def getBoundingBox(fileList):
"""Return the bounding box for this data set in the format (minLon, maxLon, minLat, maxLat)"""
if len(fileList) == 2: # Read BB from the label file
return IrgGeoFunctions.getBoundingBoxFromIsisLabel(fileList[1])
else: # No label file, read it from the the main file
return IrgGeoFunctions.getImageBoundingBox(
fileList[0]
) # This information is also available in the IMG file header
def chooseLonCenter(self):
'''Choose whether to align to the 0 centered or 180 centered basemap'''
(minLon, maxLon, minLat, maxLat) = IrgGeoFunctions.getImageBoundingBox(self._inputHrscPaths[0])
meanLon = (minLon + maxLon) / 2 # TODO: Verify how these bounds work!
# Detect if the image is located nearby the 180 degree line
if (abs(abs(meanLon)-180) < 10) or (abs(maxLon - minLon) > 200):
return 180
else: # Use the normal 0 centered image
return 0
def chooseLonCenter(self):
'''Choose whether to align to the 0 centered or 180 centered basemap'''
(minLon, maxLon, minLat,
maxLat) = IrgGeoFunctions.getImageBoundingBox(self._inputHrscPaths[0])
meanLon = (minLon + maxLon) / 2 # TODO: Verify how these bounds work!
# Detect if the image is located nearby the 180 degree line
if (abs(abs(meanLon) - 180) < 10) or (abs(maxLon - minLon) > 200):
return 180
else: # Use the normal 0 centered image
return 0
def main():
outputPath = ''
try:
try:
usage = "usage: cube2kml.py [--help][--manual]\n "
parser = optparse.OptionParser(usage=usage)
parser.add_option("--manual",
action="callback",
callback=man,
help="Read the manual.")
parser.add_option(
"-o",
"--output-path",
dest="outputPath",
help="Output path (default replace extension with .kml")
(options, args) = parser.parse_args()
if not args: parser.error("need input cube file")
cubePath = args[0]
except optparse.OptionError, msg:
raise Usage(msg)
print "Beginning processing....."
# Determine the output path
if outputPath == '':
outputPath = os.path.splitext(
cubePath)[0] + '.kml' # Default output path
outputFolder = os.path.dirname(outputPath)
# Get the four corners of the cube
bb = IrgGeoFunctions.getImageBoundingBox(cubePath)
# Generate a kml plot of the cube
generateKml(bb, outputPath)
print "Finished"
return 0
def main():
outputPath = ""
try:
try:
usage = "usage: cube2kml.py [--help][--manual]\n "
parser = optparse.OptionParser(usage=usage)
parser.add_option("--manual", action="callback", callback=man, help="Read the manual.")
parser.add_option(
"-o", "--output-path", dest="outputPath", help="Output path (default replace extension with .kml"
)
(options, args) = parser.parse_args()
if not args:
parser.error("need input cube file")
cubePath = args[0]
except optparse.OptionError, msg:
raise Usage(msg)
print "Beginning processing....."
# Determine the output path
if outputPath == "":
outputPath = os.path.splitext(cubePath)[0] + ".kml" # Default output path
outputFolder = os.path.dirname(outputPath)
# Get the four corners of the cube
bb = IrgGeoFunctions.getImageBoundingBox(cubePath)
# Generate a kml plot of the cube
generateKml(bb, outputPath)
print "Finished"
return 0
def writeLabelFile(imagePath, outputPath, dataSetName, versionId, description, extraData=None):
"""Write out a .LBL file formatted for the PDS"""
# Call functions to automatically obtain some data from the referenced image
imageSize = IrgGeoFunctions.getImageSize(imagePath)
boundingBox = IrgGeoFunctions.getImageBoundingBox(imagePath)
# Obtain the ASP version string
aspVersionString = IrgAspFunctions.getAspVersionStrings()
imageGeoInfo = IrgGeoFunctions.getImageGeoInfo(imagePath)
projCenterLatitude = imageGeoInfo['standard_parallel_1']
