def getBoundingBox(fileList):
"""Return the bounding box for this data set in the format (minLon, maxLon, minLat, maxLat)"""
if len(fileList) == 3: # ASU method
# Read plain text from the label file
return IrgGeoFunctions.getBoundingBoxFromIsisLabel(fileList[-1])
else: # Compute the corners from a geotiff
return IrgGeoFunctions.getGeoTiffBoundingBox(fileList[0])
def getBoundingBox(fileList):
"""Return the bounding box for this data set in the format (minLon, maxLon, minLat, maxLat)"""
if len(fileList) == 3: # ASU method
# Read plain text from the label file
return IrgGeoFunctions.getBoundingBoxFromIsisLabel(fileList[-1])
else: # Compute the corners from a geotiff
return IrgGeoFunctions.getGeoTiffBoundingBox(fileList[0])
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 writeColorMapInfo(colormapPath, lutFilePath, demPath, outputPath):
"""Writes a file containing the color map information"""
colormapPercentiles = []
numColorSteps = IrgFileFunctions.getFileLineCount(lutFilePath)
for i in range(0,numColorSteps): # This loop generates the percentage values from the color profile we are using
colormapPercentiles.append(i / float(numColorSteps-1))
# Get the min and max elevation of the DEM
elevationBounds = IrgGeoFunctions.getImageStats(demPath)
# Get elevation values for each percentile
colormapPercentileValues = IrgMathFunctions.getPercentileValues(elevationBounds[0][0], elevationBounds[0][1], colormapPercentiles)
# Now write out a version of the LUT file with values added
inputFile = open(lutFilePath, 'r')
outputFile = open(outputPath, 'w')
# Add a header line to the output file
outputFile.write('Percent of range, R, G, B, Elevation (meters above datum)\n')
# Write a copy of the input file with the elevation values appended to each line
for (line, value) in zip(inputFile, colormapPercentileValues):
newLine = line[:-1] + ', ' + str(round(value,2)) + '\n'
outputFile.write(newLine)
inputFile.close()
outputFile.close()
def correctPixelCoordinates(registrationResult):
'''Rescales the pixel coordinates based on the resolution they were collected at
compared to the full image resolution.'''
# TODO: Account for the image labels adjusting the image size!
sourceHeight = registrationResult['manualImageHeight']
sourceWidth = registrationResult['manualImageWidth']
(outputWidth, outputHeight) = IrgGeoFunctions.getImageSize(
registrationResult['sourceImagePath'])
if (sourceHeight != outputHeight) or (sourceWidth != outputWidth):
# Compute rescale
heightScale = float(outputHeight) / float(sourceHeight)
widthScale = float(outputWidth) / float(sourceWidth)
# Apply to each of the pixel coordinates
out = []
for pixel in registrationResult['imageInliers']:
newPixel = (pixel[0] * widthScale, pixel[1] * heightScale)
out.append(newPixel)
registrationResult['imageInliers'] = out
return registrationResult
def getPixelToGdcTransform(imagePath, pixelToProjectedTransform=None):
'''Returns a pixel to GDC transform.
The input image must either be a nicely georegistered image from Earth Engine
or a pixel to projected coordinates transform must be provided.'''
if pixelToProjectedTransform:
# Have image to projected transform, convert it to an image to GDC transform.
# Use the simple file info call (the input file may not have geo information)
(width, height) = IrgGeoFunctions.getImageSize(imagePath)
imagePoints = []
gdcPoints = []
# Loop through a spaced out grid of pixels in the image
pointPixelSpacing = (width +
height) / 20 # Results in about 100 points
for r in range(0, width, pointPixelSpacing):
for c in range(0, height, pointPixelSpacing):
# This pixel --> projected coords --> lonlat coord
thisPixel = numpy.array([float(c), float(r)])
projectedCoordinate = pixelToProjectedTransform.forward(
thisPixel)
gdcCoordinate = transform.metersToLatLon(projectedCoordinate)
imagePoints.append(thisPixel)
gdcPoints.append(gdcCoordinate)
# Solve for a transform with all of these point pairs
pixelToGdcTransform = transform.getTransform(
numpy.asarray(gdcPoints), numpy.asarray(imagePoints))
else: # Using a reference image from EE which will have nice bounds.
# Use the more thorough file info call
stats = IrgGeoFunctions.getImageGeoInfo(imagePath, False)
(width, height) = stats['image_size']
(minLon, maxLon, minLat, maxLat) = stats['lonlat_bounds']
# Make a transform from ref pixel to GDC using metadata on disk
xScale = (maxLon - minLon) / width
yScale = (maxLat - minLat) / height
transformMatrix = numpy.array([[xScale, 0, minLon],
[0, -yScale, maxLat], [0, 0, 1]])
pixelToGdcTransform = transform.LinearTransform(transformMatrix)
return pixelToGdcTransform
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 _estimateRegistration(self, baseImage, otherImage, outputPath):
'''Writes an estimated registration transform to a file based on geo metadata.'''
# This function assumes the images are in the same projection system!
# Get the projection bounds and size in both images
baseGeoInfo = IrgGeoFunctions.getImageGeoInfo(baseImage, False)
otherGeoInfo = IrgGeoFunctions.getImageGeoInfo(otherImage, False)
baseProjBounds = baseGeoInfo[ 'projection_bounds']
otherProjBounds = otherGeoInfo['projection_bounds']
baseImageSize = baseGeoInfo[ 'image_size']
otherImageSize = otherGeoInfo['image_size']
# Now estimate the bounding box of the other image in the base image
topLeftCoord = projCoordToPixelCoord(otherProjBounds[0], otherProjBounds[3], baseGeoInfo)
transform = MosaicUtilities.SpatialTransform()
transform.setShift(topLeftCoord[0], topLeftCoord[1])
transform.write(outputPath)
return topLeftCoord
def _estimateRegistration(self, baseImage, otherImage, outputPath):
'''Writes an estimated registration transform to a file based on geo metadata.'''
# This function assumes the images are in the same projection system!
# Get the projection bounds and size in both images
baseGeoInfo = IrgGeoFunctions.getImageGeoInfo(baseImage, False)
otherGeoInfo = IrgGeoFunctions.getImageGeoInfo(otherImage, False)
baseProjBounds = baseGeoInfo['projection_bounds']
otherProjBounds = otherGeoInfo['projection_bounds']
baseImageSize = baseGeoInfo['image_size']
otherImageSize = otherGeoInfo['image_size']
# Now estimate the bounding box of the other image in the base image
topLeftCoord = projCoordToPixelCoord(otherProjBounds[0],
otherProjBounds[3], baseGeoInfo)
transform = MosaicUtilities.SpatialTransform()
transform.setShift(topLeftCoord[0], topLeftCoord[1])
transform.write(outputPath)
return topLeftCoord
def cropImageLabel(jpegPath, outputPath):
'''Create a copy of a jpeg file with any label cropped off'''
# Check if there is a label using a simple command line tool
cmdPath = settings.PROJ_ROOT + '/apps/georef_imageregistration/build/detectImageTag'
cmd = [cmdPath, jpegPath]
print cmd
p = subprocess.Popen(cmd, stdout=subprocess.PIPE)
textOutput, err = p.communicate()
if 'NO_LABEL' in textOutput:
# The file is fine, just copy it.
print 'Copy ' + jpegPath + ' --> ' + outputPath
try:
shutil.copy(jpegPath, outputPath)
except:
print 'Copy failed, try again!'
# shutil.copy(jpegPath, outputPath)
os.system('cp ' + jpegPath + ' ' + outputPath)
if not os.path.exists(outputPath):
raise Exception('Still failed!')
print 'Retry successful!'
else:
lines = textOutput.strip().split(
'\n') # Get the parts of the last line
parts = lines[-1].split()
if len(parts) != 3:
raise Exception('Error running detectImageTag, got response: ' +
textOutput)
side = parts[1]
labelPos = int(parts[2])
print 'Detected image label: ' + side + ' at index ' + str(labelPos)
# Trim the label off of the bottom of the image
imageSize = IrgGeoFunctions.getImageSize(jpegPath)
x = 0
y = 0
width = imageSize[0]
height = imageSize[1]
if side == 'LEFT':
x = labelPos
width = width - labelPos
if side == 'RIGHT':
width = labelPos
if side == 'TOP':
y = labelPos
height = height - labelPos
if side == 'BOTTOM':
height = labelPos
cmd = ('gdal_translate -of jpeg -srcwin %d %d %d %d %s %s' %
(x, y, width, height, jpegPath, outputPath))
print cmd
os.system(cmd)
def recordOutputImages(sourceImagePath,
exifSourcePath,
outputPrefix,
imageInliers,
gdcInliers,
minUncertaintyMeters,
centerPointSource,
isManualRegistration=False,
overwrite=True):
'''Generates all the output image files that we create for each successfully processed image.'''
# We generate two pairs of images, one containing the image data
# and another with the same format but containing the uncertainty distances.
outputPrefix = outputPrefix + '-' + centerPointSource
uncertaintyOutputPrefix = outputPrefix + '-uncertainty'
rawUncertaintyPath = outputPrefix + '-uncertainty_raw.tif'
# Create the raw uncertainty image
(width, height) = IrgGeoFunctions.getImageSize(sourceImagePath)
posError = generateUncertaintyImage(width, height, imageInliers,
minUncertaintyMeters,
rawUncertaintyPath)
# Get a measure of the fit error
fitError = getFitError(imageInliers, gdcInliers)
# Generate the two pairs of images in the same manner
try:
(noWarpOutputPath, warpOutputPath) = \
generateGeotiff(sourceImagePath, outputPrefix, imageInliers, gdcInliers,
posError, fitError, isManualRegistration,
exifSourcePath,
writeHeaders=True, overwrite=True)
except Exception as e:
print str(e)
try:
(noWarpOutputPath, warpOutputPath) = \
generateGeotiff(rawUncertaintyPath, uncertaintyOutputPrefix, imageInliers, gdcInliers,
posError, fitError, isManualRegistration,
exifSourcePath,
writeHeaders=False, overwrite=True)
except Exception as e:
print str(e)
# Clean up the raw uncertainty image and any extraneous files
rawXmlPath = rawUncertaintyPath + '.aux.xml'
os.remove(rawUncertaintyPath)
if os.path.exists(rawXmlPath):
os.remove(rawXmlPath)
def splitImageGdal(imagePath, outputPrefix, tileSize, force=False, pool=None, maskList=[]):
'''gdal_translate based replacement for the ImageMagick convert based image split.
