def training_Classifier():
input_im = IJ.openImage(
"/home/aik19/Achintha/Sintering analysis/Scripts/Sintering_Analysis_The_New_Approach/training_sample_1.tif"
)
labels = IJ.openImage(
"/home/aik19/Achintha/Sintering analysis/Scripts/Sintering_Analysis_The_New_Approach/Filtered_training_Sample.labels.tif"
)
# for all slices in input2, add white pixels as labels for class 2 and
#black pixels as labels for class 1
segmentator = WekaSegmentation(input_im)
segmentator.addBinaryData(input_im, labels, "class 2", "class 1")
segmentator.trainClassifier()
testImage = IJ.openImage(
"/home/aik19/Achintha/Sintering analysis/Scripts/Sintering_Analysis_The_New_Approach/74122_micro_test_3.tif"
)
result = segmentator.applyClassifier(testImage)
result.show()
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()
