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()
def VesselFinder(channel_array, classifier_path):
channels = channel_array
image = channels[3]
channels = channels[0:3]
proc = image.getProcessor()
directional_op = ImagePlus("directional_op", proc)
tubes = range(5, 130, 12)
img_source = ImagePlus("image", proc)
src = clij2.push(img_source)
dst = clij2.create(src)
sigma = 2
clij2.gaussianBlur2D(src, dst, sigma, sigma)
img_blur2 = clij2.pull(dst)
src.close()
dst.close()
print("Tubeness mt start")
tubenesses = [None] * len(tubes)
rang = range(len(tubes))
threads = []
for i in rang:
threads.append(
threading.Thread(target=run_tube,
args=(img_blur2, tubes[i], i, tubenesses)))
threads[i].start()
[x.join() for x in threads]
print("Tubeness all done")
print(tubenesses)
src = clij2.push(img_source)
dst = clij2.create(src)
sigmas = [5, 20]
imgsigmas = []
for sigma in sigmas:
clij2.gaussianBlur2D(src, dst, sigma, sigma)
img = clij2.pull(dst)
imgsigmas.append(img)
print("Gaussian Blur done")
src.close()
dst.close()
variances = [5, 20]
imgvars = []
for variance in variances:
img = ImagePlus("image", proc)
IJ.run(img, "Variance...", "radius=" + str(variance))
imgvars.append(img)
print("Gaussian Blur done")
featuresArray = FeatureStackArray(image.getStackSize())
stack = ImageStack(image.getWidth(), image.getHeight())
# add new feature here (2/2) and do not forget to add it with a
# unique slice label!
stack.addSlice("directional_op", directional_op.getProcessor())
for i in range(len(sigmas)):
stack.addSlice("sigma" + str(sigmas[i]), imgsigmas[i].getProcessor())
for i in range(len(tubes)):
stack.addSlice("Tubeness" + str(tubes[i]),
tubenesses[i].getProcessor())
for i in range(len(variances)):
stack.addSlice("Variance" + str(variances[i]),
imgvars[i].getProcessor())
for i in range(len(channels)):
stack.addSlice("channel" + str(i + 1), channels[i].getProcessor())
del sigmas
del tubes
del variances
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())
del stack
wekaSegmentation = WekaSegmentation(image)
wekaSegmentation.setFeatureStackArray(featuresArray)
wekaSegmentation.loadClassifier(classifier_path +
"\\vessel-classifier_big.model")
output = wekaSegmentation.applyClassifier(image, featuresArray, 0, True)
System.gc()
return output
