def process(Destination_Directory, Current_Directory, filename, parameters):
""" Rolling ball method. """
print "Processing:"
# Opening the image
print "Open image file", filename
imp = IJ.openImage(os.path.join(Current_Directory, filename))
ip = imp.getProcessor()
# Parameters: Image processor, Rolling Ball Radius, Create background,
# light background, use parabaloid, do pre smoothing (3x3),
# correct corners
b = BackgroundSubtracter()
b.rollingBallBackground(ip,
float(parameters["ballsize"]),
ast.literal_eval(parameters["create_b"]),
ast.literal_eval(parameters["light_b"]),
ast.literal_eval(parameters["parab"]),
ast.literal_eval(parameters["smooth"]),
ast.literal_eval(parameters["corners"])
)
print "Saving to", Destination_Directory
IJ.saveAs(imp, "Tiff", os.path.join(Destination_Directory, filename))
imp.close()
def binarize(f):
Prefs.blackBackground = True
imp = IJ.openImage(f)
if imp:
binimp = imp.duplicate()
bs = BackgroundSubtracter()
ip = binimp.getProcessor()
bs.rollingBallBackground(ip, 15, False, False, False, True, True)
IJ.run(binimp, "Unsharp Mask...", "radius=2 mask=0.9")
def smooth(n=1):
for i in range(n):
IJ.run(binimp, "Smooth", "")
smooth(10)
IJ.setAutoThreshold(binimp, "Moments dark")
IJ.run(binimp, "Make Binary", "thresholded remaining")
smooth(12)
IJ.setAutoThreshold(binimp, "Moments white")
IJ.run(binimp, "Make Binary", "thresholded remaining")
IJ.run(binimp, "Erode", "")
IJ.run(binimp, "Dilate", "")
IJ.run(binimp, "Analyze Particles...",
"size=250-Infinity circularity=0.2-1.00 exclude clear add")
binimp.close()
else:
pass
def apply_rollingball(imp,
radius=30,
createBackground=False,
lightBackground=False,
useParaboloid=False,
doPresmooth=True,
correctCorners=False):
# Create BackgroundSubtracter instance
bs = BackgroundSubtracter()
stack = imp.getStack() # get the stack within the ImagePlus
nslices = stack.getSize() # get the number of slices
for index in range(1, nslices + 1):
ip = stack.getProcessor(index)
# Run public method rollingBallBackground
bs.rollingBallBackground(ip,
radius,
createBackground,
lightBackground,
useParaboloid,
doPresmooth,
correctCorners)
return imp
return SpotDetectionLog(imgBgs32, data, ops, thresholdmethod, dimensions2D, factory) def SpotDetection2(imp): imp=Duplicator().run(imp) # subtract background bgs=BackgroundSubtracter()
def back_substraction(ip1, ip2, radius_background):
bgs=BackgroundSubtracter()
bgs.rollingBallBackground(ip1, radius_background, False, False, True, True, True)
bgs.rollingBallBackground(ip2, radius_background, False, False, True, True, True)
imp1 = ImagePlus("ch1 back sub", ip1)
imp2 = ImagePlus("ch2 back sub", ip2)
return imp1, imp2
return SpotDetectionLog(imgBgs32, data, ops, thresholdmethod, dimensions2D, factory) def SpotDetection2(imp): imp=Duplicator().run(imp) # subtract background
def back_substraction(ip1, ip2, radius_background):
bgs=BackgroundSubtracter()
bgs.rollingBallBackground(ip1, radius_background, False, False, True, True, True)
bgs.rollingBallBackground(ip2, radius_background, False, False, True, True, True)
imp1 = ImagePlus("ch1 back sub", ip1)
imp2 = ImagePlus("ch2 back sub", ip2)
IJ.run(imp1, "Enhance Contrast", "saturated=0.35")
