def measure_rm(img_path, roi_path):
imp = IJ.openImage(img_path)
img_dir = imp.getOriginalFileInfo().directory
img_file = imp.getOriginalFileInfo().fileName
ip = imp.getProcessor()
cal = Calibration(imp)
rm = RoiManager()
rm = RoiManager.getInstance()
rm.runCommand("Open", roi_path)
roi_array = rm.getRoisAsArray()
moptions = Measurements.MEAN | Measurements.AREA
for roi in roi_array:
roi_name = roi.getName()
ip.setRoi(roi)
stat = ImageStatistics.getStatistics(ip, moptions, cal)
IJ.log(img_dir + "\t" + img_file + "\t" + roi_name + "\t" +
str(stat.pixelCount) + "\t" +
str(stat.pixelCount * stat.umean) + "\t" + str(stat.umean))
rm.runCommand("delete")
def measureTumor(original, locations):
'''Returns the area from the original image with the
highest kurtosis which generally corresponds to the most
in focus image. Also saves an image corresponding to a mask
of the measurement.'''
# Prevent ROI manager from appearing
roiM = RoiManager(True)
ParticleAnalyzer.setRoiManager(roiM)
# Locate particles above a minimum size and with a desired circularity
IJ.run(locations, "Analyze Particles...", "size=" + str(minimumCancerArea) +"-" + str(maxCancerArea) +" circularity=" + str(circularityCutoff) + "-1.00 show=Nothing exclude add stack");
# Choose ROI with the highest kurtosis
maxKurtosis = None
area = None
selectedROI = None
for roi in roiM.getRoisAsArray():
original.setRoi(roi)
stats = original.getStatistics(Measurements.KURTOSIS, Measurements.AREA)
currentKurtosis = stats.kurtosis
if currentKurtosis > maxKurtosis:
maxKurtosis = stats.kurtosis
area = stats.area
selectedROI = roi
original.killRoi() # Remove the remaining ROI
roiM.runCommand("Reset")
return (area, selectedROI)
def resetpressed(event):
self.__ranges.clear()
self.__image=IJ.getImage()
rm = RoiManager.getInstance()
if (rm==None): rm = RoiManager()
rm.runCommand("reset")
self.__image.killRoi()
IJ.setAutoThreshold(self.__image, "MaxEntropy")
rt=ResultsTable()
pa=ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER+ParticleAnalyzer.CLEAR_WORKSHEET , Measurements.AREA+Measurements.ELLIPSE+Measurements.MEAN, rt, 0.00, 10000.00, 0.00, 1.00)
pa.analyze(self.__image)
self.__roisArray=[]
self.__roisArray=rm.getRoisAsArray()
#rm.runCommand("Show All")
#rm.runCommand("Select All")
#rm.runCommand("Set Color", "blue")
IJ.resetThreshold(self.__image)
keys=self.__slidersDict.keys()
for k in keys:
if k.endswith("min"):
self.__slidersDict[k].setValue(0)
self.__slidersDict[k].repaint()
else:
self.__slidersDict[k].setValue(self.__slidersDict[k].getMaximum())
self.__slidersDict[k].repaint()
def load_qcd_edges2(input_file_path):
"""load edges from roi *.zip file"""
if os.path.isfile(input_file_path):
roim = RoiManager(False)
roim.runCommand("Open", input_file_path)
edges = roim.getRoisAsArray()
roim.close()
else:
edges = load_qcd_edges(os.path.splitext(input_file_path)[0] + '.json')
return edges
def apply_roi_arr(func):
rm = RoiManager()
rm = rm.getInstance()
roi_arr = rm.getRoisAsArray() # => list
result_list = []
for roi in roi_arr:
result = func(roi)
if result is not None:
result_list.append(result)
return result_list
def perform_manual_qc(imp, rois, important_channel=1):
"""given cell rois generated by automatic methods, allow user to delete/add/redraw as appropriate"""
for ch in range(imp.getNChannels()):
imp.setC(ch + 1)
sat_frac = 0.99 if (ch + 1) == important_channel else 0.01
IJ.run(imp, "Enhance Contrast", "saturated={}".format(sat_frac))
imp.setC(important_channel)
IJ.setTool("freehand")
proceed = False
roim = RoiManager()
roim.runCommand("Show all with labels")
for roi in rois:
roim.addRoi(roi)
auto_rois_only = rois
while not proceed:
dialog = NonBlockingGenericDialog("Perform manual segmentation")
dialog.setOKLabel("Proceed to next image...")
dialog.addMessage("Perform manual correction of segmentation: ")
dialog.addMessage(
"Draw around cells and add to the region of interest manager (Ctrl+T). "
)
dialog.addMessage("Delete and redraw cells as appropriate. ")
dialog.addMessage(
"Then press \"proceed to next image\" when all cells have been added. "
)
dialog.showDialog()
if dialog.wasCanceled():
print("Manual segmentation canceled")
return auto_rois_only
elif dialog.wasOKed():
if roim.getCount() == 0:
rois = []
confirm_dialog = GenericDialog("Continue?")
confirm_dialog.addMessage(
"No rois selected in this FOV. Are you sure you want to proceed?"
)
confirm_dialog.setOKLabel("Yes, proceed")
confirm_dialog.setCancelLabel("No, not yet")
confirm_dialog.showDialog()
if confirm_dialog.wasOKed():
proceed = True
else:
rois = roim.getRoisAsArray()
proceed = True
roim.reset()
roim.close()
for ch in range(imp.getNChannels()):
imp.setC(ch + 1)
IJ.run(imp, "Enhance Contrast", "saturated={}".format(0.35))
imp.setC(important_channel)
return rois
def generate_cell_rois(seg_binary_imp):
"""generate rois from which cell shape information will be gleaned"""
seg_binary_imp.killRoi()
mxsz = seg_binary_imp.width * seg_binary_imp.height
roim = RoiManager(False)
pa_options = ParticleAnalyzer.AREA | ParticleAnalyzer.PERIMETER | ParticleAnalyzer.SHAPE_DESCRIPTORS
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER, pa_options, None,
1000, mxsz)
pa.setRoiManager(roim)
roim.reset()
pa.analyze(seg_binary_imp)
rois = roim.getRoisAsArray()
roim.reset()
roim.close()
return rois
def get_no_nuclei_fully_enclosed(roi, full_nuclei_imp, overlap_threshold=0.65):
"""for a given cell roi and ImagePlus with binary nuclei, return how many nuclei lie ENTIRELY within the cell"""
bbox = roi.getBounds()
full_nuclei_imp.setRoi(roi)
cropped_nuc_imp = full_nuclei_imp.crop()
roi.setLocation(0, 0)
cropped_nuc_imp.setRoi(roi)
cropped_nuc_imp.killRoi()
roim = RoiManager(False)
mxsz = cropped_nuc_imp.getWidth() * cropped_nuc_imp.getHeight()
pa = ParticleAnalyzer(
ParticleAnalyzer.ADD_TO_MANAGER, ParticleAnalyzer.AREA
| ParticleAnalyzer.SLICE | ParticleAnalyzer.CENTROID, None, 0, mxsz)
pa.setRoiManager(roim)
pa.analyze(cropped_nuc_imp)
cell_imp = IJ.createImage("Cell binary", cropped_nuc_imp.getWidth(),
cropped_nuc_imp.getHeight(), 1, 8)
IJ.run(cell_imp, "Select All", "")
IJ.run(cell_imp, "Set...", "value=0 slice")
cell_imp.setRoi(roi)
IJ.run(cell_imp, "Set...", "value=255 slice")
no_enclosed_nuclei = 0
for idx, nuc_roi in enumerate(roim.getRoisAsArray()):
test_imp = Duplicator().run(cell_imp)
test_imp.setRoi(nuc_roi)
IJ.run(test_imp, "Set...", "value=255 slice")
test_imp.killRoi()
IJ.run(test_imp, "Create Selection", "")
IJ.run(test_imp, "Make Inverse", "")
test_roi = test_imp.getRoi()
test_roi_stats = test_roi.getStatistics()
cell_roi_stats = roi.getStatistics()
nuc_roi_stats = nuc_roi.getStatistics()
if test_roi_stats.area < (
cell_roi_stats.area +
(1 - overlap_threshold) * nuc_roi_stats.area
): # i.e. if more than (100*overlap_threshold)% of nucleus is inside cell...
no_enclosed_nuclei += 1
test_imp.changes = False
test_imp.close()
roi.setLocation(bbox.getX(), bbox.getY())
cropped_nuc_imp.changes = False
cropped_nuc_imp.close()
cell_imp.changes = False
cell_imp.close()
return no_enclosed_nuclei
def save_roi_set(imp=IJ.getImage()):
img_dir, name = get_file_info()
roi_dir = make_roi_dir(img_dir, name)
roi_path = make_roi_path(roi_dir, name)
Roi.setColor(Color.blue)
rm = RoiManager().getInstance()
rm.deselect()
rm.runCommand("Save", roi_path)
ol = imp.getOverlay()
if ol is None:
ol = Overlay()
for roi in rm.getRoisAsArray():
ol.add(roi)
imp.setOverlay(ol)
rm.runCommand("delete")
ol.setStrokeColor(Color.blue)
Roi.setColor(Color.yellow)
def manual_analysis(imp, file_name, output_folder):
"""perform analysis based on manually drawn cells"""
cal = imp.getCalibration()
channel_imps = ChannelSplitter.split(imp)
gfp_imp = channel_imps[0]
IJ.setTool("freehand")
proceed = False
roim = RoiManager()
roim.runCommand("Show all with labels")
dialog = NonBlockingGenericDialog("Perform manual segmentation")
dialog.setOKLabel("Proceed to next image...")
dialog.addMessage("Perform manual segmentation: ")
dialog.addMessage(
"Draw around cells and add to the region of interest manager (Ctrl+T)")
dialog.addMessage(
"You can see what you've added so far if you check \"show all\" on the ROI manager"
)
dialog.addMessage(
"Then press \"proceed to next image\" when all cells have been added")
dialog.showDialog()
if dialog.wasCanceled():
raise KeyboardInterrupt("Run canceled")
elif dialog.wasOKed():
rois = roim.getRoisAsArray()
roim.reset()
roim.close()
out_stats = generate_cell_shape_results(rois, gfp_imp, cal, file_name)
print("Number of cells identified = {}".format(len(out_stats)))
# save output
save_qc_image(
imp, rois, "{}_plus_overlay.tiff".format(
os.path.join(output_folder,
os.path.splitext(file_name)[0])))
save_cell_rois(rois, output_folder, os.path.splitext(file_name)[0])
imp.changes = False
imp.close()
save_output_csv(out_stats, output_folder)
return out_stats
return None
def RoiSelection():
true=1
false=0
IJ.run("Invert", "stack");
IJ.run("Fill Holes", "stack");
IJ.run("Create Selection");
rm = RoiManager()
rm.runCommand("add")
rm.runCommand("split")
#number_selected=rm.getCount()
IJ.run("Select None");
rm.runCommand("deselect")
#rm.select(0)
#print number_selected
roi_array=rm.getRoisAsArray()
max_roi=None
max_points=-1
for roi in roi_array:
polygon=roi.getPolygon()
if polygon is not None:
number_of_points = polygon.npoints
if max_points < number_of_points:
max_points=number_of_points
max_roi=roi
#print max_points
#sorted_roi_array=sorted(roi_array, key=methodcaller('getLength'), reverse=True)
#length_array=[]
#index=0
#for roi in roi_array:
# index=index+1
# length_array.append((index,roi.getLength()))
#sorted_length_array=sorted(length_array, key=itemgetter(0))
rm.runCommand("Select All")
rm.runCommand("Delete")
#for roi in roi_array:
interpolated_polygon=max_roi.getInterpolatedPolygon(20,True)
roi_polygon=PolygonRoi(interpolated_polygon,Roi.POLYGON)
rm.addRoi(roi_polygon)
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
display.createDisplay("log", data.create(ImgPlus(log)))
otsu=ops.run("threshold", ops.create( dimensions2D, BitType()), imgBgs, Otsu())
display.createDisplay("thresholded", data.create(ImgPlus(otsu)))
'''
#Utility.clearOutsideRoi(imp, clone)
IJ.run(imp, "Auto Local Threshold", "method=MidGrey radius=15 parameter_1=0 parameter_2=0 white");
IJ.run(imp, "Fill Holes", "");
IJ.run(imp, "Close-", "");
IJ.run(imp, "Watershed", "");
iplus.updateAndDraw()
# create a hidden roi manager
roim = RoiManager(True)
# count the particles
countParticles(iplus, roim, 10, 200, 0.5, 1.0)
[truecolor1.getProcessor().draw(roi) for roi in roim.getRoisAsArray()]
truecolor1.updateAndDraw()
#Prefs.blackBackground = False;
#IJ.run("Make Binary", "");
#IJ.run("LoG 3D");
#IJ.run("Duplicate...", "title="+"test")
#IJ.run("RGB Stack");
#IJ.run("Convert Stack to Images");
preview.show()
rm = RoiManager()
dialog = WaitForUserDialog("Action required", "Please select regions of interest in this image. Click OK when done.")
dialog.show()
rm.close()
splitter = ChannelSplitter()
imp1 = ImagePlus("CH1", splitter.getChannel(image, imageA))
imp2 = ImagePlus("CH2", splitter.getChannel(image, imageB))
title = image.getTitle()
title = title[:title.rfind('.')]
image.close()
preview.close()
ch1 = ImagePlusAdapter.wrap(imp1)
ch2 = ImagePlusAdapter.wrap(imp2)
for roi in rm.getRoisAsArray():
container = createContainer(roi, ch1, ch2)
img1 = container.getSourceImage1()
img2 = container.getSourceImage2()
mask = container.getMask()
thr1, thrimp1 = calculateThreshold(imp1, roi, methods[0])
thr2, thrimp2 = calculateThreshold(imp2, roi, methods[1])
cursor = TwinCursor(img1.randomAccess(), img2.randomAccess(), Views.iterable(mask).localizingCursor())
rtype = img1.randomAccess().get().createVariable()
raw = manders.calculateMandersCorrelation(cursor, rtype)
rthr1 = rtype.copy()
rthr2 = rtype.copy()
rthr1.set(thr1)
rthr2.set(thr2)
image_title = img.getShortTitle()
# make paths for moving when done.
full_img_path_orig = os.path.join(blinded_base, image_title + ".tif")
full_img_path_done = os.path.join(data, image_title + ".tif")
# get roi manager
roim = RoiManager().getRoiManager()
# make savepaths for csv and rois
roimsave = os.path.join(data, image_title + "_rois.zip")
csvsave = os.path.join(data, image_title + "_data.csv")
csv_ = [["image", "roi_name", "uncalibrated_length"]]
for roi, _ in enumerate(roim.getRoisAsArray()):
target = roim.getRoi(roi)
csv_.append([image_title, target.getName(), target.getLength()])
# write and save csv
with open(csvsave, "w") as c:
writer = csv.writer(c)
for l in csv_:
writer.writerow(l)
# save rois
roim.runCommand("Deselect")
roim.runCommand("Save", roimsave)
# close both
roim.close()
def updatepressed(event):
self.__image=IJ.getImage()
rm = RoiManager.getInstance()
if (rm==None): rm = RoiManager()
rm.runCommand("reset")
self.__image.killRoi()
IJ.run("Threshold...")