This function assumes that all relevant folders have been created.'''
# Compute the bounding box for each tile
inputImageSize = IrgGeoFunctions.getImageSize(imagePath)
#print imagePath + ' is size ' + str(inputImageSize)
numTilesX = int(math.ceil(float(inputImageSize[0]) / float(tileSize)))
numTilesY = int(math.ceil(float(inputImageSize[1]) / float(tileSize)))
print 'Using gdal_translate to generate ' + str(int(numTilesX*numTilesY)) + ' tiles!'
# Generate each of the tiles using GDAL
cmdList = []
for r in range(0,numTilesY):
for c in range(0,numTilesX):
if maskList: # Make sure this tile is not completely masked out
# Find the info for the corresponding mask tile
# If the image is larger than the mask, there is a chance there will be no
# mask tile for this image tile. If so we can still skip the image tile,,
tilePrefix = getTilePrefix(r, c)
maskTileInfo = [x for x in maskList if x['prefix'] == tilePrefix]
if not maskTileInfo or (maskTileInfo[0]['percentValid'] < MIN_TILE_PERCENT_PIXELS_VALID):
continue
# Get the pixel ROI for this tile
# - TODO: Use the Tiling class!
minCol = c*tileSize
minRow = r*tileSize
width = tileSize
height = tileSize
if (minCol + width ) > inputImageSize[0]: width = inputImageSize[0] - minCol
if (minRow + height) > inputImageSize[1]: height = inputImageSize[1] - minRow
totalNumPixels = height*width
# Generate the tile (console output suppressed)
thisPixelRoi = ('%d %d %d %d' % (minCol, minRow, width, height))
thisTilePath = outputPrefix + str(r) +'_'+ str(c) + '.tif'
cmd = MosaicUtilities.GDAL_TRANSLATE_PATH+' -q -srcwin ' + thisPixelRoi +' '+ imagePath +' '+ thisTilePath
if pool:
#print cmd
cmdList.append((cmd, thisTilePath, force))
else:
MosaicUtilities.cmdRunner(cmd, thisTilePath, force)
if pool:
# Pass all of these commands to a multiprocessing worker pool
print 'splitImageGdal is launching '+str(len(cmdList))+' gdalwarp threads...'
pool.map(MosaicUtilities.cmdRunnerWrapper, cmdList)
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 generateTileInfo(fullPath, fileName, tileSize, metadataPath, force=False):
'''Generates a metadata json file for an image tile'''
# If the metadata is saved, just reload it.
if (os.path.exists(metadataPath) and not force):
with open(metadataPath, 'r') as f:
thisTileInfo = json.load(f)
return thisTileInfo
# Figure out the position of the tile
numbers = re.findall(r"[\d']+",
fileName) # Extract all numbers from the file name
tileRow = int(numbers[-2]) # In the tile grid
tileCol = int(numbers[-1])
pixelRow = tileRow * tileSize # In pixel coordinates relative to the original image
pixelCol = tileCol * tileSize
# TODO: Counting the black pixels is a little slow, run this in parallel!
# Get other tile information
width, height = IrgGeoFunctions.getImageSize(fullPath)
totalNumPixels = height * width
blackPixelCount = MosaicUtilities.countBlackPixels(fullPath)
validPercentage = 1.0 - (float(blackPixelCount) / float(totalNumPixels))
thisTileInfo = {
'path': fullPath,
'tileRow': tileRow,
'tileCol': tileCol,
'pixelRow': pixelRow,
'pixelCol': pixelCol,
'heightPixels': height,
'widthPixels': width,
'percentValid': validPercentage,
'prefix': getTilePrefix(tileRow, tileCol)
}
# Cache the metadata to disk so we don't have to recompute
with open(metadataPath, 'w') as f:
json.dump(thisTileInfo, f)
return thisTileInfo
def generateTileInfo(fullPath, fileName, tileSize, metadataPath, force=False):
'''Generates a metadata json file for an image tile'''
# If the metadata is saved, just reload it.
if (os.path.exists(metadataPath) and not force):
with open(metadataPath, 'r') as f:
thisTileInfo = json.load(f)
return thisTileInfo
# Figure out the position of the tile
numbers = re.findall(r"[\d']+", fileName) # Extract all numbers from the file name
tileRow = int(numbers[-2]) # In the tile grid
tileCol = int(numbers[-1])
pixelRow = tileRow * tileSize # In pixel coordinates relative to the original image
pixelCol = tileCol * tileSize
# TODO: Counting the black pixels is a little slow, run this in parallel!
# Get other tile information
width, height = IrgGeoFunctions.getImageSize(fullPath)
totalNumPixels = height*width
blackPixelCount = MosaicUtilities.countBlackPixels(fullPath)
validPercentage = 1.0 - (float(blackPixelCount) / float(totalNumPixels))
thisTileInfo = {'path' : fullPath,
'tileRow' : tileRow,
'tileCol' : tileCol,
'pixelRow' : pixelRow,
'pixelCol' : pixelCol,
'heightPixels': height,
'widthPixels' : width,
'percentValid': validPercentage,
'prefix' : getTilePrefix(tileRow, tileCol)
}
# Cache the metadata to disk so we don't have to recompute
with open(metadataPath, 'w') as f:
json.dump(thisTileInfo, f)
return thisTileInfo
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 getBoundingBox(fileList):
"""Return the bounding box for this data set in the format (minLon, maxLon, minLat, maxLat)"""
return IrgGeoFunctions.getImageBoundingBox(fileList[0])
def splitImageGdal(imagePath,
outputPrefix,
tileSize,
force=False,
pool=None,
maskList=[]):
'''gdal_translate based replacement for the ImageMagick convert based image split.
This function assumes that all relevant folders have been created.'''
# Compute the bounding box for each tile
inputImageSize = IrgGeoFunctions.getImageSize(imagePath)
#print imagePath + ' is size ' + str(inputImageSize)
numTilesX = int(math.ceil(float(inputImageSize[0]) / float(tileSize)))
numTilesY = int(math.ceil(float(inputImageSize[1]) / float(tileSize)))
print 'Using gdal_translate to generate ' + str(int(
numTilesX * numTilesY)) + ' tiles!'
# Generate each of the tiles using GDAL
cmdList = []
for r in range(0, numTilesY):
for c in range(0, numTilesX):
if maskList: # Make sure this tile is not completely masked out
# Find the info for the corresponding mask tile
# If the image is larger than the mask, there is a chance there will be no
# mask tile for this image tile. If so we can still skip the image tile,,
tilePrefix = getTilePrefix(r, c)
maskTileInfo = [
x for x in maskList if x['prefix'] == tilePrefix
]
if not maskTileInfo or (maskTileInfo[0]['percentValid'] <
MIN_TILE_PERCENT_PIXELS_VALID):
continue
# Get the pixel ROI for this tile
# - TODO: Use the Tiling class!
minCol = c * tileSize
minRow = r * tileSize
width = tileSize
height = tileSize
if (minCol + width) > inputImageSize[0]:
width = inputImageSize[0] - minCol
if (minRow + height) > inputImageSize[1]:
height = inputImageSize[1] - minRow
totalNumPixels = height * width
# Generate the tile (console output suppressed)
thisPixelRoi = ('%d %d %d %d' % (minCol, minRow, width, height))
thisTilePath = outputPrefix + str(r) + '_' + str(c) + '.tif'
cmd = MosaicUtilities.GDAL_TRANSLATE_PATH + ' -q -srcwin ' + thisPixelRoi + ' ' + imagePath + ' ' + thisTilePath
if pool:
#print cmd
cmdList.append((cmd, thisTilePath, force))
else:
MosaicUtilities.cmdRunner(cmd, thisTilePath, force)
if pool:
# Pass all of these commands to a multiprocessing worker pool
print 'splitImageGdal is launching ' + str(
len(cmdList)) + ' gdalwarp threads...'
pool.map(MosaicUtilities.cmdRunnerWrapper, cmdList)
def convertTransformToGeo(imageToRefImageTransform, newImagePath, refImagePath, refImageGeoTransform=None):
'''Converts an image-to-image homography to the ProjectiveTransform
class used elsewhere in geocam.
Either the reference image must be geo-registered, or the geo transform for it
must be provided.'''
