def poreDetectionTrueColor(inputImp, inputDataset, inputRoi, ops, data, display, detectionParameters):
detectionParameters.setCalibration(inputImp);
# calculate area of roi
stats=inputImp.getStatistics()
inputRoiArea=stats.area
# get the bounding box of the active roi
inputRec = inputRoi.getBounds()
x1=long(inputRec.getX())
y1=long(inputRec.getY())
x2=x1+long(inputRec.getWidth())-1
y2=y1+long(inputRec.getHeight())-1
# crop the roi
interval=FinalInterval( array([x1, y1 ,0], 'l'), array([x2, y2, 2], 'l') )
cropped=ops.image().crop(inputDataset.getImgPlus(), interval )
datacropped=data.create(cropped)
display.createDisplay("cropped", datacropped)
croppedPlus=IJ.getImage()
# instantiate the duplicator and the substackmaker classes
duplicator=Duplicator()
substackMaker=SubstackMaker()
# duplicate the roi
duplicate=duplicator.run(croppedPlus)
# separate into RGB and get the blue channel
IJ.run(duplicate, "RGB Stack", "")
bluePlus=substackMaker.makeSubstack(duplicate, "3-3")
blue=ImgPlus(ImageJFunctions.wrapByte(bluePlus))
bluePlus.setTitle("Blue")
# duplicate and look for bright spots
thresholdedLight=SpotDetection2(bluePlus)
# duplicate and look for dark spots
thresholdedDark=SpotDetection3(bluePlus, True)
# convert to mask
Prefs.blackBackground = True
#IJ.run(thresholdedDark, "Convert to Mask", "")substackMaker
# clear the region outside the roi
clone=inputRoi.clone()
clone.setLocation(0,0)
Utility.clearOutsideRoi(thresholdedLight, clone)
Utility.clearOutsideRoi(thresholdedDark, clone)
roimClosedPores = RoiManager(True)
detectionParameters.setCalibration(thresholdedDark)
countParticles(thresholdedDark, roimClosedPores, detectionParameters.closedPoresMinSize, detectionParameters.closedPoresMaxSize, \
detectionParameters.closedPoresMinCircularity, detectionParameters.closedPoresMaxCircularity)
# count number of open pores
roimOpenPores = RoiManager(True)
detectionParameters.setCalibration(thresholdedDark)
countParticles(thresholdedDark, roimOpenPores, detectionParameters.openPoresMinSize, detectionParameters.openPoresMaxSize, \
detectionParameters.openPoresMinCircularity, detectionParameters.openPoresMaxCircularity)
# count number of sebum
roimSebum = RoiManager(True)
detectionParameters.setCalibration(thresholdedLight)
countParticles(thresholdedLight, roimSebum, detectionParameters.sebumMinSize, detectionParameters.sebumMaxSize, \
detectionParameters.sebumMinCircularity, detectionParameters.sebumMaxCircularity)
# create lists for open and closed pores
closedPoresList=[]
for roi in roimClosedPores.getRoisAsArray():
closedPoresList.append(roi.clone())
openPoresList=[]
for roi in roimOpenPores.getRoisAsArray():
openPoresList.append(roi.clone())
# create lists for sebum
sebumsList=[]
for roi in roimSebum.getRoisAsArray():
sebumsList.append(roi.clone())
# a list of all pores
allList=closedPoresList+openPoresList+sebumsList
# calculate the stats for all pores
detectionParameters.setCalibration(bluePlus)
statsDict=CountParticles.calculateParticleStats(bluePlus, allList)
poresTotalArea=0
for area in statsDict['Areas']:
poresTotalArea=poresTotalArea+area
print area
poresAverageArea=poresTotalArea/len(statsDict['Areas'])
# for each roi add the offset such that the roi is positioned in the correct location for the
# original image
[roi.setLocation(roi.getXBase()+x1, roi.getYBase()+y1) for roi in allList]
# draw the rois on the image
inputImp.getProcessor().setColor(Color.green)
IJ.run(inputImp, "Line Width...", "line=3");
inputImp.getProcessor().draw(inputRoi)
IJ.run(inputImp, "Line Width...", "line=1");
[CountParticles.drawParticleOnImage(inputImp, roi, Color.red) for roi in closedPoresList]
[CountParticles.drawParticleOnImage(inputImp, roi, Color.magenta) for roi in openPoresList]
[CountParticles.drawParticleOnImage(inputImp, roi, Color.green) for roi in sebumsList]
inputImp.updateAndDraw()
# close images that represent intermediate steps
croppedPlus.changes=False
croppedPlus.close()
bluePlus.changes=False
bluePlus.close()
print "Total ROI Area: "+str(inputRoiArea)
print "Num closed pores: "+str(len(closedPoresList))
print "Num open pores: "+str(len(openPoresList))
print "Num sebums: "+str(len(sebumsList))
