def getFFTParamsFromImps(imp1, imp2, r1=None, r2=None): if r1: v1 = getViewFromImp(imp1, r1) else: v1 = IL.wrapByte(imp1) if r2: v2 = getViewFromImp(imp2, r2) else: v2 = IL.wrapByte(imp2) extension = array(v1.numDimensions() * [10], 'i') extSize = PhaseCorrelation2Util.getExtendedSize(v1, v2, extension) paddedDimensions = array(extSize.numDimensions() * [0], 'l') fftSize = array(extSize.numDimensions() * [0], 'l') return extension, extSize, paddedDimensions, fftSize
def getViewFromImp(imp, r=None): # r is a java.awt.rectangle im = IL.wrapByte(imp) if r is None: r = Rectangle(0, 0, imp.getWidth(), imp.getHeight()) v = Views.zeroMin( Views.interval(im, [r.x, r.y], [r.x + r.width - 1, r.y + r.height - 1])) return v
def SpotDetectionGray(gray, data, display, ops, invert): # get the dimensions dimensions2D = array([gray.dimension(0), gray.dimension(1)], 'l') factory = gray.getImg().factory() # wrap as ImagePlus imp = ImageJFunctions.wrap(gray, "wrapped") # create and call background subtractor bgs = BackgroundSubtracter() bgs.rollingBallBackground(imp.getProcessor(), 50.0, False, False, True, True, True) # wrap the result of background subtraction as Img and display it iplus = ImagePlus("bgs", imp.getProcessor()) # if (invert==True): # iplus.getProcessor().invert() imgBgs = ImageJFunctions.wrapByte(iplus) display.createDisplay("back_sub", data.create(ImgPlus(imgBgs))) # convert the background subtracted image to 32 bit temp = ops.run("createimg", factory, FloatType(), dimensions2D) imgBgs32 = ImgPlus(temp) ops.convert(imgBgs32, imgBgs, ConvertPixCopy()) #display.createDisplay("back_sub 32", data.create(ImgPlus(imgBgs32))) # create the Laplacian of Gaussian filter kernel = DetectionUtils.createLoGKernel(3.0, 2, array([1.0, 1.0], 'd')) # apply the log filter and display the result log = ImgPlus(ops.run("createimg", factory, FloatType(), dimensions2D)) ops.convolve(log, imgBgs32, kernel) #display.createDisplay("log", data.create(ImgPlus(log))) # apply the threshold operation #thresholded=ops.run("threshold", ops.create( dimensions2D, BitType()), log, Triangle()) thresholded = ops.run("triangle", log) return ImgPlus(thresholded)
def SpotDetectionGray(gray, data, display, ops, invert): # get the dimensions dimensions2D=array( [gray.dimension(0), gray.dimension(1)], 'l') factory=gray.getImg().factory() # wrap as ImagePlus imp=ImageJFunctions.wrap(gray, "wrapped") # create and call background subtractor bgs=BackgroundSubtracter() bgs.rollingBallBackground(imp.getProcessor(), 50.0, False, False, True, True, True) # wrap the result of background subtraction as Img and display it iplus=ImagePlus("bgs", imp.getProcessor()) # if (invert==True): # iplus.getProcessor().invert() imgBgs=ImageJFunctions.wrapByte(iplus) display.createDisplay("back_sub", data.create(ImgPlus(imgBgs))) # convert the background subtracted image to 32 bit temp=ops.run( "createimg", factory, FloatType(), dimensions2D ) imgBgs32=ImgPlus( temp ) ops.convert(imgBgs32, imgBgs, ConvertPixCopy() ) #display.createDisplay("back_sub 32", data.create(ImgPlus(imgBgs32))) # create the Laplacian of Gaussian filter kernel = DetectionUtils.createLoGKernel( 3.0, 2, array([1.0, 1.0], 'd' ) ) # apply the log filter and display the result log=ImgPlus( ops.run("createimg", factory, FloatType(), dimensions2D) ) ops.convolve(log, imgBgs32, kernel) #display.createDisplay("log", data.create(ImgPlus(log))) # apply the threshold operation #thresholded=ops.run("threshold", ops.create( dimensions2D, BitType()), log, Triangle()) thresholded = ops.run("triangle", log) return ImgPlus(thresholded)
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 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
imp = IJ.getImage() #imp=ImageJFunctions.wrap(blue, "wrapped") # create and call background subtractor bgs = BackgroundSubtracter() bgs.rollingBallBackground(imp.getProcessor(), 20.0, False, True, True, True, True) imp.getProcessor().invert() imp.show() print imp # wrap as Img and display iplus = ImagePlus("bgs", imp.getProcessor()) imgBgs = ImageJFunctions.wrapByte(iplus) print iplus print imgBgs display.createDisplay("back_sub", data.create(ImgPlus(imgBgs))) ''' kernel = DetectionUtils.createLoGKernel( 2.0, 2, array([1.0, 1.0], 'd' ) ) print type(kernel) print type(imgBgs) bgs32=ImgPlus( ops.create( dimensions2D, FloatType()) ) ops.convert(bgs32, imgBgs, ConvertPixCopy() ) log = ops.convolve(ops.create( dimensions2D, FloatType()), bgs32, kernel) display.createDisplay("log", data.create(ImgPlus(log)))
# wrap as ImagePlus imp=IJ.getImage() #imp=ImageJFunctions.wrap(blue, "wrapped") # create and call background subtractor bgs=BackgroundSubtracter() bgs.rollingBallBackground(imp.getProcessor(), 20.0, False, True, True, True, True) imp.getProcessor().invert() imp.show() print imp # wrap as Img and display iplus=ImagePlus("bgs", imp.getProcessor()) imgBgs=ImageJFunctions.wrapByte(iplus) print iplus print imgBgs display.createDisplay("back_sub", data.create(ImgPlus(imgBgs))) ''' kernel = DetectionUtils.createLoGKernel( 2.0, 2, array([1.0, 1.0], 'd' ) ) print type(kernel) print type(imgBgs) bgs32=ImgPlus( ops.create( dimensions2D, FloatType()) ) ops.convert(bgs32, imgBgs, ConvertPixCopy() ) log = ops.convolve(ops.create( dimensions2D, FloatType()), bgs32, kernel)
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
last_10_pct_of_im_1 = crop( im_1, Roi(start_of_last_10_pct_of_im_1, 0, im_1.getWidth() - start_of_last_10_pct_of_im_1 + 1, im_1.getHeight())) # last_10_pct_of_im_1.show() # calculate the translation offset between im_1 and im_2 xy_subpixel_shift = getShiftFromImps(last_10_pct_of_im_1, im_2) end_of_im_1_in_im_2 = -xy_subpixel_shift[ 0] + last_10_pct_of_im_1.getWidth() IJ.log('end_of_im_1_in_im_2 - ' + str(end_of_im_1_in_im_2)) img_1 = IL.wrapByte(im_1) #im is an ImagePlus, img is a ImgLib2 image img_2 = IL.wrapByte(im_2) # get the fft parameters only once at the beginning because they will be te same for all calculations extension, extSize, paddedDimensions, fftSize = getFFTParamsFromImps( im_1, im_2, r1=Rectangle(0, 0, 8 + vertical_strip_width + 2, im_2.getHeight()), r2=Rectangle(0, 0, vertical_strip_width, im_2.getHeight())) # initializing list of subpixelshifts spshifts = [] # sliding the small vertical band (which will be called view v2) pixel by pixel # the start of the small vertical band in im_2 is start_of_sliding_band_in_im_2, renamed as "s" s_range = range(