def cvt_32_bit_to_8_bit(imp): """ cvt_32_bit_to_8_bit(imp) Convert a 32 bit image to an 8-bit grayscale Parameters ========== imp: ImagePlus The ImagePlus of a 32 bit/px image to convert Returns ======= wrk: ImagePlus The ImagePlus with the 8-bit/px image """ imp.show() wrk = imp.duplicate() ic = ImageConverter(wrk) ic.convertToGray8() title = imp.getTitle() wrk.setTitle(title + "_g8") wrk.updateImage() return wrk
def do_projection(self, channel, proj_type=None):
proj = ZProjector() ## create projection class
if proj_type is None:
if self.do_z[channel] == "MAX":
proj.setMethod(ZProjector.MAX_METHOD)
elif self.do_z[channel] == "AVG":
proj.setMethod(ZProjector.AVG_METHOD)
else:
print "Dont know how to project this way"
print "Just using a single slice for images!!"
return None
else:
if proj_type == "MAX":
proj.setMethod(ZProjector.MAX_METHOD)
elif proj_type == "AVG":
proj.setMethod(ZProjector.AVG_METHOD)
else:
print "Dont know how to project this way"
print "Just using a single slice for images!!"
return None
proj.setImage(self.stack_imps[channel])
proj.doProjection()
projection = proj.getProjection()
conv = ImageConverter(projection)
conv.convertToGray16()
conv.setDoScaling(False)
self.projections[channel] = projection
self.projections_done[channel] = self.do_z[channel]
def cvt_32_bit_to_8_bit(imp):
"""
cvt_32_bit_to_8_bit(imp)
Convert a 32 bit image to an 8-bit grayscale
Parameters
==========
imp: ImagePlus
The ImagePlus of a 32 bit/px image to convert
Returns
=======
wrk: ImagePlus
The ImagePlus with the 8-bit/px image
"""
imp.show()
wrk = imp.duplicate()
ic = ImageConverter(wrk)
ic.convertToGray8()
title = imp.getTitle()
wrk.setTitle(title + "_g8")
wrk.updateImage()
return wrk
def run():
helpText = "This program will batch convert any tif image to 8-bit greyscale, " + \
"and empty calibration infomation.\n\n" + \
">> Press OK to Select a directory of TIFF images."
MessageDialog(IJ.getInstance(), "Empty Calibration Guide", helpText)
srcDir = DirectoryChooser("Chose Source Dir").getDirectory()
if srcDir is None:
IJ.log("Choose Dir Canceled!")
return
for root, directories, filenames in os.walk(srcDir):
for filename in filenames:
if not filename.endswith(".tif"):
continue
imgPath = os.path.join(root, filename)
outPath = os.path.join(root, "decal-" + filename)
imp = IJ.openImage(imgPath)
imp.setCalibration(Calibration())
ic = ImageConverter(imp)
ic.convertToGray8()
IJ.saveAsTiff(imp, outPath)
print "removed calibration and saved to ", os.path.basename(
outPath)
def previewDisplaySettings(image, title, zoom, cal):
"""Apply wanted settings for previews"""
ImageConverter.setDoScaling(0)
ImageConverter(image).convertToGray16()
image.show()
IJ.run("glasbey_on_dark")
IJ.setMinAndMax(image, 0, 255)
image.setTitle(title)
image.setCalibration(cal)
IJ.run("Set... ", "zoom=" + str(zoom))
def process(imp):
try:
IJ.run(imp, "Subtract Background...", "rolling=50");
IJ.run(imp, "Subtract Background...", "rolling=50");
ImageConverter.setDoScaling(True);
IJ.run(imp, "32-bit", "");
IJ.run(imp, "Smooth", "");
IJ.setAutoThreshold(imp, "Default dark");
IJ.run(imp, "NaN Background", "");
return imp;
except(AttributeError):
print("error processing!");
def apply_threshold(imp, method='Otsu',
background_threshold='dark',
stackopt=False,
corrf=1.0):
# one threshold value for the whole stack with correction
if stackopt:
# create argument string for the IJ.setAutoThreshold
thcmd = method + ' ' + background_threshold + ' stack'
# set threshold and get the lower threshold value
IJ.setAutoThreshold(imp, thcmd)
ip = imp.getProcessor()
# get the threshold value and correct it
lowth = ip.getMinThreshold()
lowth_corr = int(round(lowth * corrf, 0))
# process stack with corrected threshold value
imp = ThresholdTools.apply_threshold_stack_corr(imp, lowth_corr,
method=method)
# threshold slice-by-slice with correction
if not stackopt:
# get the stack
stack = imp.getStack() # get the stack within the ImagePlus
nslices = stack.getSize() # get the number of slices
print('Slices: ' + str(nslices))
print('Thresholding slice-by-slice')
for index in range(1, nslices + 1):
ip = stack.getProcessor(index)
# get the histogramm
hist = ip.getHistogram()
# get the threshold value
lowth = ThresholdTools.apply_autothreshold(hist, method=method)
lowth_corr = int(round(lowth * corrf, 0))
ip.threshold(lowth_corr)
# convert to 8bit without rescaling
ImageConverter.setDoScaling(False)
ImageConverter(imp).convertToGray8()
return imp
def apply_threshold_stack_corr(imp, lowth_corr):
# get the stacks
stack = imp.getStack()
nslices = stack.getSize()
for index in range(1, nslices + 1):
ip = stack.getProcessor(index)
ip.threshold(lowth_corr)
# convert to 8bit without rescaling
ImageConverter.setDoScaling(False)
ImageConverter(imp).convertToGray8()
return imp
def edm_watershed(imp):
# get the image processor
ip = imp.getProcessor()
if ip.isBinary is False:
# convert to 8bit without rescaling
ImageConverter.setDoScaling(False)
ImageConverter(imp).convertToGray8()
else:
edm = EDM()
edm.setup("watershed", None)
edm.run(ip)
return imp
def open_images(in_folder):
#open Red folder
for i in os.listdir(os.path.join(in_folder, "Red")):
file_path = os.path.join(in_folder, "Red", i)
if os.path.isfile(file_path) and i.endswith(".tif"):
print(i)
cur_img_red = IJ.openImage(file_path)
nslices_red = cur_img_red.getStack().getSize(
) # get the number slices in stack within the ImagePlus
#open phase folder
for i in os.listdir(os.path.join(in_folder, "Phase")):
file_path = os.path.join(in_folder, "Phase", i)
if os.path.isfile(file_path) and i.endswith(".tif"):
print(i)
cur_img_phase = IJ.openImage(file_path)
nslices_phase = cur_img_phase.getStack().getSize(
) # get the number slices in stack within the ImagePlus
if nslices_red != nslices_phase:
sys.exit("Red and phase channel have unequal number of images!")
