of input dataset with multiple layers having different projection reference system is limited.

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Rasterization.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Rasterization.html deleted file mode 100644 index 3569e88c5c6b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Rasterization.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

Rasterization

Brief Description

Rasterize a vector dataset.

Tags

Vector Data Manipulation

Long Description

This application allows reprojecting and rasterize a vector dataset. The grid of the rasterized output can be set by using a reference image, or by setting all parmeters (origin, size, spacing) by hand. In the latter case, at least the spacing (ground sampling distance) is needed (other parameters are computed automatically). The rasterized output can also be in a different projection reference system than the input dataset. - There are two rasterize mode available in the application. The first is the binary mode: it allows rendering all pixels belonging to a geometry of the input dataset in the foreground color, while rendering the other in background color. The second one allows rendering pixels belonging to a geometry woth respect to an attribute of this geometry. The field of the attribute to render can be set by the user. In the second mode, the background value is still used for unassociated pixels.

Parameters

Limitations

None

Authors

OTB-Team

See Also

For now, support of input dataset with multiple layers having different projection reference system is limited.

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/ReadImageInfo.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/ReadImageInfo.html deleted file mode 100644 index 876e143d7608..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/ReadImageInfo.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ReadImageInfo

Brief Description

Get information about the image

Tags

Utilities,Image Manipulation,Image MetaData

Long Description

Display information about the input image like: image size, origin, spacing, metadata, projections...

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/RefineSensorModel.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/RefineSensorModel.html deleted file mode 100644 index 868feaeff787..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/RefineSensorModel.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

RefineSensorModel

Brief Description

Perform least-square fit of a sensor model to a set of tie points

Tags

Geometry

Long Description

This application reads a geom file containing a sensor model and a text file containing a list of ground control point, and performs a least-square fit of the sensor model adjustable parameters to these tie points. It produces an updated geom file as output, as well as an optional ground control points based statistics file and a vector file containing residues. The output geom file can then be used to ortho-rectify the data more accurately. Plaease note that for a proper use of the application, elevation must be correctly set (including DEM and geoid file). The map parameters allows one to choose a map projection in which the accuracy will be estimated in meters.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OrthoRectification,HomologousPointsExtraction

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Rescale.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Rescale.html deleted file mode 100644 index bb606af15ef8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Rescale.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Rescale

Brief Description

Rescale the image between two given values.

Tags

Image Manipulation

Long Description

This application scales the given image pixel intensity between two given values. By default min (resp. max) value is set to 0 (resp. 255).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/RigidTransformResample-id.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/RigidTransformResample-id.html deleted file mode 100644 index 1ba465d54708..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/RigidTransformResample-id.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

RigidTransformResample

Brief Description

Resample an image with a rigid transform

Tags

Conversion,Geometry

Long Description

This application performs a parametric transform on the input image. Scaling, translation and rotation with scaling factor are handled. Parameters of the transform is expressed in physical units, thus particular attention must be paid on pixel size (value, and sign). Moreover transform is expressed from input space to output space (on the contrary ITK Transforms are expressed form output space to input space).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Translation

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/RigidTransformResample-rotation.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/RigidTransformResample-rotation.html deleted file mode 100644 index 1ba465d54708..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/RigidTransformResample-rotation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

RigidTransformResample

Brief Description

Resample an image with a rigid transform

Tags

Conversion,Geometry

Long Description

This application performs a parametric transform on the input image. Scaling, translation and rotation with scaling factor are handled. Parameters of the transform is expressed in physical units, thus particular attention must be paid on pixel size (value, and sign). Moreover transform is expressed from input space to output space (on the contrary ITK Transforms are expressed form output space to input space).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Translation

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/RigidTransformResample-translation.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/RigidTransformResample-translation.html deleted file mode 100644 index 1ba465d54708..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/RigidTransformResample-translation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

RigidTransformResample

Brief Description

Resample an image with a rigid transform

Tags

Conversion,Geometry

Long Description

This application performs a parametric transform on the input image. Scaling, translation and rotation with scaling factor are handled. Parameters of the transform is expressed in physical units, thus particular attention must be paid on pixel size (value, and sign). Moreover transform is expressed from input space to output space (on the contrary ITK Transforms are expressed form output space to input space).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Translation

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/RigidTransformResample.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/RigidTransformResample.html deleted file mode 100644 index 1ba465d54708..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/RigidTransformResample.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

RigidTransformResample

Brief Description

Resample an image with a rigid transform

Tags

Conversion,Geometry

Long Description

This application performs a parametric transform on the input image. Scaling, translation and rotation with scaling factor are handled. Parameters of the transform is expressed in physical units, thus particular attention must be paid on pixel size (value, and sign). Moreover transform is expressed from input space to output space (on the contrary ITK Transforms are expressed form output space to input space).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Translation

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/SARCalibration.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/SARCalibration.html deleted file mode 100644 index bd06f4869601..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/SARCalibration.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

SARCalibration

Brief Description

Perform radiometric calibration of SAR images. Following sensors are supported: TerraSAR-X, Sentinel1 and Radarsat-2.Both Single Look Complex(SLC) and detected products are supported as input. -

Tags

Calibration,SAR

Long Description

The objective of SAR calibration is to provide imagery in which the pixel values can be directly related to the radar backscatter of the scene. This application allows computing Sigma Naught (Radiometric Calibration) for TerraSAR-X, Sentinel1 L1 and Radarsat-2 sensors. Metadata are automatically retrieved from image products.The application supports complex and non-complex images (SLC or detected products). -

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/SARDecompositions.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/SARDecompositions.html deleted file mode 100644 index e1a43aadade2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/SARDecompositions.html +++ /dev/null @@ -1,15 +0,0 @@ - - -

SARDecompositions

Brief Description

From one-band complex images (each one related to an element of the Sinclair matrix), returns the selected decomposition.

Tags

SAR

Long Description

From one-band complex images (HH, HV, VH, VV), returns the selected decomposition. - -All the decompositions implemented are intended for the mono-static case (transmitter and receiver are co-located). -There are two kinds of decomposition : coherent ones and incoherent ones. -In the coherent case, only the Pauli decomposition is available. -In the incoherent case, there the decompositions available : Huynen, Barnes, and H-alpha-A. -User must provide three one-band complex images HH, HV or VH, and VV (mono-static case <=> HV = VH). -Incoherent decompositions consist in averaging 3x3 complex coherency/covariance matrices; the user must provide the size of the averaging window, thanks to the parameter inco.kernelsize. -

Parameters

Limitations

Some decompositions output real images, while this application outputs complex images for general purpose. -Users should pay attention to extract the real part of the results provided by this application. -

Authors

OTB-Team

See Also

SARPolarMatrixConvert, SARPolarSynth

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/SARPolarMatrixConvert.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/SARPolarMatrixConvert.html deleted file mode 100644 index 063f2963a771..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/SARPolarMatrixConvert.html +++ /dev/null @@ -1,32 +0,0 @@ - - -

SARPolarMatrixConvert

Brief Description

This applications allows converting classical polarimetric matrices to each other.

Tags

SAR

Long Description

This application allows converting classical polarimetric matrices to each other. -For instance, it is possible to get the coherency matrix from the Sinclar one, or the Mueller matrix from the coherency one. -The filters used in this application never handle matrices, but images where each band is related to their elements. -As most of the time SAR polarimetry handles symmetric/hermitian matrices, only the relevant elements are stored, so that the images representing them have a minimal number of bands. -For instance, the coherency matrix size is 3x3 in the monostatic case, and 4x4 in the bistatic case : it will thus be stored in a 6-band or a 10-band complex image (the diagonal and the upper elements of the matrix). - -The Sinclair matrix is a special case : it is always represented as 3 or 4 one-band complex images (for mono- or bistatic case). -The available conversions are listed below: - ---- Monostatic case --- -1 msinclairtocoherency --> Sinclair matrix to coherency matrix (input : 3 x 1 complex channel (HH, HV or VH, VV) | output : 6 complex channels) -2 msinclairtocovariance --> Sinclair matrix to covariance matrix (input : 3 x 1 complex channel (HH, HV or VH, VV) | output : 6 complex channels) -3 msinclairtocircovariance --> Sinclair matrix to circular covariance matrix (input : 3 x 1 complex channel (HH, HV or VH, VV) | output : 6 complex channels) -4 mcoherencytomueller --> Coherency matrix to Mueller matrix (input : 6 complex channels | 16 real channels) -5 mcovariancetocoherencydegree --> Covariance matrix to coherency degree (input : 6 complex channels | 3 complex channels) -6 mcovariancetocoherency --> Covariance matrix to coherency matrix (input : 6 complex channels | 6 complex channels) -7 mlinearcovariancetocircularcovariance --> Covariance matrix to circular covariance matrix (input : 6 complex channels | output : 6 complex channels) - ---- Bistatic case --- -8 bsinclairtocoherency --> Sinclair matrix to coherency matrix (input : 4 x 1 complex channel (HH, HV, VH, VV) | 10 complex channels) -9 bsinclairtocovariance --> Sinclair matrix to covariance matrix (input : 4 x 1 complex channel (HH, HV, VH, VV) | output : 10 complex channels) -10 bsinclairtocircovariance --> Sinclair matrix to circular covariance matrix (input : 4 x 1 complex channel (HH, HV, VH, VV) | output : 10 complex channels) - ---- Both cases --- -11 sinclairtomueller --> Sinclair matrix to Mueller matrix (input : 4 x 1 complex channel (HH, HV, VH, VV) | output : 16 real channels) -12 muellertomcovariance --> Mueller matrix to covariance matrix (input : 16 real channels | output : 6 complex channels) -13 muellertopoldegandpower --> Mueller matrix to polarization degree and power (input : 16 real channels | output : 4 real channels) -

Parameters

Limitations

None

Authors

OTB-Team

See Also

SARPolarSynth, SARDecompositions

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/SARPolarSynth.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/SARPolarSynth.html deleted file mode 100644 index a374d09315af..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/SARPolarSynth.html +++ /dev/null @@ -1,32 +0,0 @@ - - -

SARPolarSynth

Brief Description

Gives, for each pixel, the power that would have been received by a SAR system with a basis different from the classical (H,V) one (polarimetric synthetis).

Tags

SAR

Long Description

This application gives, for each pixel, the power that would have been received by a SAR system with a basis different from the classical (H,V) one (polarimetric synthetis). -The new basis A and B are indicated through two Jones vectors, defined by the user thanks to orientation (psi) and ellipticity (khi) parameters. -These parameters are namely psii, khii, psir and khir. The suffixes (i) and (r) refer to the transmiting antenna and the receiving antenna respectively. -Orientations and ellipticities are given in degrees, and are between -90/90 degrees and -45/45 degrees respectively. - -Four polarization architectures can be processed : -1) HH_HV_VH_VV : full polarization, general bistatic case. -2) HH_HV_VV or HH_VH_VV : full polarization, monostatic case (transmitter and receiver are co-located). -3) HH_HV : dual polarization. -4) VH_VV : dual polarization. -The application takes a complex vector image as input, where each band correspond to a particular emission/reception polarization scheme. -User must comply with the band order given above, since the bands are used to build the Sinclair matrix. - -In order to determine the architecture, the application first relies on the number of bands of the input image. -1) Architecture HH_HV_VH_VV is the only one with four bands, there is no possible confusion. -2) Concerning HH_HV_VV and HH_VH_VV architectures, both correspond to a three channels image. But they are processed in the same way, as the Sinclair matrix is symmetric in the monostatic case. -3) Finally, the two last architectures (dual polarizations), can't be distinguished only by the number of bands of the input image. - User must then use the parameters emissionh and emissionv to indicate the architecture of the system : emissionh=1 and emissionv=0 --> HH_HV, emissionh=0 and emissionv=1 --> VH_VV. -Note : if the architecture is HH_HV, khii and psii are automatically both set to 0 degree; if the architecture is VH_VV, khii and psii are automatically set to 0 degree and 90 degrees respectively. - -It is also possible to force the calculation to co-polar or cross-polar modes. -In the co-polar case, values for psir and khir will be ignored and forced to psii and khii; same as the cross-polar mode, where khir and psir will be forced to (psii + 90 degrees) and -khii. - -Finally, the result of the polarimetric synthetis is expressed in the power domain, through a one-band scalar image. -Note: this application doesn't take into account the terms which do not depend on the polarization of the antennas. -The parameter gain can be used for this purpose. - -More details can be found in the OTB CookBook (SAR processing chapter).

Parameters

Limitations

None

Authors

OTB-Team

See Also

SARDecompositions, SARPolarMatrixConvert

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/SFSTextureExtraction.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/SFSTextureExtraction.html deleted file mode 100644 index faf6fea0fd80..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/SFSTextureExtraction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

SFSTextureExtraction

Brief Description

Computes Structural Feature Set textures on every pixel of the input image selected channel

Tags

Textures,Feature Extraction

Long Description

This application computes SFS textures on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbSFSTexturesImageFilter class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/SOMClassification.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/SOMClassification.html deleted file mode 100644 index a6fc8a522dfc..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/SOMClassification.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

SOMClassification

Brief Description

SOM image classification.

Tags

Segmentation,Learning

Long Description

Unsupervised Self Organizing Map image classification.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/SampleExtraction.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/SampleExtraction.html deleted file mode 100644 index 1b6d1b5e25d4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/SampleExtraction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

SampleExtraction

Brief Description

Extracts samples values from an image.

Tags

Learning

Long Description

The application extracts samples values from animage using positions contained in a vector data file.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/SampleSelection.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/SampleSelection.html deleted file mode 100644 index b4543bbdbf25..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/SampleSelection.html +++ /dev/null @@ -1,34 +0,0 @@ - - -

SampleSelection

Brief Description

Selects samples from a training vector data set.

Tags

Learning

Long Description

The application selects a set of samples from geometries intended for training (they should have a field giving the associated class). - -First of all, the geometries must be analyzed by the PolygonClassStatistics application to compute statistics about the geometries, which are summarized in an xml file. -Then, this xml file must be given as input to this application (parameter instats). - -The input support image and the input training vectors shall be given in parameters 'in' and 'vec' respectively. Only the sampling grid (origin, size, spacing)will be read in the input image. -There are several strategies to select samples (parameter strategy) : - - smallest (default) : select the same number of sample in each class - so that the smallest one is fully sampled. - - constant : select the same number of samples N in each class - (with N below or equal to the size of the smallest class). - - byclass : set the required number for each class manually, with an input CSV file - (first column is class name, second one is the required samples number). -There is also a choice on the sampling type to performs : - - periodic : select samples uniformly distributed - - random : select samples randomly distributed -Once the strategy and type are selected, the application outputs samples positions(parameter out). - -The other parameters to look at are : - - layer : index specifying from which layer to pick geometries. - - field : set the field name containing the class. - - mask : an optional raster mask can be used to discard samples. - - outrates : allows outputting a CSV file that summarizes the sampling rates for each class. - -As with the PolygonClassStatistics application, different types of geometry are supported : polygons, lines, points. -The behavior of this application is different for each type of geometry : - - polygon: select points whose center is inside the polygon - - lines : select points intersecting the line - - points : select closest point to the provided point -

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/SarRadiometricCalibration.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/SarRadiometricCalibration.html deleted file mode 100644 index f7375f6957b3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/SarRadiometricCalibration.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

SarRadiometricCalibration

Brief Description

Perform radiometric calibration of SAR images. Following sensors are supported: TerraSAR-X, Sentinel1 and Radarsat-2.Both Single Look Complex(SLC) and detected products are supported as input. -

Tags

Calibration,SAR

Long Description

The objective of SAR calibration is to provide imagery in which the pixel values can be directly related to the radar backscatter of the scene. This application allows computing Sigma Naught (Radiometric Calibration) for TerraSAR-X, Sentinel1 L1 and Radarsat-2 sensors. Metadata are automatically retrieved from image products.The application supports complex and non-complex images (SLC or detected products). -

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Segmentation-cc.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Segmentation-cc.html deleted file mode 100644 index d5faed62ee0b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Segmentation-cc.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

Segmentation

Brief Description

Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported.

Tags

Segmentation

Long Description

This application allows one to perform various segmentation algorithms on a multispectral image.Available segmentation algorithms are two different versions of Mean-Shift segmentation algorithm (one being multi-threaded), simple pixel based connected components according to a user-defined criterion, and watershed from the gradient of the intensity (norm of spectral bands vector). The application has two different modes that affects the nature of its output. - -In raster mode, the output of the application is a classical image of unique labels identifying the segmented regions. The labeled output can be passed to the ColorMapping application to render regions with contrasted colors. Please note that this mode loads the whole input image into memory, and as such can not handle large images. - -To segment large data, one can use the vector mode. In this case, the output of the application is a vector file or database. The input image is split into tiles (whose size can be set using the tilesize parameter), and each tile is loaded, segmented with the chosen algorithm, vectorized, and written into the output file or database. This piece-wise behavior ensure that memory will never get overloaded, and that images of any size can be processed. There are few more options in the vector mode. The simplify option allows simplifying the geometry (i.e. remove nodes in polygons) according to a user-defined tolerance. The stitch option tries to stitch together the polygons corresponding to segmented region that may have been split by the tiling scheme.

Parameters

Limitations

In raster mode, the application can not handle large input images. Stitching step of vector mode might become slow with very large input images. -MeanShift filter results depends on the number of threads used. -Watershed and multiscale geodesic morphology segmentation will be performed on the amplitude of the input image.

Authors

OTB-Team

See Also

MeanShiftSegmentation

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Segmentation-meanshift.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Segmentation-meanshift.html deleted file mode 100644 index d5faed62ee0b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Segmentation-meanshift.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

Segmentation

Brief Description

Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported.

Tags

Segmentation

Long Description

This application allows one to perform various segmentation algorithms on a multispectral image.Available segmentation algorithms are two different versions of Mean-Shift segmentation algorithm (one being multi-threaded), simple pixel based connected components according to a user-defined criterion, and watershed from the gradient of the intensity (norm of spectral bands vector). The application has two different modes that affects the nature of its output. - -In raster mode, the output of the application is a classical image of unique labels identifying the segmented regions. The labeled output can be passed to the ColorMapping application to render regions with contrasted colors. Please note that this mode loads the whole input image into memory, and as such can not handle large images. - -To segment large data, one can use the vector mode. In this case, the output of the application is a vector file or database. The input image is split into tiles (whose size can be set using the tilesize parameter), and each tile is loaded, segmented with the chosen algorithm, vectorized, and written into the output file or database. This piece-wise behavior ensure that memory will never get overloaded, and that images of any size can be processed. There are few more options in the vector mode. The simplify option allows simplifying the geometry (i.e. remove nodes in polygons) according to a user-defined tolerance. The stitch option tries to stitch together the polygons corresponding to segmented region that may have been split by the tiling scheme.

Parameters

Limitations

In raster mode, the application can not handle large input images. Stitching step of vector mode might become slow with very large input images. -MeanShift filter results depends on the number of threads used. -Watershed and multiscale geodesic morphology segmentation will be performed on the amplitude of the input image.

Authors

OTB-Team

See Also

MeanShiftSegmentation

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Segmentation-mprofiles.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Segmentation-mprofiles.html deleted file mode 100644 index d5faed62ee0b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Segmentation-mprofiles.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

Segmentation

Brief Description

Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported.

Tags

Segmentation

Long Description

This application allows one to perform various segmentation algorithms on a multispectral image.Available segmentation algorithms are two different versions of Mean-Shift segmentation algorithm (one being multi-threaded), simple pixel based connected components according to a user-defined criterion, and watershed from the gradient of the intensity (norm of spectral bands vector). The application has two different modes that affects the nature of its output. - -In raster mode, the output of the application is a classical image of unique labels identifying the segmented regions. The labeled output can be passed to the ColorMapping application to render regions with contrasted colors. Please note that this mode loads the whole input image into memory, and as such can not handle large images. - -To segment large data, one can use the vector mode. In this case, the output of the application is a vector file or database. The input image is split into tiles (whose size can be set using the tilesize parameter), and each tile is loaded, segmented with the chosen algorithm, vectorized, and written into the output file or database. This piece-wise behavior ensure that memory will never get overloaded, and that images of any size can be processed. There are few more options in the vector mode. The simplify option allows simplifying the geometry (i.e. remove nodes in polygons) according to a user-defined tolerance. The stitch option tries to stitch together the polygons corresponding to segmented region that may have been split by the tiling scheme.

Parameters

Limitations

In raster mode, the application can not handle large input images. Stitching step of vector mode might become slow with very large input images. -MeanShift filter results depends on the number of threads used. -Watershed and multiscale geodesic morphology segmentation will be performed on the amplitude of the input image.

Authors

OTB-Team

See Also

MeanShiftSegmentation

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Segmentation-watershed.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Segmentation-watershed.html deleted file mode 100644 index d5faed62ee0b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Segmentation-watershed.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

Segmentation

Brief Description

Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported.

Tags

Segmentation

Long Description

This application allows one to perform various segmentation algorithms on a multispectral image.Available segmentation algorithms are two different versions of Mean-Shift segmentation algorithm (one being multi-threaded), simple pixel based connected components according to a user-defined criterion, and watershed from the gradient of the intensity (norm of spectral bands vector). The application has two different modes that affects the nature of its output. - -In raster mode, the output of the application is a classical image of unique labels identifying the segmented regions. The labeled output can be passed to the ColorMapping application to render regions with contrasted colors. Please note that this mode loads the whole input image into memory, and as such can not handle large images. - -To segment large data, one can use the vector mode. In this case, the output of the application is a vector file or database. The input image is split into tiles (whose size can be set using the tilesize parameter), and each tile is loaded, segmented with the chosen algorithm, vectorized, and written into the output file or database. This piece-wise behavior ensure that memory will never get overloaded, and that images of any size can be processed. There are few more options in the vector mode. The simplify option allows simplifying the geometry (i.e. remove nodes in polygons) according to a user-defined tolerance. The stitch option tries to stitch together the polygons corresponding to segmented region that may have been split by the tiling scheme.

Parameters

Limitations

In raster mode, the application can not handle large input images. Stitching step of vector mode might become slow with very large input images. -MeanShift filter results depends on the number of threads used. -Watershed and multiscale geodesic morphology segmentation will be performed on the amplitude of the input image.

Authors

OTB-Team

See Also

MeanShiftSegmentation

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Segmentation.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Segmentation.html deleted file mode 100644 index d5faed62ee0b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Segmentation.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

Segmentation

Brief Description

Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported.

Tags

Segmentation

Long Description

This application allows one to perform various segmentation algorithms on a multispectral image.Available segmentation algorithms are two different versions of Mean-Shift segmentation algorithm (one being multi-threaded), simple pixel based connected components according to a user-defined criterion, and watershed from the gradient of the intensity (norm of spectral bands vector). The application has two different modes that affects the nature of its output. - -In raster mode, the output of the application is a classical image of unique labels identifying the segmented regions. The labeled output can be passed to the ColorMapping application to render regions with contrasted colors. Please note that this mode loads the whole input image into memory, and as such can not handle large images. - -To segment large data, one can use the vector mode. In this case, the output of the application is a vector file or database. The input image is split into tiles (whose size can be set using the tilesize parameter), and each tile is loaded, segmented with the chosen algorithm, vectorized, and written into the output file or database. This piece-wise behavior ensure that memory will never get overloaded, and that images of any size can be processed. There are few more options in the vector mode. The simplify option allows simplifying the geometry (i.e. remove nodes in polygons) according to a user-defined tolerance. The stitch option tries to stitch together the polygons corresponding to segmented region that may have been split by the tiling scheme.

Parameters

Limitations

In raster mode, the application can not handle large input images. Stitching step of vector mode might become slow with very large input images. -MeanShift filter results depends on the number of threads used. -Watershed and multiscale geodesic morphology segmentation will be performed on the amplitude of the input image.

Authors

OTB-Team

See Also

MeanShiftSegmentation

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Smoothing-anidif.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Smoothing-anidif.html deleted file mode 100644 index c8bcf1ce16d6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Smoothing-anidif.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Smoothing

Brief Description

Apply a smoothing filter to an image

Tags

Image Filtering

Long Description

This application applies smoothing filter to an image. Either gaussian, mean, or anisotropic diffusion are available.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Smoothing-gaussian.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Smoothing-gaussian.html deleted file mode 100644 index c8bcf1ce16d6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Smoothing-gaussian.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Smoothing

Brief Description

Apply a smoothing filter to an image

Tags

Image Filtering

Long Description

This application applies smoothing filter to an image. Either gaussian, mean, or anisotropic diffusion are available.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Smoothing-mean.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Smoothing-mean.html deleted file mode 100644 index c8bcf1ce16d6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Smoothing-mean.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Smoothing

Brief Description

Apply a smoothing filter to an image

Tags

Image Filtering

Long Description

This application applies smoothing filter to an image. Either gaussian, mean, or anisotropic diffusion are available.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Smoothing.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Smoothing.html deleted file mode 100644 index c8bcf1ce16d6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Smoothing.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Smoothing

Brief Description

Apply a smoothing filter to an image

Tags

Image Filtering

Long Description

This application applies smoothing filter to an image. Either gaussian, mean, or anisotropic diffusion are available.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/SplitImage.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/SplitImage.html deleted file mode 100644 index b589865061db..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/SplitImage.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

SplitImage

Brief Description

Split a N multiband image into N images

Tags

Image Manipulation

Long Description

This application splits a N-bands image into N mono-band images. The output images filename will be generated from the output parameter. Thus if the input image has 2 channels, and the user has set an output outimage.tif, the generated images will be outimage_0.tif and outimage_1.tif

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/StereoFramework.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/StereoFramework.html deleted file mode 100644 index 157150ba4866..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/StereoFramework.html +++ /dev/null @@ -1,16 +0,0 @@ - - -

StereoFramework

Brief Description

Compute the ground elevation based on one or multiple stereo pair(s)

Tags

Stereo

Long Description

Compute the ground elevation with a stereo block matching algorithm between one or multiple stereo pair in sensor geometry. The output is projected in desired geographic or cartographic map projection (UTM by default). The pipeline is made of the following steps: -for each sensor pair : - - compute the epipolar displacement grids from the stereo pair (direct and inverse) - - resample the stereo pair into epipolar geometry using BCO interpolation - - create masks for each epipolar image : remove black borders and resample input masks - - compute horizontal disparities with a block matching algorithm - - refine disparities to sub-pixel precision with a dichotomy algorithm - - apply an optional median filter - - filter disparities based on the correlation score and exploration bounds - - translate disparities in sensor geometry - convert disparity to 3D Map. -Then fuse all 3D maps to produce DSM.

Parameters

Limitations

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/StereoRectificationGridGenerator.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/StereoRectificationGridGenerator.html deleted file mode 100644 index 56bafc0fa3a4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/StereoRectificationGridGenerator.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

StereoRectificationGridGenerator

Brief Description

Generates two deformation fields to stereo-rectify (i.e. resample in epipolar geometry) a pair of stereo images up to the sensor model precision

Tags

Stereo

Long Description

This application generates a pair of deformation grid to stereo-rectify a pair of stereo images according to sensor modelling and a mean elevation hypothesis. The deformation grids can be passed to the StereoRectificationGridGenerator application for actual resampling in epipolar geometry.

Parameters

Limitations

Generation of the deformation grid is not streamable, pay attention to this fact when setting the grid step.

Authors

OTB-Team

See Also

otbGridBasedImageResampling

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/Superimpose.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/Superimpose.html deleted file mode 100644 index 1dc724304b1f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/Superimpose.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Superimpose

Brief Description

Using available image metadata, project one image onto another one

Tags

Geometry,Superimposition

Long Description

This application performs the projection of an image into the geometry of another one.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TestApplication.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TestApplication.html deleted file mode 100644 index aac6ba570cde..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TestApplication.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TestApplication

Brief Description

This application helps developers to test parameters types

Tags

Test

Long Description

The purpose of this application is to test parameters types.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TileFusion.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TileFusion.html deleted file mode 100644 index ff003aa4becc..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TileFusion.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TileFusion

Brief Description

Fusion of an image made of several tile files.

Tags

Image Manipulation

Long Description

Concatenate several tile files into a single image file.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-ann.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-ann.html deleted file mode 100644 index a44f1808287f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-ann.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-bayes.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-bayes.html deleted file mode 100644 index a44f1808287f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-bayes.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-boost.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-boost.html deleted file mode 100644 index a44f1808287f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-boost.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-dt.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-dt.html deleted file mode 100644 index a44f1808287f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-dt.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-gbt.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-gbt.html deleted file mode 100644 index a44f1808287f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-gbt.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-knn.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-knn.html deleted file mode 100644 index a44f1808287f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-knn.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-libsvm.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-libsvm.html deleted file mode 100644 index a44f1808287f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-libsvm.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-rf.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-rf.html deleted file mode 100644 index a44f1808287f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier-rf.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier.html deleted file mode 100644 index a44f1808287f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainImagesClassifier.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainOGRLayersClassifier.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainOGRLayersClassifier.html deleted file mode 100644 index 8658fc3c1058..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainOGRLayersClassifier.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TrainOGRLayersClassifier

Brief Description

Train a SVM classifier based on labeled geometries and a list of features to consider.

Tags

Segmentation

Long Description

This application trains a SVM classifier based on labeled geometries and a list of features to consider for classification. This application is deprecated, prefer using TrainVectorClassifier which offers access to all the classifiers.

Parameters

Limitations

Experimental. For now only shapefiles are supported. Tuning of SVM classifier is not available.

Authors

David Youssefi during internship at CNES

See Also

OGRLayerClassifier,ComputeOGRLayersFeaturesStatistics

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-ann.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-ann.html deleted file mode 100644 index 4a7c4d30c120..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-ann.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

TrainRegression

Brief Description

Train a classifier from multiple images to perform regression.

Tags

Learning

Long Description

This application trains a classifier from multiple input images or a csv file, in order to perform regression. Predictors are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The output value for each predictor is assumed to be the last band (or the last column for CSV files). Training and validation predictor lists are built such that their size is inferior to maximum bounds given by the user, and the proportion corresponds to the balance parameter. Several classifier parameters can be set depending on the chosen classifier. In the validation process, the mean square error is computed - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-dt.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-dt.html deleted file mode 100644 index 4a7c4d30c120..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-dt.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

TrainRegression

Brief Description

Train a classifier from multiple images to perform regression.

Tags

Learning

Long Description

This application trains a classifier from multiple input images or a csv file, in order to perform regression. Predictors are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The output value for each predictor is assumed to be the last band (or the last column for CSV files). Training and validation predictor lists are built such that their size is inferior to maximum bounds given by the user, and the proportion corresponds to the balance parameter. Several classifier parameters can be set depending on the chosen classifier. In the validation process, the mean square error is computed - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-gbt.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-gbt.html deleted file mode 100644 index 4a7c4d30c120..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-gbt.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

TrainRegression

Brief Description

Train a classifier from multiple images to perform regression.

Tags

Learning

Long Description

This application trains a classifier from multiple input images or a csv file, in order to perform regression. Predictors are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The output value for each predictor is assumed to be the last band (or the last column for CSV files). Training and validation predictor lists are built such that their size is inferior to maximum bounds given by the user, and the proportion corresponds to the balance parameter. Several classifier parameters can be set depending on the chosen classifier. In the validation process, the mean square error is computed - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-knn.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-knn.html deleted file mode 100644 index 4a7c4d30c120..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-knn.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

TrainRegression

Brief Description

Train a classifier from multiple images to perform regression.

Tags

Learning

Long Description

This application trains a classifier from multiple input images or a csv file, in order to perform regression. Predictors are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The output value for each predictor is assumed to be the last band (or the last column for CSV files). Training and validation predictor lists are built such that their size is inferior to maximum bounds given by the user, and the proportion corresponds to the balance parameter. Several classifier parameters can be set depending on the chosen classifier. In the validation process, the mean square error is computed - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-libsvm.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-libsvm.html deleted file mode 100644 index 4a7c4d30c120..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-libsvm.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

TrainRegression

Brief Description

Train a classifier from multiple images to perform regression.

Tags

Learning

Long Description

This application trains a classifier from multiple input images or a csv file, in order to perform regression. Predictors are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The output value for each predictor is assumed to be the last band (or the last column for CSV files). Training and validation predictor lists are built such that their size is inferior to maximum bounds given by the user, and the proportion corresponds to the balance parameter. Several classifier parameters can be set depending on the chosen classifier. In the validation process, the mean square error is computed - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-rf.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-rf.html deleted file mode 100644 index 4a7c4d30c120..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression-rf.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

TrainRegression

Brief Description

Train a classifier from multiple images to perform regression.

Tags

Learning

Long Description

This application trains a classifier from multiple input images or a csv file, in order to perform regression. Predictors are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The output value for each predictor is assumed to be the last band (or the last column for CSV files). Training and validation predictor lists are built such that their size is inferior to maximum bounds given by the user, and the proportion corresponds to the balance parameter. Several classifier parameters can be set depending on the chosen classifier. In the validation process, the mean square error is computed - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression.html deleted file mode 100644 index 4a7c4d30c120..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainRegression.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

TrainRegression

Brief Description

Train a classifier from multiple images to perform regression.

Tags

Learning

Long Description

This application trains a classifier from multiple input images or a csv file, in order to perform regression. Predictors are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The output value for each predictor is assumed to be the last band (or the last column for CSV files). Training and validation predictor lists are built such that their size is inferior to maximum bounds given by the user, and the proportion corresponds to the balance parameter. Several classifier parameters can be set depending on the chosen classifier. In the validation process, the mean square error is computed - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-ann.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-ann.html deleted file mode 100644 index 4c674772e0a5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-ann.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TrainVectorClassifier

Brief Description

Train a classifier based on labeled geometries and a list of features to consider.

Tags

Learning

Long Description

This application trains a classifier based on labeled geometries and a list of features to consider for classification.

Parameters

Limitations

Authors

OTB Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-bayes.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-bayes.html deleted file mode 100644 index 4c674772e0a5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-bayes.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TrainVectorClassifier

Brief Description

Train a classifier based on labeled geometries and a list of features to consider.

Tags

Learning

Long Description

This application trains a classifier based on labeled geometries and a list of features to consider for classification.

Parameters

Limitations

Authors

OTB Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-boost.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-boost.html deleted file mode 100644 index 4c674772e0a5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-boost.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TrainVectorClassifier

Brief Description

Train a classifier based on labeled geometries and a list of features to consider.

Tags

Learning

Long Description

This application trains a classifier based on labeled geometries and a list of features to consider for classification.

Parameters

Limitations

Authors

OTB Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-dt.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-dt.html deleted file mode 100644 index 4c674772e0a5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-dt.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TrainVectorClassifier

Brief Description

Train a classifier based on labeled geometries and a list of features to consider.

Tags

Learning

Long Description

This application trains a classifier based on labeled geometries and a list of features to consider for classification.

Parameters

Limitations

Authors

OTB Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-gbt.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-gbt.html deleted file mode 100644 index 4c674772e0a5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-gbt.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TrainVectorClassifier

Brief Description

Train a classifier based on labeled geometries and a list of features to consider.

Tags

Learning

Long Description

This application trains a classifier based on labeled geometries and a list of features to consider for classification.

Parameters

Limitations

Authors

OTB Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-knn.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-knn.html deleted file mode 100644 index 4c674772e0a5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-knn.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TrainVectorClassifier

Brief Description

Train a classifier based on labeled geometries and a list of features to consider.

Tags

Learning

Long Description

This application trains a classifier based on labeled geometries and a list of features to consider for classification.

Parameters

Limitations

Authors

OTB Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-libsvm.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-libsvm.html deleted file mode 100644 index 4c674772e0a5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-libsvm.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TrainVectorClassifier

Brief Description

Train a classifier based on labeled geometries and a list of features to consider.

Tags

Learning

Long Description

This application trains a classifier based on labeled geometries and a list of features to consider for classification.

Parameters

Limitations

Authors

OTB Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-rf.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-rf.html deleted file mode 100644 index 4c674772e0a5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier-rf.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TrainVectorClassifier

Brief Description

Train a classifier based on labeled geometries and a list of features to consider.

Tags

Learning

Long Description

This application trains a classifier based on labeled geometries and a list of features to consider for classification.

Parameters

Limitations

Authors

OTB Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier.html deleted file mode 100644 index 4c674772e0a5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/TrainVectorClassifier.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TrainVectorClassifier

Brief Description

Train a classifier based on labeled geometries and a list of features to consider.

Tags

Learning

Long Description

This application trains a classifier based on labeled geometries and a list of features to consider for classification.

Parameters

Limitations

Authors

OTB Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataDSValidation.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataDSValidation.html deleted file mode 100644 index e2cd2032ac0a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataDSValidation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

VectorDataDSValidation

Brief Description

Vector data validation based on the fusion of features using Dempster-Shafer evidence theory framework.

Tags

Feature Extraction

Long Description

This application validates or unvalidate the studied samples using the Dempster-Shafer theory.

Parameters

Limitations

None.

Authors

OTB-Team

See Also

http://en.wikipedia.org/wiki/Dempster-Shafer_theory

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataExtractROI.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataExtractROI.html deleted file mode 100644 index 5acd2390b3b6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataExtractROI.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

VectorDataExtractROI

Brief Description

Perform an extract ROI on the input vector data according to the input image extent

Tags

Vector Data Manipulation

Long Description

This application extracts the vector data features belonging to a region specified by the support image envelope. Any features intersecting the support region is copied to output. The output geometries are NOT cropped.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataReprojection-image.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataReprojection-image.html deleted file mode 100644 index 6a546974cf23..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataReprojection-image.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

VectorDataReprojection

Brief Description

Reproject a vector data using support image projection reference, or a user specified map projection -

Tags

Geometry,Vector Data Manipulation,Coordinates

Long Description

This application allows reprojecting a vector data using support image projection reference, or a user given map projection. - If given, image keywordlist can be added to reprojected vectordata.

Parameters

Limitations

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataReprojection-user.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataReprojection-user.html deleted file mode 100644 index 6a546974cf23..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataReprojection-user.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

VectorDataReprojection

Brief Description

Reproject a vector data using support image projection reference, or a user specified map projection -

Tags

Geometry,Vector Data Manipulation,Coordinates

Long Description

This application allows reprojecting a vector data using support image projection reference, or a user given map projection. - If given, image keywordlist can be added to reprojected vectordata.

Parameters

Limitations

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataReprojection.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataReprojection.html deleted file mode 100644 index 6a546974cf23..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataReprojection.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

VectorDataReprojection

Brief Description

Reproject a vector data using support image projection reference, or a user specified map projection -

Tags

Geometry,Vector Data Manipulation,Coordinates

Long Description

This application allows reprojecting a vector data using support image projection reference, or a user given map projection. - If given, image keywordlist can be added to reprojected vectordata.

Parameters

Limitations

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataSetField.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataSetField.html deleted file mode 100644 index 34a074002ed3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataSetField.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

VectorDataSetField

Brief Description

Set a field in vector data.

Tags

Vector Data Manipulation

Long Description

Set a specified field to a specified value on all features of a vector data.

Parameters

Limitations

Doesn't work with KML files yet

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataTransform.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataTransform.html deleted file mode 100644 index ad5cda0a1e68..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/VectorDataTransform.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

VectorDataTransform

Brief Description

Apply a transform to each vertex of the input VectorData

Tags

Vector Data Manipulation

Long Description

This application performs a transformation of an input vector data transforming each vertex in the vector data. The applied transformation manages translation, rotation and scale, and can be centered or not.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.6.0/doc/VertexComponentAnalysis.html b/python/plugins/processing/algs/otb/description/5.6.0/doc/VertexComponentAnalysis.html deleted file mode 100644 index 345c7725e11f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.6.0/doc/VertexComponentAnalysis.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

VertexComponentAnalysis

Brief Description

Find endmembers in hyperspectral images with Vertex Component Analysis

Tags

Hyperspectral,Dimensionality Reduction

Long Description

Applies the Vertex Component Analysis to an hyperspectral image to extract endmembers