# Convert the image-to-image transform parameters to a class
temp = numpy.array([imageToRefImageTransform[0:3],
imageToRefImageTransform[3:6],
imageToRefImageTransform[6:9]] )
imageToRefTransform = transform.ProjectiveTransform(temp)
newImageSize = IrgGeoFunctions.getImageSize(newImagePath)
refImageSize = IrgGeoFunctions.getImageSize(refImagePath)
# Get a pixel to GDC transform for the reference image
refPixelToGdcTransform = registration_common.getPixelToGdcTransform(
refImagePath, refImageGeoTransform)
# Generate a list of point pairs
imagePoints = []
projPoints = []
gdcPoints = []
print 'transform = \n' + str(imageToRefTransform.matrix)
# Loop through an evenly spaced grid of pixels in the new image
# - For each pixel, compute the desired output coordinate
pointPixelSpacing = (newImageSize[0] + newImageSize[1]) / 20 # Results in about 100 points
for r in range(0, newImageSize[0], pointPixelSpacing):
for c in range(0, newImageSize[1], pointPixelSpacing):
# Get pixel in new image and matching pixel in the reference image
thisPixel = numpy.array([float(c), float(r)])
pixelInRefImage = imageToRefTransform.forward(thisPixel)
# If any pixel transforms outside the reference image our transform
# is probably invalid but continue on skipping this pixel.
if ((not registration_common.isPixelValid(thisPixel, newImageSize)) or
(not registration_common.isPixelValid(pixelInRefImage, refImageSize))):
continue
# Compute the location of this pixel in the projected coordinate system
# used by the transform.py file.
if (not refImageGeoTransform):
# Use the geo information of the reference image
gdcCoordinate = refPixelToGdcTransform.forward(pixelInRefImage)
projectedCoordinate = transform.lonLatToMeters(gdcCoordinate)
else: # Use the user-provided transform
projectedCoordinate = refImageGeoTransform.forward(pixelInRefImage)
gdcCoordinate = transform.metersToLatLon(projectedCoordinate)
imagePoints.append(thisPixel)
projPoints.append(projectedCoordinate)
gdcPoints.append(gdcCoordinate)
#print str(thisPixel) + ' --> ' + str(gdcCoordinate) + ' <--> ' + str(projectedCoordinate)
# Compute a transform object that converts from the new image to projected coordinates
#print 'Converting transform to world coordinates...'
#testImageToProjectedTransform = transform.getTransform(numpy.asarray(worldPoints),
# numpy.asarray(imagePoints))
testImageToProjectedTransform = transform.ProjectiveTransform.fit(numpy.asarray(projPoints),
numpy.asarray(imagePoints))
testImageToGdcTransform = transform.ProjectiveTransform.fit(numpy.asarray(gdcPoints),
numpy.asarray(imagePoints))
#print refPixelToGdcTransform
#print testImageToProjectedTransform
#for i, w in zip(imagePoints, worldPoints):
# print str(i) + ' --> ' + str(w) + ' <--> ' + str(testImageToProjectedTransform.forward(i))
return (testImageToProjectedTransform, testImageToGdcTransform, refPixelToGdcTransform)
def addGeoDataToAsuJp2File(inputJp2Path, inputHeaderPath, outputPath, keep=False):
"""Does the actual work of adding the geo data"""
if not os.path.exists(inputJp2Path):
raise Exception('Input file ' + inputJp2Path + ' does not exist!')
if not os.path.exists(inputHeaderPath):
raise Exception('Input file ' + inputHeaderPath + ' does not exist!')
# Get the needed paths
prjPath = replaceFileExtension(inputJp2Path, '.prj')
vrtPath = replaceFileExtension(inputJp2Path, '.vrt')
# The perl script works best from the input folder
originalDirectory = os.getcwd()
print originalDirectory
inputFolder = os.path.dirname(inputJp2Path)
print inputFolder
if inputFolder != '':
os.chdir(inputFolder)
# Call perl script, then return to original directory
cmd = 'isis3world.pl -J -prj ' + os.path.basename(inputHeaderPath)
print cmd
os.system(cmd)
if inputFolder != '':
os.chdir(originalDirectory)
# If the first command is not called from the input folder, need to do this
#mv .j2w <INPUT FOLDER>/B01_009838_2108_XI_30N319W.j2w
#mv .prj <INPUT FOLDER>/B01_009838_2108_XI_30N319W.prj
correctedProjPath = editProjFile(prjPath)
if (not os.path.exists(correctedProjPath)):
raise Exception('Failed to correct proj file!')
# Determine the bounds of the image in projected coordinates
projectedBounds = IrgGeoFunctions.getProjectedBoundsFromIsisLabel(inputHeaderPath)
projectedBoundsString = (str(projectedBounds[0]) + ' ' +
str(projectedBounds[3]) + ' ' +
str(projectedBounds[1]) + ' ' +
str(projectedBounds[2]) )
# Next conversion command
#cmd = 'gdal_translate -of VRT -a_srs ESRI::"'+ prjPath +'" -a_nodata 0 '+ inputJp2Path +' '+ vrtPath
## Finish the conversion
#cmd = 'gdal_translate -of JP2OpenJPEG '+ vrtPath +' '+ outputPath
#print(cmd)
#os.system(cmd)
# Copy the input image to the output path if different
if (outputPath != inputJp2Path):
cmd = 'cp '+ inputJp2Path +' '+ outputPath
print(cmd)
os.system(cmd)
# Add metadata to the output image, specifying a projecting string and projected coordinate boundaries.
f = open(correctedProjPath, 'r')
prjText=f.read()
f.close()
cmd = 'gdal_edit.py -mo "AREA_OR_POINT=Area" -a_ullr ' + projectedBoundsString + ' -a_srs "'+ prjText +'" '+ outputPath
print(cmd)
os.system(cmd)
# gdal_edit.py actually puts the metadata here, the input file is not touched!
sidecarPath = outputPath + '.aux.xml'
# Clean up temporary files
if not keep:
#os.remove(vrtPath)
os.remove(prjPath)
os.remove(correctedProjPath)
return (outputPath, sidecarPath)
def addGeoDataToAsuJp2File(inputJp2Path,
inputHeaderPath,
outputPath,
keep=False):
"""Does the actual work of adding the geo data"""
if not os.path.exists(inputJp2Path):
raise Exception('Input file ' + inputJp2Path + ' does not exist!')
if not os.path.exists(inputHeaderPath):
raise Exception('Input file ' + inputHeaderPath + ' does not exist!')
# Get the needed paths
prjPath = replaceFileExtension(inputJp2Path, '.prj')
vrtPath = replaceFileExtension(inputJp2Path, '.vrt')
# The perl script works best from the input folder
originalDirectory = os.getcwd()
print originalDirectory
inputFolder = os.path.dirname(inputJp2Path)
print inputFolder
if inputFolder != '':
os.chdir(inputFolder)
# Call perl script, then return to original directory
cmd = 'isis3world.pl -J -prj ' + os.path.basename(inputHeaderPath)
print cmd
os.system(cmd)
if inputFolder != '':
os.chdir(originalDirectory)
# If the first command is not called from the input folder, need to do this
#mv .j2w <INPUT FOLDER>/B01_009838_2108_XI_30N319W.j2w
#mv .prj <INPUT FOLDER>/B01_009838_2108_XI_30N319W.prj
correctedProjPath = editProjFile(prjPath)
if (not os.path.exists(correctedProjPath)):
raise Exception('Failed to correct proj file!')
# Determine the bounds of the image in projected coordinates
projectedBounds = IrgGeoFunctions.getProjectedBoundsFromIsisLabel(
inputHeaderPath)
projectedBoundsString = (str(projectedBounds[0]) + ' ' +
str(projectedBounds[3]) + ' ' +
str(projectedBounds[1]) + ' ' +
str(projectedBounds[2]))
# Next conversion command
#cmd = 'gdal_translate -of VRT -a_srs ESRI::"'+ prjPath +'" -a_nodata 0 '+ inputJp2Path +' '+ vrtPath
## Finish the conversion
#cmd = 'gdal_translate -of JP2OpenJPEG '+ vrtPath +' '+ outputPath
#print(cmd)
#os.system(cmd)
# Copy the input image to the output path if different
if (outputPath != inputJp2Path):
cmd = 'cp ' + inputJp2Path + ' ' + outputPath
print(cmd)
os.system(cmd)
# Add metadata to the output image, specifying a projecting string and projected coordinate boundaries.
f = open(correctedProjPath, 'r')
prjText = f.read()
f.close()
cmd = 'gdal_edit.py -mo "AREA_OR_POINT=Area" -a_ullr ' + projectedBoundsString + ' -a_srs "' + prjText + '" ' + outputPath
print(cmd)
os.system(cmd)
# gdal_edit.py actually puts the metadata here, the input file is not touched!
sidecarPath = outputPath + '.aux.xml'
# Clean up temporary files
if not keep:
#os.remove(vrtPath)
os.remove(prjPath)
os.remove(correctedProjPath)
return (outputPath, sidecarPath)
def qualityGdalwarp(imagePath, outputPath, imagePoints, gdcPoints):
'''Use some workarounds to get a higher quality gdalwarp output than is normally possible.'''
# Generate a high resolution grid of fake GCPs based on a transform we compute,
# then call gdalwarp using a high order polynomial to accurately match our transform.
#trans = transform.ProjectiveTransform.fit(numpy.asarray(gdcPoints),numpy.asarray(imagePoints))ls
trans = transform.getTransform(numpy.asarray(gdcPoints),numpy.asarray(imagePoints))
transformName = trans.getJsonDict()['type']
tempPath = outputPath + '-temp.tif'
# Generate a temporary image containing the grid of fake GCPs
cmd = ('gdal_translate -co "COMPRESS=LZW" -co "tiled=yes" -co "predictor=2" -a_srs "'
+ OUTPUT_PROJECTION +'" '+ imagePath +' '+ tempPath)
# Generate the GCPs in a grid, keeping the total under about 500 points so
# that GDAL does not complain.
(width, height) = IrgGeoFunctions.getImageSize(imagePath)
xStep = width /22
yStep = height/22
MAX_DEG_SIZE = 20
minLon = 999 # Keep track of the lonlat size and don't write if it is too big.
minLat = 999 # - This would work better if it was in pixels, but how to get that size?