timepoints = nslices_red
ImageConverter(cur_img_phase).convertToGray16()
comp_stack_img = RGBStackMerge.mergeChannels(
[cur_img_red, None, None, cur_img_phase, None, None, None], True)
return (comp_stack_img, timepoints)
def Weka_Segm(dirs):
""" Loads trained classifier and segments cells """
""" in aligned images according to training. """
# Define reference image for segmentation (default is timepoint000).
w_train = os.path.join(dirs["Composites_Aligned"], "Timepoint000.tif")
trainer = IJ.openImage(w_train)
weka = WekaSegmentation()
weka.setTrainingImage(trainer)
# Select classifier model.
weka.loadClassifier(str(classifier))
weka.applyClassifier(False)
segmentation = weka.getClassifiedImage()
segmentation.show()
# Convert image to 8bit
ImageConverter(segmentation).convertToRGB()
ImageConverter(segmentation).convertToGray8()
# Threshold segmentation to soma only.
hist = segmentation.getProcessor().getHistogram()
lowth = Auto_Threshold.IJDefault(hist)
segmentation.getProcessor().threshold(lowth)
segmentation.getProcessor().setThreshold(0, 0, ImageProcessor.NO_LUT_UPDATE)
segmentation.getProcessor().invert()
segmentation.show()
# Run Watershed Irregular Features plugin, with parameters.
IJ.run(segmentation, "Watershed Irregular Features",
"erosion=20 convexity_treshold=0 separator_size=0-Infinity")
# Make selection and add to RoiManager.
RoiManager()
rm = RoiManager.getInstance()
rm.runCommand("reset")
roi = ThresholdToSelection.run(segmentation)
segmentation.setRoi(roi)
rm.addRoi(roi)
rm.runCommand("Split")
def run():
helpText = "This program will batch convert any tif image to 8-bit greyscale, " + \
"and empty calibration infomation.\n\n" + \
">> Press OK to Select a directory of TIFF images."
MessageDialog(IJ.getInstance(),"Empty Calibration Guide", helpText)
srcDir = DirectoryChooser("Chose Source Dir").getDirectory()
if srcDir is None:
IJ.log("Choose Dir Canceled!")
return
for root, directories, filenames in os.walk(srcDir):
for filename in filenames:
if not filename.endswith(".tif"):
continue
imgPath = os.path.join(root, filename)
outPath = os.path.join(root, "decal-" + filename)
imp = IJ.openImage(imgPath)
imp.setCalibration(Calibration())
ic = ImageConverter(imp)
ic.convertToGray8()
IJ.saveAsTiff(imp, outPath)
print "removed calibration and saved to ", os.path.basename(outPath)
def convert(filepath, targetFolder):
imp = IJ.openImage(filepath)
if imp.getType() == ImagePlus.GRAY32:
ic = ImageConverter(imp)
ic.setDoScaling(False)
ic.convertToGray16()
filename = os.path.basename(filepath)
writeZip(imp, os.path.join(targetFolder, filename), title=filename)
else:
syncPrint("Not a 32-bit image: " + filename)
def convertAndSaveFile(fullFilePath, convertTo8Bit=False, allowOverwrite=False):
"""
"""
print(' convertAndSaveFile() fullFilePath:', fullFilePath)
folderPath, fileName = os.path.split(fullFilePath)
fileNameNoExtension = os.path.splitext(fileName)[0]
saveFileNoExtension = os.path.join(folderPath, fileNameNoExtension)
#
# load file and build a dict of parameters like (channels, pixels, voxels)
myFileInfo, imp = bSimpleFileInfo.myLoadFile(fullFilePath, doShow=True)
if convertTo8Bit:
# from (ImagePlus.GRAY8, ImagePlus.GRAY16, ImagePlus.GRAY32, ImagePlus.COLOR_256 or ImagePlus.COLOR_RGB)
myType = imp.getType()
if myType in [1,2]:
ic = ImageConverter(imp) # converts channelImp in place
scaleConversions = True
ic.setDoScaling(scaleConversions) # scales inensities to fill 0...255
ic.convertToGray8()
#
# split channels
channelArray = ChannelSplitter.split(imp) # returns an array of imp
for channelIdx, channelImp in enumerate(channelArray):
# channelImp is an imp, this will NOT have fileinfo (we just created it)
#channelImp.show()
saveFilePath = saveFileNoExtension + '_ch' + str(channelIdx+1) + '.tif'
print(' ch:', channelIdx+1, 'saveFilePath:', saveFilePath)
if not allowOverwrite and os.path.isfile(saveFilePath):
print(' . not saving, file already exists', saveFilePath)
else:
IJ.save(channelImp, saveFilePath)
return myFileInfo
def process(subFolder, outputDirectory, filename):
imp = IJ.openImage(inputDirectory + subFolder + '/' + filename)
imp.show()
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)
dup = imp.duplicate()
dup_title = dup.getTitle()
ic.convertToGray8()
imp.updateAndDraw()
IJ.run("Threshold...")
IJ.setThreshold(218, 245)
IJ.run(imp, "Convert to Mask", "")
rm = RoiManager()
imp.getProcessor().setThreshold(0, 0, ImageProcessor.NO_LUT_UPDATE)
boundroi = ThresholdToSelection.run(imp)
rm.addRoi(boundroi)
imp.changes = False
imp.close()
images = [None] * 5
intensities = [None] * 5
blobsarea = [None] * 5
blobsnuclei = [None] * 5
cells = [None] * 5
bigareas = [None] * 5
IJ.run(dup, "Colour Deconvolution", "vectors=[H DAB]")
images[0] = getImage(dup_title + "-(Colour_1)")
images[1] = getImage(dup_title + "-(Colour_2)")
images[2] = getImage(dup_title + "-(Colour_3)")
images[2].close()
for chan in channels:
v, x = chan
imp = images[x]
imp.show()
for roi in rm.getRoiManager().getRoisAsArray():
imp.setRoi(roi)
stats = imp.getStatistics(Measurements.MEAN | Measurements.AREA)
intensities[x] = stats.mean
bigareas[x] = stats.area
rm.runCommand(imp, "Show None")
rm.close()
# Opens the ch00 image and sets default properties
imp = images[0].duplicate()
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
imp.show()
setTempCurrentImage(imp)
ic = ImageConverter(imp)
imp.updateAndDraw()
IJ.run(imp, "Gaussian Blur...", "sigma=" + str(blur))
imp.updateAndDraw()
imp.show()
IJ.run("Threshold...")
