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sarHelpers.py
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sarHelpers.py
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class Utils:
def subset(product, borderRectInGeoCoor):
from snappy import jpy
from snappy import ProductIO
from snappy import GPF
from snappy import HashMap
xmin = borderRectInGeoCoor[0]
ymin = borderRectInGeoCoor[1]
xmax = borderRectInGeoCoor[2]
ymax = borderRectInGeoCoor[3]
p1 = '%s %s' %(xmin, ymin)
p2 = '%s %s' %(xmin, ymax)
p3 = '%s %s' %(xmax, ymax)
p4 = '%s %s' %(xmax, ymin)
wkt = "POLYGON((%s, %s, %s, %s, %s))" %(p1, p2, p3, p4, p1)
WKTReader = jpy.get_type('com.vividsolutions.jts.io.WKTReader')
geom = WKTReader().read(wkt)
HashMap = jpy.get_type('java.util.HashMap')
GPF.getDefaultInstance().getOperatorSpiRegistry().loadOperatorSpis()
parameters = HashMap()
parameters.put('copyMetadata', True)
parameters.put('geoRegion', geom)
parameters.put('outputImageScaleInDb', False)
subset = GPF.createProduct('Subset', parameters, product)
return subset
def calibrate(product):
from snappy import jpy
from snappy import ProductIO
from snappy import GPF
from snappy import HashMap
parameters = HashMap()
parameters.put('outputSigmaBand', True)
parameters.put('outputImageScaleInDb', False)
Calibrate = GPF.createProduct('Calibration', parameters, product)
return Calibrate
def terrainCorrection(product):
from snappy import jpy
from snappy import ProductIO
from snappy import GPF
from snappy import HashMap
parameters = HashMap()
parameters.put('demResamplingMethod', 'NEAREST_NEIGHBOUR')
parameters.put('imgResamplingMethod', 'NEAREST_NEIGHBOUR')
parameters.put('demName', 'SRTM 3Sec')
parameters.put('pixelSpacingInMeter', 10.0)
terrain = GPF.createProduct('Terrain-Correction', parameters, product)
return terrain
def speckleFilter(product):
from snappy import jpy
from snappy import ProductIO
from snappy import GPF
from snappy import HashMap
parameters = HashMap()
parameters.put('filter', 'Lee')
parameters.put('filterSizeX', 5)
parameters.put('filterSizeY', 5)
parameters.put('dampingFactor', 2)
parameters.put('edgeThreshold', 5000.0)
parameters.put('estimateENL', True)
parameters.put('enl', 1.0)
Speckle = GPF.createProduct('Speckle-Filter', parameters, product)
return Speckle
def getGeoDataBorder(geopandasDataFilePath, geoDataIndex):
'''
Get border of geometry column in geopandas dataframe
:type geopandasDataFilePath: string
:type geoDataIndex: int
:param geopandasDataFilePath: directory of geopandas dataframe file
:param geoDataIndex: geoDataIndex of data needed to retrieve in geopandas Dataframe
:return: List of xmin, xmax, ymin, ymax of border
'''
import geopandas as gpd
from shapely.geometry import Polygon
geoData = gpd.read_file(geopandasDataFilePath)
geom = geoData.loc[geoDataIndex].geometry
xmin, ymin, xmax, ymax = geom.bounds
w, h = xmax - xmin, ymax - ymin
xmin -= 0.05*w
xmax += 0.05*w
ymin -= 0.05*h
ymax += 0.05*h
return [xmin, ymin, xmax, ymax]
def maskOutLake(watermask):
import numpy as np
from scipy.ndimage import measurements
visited, label = measurements.label(watermask)
area = measurements.sum(watermask, visited, index=np.arange(label + 1))
largestElement = np.argmax(area)
return np.where(visited==largestElement, 1, 0)
def maskWater(vh, offset = -22):
import numpy as np
return np.where(vh < offset, 1, 0)
def countPixel(mat):
return len(np.where(mat==1))
def createMaskLake(imgDir):
ds = rasterio.open(imgDir)