maxLon = -999
maxLat = -999
for r in range(0,height,yStep):
for c in range(0,width,xStep):
pixel = (c,r)
lonlat = trans.forward(pixel)
cmd += ' -gcp '+ str(c) +' '+str(r) +' '+str(lonlat[0]) +' '+str(lonlat[1])
if lonlat[0] < minLon:
minLon = lonlat[0]
if lonlat[1] < minLat:
minLat = lonlat[1]
if lonlat[0] > maxLon:
maxLon = lonlat[0]
if lonlat[1] > maxLat:
maxLat = lonlat[1]
#print cmd
os.system(cmd)
if max((maxLon - minLon), (maxLat - minLat)) > MAX_DEG_SIZE:
raise Exception('Warped image is too large to generate!\n'
'-> LonLat bounds: ' + str((minLon, minLat, maxLon, maxLat)))
# Now generate a warped geotiff.
# - "order 2" looks terrible with fewer GCPs, but "order 1" does not accurately
# capture the footprint of higher tilt images.
# - tps seems to work well with the evenly spaced grid of virtual GCPs.
cmd = ('gdalwarp -co "COMPRESS=LZW" -co "tiled=yes" -co "predictor=2"'
+ ' -dstalpha -overwrite -tps -multi -r cubic -t_srs "'
+ OUTPUT_PROJECTION +'" ' + tempPath +' '+ outputPath)
print cmd
os.system(cmd)
# Check output and cleanup
os.remove(tempPath)
if not os.path.exists(outputPath):
raise Exception('Failed to create warped geotiff file: ' + outputPath)
return transformName
def main(argsIn):
print '#################################################################################'
print "Running makeDemAndCompare.py"
try:
try:
usage = "usage: makeDemAndCompare.py [--output <path>][--manual]\n "
parser = optparse.OptionParser(usage=usage)
inputGroup = optparse.OptionGroup(parser, 'Input Paths')
inputGroup.add_option("--left", dest="leftPath", help="Path to left cube file")
inputGroup.add_option("--right", dest="rightPath", help="Path to right cube file")
inputGroup.add_option("--lola", dest="lolaPath", help="Path to LOLA DEM")
inputGroup.add_option("--asu", dest="asuPath", help="Path to ASU DEM")
inputGroup.add_option("--node-file", dest="nodeFilePath",
help="Path to file containing list of available nodes")
parser.add_option_group(inputGroup)
# The default working directory path is kind of ugly...
parser.add_option("--workDir", dest="workDir", help="Folder to store temporary files in")
parser.add_option("--prefix", dest="prefix", help="Output prefix.")
parser.add_option("--log-path", dest="logPath",
help="Where to write the output log file.")
parser.add_option("--crop", dest="cropAmount",
help="Crops the output image to reduce processing time.")
parser.add_option("--manual", action="callback", callback=man,
help="Read the manual.")
parser.add_option("--keep", action="store_true", dest="keep",
help="Do not delete the temporary files.")
(options, args) = parser.parse_args(argsIn)
if not options.leftPath:
parser.error("Need left input path")
if not options.rightPath:
parser.error("Need right input path")
if not options.prefix:
parser.error("Need output prefix")
except optparse.OptionError, msg:
raise Usage(msg)
print "Beginning processing....."
startTime = time.time()
# Make sure we have all the functions we need
functionStartupCheck()
# Set this to true to force steps after it to activate
carry = False
# Set up the output folders
outputFolder = os.path.dirname(options.prefix)
inputBaseName = os.path.basename(options.leftPath)
tempFolder = outputFolder + '/' + inputBaseName + '_stereoCalibrationTemp/'
if (options.workDir):
tempFolder = options.workDir
if not os.path.exists(outputFolder):
os.mkdir(outputFolder)
hadToCreateTempFolder = not os.path.exists(tempFolder)
if not os.path.exists(tempFolder):
os.mkdir(tempFolder)
# Set up logging
if not options.logPath:
options.logPath = options.prefix + '-Log.txt'
logging.basicConfig(filename=options.logPath,level=logging.INFO)
# Go ahead and set up all the output paths
# -- Deliverables
demPath = options.prefix + '-DEM.tif'
intersectionErrorPath = options.prefix + '-IntersectionErr.tif'
hillshadePath = options.prefix + '-Hillshade.tif'
colormapPath = options.prefix + '-Colormap.tif'
colormapLegendPath = options.prefix + '-ColormapLegend.csv'
mapProjectLeftPath = options.prefix + '-MapProjLeft.tif'
mapProjectRightPath = options.prefix + '-MapProjRight.tif'
confidenceLevelPath = options.prefix + '-Confidence.tif'
confidenceLegendPath = options.prefix + '-ConfidenceLegend.csv'
# -- Diagnostic
intersectionViewPathX = options.prefix + '-IntersectionErrorX.tif'
intersectionViewPathY = options.prefix + '-IntersectionErrorY.tif'
intersectionViewPathZ = options.prefix + '-IntersectionErrorZ.tif'
lolaDiffStatsPath = options.prefix + '-LOLA_diff_stats.txt'
lolaDiffPointsPath = options.prefix + '-LOLA_diff_points.csv'
lolaAsuDiffStatsPath = options.prefix + '-ASU_LOLA_diff_stats.txt'
lolaAsuDiffPointsPath = options.prefix + '-ASU_LOLA_diff_points.csv'
mapProjectLeftUint8Path = options.prefix + '-MapProjLeftUint8.tif'
mapProjectRightUint8Path = options.prefix + '-MapProjRightUint8.tif'
# If specified, crop the inputs that will be passed into the stereo function to reduce processing time
mainMosaicCroppedPath = os.path.join(tempFolder, 'mainMosaicCropped.cub')
stereoMosaicCroppedPath = os.path.join(tempFolder, 'stereoMosaicCropped.cub')
if options.cropAmount and (options.cropAmount > 0):
# Verify input files are present
if not os.path.exists(options.leftPath):
raise Exception('Input file ' + options.leftPath + ' not found!')
if not os.path.exists(options.rightPath):
raise Exception('Input file ' + options.rightPath + ' not found!')
if (not os.path.exists(mainMosaicCroppedPath)) or carry:
cmd = ('crop from= ' + options.leftPath + ' to= ' + mainMosaicCroppedPath +
' nlines= ' + str(options.cropAmount))# + ' line=24200')
print cmd
os.system(cmd)
if (not os.path.exists(stereoMosaicCroppedPath) or carry):
cmd = ('crop from= ' + options.rightPath + ' to= ' + stereoMosaicCroppedPath +
' nlines= ' + str(options.cropAmount))# + ' line=24200')
print cmd
os.system(cmd)
options.leftPath = mainMosaicCroppedPath
options.rightPath = stereoMosaicCroppedPath
print '\n-------------------------------------------------------------------------\n'
# Call stereo to generate a point cloud from the two images
# - This step takes a really long time.
stereoOutputPrefix = os.path.join(tempFolder, 'stereoWorkDir/stereo')
stereoOutputFolder = os.path.join(tempFolder, 'stereoWorkDir')
pointCloudPath = stereoOutputPrefix + '-PC.tif'
stereoOptionString = ('--corr-timeout 400 --alignment-method AffineEpipolar --subpixel-mode ' + str(SUBPIXEL_MODE) +
' ' + options.leftPath + ' ' + options.rightPath +
' --job-size-w 4096 --job-size-h 4096 ' + # Reduce number of tile files created
' ' + stereoOutputPrefix + ' --processes 10 --threads-multiprocess 4' +
' --threads-singleprocess 32 --compute-error-vector' + ' --filter-mode 1' +
' --erode-max-size 5000 --subpixel-kernel 35 35 --subpixel-max-levels 0')
if (not os.path.exists(pointCloudPath) and not os.path.exists(demPath)) or carry:
# Verify input files are present
if not os.path.exists(options.leftPath):
raise Exception('Input file ' + options.leftPath + ' not found!')
if not os.path.exists(options.rightPath):
raise Exception('Input file ' + options.rightPath + ' not found!')
cmd = ('parallel_stereo ' + stereoOptionString)
print cmd
os.system(cmd)
# Compute percentage of good pixels
percentGood = IrgAspFunctions.getStereoGoodPixelPercentage(stereoOutputPrefix)
print 'Stereo completed with good pixel percentage: ' + str(percentGood)
logging.info('Final stereo completed with good pixel percentage: %s', str(percentGood))
else:
print 'Stereo file ' + pointCloudPath + ' already exists, skipping stereo step.'
stereoTime = time.time()
logging.info('Stereo finished in %f seconds', stereoTime - startTime)
# Find out the center latitude of the mosaic
if os.path.exists(options.leftPath):
centerLat = IrgIsisFunctions.getCubeCenterLatitude(options.leftPath, tempFolder)
elif os.path.exists(demPath): # Input file has been deleted but we still have the info
demInfo = IrgGeoFunctions.getImageGeoInfo(demPath, False)
centerLat = demInfo['standard_parallel_1']
else:
raise Exception("Can't delete the input files before creating the DEM!")
# Generate a DEM
if (not os.path.exists(demPath)) or carry:
# Equirectangular style projection
# - Latitude of true scale = center latitude = lat_ts
# - Latitude of origin = 0 = lat+0
# - Longitude of projection center = Central meridian = lon+0
cmd = ('point2dem --dem-hole-fill-len 15 --remove-outliers --errorimage -o ' + options.prefix + ' ' + pointCloudPath +
' -r moon --tr ' + str(DEM_METERS_PER_PIXEL) + ' --t_srs "+proj=eqc +lat_ts=' + str(centerLat) +
' +lat_0=0 +a='+str(MOON_RADIUS)+' +b='+str(MOON_RADIUS)+' +units=m" --nodata ' + str(DEM_NODATA))
os.system(cmd)
else:
print 'DEM file ' + demPath + ' already exists, skipping point2dem step.'
# Create a hillshade image to visualize the output
if (not os.path.exists(hillshadePath)) or carry:
cmd = 'hillshade ' + demPath + ' -o ' + hillshadePath
print cmd
os.system(cmd)
else:
print 'Output file ' + hillshadePath + ' already exists, skipping hillshade step.'