IJ.setAutoThreshold(self.__image, "MaxEntropy")
rt=ResultsTable()
pa=ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER+ParticleAnalyzer.CLEAR_WORKSHEET , Measurements.AREA+Measurements.ELLIPSE+Measurements.MEAN, rt, 0.00, 10000.00, 0.00, 1.00)
pa.analyze(self.__image)
self.__roisArray=[]
self.__roisArray=rm.getRoisAsArray()
#for i in range(rm.getCount()) :
# rm.select(i)
# rm.runCommand("Set Color", "0000FF", 2)
IJ.resetThreshold(self.__image)
rt.show("tempRT")
areas=rt.getColumn(ResultsTable.AREA)
means=rt.getColumn(ResultsTable.MEAN)
majors=rt.getColumn(ResultsTable.MAJOR)
minors=rt.getColumn(ResultsTable.MINOR)
#print 0
if self.__slidersDict["Area_max"].getMaximum() < int(max(areas)+1):
# print 1
self.__slidersDict["Area_max"].setMaximum(int(max(areas))+1)
if self.__slidersDict["Area_min"].getMaximum() < int(max(areas)+1):
# print 2
self.__slidersDict["Area_min"].setMaximum(int(max(areas))+1)
if self.__slidersDict["Mean_max"].getMaximum() < int(max(means)+1):
# print 3
self.__slidersDict["Mean_max"].setMaximum(int(max(means))+1)
if self.__slidersDict["Mean_min"].getMaximum() < int(max(means)+1):
# print 4
self.__slidersDict["Mean_min"].setMaximum(int(max(means))+1)
if self.__slidersDict["Major_max"].getMaximum() < int(max(majors)):
# print 5
self.__slidersDict["Major_max"].setMaximum(int(max(majors))+1)
if self.__slidersDict["Major_min"].getMaximum() < int(max(majors)+1):
# print 6
self.__slidersDict["Major_min"].setMaximum(int(max(majors))+1)
if self.__slidersDict["Minor_max"].getMaximum() < int(max(minors)+1):
# print 7
self.__slidersDict["Minor_max"].setMaximum(int(max(minors))+1)
if self.__slidersDict["Minor_min"].getMaximum() < int(max(minors)+1):
# print 8
self.__slidersDict["Minor_min"].setMaximum(int(max(minors))+1)
if self.__slidersDict["AR_max"].getMaximum() < int((max(majors)+1)/min(minors)+1):
# print 9
self.__slidersDict["AR_max"].setMaximum(int((max(majors)+1)/(min(minors))))
if self.__slidersDict["AR_min"].getMaximum() < int((max(majors)+1)/min(minors)):
# print 10
self.__slidersDict["AR_min"].setMaximum(int((max(majors)+1)/(min(minors))))
#print 11
for sb in self.__slidersDict.values():
sb.repaint()
#rm.runCommand("reset")
#temprois=self.getIncludeRois()
#IJ.run(self.__image, "Remove Overlay", "")
#o=Overlay()
#for roi in temprois:
# o.addElement(roi)
#self.__image.killRoi()
#self.__image.setOverlay(o)
self.__image.updateAndDraw()
class StackCells(swing.JFrame):
def __init__(self):
swing.JFrame.__init__(self, title="Stack Cells")
self.__impD = IJ.getImage()
self.setDefaultCloseOperation(swing.JFrame.DISPOSE_ON_CLOSE)
self.__n=0
self.__widthl = "11"
self.__iplist = []
self.__init = False
self.__initDIA = False
self.__initFLUO = False
self.__skip = False
self.__avg = True
self.__mosa = True
self.__maxfinder = True
self.__appmedian = True
self.__fire = True
self.__align = True
self.__alignC = False
self.__enlarge = True
self.__measures = True
self.__sens = []
self.__listrois = []
self.__cellsrois = []
self.__Cutoff = 0
self.__labels = []
self.__maxraf = 100.0
self.__minraf = 0.0
self.__conEllipses = False
self.__dictCells = {}
self.__rm = RoiManager.getInstance()
if (self.__rm==None): self.__rm = RoiManager()
self.run()
def run(self) :
self.size=(1100, 400)
self.contentPane.layout = awt.BorderLayout()
self.__display = swing.JTextField(preferredSize=(400, 30), horizontalAlignment=swing.SwingConstants.LEFT)
self.__setDisplay()
line = BorderFactory.createEtchedBorder(EtchedBorder.LOWERED)
northpanel1=swing.JPanel(awt.FlowLayout(awt.FlowLayout.LEFT))
northpanel1.setBorder(line)
northpanel1.add(self.__display)
new = swing.JButton("New", size=(100, 70), actionPerformed=self.__new)
northpanel1.add(new)
add = swing.JButton("Add", size=(100, 70), actionPerformed=self.__add)
northpanel1.add(add)
roiman = swing.JButton("Add Roi manager", size=(100, 70), actionPerformed= self.__addroi)
northpanel1.add(roiman)
end = swing.JButton("End", size=(100, 70), actionPerformed= self.__end)
northpanel1.add(end)
#grid = awt.GridLayout()
#grid.setRows(2)
#northpanel=swing.JPanel(grid)
#northpanel.add(northpanel1)
#northpanel2=swing.JPanel(awt.FlowLayout(awt.FlowLayout.LEFT))
grid0 = awt.GridLayout()
grid0.setRows(6)
northpanel2=swing.JPanel(grid0)
northpanel2.setBorder(line)
label=swing.JLabel("Label2")
label.setText("Line width ?")
northpanel2.add(label)
self.__display2 = swing.JTextField(preferredSize=(50, 30), horizontalAlignment=swing.SwingConstants.LEFT)
self.__display2.text = "11"
northpanel2.add(self.__display2)
label=swing.JLabel("Label3")
label.setText("Noise for peaks ?")
northpanel2.add(label)
self.__display3 = swing.JTextField(preferredSize=(50, 30), horizontalAlignment=swing.SwingConstants.LEFT)
self.__display3.text = "100"
northpanel2.add(self.__display3)
label=swing.JLabel("Label4")
label.setText("Fluo threshold ?")
northpanel2.add(label)
self.__display4 = swing.JTextField(preferredSize=(50, 30), horizontalAlignment=swing.SwingConstants.LEFT)
self.__display4.text = "170"
northpanel2.add(self.__display4)
#northpanel3=swing.JPanel(awt.FlowLayout(awt.FlowLayout.LEFT))
#northpanel3.setBorder(line)
label=swing.JLabel("Label5")
label.setText("Min of length ?")
northpanel2.add(label)
self.__display5 = swing.JTextField(preferredSize=(50, 30), horizontalAlignment=swing.SwingConstants.LEFT)
self.__display5.text = "50"
northpanel2.add(self.__display5)
label=swing.JLabel("Label6")
label.setText("Max of length ?")
northpanel2.add(label)
self.__display6 = swing.JTextField(preferredSize=(50, 30), horizontalAlignment=swing.SwingConstants.LEFT)
self.__display6.text = "500"
northpanel2.add(self.__display6)
dia = swing.JButton("DIA", size=(100, 70), actionPerformed= self.__dia)
northpanel2.add(dia)
fluo = swing.JButton("FLUO", size=(100, 70), actionPerformed= self.__fluo)
northpanel2.add(fluo)
southpanel=swing.JPanel(awt.FlowLayout(awt.FlowLayout.RIGHT))
southpanel.setBorder(line)
help = swing.JButton("Help", size=(100, 70), actionPerformed=self.__help)
self.__label=swing.JLabel("Label")
southpanel.add(self.__label)
close = swing.JButton("Close", size=(100, 70), actionPerformed=self.__close)
southpanel.add(help)
southpanel.add(close)
grid = awt.GridLayout()
grid.setRows(4)
checkpanel=swing.JPanel(grid)
checkpanel.setBorder(line)
self.__box0=swing.JCheckBox(actionPerformed=self.__boxaction0)
self.__box0.setText("Skip failed ROIs")
self.__box0.setSelected(False)
self.__box1=swing.JCheckBox(actionPerformed=self.__boxaction1)
self.__box1.setText("Mosaic")
self.__box1.setSelected(True)
self.__box2=swing.JCheckBox(actionPerformed=self.__boxaction2)
self.__box2.setText("Mean Projection")
self.__box2.setSelected(True)
self.__box3=swing.JCheckBox(actionPerformed=self.__boxaction3)
self.__box3.setText("Max Finder")
self.__box3.setSelected(True)
self.__box4=swing.JCheckBox(actionPerformed=self.__boxaction4)
self.__box4.setText("Median filter")
self.__box4.setSelected(True)
self.__box5=swing.JCheckBox(actionPerformed=self.__boxaction5)
self.__box5.setText("Fire LUT")
self.__box5.setSelected(True)
self.__box6=swing.JCheckBox(actionPerformed=self.__boxaction6)
self.__box6.setText("Auto Align Left")
self.__box6.setSelected(True)
self.__box7=swing.JCheckBox(actionPerformed=self.__boxaction7)
self.__box7.setText("Auto Enlarge")
self.__box7.setSelected(True)
self.__box8=swing.JCheckBox(actionPerformed=self.__boxaction8)
self.__box8.setText("Measures")
self.__box8.setSelected(True)
self.__box9=swing.JCheckBox(actionPerformed=self.__boxaction9)
self.__box9.setText("Auto Align Center")
self.__box9.setSelected(False)
self.__box10=swing.JCheckBox(actionPerformed=self.__boxaction10)
self.__box10.setText("Use ellipses")
self.__box10.setSelected(False)
checkpanel.add(self.__box0)
checkpanel.add(self.__box1)
checkpanel.add(self.__box2)
checkpanel.add(self.__box3)
checkpanel.add(self.__box4)
checkpanel.add(self.__box5)
checkpanel.add(self.__box6)
checkpanel.add(self.__box7)
checkpanel.add(self.__box8)
checkpanel.add(self.__box9)
checkpanel.add(self.__box10)
grid = awt.GridLayout()
grid.setRows(10)
checkpanel2=swing.JPanel(grid)
checkpanel2.setBorder(line)
label=swing.JLabel("Label7")
label.setText("Max of Solidity")
checkpanel2.add(label)
self.__display7 = swing.JTextField(preferredSize=(50, 30), horizontalAlignment=swing.SwingConstants.LEFT)
self.__display7.text = "1"
checkpanel2.add(self.__display7)
label=swing.JLabel("Label8")
label.setText("Min of Solidity")
checkpanel2.add(label)
self.__display8 = swing.JTextField(preferredSize=(50, 30), horizontalAlignment=swing.SwingConstants.LEFT)
self.__display8.text = "0"
checkpanel2.add(self.__display8)
label=swing.JLabel("Label9")
label.setText("Max of Area")
checkpanel2.add(label)
self.__display9 = swing.JTextField(preferredSize=(50, 30), horizontalAlignment=swing.SwingConstants.LEFT)
self.__display9.text = "1447680"
checkpanel2.add(self.__display9)
label=swing.JLabel("Label10")
label.setText("Min of Area")
checkpanel2.add(label)
self.__display10 = swing.JTextField(preferredSize=(50, 30), horizontalAlignment=swing.SwingConstants.LEFT)
self.__display10.text = "1"
checkpanel2.add(self.__display10)
label=swing.JLabel("Label11")
label.setText("Max of Circ")
checkpanel2.add(label)
self.__display11 = swing.JTextField(preferredSize=(50, 30), horizontalAlignment=swing.SwingConstants.LEFT)
self.__display11.text = "1"
checkpanel2.add(self.__display11)
label=swing.JLabel("Label12")
label.setText("Min of Circ")
checkpanel2.add(label)
self.__display12 = swing.JTextField(preferredSize=(50, 30), horizontalAlignment=swing.SwingConstants.LEFT)
self.__display12.text = "0"
checkpanel2.add(self.__display12)
label=swing.JLabel("Label13")
label.setText("Max of AR")
checkpanel2.add(label)
self.__display13 = swing.JTextField(preferredSize=(50, 30), horizontalAlignment=swing.SwingConstants.LEFT)
self.__display13.text = "1392"
checkpanel2.add(self.__display13)
label=swing.JLabel("Label14")
label.setText("Min of AR")
checkpanel2.add(label)
self.__display14 = swing.JTextField(preferredSize=(50, 30), horizontalAlignment=swing.SwingConstants.LEFT)
self.__display14.text = "1"
checkpanel2.add(self.__display14)
label=swing.JLabel("Label15")
label.setText("Max of Feret")
checkpanel2.add(label)
self.__display15 = swing.JTextField(preferredSize=(50, 30), horizontalAlignment=swing.SwingConstants.LEFT)
self.__display15.text = "1392"
checkpanel2.add(self.__display15)
label=swing.JLabel("Label16")
label.setText("Min of Feret")
checkpanel2.add(label)
self.__display16 = swing.JTextField(preferredSize=(50, 30), horizontalAlignment=swing.SwingConstants.LEFT)
self.__display16.text = "1"
checkpanel2.add(self.__display16)
label=swing.JLabel("Label17")
label.setText("Max of Mean")
checkpanel2.add(label)
self.__display17 = swing.JTextField(preferredSize=(50, 30), horizontalAlignment=swing.SwingConstants.LEFT)
self.__display17.text = "65535"
checkpanel2.add(self.__display17)
label=swing.JLabel("Label18")
label.setText("Min of Mean")
checkpanel2.add(label)
self.__display18 = swing.JTextField(preferredSize=(50, 30), horizontalAlignment=swing.SwingConstants.LEFT)
self.__display18.text = "0"
checkpanel2.add(self.__display18)
label=swing.JLabel("Label19")
label.setText("max ratio Axis/Feret")
checkpanel2.add(label)
self.__display19 = swing.JTextField(preferredSize=(50, 30), horizontalAlignment=swing.SwingConstants.LEFT)
self.__display19.text = str(self.__maxraf)
checkpanel2.add(self.__display19)
label=swing.JLabel("Label20")
label.setText("Min ratio Axis/Feret")
checkpanel2.add(label)
self.__display20 = swing.JTextField(preferredSize=(50, 30), horizontalAlignment=swing.SwingConstants.LEFT)
self.__display20.text = str(self.__minraf)
checkpanel2.add(self.__display20)
label=swing.JLabel("Label21")
label.setText("Max MinFeret")
checkpanel2.add(label)
self.__display21 = swing.JTextField(preferredSize=(50, 30), horizontalAlignment=swing.SwingConstants.LEFT)
self.__display21.text = "1392"
checkpanel2.add(self.__display21)
label=swing.JLabel("Label22")
label.setText("Min MinFeret")
checkpanel2.add(label)
self.__display22 = swing.JTextField(preferredSize=(50, 30), horizontalAlignment=swing.SwingConstants.LEFT)
self.__display22.text = "1"
checkpanel2.add(self.__display22)
self.contentPane.add(northpanel1, awt.BorderLayout.NORTH)
self.contentPane.add(checkpanel, awt.BorderLayout.WEST)
self.contentPane.add(northpanel2, awt.BorderLayout.CENTER)
self.contentPane.add(southpanel, awt.BorderLayout.SOUTH)
self.contentPane.add(checkpanel2, awt.BorderLayout.EAST)
def __close(self, event):
self.oked = True
time.sleep(0.01)
self.dispose()
def __new(self, event):
self.__init = True
self.__n += 1
self.__name = "stackcells"+str(self.__n)
self.__display.text = self.__name
self.__sens[:] = []
self.__listrois[:] = []
self.__iplist[:] = []
self.__cellsrois[:] = []
self.__labels[:] = []
def __add(self, event):
if ( not self.__init) :
IJ.showMessage("", "please start a new stack")
return
if ( not self.__initDIA) :
IJ.showMessage("", "please select an image for DIA")
return
if ( not self.__initFLUO) :
IJ.showMessage("", "please select an image for FLUO")
return
self.__widthl = self.__display2.getText()
roi = self.__impD.getRoi()
if roi == None :
IJ.showMessage("", "No selection")
return
if roi.getType() in [6,7] :
nslice = self.__impD.getCurrentSlice()
self.__impF.setSlice(nslice)
self.__impF.setRoi(roi)
elif roi.getType() in [2,4] :
nslice = self.__impD.getCurrentSlice()
self.__impF.setSlice(nslice)
m=Morph(self.__impF, roi)
m.setMidParams(10, 2)
roi=m.MidAxis
if roi == None :
self.__display.text = "roi fail"
if not self.__skip : IJ.showMessage("", "failed roi, please draw it as polyline")
return
#if roi.getType() != 6 : self.__impF.setRoi(roi)
else :
IJ.showMessage("", "This selection is not yet allowed")
return
self.__impF.setRoi(roi)
straightener = Straightener()
new_ip = straightener.straighten(self.__impF, roi, int(self.__widthl))
self.__iplist.append(new_ip)
self.__labels.append(self.__isF.getShortSliceLabel(nslice))
self.__display.text = self.__name + " cell " + str(len(self.__iplist)) +" width="+str(new_ip.getWidth())+ " height="+ str(new_ip.getHeight())
roi.setPosition(self.__impD.getCurrentSlice())
self.__rm = RoiManager.getInstance()
if (self.__rm==None): self.__rm = RoiManager()
self.__rm.add(self.__impD, roi, len(self.__iplist))
self.__cellsrois.append((roi, self.__impD.getCurrentSlice()))