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/BandMath.xml b/python/plugins/processing/algs/otb/description/5.8.0/BandMath.xml deleted file mode 100644 index 62e199221bc2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/BandMath.xml +++ /dev/null @@ -1,42 +0,0 @@ - - BandMath - otbcli_BandMath - Band Math - Miscellaneous - Perform a mathematical operation on monoband images - - ParameterMultipleInput - il - Input image list - Image list to perform computation on. - - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterString - exp - Expression - The mathematical expression to apply. -Use im1b1 for the first band, im1b2 for the second one... - - - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/BandMathX.xml b/python/plugins/processing/algs/otb/description/5.8.0/BandMathX.xml deleted file mode 100644 index a31bb1ce55de..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/BandMathX.xml +++ /dev/null @@ -1,56 +0,0 @@ - - BandMathX - otbcli_BandMathX - Band Math X - Miscellaneous - This application performs mathematical operations on multiband images. -Mathematical formula interpretation is done via muParserX library : http://articles.beltoforion.de/article.php?a=muparserx - - ParameterMultipleInput - il - Input image list - Image list to perform computation on. - - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterString - exp - Expressions - Mathematical expression to apply. - - - False - - - ParameterFile - incontext - Import context - A txt file containing user's constants and expressions. - - True - - - OutputFile - outcontext - Export context - A txt file where to save user's constants and expressions. - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/BinaryMorphologicalOperation-closing.xml b/python/plugins/processing/algs/otb/description/5.8.0/BinaryMorphologicalOperation-closing.xml deleted file mode 100644 index 2961f167e085..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/BinaryMorphologicalOperation-closing.xml +++ /dev/null @@ -1,77 +0,0 @@ - - BinaryMorphologicalOperation-closing - otbcli_BinaryMorphologicalOperation - BinaryMorphologicalOperation (closing) - Feature Extraction - Performs morphological operations on an input image channel - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - closing - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/BinaryMorphologicalOperation-dilate.xml b/python/plugins/processing/algs/otb/description/5.8.0/BinaryMorphologicalOperation-dilate.xml deleted file mode 100644 index 23477a328fa3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/BinaryMorphologicalOperation-dilate.xml +++ /dev/null @@ -1,97 +0,0 @@ - - BinaryMorphologicalOperation-dilate - otbcli_BinaryMorphologicalOperation - BinaryMorphologicalOperation (dilate) - Feature Extraction - Performs morphological operations on an input image channel - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - dilate - - - 0 - False - - - ParameterNumber - filter.dilate.foreval - Foreground Value - The Foreground Value - - - 1 - False - - - ParameterNumber - filter.dilate.backval - Background Value - The Background Value - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/BinaryMorphologicalOperation-erode.xml b/python/plugins/processing/algs/otb/description/5.8.0/BinaryMorphologicalOperation-erode.xml deleted file mode 100644 index c25c24f0e54c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/BinaryMorphologicalOperation-erode.xml +++ /dev/null @@ -1,77 +0,0 @@ - - BinaryMorphologicalOperation-erode - otbcli_BinaryMorphologicalOperation - BinaryMorphologicalOperation (erode) - Feature Extraction - Performs morphological operations on an input image channel - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - erode - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/BinaryMorphologicalOperation-opening.xml b/python/plugins/processing/algs/otb/description/5.8.0/BinaryMorphologicalOperation-opening.xml deleted file mode 100644 index 9af9fcb74cb0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/BinaryMorphologicalOperation-opening.xml +++ /dev/null @@ -1,77 +0,0 @@ - - BinaryMorphologicalOperation-opening - otbcli_BinaryMorphologicalOperation - BinaryMorphologicalOperation (opening) - Feature Extraction - Performs morphological operations on an input image channel - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - opening - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/ClassificationMapRegularization.xml b/python/plugins/processing/algs/otb/description/5.8.0/ClassificationMapRegularization.xml deleted file mode 100644 index dde6059b01da..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/ClassificationMapRegularization.xml +++ /dev/null @@ -1,87 +0,0 @@ - - ClassificationMapRegularization - otbcli_ClassificationMapRegularization - Classification Map Regularization - Learning - Filters the input labeled image using Majority Voting in a ball shaped neighbordhood. - - ParameterRaster - io.in - Input classification image - The input labeled image to regularize. - False - - - OutputRaster - io.out - Output regularized image - The output regularized labeled image. - - - - ParameterNumber - ip.radius - Structuring element radius (in pixels) - The radius of the ball shaped structuring element (expressed in pixels). By default, 'ip.radius = 1 pixel'. - - - 1 - False - - - ParameterBoolean - ip.suvbool - Multiple majority: Undecided(X)/Original - Pixels with more than 1 majority class are marked as Undecided if this parameter is checked (true), or keep their Original labels otherwise (false). Please note that the Undecided value must be different from existing labels in the input labeled image. By default, 'ip.suvbool = false'. - True - True - - - ParameterNumber - ip.nodatalabel - Label for the NoData class - Label for the NoData class. Such input pixels keep their NoData label in the output image. By default, 'ip.nodatalabel = 0'. - - - 0 - False - - - ParameterNumber - ip.undecidedlabel - Label for the Undecided class - Label for the Undecided class. By default, 'ip.undecidedlabel = 0'. - - - 0 - False - - - ParameterBoolean - ip.onlyisolatedpixels - Process isolated pixels only - Only pixels whose label is unique in the neighbordhood will be processed. By default, 'ip.onlyisolatedpixels = false'. - True - True - - - ParameterNumber - ip.isolatedthreshold - Threshold for isolated pixels - Maximum number of neighbours with the same label as the center pixel to consider that it is an isolated pixel. By default, 'ip.isolatedthreshold = 1'. - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/ColorMapping-continuous.xml b/python/plugins/processing/algs/otb/description/5.8.0/ColorMapping-continuous.xml deleted file mode 100644 index a41a70cd7d4c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/ColorMapping-continuous.xml +++ /dev/null @@ -1,104 +0,0 @@ - - ColorMapping-continuous - otbcli_ColorMapping - ColorMapping (continuous) - Image Manipulation - Maps an input label image to 8-bits RGB using look-up tables. - - ParameterRaster - in - Input Image - Input image filename - False - - - OutputRaster - out - Output Image - Output image filename - - - - ParameterSelection - op - Operation - Selection of the operation to execute (default is : label to color). - - - labeltocolor - - - 0 - False - - - ParameterSelection - method - Color mapping method - Selection of color mapping methods and their parameters. - - - continuous - - - 0 - False - - - ParameterSelection - method.continuous.lut - Look-up tables - Available look-up tables. - - - red - green - blue - grey - hot - cool - spring - summer - autumn - winter - copper - jet - hsv - overunder - relief - - - 0 - False - - - ParameterNumber - method.continuous.min - Mapping range lower value - Set the lower input value of the mapping range. - - - 0 - False - - - ParameterNumber - method.continuous.max - Mapping range higher value - Set the higher input value of the mapping range. - - - 255 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/ColorMapping-custom.xml b/python/plugins/processing/algs/otb/description/5.8.0/ColorMapping-custom.xml deleted file mode 100644 index f327f0d9f5d8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/ColorMapping-custom.xml +++ /dev/null @@ -1,68 +0,0 @@ - - ColorMapping-custom - otbcli_ColorMapping - ColorMapping (custom) - Image Manipulation - Maps an input label image to 8-bits RGB using look-up tables. - - ParameterRaster - in - Input Image - Input image filename - False - - - OutputRaster - out - Output Image - Output image filename - - - - ParameterSelection - op - Operation - Selection of the operation to execute (default is : label to color). - - - labeltocolor - - - 0 - False - - - ParameterSelection - method - Color mapping method - Selection of color mapping methods and their parameters. - - - custom - - - 0 - False - - - ParameterFile - method.custom.lut - Look-up table file - An ASCII file containing the look-up table -with one color per line -(for instance the line '1 255 0 0' means that all pixels with label 1 will be replaced by RGB color 255 0 0) -Lines beginning with a # are ignored - - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/ColorMapping-image.xml b/python/plugins/processing/algs/otb/description/5.8.0/ColorMapping-image.xml deleted file mode 100644 index fb1776271c0e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/ColorMapping-image.xml +++ /dev/null @@ -1,94 +0,0 @@ - - ColorMapping-image - otbcli_ColorMapping - ColorMapping (image) - Image Manipulation - Maps an input label image to 8-bits RGB using look-up tables. - - ParameterRaster - in - Input Image - Input image filename - False - - - OutputRaster - out - Output Image - Output image filename - - - - ParameterSelection - op - Operation - Selection of the operation to execute (default is : label to color). - - - labeltocolor - - - 0 - False - - - ParameterSelection - method - Color mapping method - Selection of color mapping methods and their parameters. - - - image - - - 0 - False - - - ParameterRaster - method.image.in - Support Image - Support image filename. For each label, the LUT is calculated from the mean pixel value in the support image, over the corresponding labeled areas. First of all, the support image is normalized with extrema rejection - False - - - ParameterNumber - method.image.nodatavalue - NoData value - NoData value for each channel of the support image, which will not be handled in the LUT estimation. If NOT checked, ALL the pixel values of the support image will be handled in the LUT estimation. - - - 0 - True - - - ParameterNumber - method.image.low - lower quantile - lower quantile for image normalization - - - 2 - True - - - ParameterNumber - method.image.up - upper quantile - upper quantile for image normalization - - - 2 - True - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/ColorMapping-optimal.xml b/python/plugins/processing/algs/otb/description/5.8.0/ColorMapping-optimal.xml deleted file mode 100644 index bf2c08113bf4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/ColorMapping-optimal.xml +++ /dev/null @@ -1,67 +0,0 @@ - - ColorMapping-optimal - otbcli_ColorMapping - ColorMapping (optimal) - Image Manipulation - Maps an input label image to 8-bits RGB using look-up tables. - - ParameterRaster - in - Input Image - Input image filename - False - - - OutputRaster - out - Output Image - Output image filename - - - - ParameterSelection - op - Operation - Selection of the operation to execute (default is : label to color). - - - labeltocolor - - - 0 - False - - - ParameterSelection - method - Color mapping method - Selection of color mapping methods and their parameters. - - - optimal - - - 0 - False - - - ParameterNumber - method.optimal.background - Background label - Value of the background label - - - 0 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/CompareImages.xml b/python/plugins/processing/algs/otb/description/5.8.0/CompareImages.xml deleted file mode 100644 index 780fbcfcc578..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/CompareImages.xml +++ /dev/null @@ -1,101 +0,0 @@ - - CompareImages - otbcli_CompareImages - Images comparison - Miscellaneous - Estimator between 2 images. - - ParameterRaster - ref.in - Reference image - Image used as reference in the comparison - False - - - ParameterNumber - ref.channel - Reference image channel - Used channel for the reference image - - - 1 - False - - - ParameterRaster - meas.in - Measured image - Image used as measured in the comparison - False - - - ParameterNumber - meas.channel - Measured image channel - Used channel for the measured image - - - 1 - False - - - ParameterNumber - roi.startx - Start X - ROI start x position. - - - 0 - False - - - ParameterNumber - roi.starty - Start Y - ROI start y position. - - - 0 - False - - - ParameterNumber - roi.sizex - Size X - Size along x in pixels. - - - 0 - False - - - ParameterNumber - roi.sizey - Size Y - Size along y in pixels. - - - 0 - False - - - ParameterNumber - count - count - Nb of pixels which are different - - - 0.0 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/ComputeConfusionMatrix-raster.xml b/python/plugins/processing/algs/otb/description/5.8.0/ComputeConfusionMatrix-raster.xml deleted file mode 100644 index 5bf8a8210eb5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/ComputeConfusionMatrix-raster.xml +++ /dev/null @@ -1,60 +0,0 @@ - - ComputeConfusionMatrix-raster - otbcli_ComputeConfusionMatrix - ComputeConfusionMatrix (raster) - Learning - Computes the confusion matrix of a classification - - ParameterRaster - in - Input Image - The input classification image. - False - - - OutputFile - out - Matrix output - Filename to store the output matrix (csv format) - - - ParameterSelection - ref - Ground truth - Choice of ground truth format - - - raster - - - 0 - False - - - ParameterRaster - ref.raster.in - Input reference image - Input image containing the ground truth labels - False - - - ParameterNumber - nodatalabel - Value for nodata pixels - Label for the NoData class. Such input pixels will be discarded from the ground truth and from the input classification map. By default, 'nodatalabel = 0'. - - - 0 - True - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/ComputeConfusionMatrix-vector.xml b/python/plugins/processing/algs/otb/description/5.8.0/ComputeConfusionMatrix-vector.xml deleted file mode 100644 index 39c49febcf87..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/ComputeConfusionMatrix-vector.xml +++ /dev/null @@ -1,70 +0,0 @@ - - ComputeConfusionMatrix-vector - otbcli_ComputeConfusionMatrix - ComputeConfusionMatrix (vector) - Learning - Computes the confusion matrix of a classification - - ParameterRaster - in - Input Image - The input classification image. - False - - - OutputFile - out - Matrix output - Filename to store the output matrix (csv format) - - - ParameterSelection - ref - Ground truth - Choice of ground truth format - - - vector - - - 0 - False - - - ParameterFile - ref.vector.in - Input reference vector data - Input vector data of the ground truth - - False - - - ParameterString - ref.vector.field - Field name - Field name containing the label values - Class - - True - - - ParameterNumber - nodatalabel - Value for nodata pixels - Label for the NoData class. Such input pixels will be discarded from the ground truth and from the input classification map. By default, 'nodatalabel = 0'. - - - 0 - True - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/ComputeImagesStatistics.xml b/python/plugins/processing/algs/otb/description/5.8.0/ComputeImagesStatistics.xml deleted file mode 100644 index b6b31c8481d6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/ComputeImagesStatistics.xml +++ /dev/null @@ -1,41 +0,0 @@ - - ComputeImagesStatistics - otbcli_ComputeImagesStatistics - Compute Images second order statistics - Learning - Computes global mean and standard deviation for each band from a set of images and optionally saves the results in an XML file. - - ParameterMultipleInput - il - Input images - List of input images filenames. - - False - - - ParameterNumber - bv - Background Value - Background value to ignore in statistics computation. - - - 0.0 - True - - - OutputFile - out - Output XML file - XML filename where the statistics are saved for future reuse. - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/ComputeOGRLayersFeaturesStatistics.xml b/python/plugins/processing/algs/otb/description/5.8.0/ComputeOGRLayersFeaturesStatistics.xml deleted file mode 100644 index b7dd4dea07c3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/ComputeOGRLayersFeaturesStatistics.xml +++ /dev/null @@ -1,30 +0,0 @@ - - ComputeOGRLayersFeaturesStatistics - otbcli_ComputeOGRLayersFeaturesStatistics - ComputeOGRLayersFeaturesStatistics - Segmentation - Compute statistics of the features in a set of OGR Layers - - ParameterVector - inshp - Name of the input shapefile - Name of the input shapefile - - False - - - OutputFile - outstats - XML file containing mean and variance of each feature. - XML file containing mean and variance of each feature. - - - ParameterString - feat - List of features to consider for statistics. - List of features to consider for statistics. - - - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/ComputePolylineFeatureFromImage.xml b/python/plugins/processing/algs/otb/description/5.8.0/ComputePolylineFeatureFromImage.xml deleted file mode 100644 index a8a72a432205..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/ComputePolylineFeatureFromImage.xml +++ /dev/null @@ -1,57 +0,0 @@ - - ComputePolylineFeatureFromImage - otbcli_ComputePolylineFeatureFromImage - Compute Polyline Feature From Image - Feature Extraction - This application compute for each studied polyline, contained in the input VectorData, the chosen descriptors. - - ParameterRaster - in - Input Image - An image to compute the descriptors on. - False - - - ParameterVector - vd - Vector Data - Vector data containing the polylines where the features will be computed. - - False - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterString - expr - Feature expression - The feature formula (b1 < 0.3) where b1 is the standard name of input image first band - - - False - - - ParameterString - field - Feature name - The field name corresponding to the feature codename (NONDVI, ROADSA...) - - - False - - - OutputVector - out - Output Vector Data - The output vector data containing polylines with a new field - - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/ConcatenateImages.xml b/python/plugins/processing/algs/otb/description/5.8.0/ConcatenateImages.xml deleted file mode 100644 index 4f7c9f4aa70f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/ConcatenateImages.xml +++ /dev/null @@ -1,32 +0,0 @@ - - ConcatenateImages - otbcli_ConcatenateImages - Images Concatenation - Image Manipulation - Concatenate a list of images of the same size into a single multi-channel one. - - ParameterMultipleInput - il - Input images list - The list of images to concatenate - - False - - - OutputRaster - out - Output Image - The concatenated output image - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/ConcatenateVectorData.xml b/python/plugins/processing/algs/otb/description/5.8.0/ConcatenateVectorData.xml deleted file mode 100644 index 9b95a36fee95..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/ConcatenateVectorData.xml +++ /dev/null @@ -1,22 +0,0 @@ - - ConcatenateVectorData - otbcli_ConcatenateVectorData - Concatenate - Vector Data Manipulation - Concatenate VectorDatas - - ParameterMultipleInput - vd - Input VectorDatas to concatenate - VectorData files to be concatenated in an unique VectorData - - False - - - OutputVector - out - Concatenated VectorData - Output conctenated VectorData - - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/ConnectedComponentSegmentation.xml b/python/plugins/processing/algs/otb/description/5.8.0/ConnectedComponentSegmentation.xml deleted file mode 100644 index f8c5fa82352a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/ConnectedComponentSegmentation.xml +++ /dev/null @@ -1,78 +0,0 @@ - - ConnectedComponentSegmentation - otbcli_ConnectedComponentSegmentation - Connected Component Segmentation - Segmentation - Connected component segmentation and object based image filtering of the input image according to user-defined criterions. - - ParameterRaster - in - Input Image - The image to segment. - False - - - OutputVector - out - Output Shape - The segmentation shape. - - - - ParameterString - mask - Mask expression - Mask mathematical expression (only if support image is given) - - - True - - - ParameterString - expr - Connected Component Expression - Formula used for connected component segmentation - - - False - - - ParameterNumber - minsize - Minimum Object Size - Min object size (area in pixel) - - - 2 - True - - - ParameterString - obia - OBIA Expression - OBIA mathematical expression - - - True - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/Convert.xml b/python/plugins/processing/algs/otb/description/5.8.0/Convert.xml deleted file mode 100644 index b5e626721b40..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/Convert.xml +++ /dev/null @@ -1,83 +0,0 @@ - - Convert - otbcli_Convert - Image Conversion - Image Manipulation - Convert an image to a different format, eventually rescaling the data and/or changing the pixel type. - - ParameterRaster - in - Input image - Input image - False - - - ParameterSelection - type - Rescale type - Transfer function for the rescaling - - - none - linear - log2 - - - 0 - False - - - ParameterNumber - type.linear.gamma - Gamma correction factor - Gamma correction factor - - - 1 - True - - - ParameterRaster - mask - Input mask - The masked pixels won't be used to adapt the dynamic (the mask must have the same dimensions as the input image) - True - - - ParameterNumber - hcp.high - High Cut Quantile - Quantiles to cut from histogram high values before computing min/max rescaling (in percent, 2 by default) - - - 2 - True - - - ParameterNumber - hcp.low - Low Cut Quantile - Quantiles to cut from histogram low values before computing min/max rescaling (in percent, 2 by default) - - - 2 - True - - - OutputRaster - out - Output Image - Output image - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/DEMConvert.xml b/python/plugins/processing/algs/otb/description/5.8.0/DEMConvert.xml deleted file mode 100644 index 8e017ebe1336..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/DEMConvert.xml +++ /dev/null @@ -1,20 +0,0 @@ - - DEMConvert - otbcli_DEMConvert - DEM Conversion - Image Manipulation - Converts a geo-referenced DEM image into a general raster file compatible with OTB DEM handling. - - ParameterRaster - in - Input geo-referenced DEM - Input geo-referenced DEM to convert to general raster format. - False - - - OutputFile - out - Prefix of the output files - will be used to get the prefix (name withtout extensions) of the files to write. Three files - prefix.geom, prefix.omd and prefix.ras - will be generated. - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/Despeckle-frost.xml b/python/plugins/processing/algs/otb/description/5.8.0/Despeckle-frost.xml deleted file mode 100644 index e7e3d54aebcf..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/Despeckle-frost.xml +++ /dev/null @@ -1,64 +0,0 @@ - - Despeckle-frost - otbcli_Despeckle - Despeckle (frost) - Image Filtering - Perform speckle noise reduction on SAR image. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - filter - speckle filtering method - - - - frost - - - 0 - False - - - ParameterNumber - filter.frost.rad - Radius - Radius for frost filter - - - 1 - False - - - ParameterNumber - filter.frost.deramp - deramp - Decrease factor declaration - - - 0.1 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/Despeckle-gammamap.xml b/python/plugins/processing/algs/otb/description/5.8.0/Despeckle-gammamap.xml deleted file mode 100644 index 25609700b661..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/Despeckle-gammamap.xml +++ /dev/null @@ -1,64 +0,0 @@ - - Despeckle-gammamap - otbcli_Despeckle - Despeckle (gammamap) - Image Filtering - Perform speckle noise reduction on SAR image. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - filter - speckle filtering method - - - - gammamap - - - 0 - False - - - ParameterNumber - filter.gammamap.rad - Radius - Radius for GammaMAP filter - - - 1 - False - - - ParameterNumber - filter.gammamap.nblooks - nb looks - Nb looks for GammaMAP filter - - - 1 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/Despeckle-kuan.xml b/python/plugins/processing/algs/otb/description/5.8.0/Despeckle-kuan.xml deleted file mode 100644 index ac47ace38d3b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/Despeckle-kuan.xml +++ /dev/null @@ -1,64 +0,0 @@ - - Despeckle-kuan - otbcli_Despeckle - Despeckle (kuan) - Image Filtering - Perform speckle noise reduction on SAR image. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - filter - speckle filtering method - - - - kuan - - - 0 - False - - - ParameterNumber - filter.kuan.rad - Radius - Radius for Kuan filter - - - 0 - False - - - ParameterNumber - filter.kuan.nblooks - nb looks - Nb looks for Kuan filter - - - 0.0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/Despeckle-lee.xml b/python/plugins/processing/algs/otb/description/5.8.0/Despeckle-lee.xml deleted file mode 100644 index 99dad8b3254c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/Despeckle-lee.xml +++ /dev/null @@ -1,64 +0,0 @@ - - Despeckle-lee - otbcli_Despeckle - Despeckle (lee) - Image Filtering - Perform speckle noise reduction on SAR image. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - filter - speckle filtering method - - - - lee - - - 0 - False - - - ParameterNumber - filter.lee.rad - Radius - Radius for lee filter - - - 1 - False - - - ParameterNumber - filter.lee.nblooks - nb looks - Nb looks for lee filter - - - 1 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/DimensionalityReduction-ica.xml b/python/plugins/processing/algs/otb/description/5.8.0/DimensionalityReduction-ica.xml deleted file mode 100644 index 17e70b558b54..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/DimensionalityReduction-ica.xml +++ /dev/null @@ -1,95 +0,0 @@ - - DimensionalityReduction-ica - otbcli_DimensionalityReduction - DimensionalityReduction (ica) - Image Filtering - Perform Dimension reduction of the input image. - - ParameterRaster - in - Input Image - The input image to apply dimensionality reduction. - False - - - OutputRaster - out - Output Image - output image. Components are ordered by decreasing eigenvalues. - - - - OutputRaster - outinv - Inverse Output Image - reconstruct output image. - - - - ParameterSelection - method - Algorithm - Selection of the reduction dimension method. - - - ica - - - 0 - False - - - ParameterNumber - method.ica.iter - number of iterations - - - - 20 - True - - - ParameterNumber - method.ica.mu - Give the increment weight of W in [0, 1] - - - - 1 - True - - - ParameterNumber - nbcomp - Number of Components. - Number of relevant components kept. By default all components are kept. - - - 0 - True - - - ParameterBoolean - normalize - Normalize. - center AND reduce data before Dimensionality reduction. - True - True - - - OutputFile - outmatrix - Transformation matrix output (text format) - Filename to store the transformation matrix (csv format) - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/DimensionalityReduction-maf.xml b/python/plugins/processing/algs/otb/description/5.8.0/DimensionalityReduction-maf.xml deleted file mode 100644 index aaddbe0235fc..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/DimensionalityReduction-maf.xml +++ /dev/null @@ -1,68 +0,0 @@ - - DimensionalityReduction-maf - otbcli_DimensionalityReduction - DimensionalityReduction (maf) - Image Filtering - Perform Dimension reduction of the input image. - - ParameterRaster - in - Input Image - The input image to apply dimensionality reduction. - False - - - OutputRaster - out - Output Image - output image. Components are ordered by decreasing eigenvalues. - - - - ParameterSelection - method - Algorithm - Selection of the reduction dimension method. - - - maf - - - 0 - False - - - ParameterNumber - nbcomp - Number of Components. - Number of relevant components kept. By default all components are kept. - - - 0 - True - - - ParameterBoolean - normalize - Normalize. - center AND reduce data before Dimensionality reduction. - True - True - - - OutputFile - outmatrix - Transformation matrix output (text format) - Filename to store the transformation matrix (csv format) - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/DimensionalityReduction-napca.xml b/python/plugins/processing/algs/otb/description/5.8.0/DimensionalityReduction-napca.xml deleted file mode 100644 index a29de96d537a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/DimensionalityReduction-napca.xml +++ /dev/null @@ -1,95 +0,0 @@ - - DimensionalityReduction-napca - otbcli_DimensionalityReduction - DimensionalityReduction (napca) - Image Filtering - Perform Dimension reduction of the input image. - - ParameterRaster - in - Input Image - The input image to apply dimensionality reduction. - False - - - OutputRaster - out - Output Image - output image. Components are ordered by decreasing eigenvalues. - - - - OutputRaster - outinv - Inverse Output Image - reconstruct output image. - - - - ParameterSelection - method - Algorithm - Selection of the reduction dimension method. - - - napca - - - 0 - False - - - ParameterNumber - method.napca.radiusx - Set the x radius of the sliding window. - - - - 1 - False - - - ParameterNumber - method.napca.radiusy - Set the y radius of the sliding window. - - - - 1 - False - - - ParameterNumber - nbcomp - Number of Components. - Number of relevant components kept. By default all components are kept. - - - 0 - True - - - ParameterBoolean - normalize - Normalize. - center AND reduce data before Dimensionality reduction. - True - True - - - OutputFile - outmatrix - Transformation matrix output (text format) - Filename to store the transformation matrix (csv format) - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/DimensionalityReduction-pca.xml b/python/plugins/processing/algs/otb/description/5.8.0/DimensionalityReduction-pca.xml deleted file mode 100644 index 812cd61ce772..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/DimensionalityReduction-pca.xml +++ /dev/null @@ -1,75 +0,0 @@ - - DimensionalityReduction-pca - otbcli_DimensionalityReduction - DimensionalityReduction (pca) - Image Filtering - Perform Dimension reduction of the input image. - - ParameterRaster - in - Input Image - The input image to apply dimensionality reduction. - False - - - OutputRaster - out - Output Image - output image. Components are ordered by decreasing eigenvalues. - - - - OutputRaster - outinv - Inverse Output Image - reconstruct output image. - - - - ParameterSelection - method - Algorithm - Selection of the reduction dimension method. - - - pca - - - 0 - False - - - ParameterNumber - nbcomp - Number of Components. - Number of relevant components kept. By default all components are kept. - - - 0 - True - - - ParameterBoolean - normalize - Normalize. - center AND reduce data before Dimensionality reduction. - True - True - - - OutputFile - outmatrix - Transformation matrix output (text format) - Filename to store the transformation matrix (csv format) - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/EdgeExtraction-gradient.xml b/python/plugins/processing/algs/otb/description/5.8.0/EdgeExtraction-gradient.xml deleted file mode 100644 index 6bf5b003761a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/EdgeExtraction-gradient.xml +++ /dev/null @@ -1,54 +0,0 @@ - - EdgeExtraction-gradient - otbcli_EdgeExtraction - EdgeExtraction (gradient) - Feature Extraction - Computes edge features on every pixel of the input image selected channel - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - filter - Edge feature - Choice of edge feature - - - gradient - - - 0 - False - - - OutputRaster - out - Feature Output Image - Output image containing the edge features. - - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/EdgeExtraction-sobel.xml b/python/plugins/processing/algs/otb/description/5.8.0/EdgeExtraction-sobel.xml deleted file mode 100644 index e322268eb1fa..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/EdgeExtraction-sobel.xml +++ /dev/null @@ -1,54 +0,0 @@ - - EdgeExtraction-sobel - otbcli_EdgeExtraction - EdgeExtraction (sobel) - Feature Extraction - Computes edge features on every pixel of the input image selected channel - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - filter - Edge feature - Choice of edge feature - - - sobel - - - 0 - False - - - OutputRaster - out - Feature Output Image - Output image containing the edge features. - - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/EdgeExtraction-touzi.xml b/python/plugins/processing/algs/otb/description/5.8.0/EdgeExtraction-touzi.xml deleted file mode 100644 index ea043b256958..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/EdgeExtraction-touzi.xml +++ /dev/null @@ -1,64 +0,0 @@ - - EdgeExtraction-touzi - otbcli_EdgeExtraction - EdgeExtraction (touzi) - Feature Extraction - Computes edge features on every pixel of the input image selected channel - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - filter - Edge feature - Choice of edge feature - - - touzi - - - 0 - False - - - ParameterNumber - filter.touzi.xradius - The Radius - The Radius - - - 1 - False - - - OutputRaster - out - Feature Output Image - Output image containing the edge features. - - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/ExtractROI-fit.xml b/python/plugins/processing/algs/otb/description/5.8.0/ExtractROI-fit.xml deleted file mode 100644 index 973c0a19da11..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/ExtractROI-fit.xml +++ /dev/null @@ -1,61 +0,0 @@ - - ExtractROI-fit - otbcli_ExtractROI - ExtractROI (fit) - Image Manipulation - Extract a ROI defined by the user. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - mode - Extraction mode - - - - fit - - - 0 - False - - - ParameterRaster - mode.fit.ref - Reference image - Reference image to define the ROI - False - - - ParameterNumber - mode.fit.elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/ExtractROI-standard.xml b/python/plugins/processing/algs/otb/description/5.8.0/ExtractROI-standard.xml deleted file mode 100644 index e898dbf6b6cc..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/ExtractROI-standard.xml +++ /dev/null @@ -1,84 +0,0 @@ - - ExtractROI-standard - otbcli_ExtractROI - ExtractROI (standard) - Image Manipulation - Extract a ROI defined by the user. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - mode - Extraction mode - - - - standard - - - 0 - False - - - ParameterNumber - startx - Start X - ROI start x position. - - - 0 - False - - - ParameterNumber - starty - Start Y - ROI start y position. - - - 0 - False - - - ParameterNumber - sizex - Size X - size along x in pixels. - - - 0 - False - - - ParameterNumber - sizey - Size Y - size along y in pixels. - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/FusionOfClassifications-dempstershafer.xml b/python/plugins/processing/algs/otb/description/5.8.0/FusionOfClassifications-dempstershafer.xml deleted file mode 100644 index 96d4a0cbe02c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/FusionOfClassifications-dempstershafer.xml +++ /dev/null @@ -1,79 +0,0 @@ - - FusionOfClassifications-dempstershafer - otbcli_FusionOfClassifications - FusionOfClassifications (dempstershafer) - Learning - Fuses several classifications maps of the same image on the basis of class labels. - - ParameterMultipleInput - il - Input classifications - List of input classification maps to fuse. Labels in each classification image must represent the same class. - - False - - - ParameterSelection - method - Fusion method - Selection of the fusion method and its parameters. - - - dempstershafer - - - 0 - False - - - ParameterMultipleInput - method.dempstershafer.cmfl - Confusion Matrices - A list of confusion matrix files (*.CSV format) to define the masses of belief and the class labels. Each file should be formatted the following way: the first line, beginning with a '#' symbol, should be a list of the class labels present in the corresponding input classification image, organized in the same order as the confusion matrix rows/columns. - - False - - - ParameterSelection - method.dempstershafer.mob - Mass of belief measurement - Type of confusion matrix measurement used to compute the masses of belief of each classifier. - - - precision - recall - accuracy - kappa - - - 0 - False - - - ParameterNumber - nodatalabel - Label for the NoData class - Label for the NoData class. Such input pixels keep their NoData label in the output image and are not handled in the fusion process. By default, 'nodatalabel = 0'. - - - 0 - False - - - ParameterNumber - undecidedlabel - Label for the Undecided class - Label for the Undecided class. Pixels with more than 1 fused class are marked as Undecided. Please note that the Undecided value must be different from existing labels in the input classifications. By default, 'undecidedlabel = 0'. - - - 0 - False - - - OutputRaster - out - The output classification image - The output classification image resulting from the fusion of the input classification images. - - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/FusionOfClassifications-majorityvoting.xml b/python/plugins/processing/algs/otb/description/5.8.0/FusionOfClassifications-majorityvoting.xml deleted file mode 100644 index abd3f7cb1289..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/FusionOfClassifications-majorityvoting.xml +++ /dev/null @@ -1,55 +0,0 @@ - - FusionOfClassifications-majorityvoting - otbcli_FusionOfClassifications - FusionOfClassifications (majorityvoting) - Learning - Fuses several classifications maps of the same image on the basis of class labels. - - ParameterMultipleInput - il - Input classifications - List of input classification maps to fuse. Labels in each classification image must represent the same class. - - False - - - ParameterSelection - method - Fusion method - Selection of the fusion method and its parameters. - - - majorityvoting - - - 0 - False - - - ParameterNumber - nodatalabel - Label for the NoData class - Label for the NoData class. Such input pixels keep their NoData label in the output image and are not handled in the fusion process. By default, 'nodatalabel = 0'. - - - 0 - False - - - ParameterNumber - undecidedlabel - Label for the Undecided class - Label for the Undecided class. Pixels with more than 1 fused class are marked as Undecided. Please note that the Undecided value must be different from existing labels in the input classifications. By default, 'undecidedlabel = 0'. - - - 0 - False - - - OutputRaster - out - The output classification image - The output classification image resulting from the fusion of the input classification images. - - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/GrayScaleMorphologicalOperation-closing.xml b/python/plugins/processing/algs/otb/description/5.8.0/GrayScaleMorphologicalOperation-closing.xml deleted file mode 100644 index 5d5e5f146bc8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/GrayScaleMorphologicalOperation-closing.xml +++ /dev/null @@ -1,77 +0,0 @@ - - GrayScaleMorphologicalOperation-closing - otbcli_GrayScaleMorphologicalOperation - GrayScaleMorphologicalOperation (closing) - Feature Extraction - Performs morphological operations on a grayscale input image - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - closing - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/GrayScaleMorphologicalOperation-dilate.xml b/python/plugins/processing/algs/otb/description/5.8.0/GrayScaleMorphologicalOperation-dilate.xml deleted file mode 100644 index 7302c31336de..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/GrayScaleMorphologicalOperation-dilate.xml +++ /dev/null @@ -1,77 +0,0 @@ - - GrayScaleMorphologicalOperation-dilate - otbcli_GrayScaleMorphologicalOperation - GrayScaleMorphologicalOperation (dilate) - Feature Extraction - Performs morphological operations on a grayscale input image - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - dilate - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/GrayScaleMorphologicalOperation-erode.xml b/python/plugins/processing/algs/otb/description/5.8.0/GrayScaleMorphologicalOperation-erode.xml deleted file mode 100644 index 7da86e36fea2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/GrayScaleMorphologicalOperation-erode.xml +++ /dev/null @@ -1,77 +0,0 @@ - - GrayScaleMorphologicalOperation-erode - otbcli_GrayScaleMorphologicalOperation - GrayScaleMorphologicalOperation (erode) - Feature Extraction - Performs morphological operations on a grayscale input image - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - erode - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/GrayScaleMorphologicalOperation-opening.xml b/python/plugins/processing/algs/otb/description/5.8.0/GrayScaleMorphologicalOperation-opening.xml deleted file mode 100644 index e9781f67cab4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/GrayScaleMorphologicalOperation-opening.xml +++ /dev/null @@ -1,77 +0,0 @@ - - GrayScaleMorphologicalOperation-opening - otbcli_GrayScaleMorphologicalOperation - GrayScaleMorphologicalOperation (opening) - Feature Extraction - Performs morphological operations on a grayscale input image - - ParameterRaster - in - Input Image - The input image to be filtered. - False - - - OutputRaster - out - Feature Output Image - Output image containing the filtered output image. - - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - structype - Structuring Element Type - Choice of the structuring element type - - - ball - - - 0 - False - - - ParameterNumber - structype.ball.xradius - The Structuring Element Radius - The Structuring Element Radius - - - 5 - False - - - ParameterSelection - filter - Morphological Operation - Choice of the morphological operation - - - opening - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/HaralickTextureExtraction.xml b/python/plugins/processing/algs/otb/description/5.8.0/HaralickTextureExtraction.xml deleted file mode 100644 index 12a02eeacf11..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/HaralickTextureExtraction.xml +++ /dev/null @@ -1,126 +0,0 @@ - - HaralickTextureExtraction - otbcli_HaralickTextureExtraction - Haralick Texture Extraction - Feature Extraction - Computes textures on every pixel of the input image selected channel - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterNumber - parameters.xrad - X Radius - X Radius - - - 2 - False - - - ParameterNumber - parameters.yrad - Y Radius - Y Radius - - - 2 - False - - - ParameterNumber - parameters.xoff - X Offset - X Offset - - - 1 - False - - - ParameterNumber - parameters.yoff - Y Offset - Y Offset - - - 1 - False - - - ParameterNumber - parameters.min - Image Minimum - Image Minimum - - - 0 - False - - - ParameterNumber - parameters.max - Image Maximum - Image Maximum - - - 255 - False - - - ParameterNumber - parameters.nbbin - Histogram number of bin - Histogram number of bin - - - 8 - False - - - ParameterSelection - texture - Texture Set Selection - Choice of The Texture Set - - - simple - advanced - higher - - - 0 - False - - - OutputRaster - out - Output Image - Output image containing the selected texture features. - - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/HooverCompareSegmentation.xml b/python/plugins/processing/algs/otb/description/5.8.0/HooverCompareSegmentation.xml deleted file mode 100644 index 2646745b3f7e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/HooverCompareSegmentation.xml +++ /dev/null @@ -1,95 +0,0 @@ - - HooverCompareSegmentation - otbcli_HooverCompareSegmentation - Hoover compare segmentation - Segmentation - Compare two segmentations with Hoover metrics - - ParameterRaster - ingt - Input ground truth - A partial ground truth segmentation image. - False - - - ParameterRaster - inms - Input machine segmentation - A machine segmentation image. - False - - - ParameterNumber - bg - Background label - Label value of the background in the input segmentations - - - 0 - False - - - ParameterNumber - th - Overlapping threshold - Overlapping threshold used to find Hoover instances. - - - 0.75 - False - - - OutputRaster - outgt - Colored ground truth output - The colored ground truth output image. - - - - OutputRaster - outms - Colored machine segmentation output - The colored machine segmentation output image. - - - - ParameterNumber - rc - Correct detection score - Overall score for correct detection (RC) - - - 0.0 - False - - - ParameterNumber - rf - Over-segmentation score - Overall score for over segmentation (RF) - - - 0.0 - False - - - ParameterNumber - ra - Under-segmentation score - Overall score for under segmentation (RA) - - - 0.0 - False - - - ParameterNumber - rm - Missed detection score - Overall score for missed detection (RM) - - - 0.0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/ImageClassifier.xml b/python/plugins/processing/algs/otb/description/5.8.0/ImageClassifier.xml deleted file mode 100644 index 555b0eca3e43..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/ImageClassifier.xml +++ /dev/null @@ -1,72 +0,0 @@ - - ImageClassifier - otbcli_ImageClassifier - Image Classification - Learning - Performs a classification of the input image according to a model file. - - ParameterRaster - in - Input Image - The input image to classify. - False - - - ParameterRaster - mask - Input Mask - The mask allows restricting classification of the input image to the area where mask pixel values are greater than 0. - True - - - ParameterFile - model - Model file - A model file (produced by TrainImagesClassifier application, maximal class label = 65535). - - False - - - ParameterFile - imstat - Statistics file - A XML file containing mean and standard deviation to center and reduce samples before classification (produced by ComputeImagesStatistics application). - - True - - - OutputRaster - out - Output Image - Output image containing class labels - - - - OutputRaster - confmap - Confidence map - Confidence map of the produced classification. The confidence index depends on the model : - - LibSVM : difference between the two highest probabilities (needs a model with probability estimates, so that classes probabilities can be computed for each sample) - - OpenCV - * Boost : sum of votes - * DecisionTree : (not supported) - * GradientBoostedTree : (not supported) - * KNearestNeighbors : number of neighbors with the same label - * NeuralNetwork : difference between the two highest responses - * NormalBayes : (not supported) - * RandomForest : Confidence (proportion of votes for the majority class). Margin (normalized difference of the votes of the 2 majority classes) is not available for now. - * SVM : distance to margin (only works for 2-class models) - - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/ImageEnvelope.xml b/python/plugins/processing/algs/otb/description/5.8.0/ImageEnvelope.xml deleted file mode 100644 index fef99dd96131..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/ImageEnvelope.xml +++ /dev/null @@ -1,40 +0,0 @@ - - ImageEnvelope - otbcli_ImageEnvelope - Image Envelope - Geometry - Extracts an image envelope. - - ParameterRaster - in - Input Image - Input image. - False - - - OutputVector - out - Output Vector Data - Vector data file containing the envelope - - - - ParameterNumber - sr - Sampling Rate - Sampling rate for image edges (in pixel) - - - 0 - True - - - ParameterString - proj - Projection - Projection to be used to compute the envelope (default is WGS84) - - - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/KMeansClassification.xml b/python/plugins/processing/algs/otb/description/5.8.0/KMeansClassification.xml deleted file mode 100644 index 9cac46c41172..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/KMeansClassification.xml +++ /dev/null @@ -1,84 +0,0 @@ - - KMeansClassification - otbcli_KMeansClassification - Unsupervised KMeans image classification - Learning - Unsupervised KMeans image classification - - ParameterRaster - in - Input Image - Input image to classify. - False - - - OutputRaster - out - Output Image - Output image containing the class indexes. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterRaster - vm - Validity Mask - Validity mask. Only non-zero pixels will be used to estimate KMeans modes. - True - - - ParameterNumber - ts - Training set size - Size of the training set (in pixels). - - - 100 - True - - - ParameterNumber - nc - Number of classes - Number of modes, which will be used to generate class membership. - - - 5 - False - - - ParameterNumber - maxit - Maximum number of iterations - Maximum number of iterations for the learning step. - - - 1000 - True - - - ParameterNumber - ct - Convergence threshold - Convergence threshold for class centroid (L2 distance, by default 0.0001). - - - 0.0001 - True - - - OutputFile - outmeans - Centroid filename - Output text file containing centroid positions - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/KmzExport.xml b/python/plugins/processing/algs/otb/description/5.8.0/KmzExport.xml deleted file mode 100644 index 57469ba47a5c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/KmzExport.xml +++ /dev/null @@ -1,54 +0,0 @@ - - KmzExport - otbcli_KmzExport - Image to KMZ Export - Miscellaneous - Export the input image in a KMZ product. - - ParameterRaster - in - Input image - Input image - False - - - OutputFile - out - Output .kmz product - Output Kmz product directory (with .kmz extension) - - - ParameterNumber - tilesize - Tile Size - Size of the tiles in the kmz product, in number of pixels (default = 512). - - - 512 - True - - - ParameterRaster - logo - Image logo - Path to the image logo to add to the KMZ product. - True - - - ParameterRaster - legend - Image legend - Path to the image legend to add to the KMZ product. - True - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/LSMSSegmentation.xml b/python/plugins/processing/algs/otb/description/5.8.0/LSMSSegmentation.xml deleted file mode 100644 index ceb358d940ea..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/LSMSSegmentation.xml +++ /dev/null @@ -1,94 +0,0 @@ - - LSMSSegmentation - otbcli_LSMSSegmentation - Exact Large-Scale Mean-Shift segmentation, step 2 - Segmentation - Second step of the exact Large-Scale Mean-Shift segmentation workflow. - - ParameterRaster - in - Filtered image - The filtered image (cf. Adaptive MeanShift Smoothing application). - False - - - ParameterRaster - inpos - Spatial image - The spatial image. Spatial input is the displacement map (output of the Adaptive MeanShift Smoothing application). - True - - - OutputRaster - out - Output Image - The output image. The output image is the segmentation of the filtered image. It is recommended to set the pixel type to uint32. - - - - ParameterNumber - spatialr - Spatial radius - Spatial radius of the neighborhood. - - - 5 - True - - - ParameterNumber - ranger - Range radius - Range radius defining the radius (expressed in radiometry unit) in the multi-spectral space. - - - 15 - True - - - ParameterNumber - minsize - Minimum Region Size - Minimum Region Size. If, after the segmentation, a region is of size lower than this criterion, the region is deleted. - - - 0 - True - - - ParameterNumber - tilesizex - Size of tiles in pixel (X-axis) - Size of tiles along the X-axis. - - - 500 - False - - - ParameterNumber - tilesizey - Size of tiles in pixel (Y-axis) - Size of tiles along the Y-axis. - - - 500 - False - - - ParameterFile - tmpdir - Directory where to write temporary files - This applications need to write temporary files for each tile. This parameter allows choosing the path where to write those files. If disabled, the current path will be used. - - True - - - ParameterBoolean - cleanup - Temporary files cleaning - If activated, the application will try to clean all temporary files it created - True - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/LSMSSmallRegionsMerging.xml b/python/plugins/processing/algs/otb/description/5.8.0/LSMSSmallRegionsMerging.xml deleted file mode 100644 index 5962f4cbb2e8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/LSMSSmallRegionsMerging.xml +++ /dev/null @@ -1,68 +0,0 @@ - - LSMSSmallRegionsMerging - otbcli_LSMSSmallRegionsMerging - Exact Large-Scale Mean-Shift segmentation, step 3 (optional) - Segmentation - Third (optional) step of the exact Large-Scale Mean-Shift segmentation workflow. - - ParameterRaster - in - Input image - The input image. - False - - - ParameterRaster - inseg - Segmented image - The segmented image input. Segmented image input is the segmentation of the input image. - False - - - OutputRaster - out - Output Image - The output image. The output image is the input image where the minimal regions have been merged. - - - - ParameterNumber - minsize - Minimum Region Size - Minimum Region Size. If, after the segmentation, a region is of size lower than this criterion, the region is merged with the "nearest" region (radiometrically). - - - 50 - True - - - ParameterNumber - tilesizex - Size of tiles in pixel (X-axis) - Size of tiles along the X-axis. - - - 500 - False - - - ParameterNumber - tilesizey - Size of tiles in pixel (Y-axis) - Size of tiles along the Y-axis. - - - 500 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/LSMSVectorization.xml b/python/plugins/processing/algs/otb/description/5.8.0/LSMSVectorization.xml deleted file mode 100644 index 962d5d2d9411..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/LSMSVectorization.xml +++ /dev/null @@ -1,57 +0,0 @@ - - LSMSVectorization - otbcli_LSMSVectorization - Exact Large-Scale Mean-Shift segmentation, step 4 - Segmentation - Fourth step of the exact Large-Scale Mean-Shift segmentation workflow. - - ParameterRaster - in - Input Image - The input image. - False - - - ParameterRaster - inseg - Segmented image - The segmented image input. Segmented image input is the segmentation of the input image. - False - - - OutputVector - out - Output GIS vector file - The output GIS vector file, representing the vectorized version of the segmented image where the features of the polygons are the radiometric means and variances. - - - ParameterNumber - tilesizex - Size of tiles in pixel (X-axis) - Size of tiles along the X-axis. - - - 500 - False - - - ParameterNumber - tilesizey - Size of tiles in pixel (Y-axis) - Size of tiles along the Y-axis. - - - 500 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/LineSegmentDetection.xml b/python/plugins/processing/algs/otb/description/5.8.0/LineSegmentDetection.xml deleted file mode 100644 index 065723c0de70..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/LineSegmentDetection.xml +++ /dev/null @@ -1,39 +0,0 @@ - - LineSegmentDetection - otbcli_LineSegmentDetection - Line segment detection - Feature Extraction - Detect line segments in raster - - ParameterRaster - in - Input Image - Input image on which lines will be detected. - False - - - OutputVector - out - Output Detected lines - Output detected line segments (vector data). - - - - ParameterBoolean - norescale - No rescaling in [0, 255] - By default, the input image amplitude is rescaled between [0,255]. Turn on this parameter to skip rescaling - True - True - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/LocalStatisticExtraction.xml b/python/plugins/processing/algs/otb/description/5.8.0/LocalStatisticExtraction.xml deleted file mode 100644 index 663bd63bd3e9..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/LocalStatisticExtraction.xml +++ /dev/null @@ -1,51 +0,0 @@ - - LocalStatisticExtraction - otbcli_LocalStatisticExtraction - Local Statistic Extraction - Feature Extraction - Computes local statistical moments on every pixel in the selected channel of the input image - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterNumber - radius - Neighborhood radius - The computational window radius. - - - 3 - False - - - OutputRaster - out - Feature Output Image - Output image containing the local statistical moments. - - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/ManageNoData.xml b/python/plugins/processing/algs/otb/description/5.8.0/ManageNoData.xml deleted file mode 100644 index 01aa54e94416..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/ManageNoData.xml +++ /dev/null @@ -1,101 +0,0 @@ - - ManageNoData - otbcli_ManageNoData - No Data management - Image Manipulation - Manage No-Data - - ParameterRaster - in - Input image - Input image - False - - - OutputRaster - out - Output Image - Output image - - - - ParameterBoolean - usenan - Consider NaN as no-data - If active, the application will consider NaN as no-data values as well - True - True - - - ParameterSelection - mode - No-data handling mode - Allows choosing between different no-data handling options - - - buildmask - changevalue - apply - - - 0 - False - - - ParameterNumber - mode.buildmask.inv - Inside Value - Value given in the output mask to pixels that are not no data pixels - - - 1 - False - - - ParameterNumber - mode.buildmask.outv - Outside Value - Value given in the output mask to pixels that are no data pixels - - - 0 - False - - - ParameterNumber - mode.changevalue.newv - The new no-data value - The new no-data value - - - 0 - False - - - ParameterRaster - mode.apply.mask - Mask image - Mask to be applied on input image (valid pixels have non null values) - False - - - ParameterNumber - mode.apply.ndval - Nodata value used - No Data value used according to the mask image - - - 0 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/MeanShiftSmoothing.xml b/python/plugins/processing/algs/otb/description/5.8.0/MeanShiftSmoothing.xml deleted file mode 100644 index 8b4376573e39..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/MeanShiftSmoothing.xml +++ /dev/null @@ -1,96 +0,0 @@ - - MeanShiftSmoothing - otbcli_MeanShiftSmoothing - Exact Large-Scale Mean-Shift segmentation, step 1 (smoothing) - Image Filtering - Perform mean shift filtering - - ParameterRaster - in - Input Image - The input image. - False - - - OutputRaster - fout - Filtered output - The filtered output image. - - - - OutputRaster - foutpos - Spatial image - The spatial image output. Spatial image output is a displacement map (pixel position after convergence). - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterNumber - spatialr - Spatial radius - Spatial radius of the neighborhood. - - - 5 - True - - - ParameterNumber - ranger - Range radius - Range radius defining the radius (expressed in radiometry unit) in the multi-spectral space. - - - 15 - True - - - ParameterNumber - thres - Mode convergence threshold - Algorithm iterative scheme will stop if mean-shift vector is below this threshold or if iteration number reached maximum number of iterations. - - - 0.1 - True - - - ParameterNumber - maxiter - Maximum number of iterations - Algorithm iterative scheme will stop if convergence hasn't been reached after the maximum number of iterations. - - - 100 - True - - - ParameterNumber - rangeramp - Range radius coefficient - This coefficient makes dependent the ranger of the colorimetry of the filtered pixel : y = rangeramp*x+ranger. - - - 0 - True - - - ParameterBoolean - modesearch - Mode search. - If activated pixel iterative convergence is stopped if the path crosses an already converged pixel. Be careful, with this option, the result will slightly depend on thread number - True - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/MultiImageSamplingRate.xml b/python/plugins/processing/algs/otb/description/5.8.0/MultiImageSamplingRate.xml deleted file mode 100644 index 3391907bca08..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/MultiImageSamplingRate.xml +++ /dev/null @@ -1,89 +0,0 @@ - - MultiImageSamplingRate - otbcli_MultiImageSamplingRate - Multi-image sampling rate estimation - Learning - Compute sampling rate for an input set of images. - - ParameterMultipleInput - il - Input statistics - List of statistics files for each input image. - - False - - - OutputFile - out - Output sampling rates - Output filename storing sampling rates (CSV format with class name, required samples, total samples, and rate). The given filename will be used with a suffix to indicate the corresponding input index (for instance: rates.csv will give rates_1.csv, rates_2.csv, ...). - - - ParameterSelection - strategy - Sampling strategy - - - - byclass - constant - smallest - percent - total - all - - - 2 - False - - - ParameterMultipleInput - strategy.byclass.in - Number of samples by class - Number of samples by class (CSV format with class name in 1st column and required samples in the 2nd).In the case of the custom multi-image mode, several inputs may be given for each image. - - False - - - ParameterString - strategy.constant.nb - Number of samples for all classes - Number of samples for all classes.In the case of the custom multi-image mode, several values can be given for each image. - - - False - - - ParameterString - strategy.percent.p - The percentage(s) to use - The percentage(s) to use In the case of the custom multi-image mode, several values can be given for each image. - - - False - - - ParameterString - strategy.total.v - The number of samples to generate - The number of samples to generateIn the case of the custom multi-image mode, several values can be given for each image. - - - False - - - ParameterSelection - mim - Multi-Image Mode - - - - proportional - equal - custom - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/MultivariateAlterationDetector.xml b/python/plugins/processing/algs/otb/description/5.8.0/MultivariateAlterationDetector.xml deleted file mode 100644 index 3fa140a5e78d..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/MultivariateAlterationDetector.xml +++ /dev/null @@ -1,38 +0,0 @@ - - MultivariateAlterationDetector - otbcli_MultivariateAlterationDetector - Multivariate alteration detector - Feature Extraction - Multivariate Alteration Detector - - ParameterRaster - in1 - Input Image 1 - Image which describe initial state of the scene. - False - - - ParameterRaster - in2 - Input Image 2 - Image which describe scene after perturbations. - False - - - OutputRaster - out - Change Map - Image of detected changes. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/OGRLayerClassifier.xml b/python/plugins/processing/algs/otb/description/5.8.0/OGRLayerClassifier.xml deleted file mode 100644 index c9a288732461..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/OGRLayerClassifier.xml +++ /dev/null @@ -1,47 +0,0 @@ - - OGRLayerClassifier - otbcli_OGRLayerClassifier - OGRLayerClassifier - Segmentation - Classify an OGR layer based on a machine learning model and a list of features to consider. - - ParameterVector - inshp - Name of the input shapefile - Name of the input shapefile - - False - - - ParameterFile - instats - XML file containing mean and variance of each feature. - XML file containing mean and variance of each feature. - - False - - - OutputFile - insvm - Input model filename. - Input model filename. - - - ParameterString - feat - Features - Features to be calculated - - - False - - - ParameterString - cfield - Field containing the predicted class. - Field containing the predicted class - predicted - - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/OpticalCalibration.xml b/python/plugins/processing/algs/otb/description/5.8.0/OpticalCalibration.xml deleted file mode 100644 index b8871948814c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/OpticalCalibration.xml +++ /dev/null @@ -1,180 +0,0 @@ - - OpticalCalibration - otbcli_OpticalCalibration - Optical calibration - Calibration - Perform optical calibration TOA/TOC (Top Of Atmosphere/Top Of Canopy). Supported sensors: QuickBird, Ikonos, WorldView2, Formosat, Spot5, Pleiades, Spot6, Spot7. For other sensors the application also allows providing calibration parameters manually. - - ParameterRaster - in - Input - Input image filename (values in DN) - False - - - OutputRaster - out - Output - Output calibrated image filename - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - level - Calibration Level - - - - toa - toatoim - - - 0 - False - - - ParameterNumber - acqui.minute - Minute - Minute (0-59) - - - 0 - False - - - ParameterNumber - acqui.hour - Hour - Hour (0-23) - - - 12 - False - - - ParameterNumber - acqui.day - Day - Day (1-31) - - - 1 - False - - - ParameterNumber - acqui.month - Month - Month (1-12) - - - 1 - False - - - ParameterNumber - acqui.year - Year - Year - - - 2000 - False - - - ParameterNumber - acqui.sun.elev - Sun elevation angle (deg) - Sun elevation angle (in degrees) - - - 90 - False - - - ParameterNumber - acqui.sun.azim - Sun azimuth angle (deg) - Sun azimuth angle (in degrees) - - - 0 - False - - - ParameterNumber - acqui.view.elev - Viewing elevation angle (deg) - Viewing elevation angle (in degrees) - - - 90 - False - - - ParameterNumber - acqui.view.azim - Viewing azimuth angle (deg) - Viewing azimuth angle (in degrees) - - - 0 - False - - - ParameterFile - acqui.gainbias - Gains | biases - Gains | biases - - True - - - ParameterFile - acqui.solarilluminations - Solar illuminations - Solar illuminations (one value per band) - - True - - - ParameterFile - atmo.rsr - Relative Spectral Response File - Sensor relative spectral response file -By default the application gets this information in the metadata - - True - - - ParameterNumber - atmo.radius - Window radius (adjacency effects) - Window radius for adjacency effects correctionsSetting this parameters will enable the correction ofadjacency effects - - - 2 - True - - - ParameterNumber - atmo.pixsize - Pixel size (in km) - Pixel size (in km )used tocompute adjacency effects, it doesn't have tomatch the image spacing - - - 1 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/OrthoRectification-epsg.xml b/python/plugins/processing/algs/otb/description/5.8.0/OrthoRectification-epsg.xml deleted file mode 100644 index feac1abc7c86..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/OrthoRectification-epsg.xml +++ /dev/null @@ -1,124 +0,0 @@ - - OrthoRectification-epsg - otbcli_OrthoRectification - OrthoRectification (epsg) - Geometry - This application allows ortho-rectification of optical images from supported sensors. - - - ParameterRaster - io.in - Input Image - The input image to ortho-rectify - False - - - OutputRaster - io.out - Output Image - The ortho-rectified output image - - - - ParameterSelection - map - Output Cartographic Map Projection - Parameters of the output map projection to be used. - - - epsg - - - 0 - False - - - ParameterNumber - map.epsg.code - EPSG Code - See www.spatialreference.org to find which EPSG code is associated to your projection - - - 4326 - False - - - ParameterSelection - outputs.mode - Parameters estimation modes - - - - autosize - autospacing - - - 0 - False - - - ParameterNumber - outputs.default - Default pixel value - Default value to write when outside of input image. - - - 0 - True - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows one to define how the input image will be interpolated during resampling. - - - bco - nn - linear - - - 0 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows one to control the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - False - - - ParameterNumber - opt.ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - True - - - ParameterNumber - opt.gridspacing - Resampling grid spacing - Resampling is done according to a coordinate mapping deformation grid, whose pixel size is set by this parameter, and expressed in the coordinate system of the output image The closer to the output spacing this parameter is, the more precise will be the ortho-rectified image,but increasing this parameter will reduce processing time. - - - 4 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/OrthoRectification-fit-to-ortho.xml b/python/plugins/processing/algs/otb/description/5.8.0/OrthoRectification-fit-to-ortho.xml deleted file mode 100644 index 68d3fd51b22d..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/OrthoRectification-fit-to-ortho.xml +++ /dev/null @@ -1,107 +0,0 @@ - - OrthoRectification-fit-to-ortho - otbcli_OrthoRectification - OrthoRectification (fit-to-ortho) - Geometry - This application allows ortho-rectification of optical images from supported sensors. - - - ParameterRaster - io.in - Input Image - The input image to ortho-rectify - False - - - OutputRaster - io.out - Output Image - The ortho-rectified output image - - - - ParameterSelection - outputs.mode - Parameters estimation modes - - - - orthofit - - - 0 - False - - - ParameterRaster - outputs.ortho - Model ortho-image - A model ortho-image that can be used to compute size, origin and spacing of the output - True - - - ParameterNumber - outputs.default - Default pixel value - Default value to write when outside of input image. - - - 0 - True - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows one to define how the input image will be interpolated during resampling. - - - bco - nn - linear - - - 0 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows one to control the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - False - - - ParameterNumber - opt.ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - True - - - ParameterNumber - opt.gridspacing - Resampling grid spacing - Resampling is done according to a coordinate mapping deformation grid, whose pixel size is set by this parameter, and expressed in the coordinate system of the output image The closer to the output spacing this parameter is, the more precise will be the ortho-rectified image,but increasing this parameter will reduce processing time. - - - 4 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/OrthoRectification-lambert-WGS84.xml b/python/plugins/processing/algs/otb/description/5.8.0/OrthoRectification-lambert-WGS84.xml deleted file mode 100644 index 4ba3128152a4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/OrthoRectification-lambert-WGS84.xml +++ /dev/null @@ -1,116 +0,0 @@ - - OrthoRectification-lambert-WGS84 - otbcli_OrthoRectification - OrthoRectification (lambert-WGS84) - Geometry - This application allows ortho-rectification of optical images from supported sensors. - - - ParameterRaster - io.in - Input Image - The input image to ortho-rectify - False - - - OutputRaster - io.out - Output Image - The ortho-rectified output image - - - - ParameterSelection - map - Output Cartographic Map Projection - Parameters of the output map projection to be used. - - - lambert2 - lambert93 - wgs - - - 0 - False - - - ParameterSelection - outputs.mode - Parameters estimation modes - - - - autosize - autospacing - - - 0 - False - - - ParameterNumber - outputs.default - Default pixel value - Default value to write when outside of input image. - - - 0 - True - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows one to define how the input image will be interpolated during resampling. - - - bco - nn - linear - - - 0 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows one to control the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - False - - - ParameterNumber - opt.ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - True - - - ParameterNumber - opt.gridspacing - Resampling grid spacing - Resampling is done according to a coordinate mapping deformation grid, whose pixel size is set by this parameter, and expressed in the coordinate system of the output image The closer to the output spacing this parameter is, the more precise will be the ortho-rectified image,but increasing this parameter will reduce processing time. - - - 4 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/OrthoRectification-utm.xml b/python/plugins/processing/algs/otb/description/5.8.0/OrthoRectification-utm.xml deleted file mode 100644 index a5a76aa661db..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/OrthoRectification-utm.xml +++ /dev/null @@ -1,132 +0,0 @@ - - OrthoRectification-utm - otbcli_OrthoRectification - OrthoRectification (utm) - Geometry - This application allows ortho-rectification of optical images from supported sensors. - - - ParameterRaster - io.in - Input Image - The input image to ortho-rectify - False - - - OutputRaster - io.out - Output Image - The ortho-rectified output image - - - - ParameterSelection - map - Output Cartographic Map Projection - Parameters of the output map projection to be used. - - - utm - - - 0 - False - - - ParameterNumber - map.utm.zone - Zone number - The zone number ranges from 1 to 60 and allows defining the transverse mercator projection (along with the hemisphere) - - - 31 - False - - - ParameterBoolean - map.utm.northhem - Northern Hemisphere - The transverse mercator projections are defined by their zone number as well as the hemisphere. Activate this parameter if your image is in the northern hemisphere. - True - True - - - ParameterSelection - outputs.mode - Parameters estimation modes - - - - autosize - autospacing - - - 0 - False - - - ParameterNumber - outputs.default - Default pixel value - Default value to write when outside of input image. - - - 0 - True - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows one to define how the input image will be interpolated during resampling. - - - bco - nn - linear - - - 0 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows one to control the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - False - - - ParameterNumber - opt.ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - True - - - ParameterNumber - opt.gridspacing - Resampling grid spacing - Resampling is done according to a coordinate mapping deformation grid, whose pixel size is set by this parameter, and expressed in the coordinate system of the output image The closer to the output spacing this parameter is, the more precise will be the ortho-rectified image,but increasing this parameter will reduce processing time. - - - 4 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/Pansharpening-bayes.xml b/python/plugins/processing/algs/otb/description/5.8.0/Pansharpening-bayes.xml deleted file mode 100644 index 9b45d08e9e73..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/Pansharpening-bayes.xml +++ /dev/null @@ -1,71 +0,0 @@ - - Pansharpening-bayes - otbcli_Pansharpening - Pansharpening (bayes) - Geometry - Perform P+XS pansharpening - - ParameterRaster - inp - Input PAN Image - Input panchromatic image. - False - - - ParameterRaster - inxs - Input XS Image - Input XS image. - False - - - OutputRaster - out - Output image - Output image. - - - - ParameterSelection - method - Algorithm - Selection of the pan-sharpening method. - - - bayes - - - 0 - False - - - ParameterNumber - method.bayes.lambda - Weight - Set the weighting value. - - - 0.9999 - False - - - ParameterNumber - method.bayes.s - S coefficient - Set the S coefficient. - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/Pansharpening-lmvm.xml b/python/plugins/processing/algs/otb/description/5.8.0/Pansharpening-lmvm.xml deleted file mode 100644 index fd6d171f58b9..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/Pansharpening-lmvm.xml +++ /dev/null @@ -1,71 +0,0 @@ - - Pansharpening-lmvm - otbcli_Pansharpening - Pansharpening (lmvm) - Geometry - Perform P+XS pansharpening - - ParameterRaster - inp - Input PAN Image - Input panchromatic image. - False - - - ParameterRaster - inxs - Input XS Image - Input XS image. - False - - - OutputRaster - out - Output image - Output image. - - - - ParameterSelection - method - Algorithm - Selection of the pan-sharpening method. - - - lmvm - - - 0 - False - - - ParameterNumber - method.lmvm.radiusx - X radius - Set the x radius of the sliding window. - - - 3 - False - - - ParameterNumber - method.lmvm.radiusy - Y radius - Set the y radius of the sliding window. - - - 3 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/Pansharpening-rcs.xml b/python/plugins/processing/algs/otb/description/5.8.0/Pansharpening-rcs.xml deleted file mode 100644 index d8b9c1bc8480..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/Pansharpening-rcs.xml +++ /dev/null @@ -1,51 +0,0 @@ - - Pansharpening-rcs - otbcli_Pansharpening - Pansharpening (rcs) - Geometry - Perform P+XS pansharpening - - ParameterRaster - inp - Input PAN Image - Input panchromatic image. - False - - - ParameterRaster - inxs - Input XS Image - Input XS image. - False - - - OutputRaster - out - Output image - Output image. - - - - ParameterSelection - method - Algorithm - Selection of the pan-sharpening method. - - - rcs - - - 0 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/PolygonClassStatistics.xml b/python/plugins/processing/algs/otb/description/5.8.0/PolygonClassStatistics.xml deleted file mode 100644 index 3a5e2f294daa..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/PolygonClassStatistics.xml +++ /dev/null @@ -1,64 +0,0 @@ - - PolygonClassStatistics - otbcli_PolygonClassStatistics - Polygon Class Statistics - Learning - Computes statistics on a training polygon set. - - ParameterRaster - in - InputImage - Support image that will be classified - False - - - ParameterRaster - mask - InputMask - Validity mask (only pixels corresponding to a mask value greater than 0 will be used for statistics) - True - - - ParameterFile - vec - Input vectors - Input geometries to analyse - - False - - - OutputFile - out - Output Statistics - Output file to store statistics (XML format) - - - ParameterString - field - Field Name - Name of the field carrying the class name in the input vectors. - class - - True - - - ParameterNumber - layer - Layer Index - Layer index to read in the input vector file. - - - 0 - True - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/PredictRegression.xml b/python/plugins/processing/algs/otb/description/5.8.0/PredictRegression.xml deleted file mode 100644 index 56ccb8b1d080..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/PredictRegression.xml +++ /dev/null @@ -1,54 +0,0 @@ - - PredictRegression - otbcli_PredictRegression - Predict Regression - Learning - Performs a prediction of the input image according to a regression model file. - - ParameterRaster - in - Input Image - The input image to predict. - False - - - ParameterRaster - mask - Input Mask - The mask allow restricting classification of the input image to the area where mask pixel values are greater than 0. - True - - - ParameterFile - model - Model file - A regression model file (produced by TrainRegression application). - - False - - - ParameterFile - imstat - Statistics file - A XML file containing mean and standard deviation to center and reduce samples before prediction (produced by ComputeImagesStatistics application). If this file containsone more band than the sample size, the last stat of last band will beapplied to expand the output predicted value - - True - - - OutputRaster - out - Output Image - Output image containing predicted values - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/RadiometricIndices.xml b/python/plugins/processing/algs/otb/description/5.8.0/RadiometricIndices.xml deleted file mode 100644 index 41aa91db8123..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/RadiometricIndices.xml +++ /dev/null @@ -1,131 +0,0 @@ - - RadiometricIndices - otbcli_RadiometricIndices - Radiometric Indices - Feature Extraction - Compute radiometric indices. - - ParameterRaster - in - Input Image - Input image - False - - - OutputRaster - out - Output Image - Radiometric indices output image - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterNumber - channels.blue - Blue Channel - Blue channel index - - - 1 - False - - - ParameterNumber - channels.green - Green Channel - Green channel index - - - 1 - False - - - ParameterNumber - channels.red - Red Channel - Red channel index - - - 1 - False - - - ParameterNumber - channels.nir - NIR Channel - NIR channel index - - - 1 - False - - - ParameterNumber - channels.mir - Mir Channel - Mir channel index - - - 1 - False - - - ParameterSelection - list - Available Radiometric Indices - List of available radiometric indices with their relevant channels in brackets: - Vegetation:NDVI - Normalized difference vegetation index (Red, NIR) - Vegetation:TNDVI - Transformed normalized difference vegetation index (Red, NIR) - Vegetation:RVI - Ratio vegetation index (Red, NIR) - Vegetation:SAVI - Soil adjusted vegetation index (Red, NIR) - Vegetation:TSAVI - Transformed soil adjusted vegetation index (Red, NIR) - Vegetation:MSAVI - Modified soil adjusted vegetation index (Red, NIR) - Vegetation:MSAVI2 - Modified soil adjusted vegetation index 2 (Red, NIR) - Vegetation:GEMI - Global environment monitoring index (Red, NIR) - Vegetation:IPVI - Infrared percentage vegetation index (Red, NIR) - - Water:NDWI - Normalized difference water index (Gao 1996) (NIR, MIR) - Water:NDWI2 - Normalized difference water index (Mc Feeters 1996) (Green, NIR) - Water:MNDWI - Modified normalized difference water index (Xu 2006) (Green, MIR) - Water:NDPI - Normalized difference pond index (Lacaux et al.) (MIR, Green) - Water:NDTI - Normalized difference turbidity index (Lacaux et al.) (Red, Green) - - Soil:RI - Redness index (Red, Green) - Soil:CI - Color index (Red, Green) - Soil:BI - Brightness index (Red, Green) - Soil:BI2 - Brightness index 2 (NIR, Red, Green) - - - ndvi - tndvi - rvi - savi - tsavi - msavi - msavi2 - gemi - ipvi - ndwi - ndwi2 - mndwi - ndpi - ndti - ri - ci - bi - bi2 - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/Rasterization-image.xml b/python/plugins/processing/algs/otb/description/5.8.0/Rasterization-image.xml deleted file mode 100644 index 7303ef1d8eae..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/Rasterization-image.xml +++ /dev/null @@ -1,82 +0,0 @@ - - Rasterization-image - otbcli_Rasterization - Rasterization (image) - Vector Data Manipulation - Rasterize a vector dataset. - - ParameterVector - in - Input vector dataset - The input vector dataset to be rasterized - - False - - - OutputRaster - out - Output image - An output image containing the rasterized vector dataset - - - - ParameterRaster - im - Input reference image - A reference image from which to import output grid and projection reference system information. - True - - - ParameterNumber - background - Background value - Default value for pixels not belonging to any geometry - - - 0 - False - - - ParameterSelection - mode - Rasterization mode - Choice of rasterization modes - - - binary - attribute - - - 0 - False - - - ParameterNumber - mode.binary.foreground - Foreground value - Value for pixels inside a geometry - - - 255 - False - - - ParameterString - mode.attribute.field - The attribute field to burn - Name of the attribute field to burn - DN - - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/Rasterization-manual.xml b/python/plugins/processing/algs/otb/description/5.8.0/Rasterization-manual.xml deleted file mode 100644 index c607811994eb..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/Rasterization-manual.xml +++ /dev/null @@ -1,145 +0,0 @@ - - Rasterization-manual - otbcli_Rasterization - Rasterization (manual) - Vector Data Manipulation - Rasterize a vector dataset. - - ParameterVector - in - Input vector dataset - The input vector dataset to be rasterized - - False - - - OutputRaster - out - Output image - An output image containing the rasterized vector dataset - - - - ParameterNumber - szx - Output size x - Output size along x axis (useless if support image is given) - - - 0 - True - - - ParameterNumber - szy - Output size y - Output size along y axis (useless if support image is given) - - - 0 - True - - - ParameterNumber - epsg - Output EPSG code - EPSG code for the output projection reference system (EPSG 4326 for WGS84, 32631 for UTM31N...,useless if support image is given) - - - 0 - True - - - ParameterNumber - orx - Output Upper-left x - Output upper-left corner x coordinate (useless if support image is given) - - - 0.0 - True - - - ParameterNumber - ory - Output Upper-left y - Output upper-left corner y coordinate (useless if support image is given) - - - 0.0 - True - - - ParameterNumber - spx - Spacing (GSD) x - Spacing (ground sampling distance) along x axis (useless if support image is given) - - - 0.0 - True - - - ParameterNumber - spy - Spacing (GSD) y - Spacing (ground sampling distance) along y axis (useless if support image is given) - - - 0.0 - True - - - ParameterNumber - background - Background value - Default value for pixels not belonging to any geometry - - - 0 - False - - - ParameterSelection - mode - Rasterization mode - Choice of rasterization modes - - - binary - attribute - - - 0 - False - - - ParameterNumber - mode.binary.foreground - Foreground value - Value for pixels inside a geometry - - - 255 - False - - - ParameterString - mode.attribute.field - The attribute field to burn - Name of the attribute field to burn - DN - - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/ReadImageInfo.xml b/python/plugins/processing/algs/otb/description/5.8.0/ReadImageInfo.xml deleted file mode 100644 index 0e246b613137..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/ReadImageInfo.xml +++ /dev/null @@ -1,58 +0,0 @@ - - ReadImageInfo - otbcli_ReadImageInfo - Read image information - Image Manipulation - Get information about the image - - ParameterRaster - in - Input Image - Input image to analyse - False - - - ParameterBoolean - keywordlist - Display the OSSIM keywordlist - Output the OSSIM keyword list. It contains metadata information (sensor model, geometry ). Information is stored in keyword list (pairs of key/value) - True - True - - - ParameterString - gcp.ids - GCPs Id - GCPs identifier - - - False - - - ParameterString - gcp.info - GCPs Info - GCPs Information - - - False - - - ParameterString - gcp.imcoord - GCPs Image Coordinates - GCPs Image coordinates - - - False - - - ParameterString - gcp.geocoord - GCPs Geographic Coordinates - GCPs Geographic Coordinates - - - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/Rescale.xml b/python/plugins/processing/algs/otb/description/5.8.0/Rescale.xml deleted file mode 100644 index 6ae441121fea..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/Rescale.xml +++ /dev/null @@ -1,51 +0,0 @@ - - Rescale - otbcli_Rescale - Rescale Image - Image Manipulation - Rescale the image between two given values. - - ParameterRaster - in - Input Image - The image to scale. - False - - - OutputRaster - out - Output Image - The rescaled image filename. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterNumber - outmin - Output min value - Minimum value of the output image. - - - 0 - True - - - ParameterNumber - outmax - Output max value - Maximum value of the output image. - - - 255 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/RigidTransformResample-id.xml b/python/plugins/processing/algs/otb/description/5.8.0/RigidTransformResample-id.xml deleted file mode 100644 index f0b20563d594..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/RigidTransformResample-id.xml +++ /dev/null @@ -1,89 +0,0 @@ - - RigidTransformResample-id - otbcli_RigidTransformResample - RigidTransformResample (id) - Geometry - Resample an image with a rigid transform - - ParameterRaster - in - Input image - The input image to translate. - False - - - OutputRaster - out - Output image - The transformed output image. - - - - ParameterSelection - transform.type - Type of transformation - Type of transformation. Available transformations are spatial scaling, translation and rotation with scaling factor - - - id - - - 0 - False - - - ParameterNumber - transform.type.id.scalex - X scaling - Scaling factor between the output X spacing and the input X spacing - - - 1 - False - - - ParameterNumber - transform.type.id.scaley - Y scaling - Scaling factor between the output Y spacing and the input Y spacing - - - 1 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows one to define how the input image will be interpolated during resampling. - - - nn - linear - bco - - - 2 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows controlling the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - False - - - ParameterNumber - ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/RigidTransformResample-rotation.xml b/python/plugins/processing/algs/otb/description/5.8.0/RigidTransformResample-rotation.xml deleted file mode 100644 index ac2baad02aa2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/RigidTransformResample-rotation.xml +++ /dev/null @@ -1,99 +0,0 @@ - - RigidTransformResample-rotation - otbcli_RigidTransformResample - RigidTransformResample (rotation) - Geometry - Resample an image with a rigid transform - - ParameterRaster - in - Input image - The input image to translate. - False - - - OutputRaster - out - Output image - The transformed output image. - - - - ParameterSelection - transform.type - Type of transformation - Type of transformation. Available transformations are spatial scaling, translation and rotation with scaling factor - - - rotation - - - 0 - False - - - ParameterNumber - transform.type.rotation.angle - Rotation angle - The rotation angle in degree (values between -180 and 180) - - - 0 - False - - - ParameterNumber - transform.type.rotation.scalex - X scaling - Scale factor between the X spacing of the rotated output image and the X spacing of the unrotated image - - - 1 - False - - - ParameterNumber - transform.type.rotation.scaley - Y scaling - Scale factor between the Y spacing of the rotated output image and the Y spacing of the unrotated image - - - 1 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows one to define how the input image will be interpolated during resampling. - - - nn - linear - bco - - - 2 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows controlling the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - False - - - ParameterNumber - ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/RigidTransformResample-translation.xml b/python/plugins/processing/algs/otb/description/5.8.0/RigidTransformResample-translation.xml deleted file mode 100644 index 3d0700a4c494..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/RigidTransformResample-translation.xml +++ /dev/null @@ -1,109 +0,0 @@ - - RigidTransformResample-translation - otbcli_RigidTransformResample - RigidTransformResample (translation) - Geometry - Resample an image with a rigid transform - - ParameterRaster - in - Input image - The input image to translate. - False - - - OutputRaster - out - Output image - The transformed output image. - - - - ParameterSelection - transform.type - Type of transformation - Type of transformation. Available transformations are spatial scaling, translation and rotation with scaling factor - - - translation - - - 0 - False - - - ParameterNumber - transform.type.translation.tx - The X translation (in physical units) - The translation value along X axis (in physical units). - - - 0 - False - - - ParameterNumber - transform.type.translation.ty - The Y translation (in physical units) - The translation value along Y axis (in physical units) - - - 0 - False - - - ParameterNumber - transform.type.translation.scalex - X scaling - Scaling factor between the output X spacing and the input X spacing - - - 1 - False - - - ParameterNumber - transform.type.translation.scaley - Y scaling - Scaling factor between the output Y spacing and the input Y spacing - - - 1 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows one to define how the input image will be interpolated during resampling. - - - nn - linear - bco - - - 2 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows controlling the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - False - - - ParameterNumber - ram - Available RAM (Mb) - This allows setting the maximum amount of RAM available for processing. As the writing task is time consuming, it is better to write large pieces of data, which can be achieved by increasing this parameter (pay attention to your system capabilities) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/SARCalibration.xml b/python/plugins/processing/algs/otb/description/5.8.0/SARCalibration.xml deleted file mode 100644 index dc3780429458..