# Create a colorized version of the hillshade
# - Uses a blue-red color map from here: http://www.sandia.gov/~kmorel/documents/ColorMaps/
if (not os.path.exists(colormapPath)) or (not os.path.exists(colormapLegendPath)) or carry:
# The color LUT is kept with the source code
lutFilePath = os.path.join( os.path.dirname(os.path.abspath(__file__)), 'colorProfileBlueRed.csv')
# Generate the initial version of colormap
colormapTempPath = options.prefix + '-ColormapTemp.tif'
cmd = 'colormap ' + demPath + ' -o ' + colormapTempPath + ' -s ' + hillshadePath + ' --lut-file ' + lutFilePath
print cmd
os.system(cmd)
# Now convert to the final output version (remove transparency layer) and remove the temp file
IrgFileFunctions.stripRgbImageAlphaChannel(colormapTempPath, colormapPath)
os.remove(colormapTempPath)
# Generate another file storing the colormap info
writeColorMapInfo(colormapPath, lutFilePath, demPath, colormapLegendPath)
else:
print 'Output file ' + colormapPath + ' already exists, skipping colormap step.'
## Create a 3d mesh of the point cloud
#meshPath = os.path.join(outputFolder, 'mesh.ive')
#meshPrefix = os.path.join(outputFolder, 'mesh')
#cmd = 'point2mesh ' + pointCloudPath + ' ' + options.leftPath + ' -o ' + meshPrefix
#if not os.path.exists(meshPath):
# print cmd
# os.system(cmd)
if not options.keep: # Remove stereo folder here to cut down on file count before mapproject calls
IrgFileFunctions.removeFolderIfExists(stereoOutputFolder)
# Convert the intersection error to a viewable format
cmdX = 'gdal_translate -ot byte -scale 0 10 0 255 -outsize 50% 50% -b 1 ' + intersectionErrorPath + ' ' + intersectionViewPathX
cmdY = 'gdal_translate -ot byte -scale 0 10 0 255 -outsize 50% 50% -b 2 ' + intersectionErrorPath + ' ' + intersectionViewPathY
cmdZ = 'gdal_translate -ot byte -scale 0 10 0 255 -outsize 50% 50% -b 3 ' + intersectionErrorPath + ' ' + intersectionViewPathZ
if not os.path.exists(intersectionViewPathX) or carry:
print cmdX
os.system(cmdX)
if not os.path.exists(intersectionViewPathY) or carry:
print cmdY
os.system(cmdY)
if not os.path.exists(intersectionViewPathZ) or carry:
print cmdZ
os.system(cmdZ)
# Generate a confidence plot from the intersection error
if not os.path.exists(confidenceLevelPath):
thresholdString = str(PIXEL_ACCURACY_THRESHOLDS)[1:-1].replace(',', '')
cmd = ('maskFromIntersectError ' + intersectionErrorPath + ' ' + confidenceLevelPath
+ ' --legend ' + confidenceLegendPath +
' --scaleOutput --thresholds ' + thresholdString)
print cmd
os.system(cmd)
hillshadeTime = time.time()
logging.info('DEM and hillshade finished in %f seconds', hillshadeTime - stereoTime)
# Call script to compare LOLA data with the DEM
if options.lolaPath:
compareDemToLola(options.lolaPath, demPath, lolaDiffStatsPath, lolaDiffPointsPath, carry)
# Call script to compare LOLA data with the ASU DEM
if options.asuPath:
compareDemToLola(options.lolaPath, options.asuPath, lolaAsuDiffStatsPath, lolaAsuDiffPointsPath, carry)
# Generate a map projected version of the left and right images
# - This step is done last since it is so slow!
mapProjectImage(options.leftPath, demPath, mapProjectLeftPath, MAP_PROJECT_METERS_PER_PIXEL, centerLat, options.nodeFilePath, carry)
mapProjectImage(options.rightPath, demPath, mapProjectRightPath, MAP_PROJECT_METERS_PER_PIXEL, centerLat, options.nodeFilePath, carry)
# Generate 8 bit versions of the mapproject files for debugging
cmdLeft = 'gdal_translate -scale -ot byte ' + mapProjectLeftPath + ' ' + mapProjectLeftUint8Path
cmdRight = 'gdal_translate -scale -ot byte ' + mapProjectRightPath + ' ' + mapProjectRightUint8Path
if not os.path.exists(mapProjectLeftUint8Path) or carry:
print cmdLeft
os.system(cmdLeft)
if not os.path.exists(mapProjectRightUint8Path) or carry:
print cmdRight
os.system(cmdRight)
mapProjectTime = time.time()
logging.info('Map project finished in %f seconds', mapProjectTime - hillshadeTime)
# Clean up temporary files
if not options.keep:
print 'Removing temporary files'
IrgFileFunctions.removeIfExists(mainMosaicCroppedPath)
IrgFileFunctions.removeIfExists(stereoMosaicCroppedPath)
#IrgFileFunctions.removeIntermediateStereoFiles(stereoOutputPrefix) # Limited clear
IrgFileFunctions.removeFolderIfExists(stereoOutputFolder) # Larger clear
#if (hadToCreateTempFolder): Not done since stereo output needs to be retained
# IrgFileFunctions.removeFolderIfExists(tempFolder)
endTime = time.time()
logging.info('makeDemAndCompare.py finished in %f seconds', endTime - startTime)
print "Finished in " + str(endTime - startTime) + " seconds."
print '#################################################################################'
return 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
def fetchAndPrepFile(db, setName, subtype, remoteURL, workDir):
'''Retrieves a remote file and prepares it for upload'''
#print 'Uploading file ' + setName
# Images with a center over this latitude will use polar stereographic projection
# instead of simple cylindrical projection.
HIGH_LATITUDE_CUTOFF = 65 # Degrees
asuImagePath = os.path.join(workDir, setName +
'_noGeo.jp2') # Map projected image from ASU
asuLabelPath = os.path.join(workDir,
setName + '_noGeo.lbl') # Label from ASU
edrPath = os.path.join(workDir, setName + '.IMG') # Raw image from PDS
timePath = os.path.join(
workDir,
setName + '.time') # Contains only the file capture time string
#cubPath = os.path.join(workDir, setName + '.cub') # Output of mroctx2isis
#calPath = os.path.join(workDir, setName + '.cal.cub') # Output of ctxcal
mapPath = os.path.join(workDir, setName + '.map.cub') # Output of cam2map
mapLabelPath = os.path.join(workDir, setName +
'.map.pvl') # Specify projection to cam2map
# Generate the remote URLs from the data prefix and volume stored in these parameters
asuImageUrl, asuLabelUrl, edrUrl = generatePdsPath(setName, subtype)
if True: # Map project the EDR ourselves <-- This takes way too long!
print 'Projecting the EDR image using ISIS...'
localFilePath = os.path.join(workDir, setName +
'.tif') # The output file we will upload
# Check if this is a "flat" calibration image
badImage = checkForBadFile(edrUrl)
if badImage:
raise Exception('TODO: Remove bad images from the DB!')
if not os.path.exists(edrPath):
# Download the EDR file
cmd = 'wget ' + edrUrl + ' -O ' + edrPath
print cmd
os.system(cmd)
# Extract the image capture time from the .IMG file
if not os.path.exists(timePath):
timeString = getCreationTimeHelper(edrPath)
f = open(timePath, 'w')
f.write(timeString)
f.close()
# Convert and apply calibration to the CTX file
calPath = IrgIsisFunctions.prepareCtxImage(edrPath, workDir, False)
## Find out the center latitude of the file and determine if it is high latitude
centerLat = IrgIsisFunctions.getCubeCenterLatitude(calPath, workDir)
print centerLat
highLat = abs(float(centerLat)) > HIGH_LATITUDE_CUTOFF
if True: #not os.path.exists(mapLabelPath):
# Generate the map label file
generateDefaultMappingPvl(mapLabelPath, highLat)
if True: #not os.path.exists(mapPath):
# Generate the map projected file
cmd = [
'timeout', '6h', 'cam2map', 'matchmap=', 'False', 'from=',
calPath, 'to=', mapPath, 'map=', mapLabelPath
]
print cmd
#os.system(cmd)
p = subprocess.Popen(cmd)
p.communicate()
if (p.returncode != 0):
raise Exception(
'Error or timeout running cam2map, returnCode = ' +
str(p.returncode))
if True: #not os.path.exists(localFilePath):
# Generate the final image to upload
cmd = 'gdal_translate -of GTiff ' + mapPath + ' ' + localFilePath
print cmd
os.system(cmd)
# Clean up intermediate files
os.remove(mapLabelPath)
os.remove(edrPath)
os.remove(calPath)
os.remove(mapPath)
# Two local files are left around, the first should be uploaded.
return [localFilePath, timePath]
else: # Use the map projected image from the ASU web site
print 'Using ASU projected image...'
localFilePath = os.path.join(workDir, setName +
'.jp2') # The output file we will upload
# Note: ASU seems to be missing some files!
# We are using the label path in both projection cases
if not os.path.exists(asuLabelPath):
# Download the label file
cmd = 'wget "' + asuLabelUrl + '" -O ' + asuLabelPath
print cmd
os.system(cmd)
if not IrgFileFunctions.fileIsNonZero(
asuLabelPath): # Try the alternate label path
os.remove(asuLabelPath)
asuLabelUrl = asuLabelUrl.replace('.scyl.', '.ps.')
asuLabelPath = asuLabelPath.replace('.scyl.', '.ps.')
print 'Trying alternate label path: ' + asuLabelUrl
# Download the label file
cmd = 'wget "' + asuLabelUrl + '" -O ' + asuLabelPath
print cmd
os.system(cmd)
if not IrgFileFunctions.fileIsNonZero(asuLabelPath):
raise Exception('Failed to download file label at URL: ' +
asuLabelUrl)
# Check the projection type
projType = IrgGeoFunctions.getProjectionFromIsisLabel(asuLabelPath)
if projType != 'SimpleCylindrical':
print 'WARNING: projType = ' + projType
print 'Maps Engine may fail to ingest this file!'