print "Total particles: "+str(len(allList))+ " total area: "+str(poresTotalArea)
statslist=[inputRoiArea, len(allList), len(closedPoresList), len(openPoresList), len(sebumsList), poresAverageArea, 100*poresTotalArea/inputRoiArea]
header=[Messages.TotalAreaMask, Messages.TotalDetectedPores, Messages.ClosedPores, Messages.OpenPores, Messages.Sebum, Messages.PoresAverageArea, Messages.PoresFractionalArea]
return header,statslist
def poreDetectionUV(inputImp, inputDataset, inputRoi, ops, data, display, detectionParameters):
title = inputImp.getTitle()
title=title.replace('UV', 'SD')
print title
#trueColorImp= WindowManager.getImage(title)
#print type( trueColorImp)
# calculate are of roi
stats=inputImp.getStatistics()
inputRoiArea=stats.area
print inputRoi
# get the bounding box of the active roi
inputRec = inputRoi.getBounds()
x1=long(inputRec.getX())
y1=long(inputRec.getY())
x2=x1+long(inputRec.getWidth())-1
y2=y1+long(inputRec.getHeight())-1
print x1
print y1
print x2
print y2
# crop the roi
interval=FinalInterval( array([x1, y1 ,0], 'l'), array([x2, y2, 2], 'l') )
cropped=ops.crop(interval, None, inputDataset.getImgPlus() )
datacropped=data.create(cropped)
display.createDisplay("cropped", datacropped)
croppedPlus=IJ.getImage()
duplicator=Duplicator()
substackMaker=SubstackMaker()
# duplicate the roi
duplicate=duplicator.run(croppedPlus)
#duplicate.show()
# convert duplicate of roi to HSB and get brightness
IJ.run(duplicate, "HSB Stack", "");
brightnessPlus=substackMaker.makeSubstack(duplicate, "3-3")
brightness=ImgPlus(ImageJFunctions.wrapByte(brightnessPlus))
brightnessPlus.setTitle("Brightness")
#brightnessPlus.show()
# make another duplicate, split channels and get red
duplicate=duplicator.run(croppedPlus)
channels=ChannelSplitter().split(duplicate)
redPlus=channels[0]
red=ImgPlus(ImageJFunctions.wrapByte(redPlus))
redPlus.show()
# convert to lab
IJ.run(croppedPlus, "Color Transformer", "colour=Lab")
IJ.selectWindow('Lab')
labPlus=IJ.getImage()
# get the A channel
APlus=substackMaker.makeSubstack(labPlus, "2-2")
APlus.setTitle('A')
APlus.show()
APlus.getProcessor().resetMinAndMax()
APlus.updateAndDraw()
AThresholded=threshold(APlus, -10, 50)
# get the B channel
BPlus=substackMaker.makeSubstack(labPlus, "3-3")
BPlus.setTitle('B')
BPlus.show()
BPlus.getProcessor().resetMinAndMax()
BPlus.updateAndDraw()
BThresholded=threshold(BPlus, -10, 50)
# AND the Athreshold and Bthreshold to get a map of the red pixels
ic = ImageCalculator();
redMask = ic.run("AND create", AThresholded, BThresholded);
IJ.run(redMask, "Divide...", "value=255");
#redMask.show()
labPlus.close()
# threshold the spots from the red channel
thresholdedred=SpotDetectionGray(red, data, display, ops, False)
display.createDisplay("thresholdedred", data.create(thresholdedred))
impthresholdedred = ImageJFunctions.wrap(thresholdedred, "wrapped")
# threshold the spots from the brightness channel
thresholded=SpotDetectionGray(brightness, data, display, ops, False)
display.createDisplay("thresholded", data.create(thresholded))
impthresholded=ImageJFunctions.wrap(thresholded, "wrapped")
# or the thresholding results from red and brightness channel
impthresholded = ic.run("OR create", impthresholded, impthresholdedred);
# convert to mask
Prefs.blackBackground = True
IJ.run(impthresholded, "Convert to Mask", "")
# clear the region outside the roi
clone=inputRoi.clone()
clone.setLocation(0,0)
Utility.clearOutsideRoi(impthresholded, clone)
# create a hidden roi manager
roim = RoiManager(True)
# count the particlesimp.getProcessor().setColor(Color.green)
countParticles(impthresholded, roim, detectionParameters.minSize, detectionParameters.maxSize, detectionParameters.minCircularity, detectionParameters.maxCircularity)
# define a function to determine the percentage of pixels that are foreground in a binary image
# inputs:
# imp: binary image, 0=background, 1=foreground
# roi: an roi
def isRed(imp, roi):
stats = imp.getStatistics()
if (stats.mean>detectionParameters.redPercentage): return True
else: return False
def notRed(imp, roi):
stats = imp.getStatistics()
if (stats.mean>detectionParameters.redPercentage): return False