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/SARCalibration.xml +++ /dev/null @@ -1,56 +0,0 @@ - - SARCalibration - otbcli_SARCalibration - SAR Radiometric calibration - Calibration - Perform radiometric calibration of SAR images. Following sensors are supported: TerraSAR-X, Sentinel1 and Radarsat-2.Both Single Look Complex(SLC) and detected products are supported as input. - - - ParameterRaster - in - Input Image - Input complex image - False - - - OutputRaster - out - Output Image - Output calibrated image. This image contains the backscatter (sigmaNought) of the input image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterBoolean - noise - Disable Noise - Flag to disable noise. For 5.2.0 release, the noise values are only read by TerraSARX product. - True - True - - - ParameterSelection - lut - Lookup table sigma /gamma/ beta/ DN. - Lookup table values are not available with all SAR products. Products that provide lookup table with metadata are: Sentinel1, Radarsat2. - - - sigma - gamma - beta - dn - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/SARDecompositions.xml b/python/plugins/processing/algs/otb/description/5.8.0/SARDecompositions.xml deleted file mode 100644 index 97c97241e9be..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/SARDecompositions.xml +++ /dev/null @@ -1,78 +0,0 @@ - - SARDecompositions - otbcli_SARDecompositions - SARDecompositions - Miscellaneous - From one-band complex images (each one related to an element of the Sinclair matrix), returns the selected decomposition. - - ParameterRaster - inhh - Input Image - Input image (HH) - False - - - ParameterRaster - inhv - Input Image - Input image (HV) - True - - - ParameterRaster - invh - Input Image - Input image (VH) - True - - - ParameterRaster - invv - Input Image - Input image (VV) - False - - - OutputRaster - out - Output Image - Output image - - - - ParameterSelection - decomp - Decompositions - - - - haa - barnes - huynen - pauli - - - 0 - False - - - ParameterNumber - inco.kernelsize - Kernel size for spatial incoherent averaging. - Minute (0-59) - - - 3 - True - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/SARPolarSynth.xml b/python/plugins/processing/algs/otb/description/5.8.0/SARPolarSynth.xml deleted file mode 100644 index d7319cc7469c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/SARPolarSynth.xml +++ /dev/null @@ -1,106 +0,0 @@ - - SARPolarSynth - otbcli_SARPolarSynth - SARPolarSynth - Miscellaneous - Gives, for each pixel, the power that would have been received by a SAR system with a basis different from the classical (H,V) one (polarimetric synthetis). - - ParameterRaster - in - Input Image - Input image. - False - - - OutputRaster - out - Output Image - Output image. - - - - ParameterNumber - psii - psii - Orientation (transmitting antenna) - - - 0 - False - - - ParameterNumber - khii - khii - Ellipticity (transmitting antenna) - - - 0 - False - - - ParameterNumber - psir - psir - Orientation (receiving antenna) - - - 0 - False - - - ParameterNumber - khir - khir - Ellipticity (receiving antenna) - - - 0 - False - - - ParameterNumber - emissionh - Emission H - This parameter is useful in determining the polarization architecture (dual polarization case). - - - 0 - True - - - ParameterNumber - emissionv - Emission V - This parameter is useful in determining the polarization architecture (dual polarization case). - - - 0 - True - - - ParameterSelection - mode - Forced mode - - - - none - co - cross - - - 0 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/SFSTextureExtraction.xml b/python/plugins/processing/algs/otb/description/5.8.0/SFSTextureExtraction.xml deleted file mode 100644 index d4c6b1e2abd0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/SFSTextureExtraction.xml +++ /dev/null @@ -1,91 +0,0 @@ - - SFSTextureExtraction - otbcli_SFSTextureExtraction - SFS Texture Extraction - Feature Extraction - Computes Structural Feature Set textures on every pixel of the input image selected channel - - ParameterRaster - in - Input Image - The input image to compute the features on. - False - - - ParameterNumber - channel - Selected Channel - The selected channel index - - - 1 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterNumber - parameters.spethre - Spectral Threshold - Spectral Threshold - - - 50 - False - - - ParameterNumber - parameters.spathre - Spatial Threshold - Spatial Threshold - - - 100 - False - - - ParameterNumber - parameters.nbdir - Number of Direction - Number of Direction - - - 20 - False - - - ParameterNumber - parameters.alpha - Alpha - Alpha - - - 1 - False - - - ParameterNumber - parameters.maxcons - Ratio Maximum Consideration Number - Ratio Maximum Consideration Number - - - 5 - False - - - OutputRaster - out - Feature Output Image - Output image containing the SFS texture features. - - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/SOMClassification.xml b/python/plugins/processing/algs/otb/description/5.8.0/SOMClassification.xml deleted file mode 100644 index c6ebd5d63651..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/SOMClassification.xml +++ /dev/null @@ -1,155 +0,0 @@ - - SOMClassification - otbcli_SOMClassification - SOM Classification - Learning - SOM image classification. - - ParameterRaster - in - InputImage - Input image to classify. - False - - - OutputRaster - out - OutputImage - Output classified image (each pixel contains the index of its corresponding vector in the SOM). - - - - ParameterRaster - vm - ValidityMask - Validity mask (only pixels corresponding to a mask value greater than 0 will be used for learning) - True - - - ParameterNumber - tp - TrainingProbability - Probability for a sample to be selected in the training set - - - 1 - True - - - ParameterNumber - ts - TrainingSetSize - Maximum training set size (in pixels) - - - 0 - True - - - OutputRaster - som - SOM Map - Output image containing the Self-Organizing Map - - - - ParameterNumber - sx - SizeX - X size of the SOM map - - - 32 - True - - - ParameterNumber - sy - SizeY - Y size of the SOM map - - - 32 - True - - - ParameterNumber - nx - NeighborhoodX - X size of the initial neighborhood in the SOM map - - - 10 - True - - - ParameterNumber - ny - NeighborhoodY - Y size of the initial neighborhood in the SOM map - - - 10 - False - - - ParameterNumber - ni - NumberIteration - Number of iterations for SOM learning - - - 5 - True - - - ParameterNumber - bi - BetaInit - Initial learning coefficient - - - 1 - True - - - ParameterNumber - bf - BetaFinal - Final learning coefficient - - - 0.1 - True - - - ParameterNumber - iv - InitialValue - Maximum initial neuron weight - - - 0 - True - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/SampleExtraction.xml b/python/plugins/processing/algs/otb/description/5.8.0/SampleExtraction.xml deleted file mode 100644 index 4ce6056837e1..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/SampleExtraction.xml +++ /dev/null @@ -1,89 +0,0 @@ - - SampleExtraction - otbcli_SampleExtraction - Sample Extraction - Learning - Extracts samples values from an image. - - ParameterRaster - in - InputImage - Support image - False - - - ParameterFile - vec - Input sampling positions - Vector data file containing samplingpositions. (OGR format) - - False - - - OutputFile - out - Output samples - Output vector data file storing samplevalues (OGR format). If not given, the input vector data file is updated - - - ParameterSelection - outfield - Output field names - Choice between naming method for output fields - - - prefix - list - - - 0 - False - - - ParameterString - outfield.prefix.name - Output field prefix - Prefix used to form the field names thatwill contain the extracted values. - value_ - - False - - - ParameterString - outfield.list.names - Output field names - Full list of output field names. - - - False - - - ParameterString - field - Field Name - Name of the field carrying the classname in the input vectors. This field is copied to output. - class - - True - - - ParameterNumber - layer - Layer Index - Layer index to read in the input vector file. - - - 0 - True - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/SampleSelection.xml b/python/plugins/processing/algs/otb/description/5.8.0/SampleSelection.xml deleted file mode 100644 index e2fc7263b384..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/SampleSelection.xml +++ /dev/null @@ -1,168 +0,0 @@ - - SampleSelection - otbcli_SampleSelection - Sample Selection - Learning - Selects samples from a training vector data set. - - ParameterRaster - in - InputImage - Support image that will be classified - False - - - ParameterRaster - mask - InputMask - Validity mask (only pixels corresponding to a mask value greater than 0 will be used for statistics) - True - - - ParameterFile - vec - Input vectors - Input geometries to analyse - - False - - - OutputFile - out - Output vectors - Output resampled geometries - - - ParameterFile - instats - Input Statistics - Input file storing statistics (XML format) - - False - - - OutputFile - outrates - Output rates - Output rates (CSV formatted) - - - ParameterSelection - sampler - Sampler type - Type of sampling (periodic, pattern based, random) - - - periodic - random - - - 0 - False - - - ParameterNumber - sampler.periodic.jitter - Jitter amplitude - Jitter amplitude added during sample selection (0 = no jitter) - - - 0 - True - - - ParameterSelection - strategy - Sampling strategy - - - - byclass - constant - percent - total - smallest - all - - - 4 - False - - - ParameterFile - strategy.byclass.in - Number of samples by class - Number of samples by class (CSV format with class name in 1st column and required samples in the 2nd. - - False - - - ParameterNumber - strategy.constant.nb - Number of samples for all classes - Number of samples for all classes - - - 0 - False - - - ParameterNumber - strategy.percent.p - The percentage to use - The percentage to use - - - 0.5 - False - - - ParameterNumber - strategy.total.v - The number of samples to generate - The number of samples to generate - - - 1000 - False - - - ParameterString - field - Field Name - Name of the field carrying the class name in the input vectors. - class - - True - - - ParameterNumber - layer - Layer Index - Layer index to read in the input vector file. - - - 0 - True - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/Segmentation-cc.xml b/python/plugins/processing/algs/otb/description/5.8.0/Segmentation-cc.xml deleted file mode 100644 index 539431b77a4f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/Segmentation-cc.xml +++ /dev/null @@ -1,161 +0,0 @@ - - Segmentation-cc - otbcli_Segmentation - Segmentation (cc) - Segmentation - Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported. - - ParameterRaster - in - Input Image - The input image to segment - False - - - ParameterSelection - filter - Segmentation algorithm - Choice of segmentation algorithm (mean-shift by default) - - - cc - - - 0 - False - - - ParameterString - filter.cc.expr - Condition - User defined connection condition, written as a mathematical expression. Available variables are p(i)b(i), intensity_p(i) and distance (example of expression : distance < 10 ) - - - False - - - ParameterSelection - mode - Processing mode - Choice of processing mode, either raster or large-scale. - - - vector - - - 0 - False - - - OutputVector - mode.vector.out - Output vector file - The output vector file or database (name can be anything understood by OGR) - - - ParameterSelection - mode.vector.outmode - Writing mode for the output vector file - This allows one to set the writing behaviour for the output vector file. Please note that the actual behaviour depends on the file format. - - - ulco - ovw - ulovw - ulu - - - 0 - False - - - ParameterRaster - mode.vector.inmask - Mask Image - Only pixels whose mask value is strictly positive will be segmented. - True - - - ParameterBoolean - mode.vector.neighbor - 8-neighbor connectivity - Activate 8-Neighborhood connectivity (default is 4). - True - True - - - ParameterBoolean - mode.vector.stitch - Stitch polygons - Scan polygons on each side of tiles and stitch polygons which connect by more than one pixel. - True - True - - - ParameterNumber - mode.vector.minsize - Minimum object size - Objects whose size is below the minimum object size (area in pixels) will be ignored during vectorization. - - - 1 - True - - - ParameterNumber - mode.vector.simplify - Simplify polygons - Simplify polygons according to a given tolerance (in pixel). This option allows reducing the size of the output file or database. - - - 0.1 - True - - - ParameterString - mode.vector.layername - Layer name - Name of the layer in the vector file or database (default is Layer). - layer - - False - - - ParameterString - mode.vector.fieldname - Geometry index field name - Name of the field holding the geometry index in the output vector file or database. - DN - - False - - - ParameterNumber - mode.vector.tilesize - Tiles size - User defined tiles size for tile-based segmentation. Optimal tile size is selected according to available RAM if null. - - - 1024 - False - - - ParameterNumber - mode.vector.startlabel - Starting geometry index - Starting value of the geometry index field - - - 1 - False - - - ParameterString - mode.vector.ogroptions - OGR options for layer creation - A list of layer creation options in the form KEY=VALUE that will be passed directly to OGR without any validity checking. Options may depend on the file format, and can be found in OGR documentation. - - - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/Segmentation-meanshift.xml b/python/plugins/processing/algs/otb/description/5.8.0/Segmentation-meanshift.xml deleted file mode 100644 index ce1935dd4cf9..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/Segmentation-meanshift.xml +++ /dev/null @@ -1,202 +0,0 @@ - - Segmentation-meanshift - otbcli_Segmentation - Segmentation (meanshift) - Segmentation - Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported. - - ParameterRaster - in - Input Image - The input image to segment - False - - - ParameterSelection - filter - Segmentation algorithm - Choice of segmentation algorithm (mean-shift by default) - - - meanshift - - - 0 - False - - - ParameterNumber - filter.meanshift.spatialr - Spatial radius - Spatial radius of the neighborhood. - - - 5 - False - - - ParameterNumber - filter.meanshift.ranger - Range radius - Range radius defining the radius (expressed in radiometry unit) in the multispectral space. - - - 15 - False - - - ParameterNumber - filter.meanshift.thres - Mode convergence threshold - Algorithm iterative scheme will stop if mean-shift vector is below this threshold or if iteration number reached maximum number of iterations. - - - 0.1 - False - - - ParameterNumber - filter.meanshift.maxiter - Maximum number of iterations - Algorithm iterative scheme will stop if convergence hasn't been reached after the maximum number of iterations. - - - 100 - False - - - ParameterNumber - filter.meanshift.minsize - Minimum region size - Minimum size of a region (in pixel unit) in segmentation. Smaller clusters will be merged to the neighboring cluster with the closest radiometry. If set to 0 no pruning is done. - - - 100 - False - - - ParameterSelection - mode - Processing mode - Choice of processing mode, either raster or large-scale. - - - vector - - - 0 - False - - - OutputVector - mode.vector.out - Output vector file - The output vector file or database (name can be anything understood by OGR) - - - ParameterSelection - mode.vector.outmode - Writing mode for the output vector file - This allows one to set the writing behaviour for the output vector file. Please note that the actual behaviour depends on the file format. - - - ulco - ovw - ulovw - ulu - - - 0 - False - - - ParameterRaster - mode.vector.inmask - Mask Image - Only pixels whose mask value is strictly positive will be segmented. - True - - - ParameterBoolean - mode.vector.neighbor - 8-neighbor connectivity - Activate 8-Neighborhood connectivity (default is 4). - True - True - - - ParameterBoolean - mode.vector.stitch - Stitch polygons - Scan polygons on each side of tiles and stitch polygons which connect by more than one pixel. - True - True - - - ParameterNumber - mode.vector.minsize - Minimum object size - Objects whose size is below the minimum object size (area in pixels) will be ignored during vectorization. - - - 1 - True - - - ParameterNumber - mode.vector.simplify - Simplify polygons - Simplify polygons according to a given tolerance (in pixel). This option allows reducing the size of the output file or database. - - - 0.1 - True - - - ParameterString - mode.vector.layername - Layer name - Name of the layer in the vector file or database (default is Layer). - layer - - False - - - ParameterString - mode.vector.fieldname - Geometry index field name - Name of the field holding the geometry index in the output vector file or database. - DN - - False - - - ParameterNumber - mode.vector.tilesize - Tiles size - User defined tiles size for tile-based segmentation. Optimal tile size is selected according to available RAM if null. - - - 1024 - False - - - ParameterNumber - mode.vector.startlabel - Starting geometry index - Starting value of the geometry index field - - - 1 - False - - - ParameterString - mode.vector.ogroptions - OGR options for layer creation - A list of layer creation options in the form KEY=VALUE that will be passed directly to OGR without any validity checking. Options may depend on the file format, and can be found in OGR documentation. - - - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/Segmentation-mprofiles.xml b/python/plugins/processing/algs/otb/description/5.8.0/Segmentation-mprofiles.xml deleted file mode 100644 index be511f25ee3c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/Segmentation-mprofiles.xml +++ /dev/null @@ -1,192 +0,0 @@ - - Segmentation-mprofiles - otbcli_Segmentation - Segmentation (mprofiles) - Segmentation - Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported. - - ParameterRaster - in - Input Image - The input image to segment - False - - - ParameterSelection - filter - Segmentation algorithm - Choice of segmentation algorithm (mean-shift by default) - - - mprofiles - - - 0 - False - - - ParameterNumber - filter.mprofiles.size - Profile Size - Size of the profiles - - - 5 - False - - - ParameterNumber - filter.mprofiles.start - Initial radius - Initial radius of the structuring element (in pixels) - - - 1 - False - - - ParameterNumber - filter.mprofiles.step - Radius step. - Radius step along the profile (in pixels) - - - 1 - False - - - ParameterNumber - filter.mprofiles.sigma - Threshold of the final decision rule - Profiles values under the threshold will be ignored. - - - 1 - False - - - ParameterSelection - mode - Processing mode - Choice of processing mode, either raster or large-scale. - - - vector - - - 0 - False - - - OutputVector - mode.vector.out - Output vector file - The output vector file or database (name can be anything understood by OGR) - - - ParameterSelection - mode.vector.outmode - Writing mode for the output vector file - This allows one to set the writing behaviour for the output vector file. Please note that the actual behaviour depends on the file format. - - - ulco - ovw - ulovw - ulu - - - 0 - False - - - ParameterRaster - mode.vector.inmask - Mask Image - Only pixels whose mask value is strictly positive will be segmented. - True - - - ParameterBoolean - mode.vector.neighbor - 8-neighbor connectivity - Activate 8-Neighborhood connectivity (default is 4). - True - True - - - ParameterBoolean - mode.vector.stitch - Stitch polygons - Scan polygons on each side of tiles and stitch polygons which connect by more than one pixel. - True - True - - - ParameterNumber - mode.vector.minsize - Minimum object size - Objects whose size is below the minimum object size (area in pixels) will be ignored during vectorization. - - - 1 - True - - - ParameterNumber - mode.vector.simplify - Simplify polygons - Simplify polygons according to a given tolerance (in pixel). This option allows reducing the size of the output file or database. - - - 0.1 - True - - - ParameterString - mode.vector.layername - Layer name - Name of the layer in the vector file or database (default is Layer). - layer - - False - - - ParameterString - mode.vector.fieldname - Geometry index field name - Name of the field holding the geometry index in the output vector file or database. - DN - - False - - - ParameterNumber - mode.vector.tilesize - Tiles size - User defined tiles size for tile-based segmentation. Optimal tile size is selected according to available RAM if null. - - - 1024 - False - - - ParameterNumber - mode.vector.startlabel - Starting geometry index - Starting value of the geometry index field - - - 1 - False - - - ParameterString - mode.vector.ogroptions - OGR options for layer creation - A list of layer creation options in the form KEY=VALUE that will be passed directly to OGR without any validity checking. Options may depend on the file format, and can be found in OGR documentation. - - - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/Segmentation-watershed.xml b/python/plugins/processing/algs/otb/description/5.8.0/Segmentation-watershed.xml deleted file mode 100644 index 20551ccd4161..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/Segmentation-watershed.xml +++ /dev/null @@ -1,172 +0,0 @@ - - Segmentation-watershed - otbcli_Segmentation - Segmentation (watershed) - Segmentation - Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported. - - ParameterRaster - in - Input Image - The input image to segment - False - - - ParameterSelection - filter - Segmentation algorithm - Choice of segmentation algorithm (mean-shift by default) - - - watershed - - - 0 - False - - - ParameterNumber - filter.watershed.threshold - Depth Threshold - Depth threshold Units in percentage of the maximum depth in the image. - - - 0.01 - False - - - ParameterNumber - filter.watershed.level - Flood Level - flood level for generating the merge tree from the initial segmentation (between 0 and 1) - - - 0.1 - False - - - ParameterSelection - mode - Processing mode - Choice of processing mode, either raster or large-scale. - - - vector - - - 0 - False - - - OutputVector - mode.vector.out - Output vector file - The output vector file or database (name can be anything understood by OGR) - - - ParameterSelection - mode.vector.outmode - Writing mode for the output vector file - This allows one to set the writing behaviour for the output vector file. Please note that the actual behaviour depends on the file format. - - - ulco - ovw - ulovw - ulu - - - 0 - False - - - ParameterRaster - mode.vector.inmask - Mask Image - Only pixels whose mask value is strictly positive will be segmented. - True - - - ParameterBoolean - mode.vector.neighbor - 8-neighbor connectivity - Activate 8-Neighborhood connectivity (default is 4). - True - True - - - ParameterBoolean - mode.vector.stitch - Stitch polygons - Scan polygons on each side of tiles and stitch polygons which connect by more than one pixel. - True - True - - - ParameterNumber - mode.vector.minsize - Minimum object size - Objects whose size is below the minimum object size (area in pixels) will be ignored during vectorization. - - - 1 - True - - - ParameterNumber - mode.vector.simplify - Simplify polygons - Simplify polygons according to a given tolerance (in pixel). This option allows reducing the size of the output file or database. - - - 0.1 - True - - - ParameterString - mode.vector.layername - Layer name - Name of the layer in the vector file or database (default is Layer). - layer - - False - - - ParameterString - mode.vector.fieldname - Geometry index field name - Name of the field holding the geometry index in the output vector file or database. - DN - - False - - - ParameterNumber - mode.vector.tilesize - Tiles size - User defined tiles size for tile-based segmentation. Optimal tile size is selected according to available RAM if null. - - - 1024 - False - - - ParameterNumber - mode.vector.startlabel - Starting geometry index - Starting value of the geometry index field - - - 1 - False - - - ParameterString - mode.vector.ogroptions - OGR options for layer creation - A list of layer creation options in the form KEY=VALUE that will be passed directly to OGR without any validity checking. Options may depend on the file format, and can be found in OGR documentation. - - - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/Smoothing-anidif.xml b/python/plugins/processing/algs/otb/description/5.8.0/Smoothing-anidif.xml deleted file mode 100644 index 84f43f070f40..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/Smoothing-anidif.xml +++ /dev/null @@ -1,74 +0,0 @@ - - Smoothing-anidif - otbcli_Smoothing - Smoothing (anidif) - Image Filtering - Apply a smoothing filter to an image - - ParameterRaster - in - Input Image - Input image to smooth. - False - - - OutputRaster - out - Output Image - Output smoothed image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - type - Smoothing Type - Smoothing kernel to apply - - - anidif - - - 2 - False - - - ParameterNumber - type.anidif.timestep - Time Step - Diffusion equation time step - - - 0.125 - False - - - ParameterNumber - type.anidif.nbiter - Nb Iterations - Controls the sensitivity of the conductance term - - - 10 - False - - - ParameterNumber - type.anidif.conductance - Conductance - - - - 1 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/Smoothing-gaussian.xml b/python/plugins/processing/algs/otb/description/5.8.0/Smoothing-gaussian.xml deleted file mode 100644 index 49f7cc1fc551..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/Smoothing-gaussian.xml +++ /dev/null @@ -1,54 +0,0 @@ - - Smoothing-gaussian - otbcli_Smoothing - Smoothing (gaussian) - Image Filtering - Apply a smoothing filter to an image - - ParameterRaster - in - Input Image - Input image to smooth. - False - - - OutputRaster - out - Output Image - Output smoothed image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - type - Smoothing Type - Smoothing kernel to apply - - - gaussian - - - 2 - False - - - ParameterNumber - type.gaussian.radius - Radius - Gaussian radius (in pixels) - - - 2 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/Smoothing-mean.xml b/python/plugins/processing/algs/otb/description/5.8.0/Smoothing-mean.xml deleted file mode 100644 index 8e010db17ede..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/Smoothing-mean.xml +++ /dev/null @@ -1,54 +0,0 @@ - - Smoothing-mean - otbcli_Smoothing - Smoothing (mean) - Image Filtering - Apply a smoothing filter to an image - - ParameterRaster - in - Input Image - Input image to smooth. - False - - - OutputRaster - out - Output Image - Output smoothed image. - - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - - ParameterSelection - type - Smoothing Type - Smoothing kernel to apply - - - mean - - - 2 - False - - - ParameterNumber - type.mean.radius - Radius - Mean radius (in pixels) - - - 2 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/StereoFramework.xml b/python/plugins/processing/algs/otb/description/5.8.0/StereoFramework.xml deleted file mode 100644 index e06cd7fe7c55..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/StereoFramework.xml +++ /dev/null @@ -1,343 +0,0 @@ - - StereoFramework - otbcli_StereoFramework - Stereo Framework - Stereo - Compute the ground elevation based on one or multiple stereo pair(s) - - ParameterMultipleInput - input.il - Input images list - The list of images. - - False - - - ParameterString - input.co - Couples list - List of index of couples im image list. Couples must be separated by a comma. (index start at 0). for example : 0 1,1 2 will process a first couple composed of the first and the second image in image list, then the first and the third image -. note that images are handled by pairs. if left empty couples are created from input index i.e. a first couple will be composed of the first and second image, a second couple with third and fourth image etc. (in this case image list must be even). - - - True - - - ParameterNumber - input.channel - Image channel used for the block matching - Used channel for block matching (used for all images) - - - 1 - False - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - output.res - Output resolution - Spatial sampling distance of the output elevation : the cell size (in m) - - - 1 - False - - - ParameterNumber - output.nodata - NoData value - DSM empty cells are filled with this value (optional -32768 by default) - - - -32768 - True - - - ParameterSelection - output.fusionmethod - Method to fuse measures in each DSM cell - This parameter allows one to choose the method used to fuse elevation measurements in each output DSM cell - - - max - min - mean - acc - - - 0 - False - - - OutputRaster - output.out - Output DSM - Output elevation image - - - - ParameterSelection - output.mode - Parameters estimation modes - - - - fit - user - - - 0 - False - - - ParameterNumber - output.mode.user.ulx - Upper Left X - Cartographic X coordinate of upper-left corner (meters for cartographic projections, degrees for geographic ones) - - - 0.0 - False - - - ParameterNumber - output.mode.user.uly - Upper Left Y - Cartographic Y coordinate of the upper-left corner (meters for cartographic projections, degrees for geographic ones) - - - 0.0 - False - - - ParameterNumber - output.mode.user.sizex - Size X - Size of projected image along X (in pixels) - - - 0 - False - - - ParameterNumber - output.mode.user.sizey - Size Y - Size of projected image along Y (in pixels) - - - 0 - False - - - ParameterNumber - output.mode.user.spacingx - Pixel Size X - Size of each pixel along X axis (meters for cartographic projections, degrees for geographic ones) - - - 0.0 - False - - - ParameterNumber - output.mode.user.spacingy - Pixel Size Y - Size of each pixel along Y axis (meters for cartographic projections, degrees for geographic ones) - - - 0.0 - False - - - ParameterSelection - map - Output Cartographic Map Projection - Parameters of the output map projection to be used. - - - utm - lambert2 - lambert93 - wgs - epsg - - - 3 - False - - - ParameterNumber - map.utm.zone - Zone number - The zone number ranges from 1 to 60 and allows defining the transverse mercator projection (along with the hemisphere) - - - 31 - False - - - ParameterBoolean - map.utm.northhem - Northern Hemisphere - The transverse mercator projections are defined by their zone number as well as the hemisphere. Activate this parameter if your image is in the northern hemisphere. - True - True - - - ParameterNumber - map.epsg.code - EPSG Code - See www.spatialreference.org to find which EPSG code is associated to your projection - - - 4326 - False - - - ParameterNumber - stereorect.fwdgridstep - Step of the displacement grid (in pixels) - Stereo-rectification displacement grid only varies slowly. Therefore, it is recommended to use a coarser grid (higher step value) in case of large images - - - 16 - True - - - ParameterNumber - stereorect.invgridssrate - Sub-sampling rate for epipolar grid inversion - Grid inversion is an heavy process that implies spline regression on control points. To avoid eating to much memory, this parameter allows one to first sub-sample the field to invert. - - - 10 - True - - - ParameterSelection - bm.metric - Block-matching metric - - - - ssdmean - ssd - ncc - lp - - - 0 - False - - - ParameterNumber - bm.metric.lp.p - p value - Value of the p parameter in Lp pseudo-norm (must be positive) - - - 1 - False - - - ParameterNumber - bm.radius - Radius of blocks for matching filter (in pixels) - The radius of blocks in Block-Matching (in pixels) - - - 2 - True - - - ParameterNumber - bm.minhoffset - Minimum altitude offset (in meters) - Minimum altitude below the selected elevation source (in meters) - - - -20 - False - - - ParameterNumber - bm.maxhoffset - Maximum altitude offset (in meters) - Maximum altitude above the selected elevation source (in meters) - - - 20 - False - - - ParameterBoolean - postproc.bij - Use bijection consistency in block matching strategy - use bijection consistency. Right to Left correlation is computed to validate Left to Right disparities. If bijection is not found pixel is rejected. - True - True - - - ParameterBoolean - postproc.med - Use median disparities filtering - disparities output can be filtered using median post filtering (disabled by default). - True - True - - - ParameterNumber - postproc.metrict - Correlation metric threshold - Use block matching metric output to discard pixels with low correlation value (disabled by default, float value) - - - 0.6 - True - - - ParameterRaster - mask.left - Input left mask - Mask for left input image - True - - - ParameterRaster - mask.right - Input right mask - Mask for right input image - True - - - ParameterNumber - mask.variancet - Discard pixels with low local variance - This parameter allows one to discard pixels whose local variance is too small (the size of the neighborhood is given by the radius parameter) - - - 50 - True - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/Superimpose.xml b/python/plugins/processing/algs/otb/description/5.8.0/Superimpose.xml deleted file mode 100644 index 3250b909c73b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/Superimpose.xml +++ /dev/null @@ -1,97 +0,0 @@ - - Superimpose - otbcli_Superimpose - Superimpose sensor - Geometry - Using available image metadata, project one image onto another one - - ParameterRaster - inr - Reference input - The input reference image. - False - - - ParameterRaster - inm - The image to reproject - The image to reproject into the geometry of the reference input. - False - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - lms - Spacing of the deformation field - Generate a coarser deformation field with the given spacing - - - 4 - True - - - OutputRaster - out - Output image - Output reprojected image. - - - - ParameterSelection - mode - Mode - Superimposition mode - - - default - phr - - - 0 - False - - - ParameterSelection - interpolator - Interpolation - This group of parameters allows defining how the input image will be interpolated during resampling. - - - bco - nn - linear - - - 0 - False - - - ParameterNumber - interpolator.bco.radius - Radius for bicubic interpolation - This parameter allows controlling the size of the bicubic interpolation filter. If the target pixel size is higher than the input pixel size, increasing this parameter will reduce aliasing artifacts. - - - 2 - False - - - ParameterNumber - ram - Available RAM (Mb) - Available memory for processing (in MB) - - - 128 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TileFusion.xml b/python/plugins/processing/algs/otb/description/5.8.0/TileFusion.xml deleted file mode 100644 index b157b2caa49a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TileFusion.xml +++ /dev/null @@ -1,42 +0,0 @@ - - TileFusion - otbcli_TileFusion - Image Tile Fusion - Image Manipulation - Fusion of an image made of several tile files. - - ParameterMultipleInput - il - Input Tile Images - Input tiles to concatenate (in lexicographic order : (0,0) (1,0) (0,1) (1,1)). - - False - - - ParameterNumber - cols - Number of tile columns - Number of columns in the tile array - - - 0 - False - - - ParameterNumber - rows - Number of tile rows - Number of rows in the tile array - - - 0 - False - - - OutputRaster - out - Output Image - Output entire image - - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-ann.xml b/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-ann.xml deleted file mode 100644 index 4422c4dc9ea4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-ann.xml +++ /dev/null @@ -1,266 +0,0 @@ - - TrainImagesClassifier-ann - otbcli_TrainImagesClassifier - TrainImagesClassifier (ann) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - False - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - False - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - ann - - - 0 - False - - - ParameterSelection - classifier.ann.t - Train Method Type - Type of training method for the multilayer perceptron (MLP) neural network. - - - reg - back - - - 0 - False - - - ParameterString - classifier.ann.sizes - Number of neurons in each intermediate layer - The number of neurons in each intermediate layer (excluding input and output layers). - - - False - - - ParameterSelection - classifier.ann.f - Neuron activation function type - Neuron activation function. - - - ident - sig - gau - - - 1 - False - - - ParameterNumber - classifier.ann.a - Alpha parameter of the activation function - Alpha parameter of the activation function (used only with sigmoid and gaussian functions). - - - 1 - False - - - ParameterNumber - classifier.ann.b - Beta parameter of the activation function - Beta parameter of the activation function (used only with sigmoid and gaussian functions). - - - 1 - False - - - ParameterNumber - classifier.ann.bpdw - Strength of the weight gradient term in the BACKPROP method - Strength of the weight gradient term in the BACKPROP method. The recommended value is about 0.1. - - - 0.1 - False - - - ParameterNumber - classifier.ann.bpms - Strength of the momentum term (the difference between weights on the 2 previous iterations) - Strength of the momentum term (the difference between weights on the 2 previous iterations). This parameter provides some inertia to smooth the random fluctuations of the weights. It can vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough. - - - 0.1 - False - - - ParameterNumber - classifier.ann.rdw - Initial value Delta_0 of update-values Delta_{ij} in RPROP method - Initial value Delta_0 of update-values Delta_{ij} in RPROP method (default = 0.1). - - - 0.1 - False - - - ParameterNumber - classifier.ann.rdwm - Update-values lower limit Delta_{min} in RPROP method - Update-values lower limit Delta_{min} in RPROP method. It must be positive (default = 1e-7). - - - 1e-07 - False - - - ParameterSelection - classifier.ann.term - Termination criteria - Termination criteria. - - - iter - eps - all - - - 2 - False - - - ParameterNumber - classifier.ann.eps - Epsilon value used in the Termination criteria - Epsilon value used in the Termination criteria. - - - 0.01 - False - - - ParameterNumber - classifier.ann.iter - Maximum number of iterations used in the Termination criteria - Maximum number of iterations used in the Termination criteria. - - - 1000 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-bayes.xml b/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-bayes.xml deleted file mode 100644 index c5051527b855..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-bayes.xml +++ /dev/null @@ -1,133 +0,0 @@ - - TrainImagesClassifier-bayes - otbcli_TrainImagesClassifier - TrainImagesClassifier (bayes) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - False - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - False - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - bayes - - - 0 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-boost.xml b/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-boost.xml deleted file mode 100644 index 72e10d96d4fd..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-boost.xml +++ /dev/null @@ -1,179 +0,0 @@ - - TrainImagesClassifier-boost - otbcli_TrainImagesClassifier - TrainImagesClassifier (boost) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - False - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - False - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - boost - - - 0 - False - - - ParameterSelection - classifier.boost.t - Boost Type - Type of Boosting algorithm. - - - discrete - real - logit - gentle - - - 1 - False - - - ParameterNumber - classifier.boost.w - Weak count - The number of weak classifiers. - - - 100 - False - - - ParameterNumber - classifier.boost.r - Weight Trim Rate - A threshold between 0 and 1 used to save computational time. Samples with summary weight <= (1 - weight_trim_rate) do not participate in the next iteration of training. Set this parameter to 0 to turn off this functionality. - - - 0.95 - False - - - ParameterNumber - classifier.boost.m - Maximum depth of the tree - Maximum depth of the tree. - - - 1 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-dt.xml b/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-dt.xml deleted file mode 100644 index 64cb5079f951..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-dt.xml +++ /dev/null @@ -1,199 +0,0 @@ - - TrainImagesClassifier-dt - otbcli_TrainImagesClassifier - TrainImagesClassifier (dt) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - False - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - False - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - dt - - - 0 - False - - - ParameterNumber - classifier.dt.max - Maximum depth of the tree - The training algorithm attempts to split each node while its depth is smaller than the maximum possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or if the tree is pruned. - - - 65535 - False - - - ParameterNumber - classifier.dt.min - Minimum number of samples in each node - If all absolute differences between an estimated value in a node and the values of the train samples in this node are smaller than this regression accuracy parameter, then the node will not be split. - - - 10 - False - - - ParameterNumber - classifier.dt.ra - Termination criteria for regression tree - - - - 0.01 - False - - - ParameterNumber - classifier.dt.cat - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split. - - - 10 - False - - - ParameterNumber - classifier.dt.f - K-fold cross-validations - If cv_folds > 1, then it prunes a tree with K-fold cross-validation where K is equal to cv_folds. - - - 10 - False - - - ParameterBoolean - classifier.dt.r - Set Use1seRule flag to false - If true, then a pruning will be harsher. This will make a tree more compact and more resistant to the training data noise but a bit less accurate. - True - True - - - ParameterBoolean - classifier.dt.t - Set TruncatePrunedTree flag to false - If true, then pruned branches are physically removed from the tree. - True - True - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-gbt.xml b/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-gbt.xml deleted file mode 100644 index d54f637ff333..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-gbt.xml +++ /dev/null @@ -1,173 +0,0 @@ - - TrainImagesClassifier-gbt - otbcli_TrainImagesClassifier - TrainImagesClassifier (gbt) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - False - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - False - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - gbt - - - 0 - False - - - ParameterNumber - classifier.gbt.w - Number of boosting algorithm iterations - Number "w" of boosting algorithm iterations, with w*K being the total number of trees in the GBT model, where K is the output number of classes. - - - 200 - False - - - ParameterNumber - classifier.gbt.s - Regularization parameter - Regularization parameter. - - - 0.01 - False - - - ParameterNumber - classifier.gbt.p - Portion of the whole training set used for each algorithm iteration - Portion of the whole training set used for each algorithm iteration. The subset is generated randomly. - - - 0.8 - False - - - ParameterNumber - classifier.gbt.max - Maximum depth of the tree - The training algorithm attempts to split each node while its depth is smaller than the maximum possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or if the tree is pruned. - - - 3 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-knn.xml b/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-knn.xml deleted file mode 100644 index a78ca47f1b70..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-knn.xml +++ /dev/null @@ -1,143 +0,0 @@ - - TrainImagesClassifier-knn - otbcli_TrainImagesClassifier - TrainImagesClassifier (knn) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - False - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - False - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - knn - - - 0 - False - - - ParameterNumber - classifier.knn.k - Number of Neighbors - The number of neighbors to use. - - - 32 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-libsvm.xml b/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-libsvm.xml deleted file mode 100644 index 75b57373010b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-libsvm.xml +++ /dev/null @@ -1,190 +0,0 @@ - - TrainImagesClassifier-libsvm - otbcli_TrainImagesClassifier - TrainImagesClassifier (libsvm) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - False - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - False - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - libsvm - - - 0 - False - - - ParameterSelection - classifier.libsvm.k - SVM Kernel Type - SVM Kernel Type. - - - linear - rbf - poly - sigmoid - - - 0 - False - - - ParameterSelection - classifier.libsvm.m - SVM Model Type - Type of SVM formulation. - - - csvc - nusvc - oneclass - - - 0 - False - - - ParameterNumber - classifier.libsvm.c - Cost parameter C - SVM models have a cost parameter C (1 by default) to control the trade-off between training errors and forcing rigid margins. - - - 1 - False - - - ParameterBoolean - classifier.libsvm.opt - Parameters optimization - SVM parameters optimization flag. - True - True - - - ParameterBoolean - classifier.libsvm.prob - Probability estimation - Probability estimation flag. - True - True - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-rf.xml b/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-rf.xml deleted file mode 100644 index 91810de17aad..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TrainImagesClassifier-rf.xml +++ /dev/null @@ -1,203 +0,0 @@ - - TrainImagesClassifier-rf - otbcli_TrainImagesClassifier - TrainImagesClassifier (rf) - Learning - Train a classifier from multiple pairs of images and training vector data. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. - - False - - - ParameterMultipleInput - io.vd - Input Vector Data List - A list of vector data to select the training samples. - - False - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - - ParameterNumber - sample.mt - Maximum training sample size per class - Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation sample size per class - Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. - - - 1000 - False - - - ParameterNumber - sample.bm - Bound sample number by minimum - Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). - - - 1 - False - - - ParameterBoolean - sample.edg - On edge pixel inclusion - Takes pixels on polygon edge into consideration when building training and validation samples. - True - True - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterString - sample.vfn - Name of the discrimination field - Name of the field used to discriminate class labels in the input vector data files. - Class - - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - rf - - - 0 - False - - - ParameterNumber - classifier.rf.max - Maximum depth of the tree - The depth of the tree. A low value will likely underfit and conversely a high value will likely overfit. The optimal value can be obtained using cross validation or other suitable methods. - - - 5 - False - - - ParameterNumber - classifier.rf.min - Minimum number of samples in each node - If the number of samples in a node is smaller than this parameter, then the node will not be split. A reasonable value is a small percentage of the total data e.g. 1 percent. - - - 10 - False - - - ParameterNumber - classifier.rf.ra - Termination Criteria for regression tree - If all absolute differences between an estimated value in a node and the values of the train samples in this node are smaller than this regression accuracy parameter, then the node will not be split. - - - 0 - False - - - ParameterNumber - classifier.rf.cat - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split. - - - 10 - False - - - ParameterNumber - classifier.rf.var - Size of the randomly selected subset of features at each tree node - The size of the subset of features, randomly selected at each tree node, that are used to find the best split(s). If you set it to 0, then the size will be set to the square root of the total number of features. - - - 0 - False - - - ParameterNumber - classifier.rf.nbtrees - Maximum number of trees in the forest - The maximum number of trees in the forest. Typically, the more trees you have, the better the accuracy. However, the improvement in accuracy generally diminishes and reaches an asymptote for a certain number of trees. Also to keep in mind, increasing the number of trees increases the prediction time linearly. - - - 100 - False - - - ParameterNumber - classifier.rf.acc - Sufficient accuracy (OOB error) - Sufficient accuracy (OOB error). - - - 0.01 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TrainOGRLayersClassifier.xml b/python/plugins/processing/algs/otb/description/5.8.0/TrainOGRLayersClassifier.xml deleted file mode 100644 index ea4270102853..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TrainOGRLayersClassifier.xml +++ /dev/null @@ -1,47 +0,0 @@ - - TrainOGRLayersClassifier - otbcli_TrainOGRLayersClassifier - TrainOGRLayersClassifier (DEPRECATED) - Segmentation - Train a SVM classifier based on labeled geometries and a list of features to consider. - - ParameterVector - inshp - Name of the input shapefile - Name of the input shapefile - - False - - - ParameterFile - instats - XML file containing mean and variance of each feature. - XML file containing mean and variance of each feature. - - False - - - OutputFile - outsvm - Output model filename. - Output model filename. - - - ParameterString - feat - List of features to consider for classification. - List of features to consider for classification. - - - False - - - ParameterString - cfield - Field containing the class id for supervision - Field containing the class id for supervision. Only geometries with this field available will be taken into account. - class - - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-ann.xml b/python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-ann.xml deleted file mode 100644 index c76f6be09573..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-ann.xml +++ /dev/null @@ -1,233 +0,0 @@ - - TrainRegression-ann - otbcli_TrainRegression - TrainRegression (ann) - Learning - Train a classifier from multiple images to perform regression. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. First (n-1) bands should contain the predictor. The last band should contain the output value to predict. - - False - - - ParameterFile - io.csv - Input CSV file - Input CSV file containing the predictors, and the output values in last column. Only used when no input image is given - - True - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.out - Output regression model - Output file containing the model estimated (.txt format). - - - ParameterNumber - io.mse - Mean Square Error - Mean square error computed with the validation predictors - - - 0.0 - False - - - ParameterNumber - sample.mt - Maximum training predictors - Maximum number of training predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation predictors - Maximum number of validation predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - ann - - - 0 - False - - - ParameterSelection - classifier.ann.t - Train Method Type - Type of training method for the multilayer perceptron (MLP) neural network. - - - reg - back - - - 0 - False - - - ParameterString - classifier.ann.sizes - Number of neurons in each intermediate layer - The number of neurons in each intermediate layer (excluding input and output layers). - - - False - - - ParameterSelection - classifier.ann.f - Neuron activation function type - Neuron activation function. - - - ident - sig - gau - - - 1 - False - - - ParameterNumber - classifier.ann.a - Alpha parameter of the activation function - Alpha parameter of the activation function (used only with sigmoid and gaussian functions). - - - 1 - False - - - ParameterNumber - classifier.ann.b - Beta parameter of the activation function - Beta parameter of the activation function (used only with sigmoid and gaussian functions). - - - 1 - False - - - ParameterNumber - classifier.ann.bpdw - Strength of the weight gradient term in the BACKPROP method - Strength of the weight gradient term in the BACKPROP method. The recommended value is about 0.1. - - - 0.1 - False - - - ParameterNumber - classifier.ann.bpms - Strength of the momentum term (the difference between weights on the 2 previous iterations) - Strength of the momentum term (the difference between weights on the 2 previous iterations). This parameter provides some inertia to smooth the random fluctuations of the weights. It can vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough. - - - 0.1 - False - - - ParameterNumber - classifier.ann.rdw - Initial value Delta_0 of update-values Delta_{ij} in RPROP method - Initial value Delta_0 of update-values Delta_{ij} in RPROP method (default = 0.1). - - - 0.1 - False - - - ParameterNumber - classifier.ann.rdwm - Update-values lower limit Delta_{min} in RPROP method - Update-values lower limit Delta_{min} in RPROP method. It must be positive (default = 1e-7). - - - 1e-07 - False - - - ParameterSelection - classifier.ann.term - Termination criteria - Termination criteria. - - - iter - eps - all - - - 2 - False - - - ParameterNumber - classifier.ann.eps - Epsilon value used in the Termination criteria - Epsilon value used in the Termination criteria. - - - 0.01 - False - - - ParameterNumber - classifier.ann.iter - Maximum number of iterations used in the Termination criteria - Maximum number of iterations used in the Termination criteria. - - - 1000 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-dt.xml b/python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-dt.xml deleted file mode 100644 index 3a8bb610ca5e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-dt.xml +++ /dev/null @@ -1,166 +0,0 @@ - - TrainRegression-dt - otbcli_TrainRegression - TrainRegression (dt) - Learning - Train a classifier from multiple images to perform regression. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. First (n-1) bands should contain the predictor. The last band should contain the output value to predict. - - False - - - ParameterFile - io.csv - Input CSV file - Input CSV file containing the predictors, and the output values in last column. Only used when no input image is given - - True - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.out - Output regression model - Output file containing the model estimated (.txt format). - - - ParameterNumber - io.mse - Mean Square Error - Mean square error computed with the validation predictors - - - 0.0 - False - - - ParameterNumber - sample.mt - Maximum training predictors - Maximum number of training predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation predictors - Maximum number of validation predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - dt - - - 0 - False - - - ParameterNumber - classifier.dt.max - Maximum depth of the tree - The training algorithm attempts to split each node while its depth is smaller than the maximum possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or if the tree is pruned. - - - 65535 - False - - - ParameterNumber - classifier.dt.min - Minimum number of samples in each node - If all absolute differences between an estimated value in a node and the values of the train samples in this node are smaller than this regression accuracy parameter, then the node will not be split. - - - 10 - False - - - ParameterNumber - classifier.dt.ra - Termination criteria for regression tree - - - - 0.01 - False - - - ParameterNumber - classifier.dt.cat - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split. - - - 10 - False - - - ParameterNumber - classifier.dt.f - K-fold cross-validations - If cv_folds > 1, then it prunes a tree with K-fold cross-validation where K is equal to cv_folds. - - - 10 - False - - - ParameterBoolean - classifier.dt.r - Set Use1seRule flag to false - If true, then a pruning will be harsher. This will make a tree more compact and more resistant to the training data noise but a bit less accurate. - True - True - - - ParameterBoolean - classifier.dt.t - Set TruncatePrunedTree flag to false - If true, then pruned branches are physically removed from the tree. - True - True - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-gbt.xml b/python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-gbt.xml deleted file mode 100644 index 50d35edbf599..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-gbt.xml +++ /dev/null @@ -1,155 +0,0 @@ - - TrainRegression-gbt - otbcli_TrainRegression - TrainRegression (gbt) - Learning - Train a classifier from multiple images to perform regression. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. First (n-1) bands should contain the predictor. The last band should contain the output value to predict. - - False - - - ParameterFile - io.csv - Input CSV file - Input CSV file containing the predictors, and the output values in last column. Only used when no input image is given - - True - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.out - Output regression model - Output file containing the model estimated (.txt format). - - - ParameterNumber - io.mse - Mean Square Error - Mean square error computed with the validation predictors - - - 0.0 - False - - - ParameterNumber - sample.mt - Maximum training predictors - Maximum number of training predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation predictors - Maximum number of validation predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - gbt - - - 0 - False - - - ParameterSelection - classifier.gbt.t - Loss Function Type - Type of loss functionused for training. - - - sqr - abs - hub - - - 0 - False - - - ParameterNumber - classifier.gbt.w - Number of boosting algorithm iterations - Number "w" of boosting algorithm iterations, with w*K being the total number of trees in the GBT model, where K is the output number of classes. - - - 200 - False - - - ParameterNumber - classifier.gbt.s - Regularization parameter - Regularization parameter. - - - 0.01 - False - - - ParameterNumber - classifier.gbt.p - Portion of the whole training set used for each algorithm iteration - Portion of the whole training set used for each algorithm iteration. The subset is generated randomly. - - - 0.8 - False - - - ParameterNumber - classifier.gbt.max - Maximum depth of the tree - The training algorithm attempts to split each node while its depth is smaller than the maximum possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or if the tree is pruned. - - - 3 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-knn.xml b/python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-knn.xml deleted file mode 100644 index 8482f90aa78b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-knn.xml +++ /dev/null @@ -1,124 +0,0 @@ - - TrainRegression-knn - otbcli_TrainRegression - TrainRegression (knn) - Learning - Train a classifier from multiple images to perform regression. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. First (n-1) bands should contain the predictor. The last band should contain the output value to predict. - - False - - - ParameterFile - io.csv - Input CSV file - Input CSV file containing the predictors, and the output values in last column. Only used when no input image is given - - True - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.out - Output regression model - Output file containing the model estimated (.txt format). - - - ParameterNumber - io.mse - Mean Square Error - Mean square error computed with the validation predictors - - - 0.0 - False - - - ParameterNumber - sample.mt - Maximum training predictors - Maximum number of training predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation predictors - Maximum number of validation predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - knn - - - 0 - False - - - ParameterNumber - classifier.knn.k - Number of Neighbors - The number of neighbors to use. - - - 32 - False - - - ParameterSelection - classifier.knn.rule - Decision rule - Decision rule for regression output - - - mean - median - - - 0 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-libsvm.xml b/python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-libsvm.xml deleted file mode 100644 index b68db7c34911..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-libsvm.xml +++ /dev/null @@ -1,176 +0,0 @@ - - TrainRegression-libsvm - otbcli_TrainRegression - TrainRegression (libsvm) - Learning - Train a classifier from multiple images to perform regression. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. First (n-1) bands should contain the predictor. The last band should contain the output value to predict. - - False - - - ParameterFile - io.csv - Input CSV file - Input CSV file containing the predictors, and the output values in last column. Only used when no input image is given - - True - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.out - Output regression model - Output file containing the model estimated (.txt format). - - - ParameterNumber - io.mse - Mean Square Error - Mean square error computed with the validation predictors - - - 0.0 - False - - - ParameterNumber - sample.mt - Maximum training predictors - Maximum number of training predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation predictors - Maximum number of validation predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - libsvm - - - 0 - False - - - ParameterSelection - classifier.libsvm.k - SVM Kernel Type - SVM Kernel Type. - - - linear - rbf - poly - sigmoid - - - 0 - False - - - ParameterSelection - classifier.libsvm.m - SVM Model Type - Type of SVM formulation. - - - epssvr - nusvr - - - 0 - False - - - ParameterNumber - classifier.libsvm.c - Cost parameter C - SVM models have a cost parameter C (1 by default) to control the trade-off between training errors and forcing rigid margins. - - - 1 - False - - - ParameterBoolean - classifier.libsvm.opt - Parameters optimization - SVM parameters optimization flag. - True - True - - - ParameterBoolean - classifier.libsvm.prob - Probability estimation - Probability estimation flag. - True - True - - - ParameterNumber - classifier.libsvm.eps - Epsilon - - - - 0.001 - False - - - ParameterNumber - classifier.libsvm.nu - Nu - - - - 0.5 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-rf.xml b/python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-rf.xml deleted file mode 100644 index 4a2525bda97c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TrainRegression-rf.xml +++ /dev/null @@ -1,170 +0,0 @@ - - TrainRegression-rf - otbcli_TrainRegression - TrainRegression (rf) - Learning - Train a classifier from multiple images to perform regression. - - ParameterMultipleInput - io.il - Input Image List - A list of input images. First (n-1) bands should contain the predictor. The last band should contain the output value to predict. - - False - - - ParameterFile - io.csv - Input CSV file - Input CSV file containing the predictors, and the output values in last column. Only used when no input image is given - - True - - - ParameterFile - io.imstat - Input XML image statistics file - Input XML file containing the mean and the standard deviation of the input images. - - True - - - OutputFile - io.out - Output regression model - Output file containing the model estimated (.txt format). - - - ParameterNumber - io.mse - Mean Square Error - Mean square error computed with the validation predictors - - - 0.0 - False - - - ParameterNumber - sample.mt - Maximum training predictors - Maximum number of training predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.mv - Maximum validation predictors - Maximum number of validation predictors (default = 1000) (no limit = -1). - - - 1000 - False - - - ParameterNumber - sample.vtr - Training and validation sample ratio - Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). - - - 0.5 - False - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - rf - - - 0 - False - - - ParameterNumber - classifier.rf.max - Maximum depth of the tree - The depth of the tree. A low value will likely underfit and conversely a high value will likely overfit. The optimal value can be obtained using cross validation or other suitable methods. - - - 5 - False - - - ParameterNumber - classifier.rf.min - Minimum number of samples in each node - If the number of samples in a node is smaller than this parameter, then the node will not be split. A reasonable value is a small percentage of the total data e.g. 1 percent. - - - 10 - False - - - ParameterNumber - classifier.rf.ra - Termination Criteria for regression tree - If all absolute differences between an estimated value in a node and the values of the train samples in this node are smaller than this regression accuracy parameter, then the node will not be split. - - - 0 - False - - - ParameterNumber - classifier.rf.cat - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split. - - - 10 - False - - - ParameterNumber - classifier.rf.var - Size of the randomly selected subset of features at each tree node - The size of the subset of features, randomly selected at each tree node, that are used to find the best split(s). If you set it to 0, then the size will be set to the square root of the total number of features. - - - 0 - False - - - ParameterNumber - classifier.rf.nbtrees - Maximum number of trees in the forest - The maximum number of trees in the forest. Typically, the more trees you have, the better the accuracy. However, the improvement in accuracy generally diminishes and reaches an asymptote for a certain number of trees. Also to keep in mind, increasing the number of trees increases the prediction time linearly. - - - 100 - False - - - ParameterNumber - classifier.rf.acc - Sufficient accuracy (OOB error) - Sufficient accuracy (OOB error). - - - 0.01 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-ann.xml b/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-ann.xml deleted file mode 100644 index a5c94a393c7d..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-ann.xml +++ /dev/null @@ -1,237 +0,0 @@ - - TrainVectorClassifier-ann - otbcli_TrainVectorClassifier - TrainVectorClassifier (ann) - Learning - Train a classifier based on labeled geometries and a list of features to consider. - - ParameterMultipleInput - io.vd - Input Vector Data - Input geometries used for training (note : all geometries from the layer will be used) - - False - - - ParameterFile - io.stats - Input XML image statistics file - XML file containing mean and variance of each feature. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterString - feat - Field names for training features. - List of field names in the input vector data to be used as features for training. - - - False - - - ParameterString - cfield - Field containing the class id for supervision - Field containing the class id for supervision. Only geometries with this field available will be taken into account. - class - - False - - - ParameterNumber - layer - Layer Index - Index of the layer to use in the input vector file. - - - 0 - True - - - ParameterMultipleInput - valid.vd - Validation Vector Data - Geometries used for validation (must contain the same fields used for training, all geometries from the layer will be used) - - True - - - ParameterNumber - valid.layer - Layer Index - Index of the layer to use in the validation vector file. - - - 0 - True - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - ann - - - 0 - False - - - ParameterSelection - classifier.ann.t - Train Method Type - Type of training method for the multilayer perceptron (MLP) neural network. - - - reg - back - - - 0 - False - - - ParameterString - classifier.ann.sizes - Number of neurons in each intermediate layer - The number of neurons in each intermediate layer (excluding input and output layers). - - - False - - - ParameterSelection - classifier.ann.f - Neuron activation function type - Neuron activation function. - - - ident - sig - gau - - - 1 - False - - - ParameterNumber - classifier.ann.a - Alpha parameter of the activation function - Alpha parameter of the activation function (used only with sigmoid and gaussian functions). - - - 1 - False - - - ParameterNumber - classifier.ann.b - Beta parameter of the activation function - Beta parameter of the activation function (used only with sigmoid and gaussian functions). - - - 1 - False - - - ParameterNumber - classifier.ann.bpdw - Strength of the weight gradient term in the BACKPROP method - Strength of the weight gradient term in the BACKPROP method. The recommended value is about 0.1. - - - 0.1 - False - - - ParameterNumber - classifier.ann.bpms - Strength of the momentum term (the difference between weights on the 2 previous iterations) - Strength of the momentum term (the difference between weights on the 2 previous iterations). This parameter provides some inertia to smooth the random fluctuations of the weights. It can vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough. - - - 0.1 - False - - - ParameterNumber - classifier.ann.rdw - Initial value Delta_0 of update-values Delta_{ij} in RPROP method - Initial value Delta_0 of update-values Delta_{ij} in RPROP method (default = 0.1). - - - 0.1 - False - - - ParameterNumber - classifier.ann.rdwm - Update-values lower limit Delta_{min} in RPROP method - Update-values lower limit Delta_{min} in RPROP method. It must be positive (default = 1e-7). - - - 1e-07 - False - - - ParameterSelection - classifier.ann.term - Termination criteria - Termination criteria. - - - iter - eps - all - - - 2 - False - - - ParameterNumber - classifier.ann.eps - Epsilon value used in the Termination criteria - Epsilon value used in the Termination criteria. - - - 0.01 - False - - - ParameterNumber - classifier.ann.iter - Maximum number of iterations used in the Termination criteria - Maximum number of iterations used in the Termination criteria. - - - 1000 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-bayes.xml b/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-bayes.xml deleted file mode 100644 index eec2bc172725..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-bayes.xml +++ /dev/null @@ -1,104 +0,0 @@ - - TrainVectorClassifier-bayes - otbcli_TrainVectorClassifier - TrainVectorClassifier (bayes) - Learning - Train a classifier based on labeled geometries and a list of features to consider. - - ParameterMultipleInput - io.vd - Input Vector Data - Input geometries used for training (note : all geometries from the layer will be used) - - False - - - ParameterFile - io.stats - Input XML image statistics file - XML file containing mean and variance of each feature. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterString - feat - Field names for training features. - List of field names in the input vector data to be used as features for training. - - - False - - - ParameterString - cfield - Field containing the class id for supervision - Field containing the class id for supervision. Only geometries with this field available will be taken into account. - class - - False - - - ParameterNumber - layer - Layer Index - Index of the layer to use in the input vector file. - - - 0 - True - - - ParameterMultipleInput - valid.vd - Validation Vector Data - Geometries used for validation (must contain the same fields used for training, all geometries from the layer will be used) - - True - - - ParameterNumber - valid.layer - Layer Index - Index of the layer to use in the validation vector file. - - - 0 - True - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - bayes - - - 0 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-boost.xml b/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-boost.xml deleted file mode 100644 index 3afe39d62349..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-boost.xml +++ /dev/null @@ -1,150 +0,0 @@ - - TrainVectorClassifier-boost - otbcli_TrainVectorClassifier - TrainVectorClassifier (boost) - Learning - Train a classifier based on labeled geometries and a list of features to consider. - - ParameterMultipleInput - io.vd - Input Vector Data - Input geometries used for training (note : all geometries from the layer will be used) - - False - - - ParameterFile - io.stats - Input XML image statistics file - XML file containing mean and variance of each feature. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterString - feat - Field names for training features. - List of field names in the input vector data to be used as features for training. - - - False - - - ParameterString - cfield - Field containing the class id for supervision - Field containing the class id for supervision. Only geometries with this field available will be taken into account. - class - - False - - - ParameterNumber - layer - Layer Index - Index of the layer to use in the input vector file. - - - 0 - True - - - ParameterMultipleInput - valid.vd - Validation Vector Data - Geometries used for validation (must contain the same fields used for training, all geometries from the layer will be used) - - True - - - ParameterNumber - valid.layer - Layer Index - Index of the layer to use in the validation vector file. - - - 0 - True - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - boost - - - 0 - False - - - ParameterSelection - classifier.boost.t - Boost Type - Type of Boosting algorithm. - - - discrete - real - logit - gentle - - - 1 - False - - - ParameterNumber - classifier.boost.w - Weak count - The number of weak classifiers. - - - 100 - False - - - ParameterNumber - classifier.boost.r - Weight Trim Rate - A threshold between 0 and 1 used to save computational time. Samples with summary weight <= (1 - weight_trim_rate) do not participate in the next iteration of training. Set this parameter to 0 to turn off this functionality. - - - 0.95 - False - - - ParameterNumber - classifier.boost.m - Maximum depth of the tree - Maximum depth of the tree. - - - 1 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-dt.xml b/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-dt.xml deleted file mode 100644 index 1f7ca5d761ec..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-dt.xml +++ /dev/null @@ -1,170 +0,0 @@ - - TrainVectorClassifier-dt - otbcli_TrainVectorClassifier - TrainVectorClassifier (dt) - Learning - Train a classifier based on labeled geometries and a list of features to consider. - - ParameterMultipleInput - io.vd - Input Vector Data - Input geometries used for training (note : all geometries from the layer will be used) - - False - - - ParameterFile - io.stats - Input XML image statistics file - XML file containing mean and variance of each feature. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterString - feat - Field names for training features. - List of field names in the input vector data to be used as features for training. - - - False - - - ParameterString - cfield - Field containing the class id for supervision - Field containing the class id for supervision. Only geometries with this field available will be taken into account. - class - - False - - - ParameterNumber - layer - Layer Index - Index of the layer to use in the input vector file. - - - 0 - True - - - ParameterMultipleInput - valid.vd - Validation Vector Data - Geometries used for validation (must contain the same fields used for training, all geometries from the layer will be used) - - True - - - ParameterNumber - valid.layer - Layer Index - Index of the layer to use in the validation vector file. - - - 0 - True - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - dt - - - 0 - False - - - ParameterNumber - classifier.dt.max - Maximum depth of the tree - The training algorithm attempts to split each node while its depth is smaller than the maximum possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or if the tree is pruned. - - - 65535 - False - - - ParameterNumber - classifier.dt.min - Minimum number of samples in each node - If all absolute differences between an estimated value in a node and the values of the train samples in this node are smaller than this regression accuracy parameter, then the node will not be split. - - - 10 - False - - - ParameterNumber - classifier.dt.ra - Termination criteria for regression tree - - - - 0.01 - False - - - ParameterNumber - classifier.dt.cat - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split. - - - 10 - False - - - ParameterNumber - classifier.dt.f - K-fold cross-validations - If cv_folds > 1, then it prunes a tree with K-fold cross-validation where K is equal to cv_folds. - - - 10 - False - - - ParameterBoolean - classifier.dt.r - Set Use1seRule flag to false - If true, then a pruning will be harsher. This will make a tree more compact and more resistant to the training data noise but a bit less accurate. - True - True - - - ParameterBoolean - classifier.dt.t - Set TruncatePrunedTree flag to false - If true, then pruned branches are physically removed from the tree. - True - True - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-gbt.xml b/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-gbt.xml deleted file mode 100644 index 0abe5a0289c0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-gbt.xml +++ /dev/null @@ -1,144 +0,0 @@ - - TrainVectorClassifier-gbt - otbcli_TrainVectorClassifier - TrainVectorClassifier (gbt) - Learning - Train a classifier based on labeled geometries and a list of features to consider. - - ParameterMultipleInput - io.vd - Input Vector Data - Input geometries used for training (note : all geometries from the layer will be used) - - False - - - ParameterFile - io.stats - Input XML image statistics file - XML file containing mean and variance of each feature. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterString - feat - Field names for training features. - List of field names in the input vector data to be used as features for training. - - - False - - - ParameterString - cfield - Field containing the class id for supervision - Field containing the class id for supervision. Only geometries with this field available will be taken into account. - class - - False - - - ParameterNumber - layer - Layer Index - Index of the layer to use in the input vector file. - - - 0 - True - - - ParameterMultipleInput - valid.vd - Validation Vector Data - Geometries used for validation (must contain the same fields used for training, all geometries from the layer will be used) - - True - - - ParameterNumber - valid.layer - Layer Index - Index of the layer to use in the validation vector file. - - - 0 - True - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - gbt - - - 0 - False - - - ParameterNumber - classifier.gbt.w - Number of boosting algorithm iterations - Number "w" of boosting algorithm iterations, with w*K being the total number of trees in the GBT model, where K is the output number of classes. - - - 200 - False - - - ParameterNumber - classifier.gbt.s - Regularization parameter - Regularization parameter. - - - 0.01 - False - - - ParameterNumber - classifier.gbt.p - Portion of the whole training set used for each algorithm iteration - Portion of the whole training set used for each algorithm iteration. The subset is generated randomly. - - - 0.8 - False - - - ParameterNumber - classifier.gbt.max - Maximum depth of the tree - The training algorithm attempts to split each node while its depth is smaller than the maximum possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or if the tree is pruned. - - - 3 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-knn.xml b/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-knn.xml deleted file mode 100644 index abc714d0ad89..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-knn.xml +++ /dev/null @@ -1,114 +0,0 @@ - - TrainVectorClassifier-knn - otbcli_TrainVectorClassifier - TrainVectorClassifier (knn) - Learning - Train a classifier based on labeled geometries and a list of features to consider. - - ParameterMultipleInput - io.vd - Input Vector Data - Input geometries used for training (note : all geometries from the layer will be used) - - False - - - ParameterFile - io.stats - Input XML image statistics file - XML file containing mean and variance of each feature. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterString - feat - Field names for training features. - List of field names in the input vector data to be used as features for training. - - - False - - - ParameterString - cfield - Field containing the class id for supervision - Field containing the class id for supervision. Only geometries with this field available will be taken into account. - class - - False - - - ParameterNumber - layer - Layer Index - Index of the layer to use in the input vector file. - - - 0 - True - - - ParameterMultipleInput - valid.vd - Validation Vector Data - Geometries used for validation (must contain the same fields used for training, all geometries from the layer will be used) - - True - - - ParameterNumber - valid.layer - Layer Index - Index of the layer to use in the validation vector file. - - - 0 - True - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - knn - - - 0 - False - - - ParameterNumber - classifier.knn.k - Number of Neighbors - The number of neighbors to use. - - - 32 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-libsvm.xml b/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-libsvm.xml deleted file mode 100644 index 94f835ec02ed..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-libsvm.xml +++ /dev/null @@ -1,161 +0,0 @@ - - TrainVectorClassifier-libsvm - otbcli_TrainVectorClassifier - TrainVectorClassifier (libsvm) - Learning - Train a classifier based on labeled geometries and a list of features to consider. - - ParameterMultipleInput - io.vd - Input Vector Data - Input geometries used for training (note : all geometries from the layer will be used) - - False - - - ParameterFile - io.stats - Input XML image statistics file - XML file containing mean and variance of each feature. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterString - feat - Field names for training features. - List of field names in the input vector data to be used as features for training. - - - False - - - ParameterString - cfield - Field containing the class id for supervision - Field containing the class id for supervision. Only geometries with this field available will be taken into account. - class - - False - - - ParameterNumber - layer - Layer Index - Index of the layer to use in the input vector file. - - - 0 - True - - - ParameterMultipleInput - valid.vd - Validation Vector Data - Geometries used for validation (must contain the same fields used for training, all geometries from the layer will be used) - - True - - - ParameterNumber - valid.layer - Layer Index - Index of the layer to use in the validation vector file. - - - 0 - True - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - libsvm - - - 0 - False - - - ParameterSelection - classifier.libsvm.k - SVM Kernel Type - SVM Kernel Type. - - - linear - rbf - poly - sigmoid - - - 0 - False - - - ParameterSelection - classifier.libsvm.m - SVM Model Type - Type of SVM formulation. - - - csvc - nusvc - oneclass - - - 0 - False - - - ParameterNumber - classifier.libsvm.c - Cost parameter C - SVM models have a cost parameter C (1 by default) to control the trade-off between training errors and forcing rigid margins. - - - 1 - False - - - ParameterBoolean - classifier.libsvm.opt - Parameters optimization - SVM parameters optimization flag. - True - True - - - ParameterBoolean - classifier.libsvm.prob - Probability estimation - Probability estimation flag. - True - True - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-rf.xml b/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-rf.xml deleted file mode 100644 index f4b245d74913..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/TrainVectorClassifier-rf.xml +++ /dev/null @@ -1,174 +0,0 @@ - - TrainVectorClassifier-rf - otbcli_TrainVectorClassifier - TrainVectorClassifier (rf) - Learning - Train a classifier based on labeled geometries and a list of features to consider. - - ParameterMultipleInput - io.vd - Input Vector Data - Input geometries used for training (note : all geometries from the layer will be used) - - False - - - ParameterFile - io.stats - Input XML image statistics file - XML file containing mean and variance of each feature. - - True - - - OutputFile - io.confmatout - Output confusion matrix - Output file containing the confusion matrix (.csv format). - - - OutputFile - io.out - Output model - Output file containing the model estimated (.txt format). - - - ParameterString - feat - Field names for training features. - List of field names in the input vector data to be used as features for training. - - - False - - - ParameterString - cfield - Field containing the class id for supervision - Field containing the class id for supervision. Only geometries with this field available will be taken into account. - class - - False - - - ParameterNumber - layer - Layer Index - Index of the layer to use in the input vector file. - - - 0 - True - - - ParameterMultipleInput - valid.vd - Validation Vector Data - Geometries used for validation (must contain the same fields used for training, all geometries from the layer will be used) - - True - - - ParameterNumber - valid.layer - Layer Index - Index of the layer to use in the validation vector file. - - - 0 - True - - - ParameterSelection - classifier - Classifier to use for the training - Choice of the classifier to use for the training. - - - rf - - - 0 - False - - - ParameterNumber - classifier.rf.max - Maximum depth of the tree - The depth of the tree. A low value will likely underfit and conversely a high value will likely overfit. The optimal value can be obtained using cross validation or other suitable methods. - - - 5 - False - - - ParameterNumber - classifier.rf.min - Minimum number of samples in each node - If the number of samples in a node is smaller than this parameter, then the node will not be split. A reasonable value is a small percentage of the total data e.g. 1 percent. - - - 10 - False - - - ParameterNumber - classifier.rf.ra - Termination Criteria for regression tree - If all absolute differences between an estimated value in a node and the values of the train samples in this node are smaller than this regression accuracy parameter, then the node will not be split. - - - 0 - False - - - ParameterNumber - classifier.rf.cat - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split - Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split. - - - 10 - False - - - ParameterNumber - classifier.rf.var - Size of the randomly selected subset of features at each tree node - The size of the subset of features, randomly selected at each tree node, that are used to find the best split(s). If you set it to 0, then the size will be set to the square root of the total number of features. - - - 0 - False - - - ParameterNumber - classifier.rf.nbtrees - Maximum number of trees in the forest - The maximum number of trees in the forest. Typically, the more trees you have, the better the accuracy. However, the improvement in accuracy generally diminishes and reaches an asymptote for a certain number of trees. Also to keep in mind, increasing the number of trees increases the prediction time linearly. - - - 100 - False - - - ParameterNumber - classifier.rf.acc - Sufficient accuracy (OOB error) - Sufficient accuracy (OOB error). - - - 0.01 - False - - - ParameterNumber - rand - set user defined seed - Set specific seed. with integer value. - - - 0 - True - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/VectorDataExtractROI.xml b/python/plugins/processing/algs/otb/description/5.8.0/VectorDataExtractROI.xml deleted file mode 100644 index 21e8e2e96dc6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/VectorDataExtractROI.xml +++ /dev/null @@ -1,39 +0,0 @@ - - VectorDataExtractROI - otbcli_VectorDataExtractROI - VectorData Extract ROI - Vector Data Manipulation - Perform an extract ROI on the input vector data according to the input image extent - - ParameterVector - io.vd - Input Vector data - Input vector data - - False - - - ParameterRaster - io.in - Support image - Support image that specifies the extracted region - False - - - OutputVector - io.out - Output Vector data - Output extracted vector data - - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/VectorDataReprojection-image.xml b/python/plugins/processing/algs/otb/description/5.8.0/VectorDataReprojection-image.xml deleted file mode 100644 index 8a3948907e85..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/VectorDataReprojection-image.xml +++ /dev/null @@ -1,59 +0,0 @@ - - VectorDataReprojection-image - otbcli_VectorDataReprojection - VectorDataReprojection (image) - Vector Data Manipulation - Reproject a vector data using support image projection reference, or a user specified map projection - - - ParameterFile - in.vd - Input vector data - The input vector data to reproject - - False - - - ParameterRaster - in.kwl - Use image keywords list - Optional input image to fill vector data with image kwl. - True - - - OutputFile - out.vd - Output vector data - The reprojected vector data - - - ParameterSelection - out.proj - Output Projection choice - - - - image - - - 0 - False - - - ParameterRaster - out.proj.image.in - Image used to get projection map - Projection map will be found using image metadata - False - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/VectorDataReprojection-user.xml b/python/plugins/processing/algs/otb/description/5.8.0/VectorDataReprojection-user.xml deleted file mode 100644 index 0392ba55b214..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/VectorDataReprojection-user.xml +++ /dev/null @@ -1,97 +0,0 @@ - - VectorDataReprojection-user - otbcli_VectorDataReprojection - VectorDataReprojection (user) - Vector Data Manipulation - Reproject a vector data using support image projection reference, or a user specified map projection - - - ParameterFile - in.vd - Input vector data - The input vector data to reproject - - False - - - ParameterRaster - in.kwl - Use image keywords list - Optional input image to fill vector data with image kwl. - True - - - OutputFile - out.vd - Output vector data - The reprojected vector data - - - ParameterSelection - out.proj - Output Projection choice - - - - user - - - 0 - False - - - ParameterSelection - out.proj.user.map - Output Cartographic Map Projection - Parameters of the output map projection to be used. - - - utm - lambert2 - lambert93 - wgs - epsg - - - 0 - False - - - ParameterNumber - out.proj.user.map.utm.zone - Zone number - The zone number ranges from 1 to 60 and allows defining the transverse mercator projection (along with the hemisphere) - - - 31 - False - - - ParameterBoolean - out.proj.user.map.utm.northhem - Northern Hemisphere - The transverse mercator projections are defined by their zone number as well as the hemisphere. Activate this parameter if your image is in the northern hemisphere. - True - True - - - ParameterNumber - out.proj.user.map.epsg.code - EPSG Code - See www.spatialreference.org to find which EPSG code is associated to your projection - - - 4326 - False - - - ParameterNumber - elev.default - Default elevation - This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. - - - 0 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/VectorDataTransform.xml b/python/plugins/processing/algs/otb/description/5.8.0/VectorDataTransform.xml deleted file mode 100644 index e9c974ddfbd6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/VectorDataTransform.xml +++ /dev/null @@ -1,89 +0,0 @@ - - VectorDataTransform - otbcli_VectorDataTransform - Vector Data Transformation - Vector Data Manipulation - Apply a transform to each vertex of the input VectorData - - ParameterVector - vd - Input Vector data - Input vector data to transform - - False - - - OutputVector - out - Output Vector data - Output transformed vector data - - - - ParameterRaster - in - Support image - Image needed as a support to the vector data - False - - - ParameterNumber - transform.tx - Translation X - Translation in the X direction (in pixels) - - - 0 - False - - - ParameterNumber - transform.ty - Translation Y - Translation in the Y direction (in pixels) - - - 0 - False - - - ParameterNumber - transform.ro - Rotation Angle - Angle of the rotation to apply in degrees - - - 0 - False - - - ParameterNumber - transform.centerx - Center X - X coordinate of the rotation center (in physical units) - - - 0 - False - - - ParameterNumber - transform.centery - Center Y - Y coordinate of the rotation center (in physical units) - - - 0 - False - - - ParameterNumber - transform.scale - Scale - The scale to apply - - - 1 - False - - diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/BandMath.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/BandMath.html deleted file mode 100644 index 1667d53f10ae..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/BandMath.html +++ /dev/null @@ -1,10 +0,0 @@ - - -

