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)