Merge branch 'issue-2542' into projfix

doc/quickstart.rst

@@ -38,9 +38,9 @@ Install Conda
 Conda installation instructions can be found at the following links. Read
 through them a bit for your platform so you have an idea what to expect.
 
-* `Windows <https://conda.io/docs/user-guide/install/windows.html>`__
-* `macOS <https://conda.io/docs/user-guide/install/macos.html>`__
-* `Linux <https://conda.io/docs/user-guide/install/linux.html>`__
+* `Windows <https://conda.io/projects/conda/en/latest/user-guide/install/windows.html>`__
+* `macOS <https://conda.io/projects/conda/en/latest/user-guide/install/macos.html>`__
+* `Linux <https://conda.io/projects/conda/en/latest/user-guide/install/linux.html>`__
 
 .. note::
 
@@ -56,7 +56,7 @@ On macOS and Linux, all Conda commands are typed into a terminal window. On
 Windows, commands are typed into the Anaconda Prompt window. Instructions can
 be found in the Conda `Getting Started`_ guide.
 
-.. _`Getting Started`: https://conda.io/docs/user-guide/getting-started.html#starting-conda
+.. _`Getting Started`: https://conda.io/projects/conda/en/latest/user-guide/getting-started.html#starting-conda
 
 
 Test Installation
@@ -79,7 +79,7 @@ the PDAL maintenance branch.
     It is a good idea to install PDAL in it's own environment (or
     add it to an existing one). You will **NOT** want to add it to your default
     environment named ``base``. Managing environments is beyond the scope of
-    the quickstart, but you can read more about it `here <https://conda.io/docs/user-guide/getting-started.html#managing-envs>`_.
+    the quickstart, but you can read more about it `here <https://conda.io/projects/conda/en/latest/user-guide/getting-started.html#managing-envs>`_.
 
 To install the PDAL package so that we can use it to run PDAL commands, we run
 the following command to create an environment named ``myenv``, installing PDAL

doc/references.rst

@@ -52,8 +52,6 @@ Reference
 
 .. [Mesh2009] ALoopingIcon. "Meshing Point Clouds." *MESHLAB STUFF*. n.p., 7 Sept. 2009. Web. 13 Nov. 2015.
 
-.. [Mongus2012] Mongus, D., Zalik, B., 2012. Parameter-free ground filtering of LiDAR data for automatic DTM generation. ISPRS J. Photogramm. Remote Sens. 67, 1–12.
-
 .. [Pingel2013] Pingel, Thomas J., Keith C. Clarke, and William A. McBride. “An Improved Simple Morphological Filter for the Terrain Classification of Airborne LIDAR Data.” ISPRS Journal of Photogrammetry and Remote Sensing 77 (2013): 21–30.
 
 .. [Rusu2008] Rusu, Radu Bogdan, et al. "Towards 3D point cloud based object maps for household environments." Robotics and Autonomous Systems 56.11 (2008): 927-941.

doc/stages/filters.rst

@@ -36,16 +36,15 @@ invalidate an existing KD-tree.
    filters.cluster
    filters.colorinterp
    filters.colorization
+   filters.covariancefeatures
    filters.dem
    filters.eigenvalues
    filters.estimaterank
    filters.elm
    filters.ferry
    filters.hag
    filters.info
-   filters.kdistance
    filters.lof
-   filters.mongus
    filters.neighborclassifier
    filters.nndistance
    filters.normal
@@ -74,6 +73,10 @@ invalidate an existing KD-tree.
 :ref:`filters.colorization`
     Fetch and assign RGB color information from a GDAL-readable datasource.
 
+:ref:`filters.covariancefeatures`
+    Filter that calculates local features based on the covariance matrix of a
+    point's neighborhood.
+
 :ref:`filters.eigenvalues`
     Compute pointwise eigenvalues, based on k-nearest neighbors.
 
@@ -90,17 +93,10 @@ invalidate an existing KD-tree.
     Compute pointwise height above ground estimate. Requires points to be
     classified as ground/non-ground prior to estimating.
 
-:ref:`filters.kdistance`
-    Compute pointwise K-Distance (the Euclidean distance to a point's k-th
-    nearest neighbor). [Deprecated - use :ref:`filters.nndistance`]
-
 :ref:`filters.lof`
     Compute pointwise Local Outlier Factor (along with K-Distance and Local
     Reachability Distance).
 
