# Open the Drosophila larval brain sample RGB image stack
#imp = IJ.openImage("https://samples.imagej.net/samples/first-instar-brain.zip")
imp = IJ.getImage()
stack1 = imp.getStack() # of ColorProcessor
# Scale up by 1.5x
scale = 1.5
model = AffineModel2D()
model.set(scale, 0, 0, scale, 0, 0)
# An arbitrary affine, obtained from Plugins - Transform - Interactive Affine (push enter key to print it)
#model.set(1.16, 0.1484375, -0.375, 1.21875, 38.5, -39.5)
# New stack, larger
stack2 = ImageStack(int(imp.getWidth() * scale), int(imp.getHeight() * scale))
for index in xrange(1, stack1.getSize() + 1):
stack2.addSlice(ColorProcessor(stack2.getWidth(), stack2.getHeight()))
imp2 = ImagePlus("larger (push)", stack2)
# Map data from stack to stack2 using the model transform
position = zeros(2, 'd')
"""
# First approach: push (WRONG!)
width1, height1 = stack1.getWidth(), stack1.getHeight()
for index in xrange(1, 3): #stack1.size() + 1):
cp1 = stack1.getProcessor(index)
cp2 = stack2.getProcessor(index)
for y in xrange(height1):
for x in xrange(width1):
position[1] = y
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())
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)
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