IJ.setThreshold(30, lowerBounds[0])
if displayImages:
imp.show()
WaitForUserDialog(
"Title", "Adjust threshold for nuclei. Current region is: " +
region).show()
IJ.run(imp, "Convert to Mask", "")
# 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()
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER, Measurements.AREA,
table, 5, 9999999999999999, 0.05, 1.0)
pa.setHideOutputImage(True)
imp = IJ.getImage()
# imp.getProcessor().invert()
pa.analyze(imp)
imp.changes = False
imp.close()
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
maxThresholds = []
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"
)
imp.show()
setTempCurrentImage(imp)
ic = ImageConverter(imp)
ic.convertToGray8()
imp.updateAndDraw()
rm.runCommand(imp, "Show None")
rm.runCommand(imp, "Show All")
rm.runCommand(imp, "Show None")
imp.show()
IJ.selectWindow(imp.getTitle())
IJ.run("Threshold...")
IJ.setThreshold(20, lowerBounds[x])
if displayImages:
WaitForUserDialog(
"Title", "Adjust threshold for " + v +
". Current region is: " + region).show()
ip = imp.getProcessor()
maxThresholds.append(ip.getMaxThreshold())
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.getRoiManager().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()
imp.updateAndDraw()
setTempCurrentImage(imp)
roim = RoiManager()
pa = ParticleAnalyzer(ParticleAnalyzer.ADD_TO_MANAGER,
Measurements.AREA, table, 15, 9999999999999999,
0.2, 1.0)
pa.analyze(imp)
blobs = []
cell = []
for roi in roim.getRoiManager().getRoisAsArray():
imp.setRoi(roi)
stats = imp.getStatistics(Measurements.AREA)
blobs.append(stats.area)
if stats.area > tooSmallThresholdDAB and stats.area < tooBigThresholdDAB:
cell.append(stats.area)
blobsarea[x] = sum(blobs)
blobsnuclei[x] = len(blobs)
cells[x] = len(cell)
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 = [
'Directory', 'Image', 'size-average',
'too-big-(>' + str(tooBigThreshold) + ')',
'too-small-(<' + str(tooSmallThreshold) + ')', '#nuclei',
'all-negative'
]
for row in info:
if row['Animal ID'] == filename.replace('s', '-').replace(
'p', '-').split('-')[0]:
for key, value in row.items():
fieldnames.insert(0, key)
summary[key] = value
# Adds the columns for each individual marker (ignoring Dapi since it was used to count nuclei)
summary["tissue-area"] = bigareas[0]
fieldnames.append("tissue-area")
for chan in channels:
v, x = chan
summary[v + "-HEMO-cells"] = 0
fieldnames.append(v + "-HEMO-cells")
summary[v + "-intensity"] = intensities[x]
fieldnames.append(v + "-intensity")
summary[v + "-area"] = blobsarea[x]
fieldnames.append(v + "-area")
summary[v + "-area/tissue-area"] = blobsarea[x] / bigareas[0]
fieldnames.append(v + "-area/tissue-area")
summary[v + "-particles"] = blobsnuclei[x]
fieldnames.append(v + "-particles")
summary[v + "-cells"] = cells[x]
fieldnames.append(v + "-cells")
summary[v + "-particles/tissue-area"] = blobsnuclei[x] / bigareas[0]
fieldnames.append(v + "-particles/tissue-area")
fieldnames.append(v + "-HEMO-Cells/tissue-area")
# 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):
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]:
summary[chan[0] + '-HEMO-cells'] += 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
for chan in channels:
v, x = chan
summary[v + "-cells/tissue-area"] = summary[v + "-cells"] / bigareas[0]
if float(summary['#nuclei']) > 0:
summary['size-average'] = round(areaCounter / summary['#nuclei'], 2)
if displayImages:
fieldnames = ["Directory", "Image"]
for chan in channels:
v, x = chan
summary[v + "-threshold"] = maxThresholds[x]
fieldnames.append(v + "-threshold")
allMaxThresholds[v + "-" + region].append(maxThresholds[x])
# 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(outputName, 'a') as csvfile:
writer = csv.DictWriter(csvfile,
fieldnames=fieldnames,
extrasaction='ignore',
lineterminator='\n')
if os.path.getsize(outputName) < 1:
writer.writeheader()
writer.writerow(summary)
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", "")
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 duty_cycle(i, horizontal):
IJ.open(i)
IJ.run("Enhance Contrast...", "saturated=0.3 equalize")
IJ.run("Smooth", "")
'IJ.run("Anisotropic Diffusion 2D", "number=20 smoothings=1 keep=20 a1=0.50 a2=0.90 dt=20 edge=5") # use instead of smooth for better results, slower
imp = IJ.getImage()
ImageConverter(imp).convertToGray8()
file_base = str(imp.title) # convert name into string
file_base = file_base.split('.', 1)[0] # remove all characters including and after .
sample_id = file_base.split('_', 1)[0]
height = imp.height
width = imp.width
IJ.run(imp, "Set Scale...", "distance=5000 known=1 pixel=1 unit=cm")
IJ.setTool("line")
for scan in range(1, num_scans):
row_count = 0
data=[]
data2=[]
last = 0
filename = file_base + "_" + str(scan)
if not horizontal:
IJ.makeLine(0, scan*height/num_scans, width, scan*height/num_scans) # horizontal line to measure vertical grating profile
else:
IJ.makeLine(width*scan/num_scans, 9*height/10, width*scan/num_scans, 0) # vertical line to measure horizontal grating profile
IJ.run(imp, "Plot Profile", "")
imp_plot = IJ.getImage()
IJ.run(imp, "Find Peaks", setting) # nw07 params: min._peak_amplitude=60 min._peak_distance=0 min._value=100 max._value=0 exclude list
IJ.saveAs("Results", csvpath + filename + "_raw.csv")
imp_plot.close()
imp_plot = IJ.getImage()
IJ.saveAs("Png", csvpath + "Peaks in Plot of " + filename + ".png")
imp_plot.close()
IJ.selectWindow(filename + "_raw.csv")
IJ.run("Close")
with open(csvpath + filename + "_raw.csv", "rb") as fin, open(csvpath + filename + "_peaks.csv", "wb") as fout:
writer = csv.writer(fout)
for row in csv.reader(fin):
if not row[2] == '':
writer.writerow(row)
with open(csvpath + filename + "_peaks.csv", "rb") as File:
reader = csv.reader(File)
for row in reader:
row_count += 1
if row_count == 1:
continue
data.append(tuple(row)) # Append all non-header rows into a list of data as a tuple of cells
for row in sort_table(data, 2):
data2.append(tuple(row))
if len(data2) < 3:
IJ.log(filename + " no peaks detected, skipping...")