-:ref:`filters.mongus`
-    Label ground/non-ground returns using [Mongus2012]_.
-
 :ref:`filters.neighborclassifier`
     Update pointwise classification using k-nearest neighbor consensus voting.
 

filters/ApproximateCoplanarFilter.cpp

@@ -90,7 +90,7 @@ void ApproximateCoplanarFilter::filter(PointView& view)
         auto B = eigen::computeCovariance(view, ids);
 
         // perform the eigen decomposition
-        SelfAdjointEigenSolver<Matrix3f> solver(B);
+        SelfAdjointEigenSolver<Matrix3d> solver(B);
         if (solver.info() != Success)
             throwError("Cannot perform eigen decomposition.");
         auto ev = solver.eigenvalues();

filters/CovarianceFeaturesFilter.cpp

@@ -77,7 +77,7 @@ void CovarianceFeaturesFilter::addDimensions(PointLayoutPtr layout)
     if (m_featureSet == "Dimensionality")
     {
         for (auto dim: {"Linearity", "Planarity", "Scattering", "Verticality"})
-            m_extraDims[dim] = layout->registerOrAssignDim(dim, Dimension::Type::Float);
+            m_extraDims[dim] = layout->registerOrAssignDim(dim, Dimension::Type::Double);
     }
 }
 
@@ -113,43 +113,43 @@ void CovarianceFeaturesFilter::setDimensionality(PointView &view, const PointId
     auto B = eigen::computeCovariance(view, ids);
 
     // perform the eigen decomposition
-    SelfAdjointEigenSolver<Matrix3f> solver(B);
+    SelfAdjointEigenSolver<Matrix3d> solver(B);
     if (solver.info() != Success)
         throwError("Cannot perform eigen decomposition.");
 
     // Extract eigenvalues and eigenvectors in decreasing order (largest eigenvalue first)
     auto ev = solver.eigenvalues();
-    std::vector<float> lambda = {(std::max(ev[2],0.f)),
-                                 (std::max(ev[1],0.f)),
-                                 (std::max(ev[0],0.f))};
+    std::vector<double> lambda = {(std::max(ev[2],0.0)),
+                                  (std::max(ev[1],0.0)),
+                                  (std::max(ev[0],0.0))};
 
     if (lambda[0] == 0)
         throwError("Eigenvalues are all 0. Can't compute local features.");
 
     auto eigenVectors = solver.eigenvectors();
-    std::vector<float> v1(3), v2(3), v3(3);
+    std::vector<double> v1(3), v2(3), v3(3);
     for (int i=0; i < 3; i++)
     {
         v1[i] = eigenVectors.col(2)(i);
         v2[i] = eigenVectors.col(1)(i);
         v3[i] = eigenVectors.col(0)(i);
     }
 
-    float linearity  = (sqrtf(lambda[0]) - sqrtf(lambda[1])) / sqrtf(lambda[0]);
-    float planarity  = (sqrtf(lambda[1]) - sqrtf(lambda[2])) / sqrtf(lambda[0]);
-    float scattering =  sqrtf(lambda[2]) / sqrtf(lambda[0]);
+    double linearity  = (sqrt(lambda[0]) - sqrt(lambda[1])) / sqrt(lambda[0]);
+    double planarity  = (sqrt(lambda[1]) - sqrt(lambda[2])) / sqrt(lambda[0]);
+    double scattering =  sqrt(lambda[2]) / sqrt(lambda[0]);
     view.setField(m_extraDims["Linearity"], id, linearity);
     view.setField(m_extraDims["Planarity"], id, planarity);
     view.setField(m_extraDims["Scattering"], id, scattering);
 
-    std::vector<float> unary_vector(3);
-    float norm = 0;
+    std::vector<double> unary_vector(3);
+    double norm = 0;
     for (int i=0; i <3 ; i++)
     {
-        unary_vector[i] = lambda[0] * fabsf(v1[i]) + lambda[1] * fabsf(v2[i]) + lambda[2] * fabsf(v3[i]);
+        unary_vector[i] = lambda[0] * fabs(v1[i]) + lambda[1] * fabs(v2[i]) + lambda[2] * fabs(v3[i]);
         norm += unary_vector[i] * unary_vector[i];
     }
-    norm = sqrtf(norm);
+    norm = sqrt(norm);
     view.setField(m_extraDims["Verticality"], id, unary_vector[2] / norm);
 }
-}
+}

filters/EigenvaluesFilter.cpp

@@ -89,7 +89,7 @@ void EigenvaluesFilter::filter(PointView& view)
         auto B = eigen::computeCovariance(view, ids);
 
         // perform the eigen decomposition
-        SelfAdjointEigenSolver<Matrix3f> solver(B);
+        SelfAdjointEigenSolver<Matrix3d> solver(B);
         if (solver.info() != Success)
             throwError("Cannot perform eigen decomposition.");
         auto ev = solver.eigenvalues();

filters/GreedyProjection.cpp

@@ -188,7 +188,7 @@ void GreedyProjection::filter(PointView& view)
   std::vector<int> point2index (input_->points.size (), -1);
   for (int cp = 0; cp < static_cast<int> (indices_->size ()); ++cp)
   {
-    coords_.push_back(input_->points[(*indices_)[cp]].getVector3fMap());
+    coords_.push_back(input_->points[(*indices_)[cp]].getVector3dMap());
     point2index[(*indices_)[cp]] = cp;
   }
   **/