BandMath

Brief Description

Perform a mathematical operation on monoband images

Tags

Util

Long Description

This application performs a mathematical operation on monoband images.Mathematical formula interpretation is done via MuParser libraries. -For MuParser version superior to 2.0 uses '&&' and '||' logical operators, and ternary operator 'boolean_expression ? if_true : if_false' -For older version of MuParser (prior to v2) use 'and' and 'or' logical operators, and ternary operator 'if(; ; )'. -The list of features and operators is available on MuParser website: http://muparser.sourceforge.net/ -

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/BandMathX.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/BandMathX.html deleted file mode 100644 index 2b6d19b2190a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/BandMathX.html +++ /dev/null @@ -1,98 +0,0 @@ - - -

BandMathX

Brief Description

This application performs mathematical operations on multiband images. -Mathematical formula interpretation is done via muParserX library : http://articles.beltoforion.de/article.php?a=muparserx

Tags

Util

Long Description

The goal of this documentation is to give the user some hints about the syntax used in this application. -The syntax is mainly constrained by the muparserx library, which can be considered as the core of the application. - - -- Fundamentals: - -The default prefix name for variables related to the ith input is 'im(i+1)'(note the indexing from 1 to N, for N inputs). -The following list summaries the available variables for input #0 (and so on for every input): - -im1 --> a pixel from first input, made of n components (n bands) -im1bj --> jth component of a pixel from first input (first band is indexed by 1) -im1bjNkxp --> a neighbourhood ('N') of pixels of the jth component from first input, of size kxp -im1PhyX and im1PhyY --> spacing of first input in X and Y directions (horizontal and vertical) -im1bjMean im1bjMin im1bjMax im1bjSum im1bjVar --> mean, min, max, sum, variance of jth band from first input (global statistics) - -Moreover, we also have the following generic variables: -idxX and idxY --> indices of the current pixel - -Always keep in mind that this application only addresses mathematically well-defined formulas. -For instance, it is not possible to add vectors of different dimensions (this implies the addition of a row vector with a column vector), -or add a scalar to a vector or a matrix, or divide two vectors, and so on... -Thus, it is important to remember that a pixel of n components is always represented as a row vector. - -Example : - - im1 + im2 (1) - -represents the addition of pixels from first and second inputs. This expression is consistent only if -both inputs have the same number of bands. -Note that it is also possible to use the following expressions to obtain the same result: - - im1b1 + im2b1 - im1b2 + im2b2 (2) - ... - -Nevertheless, the first expression is by far much pleaseant. We call this new functionality the 'batch mode' -(performing the same operation in a band-to-band fashion). - - -- Operations involving neighborhoods of pixels: - -Another new fonctionnality is the possibility to perform operations that involve neighborhoods of pixels. -Variable related to such neighborhoods are always defined following the pattern imIbJNKxP, where: -- I is an number identifying the image input (remember, input #0 = im1, and so on) -- J is an number identifying the band (remember, first band is indexed by 1) -- KxP are two numbers that represent the size of the neighborhood (first one is related to the horizontal direction) -All neighborhood are centered, thus K and P must be odd numbers. -Many operators come with this new functionality: dotpr, mean var median min max... -For instance, if im1 represents the pixel of 3 bands image: - - im1 - mean(im1b1N5x5,im1b2N5x5,im1b3N5x5) (3) - -could represent a high pass filter (Note that by implying three neighborhoods, the operator mean returns a row vector of three components. -It is a typical behaviour for many operators of this application). - - -- Operators: - -In addition to the previous operators, other operators are available: -- existing operators/functions from muParserX, that were not originally defined for vectors and -matrices (for instance cos, sin, ...). These new operators/ functions keep the original names to which we added the prefix 'v' for vector (vcos, vsin, ...). -- mult, div and pow operators, that perform element-wise multiplication, division or exponentiation of vector/matrices (for instance im1 div im2) -- mlt, dv and pw operators, that perform multiplication, division or exponentiation of vector/matrices by a scalar (for instance im1 dv 2.0) -- bands, which is a very useful operator. It allows selecting specific bands from an image, and/or to rearrange them in a new vector; -for instance bands(im1,{1,2,1,1}) produces a vector of 4 components made of band 1, band 2, band 1 and band 1 values from the first input. -Note that curly brackets must be used in order to select the desired band indices. -... and so on. - - -- Application itself: - -The application takes the following parameters : -- Setting the list of inputs can be done with the 'il' parameter. -- Setting expressions can be done with the 'exp' parameter (see also limitations section below). -- Setting constants can be done with the 'incontext' parameter. User must provide a txt file with a specific syntax: #type name value -An example of such a file is given below: - -#F expo 1.1 -#M kernel1 { 0.1 , 0.2 , 0.3; 0.4 , 0.5 , 0.6; 0.7 , 0.8 , 0.9; 1 , 1.1 , 1.2; 1.3 , 1.4 , 1.5 } - -As we can see, #I/#F allows the definition of an integer/float constant, whereas #M allows the definition of a vector/matrix. -In the latter case, elements of a row must be separated by commas, and rows must be separated by semicolons. -It is also possible to define expressions within the same txt file, with the pattern #E expr. For instance (two expressions; see also limitations section below): - -#E $dotpr(kernel1,im1b1N3x5); im2b1^expo$ - -- The 'outcontext' parameter allows saving user's constants and expressions (context). -- Setting the output image can be done with the 'out' parameter (multi-outputs is not implemented yet). - - -Finally, we strongly recommend that the reader takes a look at the cookbook, where additional information can be found (http://www.orfeo-toolbox.org/packages/OTBCookBook.pdf). -

Parameters

Limitations

The application is currently unable to produce one output image per expression, contrary to otbBandMathXImageFilter. -Separating expressions by semi-colons (; ) will concatenate their results into a unique multiband output image.

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/BinaryMorphologicalOperation-closing.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/BinaryMorphologicalOperation-closing.html deleted file mode 100644 index 97a2b19d7065..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/BinaryMorphologicalOperation-closing.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BinaryMorphologicalOperation

Brief Description

Performs morphological operations on an input image channel

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs binary morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkBinaryDilateImageFilter, itkBinaryErodeImageFilter, itkBinaryMorphologicalOpeningImageFilter and itkBinaryMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/BinaryMorphologicalOperation-dilate.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/BinaryMorphologicalOperation-dilate.html deleted file mode 100644 index 97a2b19d7065..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/BinaryMorphologicalOperation-dilate.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BinaryMorphologicalOperation

Brief Description

Performs morphological operations on an input image channel

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs binary morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkBinaryDilateImageFilter, itkBinaryErodeImageFilter, itkBinaryMorphologicalOpeningImageFilter and itkBinaryMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/BinaryMorphologicalOperation-erode.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/BinaryMorphologicalOperation-erode.html deleted file mode 100644 index 97a2b19d7065..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/BinaryMorphologicalOperation-erode.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BinaryMorphologicalOperation

Brief Description

Performs morphological operations on an input image channel

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs binary morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkBinaryDilateImageFilter, itkBinaryErodeImageFilter, itkBinaryMorphologicalOpeningImageFilter and itkBinaryMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/BinaryMorphologicalOperation-opening.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/BinaryMorphologicalOperation-opening.html deleted file mode 100644 index 97a2b19d7065..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/BinaryMorphologicalOperation-opening.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BinaryMorphologicalOperation

Brief Description

Performs morphological operations on an input image channel

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs binary morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkBinaryDilateImageFilter, itkBinaryErodeImageFilter, itkBinaryMorphologicalOpeningImageFilter and itkBinaryMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/BinaryMorphologicalOperation.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/BinaryMorphologicalOperation.html deleted file mode 100644 index 97a2b19d7065..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/BinaryMorphologicalOperation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BinaryMorphologicalOperation

Brief Description

Performs morphological operations on an input image channel

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs binary morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkBinaryDilateImageFilter, itkBinaryErodeImageFilter, itkBinaryMorphologicalOpeningImageFilter and itkBinaryMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/BlockMatching.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/BlockMatching.html deleted file mode 100644 index 4d5caf2e62c5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/BlockMatching.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BlockMatching

Brief Description

Performs block-matching to estimate pixel-wise disparities between two images

Tags

Stereo

Long Description

This application allows one to performs block-matching to estimate pixel-wise disparities between two images. One must chose block-matching method and input masks (related to the left and right input image) of pixels for which the disparity should be investigated. Additionally, two criteria can be optionally used to disable disparity investigation for some pixel: a no-data value, and a threshold on the local variance. This allows one to speed-up computation by avoiding to investigate disparities that will not be reliable anyway. For efficiency reasons, if the optimal metric values image is desired, it will be concatenated to the output image (which will then have three bands : horizontal disparity, vertical disparity and metric value). One can split these images afterward.

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbStereoRectificationGridGenerator

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/BundleToPerfectSensor.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/BundleToPerfectSensor.html deleted file mode 100644 index 8467b5bd4c1d..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/BundleToPerfectSensor.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

BundleToPerfectSensor

Brief Description

Perform P+XS pansharpening

Tags

Geometry,Pansharpening

Long Description

This application performs P+XS pansharpening. The default mode use Pan and XS sensor models to estimate the transformation to superimpose XS over Pan before the fusion ("default mode"). The application provides also a PHR mode for Pleiades images which does not use sensor models as Pan and XS products are already coregistered but only estimate an affine transformation to superimpose XS over the Pan.Note that this option is automatically activated in case Pleiades images are detected as input.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ClassificationMapRegularization.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ClassificationMapRegularization.html deleted file mode 100644 index 006f55b08baf..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ClassificationMapRegularization.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

ClassificationMapRegularization

Brief Description

Filters the input labeled image using Majority Voting in a ball shaped neighbordhood.

Tags

Learning,Image Analysis

Long Description

This application filters the input labeled image (with a maximal class label = 65535) using Majority Voting in a ball shaped neighbordhood. Majority Voting takes the more representative value of all the pixels identified by the ball shaped structuring element and then sets the center pixel to this majority label value. - -NoData is the label of the NOT classified pixels in the input image. These input pixels keep their NoData label in the output image. - -Pixels with more than 1 majority class are marked as Undecided if the parameter 'ip.suvbool == true', or keep their Original labels otherwise.

Parameters

Limitations

The input image must be a single band labeled image (with a maximal class label = 65535). The structuring element radius must have a minimum value equal to 1 pixel. Please note that the Undecided value must be different from existing labels in the input labeled image.

Authors

OTB-Team

See Also

Documentation of the ClassificationMapRegularization application.

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ColorMapping-continuous.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ColorMapping-continuous.html deleted file mode 100644 index 6ce8d15474ab..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ColorMapping-continuous.html +++ /dev/null @@ -1,13 +0,0 @@ - - -

ColorMapping

Brief Description

Maps an input label image to 8-bits RGB using look-up tables.

Tags

Utilities,Image Manipulation,Image MetaData,Learning

Long Description

This application allows one to map a label image to a 8-bits RGB image (in both ways) using different methods. - -The custom method allows one to use a custom look-up table. The look-up table is loaded from a text file where each line describes an entry. The typical use of this method is to colorise a classification map. - -The continuous method allows mapping a range of values in a scalar input image to a colored image using continuous look-up table, in order to enhance image interpretation. Several look-up tables can been chosen with different color ranges. --The optimal method computes an optimal look-up table. When processing a segmentation label image (label to color), the color difference between adjacent segmented regions is maximized. When processing an unknown color image (color to label), all the present colors are mapped to a continuous label list. - - The support image method uses a color support image to associate an average color to each region.

Parameters

Limitations

The segmentation optimal method does not support streaming, and thus large images. The operation color to label is not implemented for the methods continuous LUT and support image LUT. - ColorMapping using support image is not threaded.

Authors

OTB-Team

See Also

ImageSVMClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ColorMapping-custom.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ColorMapping-custom.html deleted file mode 100644 index 6ce8d15474ab..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ColorMapping-custom.html +++ /dev/null @@ -1,13 +0,0 @@ - - -

ColorMapping

Brief Description

Maps an input label image to 8-bits RGB using look-up tables.