IJ.run(imp2, "Enhance Contrast", "saturated=0.35")
return imp1, imp2
IJ.run(imp, "Auto Threshold", "method=Triangle white"); return imp def SpotDetection3(imp, invert=False): # operate on a duplicate as not to change the original imp=Duplicator().run(imp) if (invert): IJ.run(imp, "Invert", ""); # subtract background bgs=BackgroundSubtracter() bgs.rollingBallBackground(imp.getProcessor(), 50.0, False, False, True, True, True)
bgs.rollingBallBackground(imp.getProcessor(), 50.0, False, False, True, True, True) IJ.run(imp, "Auto Threshold", "method=Triangle white"); return imp def SpotDetection3(imp, invert=False): # operate on a duplicate as not to change the original imp=Duplicator().run(imp) if (invert): IJ.run(imp, "Invert", ""); # subtract background bgs=BackgroundSubtracter()
def __localwand(self, x, y, ip, seuil, method, light):
self.__image.killRoi()
ip.snapshot()
if method == "mean" :
peak=ip.getPixel(x,y)
tol = (peak - self.getMean())*seuil
w = Wand(ip)
w.autoOutline(x, y, tol, Wand.EIGHT_CONNECTED)
#print "method=", method, tol, peak
elif method == "background" :
radius = self.getMinF()/4
bs = BackgroundSubtracter()
#rollingBallBackground(ImageProcessor ip, double radius, boolean createBackground, boolean lightBackground, boolean useParaboloid, boolean doPresmooth, boolean correctCorners)
bs.rollingBallBackground(ip, radius, False, light, False, True, False)
peak=ip.getPixel(x,y)
tol = peak*seuil
w = Wand(ip)
w.autoOutline(x, y, tol, Wand.EIGHT_CONNECTED)
ip.reset()
#print "method=", method, tol, radius, peak
else :
peak=ip.getPixel(x,y)
tol = peak*seuil
w = Wand(ip)
w.autoOutline(x, y, tol, Wand.EIGHT_CONNECTED)
#print "method=", method, tol
peak=ip.getPixel(x,y)
temproi=PolygonRoi(w.xpoints, w.ypoints, w.npoints, PolygonRoi.POLYGON)
self.__image.setRoi(temproi)
#self.__image.show()
#time.sleep(1)
#peakip=self.__image.getProcessor()
#stats=peakip.getStatistics()
temprt = ResultsTable()
analyser = Analyzer(self.__image, Analyzer.AREA+Analyzer.INTEGRATED_DENSITY+Analyzer.FERET, temprt)
analyser.measure()
#temprt.show("temprt")
rtValues=temprt.getRowAsString(0).split("\t")
area=float(rtValues[1])
intDen=float(rtValues[4])
feret=float(rtValues[2])
mean=intDen/area
#time.sleep(2)
temprt.reset()
self.__image.killRoi()
return [peak, area, mean, intDen, feret]
def apply_rollingball(imp, rolling_ball, createBackground, lightBackground,
useParaboloid, doPresmooth, correctCorners):
## taken from https://forum.image.sc/t/run-fiji-python-script-with-parameters-on-windows/6162/9
print 'Substracting Background - Radius:', rolling_ball
# Create BackgroundSubtracter instance
bs = BackgroundSubtracter()
# get the stacks
stack, nslices = getImageStack(imp)
for index in range(1, nslices + 1):
ip = stack.getProcessor(index)
# Run public method rollingBallBackground
bs.rollingBallBackground(ip, rolling_ball, createBackground,
lightBackground, useParaboloid, doPresmooth,
correctCorners)
return imp
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 __calRois(self, imp, indice) :
"""
Returns the ROIs of a slice given (identified with its n°) in a stack
"""
##imp=self.__dictImages[nameimages] # IL FAUT RÉCUPÉRER L'IMAGE DU STACK !!!!!
