def step(taskName, parameterByName, folderStore, options):
# Get probabilityInformation
probabilityName = parameterByName['probability name']
probabilityPath = folderStore.getProbabilityPath(probabilityName)
probabilityInformation = probability_store.Information(probabilityPath)
# Get imageInformation
imageName = probabilityInformation.getImageName()
imageInformation = folderStore.getImageInformation(imageName)
# Get parameters
positiveLocationPath = imageInformation.getPositiveLocationPath()
multispectralImage = imageInformation.getMultispectralImage()
spatialReference = multispectralImage.getSpatialReference()
evaluationRadiusInMeters = parameterByName['evaluation radius in meters']
evaluationDiameterInMeters = evaluationRadiusInMeters * 2
iterationCountPerBurst = parameterByName['iteration count per burst']
maximumDiameterInMeters = parameterByName['maximum diameter in meters']
minimumDiameterInMeters = parameterByName['minimum diameter in meters']
# Run
targetLocationPath = folderStore.fillLocationPath(taskName)
targetFolderPath = os.path.dirname(targetLocationPath)
locationInformation = {
'location': {
'path': targetLocationPath,
'spatial reference': spatialReference,
},
'probability': {
'name': probabilityName,
'path': probabilityPath,
},
'parameters': {
'iteration count per burst': iterationCountPerBurst,
'maximum diameter in meters': maximumDiameterInMeters,
'minimum diameter in meters': minimumDiameterInMeters,
'evaluation radius in meters': evaluationRadiusInMeters,
},
}
if not options.is_test:
# Cluster
print 'Clustering locations...'
probability_process.cluster(targetLocationPath, probabilityPath, iterationCountPerBurst, maximumDiameterInMeters, minimumDiameterInMeters)
# If scanning has finished,
if probabilityInformation.hasPerformance():
print 'Evaluating locations...'
# Load regions
regionPath = probabilityInformation.getRegionPath()
regionGeoFrames = region_store.loadShapefile(regionPath, multispectralImage, withGeoToPixelConversion=False)
# Evaluate locations
locationInformation['performance'] = evaluation_process.evaluateLocations(targetFolderPath, evaluationDiameterInMeters, positiveLocationPath, targetLocationPath, regionGeoFrames)
# Record
store.saveInformation(targetLocationPath, locationInformation)
def defineRegions(targetRegionPath, multispectralImagePath, panchromaticImagePath, parameterByName, options=None):
# Load
targetTestRegionPath = targetRegionPath + '-test'
multispectralImage = image_store.load(str(multispectralImagePath))
panchromaticImage = image_store.load(str(panchromaticImagePath))
regionPath = parameterByName.get('region path')
multispectralRegionFrames = parameterByName.get('multispectral region frames')
windowLengthInMeters = parameterByName.get('window length in meters')
regionLengthInWindows = parameterByName.get('region length in windows')
testFractionPerRegion = parameterByName['test fraction per region']
coverageFraction = parameterByName.get('coverage fraction', 1)
coverageFrequency = int(1 / coverageFraction)
coverageOffset = parameterByName.get('coverage offset', 0)
# If regionPath is defined, use it
if regionPath:
regionGenerator = (x for x in region_store.loadShapefile(regionPath, multispectralImage))
# If multispectralRegionFrames are defined, use them
elif multispectralRegionFrames:
regionGenerator = (x for x in multispectralRegionFrames)
# Otherwise, prepare regionGenerator
else:
regionGenerator = region_store.makeRegionGenerator(multispectralImage, panchromaticImage, regionLengthInWindows, windowLengthInMeters)
# Save regions
regionDataset = region_store.create(targetRegionPath)
testRegionDataset = region_store.create(targetTestRegionPath)
if options and not options.is_test:
for regionIndex, regionWindow in itertools.izip(itertools.count(1), regionGenerator):
if (regionIndex + coverageOffset) % coverageFrequency == 0:
regionDataset.addRegion(regionWindow)
regionFrame = region_store.getMultispectralPixelFrame(regionWindow)
testRegionDataset.addFrame(region_process.placeTestRegion(regionFrame, testFractionPerRegion))
# Save as shapefiles
regionDataset.saveShapefile(targetRegionPath, multispectralImage)
testRegionDataset.saveShapefile(targetTestRegionPath, multispectralImage)
# Prepare information
information = {
'parameters': {
'multispectral image path': multispectralImagePath,
'panchromatic image path': panchromaticImagePath,
'test fraction per region': testFractionPerRegion,
'window length in meters': windowLengthInMeters,
},
'regions': {
'path': regionDataset.getPath(),
'count': regionDataset.count(),
},
'test regions': {
'path': testRegionDataset.getPath(),
'count': testRegionDataset.count(),
},
}
if regionPath:
information['parameters'].update({
'region path': regionPath,
})
elif multispectralRegionFrames:
information['parameters'].update({
'multispectral region frames': store.stringifyNestedList(multispectralRegionFrames),
})
else:
information['parameters'].update({
'region length in windows': regionLengthInWindows,
'coverage fraction': coverageFraction,
'coverage offset': coverageOffset,
})
# Save information
store.saveInformation(targetRegionPath, information)