Tags

Utilities,Image Manipulation,Image MetaData,Learning

Long Description

This application allows one to map a label image to a 8-bits RGB image (in both ways) using different methods. - -The custom method allows one to use a custom look-up table. The look-up table is loaded from a text file where each line describes an entry. The typical use of this method is to colorise a classification map. - -The continuous method allows mapping a range of values in a scalar input image to a colored image using continuous look-up table, in order to enhance image interpretation. Several look-up tables can been chosen with different color ranges. --The optimal method computes an optimal look-up table. When processing a segmentation label image (label to color), the color difference between adjacent segmented regions is maximized. When processing an unknown color image (color to label), all the present colors are mapped to a continuous label list. - - The support image method uses a color support image to associate an average color to each region.

Parameters

Limitations

The segmentation optimal method does not support streaming, and thus large images. The operation color to label is not implemented for the methods continuous LUT and support image LUT. - ColorMapping using support image is not threaded.

Authors

OTB-Team

See Also

ImageSVMClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ColorMapping-image.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ColorMapping-image.html deleted file mode 100644 index 6ce8d15474ab..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ColorMapping-image.html +++ /dev/null @@ -1,13 +0,0 @@ - - -

ColorMapping

Brief Description

Maps an input label image to 8-bits RGB using look-up tables.

Tags

Utilities,Image Manipulation,Image MetaData,Learning

Long Description

This application allows one to map a label image to a 8-bits RGB image (in both ways) using different methods. - -The custom method allows one to use a custom look-up table. The look-up table is loaded from a text file where each line describes an entry. The typical use of this method is to colorise a classification map. - -The continuous method allows mapping a range of values in a scalar input image to a colored image using continuous look-up table, in order to enhance image interpretation. Several look-up tables can been chosen with different color ranges. --The optimal method computes an optimal look-up table. When processing a segmentation label image (label to color), the color difference between adjacent segmented regions is maximized. When processing an unknown color image (color to label), all the present colors are mapped to a continuous label list. - - The support image method uses a color support image to associate an average color to each region.

Parameters

Limitations

The segmentation optimal method does not support streaming, and thus large images. The operation color to label is not implemented for the methods continuous LUT and support image LUT. - ColorMapping using support image is not threaded.

Authors

OTB-Team

See Also

ImageSVMClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ColorMapping-optimal.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ColorMapping-optimal.html deleted file mode 100644 index 6ce8d15474ab..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ColorMapping-optimal.html +++ /dev/null @@ -1,13 +0,0 @@ - - -

ColorMapping

Brief Description

Maps an input label image to 8-bits RGB using look-up tables.

Tags

Utilities,Image Manipulation,Image MetaData,Learning

Long Description

This application allows one to map a label image to a 8-bits RGB image (in both ways) using different methods. - -The custom method allows one to use a custom look-up table. The look-up table is loaded from a text file where each line describes an entry. The typical use of this method is to colorise a classification map. - -The continuous method allows mapping a range of values in a scalar input image to a colored image using continuous look-up table, in order to enhance image interpretation. Several look-up tables can been chosen with different color ranges. --The optimal method computes an optimal look-up table. When processing a segmentation label image (label to color), the color difference between adjacent segmented regions is maximized. When processing an unknown color image (color to label), all the present colors are mapped to a continuous label list. - - The support image method uses a color support image to associate an average color to each region.

Parameters

Limitations

The segmentation optimal method does not support streaming, and thus large images. The operation color to label is not implemented for the methods continuous LUT and support image LUT. - ColorMapping using support image is not threaded.

Authors

OTB-Team

See Also

ImageSVMClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ColorMapping.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ColorMapping.html deleted file mode 100644 index 6ce8d15474ab..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ColorMapping.html +++ /dev/null @@ -1,13 +0,0 @@ - - -

ColorMapping

Brief Description

Maps an input label image to 8-bits RGB using look-up tables.

Tags

Utilities,Image Manipulation,Image MetaData,Learning

Long Description

This application allows one to map a label image to a 8-bits RGB image (in both ways) using different methods. - -The custom method allows one to use a custom look-up table. The look-up table is loaded from a text file where each line describes an entry. The typical use of this method is to colorise a classification map. - -The continuous method allows mapping a range of values in a scalar input image to a colored image using continuous look-up table, in order to enhance image interpretation. Several look-up tables can been chosen with different color ranges. --The optimal method computes an optimal look-up table. When processing a segmentation label image (label to color), the color difference between adjacent segmented regions is maximized. When processing an unknown color image (color to label), all the present colors are mapped to a continuous label list. - - The support image method uses a color support image to associate an average color to each region.

Parameters

Limitations

The segmentation optimal method does not support streaming, and thus large images. The operation color to label is not implemented for the methods continuous LUT and support image LUT. - ColorMapping using support image is not threaded.

Authors

OTB-Team

See Also

ImageSVMClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/CompareImages.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/CompareImages.html deleted file mode 100644 index a68451c51622..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/CompareImages.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

CompareImages

Brief Description

Estimator between 2 images.

Tags

Statistics

Long Description

This application computes MSE (Mean Squared Error), MAE (Mean Absolute Error) and PSNR (Peak Signal to Noise Ratio) between the channel of two images (reference and measurement). The user has to set the used channel and can specify a ROI.

Parameters

Limitations

None

Authors

OTB-Team

See Also

BandMath application, ImageStatistics

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ComputeConfusionMatrix-raster.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ComputeConfusionMatrix-raster.html deleted file mode 100644 index 8a6c9eed9ad3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ComputeConfusionMatrix-raster.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputeConfusionMatrix

Brief Description

Computes the confusion matrix of a classification

Tags

Learning

Long Description

This application computes the confusion matrix of a classification map relatively to a ground truth. This ground truth can be given as a raster or a vector data. Only reference and produced pixels with values different from NoData are handled in the calculation of the confusion matrix. The confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the output file, the reference and produced class labels are ordered according to the rows/columns of the confusion matrix.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ComputeConfusionMatrix-vector.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ComputeConfusionMatrix-vector.html deleted file mode 100644 index 8a6c9eed9ad3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ComputeConfusionMatrix-vector.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputeConfusionMatrix

Brief Description

Computes the confusion matrix of a classification

Tags

Learning

Long Description

This application computes the confusion matrix of a classification map relatively to a ground truth. This ground truth can be given as a raster or a vector data. Only reference and produced pixels with values different from NoData are handled in the calculation of the confusion matrix. The confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the output file, the reference and produced class labels are ordered according to the rows/columns of the confusion matrix.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ComputeConfusionMatrix.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ComputeConfusionMatrix.html deleted file mode 100644 index 8a6c9eed9ad3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ComputeConfusionMatrix.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputeConfusionMatrix

Brief Description

Computes the confusion matrix of a classification

Tags

Learning

Long Description

This application computes the confusion matrix of a classification map relatively to a ground truth. This ground truth can be given as a raster or a vector data. Only reference and produced pixels with values different from NoData are handled in the calculation of the confusion matrix. The confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the output file, the reference and produced class labels are ordered according to the rows/columns of the confusion matrix.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ComputeImagesStatistics.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ComputeImagesStatistics.html deleted file mode 100644 index 7cd184750210..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ComputeImagesStatistics.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputeImagesStatistics

Brief Description

Computes global mean and standard deviation for each band from a set of images and optionally saves the results in an XML file.

Tags

Learning,Image Analysis

Long Description

This application computes a global mean and standard deviation for each band of a set of images and optionally saves the results in an XML file. The output XML is intended to be used an input for the TrainImagesClassifier application to normalize samples before learning.

Parameters

Limitations

Each image of the set must contain the same bands as the others (i.e. same types, in the same order).

Authors

OTB-Team

See Also

Documentation of the TrainImagesClassifier application.

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ComputeOGRLayersFeaturesStatistics.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ComputeOGRLayersFeaturesStatistics.html deleted file mode 100644 index 42c4651f14e3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ComputeOGRLayersFeaturesStatistics.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputeOGRLayersFeaturesStatistics

Brief Description

Compute statistics of the features in a set of OGR Layers

Tags

Segmentation

Long Description

Compute statistics (mean and standard deviation) of the features in a set of OGR Layers, and write them in an XML file. This XML file can then be used by the training application.

Parameters

Limitations

Experimental. For now only shapefiles are supported.

Authors

David Youssefi during internship at CNES

See Also

OGRLayerClassifier,TrainOGRLayersClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ComputePolylineFeatureFromImage.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ComputePolylineFeatureFromImage.html deleted file mode 100644 index 646191e845f4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ComputePolylineFeatureFromImage.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ComputePolylineFeatureFromImage

Brief Description

This application compute for each studied polyline, contained in the input VectorData, the chosen descriptors.

Tags

Feature Extraction

Long Description

The first step in the classifier fusion based validation is to compute, for each studied polyline, the chosen descriptors.

Parameters

Limitations

Since it does not rely on streaming process, take care of the size of input image before launching application.

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ConcatenateImages.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ConcatenateImages.html deleted file mode 100644 index f5d2ac5e2c28..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ConcatenateImages.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ConcatenateImages

Brief Description

Concatenate a list of images of the same size into a single multi-channel one.

Tags

Image Manipulation,Concatenation,Multi-channel

Long Description

This application performs images channels concatenation. It will walk the input image list (single or multi-channel) and generates a single multi-channel image. The channel order is the one of the list.

Parameters

Limitations

All input images must have the same size.

Authors

OTB-Team

See Also

Rescale application, Convert

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ConcatenateVectorData.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ConcatenateVectorData.html deleted file mode 100644 index 1760c34db99c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ConcatenateVectorData.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ConcatenateVectorData

Brief Description

Concatenate VectorDatas

Tags

Vector Data Manipulation

Long Description

This application concatenates a list of VectorData to produce a unique VectorData as output.Note that the VectorDatas must be of the same type (Storing polygons only, lines only, or points only)

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ConnectedComponentSegmentation.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ConnectedComponentSegmentation.html deleted file mode 100644 index 96d0c3ec51b8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ConnectedComponentSegmentation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ConnectedComponentSegmentation

Brief Description

Connected component segmentation and object based image filtering of the input image according to user-defined criterions.

Tags

Image Analysis,Segmentation

Long Description

This application allows one to perform a masking, connected components segmentation and object based image filtering. First and optionally, a mask can be built based on user-defined criterions to select pixels of the image which will be segmented. Then a connected component segmentation is performed with a user defined criterion to decide whether two neighbouring pixels belong to the same segment or not. After this segmentation step, an object based image filtering is applied using another user-defined criterion reasoning on segment properties, like shape or radiometric attributes. Criterions are mathematical expressions analysed by the MuParser library (http://muparser.sourceforge.net/). For instance, expression "((b1>80) and intensity>95)" will merge two neighbouring pixel in a single segment if their intensity is more than 95 and their value in the first image band is more than 80. See parameters documentation for a list of available attributes. The output of the object based image filtering is vectorized and can be written in shapefile or KML format. If the input image is in raw geometry, resulting polygons will be transformed to WGS84 using sensor modelling before writing, to ensure consistency with GIS software. For this purpose, a Digital Elevation Model can be provided to the application. The whole processing is done on a per-tile basis for large images, so this application can handle images of arbitrary size.

Parameters

Limitations

Due to the tiling scheme in case of large images, some segments can be arbitrarily split across multiple tiles.

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Convert.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Convert.html deleted file mode 100644 index d639181f4282..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Convert.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

Convert

Brief Description

Convert an image to a different format, eventually rescaling the data and/or changing the pixel type.

Tags

Conversion,Image Dynamic,Image Manipulation

Long Description

This application performs an image pixel type conversion (short, ushort, uchar, int, uint, float and double types are handled). The output image is written in the specified format (ie. that corresponds to the given extension). - The conversion can include a rescale using the image 2 percent minimum and maximum values. The rescale can be linear or log2.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Rescale

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ConvertCartoToGeoPoint.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ConvertCartoToGeoPoint.html deleted file mode 100644 index 7d5c59ef0e7d..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ConvertCartoToGeoPoint.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ConvertCartoToGeoPoint

Brief Description

Convert cartographic coordinates to geographic one.

Tags

Coordinates,Geometry

Long Description

This application computes the geographic coordinates from a cartographic one. User has to give the X and Y coordinate and the cartographic projection (UTM/LAMBERT/LAMBERT2/LAMBERT93/SINUS/ECKERT4/TRANSMERCATOR/MOLLWEID/SVY21).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ConvertSensorToGeoPoint.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ConvertSensorToGeoPoint.html deleted file mode 100644 index 2f7c862990d8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ConvertSensorToGeoPoint.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ConvertSensorToGeoPoint

Brief Description

Sensor to geographic coordinates conversion.

Tags

Geometry

Long Description

This Application converts a sensor point of an input image to a geographic point using the Forward Sensor Model of the input image.

Parameters

Limitations

None

Authors

OTB-Team

See Also

ConvertCartoToGeoPoint application, otbObtainUTMZoneFromGeoPoint

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/DEMConvert.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/DEMConvert.html deleted file mode 100644 index e51b5030bbfd..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/DEMConvert.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DEMConvert

Brief Description

Converts a geo-referenced DEM image into a general raster file compatible with OTB DEM handling.

Tags

Image Manipulation

Long Description

In order to be understood by the Orfeo ToolBox and the underlying OSSIM library, a geo-referenced Digital Elevation Model image can be converted into a general raster image, which consists in 3 files with the following extensions: .ras, .geom and .omd. Once converted, you have to place these files in a separate directory, and you can then use this directory to set the "DEM Directory" parameter of a DEM based OTB application or filter.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/DSFuzzyModelEstimation.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/DSFuzzyModelEstimation.html deleted file mode 100644 index e5d507ba025b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/DSFuzzyModelEstimation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DSFuzzyModelEstimation

Brief Description

Estimate feature fuzzy model parameters using 2 vector data (ground truth samples and wrong samples).

Tags

Feature Extraction

Long Description

Estimate feature fuzzy model parameters using 2 vector data (ground truth samples and wrong samples).

Parameters

Limitations

None.

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Despeckle-frost.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Despeckle-frost.html deleted file mode 100644 index 7ec3875fb9d8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Despeckle-frost.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Despeckle

Brief Description

Perform speckle noise reduction on SAR image.

Tags

SAR,Image Filtering

Long Description

This application reduce speckle noise. Four methods are available: Lee, Frost, GammaMAP and Kuan.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Despeckle-gammamap.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Despeckle-gammamap.html deleted file mode 100644 index 7ec3875fb9d8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Despeckle-gammamap.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Despeckle

Brief Description

Perform speckle noise reduction on SAR image.

Tags

SAR,Image Filtering

Long Description

This application reduce speckle noise. Four methods are available: Lee, Frost, GammaMAP and Kuan.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Despeckle-kuan.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Despeckle-kuan.html deleted file mode 100644 index 7ec3875fb9d8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Despeckle-kuan.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Despeckle

Brief Description

Perform speckle noise reduction on SAR image.

Tags

SAR,Image Filtering

Long Description

This application reduce speckle noise. Four methods are available: Lee, Frost, GammaMAP and Kuan.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Despeckle-lee.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Despeckle-lee.html deleted file mode 100644 index 7ec3875fb9d8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Despeckle-lee.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Despeckle

Brief Description

Perform speckle noise reduction on SAR image.

Tags

SAR,Image Filtering

Long Description

This application reduce speckle noise. Four methods are available: Lee, Frost, GammaMAP and Kuan.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Despeckle.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Despeckle.html deleted file mode 100644 index 7ec3875fb9d8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Despeckle.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Despeckle

Brief Description

Perform speckle noise reduction on SAR image.

Tags

SAR,Image Filtering

Long Description

This application reduce speckle noise. Four methods are available: Lee, Frost, GammaMAP and Kuan.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/DimensionalityReduction-ica.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/DimensionalityReduction-ica.html deleted file mode 100644 index e8caafdf73cd..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/DimensionalityReduction-ica.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DimensionalityReduction

Brief Description

Perform Dimension reduction of the input image.

Tags

Dimensionality Reduction,Image Filtering

Long Description

Performs dimensionality reduction on input image. PCA,NA-PCA,MAF,ICA methods are available. It is also possible to compute the inverse transform to reconstruct the image. It is also possible to optionnaly export the transformation matrix to a text file.

Parameters

Limitations

This application does not provide the inverse transform and the transformation matrix export for the MAF.

Authors

OTB-Team

See Also

"Kernel maximum autocorrelation factor and minimum noise fraction transformations," IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 612-624, (2011)

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/DimensionalityReduction-maf.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/DimensionalityReduction-maf.html deleted file mode 100644 index e8caafdf73cd..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/DimensionalityReduction-maf.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DimensionalityReduction

Brief Description

Perform Dimension reduction of the input image.

Tags

Dimensionality Reduction,Image Filtering

Long Description

Performs dimensionality reduction on input image. PCA,NA-PCA,MAF,ICA methods are available. It is also possible to compute the inverse transform to reconstruct the image. It is also possible to optionnaly export the transformation matrix to a text file.

Parameters

Limitations

This application does not provide the inverse transform and the transformation matrix export for the MAF.

Authors

OTB-Team

See Also

"Kernel maximum autocorrelation factor and minimum noise fraction transformations," IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 612-624, (2011)

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/DimensionalityReduction-napca.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/DimensionalityReduction-napca.html deleted file mode 100644 index e8caafdf73cd..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/DimensionalityReduction-napca.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DimensionalityReduction

Brief Description

Perform Dimension reduction of the input image.

Tags

Dimensionality Reduction,Image Filtering

Long Description

Performs dimensionality reduction on input image. PCA,NA-PCA,MAF,ICA methods are available. It is also possible to compute the inverse transform to reconstruct the image. It is also possible to optionnaly export the transformation matrix to a text file.

Parameters

Limitations

This application does not provide the inverse transform and the transformation matrix export for the MAF.

Authors

OTB-Team

See Also

"Kernel maximum autocorrelation factor and minimum noise fraction transformations," IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 612-624, (2011)

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/DimensionalityReduction-pca.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/DimensionalityReduction-pca.html deleted file mode 100644 index e8caafdf73cd..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/DimensionalityReduction-pca.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DimensionalityReduction

Brief Description

Perform Dimension reduction of the input image.

Tags

Dimensionality Reduction,Image Filtering

Long Description

Performs dimensionality reduction on input image. PCA,NA-PCA,MAF,ICA methods are available. It is also possible to compute the inverse transform to reconstruct the image. It is also possible to optionnaly export the transformation matrix to a text file.

Parameters

Limitations

This application does not provide the inverse transform and the transformation matrix export for the MAF.

Authors

OTB-Team

See Also

"Kernel maximum autocorrelation factor and minimum noise fraction transformations," IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 612-624, (2011)

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/DimensionalityReduction.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/DimensionalityReduction.html deleted file mode 100644 index e8caafdf73cd..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/DimensionalityReduction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DimensionalityReduction

Brief Description

Perform Dimension reduction of the input image.

Tags

Dimensionality Reduction,Image Filtering

Long Description

Performs dimensionality reduction on input image. PCA,NA-PCA,MAF,ICA methods are available. It is also possible to compute the inverse transform to reconstruct the image. It is also possible to optionnaly export the transformation matrix to a text file.

Parameters

Limitations

This application does not provide the inverse transform and the transformation matrix export for the MAF.

Authors

OTB-Team

See Also

"Kernel maximum autocorrelation factor and minimum noise fraction transformations," IEEE Transactions on Image Processing, vol. 20, no. 3, pp. 612-624, (2011)

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/DisparityMapToElevationMap.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/DisparityMapToElevationMap.html deleted file mode 100644 index 303ac0e39a48..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/DisparityMapToElevationMap.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DisparityMapToElevationMap

Brief Description

Projects a disparity map into a regular elevation map

Tags

Stereo

Long Description

This application uses a disparity map computed from a stereo image pair to produce an elevation map on the ground area covered by the stereo pair. The needed inputs are : the disparity map, the stereo pair (in original geometry) and the epipolar deformation grids. These grids have to link the original geometry (stereo pair) and the epipolar geometry (disparity map).

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbStereoRectificationGridGenerator otbBlockMatching

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/DownloadSRTMTiles.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/DownloadSRTMTiles.html deleted file mode 100644 index 3c6871d7a5eb..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/DownloadSRTMTiles.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

DownloadSRTMTiles

Brief Description

Download or list SRTM tiles related to a set of images

Tags

Utilities,Image Manipulation

Long Description

This application allows selecting the appropriate SRTM tiles that covers a list of images. It builds a list of the required tiles. Two modes are available: the first one download those tiles from the USGS SRTM3 website (http://dds.cr.usgs.gov/srtm/version2_1/SRTM3/), the second one list those tiles in a local directory. In both cases, you need to indicate the directory in which directory tiles will be download or the location of local SRTM files.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/EdgeExtraction-gradient.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/EdgeExtraction-gradient.html deleted file mode 100644 index aa436906807a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/EdgeExtraction-gradient.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

EdgeExtraction

Brief Description

Computes edge features on every pixel of the input image selected channel

Tags

Edge,Feature Extraction

Long Description

This application computes edge features on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otb class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/EdgeExtraction-sobel.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/EdgeExtraction-sobel.html deleted file mode 100644 index aa436906807a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/EdgeExtraction-sobel.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

EdgeExtraction

Brief Description

Computes edge features on every pixel of the input image selected channel

Tags

Edge,Feature Extraction

Long Description

This application computes edge features on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otb class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/EdgeExtraction-touzi.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/EdgeExtraction-touzi.html deleted file mode 100644 index aa436906807a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/EdgeExtraction-touzi.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

EdgeExtraction

Brief Description

Computes edge features on every pixel of the input image selected channel

Tags

Edge,Feature Extraction

Long Description

This application computes edge features on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otb class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/EdgeExtraction.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/EdgeExtraction.html deleted file mode 100644 index aa436906807a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/EdgeExtraction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

EdgeExtraction

Brief Description

Computes edge features on every pixel of the input image selected channel

Tags

Edge,Feature Extraction

Long Description

This application computes edge features on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otb class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ExtractROI-fit.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ExtractROI-fit.html deleted file mode 100644 index 8b36b6da38c8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ExtractROI-fit.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ExtractROI

Brief Description

Extract a ROI defined by the user.

Tags

Image Manipulation

Long Description

This application extracts a Region Of Interest with user defined size, or reference image.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ExtractROI-standard.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ExtractROI-standard.html deleted file mode 100644 index 8b36b6da38c8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ExtractROI-standard.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ExtractROI

Brief Description

Extract a ROI defined by the user.

Tags

Image Manipulation

Long Description

This application extracts a Region Of Interest with user defined size, or reference image.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ExtractROI.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ExtractROI.html deleted file mode 100644 index 8b36b6da38c8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ExtractROI.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ExtractROI

Brief Description

Extract a ROI defined by the user.

Tags

Image Manipulation

Long Description

This application extracts a Region Of Interest with user defined size, or reference image.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/FineRegistration.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/FineRegistration.html deleted file mode 100644 index 211861d3ef38..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/FineRegistration.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

FineRegistration

Brief Description

Estimate disparity map between two images.

Tags

Stereo

Long Description

Estimate disparity map between two images. Output image contain x offset, y offset and metric value.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/FusionOfClassifications-dempstershafer.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/FusionOfClassifications-dempstershafer.html deleted file mode 100644 index 82c22ebd5f5a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/FusionOfClassifications-dempstershafer.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

FusionOfClassifications

Brief Description

Fuses several classifications maps of the same image on the basis of class labels.

Tags

Learning,Image Analysis

Long Description

This application allows you to fuse several classification maps and produces a single more robust classification map. Fusion is done either by mean of Majority Voting, or with the Dempster Shafer combination method on class labels. - - - MAJORITY VOTING: for each pixel, the class with the highest number of votes is selected. - - DEMPSTER SHAFER: for each pixel, the class label for which the Belief Function is maximal is selected. This Belief Function is calculated by mean of the Dempster Shafer combination of Masses of Belief, and indicates the belief that each input classification map presents for each label value. Moreover, the Masses of Belief are based on the input confusion matrices of each classification map, either by using the PRECISION or RECALL rates, or the OVERALL ACCURACY, or the KAPPA coefficient. Thus, each input classification map needs to be associated with its corresponding input confusion matrix file for the Dempster Shafer fusion. - - Input pixels with the NODATA label are not handled in the fusion of classification maps. Moreover, pixels for which all the input classifiers are set to NODATA keep this value in the output fused image. - - In case of number of votes equality, the UNDECIDED label is attributed to the pixel. -

Parameters

Limitations

None

Authors

OTB-Team

See Also

ImageClassifier application

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/FusionOfClassifications-majorityvoting.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/FusionOfClassifications-majorityvoting.html deleted file mode 100644 index 82c22ebd5f5a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/FusionOfClassifications-majorityvoting.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

FusionOfClassifications

Brief Description

Fuses several classifications maps of the same image on the basis of class labels.

Tags

Learning,Image Analysis

Long Description

This application allows you to fuse several classification maps and produces a single more robust classification map. Fusion is done either by mean of Majority Voting, or with the Dempster Shafer combination method on class labels. - - - MAJORITY VOTING: for each pixel, the class with the highest number of votes is selected. - - DEMPSTER SHAFER: for each pixel, the class label for which the Belief Function is maximal is selected. This Belief Function is calculated by mean of the Dempster Shafer combination of Masses of Belief, and indicates the belief that each input classification map presents for each label value. Moreover, the Masses of Belief are based on the input confusion matrices of each classification map, either by using the PRECISION or RECALL rates, or the OVERALL ACCURACY, or the KAPPA coefficient. Thus, each input classification map needs to be associated with its corresponding input confusion matrix file for the Dempster Shafer fusion. - - Input pixels with the NODATA label are not handled in the fusion of classification maps. Moreover, pixels for which all the input classifiers are set to NODATA keep this value in the output fused image. - - In case of number of votes equality, the UNDECIDED label is attributed to the pixel. -

Parameters

Limitations

None

Authors

OTB-Team

See Also

ImageClassifier application

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/FusionOfClassifications.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/FusionOfClassifications.html deleted file mode 100644 index 82c22ebd5f5a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/FusionOfClassifications.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

FusionOfClassifications

Brief Description

Fuses several classifications maps of the same image on the basis of class labels.

Tags

Learning,Image Analysis

Long Description

This application allows you to fuse several classification maps and produces a single more robust classification map. Fusion is done either by mean of Majority Voting, or with the Dempster Shafer combination method on class labels. - - - MAJORITY VOTING: for each pixel, the class with the highest number of votes is selected. - - DEMPSTER SHAFER: for each pixel, the class label for which the Belief Function is maximal is selected. This Belief Function is calculated by mean of the Dempster Shafer combination of Masses of Belief, and indicates the belief that each input classification map presents for each label value. Moreover, the Masses of Belief are based on the input confusion matrices of each classification map, either by using the PRECISION or RECALL rates, or the OVERALL ACCURACY, or the KAPPA coefficient. Thus, each input classification map needs to be associated with its corresponding input confusion matrix file for the Dempster Shafer fusion. - - Input pixels with the NODATA label are not handled in the fusion of classification maps. Moreover, pixels for which all the input classifiers are set to NODATA keep this value in the output fused image. - - In case of number of votes equality, the UNDECIDED label is attributed to the pixel. -

Parameters

Limitations

None

Authors

OTB-Team

See Also

ImageClassifier application

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/GeneratePlyFile.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/GeneratePlyFile.html deleted file mode 100644 index 9cefcc9d5b87..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/GeneratePlyFile.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GeneratePlyFile

Brief Description

Generate a 3D Ply file from a DEM and a color image.

Tags

Geometry

Long Description

Generate a 3D Ply file from a DEM and a color image.

Parameters

Limitations

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/GenerateRPCSensorModel.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/GenerateRPCSensorModel.html deleted file mode 100644 index 63c73dfbd0a2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/GenerateRPCSensorModel.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GenerateRPCSensorModel

Brief Description

Generate a RPC sensor model from a list of Ground Control Points.

Tags

Geometry

Long Description

This application generates a RPC sensor model from a list of Ground Control Points. At least 20 points are required for estimation wihtout elevation support, and 40 points for estimation with elevation support. Elevation support will be automatically deactivated if an insufficient amount of points is provided. The application can optionnaly output a file containing accuracy statistics for each point, and a vector file containing segments represening points residues. The map projection parameter allows defining a map projection in which the accuracy is evaluated.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OrthoRectication,HomologousPointsExtraction,RefineSensorModel

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/GrayScaleMorphologicalOperation-closing.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/GrayScaleMorphologicalOperation-closing.html deleted file mode 100644 index e3081bbf311b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/GrayScaleMorphologicalOperation-closing.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GrayScaleMorphologicalOperation

Brief Description

Performs morphological operations on a grayscale input image

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs grayscale morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkGrayscaleDilateImageFilter, itkGrayscaleErodeImageFilter, itkGrayscaleMorphologicalOpeningImageFilter and itkGrayscaleMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/GrayScaleMorphologicalOperation-dilate.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/GrayScaleMorphologicalOperation-dilate.html deleted file mode 100644 index e3081bbf311b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/GrayScaleMorphologicalOperation-dilate.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GrayScaleMorphologicalOperation

Brief Description

Performs morphological operations on a grayscale input image

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs grayscale morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkGrayscaleDilateImageFilter, itkGrayscaleErodeImageFilter, itkGrayscaleMorphologicalOpeningImageFilter and itkGrayscaleMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/GrayScaleMorphologicalOperation-erode.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/GrayScaleMorphologicalOperation-erode.html deleted file mode 100644 index e3081bbf311b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/GrayScaleMorphologicalOperation-erode.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GrayScaleMorphologicalOperation

Brief Description

Performs morphological operations on a grayscale input image

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs grayscale morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkGrayscaleDilateImageFilter, itkGrayscaleErodeImageFilter, itkGrayscaleMorphologicalOpeningImageFilter and itkGrayscaleMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/GrayScaleMorphologicalOperation-opening.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/GrayScaleMorphologicalOperation-opening.html deleted file mode 100644 index e3081bbf311b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/GrayScaleMorphologicalOperation-opening.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GrayScaleMorphologicalOperation

Brief Description

Performs morphological operations on a grayscale input image

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs grayscale morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkGrayscaleDilateImageFilter, itkGrayscaleErodeImageFilter, itkGrayscaleMorphologicalOpeningImageFilter and itkGrayscaleMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/GrayScaleMorphologicalOperation.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/GrayScaleMorphologicalOperation.html deleted file mode 100644 index e3081bbf311b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/GrayScaleMorphologicalOperation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GrayScaleMorphologicalOperation

Brief Description

Performs morphological operations on a grayscale input image

Tags

MorphologicalOperations,Feature Extraction

Long Description

This application performs grayscale morphological operations on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

itkGrayscaleDilateImageFilter, itkGrayscaleErodeImageFilter, itkGrayscaleMorphologicalOpeningImageFilter and itkGrayscaleMorphologicalClosingImageFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/GridBasedImageResampling.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/GridBasedImageResampling.html deleted file mode 100644 index 5fd5b9e1ba62..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/GridBasedImageResampling.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

GridBasedImageResampling

Brief Description

Resamples an image according to a resampling grid

Tags

Geometry

Long Description

This application allows performing image resampling from an input resampling grid.

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbStereorecificationGridGeneration

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/HaralickTextureExtraction.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/HaralickTextureExtraction.html deleted file mode 100644 index ef966758bb63..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/HaralickTextureExtraction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

HaralickTextureExtraction

Brief Description

Computes textures on every pixel of the input image selected channel

Tags

Textures,Feature Extraction

Long Description

This application computes Haralick, advanced and higher order textures on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbScalarImageToTexturesFilter, otbScalarImageToAdvancedTexturesFilter and otbScalarImageToHigherOrderTexturesFilter classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/HomologousPointsExtraction.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/HomologousPointsExtraction.html deleted file mode 100644 index dff39817e5ce..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/HomologousPointsExtraction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

HomologousPointsExtraction

Brief Description

Compute homologous points between images using keypoints

Tags

Feature Extraction

Long Description

This application allows computing homologous points between images using keypoints. SIFT or SURF keypoints can be used and the band on which keypoints are computed can be set independently for both images. The application offers two modes : the first is the full mode where keypoints are extracted from the full extent of both images (please note that in this mode large image file are not supported). The second mode, called geobins, allows one to set-up spatial binning to get fewer points spread across the entire image. In this mode, the corresponding spatial bin in the second image is estimated using geographical transform or sensor modelling, and is padded according to the user defined precision. Last, in both modes the application can filter matches whose colocalisation in first image exceed this precision. The elevation parameters are to deal more precisely with sensor modelling in case of sensor geometry data. The outvector option allows creating a vector file with segments corresponding to the localisation error between the matches. It can be useful to assess the precision of a registration for instance. The vector file is always reprojected to EPSG:4326 to allow display in a GIS. This is done via reprojection or by applying the image sensor models.

Parameters

Limitations

Full mode does not handle large images.

Authors

OTB-Team

See Also

RefineSensorModel

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/HooverCompareSegmentation.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/HooverCompareSegmentation.html deleted file mode 100644 index 7fdbe9a5043f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/HooverCompareSegmentation.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

HooverCompareSegmentation

Brief Description

Compare two segmentations with Hoover metrics

Tags

Segmentation

Long Description

This application compares a machine segmentation (MS) with a partial ground truth segmentation (GT). The Hoover metrics are used to estimate scores for correct detection, over-segmentation, under-segmentation and missed detection. - The application can output the overall Hoover scores along with coloredimages of the MS and GT segmentation showing the state of each region (correct detection, over-segmentation, under-segmentation, missed) - The Hoover metrics are described in : Hoover et al., "An experimental comparison of range image segmentation algorithms", IEEE PAMI vol. 18, no. 7, July 1996.

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbHooverMatrixFilter, otbHooverInstanceFilter, otbLabelMapToAttributeImageFilter

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/HyperspectralUnmixing.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/HyperspectralUnmixing.html deleted file mode 100644 index 17e18c1055e8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/HyperspectralUnmixing.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

HyperspectralUnmixing

Brief Description

Estimate abundance maps from an hyperspectral image and a set of endmembers.

Tags

Hyperspectral

Long Description

The application applies a linear unmixing algorithm to an hyperspectral data cube. This method supposes that the mixture between materials in the scene is macroscopic and simulates a linear mixing model of spectra. -The Linear Mixing Model (LMM) acknowledges that reflectance spectrum associated with each pixel is a linear combination of pure materials in the recovery area, commonly known as endmembers. Endmembers can be estimated using the VertexComponentAnalysis application. -The application allows one to estimate the abundance maps with several algorithms : Unconstrained Least Square (ucls), Fully Constrained Least Square (fcls), Image Space Reconstruction Algorithm (isra) and Non-negative constrained Least Square (ncls) and Minimum Dispersion Constrained Non Negative Matrix Factorization (MDMDNMF). -

Parameters

Limitations

None

Authors

OTB-Team

See Also

VertexComponentAnalysis

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ImageClassifier.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ImageClassifier.html deleted file mode 100644 index 609883188384..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ImageClassifier.html +++ /dev/null @@ -1,16 +0,0 @@ - - -

ImageClassifier

Brief Description

Performs a classification of the input image according to a model file.

Tags

Learning

Long Description

This application performs an image classification based on a model file produced by the TrainImagesClassifier application. Pixels of the output image will contain the class labels decided by the classifier (maximal class label = 65535). The input pixels can be optionally centered and reduced according to the statistics file produced by the ComputeImagesStatistics application. An optional input mask can be provided, in which case only input image pixels whose corresponding mask value is greater than 0 will be classified. The remaining of pixels will be given the label 0 in the output image.

Parameters

Limitations

The input image must have the same type, order and number of bands than the images used to produce the statistics file and the SVM model file. If a statistics file was used during training by the TrainImagesClassifier, it is mandatory to use the same statistics file for classification. If an input mask is used, its size must match the input image size.

Authors

OTB-Team

See Also

TrainImagesClassifier, ValidateImagesClassifier, ComputeImagesStatistics

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ImageEnvelope.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ImageEnvelope.html deleted file mode 100644 index 6b0e00023b41..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ImageEnvelope.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ImageEnvelope

Brief Description

Extracts an image envelope.

Tags

Geometry

Long Description

Build a vector data containing the polygon of the image envelope.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/KMeansClassification.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/KMeansClassification.html deleted file mode 100644 index 47414f7b94d3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/KMeansClassification.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

KMeansClassification

Brief Description

Unsupervised KMeans image classification

Tags

Segmentation,Learning

Long Description

Performs unsupervised KMeans image classification.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/KmzExport.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/KmzExport.html deleted file mode 100644 index 98c54781ec2e..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/KmzExport.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

KmzExport

Brief Description

Export the input image in a KMZ product.

Tags

KMZ,Export

Long Description

This application exports the input image in a kmz product that can be display in the Google Earth software. The user can set the size of the product size, a logo and a legend to the product. Furthemore, to obtain a product that fits the relief, a DEM can be used.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Conversion

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/LSMSSegmentation.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/LSMSSegmentation.html deleted file mode 100644 index ffb7cfde48da..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/LSMSSegmentation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

LSMSSegmentation

Brief Description

Second step of the exact Large-Scale Mean-Shift segmentation workflow.

Tags

Segmentation,LSMS

Long Description

This application performs the second step of the exact Large-Scale Mean-Shift segmentation workflow (LSMS). Filtered range image and spatial image should be created with the MeanShiftSmoothing application, with modesearch parameter disabled. If spatial image is not set, the application will only process the range image and spatial radius parameter will not be taken into account. This application will produce a labeled image where neighbor pixels whose range distance is below range radius (and optionally spatial distance below spatial radius) will be grouped together into the same cluster. For large images one can use the nbtilesx and nbtilesy parameters for tile-wise processing, with the guarantees of identical results. Please note that this application will generate a lot of temporary files (as many as the number of tiles), and will therefore require twice the size of the final result in term of disk space. The cleanup option (activated by default) allows removing all temporary file as soon as they are not needed anymore (if cleanup is activated, tmpdir set and tmpdir does not exists before running the application, it will be removed as well during cleanup). The tmpdir option allows defining a directory where to write the temporary files. Please also note that the output image type should be set to uint32 to ensure that there are enough labels available.

Parameters

Limitations

This application is part of the Large-Scale Mean-Shift segmentation workflow (LSMS) and may not be suited for any other purpose.

Authors

David Youssefi

See Also

MeanShiftSmoothing, LSMSSmallRegionsMerging, LSMSVectorization

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/LSMSSmallRegionsMerging.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/LSMSSmallRegionsMerging.html deleted file mode 100644 index 4ae7be132f05..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/LSMSSmallRegionsMerging.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

LSMSSmallRegionsMerging

Brief Description

Third (optional) step of the exact Large-Scale Mean-Shift segmentation workflow.

Tags

Segmentation,LSMS

Long Description

This application performs the third step of the exact Large-Scale Mean-Shift segmentation workflow (LSMS). Given a segmentation result (label image) and the original image, it will merge regions whose size in pixels is lower than minsize parameter with the adjacent regions with the adjacent region with closest radiometry and acceptable size. Small regions will be processed by size: first all regions of area, which is equal to 1 pixel will be merged with adjacent region, then all regions of area equal to 2 pixels, until regions of area minsize. For large images one can use the nbtilesx and nbtilesy parameters for tile-wise processing, with the guarantees of identical results.

Parameters

Limitations

This application is part of the Large-Scale Mean-Shift segmentation workflow (LSMS) and may not be suited for any other purpose.

Authors

David Youssefi

See Also

LSMSSegmentation, LSMSVectorization, MeanShiftSmoothing

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/LSMSVectorization.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/LSMSVectorization.html deleted file mode 100644 index f0483b43cb94..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/LSMSVectorization.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

LSMSVectorization

Brief Description

Fourth step of the exact Large-Scale Mean-Shift segmentation workflow.

Tags

Segmentation,LSMS

Long Description

This application performs the fourth step of the exact Large-Scale Mean-Shift segmentation workflow (LSMS). Given a segmentation result (label image), that may have been processed for small regions merging or not, it will convert it to a GIS vector file containing one polygon per segment. Each polygon contains additional fields: mean and variance of each channels from input image (in parameter), segmentation image label, number of pixels in the polygon. For large images one can use the nbtilesx and nbtilesy parameters for tile-wise processing, with the guarantees of identical results.

Parameters

Limitations

This application is part of the Large-Scale Mean-Shift segmentation workflow (LSMS) and may not be suited for any other purpose.

Authors

David Youssefi

See Also

MeanShiftSmoothing, LSMSSegmentation, LSMSSmallRegionsMerging

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/LineSegmentDetection.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/LineSegmentDetection.html deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/LocalStatisticExtraction.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/LocalStatisticExtraction.html deleted file mode 100644 index 6ff7bc151aa1..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/LocalStatisticExtraction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

LocalStatisticExtraction

Brief Description

Computes local statistical moments on every pixel in the selected channel of the input image

Tags

Statistics,Feature Extraction

Long Description

This application computes the 4 local statistical moments on every pixel in the selected channel of the input image, over a specified neighborhood. The output image is multi band with one statistical moment (feature) per band. Thus, the 4 output features are the Mean, the Variance, the Skewness and the Kurtosis. They are provided in this exact order in the output image.

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbRadiometricMomentsImageFunction class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ManageNoData.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ManageNoData.html deleted file mode 100644 index b008bfa00e88..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ManageNoData.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ManageNoData

Brief Description

Manage No-Data

Tags

Conversion,Image Dynamic,Image Manipulation

Long Description

This application has two modes. The first allows building a mask of no-data pixels from the no-data flags read from the image file. The second allows updating the change the no-data value of an image (pixels value and metadata). This last mode also allows replacing NaN in images with a proper no-data value. To do so, one should activate the NaN is no-data option.

Parameters

Limitations

None

Authors

OTB-Team

See Also

BanMath

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/MeanShiftSmoothing.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/MeanShiftSmoothing.html deleted file mode 100644 index c8df67c03328..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/MeanShiftSmoothing.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

MeanShiftSmoothing

Brief Description

Perform mean shift filtering

Tags

Image Filtering,LSMS

Long Description

This application performs mean shift fitlering (multi-threaded).

Parameters

Limitations

With mode search option, the result will slightly depend on thread number.

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/MultiImageSamplingRate.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/MultiImageSamplingRate.html deleted file mode 100644 index bb23609b5292..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/MultiImageSamplingRate.html +++ /dev/null @@ -1,56 +0,0 @@ - - -

MultiImageSamplingRate

Brief Description

Compute sampling rate for an input set of images.

Tags

Learning

Long Description

The application computes sampling rates for a set of input images. Before calling this application, each pair of image and training vectors has to be analysed with the application PolygonClassStatistics. The statistics file is then used to compute the sampling rates for each class in each image. Several types of sampling are implemented. Each one is a combination of a mono-image strategy and a multi-image mode. The mono-image strategies are : - - * smallest (default) : select the same number of sample in each class so that the smallest one is fully sampled. - * constant : select the same number of samples N in each class (with N below or equal to the size of the smallest class). - * byclass : set the required number for each class manually, with an input CSV file (first column is class name, second one is the required samples number). - -The multi-image modes (mim) are proportional, equal and custom. The custom mode lets the users choose the distribution of samples among the images. The different behaviours are described below. Ti(c) and Ni(c) refers resp. to the total number and needed number of samples in image i for class c. Let's call L the total number of images. - * strategy = all - - - Same behaviour for all modes : take all samples - - * strategy = constant : let's call M the global number of samples required per class. For each image i and each class c: - - - if mim = proportional, then Ni( c ) = M * Ti( c ) / sum_k( Tk(c) ) - - - if mim = equal , then Ni( c ) = M / L - - - if mim = custom , then Ni( c ) = Mi where Mi is the custom requested number of samples for image i - - * strategy = byClass : let's call M(c) the global number of samples for class c). For each image i and each class c: - - - if mim = proportional, then Ni( c ) = M(c) * Ti( c ) / sum_k( Tk(c) ) - - - if mim = equal , then Ni( c ) = M(c) / L - - - if mim = custom , then Ni( c ) = Mi(c) where Mi(c) is the custom requested number of samples for image i and class c - - * strategy = percent : For each image i and each class c: - - - if mim = proportional, then Ni( c ) = p * Ti( c ) where p is the global percentage of samples - - - if mim = equal , then Ni( c ) = p * sum_k(Tk(c)]/L where p is the global percentage of samples - - - if mim = custom , then Ni( c ) = p(i) * Ti(c) where p(i) is the percentage of samples for image i. c - - * strategy = total : For each image i and each class c: - - - if mim = proportional, then Ni( c ) = total * (sum_k(Ti(k))/sum_kl(Tl(k))) * (Ti(c)/sum_k(Ti(k))) where total is the total number of samples specified. - - - if mim = equal , then Ni( c ) = (total / L) * (Ti(c)/sum_k(Ti(k))) where total is the total number of samples specified. - - - if mim = custom , then Ni( c ) = total(i) * (Ti(c)/sum_k(Ti(k))) where total(i) is the total number of samples specified for image i. - - * strategy = smallest class - - - if mim = proportional, then the smallest class size (computed globally) is used for the strategy constant+proportional. - - - if mim = equal , then the smallest class size (computed globally) is used for the strategy constant+equal. - - - if mim = custom , then the smallest class is computed and used for each image separately. - -

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/MultiResolutionPyramid.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/MultiResolutionPyramid.html deleted file mode 100644 index 35f527bb0353..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/MultiResolutionPyramid.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

MultiResolutionPyramid

Brief Description

Build a multi-resolution pyramid of the image.

Tags

Conversion,Image Manipulation,Image MultiResolution,Util

Long Description

This application builds a multi-resolution pyramid of the input image. User can specified the number of levels of the pyramid and the subsampling factor. To speed up the process, you can use the fast scheme option

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/MultivariateAlterationDetector.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/MultivariateAlterationDetector.html deleted file mode 100644 index 2505ba3cba15..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/MultivariateAlterationDetector.html +++ /dev/null @@ -1,21 +0,0 @@ - - -

MultivariateAlterationDetector

Brief Description

Multivariate Alteration Detector

Tags

Feature Extraction

Long Description

This application detects change between two given images.

Parameters

Limitations

None

Authors

OTB-Team

See Also

This filter implements the Multivariate Alteration Detector, based on the following work: - A. A. Nielsen and K. Conradsen, Multivariate alteration detection (mad) in multispectral, bi-temporal image data: a new approach to change detection studies, Remote Sens. Environ., vol. 64, pp. 1-19, (1998) - - Multivariate Alteration Detector takes two images as inputs and produce a set of N change maps as a VectorImage (where N is the maximum of number of bands in first and second image) with the following properties: - - Change maps are differences of a pair of linear combinations of bands from image 1 and bands from image 2 chosen to maximize the correlation. - - Each change map is orthogonal to the others. - - This is a statistical method which can handle different modalities and even different bands and number of bands between images. - - If numbers of bands in image 1 and 2 are equal, then change maps are sorted by increasing correlation. If number of bands is different, the change maps are sorted by decreasing correlation. - - The GetV1() and GetV2() methods allow retrieving the linear combinations used to generate the Mad change maps as a vnl_matrix of double, and the GetRho() method allows retrieving the correlation associated to each Mad change maps as a vnl_vector. - - This filter has been implemented from the Matlab code kindly made available by the authors here: - http://www2.imm.dtu.dk/~aa/software.html - - Both cases (same and different number of bands) have been validated by comparing the output image to the output produced by the Matlab code, and the reference images for testing have been generated from the Matlab code using Octave.

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/OGRLayerClassifier.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/OGRLayerClassifier.html deleted file mode 100644 index 9b8b14bf2ff0..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/OGRLayerClassifier.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

OGRLayerClassifier

Brief Description

Classify an OGR layer based on a machine learning model and a list of features to consider.

Tags

Segmentation

Long Description

This application will apply a trained machine learning model on the selected feature to get a classification of each geometry contained in an OGR layer. The list of feature must match the list used for training. The predicted label is written in the user defined field for each geometry.

Parameters

Limitations

Experimental. Only shapefiles are supported for now.

Authors

David Youssefi during internship at CNES

See Also

ComputeOGRLayersFeaturesStatistics,TrainOGRLayersClassifier

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/OSMDownloader.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/OSMDownloader.html deleted file mode 100644 index e675bf1e6653..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/OSMDownloader.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

OSMDownloader

Brief Description

Generate a vector data from OSM on the input image extend

Tags

Image MetaData

Long Description

Generate a vector data from Open Street Map data. A DEM could be use. By default, the entire layer is downloaded, an image can be use as support for the OSM data. The application can provide also available classes in layers . This application required an Internet access. Information about the OSM project : http://www.openstreetmap.fr/

Parameters

Limitations

None

Authors

OTB-Team

See Also

Conversion

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ObtainUTMZoneFromGeoPoint.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ObtainUTMZoneFromGeoPoint.html deleted file mode 100644 index eb416fdd3d84..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ObtainUTMZoneFromGeoPoint.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ObtainUTMZoneFromGeoPoint

Brief Description

UTM zone determination from a geographic point.

Tags

Coordinates

Long Description

This application returns the UTM zone of an input geographic point.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

Obtain a UTM Zone \ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/OpticalCalibration.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/OpticalCalibration.html deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/OrthoRectification-epsg.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/OrthoRectification-epsg.html deleted file mode 100644 index e341b116768c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/OrthoRectification-epsg.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

OrthoRectification

Brief Description

This application allows ortho-rectification of optical images from supported sensors. -

Tags

Geometry

Long Description

An inverse sensor model is built from the input image metadata to convert geographical to raw geometry coordinates. This inverse sensor model is then combined with the chosen map projection to build a global coordinate mapping grid. Last, this grid is used to resample using the chosen interpolation algorithm. A Digital Elevation Model can be specified to account for terrain deformations. -In case of SPOT5 images, the sensor model can be approximated by an RPC model in order to speed-up computation.

Parameters

Limitations

Supported sensors are Pleiades, SPOT5 (TIF format), Ikonos, Quickbird, Worldview2, GeoEye.

Authors

OTB-Team

See Also

Ortho-rectification chapter from the OTB Software Guide

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/OrthoRectification-fit-to-ortho.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/OrthoRectification-fit-to-ortho.html deleted file mode 100644 index e341b116768c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/OrthoRectification-fit-to-ortho.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

OrthoRectification

Brief Description

This application allows ortho-rectification of optical images from supported sensors. -

Tags

Geometry

Long Description

An inverse sensor model is built from the input image metadata to convert geographical to raw geometry coordinates. This inverse sensor model is then combined with the chosen map projection to build a global coordinate mapping grid. Last, this grid is used to resample using the chosen interpolation algorithm. A Digital Elevation Model can be specified to account for terrain deformations. -In case of SPOT5 images, the sensor model can be approximated by an RPC model in order to speed-up computation.

Parameters

Limitations

Supported sensors are Pleiades, SPOT5 (TIF format), Ikonos, Quickbird, Worldview2, GeoEye.

Authors

OTB-Team

See Also

Ortho-rectification chapter from the OTB Software Guide

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/OrthoRectification-lambert-WGS84.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/OrthoRectification-lambert-WGS84.html deleted file mode 100644 index e341b116768c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/OrthoRectification-lambert-WGS84.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

OrthoRectification

Brief Description

This application allows ortho-rectification of optical images from supported sensors. -

Tags

Geometry

Long Description

An inverse sensor model is built from the input image metadata to convert geographical to raw geometry coordinates. This inverse sensor model is then combined with the chosen map projection to build a global coordinate mapping grid. Last, this grid is used to resample using the chosen interpolation algorithm. A Digital Elevation Model can be specified to account for terrain deformations. -In case of SPOT5 images, the sensor model can be approximated by an RPC model in order to speed-up computation.

Parameters

Limitations

Supported sensors are Pleiades, SPOT5 (TIF format), Ikonos, Quickbird, Worldview2, GeoEye.

Authors

OTB-Team

See Also

Ortho-rectification chapter from the OTB Software Guide

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/OrthoRectification-utm.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/OrthoRectification-utm.html deleted file mode 100644 index e341b116768c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/OrthoRectification-utm.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

OrthoRectification

Brief Description

This application allows ortho-rectification of optical images from supported sensors. -

Tags

Geometry

Long Description

An inverse sensor model is built from the input image metadata to convert geographical to raw geometry coordinates. This inverse sensor model is then combined with the chosen map projection to build a global coordinate mapping grid. Last, this grid is used to resample using the chosen interpolation algorithm. A Digital Elevation Model can be specified to account for terrain deformations. -In case of SPOT5 images, the sensor model can be approximated by an RPC model in order to speed-up computation.