continue
else:
peaks = len(data2)
last = data2[0]
last = last[2]
row_count = 0
diff = 0
colG = []
blank = ""
duty = zip(*data2)
for row in data2:
row_count += 1
if row_count == 1:
continue
else:
print row[2]
print last
diff = float(row[2]) - float(last)
colG.append(diff)
last = row[2]
a, b = colG[::2], colG[1::2]
avga = sum(a)/len(a)
avgb = sum(b)/len(b)
a_len_dev = len(a) - 1
b_len_dev = len(b) - 1
if b_len_dev > 1:
a_stdev = sqrt(sum((x - avga)**2 for x in a) / a_len_dev)
b_stdev = sqrt(sum((x - avgb)**2 for x in b) / b_len_dev)
else:
a_stdev = 1
b_stdev = 1
duty_cyc = avga/(avga+avgb)
perc = duty_cyc*100
invperc = 100 - perc
inv_duty = 1 - duty_cyc
duty_max = max(duty_cyc, inv_duty)
duty_min = min(duty_cyc, inv_duty)
percs = round(perc)
percs = int(percs)
percs = str(percs)
invpercs = round(invperc)
invpercs = int(invpercs)
invpercs = str(invpercs)
colG.insert(0, blank)
a.insert(0, blank)
b.insert(0, blank)
b.insert(1, blank)
i = 0
while i < len(a):
i += 2
a.insert(i, blank)
i = 1
while i < len(b):
i += 2
b.insert(i, blank)
duty.append(colG)
duty.append(a)
duty.append(b)
duty = zip(*duty)
result = [sample_id, file_base, scan, duty_cyc, inv_duty, a_stdev, b_stdev, peaks, duty_max, duty_min]
header = ["X0", "Y0", "X1", "Y1", "X2", "Y2", "diff", "a", "b"]
results.append(result)
with open(csvpath + filename + "_duty.csv", 'wb') as myfile:
wr = csv.writer(myfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)
wr.writerow(header)
wr.writerows(duty)
print(filename + " duty cycle is " + percs + "/" + invpercs + "%.")
IJ.log(filename + " duty cycle is " + percs + "/" + invpercs + "% (" + str(peaks) + " peaks found)")
with open(csvpath + "results.csv", 'wb') as myfile:
wr = csv.writer(myfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)
wr.writerows(results) # Write the result data from all images into a single csv file
def loadTif(self, allowOverwrite=1):
if self.header['b_sequence'] == 'Linescan':
bPrintLog('Skipping: bPrairie2tif does not support Linescan, email bob and have him implement this', 3)
return 0
width = None
height = None
#ip_ch1 = [] # a list of images as a list of 'image processor'
#ip_ch2 = []
#ip_ch3 = []
bPrintLog('Loading ' + str(self.header['b_numSlices']) + ' Files ...', 3)
numFiles = 0
infoStr = ''
# sort individual .tif files into ch1 and ch2
for filename in os.listdir(self.srcFolder):
if filename.endswith(".tif") and not filename.startswith('.'):
# bPrintLog('opening:' + filename, 3)
try:
imp = IJ.openImage(self.srcFolder + filename)
if imp is None:
bPrintLog("ERROR: prairie2stack() could not open image from file:" + self.srcFolder + filename)
continue
isch1 = '_Ch1_' in filename
isch2 = '_Ch2_' in filename
isch3 = '_Ch3_' in filename
if numFiles == 0:
# don;t do this, header is to big to keep with output .tif
#infoStr = imp.getProperty("Info") #get all tags, this is usefless
infoStr += '\n' + self.infoStr0 # when appending, '\n' (EOL) is important because header does NOT end in EOL
width = imp.width
height = imp.height
#stack = imp.getImageStack()
#cp = stack.getProcessor(1) # assume 1 channel
if isch1:
#ip_ch1.append(cp)
if self.imp_ch1 is None:
self.imp_ch1 = imp
else:
self.imp_ch1 = Concatenator.run(self.imp_ch1, imp)
elif isch2:
#ip_ch2.append(cp)
if self.imp_ch2 is None:
self.imp_ch2 = imp
else:
self.imp_ch2 = Concatenator.run(self.imp_ch2, imp)
elif isch3:
#ip_ch3.append(cp)
if self.imp_ch3 is None:
self.imp_ch3 = imp
else:
self.imp_ch3 = Concatenator.run(self.imp_ch3, imp)
else:
bPrintLog('ERROR: did not find channel name in file:' + filename)
numFiles += 1
except:
continue
#bPrintLog("exception error: prairie2stack() could not open image from file:" + self.srcFolder + filename)
bPrintLog('Loaded ' + str(numFiles) + ' files ...', 3)
'''
bPrintLog('ch1 has ' + str(len(ip_ch1)) + ' slices', 4)
bPrintLog('ch2 has ' + str(len(ip_ch2)) + ' slices', 4)
bPrintLog('ch3 has ' + str(len(ip_ch3)) + ' slices', 4)
'''
#20170314, need to rewrite this to loop through channels (lots of repeated code here
if self.imp_ch1 is not None:
self.imp_ch1.setProperty("Info", infoStr);
if self.imp_ch2 is not None:
self.imp_ch2.setProperty("Info", infoStr);
if self.imp_ch3 is not None:
self.imp_ch3.setProperty("Info", infoStr);
#ch1
#if ip_ch1:
if self.imp_ch1:
# bPrintLog('ch1 has ' + str(self.imp_ch1.getNSlices()) + ' slices', 4)
'''
stack_ch1 = ImageStack(width, height)
for fp in ip_ch1:
stack_ch1.addSlice(fp)
self.imp_ch1 = ImagePlus('xxx', stack_ch1)
self.imp_ch1.setProperty("Info", infoStr);
'''
# median filter
if globalOptions['medianFilter']>0:
bPrintLog('ch1: Running median filter: ' + str(globalOptions['medianFilter']), 4)
medianArgs = 'radius=' + str(globalOptions['medianFilter']) + ' stack'
IJ.run(self.imp_ch1, "Median...", medianArgs);
infoStr += 'bMedianFilter=' + str(globalOptions['medianFilter']) + '\n'
self.imp_ch1.setProperty("Info", infoStr)
if globalOptions['convertToEightBit']:
bPrintLog('converting to 8-bit by dividing image down and then convert to 8-bit with ImageConverter.setDoScaling(False)', 4)
bitDepth = 2^13
divideBy = bitDepth / 2^8
# divide the 13 bit image down to 8 bit
#run("Divide...", "value=32 stack");
bPrintLog('divideBy:' + str(divideBy), 4)
divideArgs = 'value=' + str(divideBy) + ' stack'
IJ.run(self.imp_ch1, "Divide...", divideArgs);
# convert to 8-bit will automatically scale, to turn this off use
# eval("script", "ImageConverter.setDoScaling(false)");
ImageConverter.setDoScaling(False)
# run("8-bit");
bPrintLog('converting to 8-bit with setDoScaling False', 4)
IJ.run(self.imp_ch1, "8-bit", '');