projCenterLongitude = imageGeoInfo['central_meridian']
# Currently assuming pixels are the same size
metersPerPixel = abs(imageGeoInfo['pixel size'][0])
# Compute pixels per degree
lonSpanDegrees = boundingBox[1] - boundingBox[0]
latSpanDegrees = boundingBox[3] - boundingBox[2]
pixelsPerDegreeLon = imageSize[0] / lonSpanDegrees
pixelsPerDegreeLat = imageSize[1] / latSpanDegrees
pixelsPerDegree = (pixelsPerDegreeLat + pixelsPerDegreeLon) / 2.0
# Computed by dividing 'Origin' by 'Pixel Size'
lineProjOffset = imageGeoInfo['origin'][0] / imageGeoInfo['pixel size'][1]
sampleProjOffset = imageGeoInfo['origin'][1] / imageGeoInfo['pixel size'][0]
labelFile = open(outputPath, 'w')
labelFile.write('PDS_VERSION_ID = PDS3\n')
labelFile.write('/* The source image data definition. */\n')
labelFile.write('^IMAGE = ' + os.path.basename(outputPath) +'\n')
labelFile.write('/* Identification Information */\n')
labelFile.write('DATA_SET_ID = ""\n') # Someone will tell us what to put here
labelFile.write('DATA_SET_NAME = ""\n') # Someone will tell us what to put here
labelFile.write("VOLUME_ID = ''\n") # Someone will tell us what to put here
labelFile.write('PRODUCER_INSTITUTION_NAME = "NASA AMES RESEARCH CENTER"\n')
labelFile.write('PRODUCER_ID = NASA IRG\n')
labelFile.write('PRODUCER_FULL_NAME = "ZACHARY MORATTO"\n')
labelFile.write("PRODUCT_ID = " + dataSetName + "\n")
labelFile.write("PRODUCT_VERSION_ID = " + versionId + "\n")
labelFile.write('PRODUCT_TYPE = "RDR"\n')
labelFile.write('INSTRUMENT_HOST_NAME = "LUNAR RECONNAISSANCE ORBITER"\n')
labelFile.write('INSTRUMENT_HOST_ID = "LRO"\n')
labelFile.write('INSTRUMENT_NAME = "LUNAR RECONNAISSANCE ORBITER CAMERA"\n')
labelFile.write('INSTRUMENT_ID = "LROC"\n')
labelFile.write('TARGET_NAME = MOON\n')
labelFile.write('MISSION_PHASE_NAME = "NOMINAL MISSION"\n')
labelFile.write("""RATIONALE_DESC = "Created at the request of NASA's Exploration\n""")
labelFile.write(' Systems Mission Directorate to support future\n')
labelFile.write(' human exploration"\n')
labelFile.write('SOFTWARE_NAME = "'+ aspVersionString[0] +' | '+ aspVersionString[2] +'"\n')
labelFile.write('DESCRIPTION = "' + description + '"\n')
labelFile.write('\n')
labelFile.write('/* Time Parameters */\n')
labelFile.write('PRODUCT_CREATION_TIME = ' + time.strftime("%Y-%m-%dT%H:%M:%S") + '\n')
labelFile.write('\n')
labelFile.write('/* NOTE: */\n')
labelFile.write('/* This raster image is composed of a set of pixels that represent finite */\n')
labelFile.write('/* areas, and not discrete points. The center of the upper left pixel is */\n')
labelFile.write('/* defined as line and sample (1.0,1.0). The */\n')
labelFile.write('/* [LINE,SAMPLE]_PROJECTION_OFFSET elements are the pixel offset from line */\n')
labelFile.write('/* and sample (1.0,1.0) to the map projection origin (defined by the */\n')
labelFile.write('/* CENTER_LATITUDE and CENTER_LONGITUDE elements). These offset values */\n')
labelFile.write('/* are positive when the map projection origin is to the right or below */\n')
labelFile.write('/* the center of the upper left pixel. */\n')
if extraData: # Location for additional notes
labelFile.write(extraData)
labelFile.write('\n')
labelFile.write('OBJECT = IMAGE_MAP_PROJECTION\n')
labelFile.write(' MAP_PROJECTION_TYPE = EQUIRECTANGULAR\n') # Specified by +proj=eqc
labelFile.write(' PROJECTION_LATITUDE_TYPE = PLANETOCENTRIC\n') #From gdalinfo?
labelFile.write(' A_AXIS_RADIUS = 1737.4 <KM>\n') # Fixed lunar radius
labelFile.write(' B_AXIS_RADIUS = 1737.4 <KM>\n')
labelFile.write(' C_AXIS_RADIUS = 1737.4 <KM>\n')
labelFile.write(' COORDINATE_SYSTEM_NAME = PLANETOCENTRIC\n') #From gdalinfo?
