def load(classifierPath):
# Load featureSet
classifierInformation = classifier.Information(classifierPath)
featureModuleName = classifierInformation.getFeatureModuleName()
featureModule = store.getLibraryModule(featureModuleName)
featureClassName = classifierInformation.getFeatureClassName()
featureClass = getattr(featureModule, featureClassName)
featureSet = featureClass()
# Start classifierProcess
classifierProcess = runLushProcess('classifyStream', classifierPath)
# Define
def classify(imageContent=None, matrixContent=None):
if imageContent and not matrixContent:
# Extract matrix
multispectralWindow, panchromaticWindow = imageContent
matrix = featureSet.extractFeatures(multispectralWindow, panchromaticWindow)
matrixContent = makeLushMatrixDirectly(matrix)
# Classify
classifierProcess.stdin.write(matrixContent + '\n')
line = classifierProcess.stdout.readline().rstrip()
label, probability = pattern_classifierOutput.match(line).groups()
# Return
return int(label), float(probability)
# Return
return classify
def load(classifierPath):
classifierInformation = Information(classifierPath)
classifierModuleName = classifierInformation.getClassifierModuleName()
classifierModule = store.getLibraryModule(classifierModuleName)
return classifierModule.load(classifierPath)