# print stats including intensity for the stack we just made
# Returns the dimensions of this image (width, height, nChannels, nSlices, nFrames) as a 5 element int array.
d = self.imp_ch1.getDimensions() # width, height, nChannels, nSlices, nFrames
stats = self.imp_ch1.getStatistics() # stats.min, stats.max
bPrintLog('ch1 dimensions w:' + str(d[0]) + ' h:' + str(d[1]) + ' channels:' + str(d[2]) + ' slices:' + str(d[3]) + ' frames:' + str(d[4]), 4)
bPrintLog('ch1 intensity min:' + str(stats.min) + ' max:' + str(stats.max), 4)
# set the voxel size so opening in Fiji will report correct bit depth
# run("Properties...", "channels=1 slices=300 frames=1 unit=um pixel_width=.2 pixel_height=.3 voxel_depth=.4");
#ch2
#if ip_ch2:
if self.imp_ch2:
# bPrintLog('ch2 has ' + str(self.imp_ch2.getNSlices()) + ' slices', 4)
'''
stack_ch2 = ImageStack(width, height)
for fp in ip_ch2:
stack_ch2.addSlice(fp)
self.imp_ch2 = ImagePlus('xxx', stack_ch2)
self.imp_ch2.setProperty("Info", infoStr);
'''
# median filter
if globalOptions['medianFilter']>0:
bPrintLog('ch2: Running median filter: ' + str(globalOptions['medianFilter']), 4)
medianArgs = 'radius=' + str(globalOptions['medianFilter']) + ' stack'
IJ.run(self.imp_ch2, "Median...", medianArgs);
infoStr += 'bMedianFilter=' + str(globalOptions['medianFilter']) + '\n'
self.imp_ch2.setProperty("Info", infoStr)
if globalOptions['convertToEightBit']:
bPrintLog('converting to 8-bit by dividing image down and then convert to 8-bit with ImageConverter.setDoScaling(False)', 4)
bitDepth = 2^13
divideBy = bitDepth / 2^8
# divide the 13 bit image down to 8 bit
#run("Divide...", "value=32 stack");
bPrintLog('divideBy:' + str(divideBy), 4)
divideArgs = 'value=' + str(divideBy) + ' stack'
IJ.run(self.imp_ch2, "Divide...", divideArgs);
# convert to 8-bit will automatically scale, to turn this off use
# eval("script", "ImageConverter.setDoScaling(false)");
ImageConverter.setDoScaling(False)
# run("8-bit");
bPrintLog('converting to 8-bit with setDoScaling False', 4)
IJ.run(self.imp_ch2, "8-bit", '');
# print stats including intensity for the stack we just made
d = self.imp_ch2.getDimensions() # width, height, nChannels, nSlices, nFrames
stats = self.imp_ch2.getStatistics() # stats.min, stats.max
bPrintLog('ch2 dimensions w:' + str(d[0]) + ' h:' + str(d[1]) + ' channels:' + str(d[2]) + ' slices:' + str(d[3]) + ' frames:' + str(d[4]), 4)
bPrintLog('ch2 intensity min:' + str(stats.min) + ' max:' + str(stats.max), 4)
#ch2
#if ip_ch3:
if self.imp_ch3:
# bPrintLog('ch1 has ' + str(self.imp_ch3.getNSlices()) + ' slices', 4)
'''
stack_ch3 = ImageStack(width, height)
for fp in ip_ch3:
stack_ch3.addSlice(fp)
self.imp_ch3 = ImagePlus('xxx', stack_ch3)
self.imp_ch3.setProperty("Info", infoStr);
'''
# median filter
if globalOptions['medianFilter']>0:
bPrintLog('ch3: Running median filter: ' + str(globalOptions['medianFilter']), 4)
medianArgs = 'radius=' + str(globalOptions['medianFilter']) + ' stack'
IJ.run(self.imp_ch3, "Median...", medianArgs);
infoStr += 'bMedianFilter=' + str(globalOptions['medianFilter']) + '\n'
self.imp_ch3.setProperty("Info", infoStr)
if globalOptions['convertToEightBit']:
bPrintLog('converting to 8-bit by dividing image down and then convert to 8-bit with ImageConverter.setDoScaling(False)', 4)
bitDepth = 2^13
divideBy = bitDepth / 2^8
# divide the 13 bit image down to 8 bit
#run("Divide...", "value=32 stack");
bPrintLog('divideBy:' + str(divideBy), 4)
divideArgs = 'value=' + str(divideBy) + ' stack'
IJ.run(self.imp_ch3, "Divide...", divideArgs);
# convert to 8-bit will automatically scale, to turn this off use
# eval("script", "ImageConverter.setDoScaling(false)");
ImageConverter.setDoScaling(False)
# run("8-bit");
bPrintLog('converting to 8-bit with setDoScaling False', 4)
IJ.run(self.imp_ch3, "8-bit", '');
# print stats including intensity for the stack we just made
d = self.imp_ch3.getDimensions() # width, height, nChannels, nSlices, nFrames
stats = self.imp_ch3.getStatistics() # stats.min, stats.max
bPrintLog('ch3 dimensions w:' + str(d[0]) + ' h:' + str(d[1]) + ' channels:' + str(d[2]) + ' slices:' + str(d[3]) + ' frames:' + str(d[4]), 4)
bPrintLog('ch3 intensity min:' + str(stats.min) + ' max:' + str(stats.max), 4)
return 1
image.close()
#image = ImagePlus("test", twochannel_stack)
#fs = FileSaver(image)
#filepath = directory + "/" + filename + "_twochannel.tif"
#fs.saveAsTiff(filepath)
image_dapi = ImagePlus("dapi stack", dapi_stack)
image_dapi.show()
image_green = ImagePlus("green stack", green_stack)
image_green.show()
image = ImagePlus("two channel stack", twochannel_stack)
image.show()
if auto_thresh:
con = ImageConverter(image)
con.convertToGray8()
IJ.run(image, "Auto Local Threshold", "method=Bernsen radius=15 parameter_1=0 parameter_2=0 white stack")
#image = CompositeImage(image_two)
#image = IJ.getImage()
z_slices = image.getDimensions()[3] / 2
print("order=xyczt(default) channels=2 slices="+ str(z_slices) + " frames=1 display=Color", image.getDimensions())
