def step(taskName, parameterByName, folderStore, options):
# Get parameters
trainingSize = parameterByName.get("training size")
testSize = parameterByName.get("test size")
positiveFraction = parameterByName.get("positive fraction")
# Get names
windowNames = parameterByName.get("window names", [])
windowPaths = map(folderStore.getWindowPath, windowNames)
windowFolderPaths = map(os.path.dirname, windowPaths)
windowInformations = map(folderStore.getWindowInformation, windowNames)
patchNames = parameterByName.get("patch names", [])
patchPaths = map(folderStore.getPatchPath, patchNames)
patchFolderPaths = map(os.path.dirname, patchPaths)
patchInformations = map(folderStore.getPatchInformation, patchNames)
# Set
sourceInformations = windowInformations + patchInformations
sourceFolderPaths = windowFolderPaths + patchFolderPaths
# Make sure that each dataset has the same windowGeoLength
windowLengthInMeters = store.validateSame(
[x.getWindowLengthInMeters() for x in sourceInformations],
"Datasets must have the same window length in meters: %s" % taskName,
)
# Make sure that each dataset has the same spatialReference
spatialReference = store.validateSame(
[x.getSpatialReference() for x in sourceInformations],
"Datasets must have the same spatial reference: %s" % taskName,
)
# Set
targetDatasetPath = folderStore.fillDatasetPath(taskName)
targetDatasetFolderPath = os.path.dirname(targetDatasetPath)
# Record
information = {
"parameters": {"training size": trainingSize, "test size": testSize, "positive fraction": positiveFraction},
"windows": {"spatial reference": spatialReference, "window length in meters": windowLengthInMeters},
"sources": {
"window names": store.stringifyList(windowNames),
"window paths": store.stringifyList(windowPaths),
"patch names": store.stringifyList(patchNames),
"patch paths": store.stringifyList(patchPaths),
},
}
# Combine training and test sets
if not options.is_test:
print "Combining datasets...\n\ttargetDatasetPath = %s" % targetDatasetPath
information["training set"] = sample_process.combineDatasets(
sample_process.makeTrainingPath(targetDatasetFolderPath),
map(sample_process.makeTrainingPath, sourceFolderPaths),
trainingSize,
positiveFraction,
).getStatistics()
information["test set"] = sample_process.combineDatasets(
sample_process.makeTestPath(targetDatasetFolderPath),
map(sample_process.makeTestPath, sourceFolderPaths),
testSize,
positiveFraction,
).getStatistics()
# Save
store.saveInformation(targetDatasetPath, information)
def add(imageName, ownerID, parameterByName):
"""
Add an image to a database
"""
# Show feedback
print 'Registering image: ' + imageName
# Check whether the image has already been registered
image = meta.Session.query(model.Image).filter_by(name=imageName).first()
# If the image does not exist,
if not image:
# Register the image in the database
image = model.Image(imageName, ownerID)
meta.Session.add(image)
# Prepare path
basePath = parameterByName.get('path', '')
# Load multispectral image
multispectralImagePath = store.fillPath(basePath, parameterByName['multispectral image'])
multispectralImage = image_store.load(multispectralImagePath)
multispectralSpatialReference = multispectralImage.getSpatialReference()
# Load panchromatic image
panchromaticImagePath = store.fillPath(basePath, parameterByName['panchromatic image'])
panchromaticImage = image_store.load(panchromaticImagePath)
panchromaticSpatialReference = panchromaticImage.getSpatialReference()
# Validate
spatialReference = store.validateSame([multispectralSpatialReference, panchromaticSpatialReference], 'Spatial references do not match: %s' % imageName)
# Update image
image.multispectral_path = multispectralImagePath
image.panchromatic_path = panchromaticImagePath
image.spatial_reference = spatialReference
image.is_complete = True
meta.Session.commit()