#if self.__batch : imp.hide()
#else : imp.show()
#imp.hide()
imp.show()
if self.__batch : imp.hide()
imp.setSlice(indice)
imp.killRoi()
ip = imp.getProcessor()
bs=BackgroundSubtracter()
#if str(self.__subback) == "0" or str(self.__subback) == "1" : self.__subback = bool(int(self.__subback))
#if self.__subback == True : IJ.run(imp, "Subtract Background...", "rolling="+str(self.__radius)+" light")
if self.__subback == True : bs.rollingBallBackground(ip, self.__radius, False, True, False, True, False)
if self.__runmacro :
imp.show()
imp.setSlice(indice)
imp.updateAndDraw()
IJ.runMacroFile(self.__macropath, imp.getTitle())
imp.updateAndDraw()
#if str(self.__manthresh) == "0" or str(self.__manthresh) == "1" : self.__manthresh = bool(int(self.__manthresh))
#if self.__manthresh : IJ.setThreshold(imp, self.__minthr, self.__maxthr)
if self.__manthresh :
ip.setThreshold(self.__minthr, self.__maxthr, ImageProcessor.RED_LUT)
else : self.__setThreshold(imp, indice)
rt=ResultsTable()
pa1=ParticleAnalyzer(ParticleAnalyzer.SHOW_MASKS+ParticleAnalyzer.EXCLUDE_EDGE_PARTICLES , Measurements.AREA, rt, self.__minArea, self.__maxArea, self.__minCirc, self.__maxCirc)
pa1.setHideOutputImage(True)
pa1.analyze(imp)
masks=pa1.getOutputImage()
masks.getProcessor().erode()
masks.getProcessor().dilate()
masks.getProcessor().invertLut()
masks.getProcessor().threshold(1)
rm = RoiManager.getInstance()
if (rm==None): rm = RoiManager()
rm.runCommand("reset")
#rm.hide()
pa2=ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER+ParticleAnalyzer.CLEAR_WORKSHEET+ParticleAnalyzer.EXCLUDE_EDGE_PARTICLES , Measurements.AREA, rt, self.__minArea, self.__maxArea, self.__minCirc, self.__maxCirc)
pa2.analyze(masks)
masks.close()
temparray=rm.getRoisAsArray()
for r in temparray :
tempnameroi=r.getName()
r.setPosition(indice)
r.setName(str(indice)+"-"+tempnameroi)
r.setStrokeWidth(1)
if len(self.__params) > 0 :
for k in self.__params:
#if k[0]=="Area": self.__minArea, self.__maxArea = str(k[1]), str(k[2])
if k[0]=="Area": self.__minArea, self.__maxArea = k[1], k[2]
for k in self.__params:
#if k[0]=="Circ": self.__minCirc, self.__maxCirc = str(k[1]), str(k[2])
if (k[0]=="Circ") and k[3] : self.__minCirc, self.__maxCirc = k[1], k[2]
else : self.__minCirc, self.__maxCirc = 0, 1
self.__rr.setRoisarray(temparray, imp)
self.__rr.setRange(indice, self.__params)
return self.__rr.includeRois
else : return temparray
from jarray import array
from fiji.plugin.trackmate.detection import DetectionUtils
from net.imagej.ops.convert import ConvertPixCopy
###############################################################
# Step 1: Rolling ball background subtraction (still uses IJ1)
###############################################################
# wrap as ImagePlus
imp = ImageJFunctions.wrap(inputData, "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
iplus = ImagePlus("bgs", imp.getProcessor())
imgBgs = ImageJFunctions.wrapShort(iplus)
###############################################################
# Step 2: Laplacian of Gaussian Filtering
###############################################################
# convert to 32 bit
imgBgs32 = ops.run("createimg", imgBgs, FloatType())
ops.convert(imgBgs32, imgBgs, ConvertPixCopy())
"rowNo": rowNo,
"colNo": colNo,
"nucMinSize": nucMinSize,
"cmMinSize": cmMinSize,
"allFileNames": allFileNames}
with open("savedSettings.json", "w+") as f:
json.dump(jsonStoreDict, f)
reList = []
for fileName in allFileNames:
match = re.match(formatString, fileName)
if match is None: continue
matchDict = match.groupdict()
matchDict['fileName'] = match.string
reList.append(matchDict)
opener = Opener()
bs = BackgroundSubtracter()
for outerPairs, group in getCombos(reList, groupBy).iteritems():
print(group)
nucIp, cmIp = openStitched(group, rowNo, colNo) if stitched else openUnstitched(group)
if nucIp is None or cmIp is None: continue
nucStack, cmStack = nucIp.getStack(), cmIp.getStack()