else: return True
allList=[]
for roi in roim.getRoisAsArray():
allList.append(roi.clone())
# count particles that are red
redList=CountParticles.filterParticlesWithFunction(redMask, allList, isRed)
# count particles that are red
blueList=CountParticles.filterParticlesWithFunction(redMask, allList, notRed)
print "Total particles: "+str(len(allList))
print "Filtered particles: "+str(len(redList))
# for each roi add the offset such that the roi is positioned in the correct location for the
# original image
[roi.setLocation(roi.getXBase()+x1, roi.getYBase()+y1) for roi in allList]
# create an overlay and add the rois
overlay1=Overlay()
inputRoi.setStrokeColor(Color.green)
overlay1.add(inputRoi)
[CountParticles.addParticleToOverlay(roi, overlay1, Color.red) for roi in redList]
[CountParticles.addParticleToOverlay(roi, overlay1, Color.cyan) for roi in blueList]
def drawAllRoisOnImage(imp, mainRoi, redList, blueList):
imp.getProcessor().setColor(Color.green)
IJ.run(imp, "Line Width...", "line=3");
imp.getProcessor().draw(inputRoi)
imp.updateAndDraw()
IJ.run(imp, "Line Width...", "line=1");
[CountParticles.drawParticleOnImage(imp, roi, Color.magenta) for roi in redList]
[CountParticles.drawParticleOnImage(imp, roi, Color.green) for roi in blueList]
imp.updateAndDraw()
drawAllRoisOnImage(inputImp, inputRoi, redList, blueList)
#drawAllRoisOnImage(trueColorImp, inputRoi, redList, blueList)
# draw overlay
#inputImp.setOverlay(overlay1)
#inputImp.updateAndDraw()
statsdict=CountParticles.calculateParticleStats(APlus, BPlus, redMask, roim.getRoisAsArray())
print inputRoiArea
areas=statsdict['Areas']
poreArea=0
for area in areas:
poreArea=poreArea+area
ATotal=0
ALevels=statsdict['ALevel']
for A in ALevels:
ATotal=ATotal+A
AAverage=ATotal/len(ALevels)
BTotal=0
BLevels=statsdict['BLevel']
for B in BLevels:
BTotal=BTotal+B
BAverage=BTotal/len(BLevels)
redTotal=0
redPercentages=statsdict['redPercentage']
for red in redPercentages:
redTotal=redTotal+red
redAverage=redTotal/len(redPercentages)
pixwidth=inputImp.getCalibration().pixelWidth
inputRoiArea=inputRoiArea/(pixwidth*pixwidth)
print str(len(allList))+" "+str(len(redList))+" "+str(len(blueList))+" "+str(poreArea/inputRoiArea)+" "+str(redAverage)
def main():
# define here which membrane indices will be used in the analysis, with last index the "control" index
membrane_indices = [-1, 0, 1, 3]
# for now, work with frontmost open image...
imp = IJ.getImage()
im_title = imp.getTitle()
settings = MembraneEvolutionAnalysisSettings(
membrane_indices=membrane_indices)
settings.loadPersistedSettings()
timestamp = datetime.strftime(datetime.now(), '%Y-%m-%d %H-%M-%S')
DirectoryChooser.setDefaultDirectory((settings.output_path))
dc = DirectoryChooser('Select the root folder for saving output')
output_root = dc.getDirectory()
if output_root is None:
raise IOError('no output path chosen')
settings.output_path = output_root
# get calibration
cal = imp.getCalibration()
if cal.getTimeUnit() == "sec":
cal.setTimeUnit('s')
# pop up a dialog prompting for selection of zero time point, frame interval, and time step for analysis
time_steps_not_ok = True
while time_steps_not_ok:
dialog = NonBlockingGenericDialog("Determine time parameters...")
dialog.addNumericField("0 timepoint frame (1-index): ",
settings.zero_timepoint_frame, 0)
dialog.addNumericField("Acquisition time step (s): ",
cal.frameInterval,
2) # assume stored in seconds
dialog.addNumericField(
"Time step for analysis (s): ",
cal.frameInterval * settings.analysis_frame_step, 2)
dialog.showDialog()
if dialog.wasCanceled():
return
zero_f = dialog.getNextNumber()
acq_t_step = dialog.getNextNumber()
analysis_t_step = dialog.getNextNumber()
if acq_t_step != 0 and analysis_t_step != 0:
analysis_frame_step = analysis_t_step / acq_t_step
if round(analysis_frame_step) == analysis_frame_step:
time_steps_not_ok = False
settings.zero_timepoint_frame = zero_f
settings.analysis_frame_step = analysis_frame_step
if time_steps_not_ok:
warning_dlg = GenericDialog("Error!")
warning_dlg.addMessage(
"Analysis time step must be an integer multiple of acquisition time steps, and neither should be zero!!"
