def extractRIM(impbin, iteration):
IJ.run(imp, "Options...", "iterations=1 count=1 edm=Overwrite do=Nothing");
impErode = Duplicator().run(impbin)
impDilate = Duplicator().run(impbin)
# resized = CanvasResizer().expandStack(impDilate.getStack(), impDilate.getWidth()+2*iteration, impDilate.getHeight()+2*iteration, iteration, iteration)
# impDilate.setStack(None, resized);
for j in range(impErode.getStackSize()):
ipe = impErode.getStack().getProcessor(j+1)
ipe.setBackgroundValue(0)
ipd = impDilate.getStack().getProcessor(j+1)
ipd.setBackgroundValue(0)
for i in range(iteration):
ipe.erode()
ipd.dilate()
# IJ.run(impErode, "Dilate", "stack")
# IJ.run(impDilate, "Erode", "stack")
# resized = CanvasResizer().expandStack(impDilate.getStack(), impDilate.getWidth()-2*iteration, impDilate.getHeight()-2*iteration, -1*iteration, -1*iteration)
# impDilate.setStack(None, resized);
# impErode.show()
# Duplicator().run(impDilate).show()
for i in range(1, impbin.getStackSize()+1):
impDilate.setSlice(i)
impErode.setSlice(i)
ImageCalculator().calculate("XOR", impDilate, impErode)
return impDilate;
def threshold(imPlus, edgeThreshold=2500):
mask = Duplicator().run(imPlus)
mask_stk = mask.getStack()
# First, we threshold based on edges
IJ.setThreshold(mask, edgeThreshold, 100000, "No Update")
for i in range(mask.getImageStackSize()):
mask_stk.getProcessor(i + 1).findEdges()
IJ.run(mask, "Make Binary", "method=Default background=Default black")
# Now, we need to clean up the binary images morphologically
IJ.run(mask, "Dilate", "stack")
IJ.run(mask, "Fill Holes", "stack")
IJ.run(mask, "Erode", "stack")
IJ.run(mask, "Erode", "stack")
# Finally, remove the small particles
stk = ImageStack(mask.getWidth(), mask.getHeight())
p = PA(PA.SHOW_MASKS, 0, None, 200, 100000)
p.setHideOutputImage(True)
for i in range(mask_stk.getSize()):
mask.setSliceWithoutUpdate(i + 1)
p.analyze(mask)
mmap = p.getOutputImage()
stk.addSlice(mmap.getProcessor())
mask.setStack(stk)
mask.setSliceWithoutUpdate(1)
mask.setTitle(mask_title(imPlus.getTitle()))
mask.show()
return mask
def creatResultsCompisite(pathdict, impbin, impsig):
impbin.killRoi()
impsig.killRoi()
impid = Duplicator().run(impbin)
IJ.run(impbin, "Red", "")
for i in range(impid.getStackSize()):
impid.getStack().getProcessor(i+1).setColor(0)
impid.getStack().getProcessor(i+1).fill()
impid.getStack().getProcessor(i+1).setColor(255)
for pathid, path in pathdict.iteritems():
for nuc in path.nucs:
ip = impid.getStack().getProcessor(int(nuc.frame))
ip.setColor(255)
ip.drawString(str(pathid), int(nuc.x), int(nuc.y))
#print 'Draw Path ID:',str(pathid)
images = jarray.array([impbin, impsig, impid], ImagePlus)
comb = RGBStackMerge().mergeHyperstacks(images, False)
comb.setTitle('Measurement Map.tif')
return comb
def analyze(iDataSet, tbModel, p, output_folder):
#
# LOAD FILES
#
filepath = tbModel.getFileAPth(iDataSet, "RAW", "IMG")
filename = tbModel.getFileName(iDataSet, "RAW", "IMG")
print("Analyzing: "+filepath)
IJ.run("Bio-Formats Importer", "open=["+filepath+"] color_mode=Default view=Hyperstack stack_order=XYCZT");
imp = IJ.getImage()
#
# INIT
#
IJ.run("Options...", "iterations=1 count=1");
#
# SCALING
#
IJ.run(imp, "Scale...", "x="+str(p["scale"])+" y="+str(p["scale"])+" z=1.0 interpolation=Bilinear average process create");
imp = IJ.getImage()
# save output file
output_file = filename+"--downscale_input.tif"
IJ.saveAs(IJ.getImage(), "TIFF", os.path.join(output_folder, output_file))
tbModel.setFileAPth(output_folder, output_file, iDataSet, "INPUT","IMG")