labelFile.write(' POSITIVE_LONGITUDE_DIRECTION = EAST\n') #From gdalinfo?
labelFile.write(' KEYWORD_LATITUDE_TYPE = PLANETOCENTRIC\n') #From gdalinfo?
labelFile.write(' /* NOTE: CENTER_LATITUDE and CENTER_LONGITUDE describe the location */\n')
labelFile.write(' /* of the center of projection, which is not necessarily equal to the */\n')
labelFile.write(' /* location of the center point of the image. */\n')
labelFile.write(' CENTER_LATITUDE = ' + str(projCenterLatitude) + ' <DEG>\n')
labelFile.write(' CENTER_LONGITUDE = ' + str(projCenterLongitude) + ' <DEG>\n')
labelFile.write(' LINE_FIRST_PIXEL = 1\n')
labelFile.write(' LINE_LAST_PIXEL = ' + str(imageSize[1] + 1) + '\n')
labelFile.write(' SAMPLE_FIRST_PIXEL = 1\n')
labelFile.write(' SAMPLE_LAST_PIXEL = ' + str(imageSize[0] + 1) + '\n')
labelFile.write(' MAP_PROJECTION_ROTATION = 0.0 <DEG>\n') #From gdalinfo (probably always zero)
labelFile.write(' MAP_RESOLUTION = ' + str(round(pixelsPerDegree,2)) +' <PIX/DEG>\n')
labelFile.write(' MAP_SCALE = ' + str(round(metersPerPixel,4)) + ' <METERS/PIXEL>\n')
labelFile.write(' MAXIMUM_LATITUDE = ' + str(boundingBox[3]) + ' <DEG>\n')
labelFile.write(' MINIMUM_LATITUDE = ' + str(boundingBox[2]) + ' <DEG>\n')
labelFile.write(' EASTERNMOST_LONGITUDE = ' + str(boundingBox[0]) + ' <DEG>\n')
labelFile.write(' WESTERNMOST_LONGITUDE = ' + str(boundingBox[1]) + ' <DEG>\n')
labelFile.write(' LINE_PROJECTION_OFFSET = ' + str(round(lineProjOffset,2)) +' <PIXEL>\n')
labelFile.write(' SAMPLE_PROJECTION_OFFSET = ' + str(round(sampleProjOffset,2)) +' <PIXEL>\n')
labelFile.write('END_OBJECT = IMAGE_MAP_PROJECTION\n')
labelFile.write('\n')
labelFile.write('END\n')
labelFile.close()
return True
def getBoundingBox(fileList):
"""Return the bounding box for this data set in the format (minLon, maxLon, minLat, maxLat)"""
if len(fileList) == 2: # Read BB from the label file
return IrgGeoFunctions.getBoundingBoxFromIsisLabel(fileList[1])
else: # No label file, read it from the the main file
return IrgGeoFunctions.getImageBoundingBox(fileList[0]) # This information is also available in the IMG file header
def __init__(self, sourceFileInfoDict, outputFolder,
basemapInstance, basemapInstance180,
force=False, threadPool=None):
'''Set up all the low resolution HRSC products.'''