#self.__rm.runCommand("Show All")
IJ.selectWindow(self.__impD.getTitle())
def __end(self, event):
if len(self.__iplist)==0 :
IJ.showMessage("", "Stack is empty")
return
self.__ipw=[ ip.getWidth() for ip in self.__iplist ]
self.__iph=[ ip.getHeight() for ip in self.__iplist ]
maxw=max(self.__ipw)
maxh=max(self.__iph)
if self.__enlarge :
resizelist = [ ip.resize(maxw, maxh, True) for ip in self.__iplist ]
else :
resizelist = []
for ip in self.__iplist :
tempip = ShortProcessor(maxw, maxh)
tempip.copyBits(ip, 0, 0, Blitter.COPY)
resizelist.append(tempip)
ims = ImageStack(maxw, maxh)
#for ip in resizelist : ims.addSlice("", ip)
for i in range(len(resizelist)) :
ims.addSlice(self.__labels[i], resizelist[i])
self.__impRes = ImagePlus(self.__name, ims)
self.__impRes.show()
self.__sens = [1 for i in range(len(self.__iplist)) ]
if self.__appmedian : IJ.run(self.__impRes, "Median...", "radius=1 stack")
if self.__align : self.__falign()
if self.__avg : self.__favg()
if self.__mosa : self.__fmosa()
if self.__maxfinder : self.__fmaxfinder()
if self.__fire : IJ.run(self.__impRes, "Fire", "")
if self.__measures : self.__fmeasures()
self.__sens[:] = []
self.__listrois[:] = []
self.__iplist[:] = []
self.__cellsrois[:] = []
self.__ipw[:] = []
self.__iph[:] = []
self.__init = False
def __dia(self, event):
IJ.run("Set Scale...", "distance=0 known=0 pixel=1 unit=pixel")
#IJ.run("Properties...", "channels=1 slices=1 frames=20 unit=pixel pixel_width=1.0000 pixel_height=1.0000 voxel_depth=1.0000 frame=[1 sec] origin=0,0");
self.__impD = IJ.getImage()
self.__isD = self.__impD.getImageStack()
self.__display.text = "DIA="+self.__impD.getTitle()
self.__initDIA = True
def __fluo(self, event):
IJ.run("Set Scale...", "distance=0 known=0 pixel=1 unit=pixel")
self.__impF = IJ.getImage()
self.__isF = self.__impF.getImageStack()
self.__display.text = "FLUO="+self.__impF.getTitle()
self.__initFLUO = True
def __addroi(self, event) :
if ( not self.__init) :
IJ.showMessage("", "please start a new stack")
return
if ( not self.__initDIA) :
IJ.showMessage("", "please select an image for DIA")
return
if ( not self.__initFLUO) :
IJ.showMessage("", "please select an image for FLUO")
return
twres = TextWindow("measures-"+self.__name, "label\tname\tsol\tarea\tcirc\tAR\tFeret\taxis\traf\tdMajor\tdFeret\tdArea", "", 300, 450)
tab="\t"
self.__widthl = self.__display2.getText()
IJ.selectWindow(self.__impF.getTitle())
self.__rm = RoiManager.getInstance()
if (self.__rm==None): self.__rm = RoiManager()
if self.__impF.getImageStackSize() > 1 :
roisarray =[(roi, self.__rm.getSliceNumber(roi.getName())) for roi in self.__rm.getRoisAsArray()]
else :
roisarray =[(roi, 1) for roi in self.__rm.getRoisAsArray()]
self.__rm.runCommand("reset")
#self.__rm.runCommand("Delete")
IJ.selectWindow(self.__impF.getTitle())
self.__maxraf=float(self.__display19.text)
self.__minraf=float(self.__display20.text)
count=1
for roielement in roisarray :
roi = roielement[0]
pos = roielement[1]
lab = self.__impF.getImageStack().getShortSliceLabel(pos)
if lab==None : lab=str(pos)
if self.__conEllipses :
IJ.selectWindow(self.__impF.getTitle())
self.__impF.setSlice(pos)
self.__impF.setRoi(roi)
self.__rm.runCommand("Add")
IJ.run(self.__impF, "Fit Ellipse", "")
ellipse=self.__impF.getRoi()
params = ellipse.getParams()
ferets = ellipse.getFeretValues()
imp2 = Duplicator().run(self.__impF,pos,pos)
IJ.run(imp2, "Rotate... ", "angle="+str(ferets[1])+" grid=0 interpolation=Bilinear enlarge slice")
temproi=Roi((imp2.getWidth()-ferets[0])/2.0,(imp2.getHeight()-ferets[2])/2.0,ferets[0],ferets[2])
imp2.setRoi(temproi)
imp3 = Duplicator().run(imp2,1,1)
ip3=imp3.getProcessor()
if int(self.__display5.text) < ip3.getWidth() < int(self.__display6.text) :
self.__iplist.append(ip3)
self.__display.text = self.__name + " cell " + str(len(self.__iplist))
fer=Line(params[0],params[1],params[2],params[3])
self.__cellsrois.append((fer, pos))
self.__labels.append(self.__isF.getShortSliceLabel(pos))
m=Morph(self.__impF, roi)
twres.append(lab+tab+str(roi.getName())+tab+str(m.Solidity)+tab+str(m.Area)+tab+str(m.Circ)+tab+str(m.AR)+tab+str(m.MaxFeret)+tab+str(fer.getLength())+tab+str(1)+tab+str(0)+tab+str(0)+tab+str(0))
self.__dictCells[count]=(str(roi.getName()), lab, roi)
count=count+1
continue
if roi.getType() in [6,7] :
self.__impF.setSlice(pos)
self.__impF.setRoi(roi)
self.__rm.runCommand("Add")
elif roi.getType() in [2,4] :
self.__impF.setSlice(pos)
self.__impF.setRoi(roi)
m=Morph(self.__impF, roi)
m.setMidParams(10, 2)
midroi=m.MidAxis
if midroi == None : continue
raf = m.MaxFeret/midroi.getLength()
if (self.__maxraf < raf) or (raf < self.__minraf) : continue
maxsol = float(self.__display7.text)
minsol = float(self.__display8.text)
maxarea = float(self.__display9.text)
minarea = float(self.__display10.text)
maxcirc = float(self.__display11.text)
mincirc = float(self.__display12.text)
maxar = float(self.__display13.text)
minar = float(self.__display14.text)
maxfer = float(self.__display15.text)
minfer = float(self.__display16.text)
maxmean = float(self.__display17.text)
minmean = float(self.__display18.text)
maxmferet = float(self.__display21.text)
minmferet = float(self.__display22.text)
testsol = (minsol< m.Solidity < maxsol)
testarea = (minarea< m.Area < maxarea)
testcirc = (mincirc< m.Circ < maxcirc)
testar = (minar< m.AR < maxar)
testfer = (minfer< m.MaxFeret < maxfer)
testmean = (minmean < m.Mean < maxmean)
testmferet = (minmferet < m.MinFeret < maxmferet)
#print minmferet , m.MinFeret , maxmferet
test = (testsol+testarea+testcirc+testar+testfer+testmean+testmferet)/7
if test :
fmaj, ffmx, fa =[],[],[]
for r in m.getMidSegments(10, 40, 0) :
if r == None : continue
m2=Morph(self.__impF, r)
fmaj.append(m2.Major)
ffmx.append(m2.MaxFeret)
fa.append(m2.Area)
diffmajor, diffferet, diffarea = 0,0,0
if len(fa) > 4 :
medfmaj = self.listmean(fmaj[1:-1])
medffmx = self.listmean(ffmx[1:-1])
medfa = self.listmean(fa[1:-1])
diffmajor = (max(fmaj[1:-1])-medfmaj)/medfmaj
diffferet = (max(ffmx[1:-1])-medffmx)/medffmx
diffarea = (max(fa[1:-1])-medfa)/medfa
twres.append(lab+tab+str(roi.getName())+tab+str(m.Solidity)+tab+str(m.Area)+tab+str(m.Circ)+tab+str(m.AR)+tab+str(m.MaxFeret)+tab+str(midroi.getLength())+tab+str(m.MaxFeret/midroi.getLength())+tab+str(diffmajor)+tab+str(diffferet)+tab+str(diffarea))
#print lab+tab+str(roi.getName())+tab+str(m.Solidity)+tab+str(m.Area)+tab+str(m.Circ)+tab+str(m.AR)+tab+str(m.MaxFeret)+tab+str(midroi.getLength())+tab+str(m.MaxFeret/midroi.getLength())+tab+str(diffmajor)+tab+str(diffferet)+tab+str(diffarea)
self.__impF.setRoi(roi)
self.__rm.runCommand("Add")
self.__impF.killRoi()
self.__impF.setRoi(midroi)
#self.__dictCells[str(roi.getName())]=(str(roi.getName()), lab, roi)
self.__dictCells[count]=(str(roi.getName()), lab, roi)
count=count+1
else :
#print "test falls"
continue
else :
print "out loop"
continue
straightener = Straightener()
new_ip = straightener.straighten(self.__impF, midroi, int(self.__widthl))
if int(self.__display5.text) < new_ip.getWidth() < int(self.__display6.text) :
self.__iplist.append(new_ip)
self.__display.text = self.__name + " cell " + str(len(self.__iplist))
#print "add", roi.getName(), roi.getType()
self.__cellsrois.append((midroi, pos))
self.__labels.append(self.__isF.getShortSliceLabel(pos))
#roisarray=self.__rm.getRoisAsArray()
#self.__rm.runCommand("reset")
#self.__rm.runCommand("Delete")
self.__impD.killRoi()
self.__impF.killRoi()
IJ.selectWindow(self.__impD.getTitle())
def __boxaction0(self, event):
self.__skip = event.getSource().isSelected()
def __boxaction1(self, event):
self.__mosa = event.getSource().isSelected()
#self.__setDisplay(str(event.getSource().text)+" is "+str(event.getSource().isSelected()))
def __boxaction2(self, event):
self.__avg = event.getSource().isSelected()
#self.__setDisplay(str(event.getSource().text)+" is "+str(event.getSource().isSelected()))
def __boxaction3(self, event):
self.__maxfinder = event.getSource().isSelected()
#self.__setDisplay(str(event.getSource().text)+" is "+str(event.getSource().isSelected()))
def __boxaction4(self, event):
self.__appmedian = event.getSource().isSelected()
#self.__setDisplay(str(event.getSource().text)+" is "+str(event.getSource().isSelected()))
def __boxaction5(self, event):
self.__fire = event.getSource().isSelected()
#self.__setDisplay(str(event.getSource().text)+" is "+str(event.getSource().isSelected()))
def __boxaction6(self, event):
self.__align = event.getSource().isSelected()
#self.__setDisplay(str(event.getSource().text)+" is "+str(event.getSource().isSelected()))
def __boxaction7(self, event):
self.__enlarge = event.getSource().isSelected()
#self.__setDisplay(str(event.getSource().text)+" is "+str(event.getSource().isSelected()))
def __boxaction8(self, event):
self.__measures = event.getSource().isSelected()
#self.__setDisplay(str(event.getSource().text)+" is "+str(event.getSource().isSelected()))
def __boxaction9(self, event):
self.__alignC = event.getSource().isSelected()
#self.__setDisplay(str(event.getSource().text)+" is "+str(event.getSource().isSelected()))
def __boxaction10(self, event):
self.__conEllipses = event.getSource().isSelected()
def __favg(self) :
zp = ZProjector(self.__impRes)
zp.setMethod(ZProjector.AVG_METHOD)
zp.doProjection()
imp = zp.getProjection()
imp.show()
if self.__fire : IJ.run(imp, "Fire", "")
def __fmosa(self) :
mm = MontageMaker()
imp = mm.makeMontage2(self.__impRes, 1, self.__impRes.getStackSize(), 1, 1, self.__impRes.getStackSize(), 1, 0, False)
imp.setTitle("MONTAGE"+self.__name)
imp.show()
if self.__fire : IJ.run(imp, "Fire", "")
def __fmaxfinder(self) :
#stack = self.__impRes.getStack()
self.__impD.killRoi()
self.__impF.killRoi()
stack = self.__impF.getStack()
n_slices = stack.getSize()
#newstack=ImageStack(self.__impRes.getWidth(), self.__impRes.getHeight())
newstack=ImageStack(self.__impF.getWidth(), self.__impF.getHeight())
noise = self.__display3.text
for index in range(1,n_slices+1):
IJ.selectWindow(self.__impF.getTitle())
self.__impF.setSlice(index)
ip = self.__impF.getProcessor()
mf=MaximumFinder()
ipmax = mf.findMaxima(ip, int(noise), 0, 0, False, False)
newstack.addSlice("", ipmax)
newimage=ImagePlus("max points"+self.__name, newstack)
newimage.show()
newimage.updateAndDraw()
listip = []
maxh=self.__impRes.getHeight()
for roi in self.__cellsrois :
straightener = Straightener()
newimage.setSlice(roi[1])
newimage.setRoi(roi[0])
#listip.append(straightener.straighten(newimage, roi[0], int(self.__widthl)))
listip.append(straightener.straighten(newimage, roi[0], maxh))
ipw=[ ip.getWidth() for ip in listip ]
iph=[ ip.getHeight() for ip in listip ]
maxw=max(ipw)
maxh=max(iph)
if self.__enlarge : resizelist = [ ip.resize(maxw, maxh, True) for ip in listip ]
elif self.__alignC :
resizelist = []
for ip in listip :
tempip = ByteProcessor(maxw, maxh)
tempip.copyBits(ip, 0, 0, Blitter.COPY)
resizelist.append(tempip)
else :
resizelist = []
for ip in listip :
tempip = ByteProcessor(maxw, maxh)
tempip.copyBits(ip, 0, 0, Blitter.COPY)
resizelist.append(tempip)
ims = ImageStack(maxw, maxh)
#for ip in resizelist : ims.addSlice("", ip)
for i in range(len(resizelist)) :
ims.addSlice(self.__labels[i], resizelist[i])
self.__impMax = ImagePlus(self.__name+"-max", ims)
self.__impMax.show()
stack = self.__impMax.getStack() # get the stack within the ImagePlus
n_slices = stack.getSize()
for index in range(1, n_slices+1):
self.__impMax.killRoi()
self.__impMax.setSlice(index)
roi = self.__listrois[index-1]
if self.__sens[index-1]<0 :
self.__impMax.setRoi(roi)
ip1 = self.__impMax.getProcessor()
ip1.flipHorizontal()
self.__impMax.killRoi()
self.__impMax.updateAndDraw()
ip = self.__impMax.getProcessor()
for i in range(ip.getWidth()*ip.getHeight()) :
if ip.getf(i) > 0 : ip.setf(i, 255)
#else : ip.setf(i, 0)
IJ.run(self.__impMax, "8-bit", "")
IJ.run(self.__impMax, "Options...", "iterations=2 count=1 black edm=Overwrite do=Close stack")
IJ.run(self.__impMax, "Ultimate Points", "stack")
self.__impMax.updateAndDraw()
def __falign(self) :
#self.__impRes=IJ.getImage()
stack = self.__impRes.getStack() # get the stack within the ImagePlus
n_slices = stack.getSize() # get the number of slices
ic = ImageCalculator()
w = self.__impRes.getWidth()
h = self.__impRes.getHeight()
self.__sens[:] = []
self.__listrois[:] = []
for index in range(1, n_slices+1):
self.__impRes.setSlice(index)
ip1 = stack.getProcessor(index)
imp1 = ImagePlus("imp1-"+str(index), ip1)
imp1sqr = ic.run("Multiply create 32-bit", imp1, imp1)
IJ.setThreshold(imp1sqr, 1, 4294836225)
IJ.run(imp1sqr, "Create Selection", "")
roi = imp1sqr.getRoi()
rect=roi.getBounds()
roi = Roi(rect)
self.__listrois.append(roi)
ipsqr = imp1sqr.getProcessor()
is1 = ipsqr.getStatistics()
self.__impRes.killRoi()
if is1.xCenterOfMass > w/2.00 :
self.__impRes.setRoi(roi)
ip1 = self.__impRes.getProcessor()
ip1.flipHorizontal()
self.__impRes.killRoi()
self.__sens.append(-1)
else : self.__sens.append(1)
self.__impRes.updateAndDraw()
def __fmeasures(self) :
self.__Cutoff = float(self.__display4.text)
nslices = self.__impRes.getImageStackSize()
rt = ResultsTable()
rt.show("RT-"+self.__name)
twpoints = TextWindow("points-"+self.__name, "index\tlabel\tname\tx\ty\taxis\tcellw\tcellh", "", 200, 450)
twlabels = TextWindow("labels-"+self.__name, "index\tlabel\tname\tnpoints", "", 200, 450)
isres = self.__impRes.getImageStack()
for index in range(1, nslices+1):
self.__impRes.setSlice(index)
self.__impRes.killRoi()
roi = self.__listrois[index-1]
self.__impRes.setRoi(roi)
analyser= Analyzer(self.__impRes, Analyzer.LABELS+Analyzer.CENTER_OF_MASS+Analyzer.CENTROID+Analyzer.INTEGRATED_DENSITY+Analyzer.MEAN+Analyzer.KURTOSIS+Analyzer.SKEWNESS+Analyzer.MIN_MAX+Analyzer.SLICE+Analyzer.STACK_POSITION+Analyzer.STD_DEV, rt)
analyser.measure()
rt.show("RT-"+self.__name)
rect=roi.getBounds()
ip = self.__impRes.getProcessor()
xCoord = []
yCoord = []
currentPixel = []
m00 = 0.00
m10 = 0.00
m01 = 0.00
mc20 = 0.00
mc02 = 0.00
mc11 = 0.00
mc30 = 0.00
mc03 = 0.00
mc21 = 0.00
mc12 = 0.00
mc40 = 0.00
mc04 = 0.00
mc31 = 0.00
mc13 = 0.00
mm20 = 0.00
mm02 = 0.00
mm11 = 0.00
mm30 = 0.00
mm03 = 0.00
mm21 = 0.00
mm12 = 0.00
mm40 = 0.00
mm04 = 0.00
mm31 = 0.00
mm13 = 0.00
for y in range(rect.y, rect.y+rect.height, 1) :
for x in range(rect.x, rect.x+rect.width, 1) :
xCoord.append(x+0.5)
yCoord.append(y+0.5)
#pixel=ip.getf(x,y)-self.__Cutoff
