def calculateThreshold(image, roi, method):
if roi != None:
bounds = roi.getBounds()
stack = image.getStack()
newstack = ImageStack(bounds.width, bounds.height)
for i in xrange(1, stack.getSize() + 1):
ip = stack.getProcessor(i).duplicate()
ip.fillOutside(roi)
ip.setRoi(roi)
c = ip.crop()
newstack.addSlice(str(i), c)
imp = ImagePlus("ThresholdImage", newstack)
else:
imp = image
thresholder = Auto_Threshold()
result = thresholder.exec(imp, method, False, False, True, False, False, True)
return result
def threshold_stack(imp, thvalue):
# get the stacks
stack, nslices = ImageTools.getImageStack(imp)
for index in range(1, nslices + 1):
ip = stack.getProcessor(index)
# get the histogramm
hist = ip.getHistogram()
lowth = Auto_Threshold.Otsu(hist)
ip.threshold(thvalue)
return imp
def test():
newImg = ImagePlus("GrayScaled", imp)
newip = newImg.getProcessor()
hist = newip.getHistogram()
lowTH = Auto_Threshold.IsoData(hist)
newip.setThreshold(lowTH, max(hist), ImageProcessor.BLACK_AND_WHITE_LUT)
rt = ResultsTable()
pa = ParticleAnalyzer(ParticleAnalyzer.SHOW_RESULTS | ParticleAnalyzer.SHOW_OVERLAY_OUTLINES, Measurements.AREA |Measurements.MEAN |\
Measurements.MEDIAN | Measurements.STD_DEV | Measurements.MIN_MAX | Measurements.RECT, rt,50, 200000, 0.5, 1 )
pa.setResultsTable(rt)
pa.analyze(newImg)
rt.show("Results")
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
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 getThreshold(self, imp, method): thresholder = Auto_Threshold() duplicator = Duplicator() tmp = duplicator.run(imp) return thresholder.exec(tmp, method, False, False, True, False, False, True)
filename = od.getFileName()
directory = od.getDirectory()
path = od.getPath()
print filename
print directory
print path
imp = IJ.openImage(path)
imp.show()
IJ.run(imp, "8-bit", "")
imp = IJ.getImage()
hist = imp.getProcessor().getHistogram()
lowTH = Auto_Threshold.Otsu(hist)
print lowTH
imp.getProcessor().threshold(lowTH)
#pulled from http://wiki.cmci.info/documents/120206pyip_cooking/python_imagej_cookbook#pluginauto_threshold
#imp2 = IJ.getImage()
imp.show()
IJ.run("Watershed")
IJ.run("Set Measurements...", "area min redirect=None decimal=3")
IJ.run("Analyze Particles...", "display")
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=inch pixel_width=1 pixel_height=1 voxel_depth=1.0000000 global"
)
def auto_threshold(imp_in, str_thresh, bScale=False):
"""
auto_threshold_otsu(imp_in, str_thresh="Otsu", bScale=True)
Compute an autothreshold for an image. Adapted from
http://wiki.cmci.info/documents/120206pyip_cooking/python_imagej_cookbook
Parameters
----------
imp_in ImagePlus
The image to threshold
str_thresh String (Default: Default)
The threshold: Otsu, Huang, IsoData, Intermodes, Li,
MaxEntropy, Mean, MinError, Minimum, Moments, Percentile,
RenyiEntropy, Shanbhag, Triangle, Yen, or Default
bScale Boolean (Default: False)
Return
------
imp_out ImagePlus
The binary image
thr_val integer
The threshold value
"""
ti = imp_in.getShortTitle()
imp = imp_in.duplicate()
hist = imp.getProcessor().getHistogram()
if (str_thresh == "Otsu"):
lowTH = Auto_Threshold.Otsu(hist)
elif (str_thresh == "Huang"):
lowTH = Auto_Threshold.Huang(hist)
elif (str_thresh == "Intermodes"):
lowTH = Auto_Threshold.Intermodes(hist)
elif (str_thresh == "IsoData"):
lowTH = Auto_Threshold.IsoData(hist)
elif (str_thresh == "Li"):
lowTH = Auto_Threshold.Li(hist)
elif (str_thresh == "MaxEntropy"):
lowTH = Auto_Threshold.MaxEntropy(hist)
elif (str_thresh == "Mean"):
lowTH = Auto_Threshold.Mean(hist)
elif (str_thresh == "MinError"):
lowTH = Auto_Threshold.MinError(hist)
elif (str_thresh == "Minimum"):
lowTH = Auto_Threshold.Minimum(hist)
elif (str_thresh == "Moments"):
lowTH = Auto_Threshold.Moments(hist)
elif (str_thresh == "Percentile"):
lowTH = Auto_Threshold.Percentile(hist)
elif (str_thresh == "RenyiEntropy"):
lowTH = Auto_Threshold.RenyiEntropy(hist)
elif (str_thresh == "Shanbhag"):
lowTH = Auto_Threshold.Shanbhag(hist)
elif (str_thresh == "Triangle"):
lowTH = Auto_Threshold.Triangle(hist)
elif (str_thresh == "Yen"):
lowTH = Auto_Threshold.Yen(hist)
else:
lowTH = Auto_Threshold.Default(hist)
imp.getProcessor().threshold(lowTH)
imp.setDisplayRange(0, lowTH + 1)
ImageConverter.setDoScaling(bScale)
IJ.run(imp, "8-bit", "")
imp.setDisplayRange(0, 255)
imp.setTitle(ti + "-bin-" + str_thresh)
return ([imp, lowTH])