setName = sourceFileInfoDict['setName']
self._logger = logging.getLogger('hrscImageManager')
# Echo logging to stdout
echo = logging.StreamHandler(sys.stdout)
echo.setLevel(logging.DEBUG)
echo.setFormatter(logging.Formatter(MosaicUtilities.LOG_FORMAT_STR))
self._logger.addHandler(echo)
self._logger.info('Initializing hrscImageManager for set ' + setName)
# Initialize some values to empty in case they are accessed prematurely
self._tileDict = None #
# Set up some paths
self._setName = setName
self._threadPool = threadPool
self._outputFolder = outputFolder
self._hrscBasePathOut = os.path.join(outputFolder, setName)
self._tileFolder = self._hrscBasePathOut + '_tiles'
self._lowResMaskPath = self._hrscBasePathOut + '_low_res_mask.tif'
self._highResBinaryMaskPath = self._hrscBasePathOut + '_high_res_binary_mask.tif'
self._highResMaskPath = self._hrscBasePathOut + '_high_res_mask.tif'
self._brightnessGainsPath = self._hrscBasePathOut + '_brightness_gains.csv'
self._basemapCropPath = self._hrscBasePathOut + '_local_cropped_basemap.tif' # A crop of the basemap used in several places
self._basemapGrayCropPath = self._hrscBasePathOut + '_local_gray_cropped_basemap.tif'
#self._colorPairPath = self._hrscBasePathOut + '_low_res_color_pairs.csv'
self._basemapSpatialRegistrationPath = self._hrscBasePathOut + '_low_res_spatial_transform_basemap.csv' # Transform to the low res basemap
self._croppedRegionSpatialRegistrationPath = self._hrscBasePathOut + '_cropped_region_spatial_transform.csv' # Transform to cropped region of low res basemap
self._highResSpatialRegistrationPath = self._hrscBasePathOut + '_high_res_spatial_transform_basemap.csv'
self._lowResSpatialCroppedRegistrationPath = self._hrscBasePathOut + '_low_res_cropped_spatial_transform.csv'
# Get full list of input paths from the input dictionary
# - Sort them into a fixed order defined at the top of the file
self._inputHrscPaths = []
rawList = sourceFileInfoDict['allChannelPaths']
self._inputHrscPaths.append( [s for s in rawList if 're3' in s][0] )
self._inputHrscPaths.append( [s for s in rawList if 'gr3' in s][0] )
self._inputHrscPaths.append( [s for s in rawList if 'bl3' in s][0] )
self._inputHrscPaths.append( [s for s in rawList if 'ir3' in s][0] )
self._inputHrscPaths.append( [s for s in rawList if 'nd3' in s][0] )
# TODO: Always store path to regular basemap?
# Determine if a 180-centered basemap should be used for image preprocessing.
self._isCentered180 = (self.chooseLonCenter() == 180)
if self._isCentered180:
self._logger.info('HRSC image is centered around 180')
self._basemapInstance = basemapInstance180
else: # Normal case, use the 0 centered basemap
self._basemapInstance = basemapInstance
# Record input parameters
self._basemapColorPath = self._basemapInstance.getColorBasemapPath() # Path to the color low res entire base map
print 'Generating low res image copies...'
# TODO: Warp to the correct basemap!
# Generate a copy of each input HRSC channel at the low basemap resolution
self._lowResWarpedPaths = [self._warpToProjection(path, outputFolder, '_basemap_res',
self._basemapInstance.getLowResMpp(), force)
for path in self._inputHrscPaths]
# Build up a string containing all the low res paths for convenience
self._lowResPathString = ''
for path in self._lowResWarpedPaths:
self._lowResPathString += path + ' '
print 'Generating low resolution mask...'
# Make a mask at the low resolution
cmd = './makeSimpleImageMask ' + self._lowResMaskPath +' '+ self._lowResPathString
MosaicUtilities.cmdRunner(cmd, self._lowResMaskPath, force)
self._lowResPathStringAndMask = self._lowResPathString +' '+ self._lowResMaskPath
self._lowResMaskImageSize = IrgGeoFunctions.getImageSize(self._lowResMaskPath)
# Compute the HRSC bounding box
# - This is a pretty good estimate based on the metadata
lowResNadirPath = self._lowResWarpedPaths[HRSC_NADIR]
geoInfo = IrgGeoFunctions.getImageGeoInfo(lowResNadirPath)
#print geoInfo['projection_bounds']
# print geoInfo['lonlat_bounds']
if 'lonlat_bounds' in geoInfo:
(minLon, maxLon, minLat, maxLat) = geoInfo['lonlat_bounds']
else: # This function is not as reliable!
(minLon, maxLon, minLat, maxLat) = IrgGeoFunctions.getImageBoundingBox(lowResNadirPath)
hrscBoundingBoxDegrees = MosaicUtilities.Rectangle(minLon, maxLon, minLat, maxLat)
if hrscBoundingBoxDegrees.maxX < hrscBoundingBoxDegrees.minX:
hrscBoundingBoxDegrees.maxX += 360 # If needed, get both lon values into 0-360 degree range
if (hrscBoundingBoxDegrees.minX < 0) and self._isCentered180:
# If working in the 0-360 degree space, make sure the longitude values are positive
hrscBoundingBoxDegrees.minX += 360
hrscBoundingBoxDegrees.maxX += 360
print 'Estimated HRSC bounds: ' + str(hrscBoundingBoxDegrees)
# Cut out a region from the basemap around the location of the HRSC image
# - We record the ROI in degrees and low res pixels
print 'Generating low res base basemap region around HRSC data'
CROP_BUFFER_LAT = 1.0
CROP_BUFFER_LON = 1.0
self._croppedRegionBoundingBoxDegrees = copy.copy(hrscBoundingBoxDegrees)
self._croppedRegionBoundingBoxDegrees.expand(CROP_BUFFER_LON, CROP_BUFFER_LAT)
self._croppedRegionBoundingBoxPixels = self._basemapInstance.degreeRoiToPixelRoi(
self._croppedRegionBoundingBoxDegrees, False)
self._basemapInstance.makeCroppedRegionDegrees(self._croppedRegionBoundingBoxDegrees,
self._basemapCropPath, force)
self._makeGrayscaleImage(self._basemapCropPath, self._basemapGrayCropPath)
# Compute the spatial registration from the HRSC image to the base map
self._computeBaseSpatialRegistration(self._basemapInstance, lowResNadirPath, force)