pixel = ip.getPixelValue(x,y)-self.__Cutoff
if pixel < 0 : pixel = 0
currentPixel.append(pixel)
m00 += currentPixel[-1]
m10 += currentPixel[-1]*xCoord[-1]
m01 += currentPixel[-1]*yCoord[-1]
xm = m10/(m00+0.00000001)
ym = m01/(m00+0.00000001)
xc = rect.width/2.00
yc = rect.height/2.00
for i in range(rect.width*rect.height) :
xcrel = xCoord[i]-xc
ycrel = yCoord[i]-yc
#mc20 += currentPixel[i]*(xCoord[i]-xc)*(xCoord[i]-xc)
#mc02 += currentPixel[i]*(yCoord[i]-yc)*(yCoord[i]-yc)
#mc11 += currentPixel[i]*(xCoord[i]-xc)*(yCoord[i]-yc)
#
#mc30 += currentPixel[i]*(xCoord[i]-xc)*(xCoord[i]-xc)*(xCoord[i]-xc)
#mc03 += currentPixel[i]*(yCoord[i]-yc)*(yCoord[i]-yc)*(yCoord[i]-yc)
#mc21 += currentPixel[i]*(xCoord[i]-xc)*(xCoord[i]-xc)*(yCoord[i]-yc)
#mc12 += currentPixel[i]*(xCoord[i]-xc)*(yCoord[i]-yc)*(yCoord[i]-yc)
#
#mc40 += currentPixel[i]*(xCoord[i]-xc)*(xCoord[i]-xc)*(xCoord[i]-xc)*(xCoord[i]-xc)
#mc04 += currentPixel[i]*(yCoord[i]-yc)*(yCoord[i]-yc)*(yCoord[i]-yc)*(yCoord[i]-yc)
#mc31 += currentPixel[i]*(xCoord[i]-xc)*(xCoord[i]-xc)*(xCoord[i]-xc)*(yCoord[i]-yc)
#mc13 += currentPixel[i]*(xCoord[i]-xc)*(yCoord[i]-yc)*(yCoord[i]-yc)*(yCoord[i]-yc)
mc20 += currentPixel[i]*xcrel*xcrel
mc02 += currentPixel[i]*ycrel*ycrel
mc11 += currentPixel[i]*xcrel*ycrel
mc30 += currentPixel[i]*xcrel*xcrel*xcrel
mc03 += currentPixel[i]*ycrel*ycrel*ycrel
mc21 += currentPixel[i]*xcrel*xcrel*ycrel
mc12 += currentPixel[i]*xcrel*ycrel*ycrel
mc40 += currentPixel[i]*xcrel*xcrel*xcrel*xcrel
mc04 += currentPixel[i]*ycrel*ycrel*ycrel*ycrel
mc31 += currentPixel[i]*xcrel*xcrel*xcrel*ycrel
mc13 += currentPixel[i]*xcrel*ycrel*ycrel*ycrel
for i in range(rect.width*rect.height) :
mm20 += currentPixel[i]*(xCoord[i]-xm)*(xCoord[i]-xm)
mm02 += currentPixel[i]*(yCoord[i]-ym)*(yCoord[i]-ym)
mm11 += currentPixel[i]*(xCoord[i]-xm)*(yCoord[i]-ym)
mm30 += currentPixel[i]*(xCoord[i]-xm)*(xCoord[i]-xm)*(xCoord[i]-xm)
mm03 += currentPixel[i]*(yCoord[i]-ym)*(yCoord[i]-ym)*(yCoord[i]-ym)
mm21 += currentPixel[i]*(xCoord[i]-xm)*(xCoord[i]-xm)*(yCoord[i]-ym)
mm12 += currentPixel[i]*(xCoord[i]-xm)*(yCoord[i]-ym)*(yCoord[i]-ym)
mm40 += currentPixel[i]*(xCoord[i]-xm)*(xCoord[i]-xm)*(xCoord[i]-xm)*(xCoord[i]-xm)
mm04 += currentPixel[i]*(yCoord[i]-ym)*(yCoord[i]-ym)*(yCoord[i]-ym)*(yCoord[i]-ym)
mm31 += currentPixel[i]*(xCoord[i]-xm)*(xCoord[i]-xm)*(xCoord[i]-xm)*(yCoord[i]-ym)
mm13 += currentPixel[i]*(xCoord[i]-xm)*(yCoord[i]-ym)*(yCoord[i]-ym)*(yCoord[i]-ym)
xxcVar = mc20/m00
yycVar = mc02/m00
xycVar = mc11/m00
xcSkew = mc30/(m00 * math.pow(xxcVar,(3.0/2.0)))
ycSkew = mc03/(m00 * math.pow(yycVar,(3.0/2.0)))
xcKurt = mc40 / (m00 * math.pow(xxcVar,2.0)) - 3.0
ycKurt = mc04 / (m00 * math.pow(yycVar,2.0)) - 3.0
ecc = (math.pow((mc20-mc02),2.0)+(4.0*mc11*mc11))/m00
xxmVar = mm20/m00
yymVar = mm02/m00
xymVar = mm11/m00
xmSkew = mm30/(m00 * math.pow(xxmVar,(3.0/2.0)))
ymSkew = mm03/(m00 * math.pow(yymVar,(3.0/2.0)))
xmKurt = mm40 / (m00 * math.pow(xxmVar,2.0)) - 3.0
ymKurt = mm04 / (m00 * math.pow(yymVar,2.0)) - 3.0
ecm = (math.pow((mm20-mm02),2.0)+(4.0*mm11*mm11))/m00
rt.addValue("xxcVar", xxcVar)
rt.addValue("yycVar", yycVar)
rt.addValue("xycVar", xycVar)
rt.addValue("xcSkew", xcSkew)
rt.addValue("ycSkew", ycSkew)
rt.addValue("xcKurt", xcKurt)
rt.addValue("ycKurt", ycKurt)
rt.addValue("Ecc", ecc)
rt.addValue("xxmVar", xxmVar)
rt.addValue("yymVar", yymVar)
rt.addValue("xymVar", xymVar)
rt.addValue("xmSkew", xmSkew)
rt.addValue("ymSkew", ymSkew)
rt.addValue("xmKurt", xmKurt)
rt.addValue("ymKurt", ymKurt)
rt.addValue("Ecm", ecm)
rt.addValue("roiw", rect.width)
rt.addValue("roih", rect.height)
rt.addValue("cellw", self.__ipw[index-1])
rt.addValue("cellh", self.__iph[index-1])
self.__impRes.killRoi()
xCoord[:] = []
yCoord[:] = []
currentPixel[:] = []
points = []
points[:] = []
npointsmax = 0
#lab = self.__labels[index-1]
nameroi = self.__dictCells[index][0]
lab = self.__dictCells[index][1]
self.__impMax.setSlice(index)
ipmax = self.__impMax.getProcessor()
for y in range(ipmax.getHeight()) :
for x in range(ipmax.getWidth()) :
if ipmax.getPixelValue(x,y) > 0 :
#print str(index)
#print lab
#print nameroi
#print str(x)
#print str(y)
#print str(self.__cellsrois[index-1][0].getLength())
#print str(self.__ipw[index-1])
#print str(self.__iph[index-1])
twpoints.append(str(index)+"\t"+lab+"\t"+nameroi+"\t"+str(x)+"\t"+str(y)+"\t"+str(self.__cellsrois[index-1][0].getLength())+"\t"+str(self.__ipw[index-1])+"\t"+str(self.__iph[index-1]))
npointsmax+=1
rt.addValue("npoints", npointsmax)
twlabels.append(str(index)+"\t"+lab+"\t"+nameroi+"\t"+str(npointsmax))
rt.show("RT-"+self.__name)
rt.show("RT-"+self.__name)
def __setDisplay(self, val=""):
self.__display.text = str(val)
def setLabel(self, text):
self.__label.setText(text)
def listmean(self, l) : return float(sum(l)/len(l))
def listmedian(self, l) :
s=l[:]
s.sort()
w=len(l)
return float(s[(w-1)/2]) if (w%2 == 1) else float((s[w/2]+s[(w/2)-1]))/2
def __help(self, event):
IJ.log("""
--------------------------------------------------------------------------------------
New = Starts a new process with stacked cells
Add = Adds un ROI as a new cell in the stack (poly segments line or a closed area ROI)
Add Roi manager = adds all the ROIs contained in the roi manager
End = Stops the stack process and generates images and results
--------------------------------------------------------------------------------------
--------------------------------------------------------------------------------------
Line width = width of the cells in pixels.
Noise for peaks = value passed to detect peaks function
Fluo threshold = value of the background in the fluo image. Used for acurated calculus of moments.
Min length = filter for small short cells
Max length = filter for long cells
--------------------------------------------------------------------------------------
--------------------------------------------------------------------------------------
DIA = Select the image and click to set the source image for cells ROI
FLUO = Select the image and click to set the image containig the fluorescence signal
(if Add Roi manager selected, this is not take in to account)
--------------------------------------------------------------------------------------
--------------------------------------------------------------------------------------
Skip failed ROIs = debug option
Generate Mosaic = Generates a vertcal image with all the stacked cells
Mean Projection = creates the projection of all cells by mean method
Create maxFinder = uses the maxFinder function to generate peaks information
Apply median = smooth the streched images by a 3x3 median filter
Apply Fire LUT = shows all images with false colors (Fire LUT)
Auto Align = Flip the cells to align the center of mass in the left part of the images
Auto enlarge = Stretch the cell to fit the length of the longuest cell
Generate measures = creates a text windows with measures parameters.
""")
# Make the ROIs based on the mask
IJ.setThreshold(imgMask, 1, 255, "Red")
IJ.run(imgMask, "Analyze Particles...", "size=200 exclude add stack")
roiManager.runCommand("Show None")
maskStk = imgMask.getStack()
im410Stk = img410.getStack()
im470Stk = img470.getStack()
areas = []
angles = []
xs = []
ys = []
# TODO: subtract median here?
ra = roiManager.getRoisAsArray()
for i in range(maskStk.getSize()):
ip = im410Stk.getProcessor(i+1)
ip.setRoi(ra[i])
istats = ip.getStatistics()
dataTable.setValue('Intensity410_wholePharynx', i, istats.mean)
ip = im470Stk.getProcessor(i+1)
ip.setRoi(ra[i])
istats = ip.getStatistics()
dataTable.setValue('Intensity470_wholePharynx', i, istats.mean)
# TODO: Figure out units for Area
dataTable.setValue('Area (Px)', i, istats.area)
# we don't need to keep track of these in our `data` object, just need them to do the rotations
def segmentation(imp, spot_data, channel, diameter_init, ES_tolerance, ES_area_max, ES_ctrl_pts, ES_iteration, repeat_max):
# Open files
cal = imp.getCalibration()
manager = RoiManager.getInstance()
if manager is None:
manager = RoiManager()
# Prepare log files for output
options = IS.MEDIAN | IS.AREA | IS.MIN_MAX | IS.CENTROID | IS.PERIMETER | IS.ELLIPSE | IS.SKEWNESS
convergence = []
Sintensity = []
for spot in spot_data:
repeat = 0
flag = False
spotID = int(spot[0])
Xcenter = (float(spot[1]) / cal.pixelWidth)
Ycenter = (float(spot[2]) / cal.pixelHeight)
Quality = float(spot[3])
diameter_init = float(spot[4] / cal.pixelWidth) * 2.0
while True:
manager = RoiManager.getInstance()
if manager is None:
manager = RoiManager()
Xcurrent = int(Xcenter - diameter_init / 2.0)
Ycurrent = int(Ycenter - diameter_init / 2.0)
Dcurrent1 = int(diameter_init * (1.2 - repeat / 10.0))
Dcurrent2 = int(diameter_init * (0.8 + repeat / 10.0))
roi = OvalRoi(Xcurrent, Ycurrent, Dcurrent1, Dcurrent2)
imp.setPosition(channel)
imp.setRoi(roi)
Esnake_options1 = "target_brightness=Bright control_points=" + \
str(ES_ctrl_pts) + " gaussian_blur=0 "
Esnake_options2 = "energy_type=Contour alpha=2.0E-5 max_iterations=" + \
str(ES_iteration) + " immortal=false"
IJ.run(imp, "E-Snake", Esnake_options1 + Esnake_options2)
roi_snake = manager.getRoisAsArray()
roi_ind = len(roi_snake) - 1
stats = IS.getStatistics(
imp.getProcessor(), options, imp.getCalibration())
perimeter = roi_snake[roi_ind].getLength() * cal.pixelWidth
circularity = 4.0 * 3.1417 * (stats.area / (perimeter * perimeter))
if stats.area > 17.0 and stats.area < ES_area_max and stats.skewness < -0.01 and circularity > 0.01 and stats.minor > 2.0 and boundaries(Xcenter, Ycenter, stats.xCentroid / cal.pixelWidth, stats.yCentroid / cal.pixelHeight, ES_tolerance):
Sintensity = stats.median
convergence.append(True)
break
if stats.median > 6000 and stats.area > 17.0 and stats.area < ES_area_max:
Sintensity = stats.median
convergence.append(True)
break
elif repeat > repeat_max:
manager.select(imp, roi_ind)
manager.runCommand(imp, 'Delete')
roi = OvalRoi(Xcenter + 1.0 - diameter_init / 2.0, Ycenter +
1.0 - diameter_init / 2.0, diameter_init, diameter_init)
imp.setRoi(roi)
manager.add(imp, roi, spotID)
roi_snake.append(roi)
stats = IS.getStatistics(
imp.getProcessor(), options, imp.getCalibration())
Sintensity = stats.median
convergence.append(False)
break
else:
IJ.log('Area=' + str(stats.area) + ' Skewness=' + str(stats.skewness) +
' circularity=' + str(circularity) + ' Minor=' + str(stats.minor))
manager.select(imp, roi_ind)
manager.runCommand(imp, 'Delete')
repeat += 1
# End Spot-segmentation
# End all Spots-segmentation
manager.runCommand(imp, 'Show All')
imp.setPosition(channel)
color = imp.createImagePlus()
ip = imp.getProcessor().duplicate()
color.setProcessor("segmentation" + str(channel), ip)
color.show()
IJ.selectWindow("segmentation" + str(channel))
manager.moveRoisToOverlay(color)
spot_optimal = manager.getRoisAsArray()
manager.reset()
for i in xrange(0, len(spot_optimal)):
spot = spot_optimal[i]
spot.setStrokeWidth(2)
if convergence[i]:
spot.setStrokeColor(Color.GREEN)
else:
spot.setStrokeColor(Color.MAGENTA)
imp.setRoi(spot)
manager.add(imp, spot, i)
manager.runCommand(imp, 'Show All')
imp.setPosition(channel)
def batch_open_Rois(pathRoi,
file_typeRoi=None,
name_filterRoi=None,
recursive=False):
'''Open all files in the given folder.
:param path: The path from were to open the Rois. String and java.io.File are allowed.
:param file_type: Only accept files with the given extension (default: None).
:param name_filter: Reject files that contain the given string (default: wild characters).
:param recursive: Process directories recursively (default: False).
'''
# Converting a File object to a string.
if isinstance(pathRoi, File):
pathRoi = pathRoi.getAbsolutePath()
def check_type(string):
'''This function is used to check the file type.
It is possible to use a single string or a list/tuple of strings as filter.
This function can access the variables of the surrounding function.
:param string: The filename to perform the check on.
'''
if file_typeRoi:
# The first branch is used if file_type is a list or a tuple.
if isinstance(file_typeRoi, (list, tuple)):
for file_type_ in file_typeRoi:
if string.endswith(file_type_):
# Exit the function with True.
return True
else:
# Next iteration of the for loop.
continue
# The second branch is used if file_type is a string.
elif isinstance(file_typeRoi, string):
if string.endswith(file_typeRoi):
return True
else:
return False
return False
# Accept all files if file_type is None.
else:
return True
# We collect all files to open in a list.
path_to_Roi = []
# Replacing some abbreviations (e.g. $HOME on Linux).
path = os.path.expanduser(pathRoi)
# If we don't want a recursive search, we can use os.listdir().
if not recursive:
for file_name in os.listdir(pathRoi):
full_path = os.path.join(pathRoi, file_name)
if os.path.isfile(full_path):
if check_type(file_name):
path_to_Roi.append(full_path)
# For a recursive search os.walk() is used.
else:
# os.walk() is iterable.
# Each iteration of the for loop processes a different directory.
# the first return value represents the current directory.
# The second return value is a list of included directories.
# The third return value is a list of included files.
for directory, dir_names, file_names in os.walk(pathRoi):
# We are only interested in files.
for file_name in file_names:
# The list contains only the file names.
# The full path needs to be reconstructed.
full_path = os.path.join(directory, file_name)
# Both checks are performed to filter the files.
if check_type(file_name):
# Add the file to the list of Rois to open.
path_to_Roi.append([
full_path,
os.path.basename(os.path.splitext(full_path)[0])
])
# Create the list that will be returned by this function.
RoisX = []
RoisY = []
print('path', path_to_Roi)
for roi_path in path_to_Roi:
print('path', roi_path)
# An object equals True and None equals False.
rm = RoiManager.getInstance()
if (rm == None):
rm = RoiManager()
Roi = IJ.open(roi_path)
roi_points = rm.getRoisAsArray()
table = ResultsTable()
for Roi in roi_points:
xpoints = Roi.getPolygon().xpoints
ypoints = Roi.getPolygon().ypoints
for i in range(len(xpoints)):
table.incrementCounter()
table.addValue("Index", i)
table.addValue("X", xpoints[i])
table.addValue("Y", ypoints[i])
table.show("XY-Coordinates")
return roi_points
reffn = odref.getFileName()
if reffn is None:
print "User canceled the dialog!"