#os.remove(asuLabelPath)
#raise Exception(projType + ' images on hold until Google fixes a bug!')
if not os.path.exists(asuImagePath):
# Download the image file
cmd = 'wget "' + asuImageUrl + '" -O ' + asuImagePath
print cmd
os.system(cmd)
if not IrgFileFunctions.fileIsNonZero(asuImagePath):
raise Exception('Failed to download image file at URL: ' +
asuImageUrl)
## Correct the ISIS header if needed
#fixedAsuHeaderPath = putIsisHeaderIn180(asuLabelPath)
#if (fixedAsuHeaderPath != asuLabelPath):
# os.remove(asuLabelPath) # Delete replaced header
if True: # This is fast now, so do it every time
# Correct the file - The JP2 file from ASU needs the geo data from the label file!
#cmd = 'addGeoToAsuCtxJp2.py --keep --label '+ asuLabelPath +' '+ asuImagePath +' '+ localFilePath
#print cmd
#os.system(cmd)
# TODO: Remove unnecessary image copy here
(correctedPath,
sidecarPath) = addGeoDataToAsuJp2File(asuImagePath,
asuLabelPath,
localFilePath,
keep=False)
if not IrgFileFunctions.fileIsNonZero(sidecarPath):
raise Exception('Script to add geo data to JP2 file failed!')
# Clean up
os.remove(asuImagePath)
# Three local files are left around, the first should be uploaded.
return [correctedPath, sidecarPath, asuLabelPath]
def fetchAndPrepFile(db, setName, subtype, remoteURL, workDir):
'''Retrieves a remote file and prepares it for upload'''
#print 'Uploading file ' + setName
if subtype != 'DEM': # Handles RED and COLOR images
# The label file URL is the same as the image but with a different extension
remoteLabelURL = getLabelPathFromImagePath(remoteURL)
localFilePath = os.path.join(workDir, os.path.basename(remoteURL))
localLabelPath = os.path.join(workDir,
os.path.basename(remoteLabelURL))
# Retrieve the header file
if not os.path.exists(localLabelPath):
# Try to get the label locally!
pdsStart = remoteLabelURL.find('PDS')
localPdsPath = os.path.join('/HiRISE/Data/',
remoteLabelURL[pdsStart:])
print localPdsPath
if os.path.exists(
localPdsPath): # File available locally, just copy it!
cmd = 'cp ' + localPdsPath + ' ' + localLabelPath
else: # Download the image
cmd = 'wget ' + remoteLabelURL + ' -O ' + localLabelPath
print cmd
os.system(cmd)
# Retrieve the image file
if not os.path.exists(localFilePath): # Need to get it from somewhere
# Try to get the image locally!
pdsStart = remoteURL.find('PDS')
localPdsPath = os.path.join('/HiRISE/Data/', remoteURL[pdsStart:])
if os.path.exists(
localPdsPath): # File available locally, just copy it!
cmd = 'cp ' + localPdsPath + ' ' + localFilePath
else: # Download the image
cmd = 'wget ' + remoteURL + ' -O ' + localFilePath
print cmd
os.system(cmd)
if not IrgFileFunctions.fileIsNonZero(localFilePath):
if not IrgFileFunctions.fileIsNonZero(localLabelPath):
print 'Could not find label or image file, DELETING DATA RECORD!'
common.removeDataRecord(db, common.SENSOR_TYPE_HiRISE, subtype,
setName)
raise Exception('Unable to download from URL: ' + remoteURL)
# Check if there is geo data in the JP2 file
jp2HasGeoData = IrgGeoFunctions.doesImageHaveGeoData(localFilePath)
# If there is no label file, try to generate an artificial one.
fakeLabelFile = False
if not IrgFileFunctions.fileIsNonZero(localLabelPath):
#raise Exception('Unable to download from URL: ' + remoteLabelURL)
print 'WARNING: Unable to download from URL: ' + remoteLabelURL
if not jp2HasGeoData:
raise Exception(
'No geo data in JP2 and no Label file! Cannot handle this!'
)
print 'Generating a fake LBL file to proceed...'
localLabelPath = writeFakeLabelFromJp2(localFilePath)
fakeLabelFile = True
# At this point we always have a label file but it may be fake
# Some images are missing geo information but we can add it from the label file
localImageIsTiff = False
localImagePath = localFilePath
if not jp2HasGeoData:
print 'Correcting JP2 file with no geo information!!!!'
# Correct the local file, then remove the old (bad) file
outputPrefix = localFilePath[0:-4]
localImagePath = correctAndCropImage(localFilePath, localLabelPath,
outputPrefix)
print 'Generated ' + localImagePath
if localFilePath != localImagePath:
print 'Deleting JP2 file without metadata!'
os.remove(localFilePath)
localImageIsTiff = (localImagePath[-4:] == ".TIF")
if not localImageIsTiff:
# Call code to fix the header information in the JP2 file!
cmd = FIX_JP2_TOOL + ' ' + localImagePath
print cmd
os.system(cmd)
# Check the projection type
projType = IrgGeoFunctions.getProjectionFromIsisLabel(localLabelPath)
(width, height) = IrgIsisFunctions.getImageSize(localImagePath)
if (projType == 'POLAR STEREOGRAPHIC'
) and False: #(width < MAX_POLAR_UPLOAD_WIDTH):
# Google has trouble digesting these files so handle them differently.
#os.remove(localLabelPath)
#raise Exception('POLAR STEREOGRAPHIC images on hold until Google fixes a bug!')
print 'Special handling for POLAR STEROGRAPHIC image!'
if fakeLabelFile:
print 'Cannot reprocess polar image without a label file!'
print 'All we can do is upload the file and hope for the best.'
# First file is for upload, second contains the timestamp.
return [localImagePath, localLabelPath]
# Compute how many chunks are needed for this image
numChunks = ceil(width / POLAR_WIDTH_CHUNK_SIZE)
# Determine which chunk this DB entry is for
chunkNum = getChunkNum(setName)
print 'This is chunk number ' + str(chunkNum)
if chunkNum >= numChunks: # Check for chunk number error
raise Exception('Illegal chunk number: ' + setName)
# If this is the main DB entry, we need to make sure the other DB entries exist!
if chunkNum == 0:
# Go ahead and try to add each chunk, the call will only go through if it does not already exist.
for i in range(1, numChunks):
chunkSetName = makeChunkSetName(setName, i)
print 'Add chunk set name to DB: ' + chunkSetName
common.addDataRecord(db, common.SENSOR_TYPE_HiRISE,
subtype, chunkSetName, remoteURL)
raise Exception('DEBUG')
# Now actually generate the desired chunk
# - Need to use PIRL tools to extract a chunk to an IMG format, then convert that back to JP2 so that Google can read it.
fileBasePath = os.path.splitext(localImagePath)[0]
localImgPath = fileBasePath + '.IMG'
localChunkPrefix = fileBasePath + '_' + str(chunkNum)
chunkBB = getChunkBoundingBox(width, height, chunkNum)
localChunkPath = correctAndCropImage(localImagePath,
localLabelPath,
localChunkPrefix, chunkBB)
# Just use the same label file, we don't care if the DB has per-chunk boundaries.
return [localChunkPath, localLabelPath]
else: # A normal, non-polar file.
# First file is for upload, second contains the timestamp.
return [localImagePath, localLabelPath]
# TODO: Handle POLAR DEMS
else: # Handle DEMs
# For DEMs there is no label file
localFilePath = os.path.join(workDir, os.path.basename(remoteURL))
if not os.path.exists(localFilePath):
# Download the image
cmd = 'wget ' + remoteURL + ' -O ' + localFilePath
print cmd
os.system(cmd)
if not IrgFileFunctions.fileIsNonZero(
localFilePath): # Make sure we got the file
raise Exception('Unable to download from URL: ' + remoteURL)
# Generate a header file from the IMG file
localLabelPath = localFilePath[:-4] + '.LBL'
cmd = 'head -n 90 ' + localFilePath + ' > ' + localLabelPath
print cmd
os.system(cmd)
# Check if this is a polar stereographic image
isPolar = False
f = open(localLabelPath)
for line in f:
if ("MAP_PROJECTION_TYPE" in line) and ("POLAR STEREOGRAPHIC"
in line):
isPolar = True
print 'WARNING: POLAR STEREOGRAPHIC DEM MAY NOT UPLOAD PROPERLY'
break
#os.remove(localFilePath)
#os.remove(localLabelPath)
#raise Exception('POLAR STEREOGRAPHIC DEMs on hold until Google fixes a bug!')
f.close()
# Convert from IMG to TIF
tiffFilePath = localFilePath[:-4] + '.TIF'
if not os.path.exists(tiffFilePath):
cmd = 'gdal_translate -of GTiff ' + localFilePath + ' ' + tiffFilePath
print cmd
os.system(cmd)
if not isPolar: # Only EQC files need to be corrected
# Correct projected coordinates problems
cmd = 'python /home/pirl/smcmich1/repo/Tools/geoTiffTool.py --normalize-eqc-lon ' + tiffFilePath
print cmd
os.system(cmd)
os.remove(localFilePath) # Clean up source image
return [tiffFilePath, localLabelPath]
def matchLocally(mission, roll, frame, cursor, georefDb, sourceImagePath):
'''Performs image alignment to an already aligned ISS image'''
# Load new frame info
targetFrameData = source_database.FrameInfo()
targetFrameData.loadFromDb(cursor, mission, roll, frame)
targetFrameData = computeFrameInfoMetersPerPixel(targetFrameData)
# Find candidate names to match to
possibleNearbyMatches = findNearbyResults(targetFrameData, cursor, georefDb)
if not possibleNearbyMatches:
print 'Did not find any potential local matches!'
for (otherFrame, ourResult) in possibleNearbyMatches:
print 'Trying local match with frame: ' + str(otherFrame.frame)
# Get path to other frame image
otherImagePath, exifSourcePath = source_database.getSourceImage(otherFrame)
source_database.clearExif(exifSourcePath)
otherTransform = ourResult[0] # This is still in the google projected format
#print 'otherTransform = ' + str(otherTransform.matrix)
print 'New image mpp = ' + str(targetFrameData.metersPerPixel)
print 'Local match image mpp = ' + str(otherFrame.metersPerPixel)
# If we could not estimate the MPP value of the new image, guess that it is the same as
# the local reference image we are about to try.
thisMpp = targetFrameData.metersPerPixel
if not thisMpp:
thisMpp = otherFrame.metersPerPixel
print 'Attempting to register image...'