)
warning_dlg.setOKLabel("Try again...")
warning_dlg.showDialog()
if warning_dlg.wasCanceled():
return
start_frame = int(((zero_f - 1) % analysis_frame_step) + 1)
end_frame = int(imp.getNFrames() -
(imp.getNFrames() - zero_f) % analysis_frame_step)
frames = [
f + 1
for f in range(start_frame - 1, end_frame, int(analysis_frame_step))
]
print("frames = " + str(frames))
imp.killRoi()
analysis_imp = SubstackMaker().makeSubstack(
imp,
str(start_frame) + "-" + str(end_frame) + "-" +
str(int(analysis_frame_step)))
imp.changes = False
imp.close()
analysis_imp.show()
drawn_membranes = [
TimepointsMembranes(input_image_title=im_title,
time_point_s=(t - 1) * acq_t_step) for t in frames
]
membranes_listener = UpdateRoiImageListener(drawn_membranes)
analysis_imp.addImageListener(membranes_listener)
# now attach roi listener to store all 0th membranes after showing a waitforuserdialog to prompt continuation
IJ.setTool("freeline")
for membrane_idx in membrane_indices:
# if membrane_idx>50:
# IJ.setTool("line");
analysis_imp.killRoi()
membranes_listener.resetLastFrame()
membranes_listener.setCurrentMembraneIndex(membrane_idx)
analysis_imp.setZ(1)
continue_dlg = WaitForUserDialog(
"Continue?", "Click OK once all the " + str(membrane_idx) +
"-index membranes have been drawn")
continue_dlg.show()
membranes_listener.imageUpdated(analysis_imp)
drawn_membranes = membranes_listener.getDrawnMembraneTimepointsList()
json_path = os.path.join(output_root,
"Membranes " + timestamp + ".json")
f = open(json_path, 'w+')
try:
json.dump(drawn_membranes, f, default=encode_membrane)
finally:
f.close()
# save csv containing mebrane measurements for current membrane index
csv_path = os.path.join(
output_root, ("Membrane measurements " + timestamp + ".csv"))
if membrane_idx == membrane_indices[0]:
try:
f = open(csv_path, 'wb')
writer = csv.writer(f)
writer.writerow([
"Membrane index", ("Time point, " + cal.getTimeUnit()),
("Membrane length, " + cal.getUnit()),
("Euclidean length, " + cal.getUnit()),
"Membrane sinuoisty"
])
finally:
f.close()
try:
f = open(csv_path, 'ab')
writer = csv.writer(f)
for mems in drawn_membranes:
mem = mems.getMembrane(membrane_idx)
if mem is not None:
writer.writerow([
membrane_idx, mems.time_point_s,
mem.getPathLength() * cal.pixelWidth,
mem.getEuclidean() * cal.pixelWidth,
mem.getSinuosity()
])
finally:
f.close()
settings.persistSettings()
settings.save_settings()
print("Finished getting all membranes with indices " +
str(membrane_indices))
analysis_imp.close()
def poreDetectionUV(inputImp, inputDataset, inputRoi, ops, data, display, detectionParameters):
# set calibration
detectionParameters.setCalibration(inputImp);
# calculate area of roi
stats=inputImp.getStatistics()
inputRoiArea=stats.area
# get the bounding box of the active roi
inputRec = inputRoi.getBounds()
x1=long(inputRec.getX())
y1=long(inputRec.getY())
x2=x1+long(inputRec.getWidth())-1
y2=y1+long(inputRec.getHeight())-1
# crop the roi
interval=FinalInterval( array([x1, y1 ,0], 'l'), array([x2, y2, 2], 'l') )
#cropped=ops.image().crop(interval, None, inputDataset.getImgPlus() )
cropped=ops.image().crop(inputDataset.getImgPlus() , interval)
datacropped=data.create(cropped)
display.createDisplay("cropped", datacropped)
croppedPlus=IJ.getImage()
# instantiate the duplicator and the substackmaker classes
duplicator=Duplicator()
substackMaker=SubstackMaker()
# duplicate the roi
duplicate=duplicator.run(croppedPlus)
# convert duplicate of roi to HSB and get brightness
IJ.run(duplicate, "HSB Stack", "");
brightnessPlus=substackMaker.makeSubstack(duplicate, "3-3")
brightness=ImgPlus(ImageJFunctions.wrapByte(brightnessPlus))
brightnessPlus.setTitle("Brightness")
#brightnessPlus.show()
# make another duplicate, split channels and get red
duplicate=duplicator.run(croppedPlus)