else:
refdir = odref.getDirectory()
refpath = os.path.join(refdir, reffn)
refImp = IJ.openImage(refpath)
width = refImp.width
height = refImp.height
roim = RoiManager()
roim.runCommand("open", roipath)
roiArray = roim.getRoisAsArray()
nRoi = len(roiArray)
roim.close()
bwStack = ImageStack(width, height, nRoi)
for i in xrange(1, nRoi+1):
bwStack.setProcessor(FloatProcessor(width, height, zeros('f', width * height), None), i)
for i in xrange(1, nRoi+1):
roi = roiArray[i-1]
fp = bwStack.getProcessor(i)
fp.setValue(1.0)
fp.fill(roi)
roiImp = ImagePlus("roi", bwStack)
from operator import itemgetter, attrgetter
from ij.gui import PolygonRoi, Roi
true=1
false=0
IJ.run("Invert", "stack");
IJ.run("Fill Holes", "stack");
IJ.run("Create Selection");
rm = RoiManager()
rm.runCommand("add")
rm.runCommand("split")
#number_selected=rm.getCount()
IJ.run("Select None");
rm.runCommand("deselect")
#rm.select(0)
#print number_selected
roi_array=rm.getRoisAsArray()
max_roi=None
max_points=-1
for roi in roi_array:
polygon=roi.getPolygon()
if polygon is not None:
number_of_points = polygon.npoints
if max_points < number_of_points:
max_points=number_of_points
max_roi=roi
#print max_points
#sorted_roi_array=sorted(roi_array, key=methodcaller('getLength'), reverse=True)
#length_array=[]
#index=0
#for roi in roi_array:
def process(subFolder, outputDirectory, filename):
#IJ.close()
imp = IJ.openImage(inputDirectory + subFolder + '/' +
rreplace(filename, "_ch00.tif", ".tif"))
imp.show()
# Get the pixel values from the xml file
for file in os.listdir(inputDirectory + subFolder):
if file.endswith('.xml'):
xml = os.path.join(inputDirectory + subFolder, file)
xml = "C:/Users/Harris/Desktop/test_xml_for_parsing_pixel.xml"
element_tree = ET.parse(xml)
root = element_tree.getroot()
for dimensions in root.iter('DimensionDescription'):
num_pixels = int(dimensions.attrib['NumberOfElements'])
if dimensions.attrib['Unit'] == "m":
length = float(dimensions.attrib['Length']) * 1000000
else:
length = float(dimensions.attrib['Length'])
pixel_length = length / num_pixels
else:
pixel_length = 0.8777017
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=" +
str(pixel_length) + " pixel_height=" + str(pixel_length) +
" voxel_depth=25400.0508001")
ic = ImageConverter(imp)
ic.convertToGray8()
#IJ.setThreshold(imp, 2, 255)
#Automatically selects the area of the organoid based on automated thresholding and creates a mask to be applied on
#all other images
IJ.setAutoThreshold(imp, "Mean dark no-reset")
IJ.run(imp, "Convert to Mask", "")
IJ.run(imp, "Analyze Particles...", "size=100000-Infinity add select")
rm = RoiManager.getInstance()
num_roi = rm.getCount()
for i in num_roi:
imp = getCurrentImage()
rm.select(imp, i)
IJ.setBackgroundColor(0, 0, 0)
IJ.run(imp, "Clear Outside", "")
IJ.run(imp, "Convert to Mask", "")
IJ.run(imp, "Remove Outliers...",
"radius=5" + " threshold=50" + " which=Dark")
IJ.run(imp, "Remove Outliers...",
"radius=5" + " threshold=50" + " which=Bright")
# Save the mask and open it
IJ.saveAs("tiff", inputDirectory + '/mask' + i)
mask = IJ.openImage(inputDirectory + '/mask' + i + '.tif')
if not displayImages:
imp.changes = False
imp.close()
images = [None] * 5
intensities = [None] * 5
blobsarea = [None] * 5
blobsnuclei = [None] * 5
bigAreas = [None] * 5
imp.close()
# Loop to open all the channel images
for chan in channels:
v, x = chan
images[x] = IJ.openImage(inputDirectory + subFolder + '/' +
rreplace(filename, "_ch00.tif", "_ch0" +
str(x) + ".tif"))
# Apply Mask on all the images and save them into an array
apply_mask = ImageCalculator()
images[x] = apply_mask.run("Multiply create 32 bit", mask,
images[x])
ic = ImageConverter(images[x])
ic.convertToGray8()
imp = images[x]
# Calculate the intensities for each channel as well as the organoid area
for roi in rm.getRoisAsArray():
imp.setRoi(roi)
stats_i = imp.getStatistics(Measurements.MEAN
| Measurements.AREA)
intensities[x] = stats_i.mean
bigAreas[x] = stats_i.area
rm.close()
# Opens the ch00 image and sets default properties
#Get the pixel values from the xml file
for file in os.listdir(subFolder):
if file.endswith('.xml'):
xml = os.path.join(inputDirectory + subFolder, file)
xml = "C:/Users/Harris/Desktop/test_xml_for_parsing_pixel.xml"
element_tree = ET.parse(xml)
root = element_tree.getroot()
for dimensions in root.iter('DimensionDescription'):
num_pixels = int(dimensions.attrib['NumberOfElements'])
if dimensions.attrib['Unit'] == "m":
length = float(dimensions.attrib['Length']) * 1000000
else:
length = float(dimensions.attrib['Length'])
pixel_length = length / num_pixels
else:
pixel_length = 0.8777017
imp = IJ.openImage(inputDirectory + subFolder + '/' + filename)
imp = apply_mask.run("Multiply create 32 bit", mask, imp)
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=" +
str(pixel_length) + "pixel_height=" + str(pixel_length) +
"voxel_depth=25400.0508001")
# Sets the threshold and watersheds. for more details on image processing, see https://imagej.nih.gov/ij/developer/api/ij/process/ImageProcessor.html
ic = ImageConverter(imp)
ic.convertToGray8()
IJ.run(imp, "Remove Outliers...",
"radius=2" + " threshold=50" + " which=Dark")
IJ.run(imp, "Gaussian Blur...", "sigma=" + str(blur))
IJ.setThreshold(imp, lowerBounds[0], 255)
if displayImages:
imp.show()
IJ.run(imp, "Convert to Mask", "")
IJ.run(imp, "Watershed", "")
if not displayImages:
imp.changes = False
imp.close()
# Counts and measures the area of particles and adds them to a table called areas. Also adds them to the ROI manager
table = ResultsTable()
roim = RoiManager(True)
ParticleAnalyzer.setRoiManager(roim)
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER,
Measurements.AREA, table, 15, 9999999999999999,
0.2, 1.0)
pa.setHideOutputImage(True)
# imp = impM
# imp.getProcessor().invert()
pa.analyze(imp)
areas = table.getColumn(0)
# This loop goes through the remaining channels for the other markers, by replacing the ch00 at the end with its corresponding channel
# It will save all the area fractions into a 2d array called areaFractionsArray
areaFractionsArray = [None] * 5
for chan in channels:
v, x = chan
# Opens each image and thresholds
imp = images[x]
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=0.8777017 pixel_height=0.8777017 voxel_depth=25400.0508001"
)
ic = ImageConverter(imp)
ic.convertToGray8()
IJ.setThreshold(imp, lowerBounds[x], 255)
if displayImages:
imp.show()
WaitForUserDialog("Title",
"Adjust Threshold for Marker " + v).show()
IJ.run(imp, "Convert to Mask", "")
# Measures the area fraction of the new image for each ROI from the ROI manager.
areaFractions = []
for roi in roim.getRoisAsArray():
imp.setRoi(roi)
stats = imp.getStatistics(Measurements.AREA_FRACTION)
areaFractions.append(stats.areaFraction)
# Saves the results in areaFractionArray
areaFractionsArray[x] = areaFractions
roim.close()
for chan in channels:
v, x = chan
imp = images[x]
imp.deleteRoi()
roim = RoiManager(True)
ParticleAnalyzer.setRoiManager(roim)
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER,
Measurements.AREA, table, 15,
9999999999999999, 0.2, 1.0)
pa.analyze(imp)
blobs = []
for roi in roim.getRoisAsArray():
imp.setRoi(roi)
stats = imp.getStatistics(Measurements.AREA)
blobs.append(stats.area)
blobsarea[x] = sum(
blobs
) #take this out and use intial mask tissue area from the beginning
blobsnuclei[x] = len(blobs)
if not displayImages:
imp.changes = False
imp.close()
roim.reset()
roim.close()
imp.close()
# Creates the summary dictionary which will correspond to a single row in the output csv, with each key being a column
summary = {}
summary['Image'] = filename
summary['Directory'] = subFolder
# Adds usual columns
summary['size-average'] = 0
summary['#nuclei'] = 0
summary['all-negative'] = 0
summary['too-big-(>' + str(tooBigThreshold) + ')'] = 0
summary['too-small-(<' + str(tooSmallThreshold) + ')'] = 0
# Creates the fieldnames variable needed to create the csv file at the end.
fieldnames = [
'Name', 'Directory', 'Image', 'size-average',
'too-big-(>' + str(tooBigThreshold) + ')',
'too-small-(<' + str(tooSmallThreshold) + ')', '#nuclei',
'all-negative'
]
# Adds the columns for each individual marker (ignoring Dapi since it was used to count nuclei)
summary["organoid-area"] = bigAreas[x]
fieldnames.append("organoid-area")
for chan in channels:
v, x = chan
summary[v + "-positive"] = 0
fieldnames.append(v + "-positive")
summary[v + "-intensity"] = intensities[x]
fieldnames.append(v + "-intensity")
summary[v + "-blobsarea"] = blobsarea[x]
fieldnames.append(v + "-blobsarea")
summary[v + "-blobsnuclei"] = blobsnuclei[x]
fieldnames.append(v + "-blobsnuclei")
# Adds the column for colocalization between first and second marker
if len(channels) > 2:
summary[channels[1][0] + '-' + channels[2][0] + '-positive'] = 0
fieldnames.append(channels[1][0] + '-' + channels[2][0] + '-positive')
# Adds the columns for colocalization between all three markers
if len(channels) > 3:
summary[channels[1][0] + '-' + channels[3][0] + '-positive'] = 0
summary[channels[2][0] + '-' + channels[3][0] + '-positive'] = 0
summary[channels[1][0] + '-' + channels[2][0] + '-' + channels[3][0] +
'-positive'] = 0
fieldnames.append(channels[1][0] + '-' + channels[3][0] + '-positive')
fieldnames.append(channels[2][0] + '-' + channels[3][0] + '-positive')
fieldnames.append(channels[1][0] + '-' + channels[2][0] + '-' +
channels[3][0] + '-positive')
# Loops through each particle and adds it to each field that it is True for.
areaCounter = 0
for z, area in enumerate(areas):
log.write(str(area))
log.write("\n")
if area > tooBigThreshold:
summary['too-big-(>' + str(tooBigThreshold) + ')'] += 1
elif area < tooSmallThreshold:
summary['too-small-(<' + str(tooSmallThreshold) + ')'] += 1
else:
summary['#nuclei'] += 1
areaCounter += area
temp = 0
for chan in channels:
v, x = chan
if areaFractionsArray[x][z] > areaFractionThreshold[
0]: # theres an error here im not sure why. i remember fixing it before
summary[chan[0] + '-positive'] += 1
if x != 0:
temp += 1
if temp == 0:
summary['all-negative'] += 1
if len(channels) > 2:
if areaFractionsArray[1][z] > areaFractionThreshold[1]:
if areaFractionsArray[2][z] > areaFractionThreshold[2]:
summary[channels[1][0] + '-' + channels[2][0] +
'-positive'] += 1
if len(channels) > 3:
if areaFractionsArray[1][z] > areaFractionThreshold[1]:
if areaFractionsArray[3][z] > areaFractionThreshold[3]:
summary[channels[1][0] + '-' + channels[3][0] +
'-positive'] += 1
if areaFractionsArray[2][z] > areaFractionThreshold[2]:
if areaFractionsArray[3][z] > areaFractionThreshold[3]:
summary[channels[2][0] + '-' + channels[3][0] +
'-positive'] += 1
if areaFractionsArray[1][z] > areaFractionThreshold[1]:
summary[channels[1][0] + '-' + channels[2][0] +
'-' + channels[3][0] + '-positive'] += 1
# Calculate the average of the particles sizes
if float(summary['#nuclei']) > 0:
summary['size-average'] = round(areaCounter / summary['#nuclei'], 2)
# Opens and appends one line on the final csv file for the subfolder (remember that this is still inside the loop that goes through each image)
with open(outputDirectory + "/" + outputName + ".csv", 'a') as csvfile:
writer = csv.DictWriter(csvfile,
fieldnames=fieldnames,
extrasaction='ignore',
lineterminator='\n')
if os.path.getsize(outputDirectory + "/" + outputName + ".csv") < 1:
writer.writeheader()
writer.writerow(summary)
IJ.run(imp, "Close All", "")
IJ.log("-------start at "+now+" ------")
#for cell in listfilescells :
f1 = open(rootdir+now+"-R1-MT.txt", "w")
tab="\t"
f1.write("cell"+tab+"maxFrames"+tab+"maxcumul"+tab+"nrevs"+"\n")
for cle in listcellname :
rm.runCommand("reset")
#cle = cell.rsplit("/", 1)[1][:-len(".cell")]
#cles.append(cle)
rm.runCommand("Open", dictRois[cle])
rm.runCommand("Show None")
RawroisArray=rm.getRoisAsArray()
if len(RawroisArray)< minLife : continue
roisArray=[RawroisArray[i] for i in range(0,len(RawroisArray), subs)]
IJ.showStatus(cle)
IJ.showProgress(listcellname.index(cle), len(listcellname))
dxA=[]
dyA=[]
dxB=[]
dyB=[]
dA=[]
dB=[]
sensA = 1
sensB = -1
nrev = 0
reffn = odref.getFileName()
if reffn is None:
print "User canceled the dialog!"
else:
refdir = odref.getDirectory()
refpath = os.path.join(refdir, reffn)
refImp = IJ.openImage(refpath)
width = refImp.width
height = refImp.height
roim = RoiManager()
roim.runCommand("open", roipath)
roiArray = roim.getRoisAsArray()
nRoi = len(roiArray)
roim.close()
bwStack = ImageStack(width, height, nRoi)
for i in xrange(1, nRoi + 1):
bwStack.setProcessor(
FloatProcessor(width, height, zeros('f', width * height), None), i)
for i in xrange(1, nRoi + 1):
roi = roiArray[i - 1]
fp = bwStack.getProcessor(i)
fp.setValue(1.0)
fp.fill(roi)
roiImp = ImagePlus("roi", bwStack)
from ij.plugin.frame import RoiManager from ij.plugin.filter import ParticleAnalyzer from ij import IJ # get active image imp=IJ.getImage() # set up first ROI manager table1 = ResultsTable() roim1=RoiManager() pa1 = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER, Measurements.AREA|Measurements.MEAN|Measurements.ELLIPSE, table1, 0, 100, 0, 1) pa1.setRoiManager(roim1) pa1.analyze(imp) # set up second ROI manager table2 = ResultsTable() # Pass true to second ROI manager so it will not be seen roim2=RoiManager(True) pa2 = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER, Measurements.AREA|Measurements.MEAN|Measurements.ELLIPSE, table2, 100, 500, 0, 1) pa2.setRoiManager(roim2) pa2.analyze(imp) print "rois from first manager:" for roi in roim1.getRoisAsArray(): print roi print print "rois from second manager:" for roi in roim2.getRoisAsArray(): print roi
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
class CellsSelection(swing.JFrame):
def __init__(self):
swing.JFrame.__init__(self, title="Cells Selection")
self.setFont(awt.Font("Courrier", 1, 10))
self.__dictBox = {}
self.__dictFiles = {}
self.oked = False
self.__mem=[]
self.setDefaultCloseOperation(swing.JFrame.DISPOSE_ON_CLOSE)
def run(self, cells, path) :
self.__cells=cells
cells.sort()
self.__cells.sort()
self.__path=path
if len(cells) <= 6 :
cols=len(cells)
rows=1
else :
cols=6
rows=int(len(cells)/6)+1
#print "cols", cols, "rows", rows
self.setFont(awt.Font("Courrier", 1, 10))
#self.size=(max(200*cols, 1100), max(70*rows, 300))
self.size=(max(150*cols, 800), max(50*rows, 250))
line = BorderFactory.createEtchedBorder(EtchedBorder.LOWERED)
self.contentPane.layout = awt.BorderLayout()
self.__display = swing.JTextField(preferredSize=(400, 30), horizontalAlignment=swing.SwingConstants.LEFT)
self.__setDisplay()
northpanel=swing.JPanel(awt.FlowLayout(awt.FlowLayout.LEFT))
northpanel.setBorder(line)
#northpanel.add(self.__display, awt.BorderLayout.NORTH)
northpanel.add(self.__display)
selectall = swing.JButton("select ALL", size=(100, 70), actionPerformed=self.__selectall)
#northpanel.add(selectall, awt.BorderLayout.WEST)
northpanel.add(selectall)
selectnone = swing.JButton("select NONE", size=(100, 70), actionPerformed=self.__selectnone)
#northpanel.add(selectnone, awt.BorderLayout.EAST)
northpanel.add(selectnone)
mem = swing.JButton("Memorize", size=(100, 70), actionPerformed= self.__memorize)
northpanel.add(mem)
recall = swing.JButton("Recall", size=(100, 70), actionPerformed=self.__recall)
northpanel.add(recall)
southpanel=swing.JPanel(awt.FlowLayout(awt.FlowLayout.RIGHT))
southpanel.setBorder(line)
self.__label=swing.JLabel("validate selection with ok")
southpanel.add(self.__label)
ok = swing.JButton("ok", size=(100, 70), actionPerformed=self.__ok)
southpanel.add(ok)
close = swing.JButton("close", size=(100, 70), actionPerformed=self.__close)
southpanel.add(close)
westpanel=swing.JPanel(awt.FlowLayout(awt.FlowLayout.CENTER), preferredSize=(150, 200))
westpanel.setBorder(line)
show = swing.JButton("show overlay", size=(100, 70), actionPerformed=self.__show)
westpanel.add(show)
hide = swing.JButton("hide overlay", size=(100, 70), actionPerformed=self.__hide)
westpanel.add(hide)
allframes = swing.JButton("show all", size=(100, 70), actionPerformed=self.__showall)
westpanel.add(allframes)
oneframe = swing.JButton("show one frame", size=(100, 70), actionPerformed=self.__showone)
westpanel.add(oneframe)
reset = swing.JButton("reset", size=(100, 70), actionPerformed=self.__reset)
westpanel.add(reset)
title = BorderFactory.createTitledBorder("Edit Cells")
title.setTitleJustification(TitledBorder.CENTER)
eastpanel = swing.JPanel(awt.FlowLayout(awt.FlowLayout.CENTER), preferredSize=(130, 200))
eastpanel.setBorder(title)
split = swing.JButton("split", size=(100, 70), actionPerformed=self.__split)
eastpanel.add(split)
grid = awt.GridLayout()
grid.setRows(rows)
checkpanel=swing.JPanel(grid)
checkpanel.setFont(awt.Font("Courrier", 1, 10))
self.__boxes=[swing.JCheckBox(actionPerformed=self.__boxaction) for i in range(len(cells))]
for b in self.__boxes : b.setFont(awt.Font("Courrier", 1, 10))
#self.__mem=[True for i in range(len(cells))]
for i in range(len(self.__boxes)) :
self.__dictBox[cells[i]]=(cells[i], self.__boxes[i])
for i in range(len(self.__boxes)) :
self.__boxes[i].setText(str(cells[i]))
self.__boxes[i].setSelected(True)
checkpanel.add(self.__boxes[i])
for i in range(rows*cols-len(self.__boxes)) : checkpanel.add(awt.Label(""))
self.contentPane.add(northpanel, awt.BorderLayout.NORTH)
self.contentPane.add(checkpanel, awt.BorderLayout.CENTER)
self.contentPane.add(westpanel, awt.BorderLayout.WEST)
self.contentPane.add(eastpanel, awt.BorderLayout.EAST)
self.contentPane.add(southpanel, awt.BorderLayout.SOUTH)
self.contentPane.setFont(awt.Font("Courrier", 1, 10))
self.__rm = RoiManager.getInstance()
if (self.__rm==None): self.__rm = RoiManager()
self.__rm.runCommand("reset")
listfilescells=[]
listfilescells.extend(glob.glob(path+"*.zip"))
#includecells = [filename for filename in listfilescells if filename.rsplit("/",1)[1][0:-4] in cells]
includecells = [filename for filename in listfilescells if os.path.splitext(os.path.split(filename)[1])[0] in cells]
for cell in includecells :
#c = cell.rsplit("/",1)[1][0:-4]
c=os.path.splitext(os.path.split(cell)[1])[0]
self.__dictFiles[c] = (c, cell)
#for i in range(len(cells)) :
# f=listfilescells[i].rsplit("/",1)[1][0:-4]
# #print "f=", f
# for c in cells :
# #print "c=", c, "f=", f
# if f==c :
# self.__dictFiles[c] = (c, listfilescells[i])
# #print "CS dictFiles", c, listfilescells[i]
def __selectall(self, event):
for b in self.__boxes : b.setSelected(True)
def __selectnone(self, event):
for b in self.__boxes : b.setSelected(False)
def __ok(self, event):
self.oked = True
#self.dispose()
def __close(self, event):
self.oked = True
time.sleep(0.01)
self.dispose()
def __memorize(self, event):
self.__mem[:]=[]
for i in range(len(self.__boxes)) :
if self.__boxes[i].isSelected() :
#print i, "mem", self.__boxes[i].text
self.__mem.append(True)
else : self.__mem.append(False)
def __recall(self, event):
for i in range(len(self.__boxes)) :
if self.__mem[i] :
self.__boxes[i].setSelected(True)
else : self.__boxes[i].setSelected(False)
def __show(self, event):
IJ.run("Show Overlay", "")
def __hide(self, event):
IJ.run("Hide Overlay", "")
def __showall(self, event) :
self.__rm.runCommand("Associate", "false")
self.__rm.runCommand("Show All")
def __showone(self, event) :
self.__rm.runCommand("Associate", "true")
self.__rm.runCommand("Show All")
def __reset(self, event) :
self.__rm.runCommand("reset")
def __boxaction(self, event):
self.__setDisplay(str(event.getSource().text)+" is "+str(event.getSource().isSelected()))
if event.getSource().isSelected() : #print self.__dictFiles[event.getSource().text][1]
#self.__rm.runCommand("reset")
self.__rm.runCommand("Open", self.__dictFiles[event.getSource().text][1])
def __setDisplay(self, val=""):
self.__display.text = str(val)
def __split(self, event) :
sel = self.getSelected()
if len(sel) != 1 :
IJ.showMessage("only one cell should be selected !")