band = ds.read()
waterMask = Utils.maskWater(band[0])
return Utils.maskOutLake(waterMask)
def getGeoTiffImage(sarDownloadFilePath, geopandasDataFilePath, geoDataIndex, dstPath=None):
'''
Get GeoTiff image from a .SAFE folder extracted after download
:type sarDownloadFilePath: string or list of string
:type geopandasDataFilePath: string
:type geoDataIndex: int
:type dstPath: string
:param sarDownloadFilePath: directory (or list of directory) of .SAFE folder(s)
:param geopandasDataFilePath: directory of geopandas dataframe file
:param geoDataIndex: geoDataIndex of data needed to retrieve in geopandas Dataframe
:param dstPath: directory of destination file, must have '.tif' extension
:return: None
:example: sarHelpers.getGeoTiffImage(sarDownloadFilePath='S1A_IW_GRDH_1SDV_20170221T225238_20170221T225303_015388_019405_9C41.SAFE',
geopandasDataFilePath='mekongReservoirs',
geoDataIndex=0,
dstPath='geotiff/1.tif')
'''
from snappy import jpy
from snappy import ProductIO
from snappy import GPF
from snappy import HashMap
s1meta = "manifest.safe"
s1product = "%s/%s" % (sarDownloadFilePath, s1meta)
reader = ProductIO.getProductReader("SENTINEL-1")
product = reader.readProductNodes(s1product, None)
parameters = HashMap()
borderRectInGeoCoor = Utils.getGeoDataBorder(geopandasDataFilePath, geoDataIndex)
subset = Utils.subset(product, borderRectInGeoCoor)
calibrate = Utils.calibrate(subset)
terrain = Utils.terrainCorrection(calibrate)
terrainDB = GPF.createProduct("LinearToFromdB", parameters, terrain)
speckle = Utils.speckleFilter(terrainDB)
if dstPath is None:
dstPath = sarImgPath[:-4] + '.tif'
ProductIO.writeProduct(speckle, dstPath, 'GeoTiff')
product.dispose()
subset.dispose()
calibrate.dispose()
terrain.dispose()
speckle.dispose()
del product, subset, calibrate, terrain, terrainDB, speckle
return dstPath
def getWaterBody(sarRaster):
'''
Get water body of reservoir from a raster
:type sarRaster: rasterio.io.DatasetReader (result of rasterio.open('filename'))
:param sarRaster: raster object when loading sentinel-1 .tif image by rasterio
:return: numpy array
'''
import rasterio
import numpy as np
from time import time
ds = sarRaster
band = ds.read()
waterMask = Utils.maskWater(band[0])
maskLake = Utils.maskOutLake(waterMask)
waterBodyBand = np.where(maskLake == 1, band, 0)
del maskLake, band, waterMask
return waterBodyBand
def getWaterBodyFromFile(sarImgDir):
'''
Get water body of reservoir from a tif image
:type sarImgDir: string
:param sarImgDir: directory to sentinel-1 .tif image
:return: numpy array
'''
import rasterio
import numpy as np
ds = rasterio.open(sarImgDir)
return getWaterBody(ds)
def getWaterBodyFromFileAndSave(sarImgDir, sarImgDst):
'''
Get water body of reservoir from a tif image and save result as a .tif image
:type sarImgDir: string
:type sarImgDst: string
:param sarImgDir: directory to sentinel-1 .tif image
:param sarImgDst: directory to water body image
:return: string - directory to water body image
'''
import rasterio
import numpy as np
ds = rasterio.open(sarImgDir)
profile = ds.profile
print('Getting water body')
waterBody = getWaterBody(ds)
with rasterio.open(sarImgDst, 'w', **profile) as dst:
dst.write(waterBody)
del waterBody, ds, dst
return sarImgDst
def mergeGeoTiff(sarImgDir1, sarImgDir2, sarImgDst):
'''
Function to merge two geotiff images
:type sarImgDir1: string
:type sarImgDir2: string
:type sarImgDst: string
:param sarImgDir1: directory to first sentinel-1 .tif image