channels=ChannelSplitter().split(duplicate)
redPlus=channels[0]
red=ImgPlus(ImageJFunctions.wrapByte(redPlus))
# convert to lab
IJ.run(croppedPlus, "Color Transformer", "colour=Lab")
IJ.selectWindow('Lab')
labPlus=IJ.getImage()
croppedPlus.changes=False
croppedPlus.close()
# get the A channel
APlus=substackMaker.makeSubstack(labPlus, "2-2")
APlus.setTitle('A')
#APlus.show()
APlus.getProcessor().resetMinAndMax()
#APlus.updateAndDraw()
AThresholded=threshold(APlus, -10, 50)
# get the B channel
BPlus=substackMaker.makeSubstack(labPlus, "3-3")
BPlus.setTitle('B')
#BPlus.show()
BPlus.getProcessor().resetMinAndMax()
#BPlus.updateAndDraw()
BThresholded=threshold(BPlus, -10, 50)
# AND the Athreshold and Bthreshold to get a map of the red pixels
ic = ImageCalculator();
redMask = ic.run("AND create", AThresholded, BThresholded);
IJ.run(redMask, "Divide...", "value=255");
labPlus.close()
fast=True
# threshold the spots from the red channel
if (fast==False):
thresholdedred=SpotDetectionGray(red, data, display, ops, "triangle")
impthresholdedred = ImageJFunctions.wrap(thresholdedred, "wrapped")
else:
impthresholdedred=SpotDetection2(redPlus)
# threshold the spots from the brightness channel
if (fast==False):
thresholded=SpotDetectionGray(brightness, data, display, ops, "triangle")
impthresholded=ImageJFunctions.wrap(thresholded, "wrapped")
else:
impthresholded=SpotDetection2(brightnessPlus)
# or the thresholding results from red and brightness channel
impthresholded = ic.run("OR create", impthresholded, impthresholdedred);
roim=RoiManager(True)
# convert to mask
Prefs.blackBackground = True
IJ.run(impthresholded, "Convert to Mask", "")
def isRed(imp, roi):
stats = imp.getStatistics()
if (stats.mean>detectionParameters.porphyrinRedPercentage): return True
else: return False
def notRed(imp, roi):
stats = imp.getStatistics()
if (stats.mean>detectionParameters.porphyrinRedPercentage): return False
else: return True
roiClone=inputRoi.clone()
roiClone.setLocation(0,0)
Utility.clearOutsideRoi(impthresholded, roiClone)
impthresholded.show()
countParticles(impthresholded, roim, detectionParameters.porphyrinMinSize, detectionParameters.porphyrinMaxSize, \
detectionParameters.porphyrinMinCircularity, detectionParameters.porphyrinMaxCircularity)
uvPoreList=[]
for roi in roim.getRoisAsArray():
uvPoreList.append(roi.clone())
#allList=uvPoreList+closedPoresList+openPoresList
# count particles that are porphyrins (red)
porphyrinList=CountParticles.filterParticlesWithFunction(redMask, uvPoreList, isRed)
# count particles that are visible on uv but not porphyrins
sebumList=CountParticles.filterParticlesWithFunction(redMask, uvPoreList, notRed)
# for each roi add the offset such that the roi is positioned in the correct location for the
# original image
[roi.setLocation(roi.getXBase()+x1, roi.getYBase()+y1) for roi in uvPoreList]
# draw the ROIs on to the image
inputImp.getProcessor().setColor(Color.green)
IJ.run(inputImp, "Line Width...", "line=3");
inputImp.getProcessor().draw(inputRoi)
IJ.run(inputImp, "Line Width...", "line=1");
[CountParticles.drawParticleOnImage(inputImp, roi, Color.magenta) for roi in porphyrinList]
[CountParticles.drawParticleOnImage(inputImp, roi, Color.green) for roi in sebumList]
inputImp.updateAndDraw()
# calculate stats for the UV visible particles
detectionParameters.setCalibration(APlus)
statsDictUV=CountParticles.calculateParticleStatsUV(APlus, BPlus, redMask, roim.getRoisAsArray())
totalUVPoreArea=0
for area in statsDictUV['Areas']:
totalUVPoreArea=totalUVPoreArea+area
averageUVPoreArea=totalUVPoreArea/len(statsDictUV['Areas'])
poreDiameter=0
for diameter in statsDictUV['Diameters']:
poreDiameter=poreDiameter+diameter
poreDiameter=poreDiameter/len(statsDictUV['Diameters'])
redTotal=0
for red in statsDictUV['redPercentage']:
redTotal=redTotal+red
redAverage=redTotal/len(statsDictUV['redPercentage'])
statslist=[len(porphyrinList), 100*redAverage];
statsheader=[Messages.Porphyrins, Messages.PercentageRedPixels]
print("Roi Area: "+str(inputRoiArea))
print("Total Pore Area: "+str(totalUVPoreArea))
print("Average Pore Area: "+str(averageUVPoreArea))
print str(len(uvPoreList))+" "+str(len(porphyrinList))+" "+str(len(sebumList))+" "+str(100*totalUVPoreArea/inputRoiArea)+" "+str(100*redAverage)
print "cp min circularity"+str(detectionParameters.closedPoresMinCircularity)+":"+str(detectionParameters.closedPoresMinSize)
# close the thresholded image
impthresholded.changes=False
impthresholded.close()
return uvPoreList, statslist, statsheader
def poreDetectionUV(inputImp, inputDataset, inputRoi, ops, data, display,
detectionParameters):
# set calibration
detectionParameters.setCalibration(inputImp)
# calculate area of roi
stats = inputImp.getStatistics()
inputRoiArea = stats.area
# get the bounding box of the active roi
inputRec = inputRoi.getBounds()
x1 = long(inputRec.getX())
y1 = long(inputRec.getY())
x2 = x1 + long(inputRec.getWidth()) - 1
y2 = y1 + long(inputRec.getHeight()) - 1
# crop the roi
interval = FinalInterval(array([x1, y1, 0], 'l'), array([x2, y2, 2], 'l'))
#cropped=ops.image().crop(interval, None, inputDataset.getImgPlus() )
cropped = ops.image().crop(inputDataset.getImgPlus(), interval)
datacropped = data.create(cropped)
display.createDisplay("cropped", datacropped)
croppedPlus = IJ.getImage()
# instantiate the duplicator and the substackmaker classes
duplicator = Duplicator()
substackMaker = SubstackMaker()
# duplicate the roi
duplicate = duplicator.run(croppedPlus)
# convert duplicate of roi to HSB and get brightness
IJ.run(duplicate, "HSB Stack", "")
brightnessPlus = substackMaker.makeSubstack(duplicate, "3-3")
brightness = ImgPlus(ImageJFunctions.wrapByte(brightnessPlus))
brightnessPlus.setTitle("Brightness")
#brightnessPlus.show()
# make another duplicate, split channels and get red
duplicate = duplicator.run(croppedPlus)
channels = ChannelSplitter().split(duplicate)
redPlus = channels[0]
red = ImgPlus(ImageJFunctions.wrapByte(redPlus))
# convert to lab
IJ.run(croppedPlus, "Color Transformer", "colour=Lab")
IJ.selectWindow('Lab')
labPlus = IJ.getImage()
croppedPlus.changes = False
croppedPlus.close()
# get the A channel
APlus = substackMaker.makeSubstack(labPlus, "2-2")
APlus.setTitle('A')
#APlus.show()
APlus.getProcessor().resetMinAndMax()
#APlus.updateAndDraw()
AThresholded = threshold(APlus, -10, 50)
# get the B channel
BPlus = substackMaker.makeSubstack(labPlus, "3-3")
BPlus.setTitle('B')
#BPlus.show()
BPlus.getProcessor().resetMinAndMax()
#BPlus.updateAndDraw()
BThresholded = threshold(BPlus, -10, 50)
# AND the Athreshold and Bthreshold to get a map of the red pixels
ic = ImageCalculator()
redMask = ic.run("AND create", AThresholded, BThresholded)
IJ.run(redMask, "Divide...", "value=255")
labPlus.close()
fast = True
# threshold the spots from the red channel
if (fast == False):
thresholdedred = SpotDetectionGray(red, data, display, ops, "triangle")
impthresholdedred = ImageJFunctions.wrap(thresholdedred, "wrapped")
else:
impthresholdedred = SpotDetection2(redPlus)
# threshold the spots from the brightness channel
if (fast == False):
thresholded = SpotDetectionGray(brightness, data, display, ops,
"triangle")
impthresholded = ImageJFunctions.wrap(thresholded, "wrapped")
else:
impthresholded = SpotDetection2(brightnessPlus)
# or the thresholding results from red and brightness channel
impthresholded = ic.run("OR create", impthresholded, impthresholdedred)
roim = RoiManager(True)
# convert to mask
Prefs.blackBackground = True
IJ.run(impthresholded, "Convert to Mask", "")
def isRed(imp, roi):