nucIpMedian = calcMedian(nucIp)
IJ.run(nucIp, "Subtract...", "value=" + str(nucIpMedian) + " stack")
nucIpForMeasure = nucIp.duplicate()
nucMaskIp = generateNucleusMask(nucIp, nucMethod, analyzeNucStack, nucMinSize)
cmIp, cmMaskIp = generateCardiomyocyteMask(cmIp, cmMethod, analyzeCmStack, cmMinSize, brightfield)
nucMaskIp.show()
nucIpForMeasure.show()
rm = RoiManager.getRoiManager()
rm.runCommand("Associate", "true")
rm.runCommand("Show All without labels")
from ij import IJ, Prefs
from ij.plugin.filter import BackgroundSubtracter
Prefs.blackBackground = True
imp = IJ.getImage()
binimp = imp.duplicate()
bs = BackgroundSubtracter()
ip = binimp.getProcessor()
bs.rollingBallBackground(ip, 15, False, False, False, True, True)
IJ.run(binimp, "Unsharp Mask...", "radius=2 mask=0.9")
def smooth(n=1):
for i in range(n):
IJ.run(binimp, "Smooth", "")
smooth(10)
Prefs.blackBackground = True
IJ.setAutoThreshold(binimp, "Moments dark")
IJ.run(binimp, "Make Binary", "thresholded remaining")
smooth(12)
IJ.run(binimp, "Unsharp Mask...", "radius=2 mask=0.9")
IJ.setAutoThreshold(binimp, "Moments white")
IJ.run(binimp, "Make Binary", "thresholded remaining")
red32=ImgPlus( ops.create( dimensions2D, FloatType()) )
ops.convert(red32, red, ConvertPixCopy() )
green32=ImgPlus( ops.create( dimensions2D, FloatType()) )
ops.convert(green32, green, ConvertPixCopy() )
redgreen= ops.add(red32,green32)
display.createDisplay("redgreen", data.create(redgreen))
# make a copy of the red + green image
copy=redgreen.copy()
# wrap as ImagePlus
imp=ImageJFunctions.wrap(copy, "wrapped")
# create and call background subtractor
bgs=BackgroundSubtracter()
bgs.rollingBallBackground(imp.getProcessor(), 50.0, False, False, True, True, True)
# wrap as Img and display
iplus=ImagePlus("bgs", imp.getProcessor())
print type(imp)
imgBgs=ImageJFunctions.wrapFloat(iplus)
display.createDisplay("back_sub", data.create(ImgPlus(imgBgs)))
kernel = DetectionUtils.createLoGKernel( 3.0, 2, array([1.0, 1.0], 'd' ) )
print type(kernel)
print type(imgBgs)
print type(red32.getImg())
log = ops.convolve(ops.create( dimensions2D, FloatType()), imgBgs, kernel)
# 1 - Obtain an image
composite_img = IJ.openImage(stack)
# Get the first DAPI slice
dapi = composite_img.createImagePlus()
composite_img.close()
# happens to get the first slice
dapi.setProcessor("nuclei", composite_img.getProcessor().duplicate())
dapi_ip = dapi.getProcessor()
if SHOW_IMAGES:
dapi.show()
# IJ.run("Enhance Local Contrast (CLAHE)", "blocksize=127 histogram=256 maximum=3 mask=*None* fast_(less_accurate)");
FastFlat.getInstance().run(dapi, *[a[1] for a in CLAHE])
# IJ.run("Subtract Background...", "rolling=40");
BS = BackgroundSubtracter()
BS.rollingBallBackground(dapi_ip,
*[a[1] for a in bkgd_sub])
if SHOW_IMAGES:
dapi.updateAndDraw()
# IJ.setAutoThreshold("Otsu");
# IJ.run("Convert to Mask");
dapi_ip.autoThreshold()
dapi.setProcessor(dapi_ip.convertToByteProcessor(False))
dapi_ip = dapi.getProcessor()
# IJ.run("Watershed");
EDM().toWatershed(dapi_ip)
dapi_ip.invert()
# IJ.run("Analyze Particles...", "size=30-400 show=Masks");
# Perform median filtering
processor = frame.getProcessor()
filters = RankFilters()
filters.setup("median", frame)
filters.rank(processor, filter_window, filters.MEDIAN)
# Perform gamma correction
processor.gamma(gamma)
frame = ImagePlus("Frame " + str(frame_i), processor)
# Rolling ball background subtraction
processor = frame.getProcessor()
bg_subtractor = BackgroundSubtracter()
bg_subtractor.setup("", frame)
bg_subtractor.rollingBallBackground(processor, rolling_ball_size/pixel_size, False, False, False, False, True)
frame = ImagePlus("Frame " + str(frame_i), processor)
# Calibrate pixels
calibration = Calibration()
calibration.setUnit("pixel")
calibration.pixelWidth = pixel_size
calibration.pixelHeight = pixel_size
calibration.pixelDepth = 1.0
frame.setCalibration(calibration)
# Save to output dir
