def evaluateWindowsByLength(probabilityPath, geoLength):
# Load information
probabilityInformation = probability_store.Information(probabilityPath)
multispectralImage, panchromaticImage = probabilityInformation.getImagePack()
# Load regions
regionGenerator = image_process.makeWindowGenerator(multispectralImage, panchromaticImage, geoLength, 1)
regionPixelFrames = map(region_store.getMultispectralPixelFrame, regionGenerator)
# Return
return evaluateWindowsByRegions(probabilityPath, regionPixelFrames)
def scan(targetProbabilityPath, classifierPath, multispectralImagePath, panchromaticImagePath, scanRatio, regionFrames=None):
# Initialize
classify = load(classifierPath)
classifierInformation = Information(classifierPath)
windowLengthInMeters = classifierInformation.getWindowLengthInMeters()
multispectralImage = image_store.load(multispectralImagePath)
panchromaticImage = image_store.load(panchromaticImagePath)
# If no regions are defined,
if not regionFrames:
# Use the entire image
regionFrames = [(0, 0, multispectralImage.width, multispectralImage.height)]
# Open probabilityFile
probabilityFile = open(targetProbabilityPath, 'wt')
# For each window,
for regionIndex, regionFrame in enumerate(regionFrames):
print 'Scanning region %s/%s...' % (regionIndex + 1, len(regionFrames))
for window in image_process.makeWindowGenerator(multispectralImage, panchromaticImage, windowLengthInMeters, scanRatio, regionFrame):
# Classify
classification_string = '%s %s' % classify(imageContent=window.extractImage())
windowLocation_string = '%s %s' % window.multispectralWindowLocation
probabilityFile.write('%s %s\n' % (windowLocation_string, classification_string))
# Close probabilityFile
probabilityFile.close()
def makeRegionGenerator(multispectralImage, panchromaticImage, regionLengthInWindows, windowLengthInMeters):
return image_process.makeWindowGenerator(multispectralImage, panchromaticImage, regionLengthInWindows * windowLengthInMeters, 1)