# Compute the brightness scaling gains relative to the cropped base map
# - This is done at low resolution
# - The low resolution output is smoothed out later to avoid jagged edges.
cmd = ('./computeBrightnessCorrection ' + self._basemapCropPath +' '+ self._lowResPathStringAndMask +' '
+ self._lowResSpatialCroppedRegistrationPath +' '+ self._brightnessGainsPath)
MosaicUtilities.cmdRunner(cmd, self._brightnessGainsPath, force)
print 'Finished with low resolution processing for HRSC set ' + setName
def getBoundingBox(fileList):
"""Return the bounding box for this data set in the format (minLon, maxLon, minLat, maxLat)"""
return IrgGeoFunctions.getImageBoundingBox(fileList[0])
def __init__(self,
sourceFileInfoDict,
outputFolder,
basemapInstance,
basemapInstance180,
force=False,
threadPool=None):
'''Set up all the low resolution HRSC products.'''
setName = sourceFileInfoDict['setName']
self._logger = logging.getLogger('hrscImageManager')
# Echo logging to stdout
echo = logging.StreamHandler(sys.stdout)
echo.setLevel(logging.DEBUG)
echo.setFormatter(logging.Formatter(MosaicUtilities.LOG_FORMAT_STR))
self._logger.addHandler(echo)
self._logger.info('Initializing hrscImageManager for set ' + setName)
# Initialize some values to empty in case they are accessed prematurely
self._tileDict = None #
# Set up some paths
self._setName = setName
self._threadPool = threadPool
self._outputFolder = outputFolder
self._hrscBasePathOut = os.path.join(outputFolder, setName)
self._tileFolder = self._hrscBasePathOut + '_tiles'
self._lowResMaskPath = self._hrscBasePathOut + '_low_res_mask.tif'
self._highResBinaryMaskPath = self._hrscBasePathOut + '_high_res_binary_mask.tif'
self._highResMaskPath = self._hrscBasePathOut + '_high_res_mask.tif'
self._brightnessGainsPath = self._hrscBasePathOut + '_brightness_gains.csv'
self._basemapCropPath = self._hrscBasePathOut + '_local_cropped_basemap.tif' # A crop of the basemap used in several places
self._basemapGrayCropPath = self._hrscBasePathOut + '_local_gray_cropped_basemap.tif'
#self._colorPairPath = self._hrscBasePathOut + '_low_res_color_pairs.csv'
self._basemapSpatialRegistrationPath = self._hrscBasePathOut + '_low_res_spatial_transform_basemap.csv' # Transform to the low res basemap
self._croppedRegionSpatialRegistrationPath = self._hrscBasePathOut + '_cropped_region_spatial_transform.csv' # Transform to cropped region of low res basemap
self._highResSpatialRegistrationPath = self._hrscBasePathOut + '_high_res_spatial_transform_basemap.csv'
self._lowResSpatialCroppedRegistrationPath = self._hrscBasePathOut + '_low_res_cropped_spatial_transform.csv'
# Get full list of input paths from the input dictionary
# - Sort them into a fixed order defined at the top of the file
self._inputHrscPaths = []
rawList = sourceFileInfoDict['allChannelPaths']
self._inputHrscPaths.append([s for s in rawList if 're3' in s][0])
self._inputHrscPaths.append([s for s in rawList if 'gr3' in s][0])
self._inputHrscPaths.append([s for s in rawList if 'bl3' in s][0])
self._inputHrscPaths.append([s for s in rawList if 'ir3' in s][0])
self._inputHrscPaths.append([s for s in rawList if 'nd3' in s][0])
# TODO: Always store path to regular basemap?
# Determine if a 180-centered basemap should be used for image preprocessing.
self._isCentered180 = (self.chooseLonCenter() == 180)
if self._isCentered180:
self._logger.info('HRSC image is centered around 180')
self._basemapInstance = basemapInstance180
else: # Normal case, use the 0 centered basemap
self._basemapInstance = basemapInstance
# Record input parameters
self._basemapColorPath = self._basemapInstance.getColorBasemapPath(
) # Path to the color low res entire base map
print 'Generating low res image copies...'