#
# CONVERSION
#
#IJ.run(imp, "8-bit", "");
#
# CROPPING
#
#imp.setRoi(392,386,750,762);
#IJ.run(imp, "Crop", "");
#
# BACKGROUND SUBTRACTION
#
# IJ.run(imp, "Subtract...", "value=32768 stack");
IJ.run(imp, "Z Project...", "projection=[Average Intensity]");
imp_avg = IJ.getImage()
ic = ImageCalculator();
imp = ic.run("Subtract create 32-bit stack", imp, imp_avg);
#
# REGION SEGMENTATION
#
imp1 = Duplicator().run(imp, 1, imp.getImageStackSize()-1)
imp2 = Duplicator().run(imp, 2, imp.getImageStackSize())
imp_diff = ic.run("Subtract create 32-bit stack", imp1, imp2);
#imp_diff.show()
IJ.run(imp_diff, "Z Project...", "projection=[Standard Deviation]");
imp_diff_sd = IJ.getImage()
# save
IJ.run(imp_diff_sd, "Gaussian Blur...", "sigma=5");
output_file = filename+"--sd.tif"
IJ.saveAs(imp_diff_sd, "TIFF", os.path.join(output_folder, output_file))
tbModel.setFileAPth(output_folder, output_file, iDataSet, "SD","IMG")
IJ.run(imp_diff_sd, "Enhance Contrast", "saturated=0.35");
IJ.run(imp_diff_sd, "8-bit", "");
IJ.run(imp_diff_sd, "Properties...", "unit=p pixel_width=1 pixel_height=1 voxel_depth=1");
IJ.run(imp_diff_sd, "Auto Local Threshold", "method=Niblack radius=60 parameter_1=2 parameter_2=0 white");
rm = ROIManipulator.getEmptyRm()
IJ.run(imp_diff_sd, "Analyze Particles...", "add");
# select N largest Rois
diameter_roi = []
for i in range(rm.getCount()):
roi = rm.getRoi(i)
diameter_roi.append([roi.getFeretsDiameter(), roi])
diameter_roi = sorted(diameter_roi, reverse=True)
#print diameter_roi
rm.reset()
for i in range(min(len(diameter_roi), p["n_rois"])):
rm.addRoi(diameter_roi[i][1])
# save
output_file = filename+"--rois"
ROIManipulator.svRoisToFl(output_folder, output_file, rm.getRoisAsArray())
tbModel.setFileAPth(output_folder, output_file+".zip", iDataSet, "REGIONS","ROI")
#
# FFT in each region
#
IJ.run(imp, "Variance...", "radius=2 stack");
output_file = filename+"--beats.tif"
IJ.saveAs(imp, "TIFF", os.path.join(output_folder, output_file))
tbModel.setFileAPth(output_folder, output_file, iDataSet, "BEATS","IMG")
n = rm.getCount()
for i_roi in range(n):
imp_selection = Duplicator().run(imp)
rm.select(imp_selection, i_roi)
IJ.run(imp_selection, "Clear Outside", "stack");
imp_selection.show()
# FFT using Parallel FFTJ
transformer = FloatTransformer(imp_selection.getStack())
transformer.fft()
imp_fft = transformer.toImagePlus(SpectrumType.FREQUENCY_SPECTRUM)
imp_fft.show()
# Analyze FFt
IJ.run(imp_fft, "Gaussian Blur 3D...", "x=0 y=0 z=1.5");
IJ.run(imp_fft, "Plot Z-axis Profile", "");
output_file = filename+"--Region"+str(i_roi+1)+"--fft.tif"
IJ.saveAs(IJ.getImage(), "TIFF", os.path.join(output_folder, output_file))
tbModel.setFileAPth(output_folder, output_file, iDataSet, "FFT_R"+str(i_roi+1),"IMG")
IJ.run(imp_fft, "Select All", "");
rm.addRoi(imp_fft.getRoi())
rm.select(rm.getCount())
rt = ResultsTable()
rt = rm.multiMeasure(imp_fft); #print(rt.getColumnHeadings);
x = rt.getColumn(rt.getColumnIndex("Mean1"))
#rm.runCommand("delete")
peak_height_pos = []
x_min = 10
for i in range(x_min,len(x)/2):
before = x[i-1]
center = x[i]
after = x[i+1]
if (center>before) and (center>after):
peak_height_pos.append([float(x[i]),i])
if len(peak_height_pos)>0:
peak_height_pos = sorted(peak_height_pos, reverse=True)
n_max = 3
for i_max in range(min(len(peak_height_pos),n_max)):
tbModel.setNumVal(round(float(len(x))/float(peak_height_pos[i_max][1]),2), iDataSet, "F"+str(i_max+1)+"_R"+str(i_roi+1))
tbModel.setNumVal(int(peak_height_pos[i_max][0]), iDataSet, "A"+str(i_max+1)+"_R"+str(i_roi+1))
