def analyzeImage(passImage, passModel, passChannel, passProbability,
passPixels, passOutput):
retResults = list()
windows = set()
# Register current window
registerWindow(passImage.title, windows)
# Extract the requested channel
IJ.run("Z Project...", "projection=[Max Intensity]")
registerWindow("MAX_" + passImage.title, windows)
IJ.run("Duplicate...", "title=temp")
registerWindow("temp", windows)
# Apply WEKA training model to image
wekaSeg = WekaSegmentation(WindowManager.getCurrentImage())
wekaSeg.loadClassifier(passModel)
wekaSeg.applyClassifier(True)
# Extract first slice of probability map
wekaImg = wekaSeg.getClassifiedImage()
wekaImg.show()
registerWindow("Probability maps", windows)
IJ.setSlice(1)
IJ.run("Duplicate...", "title=temp2")
registerWindow("temp2", windows)
# Apply threshold and save
IJ.setThreshold(passProbability, 1, "Black & White")
fileParts = passImage.getTitle().split(".")
IJ.save(
os.path.join(
passOutput, "{0}-probmap.png".format(fileParts[0],
'.'.join(fileParts[1:]))))
# Perform particle analysis and save
IJ.run("Analyze Particles...",
"size={0}-Infinity show=Outlines pixel clear".format(passPixels))
registerWindow("Drawing of temp2", windows)
IJ.save(
os.path.join(
passOutput, "{0}-particles.png".format(fileParts[0],
'.'.join(fileParts[1:]))))
# Get measurements
tableResults = ResultsTable.getResultsTable()
for rowIdx in range(tableResults.size()):
retResults.append(tableResults.getRowAsString(rowIdx).split())
retResults[-1].insert(
0,
WindowManager.getCurrentImage().getCalibration().unit)
retResults[-1].append(
float(retResults[-1][4]) / float(retResults[-1][3]))
# Close windows
closeWindows(windows)
return retResults
def analyzeImage(passImage, passModel, passProbability, passPixels,
passOutput):
retResults = list()
# Apply WEKA training model to image
wekaSeg = WekaSegmentation(passImage)
wekaSeg.loadClassifier(passModel)
wekaSeg.applyClassifier(True)
# Extract first slice of probability map
wekaImg = wekaSeg.getClassifiedImage()
wekaImg.show()
IJ.selectWindow("Probability maps")
IJ.setSlice(1)
IJ.run("Duplicate...", "title=temp")
# Apply threshold and save
IJ.setThreshold(passProbability, 1, "Black & White")
fileParts = passImage.getTitle().split(".")
IJ.save(
os.path.join(
passOutput, "{0}-probmap.png".format(fileParts[0],
'.'.join(fileParts[1:]))))
# Perform particle analysis and save
IJ.run("Analyze Particles...",
"size={0}-Infinity show=Outlines pixel clear".format(passPixels))
IJ.selectWindow("Drawing of temp")
IJ.save(
os.path.join(
passOutput, "{0}-particles.png".format(fileParts[0],
'.'.join(fileParts[1:]))))
# Get measurements (skip final row, this will correspond to legend)
tableResults = ResultsTable.getResultsTable()
for rowIdx in range(tableResults.size() - 1):
retResults.append(tableResults.getRowAsString(rowIdx).split())
# Close interim windows
IJ.run("Close")
IJ.selectWindow("temp")
IJ.run("Close")
IJ.selectWindow("Probability maps")
IJ.run("Close")
return retResults
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 analyzeImage(passImage, passModel, passProbability, passOutput):
retResults = list()
# Apply weka training model to image
wekaSeg = WekaSegmentation(passImage)
wekaSeg.loadClassifier(passModel)
wekaSeg.applyClassifier(True)
# Extract probability map
wekaImg = wekaSeg.getClassifiedImage()
wekaImg.show()
IJ.run("Clear Results")
# Process each slice
for sliceIdx in range(ROW_BACKGROUND + 1):
# Select slice and duplicate
IJ.selectWindow("Probability maps")
IJ.setSlice(sliceIdx + 1)
IJ.run("Duplicate...", "title=temp")
# Apply threshold to probability
IJ.setThreshold(passProbability, 1, "Black & White")
# For background, take inverse
if sliceIdx == ROW_BACKGROUND: IJ.run("Invert")
# Change background to NaN for area, then measure
IJ.run("NaN Background", ".")
IJ.run("Measure")
# Save image to output directory
fileParts = passImage.getTitle().split(".")
IJ.save(
os.path.join(
passOutput,
"{0}-{1}.{2}".format(fileParts[0], FILE_TYPE[sliceIdx],
'.'.join(fileParts[1:]))))
IJ.selectWindow("temp")
IJ.run("Close")
# Close probability maps
IJ.selectWindow("Probability maps")
IJ.run("Close")
# Obtain results
tempResults = list()
tableResults = ResultsTable.getResultsTable()
for rowIdx in range(tableResults.size()):
tempResults.append(
[float(x) for x in tableResults.getRowAsString(rowIdx).split()])
# Compile image statistics as M/(M+F), F/(M+F), M/total, F/total, U/total, E/total, M+F, total
mfTotal = tempResults[ROW_MALE][FIELD_AREA] + tempResults[ROW_FEMALE][
FIELD_AREA]
total = tempResults[ROW_BACKGROUND][FIELD_AREA]
retResults.append(tempResults[ROW_MALE][FIELD_AREA] / mfTotal)
retResults.append(tempResults[ROW_FEMALE][FIELD_AREA] / mfTotal)
retResults.append(tempResults[ROW_MALE][FIELD_AREA] / total)
retResults.append(tempResults[ROW_FEMALE][FIELD_AREA] / total)
retResults.append(tempResults[ROW_UNDIF][FIELD_AREA] / total)
retResults.append(tempResults[ROW_EXTRA][FIELD_AREA] / total)
retResults.append(mfTotal)
retResults.append(total)
return retResults
