def analyzeRegions(frames, probabilityPacks, isGoodWindow):
# Initialize
frameCount = len(frames)
performances = []
# Get
for frameIndex, frame in enumerate(frames):
# Load
regionalPacks = filter(lambda x: point_process.isInsideRegion(x, frame), probabilityPacks)
# Evaluate
correctVector = []
for probabilityPack in regionalPacks:
windowX, windowY, predictedLabel = probabilityPack[:3]
actualLabel = True if isGoodWindow((windowX, windowY)) else False
correctVector.append(predictedLabel == actualLabel)
# Compute performance
performances.append(sum(correctVector) / float(len(correctVector)))
# Show feedback
if frameIndex % 100 == 0:
view.printPercentUpdate(frameIndex + 1, frameCount)
view.printPercentFinal(frameCount)
# Return
return performances
def sampleWindows(targetWindowPath, region, location, parameterByName, options=None):
# Get parameters
exampleCountPerRegion = parameterByName['example count per region']
multispectralPixelShiftValue = parameterByName['multispectral pixel shift value']
shiftCount = parameterByName['shift count']
# Prepare regionFrames
regionSet = region.getDataset()
# regionDataset = region_store.load(region.path)
regionFrames = regionDataset.getRegionFrames()
regionFrameCount = len(regionFrames)
# Prepare counts
testRegionSet = region.getTestDataset()
# testRegionDataset = region_store.load(regionInformation.getTestRegionPath())
testFractionPerRegion = regionInformation.getTestFractionPerRegion()
# Load imageDataset
imagePath = folderStore.getImagePath(regionInformation.getImageName())
imageInformation = image_store.Information(imagePath)
multispectralImage = image_store.load(imageInformation.getMultispectralImagePath())
panchromaticImage = image_store.load(imageInformation.getPanchromaticImagePath())
# Load locations
positiveGeoLocations, spatialReference = point_store.load(imageInformation.getPositiveLocationPath())
# Convert
windowLengthInMeters = regionInformation.getWindowLengthInMeters()
windowPixelDimensions = multispectralImage.convertGeoDimensionsToPixelDimensions(windowLengthInMeters, windowLengthInMeters)
positivePixels = multispectralImage.convertGeoLocationsToPixelLocations(positiveGeoLocations)
# Place examples
exampleMachine = region_process.ExampleMachine(positivePixels, exampleCountPerRegion, testFractionPerRegion, testRegionDataset, windowPixelDimensions, multispectralPixelShiftValue, shiftCount)
examplePacks = []
if options and not options.is_test:
print 'Placing examples in %s regions for "%s"...' % (regionFrameCount, taskName)
for regionFrame in regionFrames:
examplePacks.append(exampleMachine.placeInFrame(regionFrame))
exampleCount = len(examplePacks)
if exampleCount % 10 == 0:
view.printPercentUpdate(exampleCount, regionFrameCount)
view.printPercentFinal(regionFrameCount)
exampleInformation = {}
trainingPaths = []
testPaths = []
# Set
targetWindowFolderPath = os.path.dirname(targetWindowPath)
if options and not options.is_test:
# For each exampleName,
for exampleName in examplePacks[0].keys():
# Convert
examplePixelLocations = sum((x[exampleName] for x in examplePacks), [])
exampleGeoLocations = multispectralImage.convertPixelLocationsToGeoLocations(examplePixelLocations)
examplePath = os.path.join(targetWindowFolderPath, exampleName)
exampleLabel = 1 if 'positive' in exampleName else 0
# Save
point_store.save(examplePath, exampleGeoLocations, spatialReference)
exampleInformation[exampleName + ' count'] = len(examplePixelLocations)
# Extract
print 'Extracting %s windows for %s...' % (len(examplePixelLocations), exampleName)
window_process.extract(examplePath, exampleGeoLocations, exampleLabel, windowLengthInMeters, multispectralImage, panchromaticImage)
(testPaths if 'test' in exampleName else trainingPaths).append(examplePath)
# Record
information = {
'windows': {
'window length in meters': windowLengthInMeters,
'spatial reference': spatialReference,
},
'regions': {
'name': regionName,
'path': regionPath,
'count': regionFrameCount,
},
'examples': exampleInformation,
}
# Combine
if options and not options.is_test:
information['training set'] = sample_process.combineDatasets(sample_process.makeTrainingPath(targetWindowFolderPath), trainingPaths).getStatistics()
information['test set'] = sample_process.combineDatasets(sample_process.makeTestPath(targetWindowFolderPath), testPaths).getStatistics()
# Save information
store.saveInformation(targetWindowPath, information)
