del channels # create empty feature stack features = FeatureStack(stack.getWidth(), stack.getHeight(), False) # set my features to the feature stack features.setStack(stack) # put my feature stack into the array featuresArray.set(features, 0) featuresArray.setEnabledFeatures(features.getEnabledFeatures()) mp = MultilayerPerceptron() hidden_layers = "%i,%i,%i" % (20, 14, 8) mp.setHiddenLayers(hidden_layers) mp.setLearningRate(0.7) mp.setDecay(True) mp.setTrainingTime(200) mp.setMomentum(0.3) wekaSegmentation = WekaSegmentation(image) wekaSegmentation.setFeatureStackArray(featuresArray) wekaSegmentation.setClassifier(mp) wekaSegmentation.addExample(0, posroi, 1) wekaSegmentation.addExample(1, negroi, 1) wekaSegmentation.trainClassifier() wekaSegmentation.saveClassifier(folder + "\\vessel-classifier_big.model") output = wekaSegmentation.applyClassifier(image, featuresArray, 0, True) output.show()
for i in range(len(entropies)):
stack.addSlice("entropy" + str(entropies[i]),
entropies_imgs[i].getProcessor())
for i in range(len(gaussians)):
stack.addSlice("gaussian" + str(gaussians[i]),
gaussians_imgs[i].getProcessor())
#free memory
del gaussians_imgs
del entropies_imgs
# put feature stack into array
features = FeatureStack(stack.getWidth(), stack.getHeight(), False)
features.setStack(stack)
featuresArray.set(features, 0)
featuresArray.setEnabledFeatures(features.getEnabledFeatures())
smo = SMO()
wekaSegmentation = WekaSegmentation(image)
wekaSegmentation.setFeatureStackArray(featuresArray)
wekaSegmentation.setClassifier(smo)
wekaSegmentation.addExample(0, posroi, 1)
wekaSegmentation.addExample(1, negroi, 1)
wekaSegmentation.trainClassifier()
wekaSegmentation.saveClassifier(folder + "\\vessel-classifier_scaled.model")
output = wekaSegmentation.applyClassifier(image, featuresArray, 0, True)
output.show()