(imageToProjectedTransform, imageToGdcTransform, confidence, imageInliers, gdcInliers, refMetersPerPixel) = \
register_image.register_image(sourceImagePath,
otherFrame.centerLon, otherFrame.centerLat,
thisMpp, targetFrameData.date,
refImagePath =otherImagePath,
referenceGeoTransform=otherTransform,
refMetersPerPixelIn =otherFrame.metersPerPixel,
debug=options.debug, force=True, slowMethod=False)
if not options.debug:
os.remove(otherImagePath) # Clean up the image we matched against
# Quit once we get a good match
if confidence == registration_common.CONFIDENCE_HIGH:
print 'High confidence match!'
# Convert from the image-to-image GCPs to the reference image GCPs
# located in the new image.
refFrameGdcInliers = ourResult[3] # TODO: Clean this up!
(width, height) = IrgGeoFunctions.getImageSize(sourceImagePath)
print '\n\n'
print refFrameGdcInliers
print '\n\n'
(imageInliers, gdcInliers) = registration_common.convertGcps(refFrameGdcInliers,
imageToProjectedTransform, width, height)
print imageInliers
print '\n\n'
# If none of the original GCPs fall in the new image, don't use this alignment result.
# - We could use this result, but we don't in order to maintain accuracy standards.
if imageInliers:
print 'Have inliers'
print otherFrame
return (imageToProjectedTransform, imageToGdcTransform, confidence,
imageInliers, gdcInliers, refMetersPerPixel, otherFrame)
else:
print 'Inliers out of bounds!'
# Match failure, return junk values
return (registration_common.getIdentityTransform(), registration_common.getIdentityTransform(),
registration_common.CONFIDENCE_NONE, [], [], 9999, None)
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 fetchAndPrepFile(db, setName, subtype, remoteURL, workDir):
'''Retrieves a remote file and prepares it for upload'''
#print 'Uploading file ' + setName
if subtype != 'DEM': # Handles RED and COLOR images
# The label file URL is the same as the image but with a different extension
remoteLabelURL = getLabelPathFromImagePath(remoteURL)
localFilePath = os.path.join(workDir, os.path.basename(remoteURL))
localLabelPath = os.path.join(workDir, os.path.basename(remoteLabelURL))
# Retrieve the header file
if not os.path.exists(localLabelPath):
# Try to get the label locally!
pdsStart = remoteLabelURL.find('PDS')
localPdsPath = os.path.join('/HiRISE/Data/', remoteLabelURL[pdsStart:])
print localPdsPath
if os.path.exists(localPdsPath): # File available locally, just copy it!
cmd = 'cp ' + localPdsPath +' '+ localLabelPath
else: # Download the image
cmd = 'wget ' + remoteLabelURL + ' -O ' + localLabelPath
print cmd
os.system(cmd)
# Retrieve the image file
if not os.path.exists(localFilePath): # Need to get it from somewhere
# Try to get the image locally!
pdsStart = remoteURL.find('PDS')
localPdsPath = os.path.join('/HiRISE/Data/', remoteURL[pdsStart:])
if os.path.exists(localPdsPath): # File available locally, just copy it!
cmd = 'cp ' + localPdsPath +' '+ localFilePath
else: # Download the image
cmd = 'wget ' + remoteURL + ' -O ' + localFilePath
print cmd
os.system(cmd)
if not IrgFileFunctions.fileIsNonZero(localFilePath):
if not IrgFileFunctions.fileIsNonZero(localLabelPath):
print 'Could not find label or image file, DELETING DATA RECORD!'
common.removeDataRecord(db, common.SENSOR_TYPE_HiRISE, subtype, setName)
raise Exception('Unable to download from URL: ' + remoteURL)
# Check if there is geo data in the JP2 file
jp2HasGeoData = IrgGeoFunctions.doesImageHaveGeoData(localFilePath)
# If there is no label file, try to generate an artificial one.
fakeLabelFile = False
if not IrgFileFunctions.fileIsNonZero(localLabelPath):
#raise Exception('Unable to download from URL: ' + remoteLabelURL)
print 'WARNING: Unable to download from URL: ' + remoteLabelURL
if not jp2HasGeoData:
raise Exception('No geo data in JP2 and no Label file! Cannot handle this!')
print 'Generating a fake LBL file to proceed...'
localLabelPath = writeFakeLabelFromJp2(localFilePath)
fakeLabelFile = True
# At this point we always have a label file but it may be fake
# Some images are missing geo information but we can add it from the label file
localImageIsTiff = False
localImagePath = localFilePath
if not jp2HasGeoData:
print 'Correcting JP2 file with no geo information!!!!'
# Correct the local file, then remove the old (bad) file
outputPrefix = localFilePath[0:-4]
localImagePath = correctAndCropImage(localFilePath, localLabelPath, outputPrefix)
print 'Generated ' + localImagePath
if localFilePath != localImagePath:
print 'Deleting JP2 file without metadata!'
os.remove(localFilePath)
localImageIsTiff = (localImagePath[-4:] == ".TIF")
if not localImageIsTiff:
# Call code to fix the header information in the JP2 file!
cmd = FIX_JP2_TOOL +' '+ localImagePath
print cmd
os.system(cmd)
# Check the projection type
projType = IrgGeoFunctions.getProjectionFromIsisLabel(localLabelPath)
(width, height) = IrgIsisFunctions.getImageSize(localImagePath)
if (projType == 'POLAR STEREOGRAPHIC') and False: #(width < MAX_POLAR_UPLOAD_WIDTH):
# Google has trouble digesting these files so handle them differently.
#os.remove(localLabelPath)
#raise Exception('POLAR STEREOGRAPHIC images on hold until Google fixes a bug!')
print 'Special handling for POLAR STEROGRAPHIC image!'
if fakeLabelFile:
print 'Cannot reprocess polar image without a label file!'
print 'All we can do is upload the file and hope for the best.'
# First file is for upload, second contains the timestamp.
return [localImagePath, localLabelPath]
# Compute how many chunks are needed for this image
numChunks = ceil(width / POLAR_WIDTH_CHUNK_SIZE)
# Determine which chunk this DB entry is for
chunkNum = getChunkNum(setName)
print 'This is chunk number ' + str(chunkNum)
if chunkNum >= numChunks: # Check for chunk number error
raise Exception('Illegal chunk number: ' + setName)
# If this is the main DB entry, we need to make sure the other DB entries exist!
if chunkNum == 0:
# Go ahead and try to add each chunk, the call will only go through if it does not already exist.
for i in range(1,numChunks):
chunkSetName = makeChunkSetName(setName, i)
print 'Add chunk set name to DB: ' + chunkSetName
common.addDataRecord(db, common.SENSOR_TYPE_HiRISE, subtype, chunkSetName, remoteURL)
raise Exception('DEBUG')
# Now actually generate the desired chunk
# - Need to use PIRL tools to extract a chunk to an IMG format, then convert that back to JP2 so that Google can read it.
fileBasePath = os.path.splitext(localImagePath)[0]
localImgPath = fileBasePath + '.IMG'
localChunkPrefix = fileBasePath + '_' + str(chunkNum)
chunkBB = getChunkBoundingBox(width, height, chunkNum)
localChunkPath = correctAndCropImage(localImagePath, localLabelPath, localChunkPrefix, chunkBB)
# Just use the same label file, we don't care if the DB has per-chunk boundaries.
return [localChunkPath, localLabelPath]
else: # A normal, non-polar file.
# First file is for upload, second contains the timestamp.
return [localImagePath, localLabelPath]
# TODO: Handle POLAR DEMS
else: # Handle DEMs
# For DEMs there is no label file
localFilePath = os.path.join(workDir, os.path.basename(remoteURL))
if not os.path.exists(localFilePath):
# Download the image
cmd = 'wget ' + remoteURL + ' -O ' + localFilePath
print cmd
os.system(cmd)
if not IrgFileFunctions.fileIsNonZero(localFilePath): # Make sure we got the file
raise Exception('Unable to download from URL: ' + remoteURL)
# Generate a header file from the IMG file
localLabelPath = localFilePath[:-4] + '.LBL'
cmd = 'head -n 90 ' + localFilePath +' > '+ localLabelPath
print cmd
os.system(cmd)
# Check if this is a polar stereographic image
isPolar = False
f = open(localLabelPath)
for line in f:
if ("MAP_PROJECTION_TYPE" in line) and ("POLAR STEREOGRAPHIC" in line):
isPolar = True
print 'WARNING: POLAR STEREOGRAPHIC DEM MAY NOT UPLOAD PROPERLY'
break
#os.remove(localFilePath)
#os.remove(localLabelPath)
#raise Exception('POLAR STEREOGRAPHIC DEMs on hold until Google fixes a bug!')