return
else :
cellname = sel[0]
rois = self.__rm.getRoisAsArray()
self.__rm.runCommand("reset")
n = int(IJ.getNumber("slice to split ?", 1))
for i in range(n) :
self.__rm.addRoi(rois[i])
#print self.__path+cellname+"-a.zip"
self.__rm.runCommand("Save", self.__path+cellname+"-a.zip")
self.__rm.runCommand("reset")
for i in range(n, len(rois)) :
self.__rm.addRoi(rois[i])
self.__rm.runCommand("Save", self.__path+cellname+"-b.zip")
self.__rm.runCommand("reset")
root = self.__path.rsplit(os.path.sep, 2)[0]+os.path.sep
if not path.exists(root+"Cells"+os.path.sep) :os.makedirs(root+"Cells"+os.path.sep, mode=0777)
fichiertemp = open(root+"Cells"+os.path.sep+cellname+"-a.cell","w")
fichiertemp.write("NAMECELL="+cellname+"-a\n")
fichiertemp.write("PATHCELL="+root+"Cells"+os.path.sep+cellname+"-a.cell\n")
fichiertemp.write("PATHROIS="+root+"ROIs"+os.path.sep+cellname+"-a.zip\n")
fichiertemp.write("NSLICES="+str(len(rois))+"\n")
fichiertemp.write("SLICEINIT="+str(1)+"\n")
fichiertemp.write("SLICEEND="+str(n)+"\n")
r = random.randrange(5,205,1)
g = random.randrange(10,210,1)
b = random.randrange(30,230,1)
fichiertemp.write("COLOR="+str(r)+";"+str(g)+";"+str(b)+"\n")
fichiertemp.close()
fichiertemp = open(root+"Cells"+os.path.sep+cellname+"-b.cell","w")
fichiertemp.write("NAMECELL="+cellname+"-b\n")
fichiertemp.write("PATHCELL="+root+"Cells"+os.path.sep+cellname+"-b.cell\n")
fichiertemp.write("PATHROIS="+root+"ROIs"+os.path.sep+cellname+"-b.zip\n")
fichiertemp.write("NSLICES="+str(len(rois))+"\n")
fichiertemp.write("SLICEINIT="+str(n+1)+"\n")
fichiertemp.write("SLICEEND="+str(len(rois))+"\n")
r = random.randrange(5,205,1)
g = random.randrange(10,210,1)
b = random.randrange(30,230,1)
fichiertemp.write("COLOR="+str(r)+";"+str(g)+";"+str(b)+"\n")
fichiertemp.close()
def getSelected(self) :
#selected=[self.__cells[i] for i in range(len(self.__cells)) if self.__boxes[i].isSelected()]
selected=[b.getText() for b in self.__boxes if b.isSelected()]
return selected
def setSelected(self, selected) :
for b in self.__boxes : b.setSelected(False)
#for s in selected : print str(s)
for c in self.__cells :
#print str(c)
if c in selected :
self.__dictBox[c][1].setSelected(True)
def resetok(self):
self.oked = False
def setLabel(self, text):
self.__label.setText(text)
def main():
# Get active dataset
#img = IJ.getImage()
display = displayservice.getActiveDisplay()
active_dataset = imagedisplayservice.getActiveDataset(display)
if not active_dataset:
IJ.showMessage('No image opened.')
return
# Get image path
fname = active_dataset.getSource()
dir_path = os.path.dirname(fname)
if not fname:
IJ.showMessage('Source image needs to match a file on the system.')
return
# Open ROIs
rois = RoiManager.getInstance()
if not rois:
roi_path = os.path.join(dir_path, "RoiSet.zip")
if not os.path.isfile(roi_path):
try:
roi_path = glob.glob(os.path.join(dir_path, "*.roi"))[0]
except:
roi_path = None
if not roi_path:
IJ.showMessage('No ROIs. Please use Analyze > Tools > ROI Manager...')
return
rois = RoiManager(True)
rois.reset()
rois.runCommand("Open", roi_path)
IJ.log('Image filename is %s' % fname)
dt = get_dt(active_dataset)
rois_array = rois.getRoisAsArray()
for i, roi in enumerate(rois_array):
crop_id = i + 1
IJ.log("Croping %i / %i" % (crop_id, len(rois_array)))
# Get filename and basename of the current cropped image
crop_basename = "crop%i_%s" % (crop_id, active_dataset.getName())
crop_basename = os.path.splitext(crop_basename)[0] + ".ome.tif"
crop_fname = os.path.join(os.path.dirname(fname), crop_basename)
# Get bounds and crop
bounds = roi.getBounds()
dataset = crop(ij, datasetservice, active_dataset,
bounds.x, bounds.y, bounds.width,
bounds.height, crop_basename)
# Show cropped image
ij.ui().show(dataset.getName(), dataset)
# Save cropped image (ugly hack)
IJ.log("Saving crop to %s" % crop_fname)
imp = IJ.getImage()
bfExporter = LociExporter()
macroOpts = "save=[" + crop_fname + "]"
bfExporter.setup(None, imp)
Macro.setOptions(macroOpts)
bfExporter.run(None)
imp.close()
IJ.log('Done')
def channel_segmentation(infile, diameter, tolerance, repeat_max, Zrepeat=10):
# ROI optimization by Esnake optimisation
default_options = "stack_order=XYCZT color_mode=Grayscale view=Hyperstack"
IJ.run("Bio-Formats Importer", default_options + " open=[" + infile + "]")
imp = IJ.getImage()
cal = imp.getCalibration()
channels = [i for i in xrange(1, imp.getNChannels() + 1)]
log = filename(infile)
log = re.sub('.ids', '.csv', log)
XZdrift, YZdrift = retrieve_Zdrift(log)
XZpt = [i * imp.getWidth() / Zrepeat for i in xrange(1, Zrepeat - 1)]
YZpt = [i * imp.getHeight() / Zrepeat for i in xrange(1, Zrepeat - 1)]
# Prepare head output file
for ch in channels:
csv_name = 'ch' + str(ch) + log
with open(os.path.join(folder6, csv_name), 'wb') as outfile:
SegLog = csv.writer(outfile, delimiter=',')
SegLog.writerow(['spotID', 'Xpos', 'Ypos', 'Zpos',
'Quality', 'area', 'intensity', 'min', 'max', 'std'])
# Retrieve seeds from SpotDetector
options = IS.MEDIAN | IS.AREA | IS.MIN_MAX | IS.CENTROID
spots = retrieve_seeds(log)
for ch in channels:
for spot in spots:
repeat = 0
# Spots positions are given according to calibration, need to
# convert it to pixel coordinates
spotID = int(spot[0])
Xcenter = int(float(spot[2]) / cal.pixelWidth)
Ycenter = int(float(spot[3]) / cal.pixelHeight)
Zcenter = float(spot[4]) / cal.pixelDepth
Quality = float(spot[5])
# find closest grid location in Zdrift matrix
Xpt = min(range(len(XZpt)), key=lambda i: abs(XZpt[i] - Xcenter))
Ypt = min(range(len(YZpt)), key=lambda i: abs(YZpt[i] - Ycenter))
# Calculate Z position according to SpotZ, calibration and
# channel-specific Zdrift #
Zshift = median([float(XZdrift[Xpt][ch - 1]),
float(YZdrift[Ypt][ch - 1])]) / cal.pixelDepth
correctZ = int(Zcenter - Zshift)
imp.setPosition(ch, correctZ, 1)
imp.getProcessor().setMinAndMax(0, 3000)
while True:
manager = RoiManager.getInstance()
if manager is None:
manager = RoiManager()
roi = OvalRoi(Xcenter - diameter * (1.0 + repeat / 10.0) / 2.0, Ycenter - diameter * (
1.0 + repeat / 10.0) / 2.0, diameter * (1.0 + repeat / 10.0), diameter * (1.0 + repeat / 10.0))
imp.setRoi(roi)
IJ.run(imp, "E-Snake", "target_brightness=Bright control_points=3 gaussian_blur=0 energy_type=Mixture alpha=2.0E-5 max_iterations=20 immortal=false")
roi_snake = manager.getRoisAsArray()[0]
imp.setRoi(roi_snake)
stats = IS.getStatistics(
imp.getProcessor(), options, imp.getCalibration())
manager.reset()
if stats.area > 20.0 and stats.area < 150.0 and boundaries(Xcenter, Ycenter, stats.xCentroid / cal.pixelWidth, stats.yCentroid / cal.pixelHeight, tolerance):
Sarea = stats.area
Sintensity = stats.median
Smin = stats.min
Smax = stats.max
Sstd = stats.stdDev
break
elif repeat > repeat_max:
roi = OvalRoi(Xcenter - diameter / 2.0,
Ycenter - diameter / 2.0, diameter, diameter)
imp.setRoi(roi)
manager.add(imp, roi, i)
stats = IS.getStatistics(
imp.getProcessor(), options, imp.getCalibration())
Sarea = stats.area
Sintensity = stats.median
Smin = stats.min
Smax = stats.max
Sstd = stats.stdDev
break
else:
repeat += 1
# Save results
csv_name = 'ch' + str(ch) + log
with open(os.path.join(folder6, csv_name), 'ab') as outfile:
SegLog = csv.writer(outfile, delimiter=',')
SegLog.writerow([spotID, Xcenter, Ycenter, correctZ,
Quality, Sarea, Sintensity, Smin, Smax, Sstd])
# End spot optimization
# End spots
# End channels
IJ.selectWindow(filename(infile))
IJ.run("Close")
def batch_open_images(pathRoi,
pathMask,
file_typeImage=None,
name_filterImage=None,
recursive=False):
'''Open all files in the given folder.
:param path: The path from were to open the images. String and java.io.File are allowed.
:param file_type: Only accept files with the given extension (default: None).
:param name_filter: Reject files that contain the given string (default: wild characters).
:param recursive: Process directories recursively (default: False).
'''
# Converting a File object to a string.
if isinstance(pathMask, File):
pathMask = pathMask.getAbsolutePath()
def check_type(string):
'''This function is used to check the file type.
It is possible to use a single string or a list/tuple of strings as filter.
This function can access the variables of the surrounding function.
:param string: The filename to perform the check on.
'''
if file_typeImage:
# The first branch is used if file_type is a list or a tuple.
if isinstance(file_typeImage, (list, tuple)):
for file_type_ in file_typeImage:
if string.endswith(file_type_):
# Exit the function with True.
return True
else:
# Next iteration of the for loop.
continue
# The second branch is used if file_type is a string.
elif isinstance(file_typeImage, string):
if string.endswith(file_typeImage):
return True
else:
return False
return False
# Accept all files if file_type is None.
else:
return True
def check_filter(string):
'''This function is used to check for a given filter.
It is possible to use a single string or a list/tuple of strings as filter.
This function can access the variables of the surrounding function.
:param string: The filename to perform the filtering on.
'''
if name_filterImage:
# The first branch is used if name_filter is a list or a tuple.
if isinstance(name_filterImage, (list, tuple)):
for name_filter_ in name_filterImage:
if name_filter_ in string:
# Exit the function with True.
return True
else:
# Next iteration of the for loop.
continue
# The second branch is used if name_filter is a string.
elif isinstance(name_filterImage, string):
if name_filterImage in string:
return True
else:
return False
return False
else:
# Accept all files if name_filter is None.
return True
# We collect all files to open in a list.
path_to_Image = []
# Replacing some abbreviations (e.g. $HOME on Linux).
path = os.path.expanduser(pathMask)
path = os.path.expandvars(pathMask)
# If we don't want a recursive search, we can use os.listdir().
if not recursive:
for file_name in os.listdir(pathMask):
full_path = os.path.join(pathMask, file_name)
if os.path.isfile(full_path):
if check_type(file_name):
if check_filter(file_name):
path_to_Image.append(full_path)
# For a recursive search os.walk() is used.
else:
# os.walk() is iterable.
# Each iteration of the for loop processes a different directory.
# the first return value represents the current directory.
# The second return value is a list of included directories.
# The third return value is a list of included files.
for directory, dir_names, file_names in os.walk(pathMask):
# We are only interested in files.
for file_name in file_names:
# The list contains only the file names.
# The full path needs to be reconstructed.
full_path = os.path.join(directory, file_name)
# Both checks are performed to filter the files.
if check_type(file_name):
if check_filter(file_name) is False:
# Add the file to the list of images to open.
path_to_Image.append([
full_path,
os.path.basename(os.path.splitext(full_path)[0])
])
# Create the list that will be returned by this function.
Masks = []
rois = []
ImageRois = []
for img_path, file_name in path_to_Image:
# IJ.openImage() returns an ImagePlus object or None.
if check_filter(file_name):
continue
else:
MaskName = str(pathMask) + '/' + file_name + '.tif'
Mask = IJ.openImage(MaskName)
Mask.show()
rm = RoiManager.getInstance()
if (rm == None):
rm = RoiManager()
rm.runCommand("Delete")
IJ.selectWindow(file_name + '.tif')
IJ.run("Find Edges")
IJ.setAutoThreshold(Mask, "Default dark")
IJ.run("Threshold")
IJ.setThreshold(0, 0)
IJ.run("Convert to Mask")
IJ.run("Invert")
IJ.run("Create Selection")
rm.runCommand("Add")
# An object equals True and None equals False.
rois = rm.getRoisAsArray()
rm.runCommand("Save",
str(pathRoi) + "/" + file_name + '.roi')
Mask.changes = False
Mask.close()
ImageRois.append(rois)
return ImageRois
def process(subDir, subsubDir, outputDirectory, filename):
subFolder = subDir + "/" + subsubDir
# Opens the d0 image and sets default properties
imp = IJ.openImage(inputDirectory + subFolder + '/' + filename)
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=0.8777017 pixel_height=0.8777017 voxel_depth=25400.0508001"
)
# Sets the threshold and watersheds. for more details on image processing, see https://imagej.nih.gov/ij/developer/api/ij/process/ImageProcessor.html
ic = ImageConverter(imp)
ic.convertToGray8()
imp.updateAndDraw()
dup = imp.duplicate()
IJ.run(
dup, "Convolve...",
"text1=[-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 24 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n] normalize"
)
stats = dup.getStatistics(Measurements.MEAN | Measurements.MIN_MAX
| Measurements.STD_DEV)
dup.close()
blurry = (stats.mean < 18 and stats.stdDev < 22) or stats.max < 250
IJ.setThreshold(imp, lowerBounds[0], 255)
IJ.run(imp, "Convert to Mask", "")
IJ.run(imp, "Watershed", "")
if displayImages:
imp.show()
WaitForUserDialog("Title", "Look at image").show()
# Counts and measures the area of particles and adds them to a table called areas. Also adds them to the ROI manager
table = ResultsTable()
roim = RoiManager(True)
ParticleAnalyzer.setRoiManager(roim)
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER, Measurements.AREA,
table, 15, 9999999999999999, 0.2, 1.0)
pa.setHideOutputImage(True)
pa.analyze(imp)
if not displayImages:
imp.changes = False
imp.close()
areas = table.getColumn(0)
# This loop goes through the remaining channels for the other markers, by replacing the d0 at the end with its corresponding channel
# It will save all the area fractions into a 2d array called areaFractionsArray
areaFractionsArray = []
areaMeansArray = []
means = []
totalAreas = []
for chan in channels:
v, x = chan
# Opens each image and thresholds
imp = IJ.openImage(inputDirectory + subFolder + '/' +
filename.replace("d0.TIF", "d" + str(x) + ".TIF"))
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=0.8777017 pixel_height=0.8777017 voxel_depth=25400.0508001"
)
ic = ImageConverter(imp)
ic.convertToGray8()
imp.updateAndDraw()
stats = imp.getStatistics(Measurements.MEAN)
means.append(stats.mean)
areaMeans = []
for roi in roim.getRoisAsArray():
imp.setRoi(roi)
stats = imp.getStatistics(Measurements.MEAN)
areaMeans.append(stats.mean)
IJ.setThreshold(imp, lowerBounds[x], 255)
IJ.run(imp, "Convert to Mask", "")
if displayImages:
imp.show()
WaitForUserDialog("Title", "Look at image").show()
stats = imp.getStatistics(Measurements.AREA_FRACTION)
totalAreas.append(stats.areaFraction)