:param sarImgDir2: directory to second sentinel-1 .tif image
:param sarImgDst: directory to destination of merged image
:return: string - directory to destination of merged image
'''
import rasterio
from rasterio.merge import merge
import numpy as np
ds1 = rasterio.open(sarImgDir1)
ds2 = rasterio.open(sarImgDir2)
dest, outTransform = merge([ds1, ds2])
profile = ds1.profile
profile['transform'] = outTransform
profile['height'] = dest.shape[1]
profile['width'] = dest.shape[2]
with rasterio.open(sarImgDst, 'w', **profile) as dst:
dst.write(dest)
return sarImgDst
def resize(sarImgDir, imgDst=None, maxSize=400):
'''
Get water body of reservoir from a tif image
:type sarImgDir: string
:type imgDst: string
:type maxSize: int
:param sarImgDir: directory to sentinel-1 .tif image
:param imgDst: directory to resized image. If none, use default name
:param maxSize: maximum value of width and height after resizing
:return: string - directory to resized image
'''
import math
from snappy import jpy
from snappy import ProductIO
from snappy import GPF
from snappy import HashMap
GPF.getDefaultInstance().getOperatorSpiRegistry().loadOperatorSpis()
HashMap = jpy.get_type('java.util.HashMap')
p = ProductIO.readProduct(sarImgDir)
firstBand = p.getBands()[0]
width = firstBand.getRasterWidth()
height = firstBand.getRasterHeight()
ratio = width/height
parameters = HashMap()
if ratio <= 1:
parameters.put('targetHeight', maxSize)
parameters.put('targetWidth', math.ceil(maxSize*ratio))
else:
parameters.put('targetWidth', maxSize)
parameters.put('targetHeight', math.ceil(maxSize/ratio))
product = GPF.createProduct('Resample', parameters, p)
if imgDst is None:
sourceName = imgDir.split('/')[:-4]
imgDst = sourceName + '_resized.tif'
ProductIO.writeProduct(product, imgDst, 'GeoTiff')
del p, product
return imgDst
def preprocessSarFile(sarDownloadFilePath, geopandasDataFilePath, geoDataIndex, dstPath=None):
'''
Get GeoTiff image from a .SAFE folder or a list of .SAFE foloder extracted after download
:type sarDownloadFilePath: string or list of string
:type geopandasDataFilePath: string
:type geoDataIndex: int
:type dstPath: string
:param sarDownloadFilePath: directory (or list of directory) of .SAFE folder(s)
:param geopandasDataFilePath: directory of geopandas dataframe file
:param geoDataIndex: geoDataIndex of data needed to retrieve in geopandas Dataframe
:param dstPath: directory of destination file, must have '.tif' extension
:return: string - directory to resized image
:example: sarHelpers.preprocessSarFile(sarDownloadFilePath=['SARData/S1A_IW_GRDH_1SDV_20170221T225238_20170221T225303_015388_019405_9C41.SAFE',
'SARData/S1A_IW_GRDH_1SDV_20170221T225303_20170221T225328_015388_019405_0815.SAFE'],
geopandasDataFilePath='GeoData/mekongReservoirs',
geoDataIndex=0,
dstPath='GeoTiff/out.tif')
sarHelpers.preprocessSarFile(sarDownloadFilePath='SARData/S1A_IW_GRDH_1SDV_20170221T225238_20170221T225303_015388_019405_9C41.SAFE',
geopandasDataFilePath='GeoData/mekongReservoirs',
geoDataIndex=0,
dstPath='GeoTiff1/out.tif')
'''
import subprocess
from time import time
filenamePrefix = dstPath[:-4]
rawImg = getGeoTiffImage(sarDownloadFilePath=sarDownloadFilePath,
geopandasDataFilePath=geopandasDataFilePath,
geoDataIndex=geoDataIndex,
dstPath=dstPath)
print('Raw Tiff saved at {0}'.format(rawImg))
waterBodyFilename = getWaterBodyFromFileAndSave(rawImg, filenamePrefix + '_waterBody.tif')
print('Water Body saved at {0}'.format(waterBodyFilename))