Parameters

Limitations

Supported sensors are Pleiades, SPOT5 (TIF format), Ikonos, Quickbird, Worldview2, GeoEye.

Authors

OTB-Team

See Also

Ortho-rectification chapter from the OTB Software Guide

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/OrthoRectification.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/OrthoRectification.html deleted file mode 100644 index e341b116768c..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/OrthoRectification.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

OrthoRectification

Brief Description

This application allows ortho-rectification of optical images from supported sensors. -

Tags

Geometry

Long Description

An inverse sensor model is built from the input image metadata to convert geographical to raw geometry coordinates. This inverse sensor model is then combined with the chosen map projection to build a global coordinate mapping grid. Last, this grid is used to resample using the chosen interpolation algorithm. A Digital Elevation Model can be specified to account for terrain deformations. -In case of SPOT5 images, the sensor model can be approximated by an RPC model in order to speed-up computation.

Parameters

Limitations

Supported sensors are Pleiades, SPOT5 (TIF format), Ikonos, Quickbird, Worldview2, GeoEye.

Authors

OTB-Team

See Also

Ortho-rectification chapter from the OTB Software Guide

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Pansharpening-bayes.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Pansharpening-bayes.html deleted file mode 100644 index dd25169010ad..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Pansharpening-bayes.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Pansharpening

Brief Description

Perform P+XS pansharpening

Tags

Geometry,Pansharpening

Long Description

This application performs P+XS pansharpening. Pansharpening is a process of merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution color image. Algorithms available in the applications are: RCS, bayesian fusion and Local Mean and Variance Matching(LMVM).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Pansharpening-lmvm.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Pansharpening-lmvm.html deleted file mode 100644 index dd25169010ad..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Pansharpening-lmvm.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Pansharpening

Brief Description

Perform P+XS pansharpening

Tags

Geometry,Pansharpening

Long Description

This application performs P+XS pansharpening. Pansharpening is a process of merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution color image. Algorithms available in the applications are: RCS, bayesian fusion and Local Mean and Variance Matching(LMVM).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Pansharpening-rcs.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Pansharpening-rcs.html deleted file mode 100644 index dd25169010ad..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Pansharpening-rcs.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Pansharpening

Brief Description

Perform P+XS pansharpening

Tags

Geometry,Pansharpening

Long Description

This application performs P+XS pansharpening. Pansharpening is a process of merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution color image. Algorithms available in the applications are: RCS, bayesian fusion and Local Mean and Variance Matching(LMVM).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Pansharpening.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Pansharpening.html deleted file mode 100644 index dd25169010ad..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Pansharpening.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Pansharpening

Brief Description

Perform P+XS pansharpening

Tags

Geometry,Pansharpening

Long Description

This application performs P+XS pansharpening. Pansharpening is a process of merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution color image. Algorithms available in the applications are: RCS, bayesian fusion and Local Mean and Variance Matching(LMVM).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/PixelValue.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/PixelValue.html deleted file mode 100644 index 53b7cab54fcf..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/PixelValue.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

PixelValue

Brief Description

Get the value of a pixel.

Tags

Utilities,Coordinates,Raster

Long Description

Get the value of a pixel. -Pay attention, index starts at 0.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/PolygonClassStatistics.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/PolygonClassStatistics.html deleted file mode 100644 index 5c21f9f73f34..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/PolygonClassStatistics.html +++ /dev/null @@ -1,12 +0,0 @@ - - -

PolygonClassStatistics

Brief Description

Computes statistics on a training polygon set.

Tags

Learning

Long Description

The application processes a set of geometries intended for training (they should have a field giving the associated class). The geometries are analysed against a support image to compute statistics : - - number of samples per class - - number of samples per geometry -An optional raster mask can be used to discard samples. Different types of geometry are supported : polygons, lines, points. The behaviour is different for each type of geometry : - - polygon: select pixels whose center is inside the polygon - - lines : select pixels intersecting the line - - points : select closest pixel to the point -

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/PredictRegression.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/PredictRegression.html deleted file mode 100644 index d86079ee3811..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/PredictRegression.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

PredictRegression

Brief Description

Performs a prediction of the input image according to a regression model file.

Tags

Learning

Long Description

This application predict output values from an input image, based on a regression model file produced by the TrainRegression application. Pixels of the output image will contain the predicted values fromthe regression model (single band). The input pixels can be optionally centered and reduced according to the statistics file produced by the ComputeImagesStatistics application. An optional input mask can be provided, in which case only input image pixels whose corresponding mask value is greater than 0 will be processed. The remaining of pixels will be given the value 0 in the output image.

Parameters

Limitations

The input image must contain the feature bands used for the model training (without the predicted value). If a statistics file was used during training by the TrainRegression, it is mandatory to use the same statistics file for prediction. If an input mask is used, its size must match the input image size.

Authors

OTB-Team

See Also

TrainRegression, ComputeImagesStatistics

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Quicklook.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Quicklook.html deleted file mode 100644 index c84a0abaf342..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Quicklook.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

Quicklook

Brief Description

Generates a subsampled version of an image extract

Tags

Image Manipulation

Long Description

Generates a subsampled version of an extract of an image defined by ROIStart and ROISize. - This extract is subsampled using the ratio OR the output image Size.

Parameters

Limitations

This application does not provide yet the optimal way to decode coarser level of resolution from JPEG2000 images (like in Monteverdi). -Trying to subsampled huge JPEG200 image with the application will lead to poor performances for now.

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/RadiometricIndices.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/RadiometricIndices.html deleted file mode 100644 index 9686e915d862..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/RadiometricIndices.html +++ /dev/null @@ -1,25 +0,0 @@ - - -

RadiometricIndices

Brief Description

Compute radiometric indices.

Tags

Radiometric Indices,Feature Extraction

Long Description

This application computes radiometric indices using the relevant channels of the input image. The output is a multi band image into which each channel is one of the selected indices.

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbVegetationIndicesFunctor, otbWaterIndicesFunctor and otbSoilIndicesFunctor classes

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Rasterization-image.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Rasterization-image.html deleted file mode 100644 index 3569e88c5c6b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Rasterization-image.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

Rasterization

Brief Description

Rasterize a vector dataset.

Tags

Vector Data Manipulation

Long Description

This application allows reprojecting and rasterize a vector dataset. The grid of the rasterized output can be set by using a reference image, or by setting all parmeters (origin, size, spacing) by hand. In the latter case, at least the spacing (ground sampling distance) is needed (other parameters are computed automatically). The rasterized output can also be in a different projection reference system than the input dataset. - There are two rasterize mode available in the application. The first is the binary mode: it allows rendering all pixels belonging to a geometry of the input dataset in the foreground color, while rendering the other in background color. The second one allows rendering pixels belonging to a geometry woth respect to an attribute of this geometry. The field of the attribute to render can be set by the user. In the second mode, the background value is still used for unassociated pixels.

Parameters

Limitations

None

Authors

OTB-Team

See Also

For now, support of input dataset with multiple layers having different projection reference system is limited.

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Rasterization-manual.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Rasterization-manual.html deleted file mode 100644 index 3569e88c5c6b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Rasterization-manual.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

Rasterization

Brief Description

Rasterize a vector dataset.

Tags

Vector Data Manipulation

Long Description

This application allows reprojecting and rasterize a vector dataset. The grid of the rasterized output can be set by using a reference image, or by setting all parmeters (origin, size, spacing) by hand. In the latter case, at least the spacing (ground sampling distance) is needed (other parameters are computed automatically). The rasterized output can also be in a different projection reference system than the input dataset. - There are two rasterize mode available in the application. The first is the binary mode: it allows rendering all pixels belonging to a geometry of the input dataset in the foreground color, while rendering the other in background color. The second one allows rendering pixels belonging to a geometry woth respect to an attribute of this geometry. The field of the attribute to render can be set by the user. In the second mode, the background value is still used for unassociated pixels.

Parameters

Limitations

None

Authors

OTB-Team

See Also

For now, support of input dataset with multiple layers having different projection reference system is limited.

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Rasterization.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Rasterization.html deleted file mode 100644 index 3569e88c5c6b..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Rasterization.html +++ /dev/null @@ -1,6 +0,0 @@ - - -

Rasterization

Brief Description

Rasterize a vector dataset.

Tags

Vector Data Manipulation

Long Description

This application allows reprojecting and rasterize a vector dataset. The grid of the rasterized output can be set by using a reference image, or by setting all parmeters (origin, size, spacing) by hand. In the latter case, at least the spacing (ground sampling distance) is needed (other parameters are computed automatically). The rasterized output can also be in a different projection reference system than the input dataset. - There are two rasterize mode available in the application. The first is the binary mode: it allows rendering all pixels belonging to a geometry of the input dataset in the foreground color, while rendering the other in background color. The second one allows rendering pixels belonging to a geometry woth respect to an attribute of this geometry. The field of the attribute to render can be set by the user. In the second mode, the background value is still used for unassociated pixels.

Parameters

Limitations

None

Authors

OTB-Team

See Also

For now, support of input dataset with multiple layers having different projection reference system is limited.

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/ReadImageInfo.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/ReadImageInfo.html deleted file mode 100644 index 49a2964be36a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/ReadImageInfo.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

ReadImageInfo

Brief Description

Get information about the image

Tags

Utilities,Image Manipulation,Image MetaData

Long Description

Display information about the input image like: image size, origin, spacing, metadata, projections...

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/RefineSensorModel.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/RefineSensorModel.html deleted file mode 100644 index 868feaeff787..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/RefineSensorModel.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

RefineSensorModel

Brief Description

Perform least-square fit of a sensor model to a set of tie points

Tags

Geometry

Long Description

This application reads a geom file containing a sensor model and a text file containing a list of ground control point, and performs a least-square fit of the sensor model adjustable parameters to these tie points. It produces an updated geom file as output, as well as an optional ground control points based statistics file and a vector file containing residues. The output geom file can then be used to ortho-rectify the data more accurately. Plaease note that for a proper use of the application, elevation must be correctly set (including DEM and geoid file). The map parameters allows one to choose a map projection in which the accuracy will be estimated in meters.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OrthoRectification,HomologousPointsExtraction

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Rescale.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Rescale.html deleted file mode 100644 index bb606af15ef8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Rescale.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Rescale

Brief Description

Rescale the image between two given values.

Tags

Image Manipulation

Long Description

This application scales the given image pixel intensity between two given values. By default min (resp. max) value is set to 0 (resp. 255).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/RigidTransformResample-id.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/RigidTransformResample-id.html deleted file mode 100644 index 455ea4d8b600..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/RigidTransformResample-id.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

RigidTransformResample

Brief Description

Resample an image with a rigid transform

Tags

Conversion,Geometry

Long Description

This application performs a parametric transform on the input image. Scaling, translation and rotation with scaling factor are handled. Parameters of the transform is expressed in physical units, thus particular attention must be paid on pixel size (value, and sign). Moreover transform is expressed from input space to output space (on the contrary ITK Transforms are expressed form output space to input space).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Translation

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/RigidTransformResample-rotation.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/RigidTransformResample-rotation.html deleted file mode 100644 index 455ea4d8b600..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/RigidTransformResample-rotation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

RigidTransformResample

Brief Description

Resample an image with a rigid transform

Tags

Conversion,Geometry

Long Description

This application performs a parametric transform on the input image. Scaling, translation and rotation with scaling factor are handled. Parameters of the transform is expressed in physical units, thus particular attention must be paid on pixel size (value, and sign). Moreover transform is expressed from input space to output space (on the contrary ITK Transforms are expressed form output space to input space).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Translation

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/RigidTransformResample-translation.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/RigidTransformResample-translation.html deleted file mode 100644 index 455ea4d8b600..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/RigidTransformResample-translation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

RigidTransformResample

Brief Description

Resample an image with a rigid transform

Tags

Conversion,Geometry

Long Description

This application performs a parametric transform on the input image. Scaling, translation and rotation with scaling factor are handled. Parameters of the transform is expressed in physical units, thus particular attention must be paid on pixel size (value, and sign). Moreover transform is expressed from input space to output space (on the contrary ITK Transforms are expressed form output space to input space).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Translation

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/RigidTransformResample.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/RigidTransformResample.html deleted file mode 100644 index 455ea4d8b600..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/RigidTransformResample.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

RigidTransformResample

Brief Description

Resample an image with a rigid transform

Tags

Conversion,Geometry

Long Description

This application performs a parametric transform on the input image. Scaling, translation and rotation with scaling factor are handled. Parameters of the transform is expressed in physical units, thus particular attention must be paid on pixel size (value, and sign). Moreover transform is expressed from input space to output space (on the contrary ITK Transforms are expressed form output space to input space).

Parameters

Limitations

None

Authors

OTB-Team

See Also

Translation

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/SARCalibration.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/SARCalibration.html deleted file mode 100644 index bd06f4869601..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/SARCalibration.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

SARCalibration

Brief Description

Perform radiometric calibration of SAR images. Following sensors are supported: TerraSAR-X, Sentinel1 and Radarsat-2.Both Single Look Complex(SLC) and detected products are supported as input. -

Tags

Calibration,SAR

Long Description

The objective of SAR calibration is to provide imagery in which the pixel values can be directly related to the radar backscatter of the scene. This application allows computing Sigma Naught (Radiometric Calibration) for TerraSAR-X, Sentinel1 L1 and Radarsat-2 sensors. Metadata are automatically retrieved from image products.The application supports complex and non-complex images (SLC or detected products). -

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/SARDecompositions.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/SARDecompositions.html deleted file mode 100644 index e1a43aadade2..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/SARDecompositions.html +++ /dev/null @@ -1,15 +0,0 @@ - - -

SARDecompositions

Brief Description

From one-band complex images (each one related to an element of the Sinclair matrix), returns the selected decomposition.

Tags

SAR

Long Description

From one-band complex images (HH, HV, VH, VV), returns the selected decomposition. - -All the decompositions implemented are intended for the mono-static case (transmitter and receiver are co-located). -There are two kinds of decomposition : coherent ones and incoherent ones. -In the coherent case, only the Pauli decomposition is available. -In the incoherent case, there the decompositions available : Huynen, Barnes, and H-alpha-A. -User must provide three one-band complex images HH, HV or VH, and VV (mono-static case <=> HV = VH). -Incoherent decompositions consist in averaging 3x3 complex coherency/covariance matrices; the user must provide the size of the averaging window, thanks to the parameter inco.kernelsize. -

Parameters

Limitations

Some decompositions output real images, while this application outputs complex images for general purpose. -Users should pay attention to extract the real part of the results provided by this application. -

Authors

OTB-Team

See Also

SARPolarMatrixConvert, SARPolarSynth

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/SARPolarMatrixConvert.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/SARPolarMatrixConvert.html deleted file mode 100644 index 063f2963a771..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/SARPolarMatrixConvert.html +++ /dev/null @@ -1,32 +0,0 @@ - - -

SARPolarMatrixConvert

Brief Description

This applications allows converting classical polarimetric matrices to each other.

Tags

SAR

Long Description

This application allows converting classical polarimetric matrices to each other. -For instance, it is possible to get the coherency matrix from the Sinclar one, or the Mueller matrix from the coherency one. -The filters used in this application never handle matrices, but images where each band is related to their elements. -As most of the time SAR polarimetry handles symmetric/hermitian matrices, only the relevant elements are stored, so that the images representing them have a minimal number of bands. -For instance, the coherency matrix size is 3x3 in the monostatic case, and 4x4 in the bistatic case : it will thus be stored in a 6-band or a 10-band complex image (the diagonal and the upper elements of the matrix). - -The Sinclair matrix is a special case : it is always represented as 3 or 4 one-band complex images (for mono- or bistatic case). -The available conversions are listed below: - ---- Monostatic case --- -1 msinclairtocoherency --> Sinclair matrix to coherency matrix (input : 3 x 1 complex channel (HH, HV or VH, VV) | output : 6 complex channels) -2 msinclairtocovariance --> Sinclair matrix to covariance matrix (input : 3 x 1 complex channel (HH, HV or VH, VV) | output : 6 complex channels) -3 msinclairtocircovariance --> Sinclair matrix to circular covariance matrix (input : 3 x 1 complex channel (HH, HV or VH, VV) | output : 6 complex channels) -4 mcoherencytomueller --> Coherency matrix to Mueller matrix (input : 6 complex channels | 16 real channels) -5 mcovariancetocoherencydegree --> Covariance matrix to coherency degree (input : 6 complex channels | 3 complex channels) -6 mcovariancetocoherency --> Covariance matrix to coherency matrix (input : 6 complex channels | 6 complex channels) -7 mlinearcovariancetocircularcovariance --> Covariance matrix to circular covariance matrix (input : 6 complex channels | output : 6 complex channels) - ---- Bistatic case --- -8 bsinclairtocoherency --> Sinclair matrix to coherency matrix (input : 4 x 1 complex channel (HH, HV, VH, VV) | 10 complex channels) -9 bsinclairtocovariance --> Sinclair matrix to covariance matrix (input : 4 x 1 complex channel (HH, HV, VH, VV) | output : 10 complex channels) -10 bsinclairtocircovariance --> Sinclair matrix to circular covariance matrix (input : 4 x 1 complex channel (HH, HV, VH, VV) | output : 10 complex channels) - ---- Both cases --- -11 sinclairtomueller --> Sinclair matrix to Mueller matrix (input : 4 x 1 complex channel (HH, HV, VH, VV) | output : 16 real channels) -12 muellertomcovariance --> Mueller matrix to covariance matrix (input : 16 real channels | output : 6 complex channels) -13 muellertopoldegandpower --> Mueller matrix to polarization degree and power (input : 16 real channels | output : 4 real channels) -

Parameters

Limitations

None

Authors

OTB-Team

See Also

SARPolarSynth, SARDecompositions

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/SARPolarSynth.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/SARPolarSynth.html deleted file mode 100644 index f5e32ae227bb..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/SARPolarSynth.html +++ /dev/null @@ -1,35 +0,0 @@ - - -

SARPolarSynth

Brief Description

Gives, for each pixel, the power that would have been received by a SAR system with a basis different from the classical (H,V) one (polarimetric synthetis).

Tags

SAR

Long Description

This application gives, for each pixel, the power that would have been received by a SAR system with a basis different from the classical (H,V) one (polarimetric synthetis). -The new basis A and B are indicated through two Jones vectors, defined by the user thanks to orientation (psi) and ellipticity (khi) parameters. -These parameters are namely psii, khii, psir and khir. The suffixes (i) and (r) refer to the transmitting antenna and the receiving antenna respectively. -Orientations and ellipticities are given in degrees, and are between -90/90 degrees and -45/45 degrees respectively. - -Four polarization architectures can be processed : - -1. HH_HV_VH_VV : full polarization, general bistatic case. -2. HH_HV_VV or HH_VH_VV : full polarization, monostatic case (transmitter and receiver are co-located). -3. HH_HV : dual polarization. -4. VH_VV : dual polarization. - -The application takes a complex vector image as input, where each band correspond to a particular emission/reception polarization scheme. -User must comply with the band order given above, since the bands are used to build the Sinclair matrix. - -In order to determine the architecture, the application first relies on the number of bands of the input image. - -1. Architecture HH_HV_VH_VV is the only one with four bands, there is no possible confusion. -2. Concerning HH_HV_VV and HH_VH_VV architectures, both correspond to a three channels image. But they are processed in the same way, as the Sinclair matrix is symmetric in the monostatic case. -3. Finally, the two last architectures (dual polarizations), can't be distinguished only by the number of bands of the input image. User must then use the parameters emissionh and emissionv to indicate the architecture of the system : emissionh=1 and emissionv=0 --> HH_HV, emissionh=0 and emissionv=1 --> VH_VV. - -Note : if the architecture is HH_HV, khii and psii are automatically both set to 0 degree; if the architecture is VH_VV, khii and psii are automatically set to 0 degree and 90 degrees respectively. - -It is also possible to force the calculation to co-polar or cross-polar modes. -In the co-polar case, values for psir and khir will be ignored and forced to psii and khii; same as the cross-polar mode, where khir and psir will be forced to (psii + 90 degrees) and -khii. - -Finally, the result of the polarimetric synthetis is expressed in the power domain, through a one-band scalar image. -Note: this application doesn't take into account the terms which do not depend on the polarization of the antennas. -The parameter gain can be used for this purpose. - -More details can be found in the OTB CookBook (SAR processing chapter).

Parameters

Limitations

None

Authors

OTB-Team

See Also

SARDecompositions, SARPolarMatrixConvert

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/SFSTextureExtraction.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/SFSTextureExtraction.html deleted file mode 100644 index faf6fea0fd80..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/SFSTextureExtraction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

SFSTextureExtraction

Brief Description

Computes Structural Feature Set textures on every pixel of the input image selected channel

Tags

Textures,Feature Extraction

Long Description

This application computes SFS textures on a mono band image

Parameters

Limitations

None

Authors

OTB-Team

See Also

otbSFSTexturesImageFilter class

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/SOMClassification.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/SOMClassification.html deleted file mode 100644 index a6fc8a522dfc..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/SOMClassification.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

SOMClassification

Brief Description

SOM image classification.

Tags

Segmentation,Learning

Long Description

Unsupervised Self Organizing Map image classification.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/SampleExtraction.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/SampleExtraction.html deleted file mode 100644 index 1b6d1b5e25d4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/SampleExtraction.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

SampleExtraction

Brief Description

Extracts samples values from an image.

Tags

Learning

Long Description

The application extracts samples values from animage using positions contained in a vector data file.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/SampleSelection.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/SampleSelection.html deleted file mode 100644 index 3cbf65f57394..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/SampleSelection.html +++ /dev/null @@ -1,41 +0,0 @@ - - -

SampleSelection

Brief Description

Selects samples from a training vector data set.

Tags

Learning

Long Description

The application selects a set of samples from geometries intended for training (they should have a field giving the associated class). - -First of all, the geometries must be analyzed by the PolygonClassStatistics application to compute statistics about the geometries, which are summarized in an xml file. -Then, this xml file must be given as input to this application (parameter instats). - -The input support image and the input training vectors shall be given in parameters 'in' and 'vec' respectively. Only the sampling grid (origin, size, spacing)will be read in the input image. -There are several strategies to select samples (parameter strategy) : - - - smallest (default) : select the same number of sample in each class so that the smallest one is fully sampled. - - constant : select the same number of samples N in each class (with N below or equal to the size of the smallest class). - - byclass : set the required number for each class manually, with an input CSV file (first column is class name, second one is the required samples number). - - - percent: set a target global percentage of samples to use. Class proportions will be respected. - - - total: set a target total number of samples to use. Class proportions will be respected. - -There is also a choice on the sampling type to performs : - - - periodic : select samples uniformly distributed - - random : select samples randomly distributed - -Once the strategy and type are selected, the application outputs samples positions(parameter out). - -The other parameters to look at are : - - - layer : index specifying from which layer to pick geometries. - - field : set the field name containing the class. - - mask : an optional raster mask can be used to discard samples. - - outrates : allows outputting a CSV file that summarizes the sampling rates for each class. - -As with the PolygonClassStatistics application, different types of geometry are supported : polygons, lines, points. -The behavior of this application is different for each type of geometry : - - - polygon: select points whose center is inside the polygon - - lines : select points intersecting the line - - points : select closest point to the provided point -

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/SarRadiometricCalibration.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/SarRadiometricCalibration.html deleted file mode 100644 index f7375f6957b3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/SarRadiometricCalibration.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

SarRadiometricCalibration

Brief Description

Perform radiometric calibration of SAR images. Following sensors are supported: TerraSAR-X, Sentinel1 and Radarsat-2.Both Single Look Complex(SLC) and detected products are supported as input. -

Tags

Calibration,SAR

Long Description

The objective of SAR calibration is to provide imagery in which the pixel values can be directly related to the radar backscatter of the scene. This application allows computing Sigma Naught (Radiometric Calibration) for TerraSAR-X, Sentinel1 L1 and Radarsat-2 sensors. Metadata are automatically retrieved from image products.The application supports complex and non-complex images (SLC or detected products). -

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Segmentation-cc.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Segmentation-cc.html deleted file mode 100644 index e4e557745baf..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Segmentation-cc.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

Segmentation

Brief Description

Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported.

Tags

Segmentation

Long Description

This application allows one to perform various segmentation algorithms on a multispectral image.Available segmentation algorithms are two different versions of Mean-Shift segmentation algorithm (one being multi-threaded), simple pixel based connected components according to a user-defined criterion, and watershed from the gradient of the intensity (norm of spectral bands vector). The application has two different modes that affects the nature of its output. - -In raster mode, the output of the application is a classical image of unique labels identifying the segmented regions. The labeled output can be passed to the ColorMapping application to render regions with contrasted colours. Please note that this mode loads the whole input image into memory, and as such can not handle large images. - - To segment large data, one can use the vector mode. In this case, the output of the application is a vector file or database. The input image is split into tiles (whose size can be set using the tilesize parameter), and each tile is loaded, segmented with the chosen algorithm, vectorized, and written into the output file or database. This piece-wise behavior ensure that memory will never get overloaded, and that images of any size can be processed. There are few more options in the vector mode. The simplify option allows simplifying the geometry (i.e. remove nodes in polygons) according to a user-defined tolerance. The stitch option tries to stitch together the polygons corresponding to segmented region that may have been split by the tiling scheme.

Parameters

Limitations

In raster mode, the application can not handle large input images. Stitching step of vector mode might become slow with very large input images. -MeanShift filter results depends on the number of threads used. -Watershed and multiscale geodesic morphology segmentation will be performed on the amplitude of the input image.

Authors

OTB-Team

See Also

MeanShiftSegmentation

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Segmentation-meanshift.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Segmentation-meanshift.html deleted file mode 100644 index e4e557745baf..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Segmentation-meanshift.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

Segmentation

Brief Description

Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported.

Tags

Segmentation

Long Description

This application allows one to perform various segmentation algorithms on a multispectral image.Available segmentation algorithms are two different versions of Mean-Shift segmentation algorithm (one being multi-threaded), simple pixel based connected components according to a user-defined criterion, and watershed from the gradient of the intensity (norm of spectral bands vector). The application has two different modes that affects the nature of its output. - -In raster mode, the output of the application is a classical image of unique labels identifying the segmented regions. The labeled output can be passed to the ColorMapping application to render regions with contrasted colours. Please note that this mode loads the whole input image into memory, and as such can not handle large images. - - To segment large data, one can use the vector mode. In this case, the output of the application is a vector file or database. The input image is split into tiles (whose size can be set using the tilesize parameter), and each tile is loaded, segmented with the chosen algorithm, vectorized, and written into the output file or database. This piece-wise behavior ensure that memory will never get overloaded, and that images of any size can be processed. There are few more options in the vector mode. The simplify option allows simplifying the geometry (i.e. remove nodes in polygons) according to a user-defined tolerance. The stitch option tries to stitch together the polygons corresponding to segmented region that may have been split by the tiling scheme.

Parameters

Limitations

In raster mode, the application can not handle large input images. Stitching step of vector mode might become slow with very large input images. -MeanShift filter results depends on the number of threads used. -Watershed and multiscale geodesic morphology segmentation will be performed on the amplitude of the input image.

Authors

OTB-Team

See Also

MeanShiftSegmentation

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Segmentation-mprofiles.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Segmentation-mprofiles.html deleted file mode 100644 index e4e557745baf..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Segmentation-mprofiles.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

Segmentation

Brief Description

Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported.

Tags

Segmentation

Long Description

This application allows one to perform various segmentation algorithms on a multispectral image.Available segmentation algorithms are two different versions of Mean-Shift segmentation algorithm (one being multi-threaded), simple pixel based connected components according to a user-defined criterion, and watershed from the gradient of the intensity (norm of spectral bands vector). The application has two different modes that affects the nature of its output. - -In raster mode, the output of the application is a classical image of unique labels identifying the segmented regions. The labeled output can be passed to the ColorMapping application to render regions with contrasted colours. Please note that this mode loads the whole input image into memory, and as such can not handle large images. - - To segment large data, one can use the vector mode. In this case, the output of the application is a vector file or database. The input image is split into tiles (whose size can be set using the tilesize parameter), and each tile is loaded, segmented with the chosen algorithm, vectorized, and written into the output file or database. This piece-wise behavior ensure that memory will never get overloaded, and that images of any size can be processed. There are few more options in the vector mode. The simplify option allows simplifying the geometry (i.e. remove nodes in polygons) according to a user-defined tolerance. The stitch option tries to stitch together the polygons corresponding to segmented region that may have been split by the tiling scheme.

Parameters

Limitations

In raster mode, the application can not handle large input images. Stitching step of vector mode might become slow with very large input images. -MeanShift filter results depends on the number of threads used. -Watershed and multiscale geodesic morphology segmentation will be performed on the amplitude of the input image.

Authors

OTB-Team

See Also

MeanShiftSegmentation

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Segmentation-watershed.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Segmentation-watershed.html deleted file mode 100644 index e4e557745baf..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Segmentation-watershed.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

Segmentation

Brief Description

Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported.

Tags

Segmentation

Long Description

This application allows one to perform various segmentation algorithms on a multispectral image.Available segmentation algorithms are two different versions of Mean-Shift segmentation algorithm (one being multi-threaded), simple pixel based connected components according to a user-defined criterion, and watershed from the gradient of the intensity (norm of spectral bands vector). The application has two different modes that affects the nature of its output. - -In raster mode, the output of the application is a classical image of unique labels identifying the segmented regions. The labeled output can be passed to the ColorMapping application to render regions with contrasted colours. Please note that this mode loads the whole input image into memory, and as such can not handle large images. - - To segment large data, one can use the vector mode. In this case, the output of the application is a vector file or database. The input image is split into tiles (whose size can be set using the tilesize parameter), and each tile is loaded, segmented with the chosen algorithm, vectorized, and written into the output file or database. This piece-wise behavior ensure that memory will never get overloaded, and that images of any size can be processed. There are few more options in the vector mode. The simplify option allows simplifying the geometry (i.e. remove nodes in polygons) according to a user-defined tolerance. The stitch option tries to stitch together the polygons corresponding to segmented region that may have been split by the tiling scheme.

Parameters

Limitations

In raster mode, the application can not handle large input images. Stitching step of vector mode might become slow with very large input images. -MeanShift filter results depends on the number of threads used. -Watershed and multiscale geodesic morphology segmentation will be performed on the amplitude of the input image.

Authors

OTB-Team

See Also

MeanShiftSegmentation

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Segmentation.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Segmentation.html deleted file mode 100644 index e4e557745baf..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Segmentation.html +++ /dev/null @@ -1,11 +0,0 @@ - - -

Segmentation

Brief Description

Performs segmentation of an image, and output either a raster or a vector file. In vector mode, large input datasets are supported.

Tags

Segmentation

Long Description

This application allows one to perform various segmentation algorithms on a multispectral image.Available segmentation algorithms are two different versions of Mean-Shift segmentation algorithm (one being multi-threaded), simple pixel based connected components according to a user-defined criterion, and watershed from the gradient of the intensity (norm of spectral bands vector). The application has two different modes that affects the nature of its output. - -In raster mode, the output of the application is a classical image of unique labels identifying the segmented regions. The labeled output can be passed to the ColorMapping application to render regions with contrasted colours. Please note that this mode loads the whole input image into memory, and as such can not handle large images. - - To segment large data, one can use the vector mode. In this case, the output of the application is a vector file or database. The input image is split into tiles (whose size can be set using the tilesize parameter), and each tile is loaded, segmented with the chosen algorithm, vectorized, and written into the output file or database. This piece-wise behavior ensure that memory will never get overloaded, and that images of any size can be processed. There are few more options in the vector mode. The simplify option allows simplifying the geometry (i.e. remove nodes in polygons) according to a user-defined tolerance. The stitch option tries to stitch together the polygons corresponding to segmented region that may have been split by the tiling scheme.

Parameters

Limitations

In raster mode, the application can not handle large input images. Stitching step of vector mode might become slow with very large input images. -MeanShift filter results depends on the number of threads used. -Watershed and multiscale geodesic morphology segmentation will be performed on the amplitude of the input image.

Authors

OTB-Team

See Also

MeanShiftSegmentation

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Smoothing-anidif.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Smoothing-anidif.html deleted file mode 100644 index c8bcf1ce16d6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Smoothing-anidif.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Smoothing

Brief Description

Apply a smoothing filter to an image

Tags

Image Filtering

Long Description

This application applies smoothing filter to an image. Either gaussian, mean, or anisotropic diffusion are available.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Smoothing-gaussian.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Smoothing-gaussian.html deleted file mode 100644 index c8bcf1ce16d6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Smoothing-gaussian.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Smoothing

Brief Description

Apply a smoothing filter to an image

Tags

Image Filtering

Long Description

This application applies smoothing filter to an image. Either gaussian, mean, or anisotropic diffusion are available.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Smoothing-mean.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Smoothing-mean.html deleted file mode 100644 index c8bcf1ce16d6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Smoothing-mean.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Smoothing

Brief Description

Apply a smoothing filter to an image

Tags

Image Filtering

Long Description

This application applies smoothing filter to an image. Either gaussian, mean, or anisotropic diffusion are available.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Smoothing.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Smoothing.html deleted file mode 100644 index c8bcf1ce16d6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Smoothing.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Smoothing

Brief Description

Apply a smoothing filter to an image

Tags

Image Filtering

Long Description

This application applies smoothing filter to an image. Either gaussian, mean, or anisotropic diffusion are available.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/SplitImage.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/SplitImage.html deleted file mode 100644 index b2adfafe6a27..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/SplitImage.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

SplitImage

Brief Description

Split a N multiband image into N images

Tags

Image Manipulation

Long Description

This application splits a N-bands image into N mono-band images. The output images filename will be generated from the output parameter. Thus if the input image has 2 channels, and the user has set an output outimage.tif, the generated images will be outimage_0.tif and outimage_1.tif

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/StereoFramework.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/StereoFramework.html deleted file mode 100644 index a59f3eb796e4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/StereoFramework.html +++ /dev/null @@ -1,18 +0,0 @@ - - -

StereoFramework

Brief Description

Compute the ground elevation based on one or multiple stereo pair(s)

Tags

Stereo

Long Description

Compute the ground elevation with a stereo block matching algorithm between one or multiple stereo pair in sensor geometry. The output is projected in desired geographic or cartographic map projection (UTM by default). The pipeline is made of the following steps: -for each sensor pair : - - - compute the epipolar displacement grids from the stereo pair (direct and inverse) - - resample the stereo pair into epipolar geometry using BCO interpolation - - create masks for each epipolar image : remove black borders and resample input masks - - compute horizontal disparities with a block matching algorithm - - refine disparities to sub-pixel precision with a dichotomy algorithm - - apply an optional median filter - - filter disparities based on the correlation score and exploration bounds - - translate disparities in sensor geometry - convert disparity to 3D Map. - -Then fuse all 3D maps to produce DSM.

Parameters

Limitations

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/StereoRectificationGridGenerator.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/StereoRectificationGridGenerator.html deleted file mode 100644 index 56bafc0fa3a4..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/StereoRectificationGridGenerator.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

StereoRectificationGridGenerator

Brief Description

Generates two deformation fields to stereo-rectify (i.e. resample in epipolar geometry) a pair of stereo images up to the sensor model precision

Tags

Stereo

Long Description

This application generates a pair of deformation grid to stereo-rectify a pair of stereo images according to sensor modelling and a mean elevation hypothesis. The deformation grids can be passed to the StereoRectificationGridGenerator application for actual resampling in epipolar geometry.

Parameters

Limitations

Generation of the deformation grid is not streamable, pay attention to this fact when setting the grid step.

Authors

OTB-Team

See Also

otbGridBasedImageResampling

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/Superimpose.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/Superimpose.html deleted file mode 100644 index e880a9f82423..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/Superimpose.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

Superimpose

Brief Description

Using available image metadata, project one image onto another one

Tags

Geometry,Superimposition

Long Description

This application performs the projection of an image into the geometry of another one.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TestApplication.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TestApplication.html deleted file mode 100644 index aac6ba570cde..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TestApplication.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TestApplication

Brief Description

This application helps developers to test parameters types

Tags

Test

Long Description

The purpose of this application is to test parameters types.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TileFusion.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TileFusion.html deleted file mode 100644 index ff003aa4becc..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TileFusion.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TileFusion

Brief Description

Fusion of an image made of several tile files.

Tags

Image Manipulation

Long Description

Concatenate several tile files into a single image file.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-ann.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-ann.html deleted file mode 100644 index dc36736df3e8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-ann.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and OpenCV Machine Learning (2.3.1 and later).

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-bayes.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-bayes.html deleted file mode 100644 index dc36736df3e8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-bayes.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and OpenCV Machine Learning (2.3.1 and later).

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-boost.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-boost.html deleted file mode 100644 index dc36736df3e8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-boost.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and OpenCV Machine Learning (2.3.1 and later).

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-dt.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-dt.html deleted file mode 100644 index dc36736df3e8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-dt.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and OpenCV Machine Learning (2.3.1 and later).

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-gbt.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-gbt.html deleted file mode 100644 index dc36736df3e8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-gbt.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and OpenCV Machine Learning (2.3.1 and later).

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-knn.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-knn.html deleted file mode 100644 index dc36736df3e8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-knn.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and OpenCV Machine Learning (2.3.1 and later).

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-libsvm.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-libsvm.html deleted file mode 100644 index dc36736df3e8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-libsvm.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and OpenCV Machine Learning (2.3.1 and later).

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-rf.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-rf.html deleted file mode 100644 index dc36736df3e8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier-rf.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and OpenCV Machine Learning (2.3.1 and later).

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier.html deleted file mode 100644 index dc36736df3e8..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainImagesClassifier.html +++ /dev/null @@ -1,8 +0,0 @@ - - -

TrainImagesClassifier

Brief Description

Train a classifier from multiple pairs of images and training vector data.

Tags

Learning

Long Description

This application performs a classifier training from multiple pairs of input images and training vector data. Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The training vector data must contain polygons with a positive integer field representing the class label. The name of this field can be set using the "Class label field" parameter. Training and validation sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio between the number of samples in training and validation sets. Two parameters allow managing the size of the training and validation sets per class and per image. - Several classifier parameters can be set depending on the chosen classifier. In the validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels are ordered according to the rows/columns of the confusion matrix. - This application is based on LibSVM and OpenCV Machine Learning (2.3.1 and later).

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainOGRLayersClassifier.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainOGRLayersClassifier.html deleted file mode 100644 index 8658fc3c1058..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainOGRLayersClassifier.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TrainOGRLayersClassifier

Brief Description

Train a SVM classifier based on labeled geometries and a list of features to consider.

Tags

Segmentation

Long Description

This application trains a SVM classifier based on labeled geometries and a list of features to consider for classification. This application is deprecated, prefer using TrainVectorClassifier which offers access to all the classifiers.

Parameters

Limitations

Experimental. For now only shapefiles are supported. Tuning of SVM classifier is not available.

Authors

David Youssefi during internship at CNES

See Also

OGRLayerClassifier,ComputeOGRLayersFeaturesStatistics

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-ann.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-ann.html deleted file mode 100644 index 4a7c4d30c120..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-ann.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

TrainRegression

Brief Description

Train a classifier from multiple images to perform regression.

Tags

Learning

Long Description

This application trains a classifier from multiple input images or a csv file, in order to perform regression. Predictors are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The output value for each predictor is assumed to be the last band (or the last column for CSV files). Training and validation predictor lists are built such that their size is inferior to maximum bounds given by the user, and the proportion corresponds to the balance parameter. Several classifier parameters can be set depending on the chosen classifier. In the validation process, the mean square error is computed - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-dt.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-dt.html deleted file mode 100644 index 4a7c4d30c120..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-dt.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

TrainRegression

Brief Description

Train a classifier from multiple images to perform regression.

Tags

Learning

Long Description

This application trains a classifier from multiple input images or a csv file, in order to perform regression. Predictors are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The output value for each predictor is assumed to be the last band (or the last column for CSV files). Training and validation predictor lists are built such that their size is inferior to maximum bounds given by the user, and the proportion corresponds to the balance parameter. Several classifier parameters can be set depending on the chosen classifier. In the validation process, the mean square error is computed - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-gbt.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-gbt.html deleted file mode 100644 index 4a7c4d30c120..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-gbt.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

TrainRegression

Brief Description

Train a classifier from multiple images to perform regression.

Tags

Learning

Long Description

This application trains a classifier from multiple input images or a csv file, in order to perform regression. Predictors are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The output value for each predictor is assumed to be the last band (or the last column for CSV files). Training and validation predictor lists are built such that their size is inferior to maximum bounds given by the user, and the proportion corresponds to the balance parameter. Several classifier parameters can be set depending on the chosen classifier. In the validation process, the mean square error is computed - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-knn.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-knn.html deleted file mode 100644 index 4a7c4d30c120..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-knn.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

TrainRegression

Brief Description

Train a classifier from multiple images to perform regression.

Tags

Learning

Long Description

This application trains a classifier from multiple input images or a csv file, in order to perform regression. Predictors are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The output value for each predictor is assumed to be the last band (or the last column for CSV files). Training and validation predictor lists are built such that their size is inferior to maximum bounds given by the user, and the proportion corresponds to the balance parameter. Several classifier parameters can be set depending on the chosen classifier. In the validation process, the mean square error is computed - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-libsvm.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-libsvm.html deleted file mode 100644 index 4a7c4d30c120..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-libsvm.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

TrainRegression

Brief Description

Train a classifier from multiple images to perform regression.

Tags

Learning

Long Description

This application trains a classifier from multiple input images or a csv file, in order to perform regression. Predictors are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The output value for each predictor is assumed to be the last band (or the last column for CSV files). Training and validation predictor lists are built such that their size is inferior to maximum bounds given by the user, and the proportion corresponds to the balance parameter. Several classifier parameters can be set depending on the chosen classifier. In the validation process, the mean square error is computed - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-rf.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-rf.html deleted file mode 100644 index 4a7c4d30c120..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression-rf.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

TrainRegression

Brief Description

Train a classifier from multiple images to perform regression.

Tags

Learning

Long Description

This application trains a classifier from multiple input images or a csv file, in order to perform regression. Predictors are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The output value for each predictor is assumed to be the last band (or the last column for CSV files). Training and validation predictor lists are built such that their size is inferior to maximum bounds given by the user, and the proportion corresponds to the balance parameter. Several classifier parameters can be set depending on the chosen classifier. In the validation process, the mean square error is computed - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression.html deleted file mode 100644 index 4a7c4d30c120..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainRegression.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

TrainRegression

Brief Description

Train a classifier from multiple images to perform regression.

Tags

Learning

Long Description

This application trains a classifier from multiple input images or a csv file, in order to perform regression. Predictors are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by the ComputeImagesStatistics application. - The output value for each predictor is assumed to be the last band (or the last column for CSV files). Training and validation predictor lists are built such that their size is inferior to maximum bounds given by the user, and the proportion corresponds to the balance parameter. Several classifier parameters can be set depending on the chosen classifier. In the validation process, the mean square error is computed - This application is based on LibSVM and on OpenCV Machine Learning classifiers, and is compatible with OpenCV 2.3.1 and later.

Parameters

Limitations

None

Authors

OTB-Team

See Also

OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-ann.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-ann.html deleted file mode 100644 index 4c674772e0a5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-ann.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TrainVectorClassifier

Brief Description

Train a classifier based on labeled geometries and a list of features to consider.

Tags

Learning

Long Description

This application trains a classifier based on labeled geometries and a list of features to consider for classification.

Parameters

Limitations

Authors

OTB Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-bayes.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-bayes.html deleted file mode 100644 index 4c674772e0a5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-bayes.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TrainVectorClassifier

Brief Description

Train a classifier based on labeled geometries and a list of features to consider.

Tags

Learning

Long Description

This application trains a classifier based on labeled geometries and a list of features to consider for classification.

Parameters

Limitations

Authors

OTB Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-boost.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-boost.html deleted file mode 100644 index 4c674772e0a5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-boost.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TrainVectorClassifier

Brief Description

Train a classifier based on labeled geometries and a list of features to consider.

Tags

Learning

Long Description

This application trains a classifier based on labeled geometries and a list of features to consider for classification.

Parameters

Limitations

Authors

OTB Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-dt.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-dt.html deleted file mode 100644 index 4c674772e0a5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-dt.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TrainVectorClassifier

Brief Description

Train a classifier based on labeled geometries and a list of features to consider.

Tags

Learning

Long Description

This application trains a classifier based on labeled geometries and a list of features to consider for classification.

Parameters

Limitations

Authors

OTB Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-gbt.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-gbt.html deleted file mode 100644 index 4c674772e0a5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-gbt.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TrainVectorClassifier

Brief Description

Train a classifier based on labeled geometries and a list of features to consider.

Tags

Learning

Long Description

This application trains a classifier based on labeled geometries and a list of features to consider for classification.

Parameters

Limitations

Authors

OTB Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-knn.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-knn.html deleted file mode 100644 index 4c674772e0a5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-knn.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TrainVectorClassifier

Brief Description

Train a classifier based on labeled geometries and a list of features to consider.

Tags

Learning

Long Description

This application trains a classifier based on labeled geometries and a list of features to consider for classification.

Parameters

Limitations

Authors

OTB Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-libsvm.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-libsvm.html deleted file mode 100644 index 4c674772e0a5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-libsvm.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TrainVectorClassifier

Brief Description

Train a classifier based on labeled geometries and a list of features to consider.

Tags

Learning

Long Description

This application trains a classifier based on labeled geometries and a list of features to consider for classification.

Parameters

Limitations

Authors

OTB Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-rf.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-rf.html deleted file mode 100644 index 4c674772e0a5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier-rf.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TrainVectorClassifier

Brief Description

Train a classifier based on labeled geometries and a list of features to consider.

Tags

Learning

Long Description

This application trains a classifier based on labeled geometries and a list of features to consider for classification.

Parameters

Limitations

Authors

OTB Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier.html deleted file mode 100644 index 4c674772e0a5..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/TrainVectorClassifier.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

TrainVectorClassifier

Brief Description

Train a classifier based on labeled geometries and a list of features to consider.

Tags

Learning

Long Description

This application trains a classifier based on labeled geometries and a list of features to consider for classification.

Parameters

Limitations

Authors

OTB Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataDSValidation.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataDSValidation.html deleted file mode 100644 index e2cd2032ac0a..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataDSValidation.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

VectorDataDSValidation

Brief Description

Vector data validation based on the fusion of features using Dempster-Shafer evidence theory framework.

Tags

Feature Extraction

Long Description

This application validates or unvalidate the studied samples using the Dempster-Shafer theory.

Parameters

Limitations

None.

Authors

OTB-Team

See Also

http://en.wikipedia.org/wiki/Dempster-Shafer_theory

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataExtractROI.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataExtractROI.html deleted file mode 100644 index 5acd2390b3b6..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataExtractROI.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

VectorDataExtractROI

Brief Description

Perform an extract ROI on the input vector data according to the input image extent

Tags

Vector Data Manipulation

Long Description

This application extracts the vector data features belonging to a region specified by the support image envelope. Any features intersecting the support region is copied to output. The output geometries are NOT cropped.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataReprojection-image.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataReprojection-image.html deleted file mode 100644 index 6a546974cf23..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataReprojection-image.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

VectorDataReprojection

Brief Description

Reproject a vector data using support image projection reference, or a user specified map projection -

Tags

Geometry,Vector Data Manipulation,Coordinates

Long Description

This application allows reprojecting a vector data using support image projection reference, or a user given map projection. - If given, image keywordlist can be added to reprojected vectordata.

Parameters

Limitations

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataReprojection-user.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataReprojection-user.html deleted file mode 100644 index 6a546974cf23..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataReprojection-user.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

VectorDataReprojection

Brief Description

Reproject a vector data using support image projection reference, or a user specified map projection -

Tags

Geometry,Vector Data Manipulation,Coordinates

Long Description

This application allows reprojecting a vector data using support image projection reference, or a user given map projection. - If given, image keywordlist can be added to reprojected vectordata.

Parameters

Limitations

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataReprojection.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataReprojection.html deleted file mode 100644 index 6a546974cf23..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataReprojection.html +++ /dev/null @@ -1,7 +0,0 @@ - - -

VectorDataReprojection

Brief Description

Reproject a vector data using support image projection reference, or a user specified map projection -

Tags

Geometry,Vector Data Manipulation,Coordinates

Long Description

This application allows reprojecting a vector data using support image projection reference, or a user given map projection. - If given, image keywordlist can be added to reprojected vectordata.

Parameters

Limitations

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataSetField.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataSetField.html deleted file mode 100644 index 34a074002ed3..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataSetField.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

VectorDataSetField

Brief Description

Set a field in vector data.

Tags

Vector Data Manipulation

Long Description

Set a specified field to a specified value on all features of a vector data.

Parameters

Limitations

Doesn't work with KML files yet

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataTransform.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataTransform.html deleted file mode 100644 index ad5cda0a1e68..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/VectorDataTransform.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

VectorDataTransform

Brief Description

Apply a transform to each vertex of the input VectorData

Tags

Vector Data Manipulation

Long Description

This application performs a transformation of an input vector data transforming each vertex in the vector data. The applied transformation manages translation, rotation and scale, and can be centered or not.