# Measures the area fraction of the new image for each ROI from the ROI manager.
areaFractions = []
for roi in roim.getRoisAsArray():
imp.setRoi(roi)
stats = imp.getStatistics(Measurements.AREA_FRACTION)
areaFractions.append(stats.areaFraction)
# Saves the results in areaFractionArray
areaFractionsArray.append(areaFractions)
areaMeansArray.append(sum(areaMeans) / len(areaMeans))
if not displayImages:
imp.changes = False
imp.close()
roim.close()
# Figures out what well the image is a part of
ind = filename.index("p00_0_")
row = filename[ind + 6:ind + 7]
column = str(int(filename[ind + 7:ind + 9]))
# Creates the summary dictionary which will correspond to a single row in the output csv, with each key being a column
summary = {}
# Finds the name of the well from the nameArray 2d array
if row in nameArray:
if column in nameArray[row]:
summary['Name'] = nameArray[row][column]
summary['Image'] = filename
summary['Directory'] = subDir
summary['SubDirectory'] = subsubDir
summary['Row'] = row
summary['Column'] = column
# Adds usual columns
summary['size-average'] = 0
summary['#nuclei'] = 0
summary['all-negative'] = 0
summary['too-big-(>' + str(tooBigThreshold) + ')'] = 0
summary['too-small-(<' + str(tooSmallThreshold) + ')'] = 0
summary['image-quality'] = blurry
# Creates the fieldnames variable needed to create the csv file at the end.
fieldnames = [
'Name', 'Directory', 'SubDirectory', 'Image', 'Row', 'Column',
'size-average', 'image-quality',
'too-big-(>' + str(tooBigThreshold) + ')',
'too-small-(<' + str(tooSmallThreshold) + ')', '#nuclei',
'all-negative'
]
# Adds the columns for each individual marker (ignoring Dapi since it was used to count nuclei)
for chan in channels:
v, x = chan
summary[v + "-positive"] = 0
summary[v + "-intensity"] = means[x]
summary[v + "-area"] = totalAreas[x]
summary[v + "-intensity-in-nuclei"] = areaMeansArray[x]
summary[v + "-area-fraction-in-nuclei"] = sum(
areaFractionsArray[x]) / len(areaFractionsArray[x])
fieldnames.append(v + "-positive")
fieldnames.append(v + "-intensity")
fieldnames.append(v + "-area")
fieldnames.append(v + "-intensity-in-nuclei")
fieldnames.append(v + "-area-fraction-in-nuclei")
# Adds the column for colocalization between first and second marker
if len(channels) > 2:
summary[channels[1][0] + '-' + channels[2][0] + '-positive'] = 0
fieldnames.append(channels[1][0] + '-' + channels[2][0] + '-positive')
# Adds the columns for colocalization between all three markers
if len(channels) > 3:
summary[channels[1][0] + '-' + channels[3][0] + '-positive'] = 0
summary[channels[2][0] + '-' + channels[3][0] + '-positive'] = 0
summary[channels[1][0] + '-' + channels[2][0] + '-' + channels[3][0] +
'-positive'] = 0
fieldnames.append(channels[1][0] + '-' + channels[3][0] + '-positive')
fieldnames.append(channels[2][0] + '-' + channels[3][0] + '-positive')
fieldnames.append(channels[1][0] + '-' + channels[2][0] + '-' +
channels[3][0] + '-positive')
# Loops through each particle and adds it to each field that it is True for.
areaCounter = 0
if not (areas is None):
for z, area in enumerate(areas):
if not (area is None or summary is None):
if area > tooBigThreshold:
summary['too-big-(>' + str(tooBigThreshold) + ')'] += 1
elif area < tooSmallThreshold:
summary['too-small-(<' + str(tooSmallThreshold) + ')'] += 1
else:
summary['#nuclei'] += 1
areaCounter += area
temp = 0
for y, chan in enumerate(channels):
v, x = chan
if areaFractionsArray[y][z] > areaFractionThreshold:
summary[chan[0] + '-positive'] += 1
if x != 0:
temp += 1
if temp == 0:
summary['all-negative'] += 1
if len(channels) > 2:
if areaFractionsArray[1][z] > areaFractionThreshold:
if areaFractionsArray[2][z] > areaFractionThreshold:
summary[channels[1][0] + '-' + channels[2][0] +
'-positive'] += 1
if len(channels) > 3:
if areaFractionsArray[1][z] > areaFractionThreshold:
if areaFractionsArray[3][z] > areaFractionThreshold:
summary[channels[1][0] + '-' + channels[3][0] +
'-positive'] += 1
if areaFractionsArray[2][z] > areaFractionThreshold:
if areaFractionsArray[3][z] > areaFractionThreshold:
summary[channels[2][0] + '-' + channels[3][0] +
'-positive'] += 1
if areaFractionsArray[1][
z] > areaFractionThreshold:
summary[channels[1][0] + '-' +
channels[2][0] + '-' +
channels[3][0] + '-positive'] += 1
# Calculate the average of the particles sizes
if float(summary['#nuclei']) > 0:
summary['size-average'] = round(areaCounter / summary['#nuclei'], 2)
# Opens and appends one line on the final csv file for the subfolder (remember that this is still inside the loop that goes through each image)
with open(outputDirectory + "/" + outputName + ".csv", 'a') as csvfile:
writer = csv.DictWriter(csvfile,
fieldnames=fieldnames,
extrasaction='ignore',
lineterminator='\n')
if os.path.getsize(outputDirectory + "/" + outputName + ".csv") < 1:
writer.writeheader()
writer.writerow(summary)
)
writer.writeheader()
writer.writerows(current)
print("wrote results to {}".format(out_path))
# imp = IJ.openImage(th_files[0])
# imp.show()
# get open image
imp = IJ.getImage()
image_name = imp.getShortTitle()
# get roi manager
roi_manager = RoiManager().getInstance()
all_rois = roi_manager.getRoisAsArray()
csv_name = make_path(out_dir, image_name, ".csv")
roi_name = make_path(out_dir, image_name, "_rois.zip")
old_name = make_path(target_dir, image_name, ".tif")
new_name = make_path(out_dir, image_name, ".tif")
current = []
for roi in all_rois:
res = get_stats(2, roi, imp)
current.append(res)
# write csv
write_csv(current, csv_name)
# save all rois
save_rois(roi_manager, roi_name)
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 batch_open_images(pathImage, pathRoi, pathMask, file_typeImage=None, name_filterImage=None, recursive=False):
'''Open all files in the given folder.
:param path: The path from were to open the images. String and java.io.File are allowed.
:param file_type: Only accept files with the given extension (default: None).
:param name_filter: Reject files that contain the given string (default: wild characters).
:param recursive: Process directories recursively (default: False).
'''
# Converting a File object to a string.
if isinstance(pathImage, File):
pathImage = pathImage.getAbsolutePath()
def check_type(string):
'''This function is used to check the file type.
It is possible to use a single string or a list/tuple of strings as filter.
This function can access the variables of the surrounding function.
:param string: The filename to perform the check on.
'''
if file_typeImage:
# The first branch is used if file_type is a list or a tuple.
if isinstance(file_typeImage, (list, tuple)):
for file_type_ in file_typeImage:
if string.endswith(file_type_):
# Exit the function with True.
return True
else:
# Next iteration of the for loop.
continue
# The second branch is used if file_type is a string.
elif isinstance(file_typeImage, string):
if string.endswith(file_typeImage):
return True
else:
return False
return False
# Accept all files if file_type is None.
else:
return True
def dog_detection(overlay,img, imp, cal):
# Create a variable of the correct type (UnsignedByteType) for the value-extended view
zero = img.randomAccess().get().createVariable()
# Run the difference of Gaussian
cell = 8.0 # microns in diameter
min_peak = 2.0 # min intensity for a peak to be considered
dog = DogDetection(Views.extendValue(img, zero), img,
[cal.pixelWidth, cal.pixelHeight,cal.pixelDepth],
cell / 2, cell,
DogDetection.ExtremaType.MINIMA,
min_peak, False,
DoubleType())
peaks = dog.getPeaks()
roi = OvalRoi(0, 0, cell/cal.pixelWidth, cell/cal.pixelHeight)
print ('Number of cells = ', len(peaks))
p = zeros(img.numDimensions(), 'i')
boundRect = imp.getRoi()
for peak in peaks:
# Read peak coordinates into an array of integers XYZ location of spots
peak.localize(p)
print(p)
if(boundRect is not None and boundRect.contains(p[0], p[1])):
oval = OvalRoi(p[0], p[1],cell/cal.pixelWidth, cell/cal.pixelHeight)
oval.setColor(Color.RED)
overlay.add(oval)
def check_filter(string):
'''This function is used to check for a given filter.
It is possible to use a single string or a list/tuple of strings as filter.
This function can access the variables of the surrounding function.
:param string: The filename to perform the filtering on.
'''
if name_filterImage:
# The first branch is used if name_filter is a list or a tuple.
if isinstance(name_filterImage, (list, tuple)):
for name_filter_ in name_filterImage:
if name_filter_ in string:
# Exit the function with True.
return True
else:
# Next iteration of the for loop.
continue
# The second branch is used if name_filter is a string.
elif isinstance(name_filterImage, string):
if name_filterImage in string:
return True
else:
return False
return False
else:
# Accept all files if name_filter is None.
return True
# We collect all files to open in a list.
path_to_Image = []
# Replacing some abbreviations (e.g. $HOME on Linux).
path = os.path.expanduser(pathImage)
path = os.path.expandvars(pathImage)
# If we don't want a recursive search, we can use os.listdir().
if not recursive:
for file_name in os.listdir(pathImage):
full_path = os.path.join(pathImage, file_name)
if os.path.isfile(full_path):
if check_type(file_name):
if check_filter(file_name):
path_to_Image.append(full_path)
# For a recursive search os.walk() is used.
else:
# os.walk() is iterable.
# Each iteration of the for loop processes a different directory.
# the first return value represents the current directory.
# The second return value is a list of included directories.
# The third return value is a list of included files.
for directory, dir_names, file_names in os.walk(pathImage):
# We are only interested in files.
for file_name in file_names:
# The list contains only the file names.
# The full path needs to be reconstructed.
full_path = os.path.join(directory, file_name)
# Both checks are performed to filter the files.
if check_type(file_name):
if check_filter(file_name) is False:
# Add the file to the list of images to open.
path_to_Image.append([full_path, os.path.basename(os.path.splitext(full_path)[0])])
# Create the list that will be returned by this function.
Images = []
Rois = []
for img_path, file_name in path_to_Image:
# IJ.openImage() returns an ImagePlus object or None.
imp = IJ.openImage(img_path)
imp.show()
print(img_path)
if check_filter(file_name):
continue;
else:
print(file_name , pathRoi)
RoiName = str(pathRoi) + '/'+ file_name + '_rois' + '.zip'
if os.path.exists(RoiName):
Roi = IJ.open(RoiName)
imp = IJ.getImage()
cal= imp.getCalibration()# in microns
img = IJF.wrap(imp)
print('Image Dimensions', img.dimensions, 'Calibration', cal)
print(Roi)
# An object equals True and None equals False.
rm = RoiManager.getInstance()
if (rm==None):
rm = RoiManager()
try:
rm.runCommand('Delete')
except:
pass
rm.runCommand("Open", RoiName)
rois = rm.getRoisAsArray()
overlay = Overlay()
for (i in range(0,len(rois))):
overlay.add(rois[i])
def load_rois(roifile):
rm = RoiManager(False)
rm.reset()
rm.runCommand("Open", roifile)
rois = rm.getRoisAsArray()
return rois
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)
# 4. The script will pop up a file browser. Specify the output file for the ROIs. Press "Open". The coordinates of all
# ROI polygons will be save to a text file in JSON format.
#
# Frank Vernaillen
# VIB - Vlaams Instituut voor Biotechnologie
# October 2018
from ij import IJ
from ij.plugin.frame import RoiManager
import sys
rm = RoiManager.getInstance()
if rm == None:
rm = RoiManager()
rois = rm.getRoisAsArray()
if rois == None or len(rois) == 0:
sys.exit('The ROI manager has no ROIs')
filename = IJ.getFilePath(
'Specify the output file for the ROIs in JSON format (e.g. myrois.json)')
if filename == None:
sys.exit('ROI saving canceled by the user.')
# Save the ROIs as JSON arrays
IJ.log("Saving {} ROIs to {}".format(len(rois), filename))
with open(filename, 'w') as f:
f.write('[\n')
for i, roi in enumerate(rois):
polygon = roi.getPolygon()
f.write('[\n')
def analyze_homogeneity(image_title):
IJ.selectWindow(image_title)
raw_imp = IJ.getImage()
IJ.run(raw_imp, "Duplicate...", "title=Homogeneity duplicate")
IJ.selectWindow('Homogeneity')
hg_imp = IJ.getImage()
# Get a 2D image
if hg_imp.getNSlices() > 1:
IJ.run(hg_imp, "Z Project...", "projection=[Average Intensity]")
hg_imp.close()
IJ.selectWindow('MAX_Homogeneity')
hg_imp = IJ.getImage()
hg_imp.setTitle('Homogeneity')
# Blur and BG correct the image
IJ.run(hg_imp, 'Gaussian Blur...', 'sigma=' + str(HOMOGENEITY_RADIUS) + ' stack')
# Detect the spots
IJ.setAutoThreshold(hg_imp, HOMOGENEITY_THRESHOLD + " dark")
rm = RoiManager(True)
table = ResultsTable()
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER,
ParticleAnalyzer.EXCLUDE_EDGE_PARTICLES,
Measurements.AREA, # measurements
table, # Output table
0, # MinSize
500, # MaxSize
0.0, # minCirc
1.0) # maxCirc
pa.setHideOutputImage(True)
pa.analyze(hg_imp)
areas = table.getColumn(table.getHeadings().index('Area'))
median_areas = compute_median(areas)
st_dev_areas = compute_std_dev(areas, median_areas)
thresholds_areas = (median_areas - (2 * st_dev_areas), median_areas + (2 * st_dev_areas))
roi_measurements = {'integrated_density': [],
'max': [],
'area': []}
IJ.setForegroundColor(0, 0, 0)
for roi in rm.getRoisAsArray():
hg_imp.setRoi(roi)
if REMOVE_CROSS and hg_imp.getStatistics().AREA > thresholds_areas[1]:
rm.runCommand('Fill')
else:
roi_measurements['integrated_density'].append(hg_imp.getStatistics().INTEGRATED_DENSITY)
roi_measurements['max'].append(hg_imp.getStatistics().MIN_MAX)
roi_measurements['integrated_densities'].append(hg_imp.getStatistics().AREA)
rm.runCommand('Delete')
measuremnts = {'mean_integrated_density': compute_mean(roi_measurements['integrated_density']),
'median_integrated_density': compute_median(roi_measurements['integrated_density']),
'std_dev_integrated_density': compute_std_dev(roi_measurements['integrated_density']),
'mean_max': compute_mean(roi_measurements['max']),
'median_max': compute_median(roi_measurements['max']),
'std_dev_max': compute_std_dev(roi_measurements['max']),
'mean_area': compute_mean(roi_measurements['max']),
'median_area': compute_median(roi_measurements['max']),
'std_dev_area': compute_std_dev(roi_measurements['max']),
}
# generate homogeinity image
# calculate interpoint distance in pixels
nr_point_columns = int(sqrt(len(measuremnts['mean_max'])))
# TODO: This is a rough estimation that does not take into account margins or rectangular FOVs
inter_point_dist = hg_imp.getWidth() / nr_point_columns
IJ.run(hg_imp, "Maximum...", "radius="+(inter_point_dist*1.22))
# Normalize to 100
IJ.run(hg_imp, "Divide...", "value=" + max(roi_measurements['max'] / 100))
IJ.run(hg_imp, "Gaussian Blur...", "sigma=" + (inter_point_dist/2))
hg_imp.getProcessor.setMinAndMax(0, 255)
# Create a LUT based on a predefined threshold
red = zeros(256, 'b')
green = zeros(256, 'b')
blue = zeros(256, 'b')
acceptance_threshold = HOMOGENEITY_ACCEPTANCE_THRESHOLD * 256 / 100
for i in range(256):
red[i] = (i - acceptance_threshold)
green[i] = (i)
homogeneity_LUT = LUT(red, green, blue)
hg_imp.setLut(homogeneity_LUT)
return hg_imp, measuremnts
def process(subFolder, outputDirectory, filename):
imp = IJ.openImage(inputDirectory + subFolder + '/' +
rreplace(filename, "_ch00.tif", ".tif"))
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=0.8777017 pixel_height=0.8777017 voxel_depth=25400.0508001"
)
ic = ImageConverter(imp)
ic.convertToGray8()
IJ.setThreshold(imp, 2, 255)
IJ.run(imp, "Convert to Mask", "")
IJ.run(imp, "Remove Outliers...",
"radius=5" + " threshold=50" + " which=Dark")
IJ.run(imp, "Remove Outliers...",
"radius=5" + " threshold=50" + " which=Bright")
imp.getProcessor().invert()
rm = RoiManager(True)
imp.getProcessor().setThreshold(0, 0, ImageProcessor.NO_LUT_UPDATE)
boundroi = ThresholdToSelection.run(imp)
rm.addRoi(boundroi)
if not displayImages:
imp.changes = False
imp.close()
images = [None] * 5
intensities = [None] * 5
blobsarea = [None] * 5
blobsnuclei = [None] * 5
bigAreas = [None] * 5
for chan in channels:
v, x = chan
images[x] = IJ.openImage(inputDirectory + subFolder + '/' +
rreplace(filename, "_ch00.tif", "_ch0" +
str(x) + ".tif"))
imp = images[x]
for roi in rm.getRoisAsArray():
imp.setRoi(roi)
stats = imp.getStatistics(Measurements.MEAN | Measurements.AREA)
intensities[x] = stats.mean
bigAreas[x] = stats.area
rm.close()
# Opens the ch00 image and sets default properties
imp = IJ.openImage(inputDirectory + subFolder + '/' + filename)
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=0.8777017 pixel_height=0.8777017 voxel_depth=25400.0508001"
)
# Sets the threshold and watersheds. for more details on image processing, see https://imagej.nih.gov/ij/developer/api/ij/process/ImageProcessor.html
ic = ImageConverter(imp)
ic.convertToGray8()
IJ.run(imp, "Remove Outliers...",
"radius=2" + " threshold=50" + " which=Dark")
IJ.run(imp, "Gaussian Blur...", "sigma=" + str(blur))
IJ.setThreshold(imp, lowerBounds[0], 255)
if displayImages:
imp.show()
IJ.run(imp, "Convert to Mask", "")
IJ.run(imp, "Watershed", "")
if not displayImages:
imp.changes = False
imp.close()
# Counts and measures the area of particles and adds them to a table called areas. Also adds them to the ROI manager
table = ResultsTable()
roim = RoiManager(True)
ParticleAnalyzer.setRoiManager(roim)
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER, Measurements.AREA,
table, 15, 9999999999999999, 0.2, 1.0)
pa.setHideOutputImage(True)
#imp = impM
# imp.getProcessor().invert()
pa.analyze(imp)
areas = table.getColumn(0)
# This loop goes through the remaining channels for the other markers, by replacing the ch00 at the end with its corresponding channel
# It will save all the area fractions into a 2d array called areaFractionsArray
areaFractionsArray = [None] * 5
for chan in channels:
v, x = chan
# Opens each image and thresholds
imp = images[x]
IJ.run(
imp, "Properties...",
"channels=1 slices=1 frames=1 unit=um pixel_width=0.8777017 pixel_height=0.8777017 voxel_depth=25400.0508001"
)
ic = ImageConverter(imp)
ic.convertToGray8()
IJ.setThreshold(imp, lowerBounds[x], 255)
if displayImages:
imp.show()
WaitForUserDialog("Title",
"Adjust Threshold for Marker " + v).show()
IJ.run(imp, "Convert to Mask", "")