image_two = HyperStackConverter.toHyperStack(image,2,z_slices,1)
image = CompositeImage(image_two)
image.show()
rt = run_comdet(image)
rt.save(directory+"/"+filename+"_results.csv" )
image = IJ.getImage()
# root = '/data/hanslovskyp/crack_from_john/substacks/03/'
# root = '/data/hanslovskyp/davi_toy_set/'
# root = '/data/hanslovskyp/davi_toy_set/substacks/remove/01/'
# root = '/data/hanslovskyp/davi_toy_set/substacks/replace_by_average/01/'
# root = '/data/hanslovskyp/davi_toy_set/substacks/shuffle/03/'
# root = '/data/hanslovskyp/jain-nobackup/234/'
# root = '/data/hanslovskyp/jain-nobackup/234_data_downscaled/crop-150x150+75+175/'
# root = '/data/hanslovskyp/jain-nobackup/234/substacks/crop-150x150+75+175/'
# root = '/data/hanslovskyp/forPhilipp/substacks/04/'
# root = '/data/hanslovskyp/forPhilipp/substacks/05/'
root = '/data/hanslovskyp/jain-nobackup/234_data_downscaled/crop-100x100+100+200/'
IJ.run("Image Sequence...", "open=%s/data number=%d sort" % ( root.rstrip(), nImages ) );
# imgSource = FolderOpener().open( '%s/data' % root.rstrip('/') )
imgSource = IJ.getImage()
# imgSource.show()
conv = ImageConverter( imgSource )
conv.convertToGray32()
stackSource = imgSource.getStack()
nThreads = 50
scale = 1.0
scaleZBy = 1.0 # 5.0
# stackMin, stackMax = ( None, 300 )
# xyScale = 0.25 # fibsem (crack from john) ~> 0.25
# xyScale = 0.1 # fibsem (crop from john) ~> 0.1? # boergens
nImages = stackSource.getSize()
xyScale = 1.0
doXYScale = False
matrixSize = nImages
matrixScale = 1
serializeCorrelations = False
deserializeCorrelations = not serializeCorrelations
def getGrayScaleImage(currIP, c, chanName, cfg):
if (cfg.hasValue(ELMConfig.upperLeftExclusionX)):
ulExclusionX = cfg.getValue(ELMConfig.upperLeftExclusionX)
else:
ulExclusionX = 0
if (cfg.hasValue(ELMConfig.upperLeftExclusionY)):
ulExclusionY = cfg.getValue(ELMConfig.upperLeftExclusionY)
else:
ulExclusionY = 0
if (cfg.hasValue(ELMConfig.lowerRightExclusionX)):
lrExclusionX = cfg.getValue(ELMConfig.lowerRightExclusionX)
else:
lrExclusionX = currIP.getWidth()
if (cfg.hasValue(ELMConfig.lowerRightExclusionY)):
lrExclusionY = cfg.getValue(ELMConfig.lowerRightExclusionY)
else:
lrExclusionY = currIP.getHeight()
imgType = currIP.getType()
if (chanName in cfg.getValue(
ELMConfig.chansToSkip)): # Don't process skip channels
currIP.close()
return None
elif imgType == ImagePlus.COLOR_RGB or imgType == ImagePlus.COLOR_256:
if (chanName == ELMConfig.BRIGHTFIELD):
toGray = ImageConverter(currIP)
toGray.convertToGray8()
elif (chanName == ELMConfig.BLUE) \
or (cfg.getValue(ELMConfig.chanLabel)[c] == ELMConfig.RED) \
or (cfg.getValue(ELMConfig.chanLabel)[c] == ELMConfig.GREEN): #
chanIdx = 2
if (cfg.getValue(ELMConfig.chanLabel)[c] == ELMConfig.RED):
chanIdx = 0
elif (cfg.getValue(ELMConfig.chanLabel)[c] == ELMConfig.GREEN):
chanIdx = 1
imgChanns = ChannelSplitter.split(currIP)
currIP.close()
currIP = imgChanns[chanIdx]
# Clear the Exclusion zone, so it doesn't mess with thresholding
imgProc = currIP.getProcessor()
imgProc.setColor(Color(0, 0, 0))
imgProc.fillRect(lrExclusionX, lrExclusionY, currIP.getWidth(),
currIP.getHeight())
imgProc.fillRect(0, 0, ulExclusionX, ulExclusionY)
elif (chanName == ELMConfig.YELLOW):
# Clear the Exclusion zone, so it doesn't mess with thresholding
imgProc = currIP.getProcessor()
imgProc.setColor(Color(0, 0, 0))
imgProc.fillRect(lrExclusionX, lrExclusionY, currIP.getWidth(),
currIP.getHeight())
imgProc.fillRect(0, 0, ulExclusionX, ulExclusionY)
# Create a new image that consists of the average of the red & green channels
title = currIP.getTitle()
width = currIP.getWidth()
height = currIP.getHeight()
newPix = ByteProcessor(width, height)
for x in range(0, width):
for y in range(0, height):
currPix = currIP.getPixel(x, y)
newPix.putPixel(x, y, (currPix[0] + currPix[1]) / 2)
currIP.close()
currIP = ImagePlus(title, newPix)
else:
print "ERROR: Unrecognized channel name! Name: " + chanName
currIP.close()
return None
elif imgType == ImagePlus.GRAY16 or imgType == ImagePlus.GRAY32 or imgType == ImagePlus.GRAY8:
if not imgType == ImagePlus.GRAY8:
toGray = ImageConverter(currIP)
toGray.convertToGray8()
else:
print "ERROR: Unrecognized channel name & image type! Channel: " + chanName + ", imgType: " + str(
imgType)
currIP.close()
return None
return currIP
def getThresholdedMask(currIP, c, z, t, chanName, cfg, wellPath, dbgOutDesc):
if (cfg.hasValue(ELMConfig.upperLeftExclusionX)):
ulExclusionX = cfg.getValue(ELMConfig.upperLeftExclusionX)
else:
ulExclusionX = 0
if (cfg.hasValue(ELMConfig.upperLeftExclusionY)):
ulExclusionY = cfg.getValue(ELMConfig.upperLeftExclusionY)
else:
ulExclusionY = 0
if (cfg.hasValue(ELMConfig.lowerRightExclusionX)):
lrExclusionX = cfg.getValue(ELMConfig.lowerRightExclusionX)
else:
lrExclusionX = currIP.getWidth()
if (cfg.hasValue(ELMConfig.lowerRightExclusionY)):
lrExclusionY = cfg.getValue(ELMConfig.lowerRightExclusionY)
else:
lrExclusionY = currIP.getHeight()
imgType = currIP.getType()
if (chanName in cfg.getValue(
ELMConfig.chansToSkip)): # Don't process skip channels