stats = imp.getStatistics()
if (stats.mean > detectionParameters.porphyrinRedPercentage):
return True
else:
return False
def notRed(imp, roi):
stats = imp.getStatistics()
if (stats.mean > detectionParameters.porphyrinRedPercentage):
return False
else:
return True
roiClone = inputRoi.clone()
roiClone.setLocation(0, 0)
Utility.clearOutsideRoi(impthresholded, roiClone)
impthresholded.show()
countParticles(impthresholded, roim, detectionParameters.porphyrinMinSize, detectionParameters.porphyrinMaxSize, \
detectionParameters.porphyrinMinCircularity, detectionParameters.porphyrinMaxCircularity)
uvPoreList = []
for roi in roim.getRoisAsArray():
uvPoreList.append(roi.clone())
#allList=uvPoreList+closedPoresList+openPoresList
# count particles that are porphyrins (red)
porphyrinList = CountParticles.filterParticlesWithFunction(
redMask, uvPoreList, isRed)
# count particles that are visible on uv but not porphyrins
sebumList = CountParticles.filterParticlesWithFunction(
redMask, uvPoreList, notRed)
# for each roi add the offset such that the roi is positioned in the correct location for the
# original image
[
roi.setLocation(roi.getXBase() + x1,
roi.getYBase() + y1) for roi in uvPoreList
]
# draw the ROIs on to the image
inputImp.getProcessor().setColor(Color.green)
IJ.run(inputImp, "Line Width...", "line=3")
inputImp.getProcessor().draw(inputRoi)
IJ.run(inputImp, "Line Width...", "line=1")
[
CountParticles.drawParticleOnImage(inputImp, roi, Color.magenta)
for roi in porphyrinList
]
[
CountParticles.drawParticleOnImage(inputImp, roi, Color.green)
for roi in sebumList
]
inputImp.updateAndDraw()
# calculate stats for the UV visible particles
detectionParameters.setCalibration(APlus)
statsDictUV = CountParticles.calculateParticleStatsUV(
APlus, BPlus, redMask, roim.getRoisAsArray())
totalUVPoreArea = 0
for area in statsDictUV['Areas']:
totalUVPoreArea = totalUVPoreArea + area
averageUVPoreArea = totalUVPoreArea / len(statsDictUV['Areas'])
poreDiameter = 0
for diameter in statsDictUV['Diameters']:
poreDiameter = poreDiameter + diameter
poreDiameter = poreDiameter / len(statsDictUV['Diameters'])
redTotal = 0
for red in statsDictUV['redPercentage']:
redTotal = redTotal + red
redAverage = redTotal / len(statsDictUV['redPercentage'])
statslist = [len(porphyrinList), 100 * redAverage]
statsheader = [Messages.Porphyrins, Messages.PercentageRedPixels]
print("Roi Area: " + str(inputRoiArea))
print("Total Pore Area: " + str(totalUVPoreArea))
print("Average Pore Area: " + str(averageUVPoreArea))
print str(len(uvPoreList)) + " " + str(len(porphyrinList)) + " " + str(
len(sebumList)) + " " + str(
100 * totalUVPoreArea / inputRoiArea) + " " + str(100 * redAverage)
print "cp min circularity" + str(
detectionParameters.closedPoresMinCircularity) + ":" + str(
detectionParameters.closedPoresMinSize)
# close the thresholded image
impthresholded.changes = False
impthresholded.close()
return uvPoreList, statslist, statsheader
def poreDetectionTrueColor(inputImp, inputDataset, inputRoi, ops, data,
display, detectionParameters):
detectionParameters.setCalibration(inputImp)
# calculate area of roi
stats = inputImp.getStatistics()
inputRoiArea = stats.area
# get the bounding box of the active roi
inputRec = inputRoi.getBounds()
x1 = long(inputRec.getX())
y1 = long(inputRec.getY())
x2 = x1 + long(inputRec.getWidth()) - 1
y2 = y1 + long(inputRec.getHeight()) - 1
# crop the roi
interval = FinalInterval(array([x1, y1, 0], 'l'), array([x2, y2, 2], 'l'))
cropped = ops.crop(interval, None, inputDataset.getImgPlus())
datacropped = data.create(cropped)
display.createDisplay("cropped", datacropped)
croppedPlus = IJ.getImage()
duplicator = Duplicator()
substackMaker = SubstackMaker()
# duplicate the roi
duplicate = duplicator.run(croppedPlus)