# Measures the area fraction of the new image for each ROI from the ROI manager.
areaFractions = []
for roi in roim.getRoisAsArray():
imp.setRoi(roi)
stats = imp.getStatistics(Measurements.AREA_FRACTION)
areaFractions.append(stats.areaFraction)
# Saves the results in areaFractionArray
areaFractionsArray[x] = areaFractions
roim.close()
for chan in channels:
v, x = chan
imp = images[x]
imp.deleteRoi()
roim = RoiManager(True)
ParticleAnalyzer.setRoiManager(roim)
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER,
Measurements.AREA, table, 15, 9999999999999999,
0.2, 1.0)
pa.analyze(imp)
blobs = []
for roi in roim.getRoisAsArray():
imp.setRoi(roi)
stats = imp.getStatistics(Measurements.AREA)
blobs.append(stats.area)
blobsarea[x] = sum(blobs)
blobsnuclei[x] = len(blobs)
if not displayImages:
imp.changes = False
imp.close()
roim.reset()
roim.close()
# Creates the summary dictionary which will correspond to a single row in the output csv, with each key being a column
summary = {}
summary['Image'] = filename
summary['Directory'] = subFolder
# Adds usual columns
summary['size-average'] = 0
summary['#nuclei'] = 0
summary['all-negative'] = 0
summary['too-big-(>' + str(tooBigThreshold) + ')'] = 0
summary['too-small-(<' + str(tooSmallThreshold) + ')'] = 0
# Creates the fieldnames variable needed to create the csv file at the end.
fieldnames = [
'Name', 'Directory', 'Image', 'size-average',
'too-big-(>' + str(tooBigThreshold) + ')',
'too-small-(<' + str(tooSmallThreshold) + ')', '#nuclei',
'all-negative'
]
# Adds the columns for each individual marker (ignoring Dapi since it was used to count nuclei)
summary["organoid-area"] = bigAreas[x]
fieldnames.append("organoid-area")
for chan in channels:
v, x = chan
summary[v + "-positive"] = 0
fieldnames.append(v + "-positive")
summary[v + "-intensity"] = intensities[x]
fieldnames.append(v + "-intensity")
summary[v + "-blobsarea"] = blobsarea[x]
fieldnames.append(v + "-blobsarea")
summary[v + "-blobsnuclei"] = blobsnuclei[x]
fieldnames.append(v + "-blobsnuclei")
# Adds the column for colocalization between first and second marker
if len(channels) > 2:
summary[channels[1][0] + '-' + channels[2][0] + '-positive'] = 0
fieldnames.append(channels[1][0] + '-' + channels[2][0] + '-positive')
# Adds the columns for colocalization between all three markers
if len(channels) > 3:
summary[channels[1][0] + '-' + channels[3][0] + '-positive'] = 0
summary[channels[2][0] + '-' + channels[3][0] + '-positive'] = 0
summary[channels[1][0] + '-' + channels[2][0] + '-' + channels[3][0] +
'-positive'] = 0
fieldnames.append(channels[1][0] + '-' + channels[3][0] + '-positive')
fieldnames.append(channels[2][0] + '-' + channels[3][0] + '-positive')
fieldnames.append(channels[1][0] + '-' + channels[2][0] + '-' +
channels[3][0] + '-positive')
# Loops through each particle and adds it to each field that it is True for.
areaCounter = 0
for z, area in enumerate(areas):
log.write(str(area))
log.write("\n")
if area > tooBigThreshold:
summary['too-big-(>' + str(tooBigThreshold) + ')'] += 1
elif area < tooSmallThreshold:
summary['too-small-(<' + str(tooSmallThreshold) + ')'] += 1
else:
summary['#nuclei'] += 1
areaCounter += area
temp = 0
for chan in channels:
v, x = chan
if areaFractionsArray[x][z] > areaFractionThreshold[
0]: #theres an error here im not sure why. i remember fixing it before
summary[chan[0] + '-positive'] += 1
if x != 0:
temp += 1
if temp == 0:
summary['all-negative'] += 1
if len(channels) > 2:
if areaFractionsArray[1][z] > areaFractionThreshold[1]:
if areaFractionsArray[2][z] > areaFractionThreshold[2]:
summary[channels[1][0] + '-' + channels[2][0] +
'-positive'] += 1
if len(channels) > 3:
if areaFractionsArray[1][z] > areaFractionThreshold[1]:
if areaFractionsArray[3][z] > areaFractionThreshold[3]:
summary[channels[1][0] + '-' + channels[3][0] +
'-positive'] += 1
if areaFractionsArray[2][z] > areaFractionThreshold[2]:
if areaFractionsArray[3][z] > areaFractionThreshold[3]:
summary[channels[2][0] + '-' + channels[3][0] +
'-positive'] += 1
if areaFractionsArray[1][z] > areaFractionThreshold[1]:
summary[channels[1][0] + '-' + channels[2][0] +
'-' + channels[3][0] + '-positive'] += 1
# Calculate the average of the particles sizes
if float(summary['#nuclei']) > 0:
summary['size-average'] = round(areaCounter / summary['#nuclei'], 2)
# Opens and appends one line on the final csv file for the subfolder (remember that this is still inside the loop that goes through each image)
with open(outputDirectory + "/" + outputName + ".csv", 'a') as csvfile:
writer = csv.DictWriter(csvfile,
fieldnames=fieldnames,
extrasaction='ignore',
lineterminator='\n')
if os.path.getsize(outputDirectory + "/" + outputName + ".csv") < 1:
writer.writeheader()
writer.writerow(summary)
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
otsu=ops.run("threshold", ops.create( dimensions2D, BitType()), imgBgs, Otsu())
display.createDisplay("thresholded", data.create(ImgPlus(otsu)))
'''
#Utility.clearOutsideRoi(imp, clone)
IJ.run(imp, "Auto Local Threshold",
"method=MidGrey radius=15 parameter_1=0 parameter_2=0 white")
IJ.run(imp, "Fill Holes", "")
IJ.run(imp, "Close-", "")
IJ.run(imp, "Watershed", "")
iplus.updateAndDraw()
# create a hidden roi manager
roim = RoiManager(True)
# count the particles
countParticles(iplus, roim, 10, 200, 0.5, 1.0)
[truecolor1.getProcessor().draw(roi) for roi in roim.getRoisAsArray()]
truecolor1.updateAndDraw()
#Prefs.blackBackground = False;
#IJ.run("Make Binary", "");
#IJ.run("LoG 3D");
#IJ.run("Duplicate...", "title="+"test")
#IJ.run("RGB Stack");
#IJ.run("Convert Stack to Images");
if pa.analyze(blueImp):
print "All ok"
else:
print "There was a problem in analyzing", blueImp
#Export roi center and radius to TSV
default_name = imp.getShortTitle() + "_cells"
sd = SaveDialog('Save ellypses to file', default_name, '.tsv')
dirToSave = sd.getDirectory()
fileh = open(dirToSave + sd.getFileName(), "w")
#fileh.write("name\tx1\tx2\ty1\ty2\n")
roim = RoiManager.getInstance()
if roim is None:
sys.exit(1)
rois = roim.getRoisAsArray()
print rois
for roi in rois:
bounds = roi.getBounds()
x1 = (float(bounds.x) + float(bounds.x + bounds.width)) / 2.
y1 = (float(bounds.y) + float(bounds.y + bounds.height)) / 2.
fileh.write("\t".join([
roi.getName(),
str(x1),
str(y1),
str(bounds.width),
str(bounds.height)
]) + "\n")
fileh.close()
# Binarize
imp = IJ.getImage()
imp1 = imp.duplicate()
IJ.run(imp1, "Subtract Background...", "rolling=15 stack")
IJ.run(imp1, "Median...", "radius=2 stack")
IJ.run(imp1, "Gaussian Blur...", "sigma=2 stack")
Prefs.blackBackground = True;
IJ.run(imp1, "Convert to Mask", "method=MaxEntropy background=Dark calculate black")
IJ.run(imp1, "Analyze Particles...", "size=400-Infinity pixel exclude add stack")
imp1.show()
# Analyze by using ROI manger
rm = RoiManager()
rm = RoiManager.getInstance()
roi_array = rm.getRoisAsArray()
# xyで構成される入れ子構造を組みかえる
def xypoint_flatten(roi_array = RoiManager.getInstance().getRoisAsArray()):
if roi_array is not None:
xpoint = [[] for _ in range(len(roi_array))]
ypoint = [[] for _ in range(len(roi_array))]
for i, roi in enumerate(roi_array):
xy_coord = list(roi.getContainedPoints())
for j, coord in enumerate(xy_coord):
xpoint[i].append(coord.x)
ypoint[i].append(coord.y)
return xpoint, ypoint
xpoint,ypoint = xypoint_flatten()
def batch_open_images(pathImage, file_typeImage, name_filterImage=None):
if isinstance(pathImage, File):
pathImage = pathImage.getAbsolutePath()
def check_filter(string):
'''This function is used to check for a given filter.
It is possible to use a single string or a list/tuple of strings as filter.
This function can access the variables of the surrounding function.
:param string: The filename to perform the filtering on.
'''
if name_filterImage:
# The first branch is used if name_filter is a list or a tuple.
if isinstance(name_filterImage, (list, tuple)):
for name_filter_ in name_filterImage:
if name_filter_ in string:
# Exit the function with True.
return True
else:
# Next iteration of the for loop.
continue
# The second branch is used if name_filter is a string.
elif isinstance(name_filterImage, string):
if name_filterImage in string:
return True
else:
return False
return False
else:
# Accept all files if name_filter is None.
return True
def check_type(string):
'''This function is used to check the file type.
It is possible to use a single string or a list/tuple of strings as filter.
This function can access the variables of the surrounding function.
:param string: The filename to perform the check on.
'''
if file_typeImage:
# The first branch is used if file_type is a list or a tuple.
if isinstance(file_typeImage, (list, tuple)):
for file_type_ in file_typeImage:
if string.endswith(file_type_):
# Exit the function with True.
return True
else:
# Next iteration of the for loop.
continue
# The second branch is used if file_type is a string.
elif isinstance(file_typeImage, string):
if string.endswith(file_typeImage):
return True
else:
return False
return False
# Accept all files if file_type is None.
else:
return True
# We collect all files to open in a list.
path_to_Image = []
# Replacing some abbreviations (e.g. $HOME on Linux).
path = os.path.expanduser(pathImage)
path = os.path.expandvars(pathImage)
# If we don't want a recursive search, we can use os.listdir().
for directory, dir_names, file_names in os.walk(pathImage):
# We are only interested in files.
for file_name in file_names:
# The list contains only the file names.
# The full path needs to be reconstructed.
full_path = os.path.join(directory, file_name)
# Both checks are performed to filter the files.
if check_type(file_name):
if check_filter(file_name) is False:
# Add the file to the list of images to open.
path_to_Image.append([
full_path,
os.path.basename(os.path.splitext(full_path)[0])
])
Images = []
for img_path, file_name in path_to_Image:
imp = IJ.openImage(img_path)
maskimage = ops.run("create.img", imp)
cursor = maskimage.localizingCursor()
imp.show()
IJ.run("Select None")
overlay = imp.getOverlay()
if overlay == None:
overlay = Overlay()
imp.setOverlay(overlay)
else:
overlay.clear()
imp.updateAndDraw()
impY = imp.getHeight()
impX = imp.getWidth()
print(impY, impX)
rm = RoiManager.getInstance()
if not rm:
rm = RoiManager()
rm.runCommand("reset")
WaitForUserDialog("Select the landmark and the second point").show()
rm.runCommand("Add")
roi_points = rm.getRoisAsArray()
for Roi in roi_points:
xpoints = Roi.getPolygon().xpoints
ypoints = Roi.getPolygon().ypoints
print(xpoints, ypoints)
print('Start Landmark', xpoints[0], ypoints[0])
fixedpointX = xpoints[0]
fixedpointY = ypoints[0]
print('End Landmark', xpoints[1], ypoints[1])
IJ.makeLine(xpoints[0], ypoints[0], xpoints[1], ypoints[1])
gui = GenericDialog("Rotation Angle")
gui.addNumericField("Choose Angle", 15, 0)
gui.showDialog()
if gui.wasOKed():
rotateangle = gui.getNextNumber()
IJ.run("Rotate...", "angle=" + str(int(float(rotateangle))))
rm.runCommand("reset")
overlay = imp.getOverlay()
rm.runCommand("Add")
roi_points = rm.getRoisAsArray()
for Roi in roi_points:
xpoints = Roi.getPolygon().xpoints
ypoints = Roi.getPolygon().ypoints
print(xpoints, ypoints)
print('Rotated Start Landmark', xpoints[0], ypoints[0])
print('Rotated End Landmark', xpoints[1], ypoints[1])
slope = (ypoints[1] - ypoints[0]) / (xpoints[1] - xpoints[0] + 1.0E-20)
intercept = fixedpointY - slope * fixedpointX
print(fixedpointX, fixedpointY)
print('Slope', slope, 'Intercept', intercept)
XwY0 = -intercept / slope
YxwY0 = slope * XwY0 + intercept
XwYmax = (impY - intercept) / slope
YxwYmax = slope * XwYmax + intercept
YwX0 = intercept
XywX0 = (YwX0 - intercept) / slope
YwXmax = impX * slope + intercept
XxwXmax = (YwXmax - intercept) / slope
rm.runCommand("reset")
if XwY0 > 0:
lineROIA = Line(fixedpointX, fixedpointY, XwY0, YxwY0)
lineROIB = Line(fixedpointX, fixedpointY, XwYmax, YxwYmax)
overlay.add(lineROIA)
overlay.add(lineROIB)
if XwY0 < 0:
lineROIA = Line(fixedpointX, fixedpointY, XywX0, YwX0)
lineROIB = Line(fixedpointX, fixedpointY, XxwXmax, YwXmax)
overlay.add(lineROIA)
overlay.add(lineROIB)
while cursor.hasNext():
cursor.fwd()
X = cursor.getDoublePosition(0)
Y = cursor.getDoublePosition(1)
if abs(Y - slope * X - intercept) <= 4:
cursor.get().set(0)
else:
cursor.get().set(1)
labeling = ops.labeling().cca(maskimage,
StructuringElement.EIGHT_CONNECTED)
# get the index image (each object will have a unique gray level)
labelingIndex = labeling.getIndexImg()
dataImg = ds.create(labelingIndex)
location = ls.resolve(
str(savedir) + '/' + file_name + '.' + file_type_image)
dio.save(dataImg, location)
imp.close()
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
# set up first ROI manager
table1 = ResultsTable()
roim1 = RoiManager()
pa1 = ParticleAnalyzer(
ParticleAnalyzer.ADD_TO_MANAGER,
Measurements.AREA | Measurements.MEAN | Measurements.ELLIPSE, table1, 0,
100, 0, 1)
pa1.setRoiManager(roim1)
pa1.analyze(imp)
# set up second ROI manager
table2 = ResultsTable()
# Pass true to second ROI manager so it will not be seen
roim2 = RoiManager(True)
pa2 = ParticleAnalyzer(
ParticleAnalyzer.ADD_TO_MANAGER,
Measurements.AREA | Measurements.MEAN | Measurements.ELLIPSE, table2, 100,
500, 0, 1)
pa2.setRoiManager(roim2)
pa2.analyze(imp)
print "rois from first manager:"
for roi in roim1.getRoisAsArray():
print roi
print
print "rois from second manager:"
for roi in roim2.getRoisAsArray():
print roi