currIP.close()
return None
elif imgType == ImagePlus.COLOR_RGB or imgType == ImagePlus.COLOR_256:
if (chanName == ELMConfig.BRIGHTFIELD):
toGray = ImageConverter(currIP)
toGray.convertToGray8()
if cfg.params[ELMConfig.imgType] == "png":
darkBackground = True
else:
darkBackground = False
elif (chanName == ELMConfig.BLUE) \
or (cfg.getValue(ELMConfig.chanLabel)[c] == ELMConfig.RED) \
or (cfg.getValue(ELMConfig.chanLabel)[c] == ELMConfig.GREEN): #
chanIdx = 2
if (cfg.getValue(ELMConfig.chanLabel)[c] == ELMConfig.RED):
chanIdx = 0
elif (cfg.getValue(ELMConfig.chanLabel)[c] == ELMConfig.GREEN):
chanIdx = 1
imgChanns = ChannelSplitter.split(currIP)
currIP.close()
currIP = imgChanns[chanIdx]
# Clear the Exclusion zone, so it doesn't mess with thresholding
imgProc = currIP.getProcessor()
imgProc.setColor(Color(0, 0, 0))
imgProc.fillRect(lrExclusionX, lrExclusionY, currIP.getWidth(),
currIP.getHeight())
imgProc.fillRect(0, 0, ulExclusionX, ulExclusionY)
darkBackground = True
elif (chanName == ELMConfig.YELLOW):
# Clear the Exclusion zone, so it doesn't mess with thresholding
imgProc = currIP.getProcessor()
imgProc.setColor(Color(0, 0, 0))
imgProc.fillRect(lrExclusionX, lrExclusionY, currIP.getWidth(),
currIP.getHeight())
imgProc.fillRect(0, 0, ulExclusionX, ulExclusionY)
# Create a new image that consists of the average of the red & green channels
title = currIP.getTitle()
width = currIP.getWidth()
height = currIP.getHeight()
newPix = ByteProcessor(width, height)
for x in range(0, width):
for y in range(0, height):
currPix = currIP.getPixel(x, y)
newPix.putPixel(x, y, (currPix[0] + currPix[1]) / 2)
currIP.close()
currIP = ImagePlus(title, newPix)
darkBackground = True
else:
print "ERROR: Unrecognized channel name! Name: " + chanName
currIP.close()
return None
elif imgType == ImagePlus.GRAY16 or imgType == ImagePlus.GRAY32 or imgType == ImagePlus.GRAY8:
if (chanName == ELMConfig.BRIGHTFIELD):
if cfg.params[ELMConfig.imgType] == "png":
darkBackground = True
else:
darkBackground = False
else:
darkBackground = True
if not imgType == ImagePlus.GRAY8:
toGray = ImageConverter(currIP)
toGray.convertToGray8()
else:
print "ERROR: Unrecognized channel name & image type! Channel: " + chanName + ", imgType: " + str(
imgType)
currIP.close()
return None
WindowManager.setTempCurrentImage(currIP)
if cfg.getValue(ELMConfig.debugOutput):
IJ.saveAs('png',
os.path.join(wellPath, "Processing_" + dbgOutDesc + ".png"))
upperThreshImg = currIP.duplicate()
# If threshold value is set, use it
if (cfg.hasValue(ELMConfig.imageThreshold)):
thresh = cfg.getValue(ELMConfig.imageThreshold)
if (darkBackground):
currIP.getProcessor().setThreshold(thresh, 255,
ImageProcessor.NO_LUT_UPDATE)
else:
currIP.getProcessor().setThreshold(0, thresh,
ImageProcessor.NO_LUT_UPDATE)
else: # Otherise, automatically compute threshold
threshMethod = "Default"
if cfg.hasValue(ELMConfig.thresholdMethod):
threshMethod = cfg.getValue(ELMConfig.thresholdMethod)
currIP.getProcessor().setAutoThreshold(threshMethod, darkBackground,
ImageProcessor.NO_LUT_UPDATE)
threshRange = currIP.getProcessor().getMaxThreshold(
) - currIP.getProcessor().getMinThreshold()
#print "\t\tZ = " + str(z) + ", T = " + str(t) + ", chan " + chanName + ": Using default threshold of minThresh: " + str(currIP.getProcessor().getMinThreshold()) + ", maxThresh: " + str(currIP.getProcessor().getMaxThreshold())
if currIP.getType() != ImagePlus.GRAY8:
print "\tChannel " + chanName + " is not GRAY8, instead type is %d" % currIP.getType(
)
if threshRange > cfg.getValue(ELMConfig.maxThreshRange):
if (cfg.hasValue(ELMConfig.defaultThreshold)):
thresh = cfg.getValue(ELMConfig.defaultThreshold)
print "\t\tZ = " + str(z) + ", T = " + str(
t
) + ", chan " + chanName + ": Using default threshold of " + str(
thresh) + ", minThresh: " + str(currIP.getProcessor(
).getMinThreshold()) + ", maxThresh: " + str(
currIP.getProcessor().getMaxThreshold())
if (darkBackground):
currIP.getProcessor().setThreshold(
thresh, 255, ImageProcessor.NO_LUT_UPDATE)
else:
currIP.getProcessor().setThreshold(
0, thresh, ImageProcessor.NO_LUT_UPDATE)
else:
print "\t\tZ = " + str(z) + ", T = " + str(
t
) + ", chan " + chanName + ": Ignored Objects due to threshold range! minThresh: " + str(
currIP.getProcessor().getMinThreshold(
)) + ", maxThresh: " + str(
currIP.getProcessor().getMaxThreshold())
currIP.close()
return None
IJ.run(currIP, "Convert to Mask", "")
# Clear out exclusion zones
imgProc = currIP.getProcessor()
imgProc.fillRect(lrExclusionX, lrExclusionY, currIP.getWidth(),
currIP.getHeight())
imgProc.fillRect(0, 0, ulExclusionX, ulExclusionY)
IJ.run(currIP, "Close-", "")
# Brightfield has an additional thresholding step
if cfg.getValue(ELMConfig.chanLabel)[c] == ELMConfig.BRIGHTFIELD:
if cfg.getValue(ELMConfig.debugOutput):
IJ.saveAs(
'png', os.path.join(wellPath,
"OrigMask_" + dbgOutDesc + ".png"))
upperThresh = 255 * 0.95
upperThreshImg.getProcessor().setThreshold(
upperThresh, 255, ImageProcessor.NO_LUT_UPDATE)
IJ.run(upperThreshImg, "Convert to Mask", "")
