def Weka_Segm(dirs):
""" Loads trained classifier and segments cells """
""" in aligned images according to training. """
# Define reference image for segmentation (default is timepoint000).
w_train = os.path.join(dirs["Composites_Aligned"], "Timepoint000.tif")
trainer = IJ.openImage(w_train)
weka = WekaSegmentation()
weka.setTrainingImage(trainer)
# Select classifier model.
weka.loadClassifier(str(classifier))
weka.applyClassifier(False)
segmentation = weka.getClassifiedImage()
segmentation.show()
# Convert image to 8bit
ImageConverter(segmentation).convertToRGB()
ImageConverter(segmentation).convertToGray8()
# Threshold segmentation to soma only.
hist = segmentation.getProcessor().getHistogram()
lowth = Auto_Threshold.IJDefault(hist)
segmentation.getProcessor().threshold(lowth)
segmentation.getProcessor().setThreshold(0, 0, ImageProcessor.NO_LUT_UPDATE)
segmentation.getProcessor().invert()
segmentation.show()
# Run Watershed Irregular Features plugin, with parameters.
IJ.run(segmentation, "Watershed Irregular Features",
"erosion=20 convexity_treshold=0 separator_size=0-Infinity")
# Make selection and add to RoiManager.
RoiManager()
rm = RoiManager.getInstance()
rm.runCommand("reset")
roi = ThresholdToSelection.run(segmentation)
segmentation.setRoi(roi)
rm.addRoi(roi)
rm.runCommand("Split")
def apply_autothreshold(hist, method='Otsu'):
if method == 'Otsu':
lowthresh = Auto_Threshold.Otsu(hist)
if method == 'Triangle':
lowthresh = Auto_Threshold.Triangle(hist)
if method == 'IJDefault':
lowthresh = Auto_Threshold.IJDefault(hist)
if method == 'Huang':
lowthresh = Auto_Threshold.Huang(hist)
if method == 'MaxEntropy':
lowthresh = Auto_Threshold.MaxEntropy(hist)
if method == 'Mean':
lowthresh = Auto_Threshold.Mean(hist)
if method == 'Shanbhag':
lowthresh = Auto_Threshold.Shanbhag(hist)
if method == 'Yen':
lowthresh = Auto_Threshold.Yen(hist)
if method == 'Li':
lowthresh = Auto_Threshold.Li(hist)
return lowthresh