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/description/5.8.0/doc/VertexComponentAnalysis.html b/python/plugins/processing/algs/otb/description/5.8.0/doc/VertexComponentAnalysis.html deleted file mode 100644 index 345c7725e11f..000000000000 --- a/python/plugins/processing/algs/otb/description/5.8.0/doc/VertexComponentAnalysis.html +++ /dev/null @@ -1,5 +0,0 @@ - - -

VertexComponentAnalysis

Brief Description

Find endmembers in hyperspectral images with Vertex Component Analysis

Tags

Hyperspectral,Dimensionality Reduction

Long Description

Applies the Vertex Component Analysis to an hyperspectral image to extract endmembers

Parameters

Limitations

None

Authors

OTB-Team

See Also

Example of use

\ No newline at end of file diff --git a/python/plugins/processing/algs/otb/helper/generate_application_descriptors.py b/python/plugins/processing/algs/otb/helper/generate_application_descriptors.py deleted file mode 100644 index ac5c6386c5e8..000000000000 --- a/python/plugins/processing/algs/otb/helper/generate_application_descriptors.py +++ /dev/null @@ -1,414 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - generate_application_descriptors.py - --------------------- - Date : August 2012 - Copyright : (C) 2012 by Victor Olaya - Email : volayaf at gmail dot com -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from __future__ import print_function -from builtins import str - -__author__ = 'Victor Olaya' -__date__ = 'August 2012' -__copyright__ = '(C) 2012, Victor Olaya' - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = '$Format:%H$' - -import os -import otbApplication -from qgis.core import QgsApplication - -outputpath = str(QgsApplication.qgisSettingsDirPath() - + 'python/plugins/processing/otb/description') -endl = os.linesep - - -def convertendl(s): - """Convert a string for compatibility in txt dump.""" - return s.replace('\n', '\\n') - - -def get_app_list(): - blackList = ['TestApplication'] - appNames = [app for app in - otbApplication.Registry.GetAvailableApplications() if app - not in blackList] - return appNames - - -def generate_all_app_descriptors(): - appliIdx = 0 - for appliname in get_app_list(): - appliIdx = appliIdx + 1 - generate_app_descriptor(appliname) - - -def generate_app_descriptor(appliname): - # fix_print_with_import - print(appliname) - - appInstance = otbApplication.Registry.CreateApplication(appliname) - appInstance.UpdateParameters() # TODO need this ? - - out = '' - - # the application key - out += appliname + endl - - # the executable - out += 'otbcli_' + appliname + endl - - # long name - out += convertendl(appInstance.GetDocName()) + endl - - # group - out += get_group(appInstance) + endl - - for paramKey in appInstance.GetParametersKeys(): - pdesc = generate_param_descriptor(appInstance, paramKey) - if len(pdesc) > 0: - out += pdesc + endl - - with open(os.path.join(outputpath, appliname + '.txt'), 'w') as outfile: - outfile.write(out) - - -def generate_app_doc(appliname): - appInstance = otbApplication.Registry.CreateApplication(appliname) - appInstance.UpdateParameters() # TODO need this ? - html = appInstance.GetHtmlExample() - - docpath = os.path.join(outputpath, 'doc') - if not os.path.exists(docpath): - os.makedirs(docpath) - - with open(os.path.join(docpath, appliname + '.html'), 'w') as outfile: - outfile.write(html) - - -def get_group(appInstance): - tags = appInstance.GetDocTags() - sectionTags = [ - 'Image Manipulation', - 'Vector Data Manipulation', - 'Calibration', - 'Geometry', - 'Image Filtering', - 'Feature Extraction', - 'Stereo', - 'Learning', - 'Segmentation', - ] - for sectionTag in sectionTags: - for tag in tags: - if tag == sectionTag: - return sectionTag - return 'Miscellaneous' - - -def generate_param_descriptor(appInstance, paramKey): - paramcreationfunction = { - otbApplication.ParameterType_Empty: generate_parameter_Empty, - otbApplication.ParameterType_Int: generate_parameter_Int, - otbApplication.ParameterType_Float: generate_parameter_Float, - otbApplication.ParameterType_String: generate_parameter_String, - otbApplication.ParameterType_StringList: generate_parameter_NOTHANDLED, - otbApplication.ParameterType_InputFilename: generate_parameter_InputFilename, - otbApplication.ParameterType_OutputFilename: generate_parameter_OutputFilename, - otbApplication.ParameterType_Directory: generate_parameter_Directory, - otbApplication.ParameterType_Choice: generate_parameter_Choice, - otbApplication.ParameterType_InputImage: generate_parameter_InputImage, - otbApplication.ParameterType_InputImageList: generate_parameter_InputImageList, - otbApplication.ParameterType_InputVectorData: generate_parameter_InputVectorData, - otbApplication.ParameterType_InputVectorDataList: generate_parameter_InputVectorDataList, - otbApplication.ParameterType_OutputImage: generate_parameter_OutputImage, - otbApplication.ParameterType_OutputVectorData: generate_parameter_OutputVectorData, - otbApplication.ParameterType_Radius: generate_parameter_Radius, - otbApplication.ParameterType_Group: generate_parameter_NOTHANDLED, - otbApplication.ParameterType_ListView: generate_parameter_NOTHANDLED, - otbApplication.ParameterType_ComplexInputImage: generate_parameter_ComplexInputImage, - otbApplication.ParameterType_ComplexOutputImage: generate_parameter_ComplexOutputImage, - otbApplication.ParameterType_RAM: generate_parameter_RAM, - } - return paramcreationfunction[appInstance.GetParameterType(paramKey)]( - appInstance, paramKey) - - -def generate_parameter_Empty(appInstance, paramKey): - out = 'ParameterBoolean' - out += '|' - out += '-' + paramKey - out += '|' - out += convertendl(appInstance.GetParameterName(paramKey)) - out += '|' - if appInstance.IsParameterEnabled(paramKey): - out += 'True' - return out - - -def generate_parameter_Int(appInstance, paramKey): - out = 'ParameterNumber' - out += '|' - - out += '-' + paramKey - out += '|' - - out += convertendl(appInstance.GetParameterName(paramKey)) - out += '|' - - out += 'None' - out += '|' - out += 'None' - out += '|' - - defaultVal = '0' - try: - defaultVal = str(appInstance.GetParameterInt(paramKey)) - except: - pass - out += defaultVal - return out - - -def generate_parameter_Float(appInstance, paramKey): - out = 'ParameterNumber' - out += '|' - - out += '-' + paramKey - out += '|' - - out += convertendl(appInstance.GetParameterName(paramKey)) - out += '|' - - out += 'None' - out += '|' - out += 'None' - out += '|' - - defaultVal = '0.0' - try: - defaultVal = str(appInstance.GetParameterFloat(paramKey)) - except: - pass - out += defaultVal - return out - - -def generate_parameter_String(appInstance, paramKey): - out = 'ParameterString' - out += '|' - - out += '-' + paramKey - out += '|' - - out += convertendl(appInstance.GetParameterName(paramKey)) - out += '|' - - defaultVal = '' - try: - defaultVal = str(appInstance.GetParameterString(paramKey)) - except: - pass - out += defaultVal - return out - - -def generate_parameter_InputFilename(appInstance, paramKey): - out = 'ParameterFile' - out += '|' - - out += '-' + paramKey - out += '|' - - out += convertendl(appInstance.GetParameterName(paramKey)) - out += '|' - - try: - defaultVal = str(appInstance.GetParameterString(paramKey)) - out += '|' + defaultVal - except: - pass - - return out - - -def generate_parameter_OutputFilename(appInstance, paramKey): - out = 'OutputFile' - out += '|' - - out += '-' + paramKey - out += '|' - - out += convertendl(appInstance.GetParameterName(paramKey)) - - return out - - -def generate_parameter_Directory(appInstance, paramKey): - out = 'ParameterFile' - out += '|' - - out += '-' + paramKey - out += '|' - - out += convertendl(appInstance.GetParameterName(paramKey)) - - try: - defaultVal = str(appInstance.GetParameterString(paramKey)) - out += '|' + defaultVal - except: - pass - - return out - - -def generate_parameter_Choice(appInstance, paramKey): - out = 'ParameterSelection' - out += '|' - - out += '-' + paramKey - out += '|' - - out += convertendl(appInstance.GetParameterName(paramKey)) - out += '|' - - choices = '' - for choice in appInstance.GetChoiceKeys(paramKey): - choices += choice - choices += ';' - out += choices[:-1] - - out += '|' - out += str(appInstance.GetParameterInt(paramKey)) - - return out - - -def generate_parameter_InputImage(appInstance, paramKey): - out = 'ParameterRaster' - out += '|' - - out += '-' + paramKey - out += '|' - - out += convertendl(appInstance.GetParameterName(paramKey)) - out += '|' - - out += str(not appInstance.IsMandatory(paramKey)) - - return out - - -def generate_parameter_InputImageList(appInstance, paramKey): - out = 'ParameterMultipleInput' - out += '|' - - out += '-' + paramKey - out += '|' - - out += convertendl(appInstance.GetParameterName(paramKey)) - out += '|' - - out += '3' - out += '|' - - out += str(not appInstance.IsMandatory(paramKey)) - - return out - - -def generate_parameter_InputVectorData(appInstance, paramKey): - out = 'ParameterVector' - out += '|' - - out += '-' + paramKey - out += '|' - - out += convertendl(appInstance.GetParameterName(paramKey)) - out += '|' - - out += '-1' - out += '|' - - out += str(not appInstance.IsMandatory(paramKey)) - - return out - - -def generate_parameter_InputVectorDataList(appInstance, paramKey): - out = 'ParameterMultipleInput' - out += '|' - - out += '-' + paramKey - out += '|' - - out += convertendl(appInstance.GetParameterName(paramKey)) - out += '|' - - out += '-1' - out += '|' - - out += str(not appInstance.IsMandatory(paramKey)) - - return out - - -def generate_parameter_OutputImage(appInstance, paramKey): - out = 'OutputRaster' - out += '|' - - out += '-' + paramKey - out += '|' - - out += convertendl(appInstance.GetParameterName(paramKey)) - - return out - - -def generate_parameter_NOTHANDLED(appInstance, paramKey): - return '' - - -def generate_parameter_OutputVectorData(appInstance, paramKey): - out = 'OutputVector' - out += '|' - - out += '-' + paramKey - out += '|' - - out += convertendl(appInstance.GetParameterName(paramKey)) - - return out - - -def generate_parameter_Radius(appInstance, paramKey): - return generate_parameter_Int(appInstance, paramKey) - - -def generate_parameter_ComplexInputImage(appInstance, paramKey): - return generate_parameter_InputImage(appInstance, paramKey) - - -def generate_parameter_ComplexOutputImage(appInstance, paramKey): - return generate_parameter_OutputImage(appInstance, paramKey) - - -def generate_parameter_RAM(appInstance, paramKey): - return generate_parameter_Int(appInstance, paramKey) - - -if __name__ == '__main__': - generate_all_app_descriptors() diff --git a/python/plugins/processing/algs/otb/maintenance/OTBHelper.py b/python/plugins/processing/algs/otb/maintenance/OTBHelper.py deleted file mode 100644 index 34cbc8fa7d19..000000000000 --- a/python/plugins/processing/algs/otb/maintenance/OTBHelper.py +++ /dev/null @@ -1,733 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - OTBHelper.py - --------------------- - Copyright : (C) 2013 by CS Systemes d'information (CS SI) - Email : otb at c-s dot fr (CS SI) - Contributors : Julien Malik (CS SI) - File creation - Oscar Picas (CS SI) - - Alexia Mondot (CS SI) - Add particular case in xml creation -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from __future__ import print_function -from builtins import filter -from builtins import map -from builtins import str -__author__ = 'Julien Malik, Oscar Picas, Alexia Mondot' -__copyright__ = '(C) 2013, CS Systemes d\'information (CS SI)' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' -__version__ = "3.8" - -import os -import copy - -import xml.etree.ElementTree as ET -import traceback - -from contextlib import contextmanager -import shutil - - -@contextmanager -def tag(name, c): - c.append("<%s>" % name) - yield - if ' ' in name: - c.append("" % name.split(' ')[0]) - else: - c.append("" % name) - - -@contextmanager -def opentag(name, c): - c.append("<%s>" % name) - yield - - -def get_group(appInstance): - tags = appInstance.GetDocTags() - sectionTags = ["Image Manipulation", "Vector Data Manipulation", "Calibration", "Geometry", "Image Filtering", "Feature Extraction", "Stereo", "Learning", "Segmentation"] - for sectionTag in sectionTags: - for tag in tags: - if tag == sectionTag: - return sectionTag - return "Miscellaneous" - - -def set_OTB_log(): - import logging - logger = logging.getLogger('OTBGenerator') - hdlr = logging.FileHandler('OTBGenerator.log') - hdlr.setLevel(logging.DEBUG) - cons = logging.StreamHandler() - cons.setLevel(logging.CRITICAL) - formatter = logging.Formatter('%(asctime)s %(levelname)s %(message)s') - hdlr.setFormatter(formatter) - logger.addHandler(hdlr) - logger.addHandler(cons) - logger.setLevel(logging.DEBUG) - - -def get_OTB_log(): - import logging - logger = logging.getLogger('OTBGenerator') - if not logger.handlers: - set_OTB_log() - logger = logging.getLogger('OTBGenerator') - return logger - - -def indent(elem, level=0): - i = "\n" + level * " " - if len(elem): - if not elem.text or not elem.text.strip(): - elem.text = i + " " - if not elem.tail or not elem.tail.strip(): - elem.tail = i - for elem in elem: - indent(elem, level + 1) - if not elem.tail or not elem.tail.strip(): - elem.tail = i - else: - if level and (not elem.tail or not elem.tail.strip()): - elem.tail = i - -set_OTB_log() - - -def get_parameters(): - parameters = {getattr(otbApplication, each): each for each in dir(otbApplication) if 'ParameterType_' in each} - return parameters - - -def get_inverted_parameters(): - """ - This function allows mapping otb parameters with processing parameters. - """ - parameters = {getattr(otbApplication, each): each for each in dir(otbApplication) if 'ParameterType_' in each} - - inverted_parameters = {key: value for value, key in list(parameters.items())} - inverted_parameters['ParameterType_Radius'] = 1 - inverted_parameters['ParameterType_RAM'] = 1 - inverted_parameters['ParameterType_ComplexInputImage'] = 9 - inverted_parameters['ParameterType_ComplexOutputImage'] = 13 - - inverted_parameters_clone = copy.deepcopy(inverted_parameters) - inverted_parameters_clone['ParameterType_Empty'] = 'ParameterBoolean' - inverted_parameters_clone['ParameterType_Int'] = 'ParameterNumber' - inverted_parameters_clone['ParameterType_Float'] = 'ParameterNumber' - inverted_parameters_clone['ParameterType_String'] = 'ParameterString' - inverted_parameters_clone['ParameterType_StringList'] = 'ParameterString' - inverted_parameters_clone['ParameterType_InputFilename'] = 'ParameterFile' - inverted_parameters_clone['ParameterType_OutputFilename'] = 'OutputFile' - inverted_parameters_clone['ParameterType_Directory'] = 'ParameterFile' - inverted_parameters_clone['ParameterType_Choice'] = 'ParameterSelection' - inverted_parameters_clone['ParameterType_InputImage'] = 'ParameterRaster' - inverted_parameters_clone['ParameterType_InputImageList'] = 'ParameterMultipleInput' - inverted_parameters_clone['ParameterType_InputVectorData'] = 'ParameterVector' - inverted_parameters_clone['ParameterType_InputVectorDataList'] = 'ParameterMultipleInput' - inverted_parameters_clone['ParameterType_OutputImage'] = 'OutputRaster' - inverted_parameters_clone['ParameterType_OutputVectorData'] = 'OutputVector' - inverted_parameters_clone['ParameterType_Radius'] = 'ParameterNumber' - inverted_parameters_clone['ParameterType_Group'] = None - inverted_parameters_clone['ParameterType_ListView'] = 'ParameterSelection' - inverted_parameters_clone['ParameterType_ComplexInputImage'] = 'ParameterRaster' - inverted_parameters_clone['ParameterType_ComplexOutputImage'] = 'OutputRaster' - inverted_parameters_clone['ParameterType_RAM'] = 'ParameterNumber' - inverted_parameters_clone['ParameterType_InputProcessXML'] = 'ParameterFile' - inverted_parameters_clone['ParameterType_OutputProcessXML'] = 'ParameterFile' - inverted_parameters_clone['ParameterType_InputFilenameList'] = 'ParameterMultipleInput' # 'ParameterString' - - return inverted_parameters_clone - - -def retrieve_module_name(param): - """ - returns the file parameter of the given processing parameter - """ - if param: - try: - import processing.core - dir_p = os.path.dirname(processing.core.__file__) - if 'Parameter' in param: - exec("from processing.core.parameters import %s" % param) - return os.path.join(dir_p, "parameters.py") - if 'Output' in param: - exec("from processing.core.outputs import %s" % param) - return os.path.join(dir_p, "outputs.py") - except ImportError: - # fix_print_with_import - print("Error parsing ", param) - return None - - -def get_constructor_parameters_from_filename(py_file, param=""): - """ - Get all parameters from the constructor of the class param in the given py_file - """ - import ast - asto = ast.parse(open(py_file).read()) - # get all class definitions corresponding to param given len(e1) should be 1 - e1 = [each for each in asto.body if isinstance(each, ast.ClassDef) and each.name == param] - - # e1[0].body lists all functions from the class e1[0] - # e2 is a list of __init__ functions of class e1[0] - e2 = [each for each in e1[0].body if hasattr(each, "name") and each.name == "__init__"] - if len(e2) > 0: - e4 = e2[0].args.args - else: - e4 = [] - e5 = [each.id for each in e4] - return e5 - - -def get_customize_app_functions(): - """ - Get all parameters from the constructor of the class param in the given py_file - """ - import ast - - py_file = os.path.join(os.path.dirname(__file__), "OTBSpecific_XMLcreation.py") - asto = ast.parse(open(py_file).read()) - # get all class definitions corresponding to param given len(e1) should be 1 - e1 = [each.name for each in asto.body if isinstance(each, ast.FunctionDef) and each.name.startswith("get")] - - return e1 - - -def get_xml_description_from_application_name(our_app, criteria=None): - """ - creates an xml containing information about the given our_app - """ - # creates the application to get the description - # header - app_instance = otbApplication.Registry.CreateApplication(our_app) - root = ET.Element('root') - app = ET.SubElement(root, 'key') - app.text = our_app - executable = ET.SubElement(root, 'exec') - executable.text = "otbcli_" + our_app - longname = ET.SubElement(root, 'longname') - longname.text = app_instance.GetDocName() - group = ET.SubElement(root, 'group') - group.text = get_group(app_instance) - desc = ET.SubElement(root, 'description') - desc.text = app_instance.GetDescription() - - if not criteria: - def real_criteria(x): - return True - else: - if not callable(criteria): - raise Exception("criteria parameter must be a valid python callable") - - real_criteria = criteria - - if len(our_app) == 0: - raise Exception("App name is empty!") - - # get parameters - param_keys = [param_key for param_key in app_instance.GetParametersKeys()] - param_keys = list(filter(real_criteria, param_keys)) - - for param_key in param_keys: - if not param_key == "inxml" and not param_key == "outxml": - get_param_descriptor(app.text, app_instance, param_key, root) - indent(root) - return root - - -def get_the_choices(app_instance, our_descriptor, root): - choices = ET.SubElement(root, 'choices') - for choice in app_instance.GetChoiceKeys(our_descriptor): - choice_node = ET.SubElement(choices, 'choice') - choice_node.text = choice - - -def get_param_descriptor(appkey, app_instance, our_descriptor, root): - """ - update the root xml with the data of the parameter given by "our_descriptor" - """ - logger = get_OTB_log() - parameters = get_parameters() - our_type = parameters[app_instance.GetParameterType(our_descriptor)] - - #get the list of mapped parameters (otb/processing) - inverted_parameters = get_inverted_parameters() - - mapped_parameter = inverted_parameters[our_type] - - file_parameter = retrieve_module_name(mapped_parameter) - - if not file_parameter: - logger.info("Type %s is not handled yet. (%s, %s)" % (our_type, appkey, our_descriptor)) - return - the_params = get_constructor_parameters_from_filename(file_parameter, mapped_parameter) - - # special for default values of OpticalCalibration - if appkey == "OpticalCalibration": - if "default" in the_params: - try: - app_instance.GetParameterAsString(our_descriptor) - except RuntimeError: - return - - param = ET.SubElement(root, 'parameter') - attrs = {'source_parameter_type': parameters[app_instance.GetParameterType(our_descriptor)]} - if appkey == "Segmentation": - if parameters[app_instance.GetParameterType(our_descriptor)] == "ParameterType_OutputFilename": - attrs = {'source_parameter_type': 'ParameterType_OutputVectorData'} - if appkey == "LSMSVectorization": - if parameters[app_instance.GetParameterType(our_descriptor)] == "ParameterType_OutputFilename": - attrs = {'source_parameter_type': 'ParameterType_OutputVectorData'} - if appkey == "SplitImage": - if parameters[app_instance.GetParameterType(our_descriptor)] == "ParameterType_OutputImage": - attrs = {'source_parameter_type': 'ParameterType_OutputFilename'} - - if parameters[app_instance.GetParameterType(our_descriptor)] == "ParameterType_ListView": - if not appkey == "RadiometricIndices": - attrs = {'source_parameter_type': 'ParameterType_StringList'} - - param_type = ET.SubElement(param, 'parameter_type', attrib=attrs) - - param_type.text = inverted_parameters[parameters[app_instance.GetParameterType(our_descriptor)]] - if appkey == "Segmentation": - if parameters[app_instance.GetParameterType(our_descriptor)] == "ParameterType_OutputFilename": - param_type.text = "OutputVector" - if appkey == "LSMSVectorization": - if parameters[app_instance.GetParameterType(our_descriptor)] == "ParameterType_OutputFilename": - param_type.text = "OutputVector" - if appkey == "SplitImage": - if parameters[app_instance.GetParameterType(our_descriptor)] == "ParameterType_OutputImage": - param_type.text = "OutputFile" - if parameters[app_instance.GetParameterType(our_descriptor)] == "ParameterType_ListView": - if not appkey == "RadiometricIndices": - param_type.text = "ParameterString" - - # {the_params = get_constructor_parameters_from_filename(file_parameter, mapped_parameter) - if len(the_params) == 0: - # if 'Output' in file_parameter: - if 'output' in file_parameter: - file_path = os.path.join(os.path.dirname(file_parameter), 'outputs.py') - the_params = get_constructor_parameters_from_filename(file_path, "Output") - if 'parameter' in file_parameter: - file_path = os.path.join(os.path.dirname(file_parameter), 'parameters.py') - the_params = (file_path) - the_params = get_constructor_parameters_from_filename(file_path, "Parameter") - - if "self" in the_params: - #remove self - the_params.remove("self") # the_params[1:] - # to be identical as before ! - if "isSource" in the_params: - the_params.remove("isSource") - if "showSublayersDialog" in the_params: - the_params.remove("showSublayersDialog") - if "ext" in the_params: - the_params.remove("ext") - else: - raise Exception("Unexpected constructor parameters") - - key = ET.SubElement(param, 'key') - key.text = our_descriptor - is_choice_type = False - - for each in the_params: - if each == "name": - name = ET.SubElement(param, 'name') - - nametext = app_instance.GetParameterName(our_descriptor) - if "angle" in nametext: - name.text = nametext.replace("\xc2\xb0", "deg") - else: - name.text = app_instance.GetParameterName(our_descriptor) - if our_descriptor == "acqui.fluxnormcoeff": - pass - elif each == "description": - desc = ET.SubElement(param, 'description') - desc.text = app_instance.GetParameterDescription(our_descriptor) - elif each == "optional": - optional = ET.SubElement(param, 'optional') - optional.text = str(not app_instance.IsMandatory(our_descriptor)) - elif each == "default": - done = False - reason = [] - try: - default_value = str(app_instance.GetParameterAsString(our_descriptor)) - done = True - except: - reason.append(traceback.format_exc()) - if not done: - try: - default_value = str(app_instance.GetParameterFloat(our_descriptor)) - done = True - except: - reason.append(traceback.format_exc()) - if not done: - try: - default_value = str(app_instance.GetParameterInt(our_descriptor)) - done = True - except: - reason.append(traceback.format_exc()) - - if done: - default = ET.SubElement(param, 'default') - default.text = default_value - - if is_choice_type: - the_keys = [a_key for a_key in app_instance.GetChoiceKeys(our_descriptor)] - if default_value in the_keys: - default.text = str(the_keys.index(default_value)) - else: - default.text = '' - else: - logger.debug("A parameter transformation failed, trying default values : for %s, %s, type %s!, conversion message: %s" % (appkey, our_descriptor, parameters[app_instance.GetParameterType(our_descriptor)], str(reason))) - the_type = parameters[app_instance.GetParameterType(our_descriptor)] - if the_type == "ParameterType_Int": - default_value = "0" - elif the_type == "ParameterType_Float": - default_value = "0.0" - elif the_type == "ParameterType_Empty": - default_value = "True" - else: - raise Exception("Unable to adapt %s, %s, %s, conversion message: %s" % (appkey, our_descriptor, parameters[app_instance.GetParameterType(our_descriptor)], str(reason))) - - default = ET.SubElement(param, 'default') - default.text = default_value - else: - is_choice_type = 'Selection' in param_type.text - node = ET.SubElement(param, each) - if is_choice_type: - get_the_choices(app_instance, our_descriptor, node) - - -def get_default_parameter_value(app_instance, param): - parameters = get_parameters() - try: - return app_instance.GetParameterAsString(param) - except: - the_type = parameters[app_instance.GetParameterType(param)] - default_value = "0" - if the_type == "ParameterType_Int": - default_value = "0" - elif the_type == "ParameterType_Float": - default_value = "0.0" - elif the_type == "ParameterType_Empty": - default_value = "True" - return default_value - - -def escape_html(par): - if 'Int' in par: - return '<int32>' - if 'Float' in par: - return '<float>' - if 'Empty' in par: - return '<boolean>' - if 'Radius' in par: - return '<int32>' - if 'RAM' in par: - return '<int32>' - return '<string>' - - -def is_a_parameter(app_instance, param): - if app_instance.GetName() == "HaralickTextureExtraction": - if param.startswith("parameters."): - return True - if '.' in param: - return False - try: - app_instance.GetChoiceKeys(param) - return False - except: - return True - - -def describe_app(app_instance): - parameters = get_parameters() - result = [] - with tag('html', result): - with tag('head', result): - how = """ - -""" - result.append(how) - with tag('body', result): - with tag('h1', result): - result.append(app_instance.GetName()) - with tag('h2', result): - result.append('Brief Description') - result.append(app_instance.GetDescription()) - with tag('h2', result): - result.append('Tags') - result.append(','.join(app_instance.GetDocTags())) - with tag('h2', result): - result.append('Long Description') - result.append(app_instance.GetDocLongDescription()) - with tag('h2', result): - result.append('Parameters') - params = app_instance.GetParametersKeys() - with tag('ul', result): - for param in params: - if is_a_parameter(app_instance, param): - with tag('li', result): - result.append('%s -%s %s ' % ('[param]', param, escape_html(parameters[app_instance.GetParameterType(param)]))) - result.append('%s. Mandatory: %s. Default Value: "%s"' % (app_instance.GetParameterDescription(param), str(app_instance.IsMandatory(param)), get_default_parameter_value(app_instance, param))) - choices_tags = [each for each in params if (not is_a_parameter(app_instance, each)) and '.' not in each] - for choice in choices_tags: - result.append('%s -%s %s %s. Mandatory: %s. Default Value: "%s"' % ('[choice]', choice, app_instance.GetParameterDescription(choice), ','.join(app_instance.GetChoiceKeys(choice)), str(app_instance.IsMandatory(choice)), get_default_parameter_value(app_instance, choice))) - choices = app_instance.GetChoiceKeys(choice) - - with tag('ul', result): - for subchoice in choices: - with tag('li', result): - result.append('%s -%s' % ('[group]', subchoice)) - with tag('ul', result): - param_tags = [each for each in params if '.%s' % subchoice in each] - for param_tag in param_tags: - with tag('li', result): - result.append('%s -%s ' % ('[param]', param_tag)) - result.append("%s %s. Mandatory: %s. Default Value: "%s"" % (escape_html(parameters[app_instance.GetParameterType(param_tag)]), app_instance.GetParameterDescription(param_tag), str(app_instance.IsMandatory(param_tag)), get_default_parameter_value(app_instance, param_tag))) - with tag('h2', result): - result.append('Limitations') - result.append(app_instance.GetDocLimitations()) - with tag('h2', result): - result.append('Authors') - result.append(app_instance.GetDocAuthors()) - with tag('h2', result): - result.append('See Also') - result.append(app_instance.GetDocSeeAlso()) - with tag('h2', result): - result.append('Example of use') - result.append(app_instance.GetHtmlExample()) - if app_instance.GetName() == "HaralickTextureExtraction": - index = result.index("[param] -parameters <string> ") - del result[index + 2] - del result[index + 1] - del result[index] - del result[index - 1] - return "".join(result) - - -def get_list_from_node(myet, available_app): - all_params = [] - for parameter in myet.iter('parameter'): - rebuild = [] - par_type = parameter.find('parameter_type').text - key = parameter.find('key').text - name = parameter.find('name').text - source_par_type = parameter.find('parameter_type').attrib['source_parameter_type'] - rebuild.append(source_par_type) - rebuild.append(par_type) - rebuild.append(key) - rebuild.append(name) - for each in parameter[4:]: - if each.tag not in ["hidden"]: - if len(each.getchildren()) == 0: - if each.tag in ["default"]: - if "-" in available_app: - available_app = available_app.split("-")[0] - app_instance = otbApplication.Registry.CreateApplication(available_app) - rebuild.append(get_default_parameter_value(app_instance, key)) - else: - rebuild.append(each.text) - else: - rebuild.append([item.text for item in each.iter('choice')]) - all_params.append(rebuild) - return all_params - - -def adapt_list_to_string(c_list): - a_list = c_list[1:] - if a_list[0] in ["ParameterVector", "ParameterMultipleInput"]: - if c_list[0] == "ParameterType_InputImageList": - a_list[3] = 3 - else: - a_list[3] = -1 - - if a_list[0] in ["ParameterRaster", "ParameterFile", "ParameterMultipleInput", "OutputRaster", "OutputFile"]: - if "Output" in a_list[0]: - a_list.append("/tmp/processing/output.tif") - else: - import os - a_list.append(os.path.join(os.path.abspath(os.curdir), "helper/QB_Toulouse_Ortho_PAN.tif")) - - if a_list[0] in ["ParameterSelection"]: - pass - - a_list[1] = "-%s" % a_list[1] - - def mystr(par): - if isinstance(par, list): - return ";".join(par) - return str(par) - - if a_list[-1] is None: - return "" - - b_list = list(map(mystr, a_list)) - b_list = [b_list[1], b_list[-1]] - res = " ".join(b_list) - return res - - -def get_automatic_ut_from_xml_description(the_root): - dom_model = the_root - - try: - appkey = dom_model.find('key').text - cliName = dom_model.find('exec').text - - if not cliName.startswith("otbcli_"): - raise Exception('Wrong client executable') - - rebu = get_list_from_node(dom_model, appkey) - the_result = list(map(adapt_list_to_string, rebu)) - ut_command = cliName + " " + " ".join(the_result) - return ut_command - except Exception: - ET.dump(dom_model) - raise - - -def list_reader(file_name, version): - tree = ET.parse(file_name) - root = tree.getroot() - nodes = [each.text for each in root.findall("./version[@id='%s']/app_name" % version)] - return nodes - - -def get_otb_version(): - #TODO Find a way to retrieve installed otb version, force exception and parse otb-X.XX.X ? - return "5.8" - - -def get_white_list(): - nodes = list_reader("white_list.xml", get_otb_version()) - return nodes - - -def get_black_list(): - nodes = list_reader("black_list.xml", get_otb_version()) - return nodes - - -def create_xml_descriptors(): - import os - if not os.path.exists("description"): - os.mkdir("description") - if not os.path.exists("html"): - os.mkdir("html") - - logger = get_OTB_log() - - white_list = get_white_list() - black_list = get_black_list() - custom_apps_available = get_customize_app_functions() - - for available_app in otbApplication.Registry.GetAvailableApplications(): - # try: - if 'get%s' % available_app in custom_apps_available: - if available_app in white_list and available_app not in black_list: - the_list = [] - the_root = get_xml_description_from_application_name(available_app) - function_to_call = "the_list = OTBSpecific_XMLcreation.get%s(available_app,the_root)" % available_app - exec(function_to_call) - # the_list = locals()['get%s' % available_app](available_app, the_root) - if the_list: - for each_dom in the_list: - try: - ut_command = get_automatic_ut_from_xml_description(each_dom) # NOQA - except: - logger.error("Unit test for command %s must be fixed: %s" % (available_app, traceback.format_exc())) - else: - logger.warning("%s (custom app) is not in white list." % available_app) - - else: - if available_app in white_list and available_app not in black_list: - logger.warning("There is no adaptor for %s, check white list and versions" % available_app) - # TODO Remove this default code when all apps are tested... - with open("description/%s.xml" % available_app, "w") as fh: - the_root = get_xml_description_from_application_name(available_app) - ET.ElementTree(the_root).write(fh) - try: - get_automatic_ut_from_xml_description(the_root) - except: - logger.error("Unit test for command %s must be fixed: %s" % (available_app, traceback.format_exc())) - else: - logger.warning("%s (not custom app) is not in white list." % available_app) - # except Exception, e: - # logger.error(traceback.format_exc()) - - -def create_html_description(): - logger = get_OTB_log() - - if not os.path.exists("description/doc"): - os.mkdir("description/doc") - - for available_app in otbApplication.Registry.GetAvailableApplications(): - try: - with open("description/doc/%s.html" % available_app, "w") as fh: - app_instance = otbApplication.Registry.CreateApplication(available_app) - app_instance.UpdateParameters() - ct = describe_app(app_instance) - fh.write(ct) - except Exception: - logger.error(traceback.format_exc()) - - sub_algo = [each for each in os.listdir("description") if "-" in each and ".xml" in each] - for key in sub_algo: - shutil.copy("description/doc/%s" % key.split("-")[0] + ".html", "description/doc/%s" % key.split(".")[0] + ".html") - -if __name__ == "__main__": - # Prepare the environment - from qgis.core import QgsApplication - - app = QgsApplication([], True) - QgsApplication.initQgis() - - # Prepare processing framework - from processing.core.Processing import Processing - Processing.initialize() - - import OTBSpecific_XMLcreation -# try: -# import processing -# except ImportError, e: -# raise Exception("Processing must be installed and available in PYTHONPATH") - - try: - import otbApplication - except ImportError as e: - raise Exception("OTB python plugins must be installed and available in PYTHONPATH") - - create_xml_descriptors() - create_html_description() - - #Check if some application are not listed in the white/black list - logger = get_OTB_log() - white_list = get_white_list() - black_list = get_black_list() - for available_app in otbApplication.Registry.GetAvailableApplications(): - try: - if available_app not in white_list and available_app not in black_list: - logger.error("Application " + available_app + " is not listed in white_list.xml or black_list.xml. Need to be fix.") - except Exception: - logger.error(traceback.format_exc()) - - # Exit applications - QgsApplication.exitQgis() diff --git a/python/plugins/processing/algs/otb/maintenance/OTBSpecific_XMLcreation.py b/python/plugins/processing/algs/otb/maintenance/OTBSpecific_XMLcreation.py deleted file mode 100644 index 4404ff088eba..000000000000 --- a/python/plugins/processing/algs/otb/maintenance/OTBSpecific_XMLcreation.py +++ /dev/null @@ -1,763 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - OTBUtils.py - --------------------- - Date : 11-12-13 - Copyright : (C) 2013 by CS Systemes d'information (CS SI) - Email : otb at c-s dot fr (CS SI) - Contributors : Julien Malik (CS SI) - creation of otbspecific - Oscar Picas (CS SI) - - Alexia Mondot (CS SI) - split otbspecific into 2 files - add functions -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** - -When QGIS is run, OTB algorithms are created according to xml files from description/ directory. -""" - -__author__ = 'Julien Malik, Oscar Picas, Alexia Mondot' -__date__ = 'December 2013' -__copyright__ = '(C) 2013, CS Systemes d\'information (CS SI)' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' -__version__ = "3.8" - -import copy - -from processing.algs.otb.OTBUtils import (renameValueField, - remove_dependent_choices, - remove_other_choices, - remove_parameter_by_key, - defaultSplit, - split_by_choice, - defaultWrite, - remove_choice, - remove_independent_choices) - - -def getBinaryMorphologicalOperation(available_app, original_dom_document): - """ - Let ball as only available structype. - Split the application according to its filter dilate, erode, opening, closing. - """ - the_root = original_dom_document - renameValueField(the_root, 'structype.ball.xradius', 'name', 'The Structuring Element Radius') - renameValueField(the_root, 'structype.ball.xradius', 'description', 'The Structuring Element Radius') - remove_dependent_choices(the_root, 'structype', 'ball') - remove_other_choices(the_root, 'structype', 'ball') - remove_dependent_choices(the_root, 'filter', 'dilate') - remove_parameter_by_key(the_root, 'structype.ball.yradius') - the_list = defaultSplit(available_app, the_root, 'filter') - return the_list - - -def getEdgeExtraction(available_app, original_dom_document): - """ - Let ball as only available filter (not an oval). - Split the application according to its filter gradient, sobel, touzi. - """ - the_root = original_dom_document - renameValueField(the_root, 'filter.touzi.xradius', 'name', 'The Radius') - renameValueField(the_root, 'filter.touzi.xradius', 'description', 'The Radius') - remove_parameter_by_key(the_root, 'filter.touzi.yradius') - split = split_by_choice(the_root, 'filter') - the_list = [] - for key in split: - defaultWrite('%s-%s' % (available_app, key), split[key]) - the_list.append(split[key]) - return the_list - - -def getGrayScaleMorphologicalOperation(available_app, original_dom_document): - """ - Let ball as only available structype. - Split the application according to its filter dilate, erode, opening, closing. - """ - the_root = original_dom_document - renameValueField(the_root, 'structype.ball.xradius', 'name', 'The Structuring Element Radius') - renameValueField(the_root, 'structype.ball.xradius', 'description', 'The Structuring Element Radius') - remove_dependent_choices(the_root, 'structype', 'ball') - remove_other_choices(the_root, 'structype', 'ball') - remove_parameter_by_key(the_root, 'structype.ball.yradius') - - split = defaultSplit(available_app, the_root, 'filter') - return split - - -def getOrthoRectification(available_app, original_dom_document): - """ - Let only mode auto. - Remove all parameters which should be updated once the input file given. - Split by SRS : EPSG, fit to ortho, lambert-wgs84 and UTM. - Each of these SRS have their own parameters modified in this fonction. - Delete GEOID and DEM parameter as they are not updated at the creation of the otb algorithms when you launch QGIS. - The values are picked from the settings. - """ - the_root = original_dom_document - - remove_choice(the_root, 'outputs.mode', 'auto') - remove_independent_choices(the_root, 'outputs.mode', 'auto') - remove_choice(the_root, 'outputs.mode', 'outputroi') - remove_independent_choices(the_root, 'outputs.mode', 'outputroi') - remove_parameter_by_key(the_root, 'outputs.ulx') - remove_parameter_by_key(the_root, 'outputs.uly') - remove_parameter_by_key(the_root, 'outputs.sizex') - remove_parameter_by_key(the_root, 'outputs.sizey') - remove_parameter_by_key(the_root, 'outputs.spacingx') - remove_parameter_by_key(the_root, 'outputs.spacingy') - remove_parameter_by_key(the_root, 'outputs.lrx') - remove_parameter_by_key(the_root, 'outputs.lry') - remove_parameter_by_key(the_root, 'opt.rpc') - - deleteGeoidSrtm(the_root) - - remove_parameter_by_key(the_root, 'outputs.isotropic') - - emptyMap = copy.deepcopy(the_root) - - remove_parameter_by_key(the_root, 'outputs.ortho') - remove_choice(the_root, 'outputs.mode', 'orthofit') - remove_independent_choices(the_root, 'outputs.mode', 'orthofit') - merged = copy.deepcopy(the_root) - - split = split_by_choice(the_root, 'map') - the_list = [] - - for key in split: - if key == 'utm': - the_doc = split[key] - remove_parameter_by_key(the_doc, 'map.epsg.code') - defaultWrite('%s-%s' % (available_app, key), the_doc) - the_list.append(the_doc) - elif key == 'epsg': - the_doc = split[key] - remove_parameter_by_key(the_doc, 'map.utm.northhem') - remove_parameter_by_key(the_doc, 'map.utm.zone') - defaultWrite('%s-%s' % (available_app, key), the_doc) - the_list.append(the_doc) - - remove_choice(merged, 'map', 'utm') - remove_choice(merged, 'map', 'epsg') - remove_parameter_by_key(merged, 'map.epsg.code') - remove_parameter_by_key(merged, 'map.utm.northhem') - remove_parameter_by_key(merged, 'map.utm.zone') - old_app_name = merged.find('key').text - merged.find('key').text = '%s-%s' % (old_app_name, 'lambert-WGS84') - merged.find('longname').text = '%s (%s)' % (old_app_name, 'lambert-WGS84') - defaultWrite('%s-%s' % (available_app, 'lambert-WGS84'), merged) - the_list.append(merged) - - remove_parameter_by_key(emptyMap, 'map') - remove_parameter_by_key(emptyMap, 'map.epsg.code') - remove_parameter_by_key(emptyMap, 'map.utm.northhem') - remove_parameter_by_key(emptyMap, 'map.utm.zone') - remove_choice(emptyMap, 'outputs.mode', 'autosize') - remove_independent_choices(emptyMap, 'outputs.mode', 'autosize') - remove_choice(emptyMap, 'outputs.mode', 'autospacing') - remove_independent_choices(emptyMap, 'outputs.mode', 'autospacing') - old_app_name = emptyMap.find('key').text - emptyMap.find('key').text = '%s-%s' % (old_app_name, 'fit-to-ortho') - emptyMap.find('longname').text = '%s (%s)' % (old_app_name, 'fit-to-ortho') - defaultWrite('%s-%s' % (available_app, 'fit-to-ortho'), emptyMap) - the_list.append(emptyMap) - - return the_list - - -def getDimensionalityReduction(available_app, original_dom_document): - """ - Remove rescale.outmin and rescale.outmax and split by method (ica, maf, napca and pca) and adjust parameters of each resulting app. - """ - the_root = original_dom_document - remove_parameter_by_key(the_root, 'rescale.outmin') - remove_parameter_by_key(the_root, 'rescale.outmax') - split = split_by_choice(the_root, 'method') - the_list = [] - for key in split: - if key == 'maf': - the_doc = split[key] - remove_parameter_by_key(the_doc, 'outinv') - defaultWrite('%s-%s' % (available_app, key), the_doc) - the_list.append(the_doc) - else: - defaultWrite('%s-%s' % (available_app, key), split[key]) - the_list.append(split[key]) - return the_list - - -def getPansharpening(available_app, original_dom_document): - """ - Split by method (bayes, lmvm, rcs) - """ - the_root = original_dom_document - split = split_by_choice(the_root, 'method') - the_list = [] - for key in split: - defaultWrite('%s-%s' % (available_app, key), split[key]) - the_list.append(split[key]) - return the_list - - -def getPixelValue(available_app, original_dom_document): - the_root = original_dom_document - remove_parameter_by_key(the_root, 'cl') - defaultWrite(available_app, the_root) - return [the_root] - - -def getExtractROI(available_app, original_dom_document): - """ - Split by mode (standard, fit) - Adapt parameters of each resulting app. - Delete GEOID and DEM parameter as they are not updated at the creation of the otb algorithms when you launch QGIS. - The values are picked from the settings. - """ - the_root = original_dom_document - remove_parameter_by_key(the_root, 'cl') - deleteGeoidSrtm(the_root) - split = split_by_choice(the_root, 'mode') - the_list = [] - for key in split: - if key == 'standard': - the_doc = split[key] - remove_parameter_by_key(the_doc, 'mode.fit.elev.dem') - remove_parameter_by_key(the_doc, 'mode.fit.elev.geoid') - remove_parameter_by_key(the_doc, 'mode.fit.elev.default') - remove_parameter_by_key(the_doc, 'mode.fit.ref') - defaultWrite('%s-%s' % (available_app, key), the_doc) - the_list.append(the_doc) - else: - #key == 'fit' - the_doc = split[key] - remove_parameter_by_key(the_doc, 'startx') - remove_parameter_by_key(the_doc, 'starty') - remove_parameter_by_key(the_doc, 'sizex') - remove_parameter_by_key(the_doc, 'sizey') - defaultWrite('%s-%s' % (available_app, key), the_doc) - the_list.append(split[key]) - return the_list - - -def getQuicklook(available_app, original_dom_document): - the_root = original_dom_document - remove_parameter_by_key(the_root, 'cl') - defaultWrite(available_app, the_root) - return [the_root] - - -def getRigidTransformResample(available_app, original_dom_document): - """ - split by transformation (id, rotation, translation) - """ - the_root = original_dom_document - split = split_by_choice(the_root, 'transform.type') - the_list = [] - for key in split: - defaultWrite('%s-%s' % (available_app, key), split[key]) - the_list.append(split[key]) - return the_list - - -def getHomologousPointsExtraction(available_app, original_dom_document): - the_list = defaultSplit(available_app, original_dom_document, 'mode') - return the_list - - -def getGenerateRPCSensorModel(available_app, original_dom_document): - the_root = original_dom_document - remove_dependent_choices(the_root, 'map', 'wgs') - remove_other_choices(the_root, 'map', 'wgs') - defaultWrite(available_app, the_root) - return [the_root] - - -def getRefineSensorModel(available_app, original_dom_document): - the_root = original_dom_document - remove_dependent_choices(the_root, 'map', 'wgs') - remove_other_choices(the_root, 'map', 'wgs') - defaultWrite(available_app, the_root) - return [the_root] - - -def getSegmentation(available_app, original_dom_document): - """ - Remove the choice raster and split by filter (cc, edison, meanshift, mprofiles, watershed) - """ - the_root = original_dom_document - #remove_choice(the_root, 'filter', 'edison') - #remove_independent_choices(the_root, 'filter', 'edison') - #remove_choice(the_root, 'filter', 'meanshift') - #remove_independent_choices(the_root, 'filter', 'meanshift') - remove_choice(the_root, 'mode', 'raster') - remove_independent_choices(the_root, 'mode', 'raster') - split = split_by_choice(the_root, 'filter') - the_list = [] - for key in split: - defaultWrite('%s-%s' % (available_app, key), split[key]) - the_list.append(split[key]) - return the_list - - -def getKMeansClassification(available_app, original_dom_document): - the_root = original_dom_document - remove_parameter_by_key(the_root, 'rand') - defaultWrite(available_app, the_root) - return [the_root] - - -def getTrainSVMImagesClassifier(available_app, original_dom_document): - the_root = original_dom_document - remove_parameter_by_key(the_root, 'rand') - defaultWrite(available_app, the_root) - return [the_root] - - -def getComputeConfusionMatrix(available_app, original_dom_document): - """ - Split by ref (raster, vector) - """ - the_root = original_dom_document - #remove_independent_choices(the_root, 'ref', 'vector') - #remove_choice(the_root, 'ref', 'vector') - #defaultWrite(available_app, the_root) - - split = split_by_choice(the_root, 'ref') - the_list = [] - for key in split: - defaultWrite('%s-%s' % (available_app, key), split[key]) - the_list.append(split[key]) - return the_list - - return [the_root] - - -def getOpticalCalibration(available_app, original_dom_document): - """ - Remove toc options (let toa) and remove all about atmo - """ - #the_list = defaultSplit(available_app, original_dom_document, 'level') - the_root = original_dom_document - remove_independent_choices(the_root, 'level', 'toc') - remove_choice(the_root, 'level', 'toc') - remove_parameter_by_key(the_root, 'atmo.aerosol') - remove_parameter_by_key(the_root, 'atmo.oz') - remove_parameter_by_key(the_root, 'atmo.wa') - remove_parameter_by_key(the_root, 'atmo.pressure') - remove_parameter_by_key(the_root, 'atmo.opt') - remove_parameter_by_key(the_root, 'atmo.aeronet') - remove_parameter_by_key(the_root, 'radius') - defaultWrite(available_app, the_root) - return [the_root] - - -def getSarRadiometricCalibration(available_app, original_dom_document): - # TODO ** before doing anything, check support for SAR data in Qgis - the_root = original_dom_document - defaultWrite(available_app, the_root) - return [the_root] - - -def getSmoothing(available_app, original_dom_document): - """ - Split by type (anidif, gaussian, mean) - """ - - #import copy - #the_root = copy.deepcopy(original_dom_document) - #remove_dependent_choices(the_root, 'type', 'anidif') - #remove_other_choices(the_root, 'type', 'anidif') - #defaultWrite('%s-anidif' % available_app, the_root) - - #the_root = copy.deepcopy(original_dom_document) - #remove_independent_choices(the_root, 'type', 'anidif') - #remove_choice(the_root, 'type', 'anidif') - #defaultWrite(available_app, the_root) - - the_root = original_dom_document - split = split_by_choice(the_root, 'type') - the_list = [] - for key in split: - defaultWrite('%s-%s' % (available_app, key), split[key]) - the_list.append(split[key]) - - return the_list - #split = split_by_choice(the_root, 'type') - #the_list = [] - #for key in split: - # defaultWrite('%s-%s' % (available_app, key), split[key]) - # the_list.append(split[key]) - #return the_list - - -def getColorMapping(available_app, original_dom_document): - """ - Remove the option colortolabel - Split by method : custom, continuous, optimal and image and adapt parameters of each resulting app - """ - the_root = original_dom_document - remove_independent_choices(the_root, 'op', 'colortolabel') - remove_choice(the_root, 'op', 'colortolabel') - split = split_by_choice(the_root, 'method') - the_list = [] - for key in split: - if key == 'custom': - the_doc = split[key] - remove_parameter_by_key(the_doc, 'method.continuous.lut') - remove_parameter_by_key(the_doc, 'method.continuous.min') - remove_parameter_by_key(the_doc, 'method.continuous.max') - remove_parameter_by_key(the_doc, 'method.optimal.background') - remove_parameter_by_key(the_doc, 'method.image.in') - remove_parameter_by_key(the_doc, 'method.image.low') - remove_parameter_by_key(the_doc, 'method.image.up') - defaultWrite('%s-%s' % (available_app, key), the_doc) - the_list.append(the_doc) - elif key == 'continuous': - the_doc = split[key] - remove_parameter_by_key(the_doc, 'method.custom.lut') - remove_parameter_by_key(the_doc, 'method.optimal.background') - remove_parameter_by_key(the_doc, 'method.image.in') - remove_parameter_by_key(the_doc, 'method.image.low') - remove_parameter_by_key(the_doc, 'method.image.up') - defaultWrite('%s-%s' % (available_app, key), the_doc) - the_list.append(the_doc) - elif key == 'optimal': - the_doc = split[key] - remove_parameter_by_key(the_doc, 'method.custom.lut') - remove_parameter_by_key(the_doc, 'method.continuous.lut') - remove_parameter_by_key(the_doc, 'method.continuous.min') - remove_parameter_by_key(the_doc, 'method.continuous.max') - remove_parameter_by_key(the_doc, 'method.image.in') - remove_parameter_by_key(the_doc, 'method.image.low') - remove_parameter_by_key(the_doc, 'method.image.up') - defaultWrite('%s-%s' % (available_app, key), the_doc) - the_list.append(the_doc) - else: - #key == 'image' - the_doc = split[key] - remove_parameter_by_key(the_doc, 'method.custom.lut') - remove_parameter_by_key(the_doc, 'method.continuous.lut') - remove_parameter_by_key(the_doc, 'method.continuous.min') - remove_parameter_by_key(the_doc, 'method.continuous.max') - remove_parameter_by_key(the_doc, 'method.optimal.background') - defaultWrite('%s-%s' % (available_app, key), the_doc) - the_list.append(split[key]) - return the_list - - -def getFusionOfClassifications(available_app, original_dom_document): - """ - Split by method of fusion of classification (dempstershafer, majorityvoting) - """ - the_root = original_dom_document - split = split_by_choice(the_root, 'method') - the_list = [] - for key in split: - defaultWrite('%s-%s' % (available_app, key), split[key]) - the_list.append(split[key]) - return the_list - - -def getTrainImagesClassifier(available_app, original_dom_document): - """ - Split by classifier (ann, bayes, boost, dt, gbt, knn, libsvm, rf, svm) - Delete GEOID and DEM parameter as they are not updated at the creation of the otb algorithms when you launch QGIS. - The values are picked from the settings. - """ - the_root = original_dom_document - deleteGeoidSrtm(the_root) - split = split_by_choice(the_root, 'classifier') - the_list = [] - for key in split: - defaultWrite('%s-%s' % (available_app, key), split[key]) - the_list.append(split[key]) - return the_list - - -def getTrainRegression(available_app, original_dom_document): - """ - Split by classifier (ann, dt, gbt, knn, libsvm, rf) - Delete GEOID and DEM parameter as they are not updated at the creation of the otb algorithms when you launch QGIS. - The values are picked from the settings. - """ - the_root = original_dom_document - deleteGeoidSrtm(the_root) - split = split_by_choice(the_root, 'classifier') - the_list = [] - for key in split: - defaultWrite('%s-%s' % (available_app, key), split[key]) - the_list.append(split[key]) - return the_list - - -def getTrainVectorClassifier(available_app, original_dom_document): - """ - Split by classifier (ann, dt, gbt, knn, libsvm, rf) - Delete GEOID and DEM parameter as they are not updated at the creation of the otb algorithms when you launch QGIS. - The values are picked from the settings. - """ - the_root = original_dom_document - deleteGeoidSrtm(the_root) - split = split_by_choice(the_root, 'classifier') - the_list = [] - for key in split: - defaultWrite('%s-%s' % (available_app, key), split[key]) - the_list.append(split[key]) - return the_list - - -def getLineSegmentDetection(available_app, original_dom_document): - """ - Delete GEOID and DEM parameter as they are not updated at the creation of the otb algorithms when you launch QGIS. - The values are picked from the settings. - """ - the_root = original_dom_document - remove_parameter_by_key(the_root, 'elev.default') - remove_parameter_by_key(the_root, 'elev.geoid') - remove_parameter_by_key(the_root, 'elev.dem') - defaultWrite(available_app, the_root) - return [the_root] - - -def getImageEnvelope(available_app, original_dom_document): - """ - Delete GEOID and DEM parameter as they are not updated at the creation of the otb algorithms when you launch QGIS. - The values are picked from the settings. - """ - the_root = original_dom_document - remove_parameter_by_key(the_root, 'elev.default') - remove_parameter_by_key(the_root, 'elev.geoid') - remove_parameter_by_key(the_root, 'elev.dem') - defaultWrite(available_app, the_root) - return [the_root] - - -def getReadImageInfo(available_app, original_dom_document): - """ - Remove parameters that are output of the application. - """ - the_root = original_dom_document - remove_parameter_by_key(the_root, 'outkwl') - remove_parameter_by_key(the_root, 'indexx') - remove_parameter_by_key(the_root, 'indexy') - remove_parameter_by_key(the_root, 'sizex') - remove_parameter_by_key(the_root, 'sizey') - remove_parameter_by_key(the_root, 'spacingx') - remove_parameter_by_key(the_root, 'spacingy') - remove_parameter_by_key(the_root, 'originx') - remove_parameter_by_key(the_root, 'originy') - remove_parameter_by_key(the_root, 'estimatedgroundspacingx') - remove_parameter_by_key(the_root, 'estimatedgroundspacingy') - remove_parameter_by_key(the_root, 'numberbands') - remove_parameter_by_key(the_root, 'sensor') - remove_parameter_by_key(the_root, 'id') - remove_parameter_by_key(the_root, 'time') - remove_parameter_by_key(the_root, 'ullat') - remove_parameter_by_key(the_root, 'ullon') - remove_parameter_by_key(the_root, 'urlat') - remove_parameter_by_key(the_root, 'urlon') - remove_parameter_by_key(the_root, 'lrlat') - remove_parameter_by_key(the_root, 'lrlon') - remove_parameter_by_key(the_root, 'lllat') - remove_parameter_by_key(the_root, 'lllon') - remove_parameter_by_key(the_root, 'town') - remove_parameter_by_key(the_root, 'country') - remove_parameter_by_key(the_root, 'rgb.r') - remove_parameter_by_key(the_root, 'rgb.g') - remove_parameter_by_key(the_root, 'rgb.b') - remove_parameter_by_key(the_root, 'projectionref') - remove_parameter_by_key(the_root, 'keyword') - remove_parameter_by_key(the_root, 'gcp.count') - remove_parameter_by_key(the_root, 'gcp.proj') - defaultWrite(available_app, the_root) - return [the_root] - - -def getComputeModulusAndPhase(available_app, original_dom_document): - """ - Split the application according the field nbinput. - For each of the resulting apps, give a new name. - """ - the_root = original_dom_document - split = split_by_choice(the_root, 'nbinput') - the_list = [] - for key in split: - if key == 'one': - the_doc = split[key] - old_app_name = the_doc.find('key').text - the_doc.find('key').text = '%s-%s' % (old_app_name, 'OneEntry') - the_doc.find('longname').text = '%s (%s)' % (old_app_name, 'OneEntry') - defaultWrite('%s-%s' % (available_app, 'OneEntry'), the_doc) - the_list.append(the_doc) - else: - the_doc = split[key] - old_app_name = the_doc.find('key').text - the_doc.find('key').text = '%s-%s' % (old_app_name, 'TwoEntries') - the_doc.find('longname').text = '%s (%s)' % (old_app_name, 'TwoEntries') - defaultWrite('%s-%s' % (available_app, 'TwoEntries'), the_doc) - the_list.append(the_doc) - return the_list - - -def getCompareImages(available_app, original_dom_document): - """ - Remove mse, mae, psnr as they are output of the algorithm. - """ - the_root = original_dom_document - remove_parameter_by_key(the_root, 'mse') - remove_parameter_by_key(the_root, 'mae') - remove_parameter_by_key(the_root, 'psnr') - defaultWrite(available_app, the_root) - return [the_root] - - -def getRadiometricIndices(available_app, original_dom_document): - """ - These 3 indices are missing. Remove them from the list. - """ - the_root = original_dom_document - remove_choice(the_root, 'list', 'laindvilog') - remove_choice(the_root, 'list', 'lairefl') - remove_choice(the_root, 'list', 'laindviformo') - defaultWrite(available_app, the_root) - return [the_root] - - -def getConnectedComponentSegmentation(available_app, original_dom_document): - """ - Delete GEOID and DEM parameter as they are not updated at the creation of the otb algorithms when you launch QGIS. - The values are picked from the settings. - """ - the_root = original_dom_document - deleteGeoidSrtm(the_root) - defaultWrite(available_app, the_root) - return [the_root] - - -def getKmzExport(available_app, original_dom_document): - """ - Delete GEOID and DEM parameter as they are not updated at the creation of the otb algorithms when you launch QGIS. - The values are picked from the settings. - """ - the_root = original_dom_document - deleteGeoidSrtm(the_root) - defaultWrite(available_app, the_root) - return [the_root] - - -def getSuperimpose(available_app, original_dom_document): - """ - Delete GEOID and DEM parameter as they are not updated at the creation of the otb algorithms when you launch QGIS. - The values are picked from the settings. - """ - the_root = original_dom_document - deleteGeoidSrtm(the_root) - defaultWrite(available_app, the_root) - return [the_root] - - -def getStereoFramework(available_app, original_dom_document): - """ - Delete GEOID and DEM parameter as they are not updated at the creation of the otb algorithms when you launch QGIS. - The values are picked from the settings. - """ - the_root = original_dom_document - deleteGeoidSrtm(the_root) - defaultWrite(available_app, the_root) - return [the_root] - - -def getRasterization(available_app, original_dom_document): - """ - Let only rasterization with an reference image - Let only mode auto. - Remove all parameters which should be updated once the input file given. - Split by SRS : EPSG, fit to ortho, lambert-wgs84 and UTM. - Each of these SRS have their own parameters modified in this fonction. - Delete GEOID and DEM parameter as they are not updated at the creation of the otb algorithms when you launch QGIS. - The values are picked from the settings. - """ - rasterization_image = original_dom_document - - import copy - rasterization_manual = copy.deepcopy(original_dom_document) - - old_app_name = rasterization_image.find('key').text - - remove_parameter_by_key(rasterization_image, 'szx') - remove_parameter_by_key(rasterization_image, 'szy') - remove_parameter_by_key(rasterization_image, 'epsg') - remove_parameter_by_key(rasterization_image, 'orx') - remove_parameter_by_key(rasterization_image, 'ory') - remove_parameter_by_key(rasterization_image, 'spx') - remove_parameter_by_key(rasterization_image, 'spy') - - remove_parameter_by_key(rasterization_manual, 'im') - - # set a new name according to the choice - rasterization_image.find('key').text = '%s-%s' % (old_app_name, "image") - rasterization_image.find('longname').text = '%s (%s)' % (old_app_name, "image") - defaultWrite('%s-%s' % (old_app_name, "image"), rasterization_image) - rasterization_manual.find('key').text = '%s-%s' % (old_app_name, "manual") - rasterization_manual.find('longname').text = '%s (%s)' % (old_app_name, "manual") - defaultWrite('%s-%s' % (old_app_name, "manual"), rasterization_manual) - return [rasterization_image, rasterization_manual] - - -def getVectorDataExtractROI(available_app, original_dom_document): - """ - Delete GEOID and DEM parameter as they are not updated at the creation of the otb algorithms when you launch QGIS. - The values are picked from the settings. - """ - the_root = original_dom_document - deleteGeoidSrtm(the_root) - defaultWrite(available_app, the_root) - return [the_root] - - -def getVectorDataReprojection(available_app, original_dom_document): - """ - """ - the_root = original_dom_document - deleteGeoidSrtm(the_root) - return defaultSplit(available_app, the_root, 'out.proj') - - -def getComputePolylineFeatureFromImage(available_app, original_dom_document): - """ - Delete GEOID and DEM parameter as they are not updated at the creation of the otb algorithms when you launch QGIS. - The values are picked from the settings. - """ - the_root = original_dom_document - deleteGeoidSrtm(the_root) - defaultWrite(available_app, the_root) - return [the_root] - - -def getDespeckle(available_app, original_dom_document): - """ - """ - the_root = original_dom_document - the_list = defaultSplit(available_app, the_root, 'filter') - return the_list - - -def deleteGeoidSrtm(doc): - """ - Delete GEOID and DEM parameter as they are not updated at the creation of the otb algorithms when you launch QGIS. - The values are picked from the settings. - """ - t4 = [item for item in doc.findall('.//parameter') if item.find('key').text.endswith("elev.geoid")] - for t5 in t4: - doc.remove(t5) - - t4 = [item for item in doc.findall('.//parameter') if item.find('key').text.endswith("elev.dem")] - for t5 in t4: - doc.remove(t5) diff --git a/python/plugins/processing/algs/otb/maintenance/OTBTester.py b/python/plugins/processing/algs/otb/maintenance/OTBTester.py deleted file mode 100644 index d2717838590e..000000000000 --- a/python/plugins/processing/algs/otb/maintenance/OTBTester.py +++ /dev/null @@ -1,442 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - OTBTester.py - --------------------- - Copyright : (C) 2013 by CS Systemes d'information (CS SI) - Email : otb at c-s dot fr (CS SI) - Contributors : Julien Malik (CS SI) - Oscar Picas (CS SI) -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from future import standard_library -standard_library.install_aliases() -from builtins import zip -from builtins import str -from builtins import range -from builtins import object -__author__ = 'Julien Malik, Oscar Picas' -__copyright__ = '(C) 2013, CS Systemes d\'information (CS SI)' -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -from parsing import parse - -from string import Template -import os -import traceback - -from configparser import SafeConfigParser - -from processing.otb.OTBHelper import get_OTB_log - - -class LowerTemplate(Template): - - def safe_substitute(self, param): - ret = super(LowerTemplate, self).safe_substitute(param).lower() - return ret - - -class MakefileParser(object): - - def __init__(self): - self.maxDiff = None - self.parser = SafeConfigParser() - self.parser.read('otbcfg.ini') - if not os.path.exists('otbcfg.ini'): - raise Exception("OTB_SOURCE_DIR and OTB_BINARY_DIR must be specified in the file otbcfg.ini") - - self.root_dir = self.parser.get('otb', 'checkout_dir') - if not os.path.exists(self.root_dir): - raise Exception("Check otbcfg.ini : OTB_SOURCE_DIR and OTB_BINARY_DIR must be specified there") - self.build_dir = self.parser.get('otb', 'build_dir') - if not os.path.exists(self.build_dir): - raise Exception("Check otbcfg.ini : OTB_SOURCE_DIR and OTB_BINARY_DIR must be specified there") - self.logger = get_OTB_log() - - def test_CMakelists(self): - provided = {} - provided["OTB_SOURCE_DIR"] = self.root_dir - provided["OTB_BINARY_DIR"] = self.build_dir - provided["OTB_DATA_LARGEINPUT_ROOT"] = os.path.normpath(os.path.join(self.root_dir, "../OTB-Data/Input")) - - try: - with open(os.path.join(self.root_dir, "CMakeLists.txt")) as file_input: - content = file_input.read() - output = parse(content) - - defined_paths = [each for each in output if 'Command' in str(type(each)) and "FIND_PATH" in each.name] - the_paths = {key.body[0].contents: [thing.contents for thing in key.body[1:]] for key in defined_paths} - - the_sets = [each for each in output if 'Command' in str(type(each)) and "SET" in each.name.upper()] - the_sets = {key.body[0].contents: [thing.contents for thing in key.body[1:]] for key in the_sets} - the_sets = {key: " ".join(the_sets[key]) for key in the_sets} - - the_strings = set([each.body[-1].contents for each in output if 'Command' in str(type(each)) and "STRING" in each.name.upper()]) - - def mini_clean(item): - if item.startswith('"') and item.endswith('"') and " " not in item: - return item[1:-1] - return item - - the_sets = {key: mini_clean(the_sets[key]) for key in the_sets} - - def templatize(item): - if "$" in item: - return Template(item) - return item - - for key in the_sets: - if key in the_strings: - the_sets[key] = the_sets[key].lower() - - the_sets = {key: templatize(the_sets[key]) for key in the_sets} - - for path in the_paths: - target_file = the_paths[path][1] - suggested_paths = [] - if len(the_paths[path]) > 2: - suggested_paths = the_paths[path][2:] - - try: - provided[path] = find_file(target_file) - except Exception as e: - for each in suggested_paths: - st = Template(each) - pac = os.path.abspath(st.safe_substitute(provided)) - if os.path.exists(pac): - provided[path] = pac - break - - resolve_dict(provided, the_sets) - provided.update(the_sets) - - return provided - except Exception as e: - traceback.print_exc() - self.fail(str(e)) - - def add_make(self, previous_context, new_file): - with open(new_file) as f: - input = f.read() - output = parse(input) - apps = [each for each in output if 'Command' in str(type(each))] - setcommands = [each for each in apps if 'SET' in each.name.upper()] - stringcommands = [each for each in apps if 'STRING' in each.name.upper()] - - environment = previous_context - - def mini_clean(item): - if item.startswith('"') and item.endswith('"') and " " not in item: - return item[1:-1] - return item - - new_env = {} - for command in setcommands: - key = command.body[0].contents - ct = " ".join([item.contents for item in command.body[1:]]) - ct = mini_clean(ct) - - if "$" in ct: - values = Template(ct) - else: - values = ct - - new_env[key] = values - - for stringcommand in stringcommands: - key = stringcommand.body[-1].contents - ct = stringcommand.body[-2].contents - ct = mini_clean(ct.lower()) - - if "$" in ct: - values = LowerTemplate(ct) - else: - values = ct - new_env[key] = values - - resolve_dict(environment, new_env) - environment.update(new_env) - - return environment - - def get_apps(self, the_makefile, the_dict): - with open(the_makefile) as f: - input = f.read() - output = parse(input) - apps = [each for each in output if 'Command' in str(type(each))] - otb_apps = [each for each in apps if 'OTB_TEST_APPLICATION' in each.name.upper()] - return otb_apps - - def get_tests(self, the_makefile, the_dict): - with open(the_makefile) as f: - input = f.read() - output = parse(input) - apps = [each for each in output if 'Command' in str(type(each))] - otb_tests = [each for each in apps if 'ADD_TEST' in each.name.upper()] - return otb_tests - - def get_apps_with_context(self, the_makefile, the_dict): - with open(the_makefile) as f: - input = f.read() - output = parse(input) - - def is_a_command(item): - return 'Command' in str(type(item)) - - appz = [] - context = [] - for each in output: - if is_a_command(each): - if 'FOREACH' in each.name and 'ENDFOREACH' not in each.name: - args = [item.contents for item in each.body] - context.append(args) - elif 'ENDFOREACH' in each.name: - context.pop() - elif 'OTB_TEST_APPLICATION' in each.name.upper(): - appz.append((each, context[:])) - return appz - - def get_name_line(self, the_list, the_dict): - items = ('NAME', 'APP', 'OPTIONS', 'TESTENVOPTIONS', 'VALID') - itemz = [[], [], [], [], []] - last_index = 0 - for each in the_list: - if each.contents in items: - last_index = items.index(each.contents) - else: - itemz[last_index].append(each.contents) - result = itemz[0][0] - the_string = Template(result).safe_substitute(the_dict) - - if '$' in the_string: - neo_dict = the_dict - the_string = Template(the_string).safe_substitute(neo_dict) - while '$' in the_string: - try: - the_string = Template(the_string).substitute(neo_dict) - except KeyError as e: - self.logger.warning("Key %s is not found in makefiles" % str(e)) - neo_dict[str(e)] = "" - - if 'string.Template' in the_string: - raise Exception("Unexpected toString call in %s" % the_string) - - return the_string - - def get_command_line(self, the_list, the_dict): - items = ('NAME', 'APP', 'OPTIONS', 'TESTENVOPTIONS', 'VALID') - itemz = [[], [], [], [], []] - last_index = 0 - for each in the_list: - if each.contents in items: - last_index = items.index(each.contents) - else: - itemz[last_index].append(each.contents) - result = [] - result.extend(["otbcli_%s" % each for each in itemz[1]]) - - if len(result[0]) == 7: - raise Exception("App name is empty!") - - result.extend(itemz[2]) - result.append("-testenv") - result.extend(itemz[3]) - the_string = Template(" ".join(result)).safe_substitute(the_dict) - - if '$' in the_string: - neo_dict = the_dict - the_string = Template(" ".join(result)).safe_substitute(neo_dict) - while '$' in the_string: - try: - the_string = Template(the_string).substitute(neo_dict) - except KeyError as e: - self.logger.warning("Key %s is not found in makefiles" % str(e)) - neo_dict[str(e)] = "" - - if 'string.Template' in the_string: - raise Exception("Unexpected toString call in %s" % the_string) - - return the_string - - def get_test(self, the_list, the_dict): - items = ('NAME', 'APP', 'OPTIONS', 'TESTENVOPTIONS', 'VALID') - itemz = [[], [], [], [], []] - last_index = 0 - for each in the_list: - if each.contents in items: - last_index = items.index(each.contents) - else: - itemz[last_index].append(each.contents) - result = ["otbTestDriver"] - result.extend(itemz[4]) - - if len(result) == 1: - return "" - - the_string = Template(" ".join(result)).safe_substitute(the_dict) - - if '$' in the_string: - neo_dict = the_dict - the_string = Template(" ".join(result)).safe_substitute(neo_dict) - while '$' in the_string: - try: - the_string = Template(the_string).substitute(neo_dict) - except KeyError as e: - self.logger.warning("Key %s is not found in makefiles" % str(e)) - neo_dict[str(e)] = "" - - if 'string.Template' in the_string: - raise Exception("Unexpected toString call in %s" % the_string) - - return the_string - - def test_algos(self): - tests = {} - - algos_dir = os.path.join(self.root_dir, "Testing/Applications") - makefiles = find_files("CMakeLists.txt", algos_dir) - to_be_excluded = os.path.join(self.root_dir, "Testing/Applications/CMakeLists.txt") - if to_be_excluded in makefiles: - makefiles.remove(to_be_excluded) - - resolve_algos = {} - for makefile in makefiles: - intermediate_makefiles = [] - path = makefile.split(os.sep)[len(self.root_dir.split(os.sep)):-1] - for ind in range(len(path)): - tmp_path = path[:ind + 1] - tmp_path.append("CMakeLists.txt") - tmp_path = os.sep.join(tmp_path) - candidate_makefile = os.path.join(self.root_dir, tmp_path) - if os.path.exists(candidate_makefile): - intermediate_makefiles.append(candidate_makefile) - resolve_algos[makefile] = intermediate_makefiles - - dict_for_algo = {} - for makefile in makefiles: - basic = self.test_CMakelists() - last_context = self.add_make(basic, os.path.join(self.root_dir, "Testing/Utilities/CMakeLists.txt")) - for intermediate_makefile in resolve_algos[makefile]: - last_context = self.add_make(last_context, intermediate_makefile) - dict_for_algo[makefile] = last_context - - for makefile in makefiles: - appz = self.get_apps_with_context(makefile, dict_for_algo[makefile]) - - for app, context in appz: - if len(context) == 0: - import copy - ddi = copy.deepcopy(dict_for_algo[makefile]) - tk_dict = autoresolve(ddi) - tk_dict = autoresolve(tk_dict) - - name_line = self.get_name_line(app.body, tk_dict) - command_line = self.get_command_line(app.body, tk_dict) - test_line = self.get_test(app.body, tk_dict) - - if '$' in test_line or '$' in command_line: - if '$' in command_line: - self.logger.error(command_line) - if '$' in test_line: - self.logger.warning(test_line) - else: - tests[name_line] = (command_line, test_line) - else: - contexts = {} - for iteration in context: - key = iteration[0] - values = [each[1:-1].lower() for each in iteration[1:]] - contexts[key] = values - - keyorder = list(contexts.keys()) - import itertools - pool = [each for each in itertools.product(*list(contexts.values()))] - - import copy - for poolinstance in pool: - neo_dict = copy.deepcopy(dict_for_algo[makefile]) - zipped = list(zip(keyorder, poolinstance)) - for each in zipped: - neo_dict[each[0]] = each[1] - - ak_dict = autoresolve(neo_dict) - ak_dict = autoresolve(ak_dict) - ak_dict = autoresolve(ak_dict) - - ddi = ak_dict - - name_line = self.get_name_line(app.body, ddi) - command_line = self.get_command_line(app.body, ddi) - test_line = self.get_test(app.body, ddi) - - if '$' in command_line or '$' not in test_line: - if '$' in command_line: - self.logger.error(command_line) - if '$' in test_line: - self.logger.warning(test_line) - else: - tests[name_line] = (command_line, test_line) - - return tests - - -def autoresolve(a_dict): - def as_template(item, b_dict): - if hasattr(item, 'safe_substitute'): - return item.safe_substitute(b_dict) - ate = Template(item) - return ate.safe_substitute(b_dict) - templatized = {key: as_template(a_dict[key], a_dict) for key in list(a_dict.keys())} - return templatized - - -def find_file(file_name, base_dir=os.curdir): - import os - for root, dirs, files in os.walk(base_dir, topdown=False): - for name in files: - if name == file_name: - return os.path.join(root, name) - raise Exception("File not found %s" % file_name) - - -def find_files(file_name, base_dir=os.curdir): - import os - result = [] - for root, dirs, files in os.walk(base_dir, topdown=False): - for name in files: - if name == file_name: - result.append(os.path.join(root, name)) - return result - - -def resolve_dict(adia, adib): - init = len(adia) - fin = len(adia) + 1 - - def _resolve_dict(dia, dib): - for key in dib: - cand_value = dib[key] - if hasattr(cand_value, 'safe_substitute'): - value = cand_value.safe_substitute(dia) - if isinstance(value, str) and "$" not in value: - dia[key] = value - else: - dia[key] = cand_value - for key in dia: - if key in dib: - del dib[key] - - while(init != fin): - init = len(adia) - _resolve_dict(adia, adib) - fin = len(adia) diff --git a/python/plugins/processing/algs/otb/maintenance/README.md b/python/plugins/processing/algs/otb/maintenance/README.md deleted file mode 100644 index dc4bb0a6754a..000000000000 --- a/python/plugins/processing/algs/otb/maintenance/README.md +++ /dev/null @@ -1,69 +0,0 @@ -Requirements -============ - -Set OTB environment --------------------- -``` -export PYTHONPATH=/path/to/OTB/install/lib/otb/python/:$PYTHONPATH -# Environment variable for old OTB versions (< 5.2) -export ITK_AUTOLOAD_PATH=/path/to/OTB/install/lib/otb/applications/ -# Environment variable for new OTB versions (>= 5.2) -export OTB_APPLICATION_PATH=/path/to/OTB/install/lib/otb/applications/ -# Set LD_LIBRARY_PATH -export LD_LIBRARY_PATH=/path/to/OTB/install/lib/:$LD_LIBRARY_PATH -``` - -Set QGIS environment ---------------------- - -``` -export QGIS_PREFIX_PATH=/path/to/QGIS/install -export PYTHONPATH=$QGIS_PREFIX_PATH/share/qgis/python:$QGIS_PREFIX_PATH/share/qgis/python/plugins:$PYTHONPATH -# Set LD_LIBRARY_PATH -export LD_LIBRARY_PATH=$QGIS_PREFIX_PATH/lib/:$LD_LIBRARY_PATH -# Add maintenance folder to python path -export PYTHONPATH=/path/to/QGIS/src/python/plugins/processing/algs/otb/maintenance:$PYTHONPATH -``` - -Check the white and black list for the current OTB version ----------------------------------------------------------- -In the maintenance directory, the OTB applications are split in two files `black_list.xml` and `white_list.xml`. -These files are organized as follows. For each OTB version, a new node version with id as attribute is added -to the node data. Each application is then added in the node app_name. - -```xml - - - - BinaryMorphologicalOperation - EdgeExtraction - GrayScaleMorphologicalOperation - DimensionalityReduction - Pansharpening - ExtractROI - RigidTransformResample - Segmentation - KMeansClassification - TrainSVMImagesClassifier - ComputeConfusionMatrix - OpticalCalibration - SarRadiometricCalibration - Smoothing - - -``` - -The list of available applications for each version is not fixed. - -OTBSpecific_XMLcreation.py --------------------------- -Warning: Some of the applications needs to be split to be user-friendly. Here comes the file `OTBSpecific_XMLcreation.py`. -Each function follows the pattern `getNameOfTheOTBApplication()`. - -Creating xml files ------------------- - -``` -cd /path/to/QGIS/src/python/plugins/processing/algs/otb/maintenance -python ./OTBHelper.py -``` diff --git a/python/plugins/processing/algs/otb/maintenance/TestOTBAlgorithms.py b/python/plugins/processing/algs/otb/maintenance/TestOTBAlgorithms.py deleted file mode 100644 index bbfce3238333..000000000000 --- a/python/plugins/processing/algs/otb/maintenance/TestOTBAlgorithms.py +++ /dev/null @@ -1,208 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - TestOTBAlgorithms.py - --------------------- - Copyright : (C) 2013 by CS Systemes d'information - Email : otb at c-s dot fr - Contributors : Oscar Picas -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" - -# This will get replaced with a git SHA1 when you do a git archive -__revision__ = '$Format:%H$' - -import unittest -import signal -import os -import shlex -import subprocess -import shelve - -try: - import processing # NOQA -except ImportError as e: - raise Exception("Processing must be installed and available in PYTHONPATH") - -try: - import otbApplication -except ImportError as e: - raise Exception("OTB python plugins must be installed and available in PYTHONPATH") - -from processing.algs.otb.OTBHelper import get_OTB_log, create_xml_descriptors -from processing.algs.otb.OTBTester import MakefileParser - - -class Alarm(Exception): - pass - - -def alarm_handler(signum, frame): - raise Alarm - - -class AlgoTestCase(unittest.TestCase): - - def setUp(self): - self.logger = get_OTB_log() - self.the_files = [os.path.join(os.path.join(os.path.abspath(os.curdir), 'description'), each) for each in os.listdir(os.path.join(os.path.abspath(os.curdir), 'description')) if '.xml' in each] - - def tearDown(self): - self.logger = None - - -class TestSequence(unittest.TestCase): - - def setUp(self): - self.data = shelve.open("tests.shelve", writeback=True) - - def tearDown(self): - self.data.close() - - -def ut_generator(test_name, a_tuple): - def test(self): - logger = get_OTB_log() - - needs_update = False - if test_name not in self.data: - needs_update = True - if test_name in self.data: - if (self.data[test_name][0] != a_tuple[0]) or (self.data[test_name][1] != a_tuple[1]) or (self.data[test_name][2] is False): - needs_update = True - - if needs_update: - signal.signal(signal.SIGALRM, alarm_handler) - signal.alarm(6 * 60) # 6 minutes - - black_list = [] - - ut_command = a_tuple[0] - self.assertTrue(ut_command is not None) - self.assertTrue(ut_command != "") - - ut_command_validation = a_tuple[1] - self.assertTrue(ut_command_validation is not None) - self.assertTrue(ut_command_validation != "") - - if ut_command.split(" ")[0] in black_list: - raise Exception("Blacklisted test!") - - args = shlex.split(ut_command) - failed = False - logger.info("Running [%s]" % ut_command) - p = subprocess.Popen(args, stdout=subprocess.PIPE, stderr=subprocess.PIPE) - (pout, perr) = p.communicate() - if ("ERROR" in pout or "ERROR" in perr) or ("FATAL" in pout or "FATAL" in perr) or ("CRITICAL" in pout or "CRITICAL" in perr): - error_text = "Command [%s] returned [%s]" % (ut_command, pout) - if "Invalid image filename" in pout or "Invalid vector data filename" in pout or "Failed to open" in pout: - logger.warning(error_text) - else: - logger.error(error_text) - self.fail(error_text) - failed = True - else: - logger.info(pout) - - if (len(ut_command_validation) > 0) and not failed: - new_ut_command_validation = ut_command_validation + " Execute " + ut_command - - logger.info("Running Unit test [%s]" % new_ut_command_validation) - argz = shlex.split(new_ut_command_validation) - q = subprocess.Popen(argz, stdout=subprocess.PIPE, stderr=subprocess.PIPE) - (qout, qerr) = q.communicate() - - if not ("Test EXIT SUCCESS" in qout or "Test EXIT SUCCESS" in qerr): - error_text = "Unit test [%s] returned [%s]" % (new_ut_command_validation, qout) - if "Invalid image filename" in qout or "Invalid vector data filename" in qout or "Failed to open" in qout: - logger.warning(error_text) - else: - logger.error(error_text) - self.fail(error_text) - else: - logger.info(qout) - - signal.alarm(0) - self.data[test_name] = [a_tuple[0], a_tuple[1], failed] - else: - logger.info("Passed test: %s" % test_name) - - return test - - -def get_client_apps(): - app_clients = [] - for available_app in otbApplication.Registry.GetAvailableApplications(): - app_instance = otbApplication.Registry.CreateApplication(available_app) - app_instance.UpdateParameters() - ct = "otbcli_" + available_app - app_clients.append(ct) - return app_clients - - -def unfiltered_processing_mapping(): - mkf = MakefileParser() - the_tests = mkf.test_algos() - for t in the_tests: - test_name = 'test_std_%s' % t - if the_tests[t][1] is None: - skip = True - else: - if the_tests[t][1] == "": - skip = True - - if not skip: - test = ut_generator(test_name, the_tests[t]) - setattr(TestSequence, test_name, test) - - suite = unittest.TestLoader().loadTestsFromTestCase(TestSequence) - unittest.TextTestRunner(verbosity=2).run(suite) - - -def test_processing_mapping(): - mkf = MakefileParser() - the_tests = mkf.test_algos() - clients = get_client_apps() - - already_tested = set() - - for t in the_tests: - test_name = 'test_%s' % t - if the_tests[t][0].split(" ")[0] in clients: - skip = False - if the_tests[t][1] is None: - skip = True - else: - if the_tests[t][1] == "": - skip = True - - if not skip: - runnable = the_tests[t][0].split(" ")[0] - if runnable not in already_tested: - test = ut_generator(test_name, the_tests[t]) - setattr(TestSequence, test_name, test) - already_tested.add(runnable) - - suite = unittest.TestLoader().loadTestsFromTestCase(TestSequence) - unittest.TextTestRunner(verbosity=2).run(suite) - - -def test_xml_generation(): - create_xml_descriptors() - -if __name__ == '__main__': - mkf = MakefileParser() - the_tests = mkf.test_algos() - for t in the_tests: - test_name = 'test_%s' % t - test = ut_generator(test_name, the_tests[t]) - setattr(TestSequence, test_name, test) - unittest.main() diff --git a/python/plugins/processing/algs/otb/maintenance/black_list.xml b/python/plugins/processing/algs/otb/maintenance/black_list.xml deleted file mode 100644 index 6bf74a294002..000000000000 --- a/python/plugins/processing/algs/otb/maintenance/black_list.xml +++ /dev/null @@ -1,119 +0,0 @@ - - - - SarRadiometricCalibration - PixelValue - Quicklook - ConvertCartoToGeoPoint - ConvertSensorToGeoPoint - ObtainUTMZoneFromGeoPoint - BundleToPerfectSensor - Example - DSFuzzyModelEstimation - HomologousPointsExtraction - VectorDataDSValidation - GenerateRPCSensorModel - GridBasedImageResampling - GeneratePlyFile - RefineSensorModel - MultiResolutionPyramid - HyperspectralUnmixing - OSMDownloader - VertexComponentAnalysis - VectorDataSetField - DownloadSRTMTiles - DisparityMapToElevationMap - FineRegistration - StereoRectificationGridGenerator - BlockMatching - SplitImage - - - SarRadiometricCalibration - PixelValue - Quicklook - ConvertCartoToGeoPoint - ConvertSensorToGeoPoint - ObtainUTMZoneFromGeoPoint - BundleToPerfectSensor - Example - DSFuzzyModelEstimation - HomologousPointsExtraction - VectorDataDSValidation - GenerateRPCSensorModel - GridBasedImageResampling - GeneratePlyFile - RefineSensorModel - MultiResolutionPyramid - HyperspectralUnmixing - OSMDownloader - VertexComponentAnalysis - VectorDataSetField - DownloadSRTMTiles - DisparityMapToElevationMap - FineRegistration - StereoRectificationGridGenerator - BlockMatching - SplitImage - - - ApplicationExample - SarRadiometricCalibration - SARPolarMatrixConvert - PixelValue - Quicklook - ConvertCartoToGeoPoint - ConvertSensorToGeoPoint - ObtainUTMZoneFromGeoPoint - BundleToPerfectSensor - DSFuzzyModelEstimation - HomologousPointsExtraction - VectorDataDSValidation - GenerateRPCSensorModel - GridBasedImageResampling - GeneratePlyFile - RefineSensorModel - MultiResolutionPyramid - HyperspectralUnmixing - OSMDownloader - VertexComponentAnalysis - VectorDataSetField - DownloadSRTMTiles - DisparityMapToElevationMap - FineRegistration - StereoRectificationGridGenerator - BlockMatching - SplitImage - TestApplication - - - ApplicationExample - SarRadiometricCalibration - SARPolarMatrixConvert - PixelValue - Quicklook - ConvertCartoToGeoPoint - ConvertSensorToGeoPoint - ObtainUTMZoneFromGeoPoint - BundleToPerfectSensor - DSFuzzyModelEstimation - HomologousPointsExtraction - VectorDataDSValidation - GenerateRPCSensorModel - GridBasedImageResampling - GeneratePlyFile - RefineSensorModel - MultiResolutionPyramid - HyperspectralUnmixing - OSMDownloader - VertexComponentAnalysis - VectorDataSetField - DownloadSRTMTiles - DisparityMapToElevationMap - FineRegistration - StereoRectificationGridGenerator - BlockMatching - SplitImage - TestApplication - - diff --git a/python/plugins/processing/algs/otb/maintenance/parsing.py b/python/plugins/processing/algs/otb/maintenance/parsing.py deleted file mode 100644 index 66f930543db6..000000000000 --- a/python/plugins/processing/algs/otb/maintenance/parsing.py +++ /dev/null @@ -1,190 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - parsing.py - --------------------- - Copyright : (C) 2013 by CS Systemes d'information (CS SI) - Email : otb at c-s dot fr (CS SI) - Contributors : Julien Malik (CS SI) - Oscar Picas (CS SI) -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" -from builtins import next -from builtins import str -from builtins import range -__author__ = 'Julien Malik, Oscar Picas' -__copyright__ = '(C) 2013, CS Systemes d\'information (CS SI)' - -from collections import namedtuple -import re - - -def merge_pairs(list, should_merge, merge): - """ - Merges adjacent elements of list using the function merge - if they satisfy the predicate should_merge. - """ - ret = [] - i = 0 - while i < len(list) - 1: - a = list[i] - b = list[i + 1] - if should_merge(a, b): - ret.append(merge(a, b)) - i += 2 - else: - ret.append(a) - i += 1 - if i == len(list) - 1: - ret.append(list[i]) - return ret - -QuotedString = namedtuple('QuotedString', 'contents comments') -_Arg = namedtuple('Arg', 'contents comments') -_Command = namedtuple('Command', 'name body comment') -BlankLine = namedtuple('BlankLine', '') - - -class File(list): - - def __repr__(self): - return 'File(' + repr(list(self)) + ')' - - -class Comment(str): - - def __repr__(self): - return 'Comment(' + str(self) + ')' - - -def Arg(contents, comments=None): - return _Arg(contents, comments or []) - - -def Command(name, body, comment=None): - return _Command(name, body, comment) - - -class CMakeParseError(Exception): - pass - - -def prettify(s): - """ - Returns the pretty-print of the contents of a CMakeLists file. - """ - return str(parse(s)) - - -def parse(s): - ''' - Parses a string s in CMakeLists format whose - contents are assumed to have come from the - file at the given path. - ''' - nums_toks = tokenize(s) - nums_items = list(parse_file(nums_toks)) - nums_items = attach_comments_to_commands(nums_items) - items = [item for _, item in nums_items] - return File(items) - - -def parse_file(toks): - ''' - Yields line number ranges and top-level elements of the syntax tree for - a CMakeLists file, given a generator of tokens from the file. - - toks must really be a generator, not a list, for this to work. - ''' - prev_type = 'newline' - for line_num, (typ, tok_contents) in toks: - if typ == 'comment': - yield ([line_num], Comment(tok_contents)) - elif typ == 'newline' and prev_type == 'newline': - yield ([line_num], BlankLine()) - elif typ == 'word': - line_nums, cmd = parse_command(line_num, tok_contents, toks) - yield (line_nums, cmd) - prev_type = typ - - -def attach_comments_to_commands(nodes): - return merge_pairs(nodes, command_then_comment, attach_comment_to_command) - - -def command_then_comment(a, b): - line_nums_a, thing_a = a - line_nums_b, thing_b = b - return (isinstance(thing_a, _Command) and - isinstance(thing_b, Comment) and - set(line_nums_a).intersection(line_nums_b)) - - -def attach_comment_to_command(lnums_command, lnums_comment): - command_lines, command = lnums_command - _, comment = lnums_comment - return command_lines, Command(command.name, command.body[:], comment) - - -def parse_command(start_line_num, command_name, toks): - cmd = Command(name=command_name, body=[], comment=None) - expect('left paren', toks) - for line_num, (typ, tok_contents) in toks: - if typ == 'right paren': - line_nums = list(range(start_line_num, line_num + 1)) - return line_nums, cmd - elif typ == 'left paren': - raise ValueError('Unexpected left paren at line %s' % line_num) - elif typ in ('word', 'string'): - cmd.body.append(Arg(tok_contents, [])) - elif typ == 'comment': - c = tok_contents - if cmd.body: - cmd.body[-1].comments.append(c) - else: - cmd.comments.append(c) - msg = 'File ended while processing command "%s" started at line %s' % ( - command_name, start_line_num) - raise CMakeParseError(msg) - - -def expect(expected_type, toks): - line_num, (typ, tok_contents) = next(toks) - if typ != expected_type: - msg = 'Expected a %s, but got "%s" at line %s' % ( - expected_type, tok_contents, line_num) - raise CMakeParseError(msg) - -# http://stackoverflow.com/questions/691148/pythonic-way-to-implement-a-tokenizer -scanner = re.Scanner([ - (r'#.*', lambda scanner, token: ("comment", token)), - (r'"[^"]*"', lambda scanner, token: ("string", token)), - (r"\(", lambda scanner, token: ("left paren", token)), - (r"\)", lambda scanner, token: ("right paren", token)), - (r'[^ \t\r\n()#"]+', lambda scanner, token: ("word", token)), - (r'\n', lambda scanner, token: ("newline", token)), - (r"\s+", None), # skip other whitespace -]) - - -def tokenize(s): - """ - Yields pairs of the form (line_num, (token_type, token_contents)) - given a string containing the contents of a CMakeLists file. - """ - toks, remainder = scanner.scan(s) - line_num = 1 - if remainder != '': - msg = 'Unrecognized tokens at line %s: %s' % (line_num, remainder) - raise ValueError(msg) - for tok_type, tok_contents in toks: - yield line_num, (tok_type, tok_contents.strip()) - line_num += tok_contents.count('\n') diff --git a/python/plugins/processing/algs/otb/maintenance/white_list.xml b/python/plugins/processing/algs/otb/maintenance/white_list.xml deleted file mode 100644 index 73eaeb0d70ea..000000000000 --- a/python/plugins/processing/algs/otb/maintenance/white_list.xml +++ /dev/null @@ -1,257 +0,0 @@ - - - - MultivariateAlterationDetector - OpticalCalibration - StereoFramework - BinaryMorphologicalOperation - DimensionalityReduction - EdgeExtraction - GrayScaleMorphologicalOperation - LineSegmentDetection - LocalStatisticExtraction - RadiometricIndices - ConnectedComponentSegmentation - MeanShiftSmoothing - Segmentation - BandMath - ColorMapping - CompareImages - ConcatenateImages - ConcatenateVectorData - ExtractROI - KmzExport - ReadImageInfo - Rescale - Smoothing - TileFusion - Pansharpening - ClassificationMapRegularization - ComputeConfusionMatrix - ComputeImagesStatistics - FusionOfClassifications - ImageClassifier - KMeansClassification - SOMClassification - TrainImagesClassifier - ImageEnvelope - OrthoRectification - RigidTransformResample - Superimpose - ComputeModulusAndPhase - HaralickTextureExtraction - HooverCompareSegmentation - LSMSSegmentation - LSMSSmallRegionsMerging - LSMSVectorization - ComputePolylineFeatureFromImage - SFSTextureExtraction - Rasterization - VectorDataTransform - VectorDataReprojection - VectorDataExtractROI - Convert - BandMathX - ComputeOGRLayersFeaturesStatistics - DEMConvert - Despeckle - OGRLayerClassifier - TrainOGRLayersClassifier - - - BandMath - BandMathX - BinaryMorphologicalOperation - ClassificationMapRegularization - CompareImages - ColorMapping - ComputeConfusionMatrix - ComputeImagesStatistics - ComputeModulusAndPhase - ComputeOGRLayersFeaturesStatistics - ComputePolylineFeatureFromImage - ConcatenateImages - ConcatenateVectorData - ConnectedComponentSegmentation - Convert - DEMConvert - Despeckle - DimensionalityReduction - ExtractROI - EdgeExtraction - FusionOfClassifications - GrayScaleMorphologicalOperation - HaralickTextureExtraction - HooverCompareSegmentation - ImageClassifier - ImageEnvelope - KMeansClassification - KmzExport - LineSegmentDetection - LSMSSegmentation - LSMSSmallRegionsMerging - LSMSVectorization - LocalStatisticExtraction - MeanShiftSmoothing - MultivariateAlterationDetector - OGRLayerClassifier - OpticalCalibration - OrthoRectification - Pansharpening - RadiometricIndices - Rasterization - ReadImageInfo - Rescale - RigidTransformResample - Segmentation - SFSTextureExtraction - Smoothing - SOMClassification - Superimpose - StereoFramework - TileFusion - TrainImagesClassifier - TrainOGRLayersClassifier - VectorDataTransform - VectorDataReprojection - VectorDataExtractROI - - - BandMath - BandMathX - BinaryMorphologicalOperation - ClassificationMapRegularization - CompareImages - ColorMapping - ComputeConfusionMatrix - ComputeImagesStatistics - ComputeModulusAndPhase - ComputeOGRLayersFeaturesStatistics - ComputePolylineFeatureFromImage - ConcatenateImages - ConcatenateVectorData - ConnectedComponentSegmentation - Convert - DEMConvert - Despeckle - DimensionalityReduction - ExtractROI - EdgeExtraction - FusionOfClassifications - GrayScaleMorphologicalOperation - HaralickTextureExtraction - HooverCompareSegmentation - ImageClassifier - ImageEnvelope - KMeansClassification - KmzExport - LineSegmentDetection - LSMSSegmentation - LSMSSmallRegionsMerging - LSMSVectorization - LocalStatisticExtraction - MeanShiftSmoothing - ManageNoData - MultivariateAlterationDetector - OGRLayerClassifier - OpticalCalibration - OrthoRectification - Pansharpening - PolygonClassStatistics - PredictRegression - RadiometricIndices - Rasterization - ReadImageInfo - Rescale - RigidTransformResample - SARCalibration - SARDecompositions - SARPolarSynth - SampleExtraction - SampleSelection - Segmentation - SFSTextureExtraction - Smoothing - SOMClassification - Superimpose - StereoFramework - TileFusion - TrainImagesClassifier - TrainOGRLayersClassifier - TrainVectorClassifier - TrainRegression - VectorDataTransform - VectorDataReprojection - VectorDataExtractROI - MultiImageSamplingRate - - - BandMath - BandMathX - BinaryMorphologicalOperation - ClassificationMapRegularization - CompareImages - ColorMapping - ComputeConfusionMatrix - ComputeImagesStatistics - ComputeModulusAndPhase - ComputeOGRLayersFeaturesStatistics - ComputePolylineFeatureFromImage - ConcatenateImages - ConcatenateVectorData - ConnectedComponentSegmentation - Convert - DEMConvert - Despeckle - DimensionalityReduction - ExtractROI - EdgeExtraction - FusionOfClassifications - GrayScaleMorphologicalOperation - HaralickTextureExtraction - HooverCompareSegmentation - ImageClassifier - ImageEnvelope - KMeansClassification - KmzExport - LineSegmentDetection - LSMSSegmentation - LSMSSmallRegionsMerging - LSMSVectorization - LocalStatisticExtraction - MeanShiftSmoothing - ManageNoData - MultivariateAlterationDetector - OGRLayerClassifier - OpticalCalibration - OrthoRectification - Pansharpening - PolygonClassStatistics - PredictRegression - RadiometricIndices - Rasterization - ReadImageInfo - Rescale - RigidTransformResample - SARCalibration - SARDecompositions - SARPolarSynth - SampleExtraction - SampleSelection - Segmentation - SFSTextureExtraction - Smoothing - SOMClassification - Superimpose - StereoFramework - TileFusion - TrainImagesClassifier - TrainOGRLayersClassifier - TrainVectorClassifier - TrainRegression - VectorDataTransform - VectorDataReprojection - VectorDataExtractROI - MultiImageSamplingRate - - From 0c11b8dd8f96c0e9c2ac63833d0c59b63805780a Mon Sep 17 00:00:00 2001 From: volaya Date: Mon, 30 Jan 2017 10:28:37 +0100 Subject: [PATCH 2/4] fixed Cmake file --- python/plugins/processing/algs/CMakeLists.txt | 2 -- 1 file changed, 2 deletions(-) diff --git a/python/plugins/processing/algs/CMakeLists.txt b/python/plugins/processing/algs/CMakeLists.txt index f27b362151b8..46fa2dd71060 100644 --- a/python/plugins/processing/algs/CMakeLists.txt +++ b/python/plugins/processing/algs/CMakeLists.txt @@ -4,8 +4,6 @@ ADD_SUBDIRECTORY(help) ADD_SUBDIRECTORY(gdal) ADD_SUBDIRECTORY(grass7) ADD_SUBDIRECTORY(saga) -ADD_SUBDIRECTORY(otb) -ADD_SUBDIRECTORY(lidar) ADD_SUBDIRECTORY(qgis) ADD_SUBDIRECTORY(r) ADD_SUBDIRECTORY(exampleprovider) From 6002ca749f112051a7bdc47d3cd03ea7d710447e Mon Sep 17 00:00:00 2001 From: volaya Date: Mon, 30 Jan 2017 10:56:51 +0100 Subject: [PATCH 3/4] [processing] removed providers imports --- python/plugins/processing/core/Processing.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/python/plugins/processing/core/Processing.py b/python/plugins/processing/core/Processing.py index 830bc49304ae..2cafd08dff3a 100644 --- a/python/plugins/processing/core/Processing.py +++ b/python/plugins/processing/core/Processing.py @@ -57,9 +57,7 @@ from processing.modeler.ModelerAlgorithmProvider import ModelerAlgorithmProvider from processing.algs.qgis.QGISAlgorithmProvider import QGISAlgorithmProvider from processing.algs.grass7.Grass7AlgorithmProvider import Grass7AlgorithmProvider -from processing.algs.lidar.LidarToolsAlgorithmProvider import LidarToolsAlgorithmProvider from processing.algs.gdal.GdalAlgorithmProvider import GdalAlgorithmProvider -from processing.algs.otb.OTBAlgorithmProvider import OTBAlgorithmProvider from processing.algs.r.RAlgorithmProvider import RAlgorithmProvider from processing.algs.saga.SagaAlgorithmProvider import SagaAlgorithmProvider from processing.script.ScriptAlgorithmProvider import ScriptAlgorithmProvider From f94f0d753c2eb20e06fa253bb335d94b91fedbca Mon Sep 17 00:00:00 2001 From: volaya Date: Mon, 30 Jan 2017 11:59:49 +0100 Subject: [PATCH 4/4] [processing] removed otb tests --- .../processing/tests/AlgorithmsTestBase.py | 2 - .../plugins/processing/tests/CMakeLists.txt | 1 - .../processing/tests/OTBAlgorithmsTest.py | 58 ------------------- .../tests/testdata/otb_algorithm_tests.yaml | 20 ------- 4 files changed, 81 deletions(-) delete mode 100644 python/plugins/processing/tests/OTBAlgorithmsTest.py delete mode 100644 python/plugins/processing/tests/testdata/otb_algorithm_tests.yaml diff --git a/python/plugins/processing/tests/AlgorithmsTestBase.py b/python/plugins/processing/tests/AlgorithmsTestBase.py index df796e5f43d9..185d07a22271 100644 --- a/python/plugins/processing/tests/AlgorithmsTestBase.py +++ b/python/plugins/processing/tests/AlgorithmsTestBase.py @@ -48,9 +48,7 @@ from processing.modeler.ModelerAlgorithmProvider import ModelerAlgorithmProvider from processing.algs.qgis.QGISAlgorithmProvider import QGISAlgorithmProvider from processing.algs.grass7.Grass7AlgorithmProvider import Grass7AlgorithmProvider -from processing.algs.lidar.LidarToolsAlgorithmProvider import LidarToolsAlgorithmProvider from processing.algs.gdal.GdalAlgorithmProvider import GdalAlgorithmProvider -from processing.algs.otb.OTBAlgorithmProvider import OTBAlgorithmProvider from processing.algs.r.RAlgorithmProvider import RAlgorithmProvider from processing.algs.saga.SagaAlgorithmProvider import SagaAlgorithmProvider from processing.script.ScriptAlgorithmProvider import ScriptAlgorithmProvider diff --git a/python/plugins/processing/tests/CMakeLists.txt b/python/plugins/processing/tests/CMakeLists.txt index 09fb9f4fa3f9..d5e2cbb1d323 100644 --- a/python/plugins/processing/tests/CMakeLists.txt +++ b/python/plugins/processing/tests/CMakeLists.txt @@ -13,5 +13,4 @@ IF(ENABLE_TESTS) ADD_PYTHON_TEST(ProcessingGdalAlgorithmsTest GdalAlgorithmsTest.py) ADD_PYTHON_TEST(ProcessingGrass7AlgorithmsImageryTest Grass7AlgorithmsImageryTest.py) ADD_PYTHON_TEST(ProcessingGrass7AlgorithmsRasterTest Grass7AlgorithmsRasterTest.py) - ADD_PYTHON_TEST(ProcessingOTBAlgorithmsTest OTBAlgorithmsTest.py) ENDIF(ENABLE_TESTS) diff --git a/python/plugins/processing/tests/OTBAlgorithmsTest.py b/python/plugins/processing/tests/OTBAlgorithmsTest.py deleted file mode 100644 index 0d2d3585d1bb..000000000000 --- a/python/plugins/processing/tests/OTBAlgorithmsTest.py +++ /dev/null @@ -1,58 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -*************************************************************************** - OTBAlgorithmTests.py - --------------------- - Date : August 2016 - Copyright : (C) 2016 by Manuel Grizonnet - Email : manuel.grizonnet@cnes.fr -*************************************************************************** -* * -* This program is free software; you can redistribute it and/or modify * -* it under the terms of the GNU General Public License as published by * -* the Free Software Foundation; either version 2 of the License, or * -* (at your option) any later version. * -* * -*************************************************************************** -""" - -__author__ = 'Manuel Grizonnet' -__date__ = 'August 2016' -__copyright__ = '(C) 2016, Manuel Grizonnet' - -# This will get replaced with a git SHA1 when you do a git archive - -__revision__ = ':%H$' - -import AlgorithmsTestBase - -import nose2 -import shutil - -from qgis.testing import ( - start_app, - unittest -) - - -class TestOTBAlgorithms(unittest.TestCase, AlgorithmsTestBase.AlgorithmsTest): - - @classmethod - def setUpClass(cls): - start_app() - from processing.core.Processing import Processing - Processing.initialize() - cls.cleanup_paths = [] - - @classmethod - def tearDownClass(cls): - for path in cls.cleanup_paths: - shutil.rmtree(path) - - def test_definition_file(self): - return 'otb_algorithm_tests.yaml' - - -if __name__ == '__main__': - nose2.main() diff --git a/python/plugins/processing/tests/testdata/otb_algorithm_tests.yaml b/python/plugins/processing/tests/testdata/otb_algorithm_tests.yaml deleted file mode 100644 index 785166eaa630..000000000000 --- a/python/plugins/processing/tests/testdata/otb_algorithm_tests.yaml +++ /dev/null @@ -1,20 +0,0 @@ -# See ../README.md for a description of the file format - -tests: - - - algorithm: otb:imageconversion - name: Test (otb:imageconversion) - params: - -hcp.high: 2 - -hcp.low: 2 - -in: - name: raster.tif - type: raster - -ram: 128 - -type: '1' - -type.linear.gamma: 1 - results: - -out: - hash: b3657f4d848b64f688db41638ea6d86d9de1d0a169bc1bafef8af82a - type: rasterhash -