f.close()
# Convert from IMG to TIF
tiffFilePath = localFilePath[:-4] + '.TIF'
if not os.path.exists(tiffFilePath):
cmd = 'gdal_translate -of GTiff ' + localFilePath +' '+ tiffFilePath
print cmd
os.system(cmd)
if not isPolar: # Only EQC files need to be corrected
# Correct projected coordinates problems
cmd = 'python /home/pirl/smcmich1/repo/Tools/geoTiffTool.py --normalize-eqc-lon ' + tiffFilePath
print cmd
os.system(cmd)
os.remove(localFilePath) # Clean up source image
return [tiffFilePath, localLabelPath]
def fetchAndPrepFile(db, setName, subtype, remoteURL, workDir):
'''Retrieves a remote file and prepares it for upload'''
#print 'Uploading file ' + setName
# Images with a center over this latitude will use polar stereographic projection
# instead of simple cylindrical projection.
HIGH_LATITUDE_CUTOFF = 65 # Degrees
asuImagePath = os.path.join(workDir, setName + '_noGeo.jp2') # Map projected image from ASU
asuLabelPath = os.path.join(workDir, setName + '_noGeo.lbl') # Label from ASU
edrPath = os.path.join(workDir, setName + '.IMG') # Raw image from PDS
timePath = os.path.join(workDir, setName + '.time') # Contains only the file capture time string
#cubPath = os.path.join(workDir, setName + '.cub') # Output of mroctx2isis
#calPath = os.path.join(workDir, setName + '.cal.cub') # Output of ctxcal
mapPath = os.path.join(workDir, setName + '.map.cub') # Output of cam2map
mapLabelPath = os.path.join(workDir, setName + '.map.pvl') # Specify projection to cam2map
# Generate the remote URLs from the data prefix and volume stored in these parameters
asuImageUrl, asuLabelUrl, edrUrl = generatePdsPath(setName, subtype)
if True: # Map project the EDR ourselves <-- This takes way too long!
print 'Projecting the EDR image using ISIS...'
localFilePath = os.path.join(workDir, setName + '.tif') # The output file we will upload
# Check if this is a "flat" calibration image
badImage = checkForBadFile(edrUrl)
if badImage:
raise Exception('TODO: Remove bad images from the DB!')
if not os.path.exists(edrPath):
# Download the EDR file
cmd = 'wget ' + edrUrl + ' -O ' + edrPath
print cmd
os.system(cmd)
# Extract the image capture time from the .IMG file
if not os.path.exists(timePath):
timeString = getCreationTimeHelper(edrPath)
f = open(timePath, 'w')
f.write(timeString)
f.close()
# Convert and apply calibration to the CTX file
calPath = IrgIsisFunctions.prepareCtxImage(edrPath, workDir, False)
## Find out the center latitude of the file and determine if it is high latitude
centerLat = IrgIsisFunctions.getCubeCenterLatitude(calPath, workDir)
print centerLat
highLat = abs(float(centerLat)) > HIGH_LATITUDE_CUTOFF
if True:#not os.path.exists(mapLabelPath):
# Generate the map label file
generateDefaultMappingPvl(mapLabelPath, highLat)
if True:#not os.path.exists(mapPath):
# Generate the map projected file
cmd = ['timeout', '6h', 'cam2map', 'matchmap=','False', 'from=', calPath, 'to=', mapPath, 'map=', mapLabelPath]
print cmd
#os.system(cmd)
p = subprocess.Popen(cmd)
p.communicate()
if (p.returncode != 0):
raise Exception('Error or timeout running cam2map, returnCode = ' + str(p.returncode))
if True: #not os.path.exists(localFilePath):
# Generate the final image to upload
cmd = 'gdal_translate -of GTiff ' + mapPath + ' ' + localFilePath
print cmd
os.system(cmd)
# Clean up intermediate files
os.remove(mapLabelPath)
os.remove(edrPath)
os.remove(calPath)
os.remove(mapPath)
# Two local files are left around, the first should be uploaded.
return [localFilePath, timePath]
else: # Use the map projected image from the ASU web site
print 'Using ASU projected image...'
localFilePath = os.path.join(workDir, setName + '.jp2') # The output file we will upload
# Note: ASU seems to be missing some files!
# We are using the label path in both projection cases
if not os.path.exists(asuLabelPath):
# Download the label file
cmd = 'wget "' + asuLabelUrl + '" -O ' + asuLabelPath
print cmd
os.system(cmd)
if not IrgFileFunctions.fileIsNonZero(asuLabelPath): # Try the alternate label path
os.remove(asuLabelPath)
asuLabelUrl = asuLabelUrl.replace( '.scyl.', '.ps.')
asuLabelPath = asuLabelPath.replace('.scyl.', '.ps.')
print 'Trying alternate label path: ' + asuLabelUrl
# Download the label file
cmd = 'wget "' + asuLabelUrl + '" -O ' + asuLabelPath
print cmd
os.system(cmd)
if not IrgFileFunctions.fileIsNonZero(asuLabelPath):
raise Exception('Failed to download file label at URL: ' + asuLabelUrl)
# Check the projection type
projType = IrgGeoFunctions.getProjectionFromIsisLabel(asuLabelPath)
if projType != 'SimpleCylindrical':
print 'WARNING: projType = ' + projType
print 'Maps Engine may fail to ingest this file!'
#os.remove(asuLabelPath)
#raise Exception(projType + ' images on hold until Google fixes a bug!')
if not os.path.exists(asuImagePath):
# Download the image file
cmd = 'wget "' + asuImageUrl + '" -O ' + asuImagePath
print cmd
os.system(cmd)
if not IrgFileFunctions.fileIsNonZero(asuImagePath):
raise Exception('Failed to download image file at URL: ' + asuImageUrl)
## Correct the ISIS header if needed
#fixedAsuHeaderPath = putIsisHeaderIn180(asuLabelPath)
#if (fixedAsuHeaderPath != asuLabelPath):
# os.remove(asuLabelPath) # Delete replaced header
if True: # This is fast now, so do it every time
# Correct the file - The JP2 file from ASU needs the geo data from the label file!
#cmd = 'addGeoToAsuCtxJp2.py --keep --label '+ asuLabelPath +' '+ asuImagePath +' '+ localFilePath
#print cmd
#os.system(cmd)
# TODO: Remove unnecessary image copy here
(correctedPath, sidecarPath) = addGeoDataToAsuJp2File(asuImagePath, asuLabelPath, localFilePath, keep=False)
if not IrgFileFunctions.fileIsNonZero(sidecarPath):
raise Exception('Script to add geo data to JP2 file failed!')
# Clean up
os.remove(asuImagePath)
# Three local files are left around, the first should be uploaded.
return [correctedPath, sidecarPath, asuLabelPath]
def loadFrame(self, mission, roll, frame):
'''Populate from an entry in the database'''
self._dbCursor.execute('select * from Frames where trim(MISSION)=? and trim(ROLL)=? and trim(FRAME)=?',
(mission, roll, frame))
rows = self._dbCursor.fetchall()
if len(rows) != 1: # Make sure we found the next lines
raise Exception('Could not find any data for frame: ' +
source_image_utils.getFrameString(mission, roll, frame))
output = source_image_utils.FrameInfo()
rows = rows[0]
#print rows
output.mission = mission
output.roll = roll
output.frame = frame
output.exposure = str(rows[0]).strip()
output.tilt = str(rows[3]).strip()
output.time = str(rows[8]).strip()
output.date = str(rows[9]).strip()
output.cloudPercentage = float(rows[13]) / 100
output.altitude = float(rows[15])
output.focalLength = float(rows[18])
output.centerLat = float(rows[19])
output.centerLon = float(rows[20])
output.nadirLat = float(rows[21])
output.nadirLon = float(rows[22])
output.camera = str(rows[23]).strip()
output.film = str(rows[24]).strip()
if (output.centerLat) and (output.centerLon):
output.centerPointSource = georefDbWrapper.AUTOWCENTER
output.metersPerPixel = None # This information is not stored in the database
# Clean up the time format
output.time = output.time[0:2] +':'+ output.time[2:4] +':'+ output.time[4:6]
# The input tilt can be in letters or degrees so convert
# it so that it is always in degrees.
if (output.tilt == 'NV') or not output.tilt:
output.tilt = '0'
if output.tilt == 'LO': # We want to try these
output.tilt = '30'
if output.tilt == 'HO': # Do not want to try these!
output.tilt = '80'
output.tilt = float(output.tilt)
# Convert the date to 'YYYY.MM.DD' format that the image fetcher wants
# - TODO: Use a standardized format!
output.date = output.date[0:4] + '.' + output.date[4:6] + '.' + output.date[6:8]
# Get the sensor size
(output.sensorWidth, output.sensorHeight) = \
source_image_utils.getSensorSize(output.camera)
#if not output.isGoodAlignmentCandidate():
# return # In this case don't bother finding the images
# Fetch the associated non-raw image files
dbCursor.execute('select * from Images where trim(MISSION)=? and trim(ROLL)=? and trim(FRAME)=?',
(mission, roll, frame))
rows = dbCursor.fetchall()
if len(rows) < 1: # No images provided
return
# Record the image paths
bestNumPixels = 0
for row in rows:
# Get the file path and verify it exists
folder = str(row[4]).strip()
name = str(row[5]).strip()
path = os.path.join(folder, name)
if not os.path.exists(path):
continue
output.imageList.append(path)
# Record if this is the highest resolution image
width = int(row[6])
height = int(row[7])
numPixels = width*height
if numPixels > bestNumPixels:
output.width = width
output.height = height
# Try to find an associated RAW file
thisRaw = source_image_utils.getRawPath(output.mission, output.roll, output.frame)
if os.path.exists(thisRaw):
output.rawPath = thisRaw
else:
print 'Did not find: ' + thisRaw
# TODO: Handle images with no RAW data
# Get the image size
if output.rawPath:
(output.width, output.height) = \
source_image_utils.getRawImageSize(output.rawPath)
if output.width == 0:
[outputPath, exifSourcePath] = source_image_utils.getSourceImage(output)
output.width, output.height = IrgGeoFunctions.getImageSize(outputPath)
print "width is %d" % output.width
print "height is %d" % output.height
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 __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