# separate into RGB and get the blue channel
IJ.run(duplicate, "RGB Stack", "")
bluePlus = substackMaker.makeSubstack(duplicate, "3-3")
blue = ImgPlus(ImageJFunctions.wrapByte(bluePlus))
bluePlus.setTitle("Blue")
# duplicate and look for bright spots
thresholdedLight = SpotDetection2(bluePlus)
# duplicate and look for dark spots
thresholdedDark = SpotDetection3(bluePlus, True)
# convert to mask
Prefs.blackBackground = True
#IJ.run(thresholdedDark, "Convert to Mask", "")
# clear the region outside the roi
clone = inputRoi.clone()
clone.setLocation(0, 0)
Utility.clearOutsideRoi(thresholdedLight, clone)
Utility.clearOutsideRoi(thresholdedDark, clone)
roimClosedPores = RoiManager(True)
detectionParameters.setCalibration(thresholdedDark)
countParticles(thresholdedDark, roimClosedPores, detectionParameters.closedPoresMinSize, detectionParameters.closedPoresMaxSize, \
detectionParameters.closedPoresMinCircularity, detectionParameters.closedPoresMaxCircularity)
# count number of open pores
roimOpenPores = RoiManager(True)
detectionParameters.setCalibration(thresholdedDark)
countParticles(thresholdedDark, roimOpenPores, detectionParameters.openPoresMinSize, detectionParameters.openPoresMaxSize, \
detectionParameters.openPoresMinCircularity, detectionParameters.openPoresMaxCircularity)
# count number of sebum
roimSebum = RoiManager(True)
detectionParameters.setCalibration(thresholdedLight)
countParticles(thresholdedLight, roimSebum, detectionParameters.sebumMinSize, detectionParameters.sebumMaxSize, \
detectionParameters.sebumMinCircularity, detectionParameters.sebumMaxCircularity)
# create lists for open and closed pores
closedPoresList = []
for roi in roimClosedPores.getRoisAsArray():
closedPoresList.append(roi.clone())
openPoresList = []
for roi in roimOpenPores.getRoisAsArray():
openPoresList.append(roi.clone())
# create lists for sebum
sebumsList = []
for roi in roimSebum.getRoisAsArray():
sebumsList.append(roi.clone())
# a list of all pores
allList = closedPoresList + openPoresList + sebumsList
# calculate the stats for all pores
detectionParameters.setCalibration(bluePlus)
statsDict = CountParticles.calculateParticleStats(bluePlus, allList)
poresTotalArea = 0
for area in statsDict['Areas']:
poresTotalArea = poresTotalArea + area
print area
poresAverageArea = poresTotalArea / len(statsDict['Areas'])
# for each roi add the offset such that the roi is positioned in the correct location for the
# original image
[
roi.setLocation(roi.getXBase() + x1,
roi.getYBase() + y1) for roi in allList
]
# draw the rois on the image
inputImp.getProcessor().setColor(Color.green)
IJ.run(inputImp, "Line Width...", "line=3")
inputImp.getProcessor().draw(inputRoi)
IJ.run(inputImp, "Line Width...", "line=1")
[
CountParticles.drawParticleOnImage(inputImp, roi, Color.red)
for roi in closedPoresList
]
[
CountParticles.drawParticleOnImage(inputImp, roi, Color.magenta)
for roi in openPoresList
]
[
CountParticles.drawParticleOnImage(inputImp, roi, Color.green)
for roi in sebumsList
]
inputImp.updateAndDraw()
# close images that represent intermediate steps
croppedPlus.changes = False
croppedPlus.close()
bluePlus.changes = False
bluePlus.close()
print "Total ROI Area: " + str(inputRoiArea)
print "Num closed pores: " + str(len(closedPoresList))
print "Num open pores: " + str(len(openPoresList))
print "Num sebums: " + str(len(sebumsList))
print "Total particles: " + str(
len(allList)) + " total area: " + str(poresTotalArea)
statslist = [
inputRoiArea,
len(allList),
len(closedPoresList),
len(openPoresList),
len(sebumsList), poresAverageArea, 100 * poresTotalArea / inputRoiArea
]
header = [
Messages.TotalAreaMask, Messages.TotalDetectedPores,
Messages.ClosedPores, Messages.OpenPores, Messages.Sebum,
Messages.PoresAverageArea, Messages.PoresFractionalArea
]
return header, statslist