# TODO: Warp to the correct basemap!
# Generate a copy of each input HRSC channel at the low basemap resolution
self._lowResWarpedPaths = [
self._warpToProjection(path, outputFolder, '_basemap_res',
self._basemapInstance.getLowResMpp(), force)
for path in self._inputHrscPaths
]
# Build up a string containing all the low res paths for convenience
self._lowResPathString = ''
for path in self._lowResWarpedPaths:
self._lowResPathString += path + ' '
print 'Generating low resolution mask...'
# Make a mask at the low resolution
cmd = './makeSimpleImageMask ' + self._lowResMaskPath + ' ' + self._lowResPathString
MosaicUtilities.cmdRunner(cmd, self._lowResMaskPath, force)
self._lowResPathStringAndMask = self._lowResPathString + ' ' + self._lowResMaskPath
self._lowResMaskImageSize = IrgGeoFunctions.getImageSize(
self._lowResMaskPath)
# Compute the HRSC bounding box
# - This is a pretty good estimate based on the metadata
lowResNadirPath = self._lowResWarpedPaths[HRSC_NADIR]
geoInfo = IrgGeoFunctions.getImageGeoInfo(lowResNadirPath)
#print geoInfo['projection_bounds']
# print geoInfo['lonlat_bounds']
if 'lonlat_bounds' in geoInfo:
(minLon, maxLon, minLat, maxLat) = geoInfo['lonlat_bounds']
else: # This function is not as reliable!
(minLon, maxLon, minLat,
maxLat) = IrgGeoFunctions.getImageBoundingBox(lowResNadirPath)
hrscBoundingBoxDegrees = MosaicUtilities.Rectangle(
minLon, maxLon, minLat, maxLat)
if hrscBoundingBoxDegrees.maxX < hrscBoundingBoxDegrees.minX:
hrscBoundingBoxDegrees.maxX += 360 # If needed, get both lon values into 0-360 degree range
if (hrscBoundingBoxDegrees.minX < 0) and self._isCentered180:
# If working in the 0-360 degree space, make sure the longitude values are positive
hrscBoundingBoxDegrees.minX += 360
hrscBoundingBoxDegrees.maxX += 360
print 'Estimated HRSC bounds: ' + str(hrscBoundingBoxDegrees)
# Cut out a region from the basemap around the location of the HRSC image
# - We record the ROI in degrees and low res pixels
print 'Generating low res base basemap region around HRSC data'
CROP_BUFFER_LAT = 1.0
CROP_BUFFER_LON = 1.0
self._croppedRegionBoundingBoxDegrees = copy.copy(
hrscBoundingBoxDegrees)
self._croppedRegionBoundingBoxDegrees.expand(CROP_BUFFER_LON,
CROP_BUFFER_LAT)
self._croppedRegionBoundingBoxPixels = self._basemapInstance.degreeRoiToPixelRoi(
self._croppedRegionBoundingBoxDegrees, False)
self._basemapInstance.makeCroppedRegionDegrees(
self._croppedRegionBoundingBoxDegrees, self._basemapCropPath,
force)
self._makeGrayscaleImage(self._basemapCropPath,
self._basemapGrayCropPath)
# Compute the spatial registration from the HRSC image to the base map
self._computeBaseSpatialRegistration(self._basemapInstance,
lowResNadirPath, force)
# Compute the brightness scaling gains relative to the cropped base map
# - This is done at low resolution
# - The low resolution output is smoothed out later to avoid jagged edges.
cmd = ('./computeBrightnessCorrection ' + self._basemapCropPath + ' ' +
self._lowResPathStringAndMask + ' ' +
self._lowResSpatialCroppedRegistrationPath + ' ' +
self._brightnessGainsPath)
MosaicUtilities.cmdRunner(cmd, self._brightnessGainsPath, force)
print 'Finished with low resolution processing for HRSC set ' + setName
