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 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
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])
