def step(taskName, parameterByName, folderStore, options):
# Get parameters
classifierName = parameterByName['classifier name']
classifierPath = folderStore.getClassifierPath(classifierName)
classifierInformation = folderStore.getClassifierInformation(classifierName)
windowLengthInMeters = classifierInformation.getWindowLengthInMeters()
scanRatio = parameterByName['scan ratio']
regionName = parameterByName['region name']
regionPath = folderStore.getRegionPath(regionName)
regionInformation = folderStore.getRegionInformation(regionName)
regionDataset = regionInformation.getRegionDataset()
# Prepare
imageName = regionInformation.getImageName()
imagePath = folderStore.getImagePath(imageName)
imageInformation = folderStore.getImageInformation(imageName)
multispectralImagePath = imageInformation.getMultispectralImagePath()
multispectralImage = image_store.load(multispectralImagePath)
panchromaticImagePath = imageInformation.getPanchromaticImagePath()
positiveLocationPath = imageInformation.getPositiveLocationPath()
regionFrames = regionDataset.getRegionFrames()
# Record
targetProbabilityPath = folderStore.fillProbabilityPath(taskName)
probabilityInformation = {
'classifier': {
'name': classifierName,
'path': classifierPath,
},
'parameters': {
'window length in meters': windowLengthInMeters,
'scan ratio': scanRatio,
},
'probability': {
'region name': regionName,
'region path': regionPath,
'image name': imageName,
'image path': imagePath,
},
}
store.saveInformation(targetProbabilityPath, probabilityInformation)
# If this is not a test,
if not options.is_test:
# Scan
info = classifier.scan(targetProbabilityPath, classifierPath, multispectralImagePath, panchromaticImagePath, scanRatio, regionFrames)
# Save
print 'Saving probability matrix as a shapefile...'
probability_store.saveShapefile(targetProbabilityPath, targetProbabilityPath, image_store.load(multispectralImagePath), windowLengthInMeters)
# Evaluate
print 'Evaluating windows...'
windowPerformance, wrongPixelCenters, actualPixelPointMachine = evaluation_process.evaluateWindows(targetProbabilityPath, positiveLocationPath, multispectralImagePath, windowLengthInMeters)
probabilityInformation['performance'] = windowPerformance
probabilityInformation['performance'].update(info)
else:
wrongPixelCenters = []
windowPixelWidth, windowPixelHeight = multispectralImage.convertGeoDimensionsToPixelDimensions(windowLengthInMeters, windowLengthInMeters)
actualPixelPointMachine = point_process.PointMachine([], 'INTEGER', windowPixelWidth, windowPixelHeight)
# Update
store.saveInformation(targetProbabilityPath, probabilityInformation)
# Build patch
patch_process.buildPatchFromScan(wrongPixelCenters, folderStore, taskName, targetProbabilityPath, multispectralImagePath, panchromaticImagePath, actualPixelPointMachine, classifierInformation, options.is_test)
def step(taskName, parameterByName, folderStore, options):
# Get parameters
imageName = parameterByName['image name']
imagePath = folderStore.getImagePath(imageName)
imageInformation = folderStore.getImageInformation(imageName)
multispectralImagePath = imageInformation.getMultispectralImagePath()
multispectralImage = image_store.load(multispectralImagePath)
scanRatio = float(parameterByName['scan ratio'])
classifierName = parameterByName['classifier name']
classifierPath = folderStore.getClassifierPath(classifierName)
classifierInformation = folderStore.getClassifierInformation(classifierName)
windowLengthInMeters = classifierInformation.getWindowLengthInMeters()
panchromaticImagePath = imageInformation.getPanchromaticImagePath()
positiveLocationPath = imageInformation.getPositiveLocationPath()
coverageFraction = parameterByName.get('coverage fraction', 1)
# Record
targetProbabilityPath = folderStore.fillProbabilityPath(taskName)
regionPath = targetProbabilityPath + '_region'
probabilityInformation = {
'classifier': {
'name': classifierName,
'path': classifierPath,
},
'parameters': {
'window length in meters': windowLengthInMeters,
'scan ratio': scanRatio,
'coverage fraction': coverageFraction,
},
'probability': {
'region path': regionPath,
'image name': imageName,
'image path': imagePath,
},
}
# Run
store.saveInformation(targetProbabilityPath, probabilityInformation)
if not options.is_test:
# Frame
xMax = multispectralImage.width
yMax = multispectralImage.height
xMargin = int(xMax * (1 - coverageFraction) / 2)
yMargin = int(yMax * (1 - coverageFraction) / 2)
regionFrames = [(xMargin, yMargin, xMax - xMargin, yMax - yMargin)]
regionDataset = region_store.save(regionPath, regionFrames)
regionDataset.saveShapefile(regionPath, multispectralImage)
# Scan
info = classifier.scan(targetProbabilityPath, classifierPath, multispectralImagePath, panchromaticImagePath, scanRatio, regionFrames)
# Save
print 'Saving probability matrix as a shapefile...'
probability_store.saveShapefile(targetProbabilityPath, targetProbabilityPath, multispectralImage, windowLengthInMeters)
# Evaluate windows
windowPerformance, wrongPixelCenters, actualPixelPointMachine = evaluation_process.evaluateWindows(targetProbabilityPath, positiveLocationPath, multispectralImagePath, windowLengthInMeters)
probabilityInformation['performance'] = windowPerformance
probabilityInformation['performance'].update(info)
else:
wrongPixelCenters = []
windowPixelWidth, windowPixelHeight = multispectralImage.convertGeoDimensionsToPixelDimensions(windowLengthInMeters, windowLengthInMeters)
actualPixelPointMachine = point_process.PointMachine([], 'INTEGER', windowPixelWidth, windowPixelHeight)
# Update
store.saveInformation(targetProbabilityPath, probabilityInformation)
# Save patch
patch_process.buildPatchFromScan(wrongPixelCenters, folderStore, taskName, targetProbabilityPath, multispectralImagePath, panchromaticImagePath, actualPixelPointMachine, classifierInformation, options.is_test)