IJ.run(upperThreshImg, "Close-", "")
if cfg.getValue(ELMConfig.debugOutput):
WindowManager.setTempCurrentImage(upperThreshImg)
IJ.saveAs(
'png',
os.path.join(wellPath,
"UpperThreshMask_" + dbgOutDesc + ".png"))
ic = ImageCalculator()
compositeMask = ic.run("OR create", currIP, upperThreshImg)
IJ.run(compositeMask, "Close-", "")
currIP.close()
currIP = compositeMask
WindowManager.setTempCurrentImage(currIP)
if cfg.getValue(ELMConfig.debugOutput):
WindowManager.setTempCurrentImage(currIP)
IJ.saveAs('png', os.path.join(wellPath,
"Binary_" + dbgOutDesc + ".png"))
upperThreshImg.close()
return currIP
for channel in range(1, (number_of_channels_in_mouse + 1)):
channel_files = getChannelFiles(MouseIDFiles, channel)
# get the full path
chf_fpaths = [path.join(In_dir, x) for x in channel_files]
# get the minimum and maximum pixel value
min_pixval, max_pixval = get_enhance_bounds(
chf_fpaths, low_theshold, high_threshold)
IJ.log("Found pixel bounds " + str(min_pixval) + " and " +
str(max_pixval) + " for channel " + str(channel))
counter = 1
for chfile in chf_fpaths:
# open file
ch_img = Opener().openImage(chfile)
ch_tit = ch_img.getTitle()
# adjust contrast
ch_img.getProcessor().setMinAndMax(min_pixval, max_pixval)
# convert to 8-bit (which also applies the contrast)
ImageConverter(ch_img).convertToGray8()
# save
IJ.saveAsTiff(ch_img, path.join(Out_dir, ch_tit))
# close and flush
ch_img.close()
ch_img.flush()
print("Image " + str(counter) + " of " + str(len(chf_fpaths)) +
" processed")
counter += 1
IJ.log('Mouse ' + MouseID + ' processed')
print("DONE, find your results in " + Out_dir)
for al in als:
al.setProperty("label", None)
al.setVisible(False, True)
for i in range(1, Label_Max + 1):
NN = "N" + str(i)
bFound = False
for al in als:
if NN == al.title:
bFound = True
al.setProperty("label", str(i))
break
if (not bFound):
print(NN)
print("miss")
File(savDir).mkdirs()
for al in als:
if al.title.startswith('N'):
print("processing: " + al.title)
al.setVisible(True, True)
al.exportAsLabels([al], None, 1, 0, int(ls.getDepth()), True, False,
False)
al.setVisible(False, True)
imp = WM.getImage("Labels")
ImageConverter(imp).convertToGray16()
FileSaver(imp).saveAsTiff(savDir + al.title + ".tif")
imp.close()
print("done")
subfolders = [""]
for subfolder in subfolders:
#Opens each image
for filename in os.listdir(inputDirectory + subfolder):
imp = IJ.openImage(inputDirectory + subfolder + '/' + filename)
if imp:
# 10X objective
IJ.run(imp, "Properties...", "channels=1 slices=1 frames=1 unit=um pixel_width=" +str(pix_width)+ " pixel_height=" +str(pix_height)+" voxel_depth=25400.0508001") # Change to a GUI option later?
# Threshold, fills hole and watershed
ic = ImageConverter(imp);
ic.convertToGray8();
IJ.setAutoThreshold(imp, "Default dark")
if thresholdMode:
imp.show()
IJ.run("Threshold...")
WaitForUserDialog("Title", "Adjust threshold").show()
IJ.run(imp, "Convert to Mask", "")
if not inverted:
IJ.run(imp, "Invert", "")
IJ.run(imp, "Fill Holes", "")
if watershedMode:
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)
os.mkdir(transformation_outputdir)
############################################ preprocessing
# open EM image
EM = IJ.openImage(EMfilepath)
# equalize histogramm
IJ.run(EM, "Enhance Contrast...", "saturated=0.3 equalize")
# resize EM image
ip = EM.getProcessor()
width = ip.getWidth()
height = ip.getHeight()
EM.setProcessor(EM.getTitle(), EM.getProcessor().resize(256, 256))
ImageConverter(EM).convertToGray8()
# create stack z=2
EMstack = ImageStack(EM.getWidth(), EM.getHeight())
EMstack.addSlice(str(1), EM.getProcessor())
EMstack.addSlice(str(2), EM.getProcessor())
EM = ImagePlus(EM.getTitle(), EMstack)
# save EM image
saveEMfilepath = os.path.join(workdir, "EM.tif")
fs = FileSaver(EM)
fs.saveAsTiff(saveEMfilepath)
print("(- )preprocessing done")
############################################ predict image
def natural_keys(text):
return [ atoi(c) for c in re.split('(\d+)', text) ]
os.chdir(mypath)
alist = os.listdir(mypath)
alist.sort(key=natural_keys)
imp = IJ.openImage(mypath + alist[0])
signedpix = imp.getProcessor().toFloat(1, None)
width=signedpix.getWidth()
height=signedpix.getHeight()
stack = ImageStack(width, height)
i=0
for file in alist[:10]:
print("Current File Being Processed is: " + file)
imp = IJ.openImage(mypath + file)
signedpix = imp.getProcessor().toFloat(1, None)
stack.addSlice(str(i), signedpix)
i = i + 1
imp = ImagePlus("", stack)
ImageConverter(imp).convertToGray8()
imp.show()
IJ.runPlugIn("ij3d.ImageJ3DViewer", "")
dstpath = os.path.join(workDir, dstFile)
# function: thresholding
def threshold(p):
if p > th[i][0] and p < th[i][1]:
return p
else:
return 0.0
# open image (RGB)
imp = IJ.openImage(srcpath)
# convert
ImageConverter(imp).convertToLab()
## imageProcessor
ip = imp.getProcessor()
# FloatProcessor by channel
chpx = [] # channel pixels
filtered = [] # filtered FloatProcessor
for i in range(3):
chpx.append(ip.toFloat(i, None).getPixels())
_filtered = [threshold(p) for p in chpx[i]] # filtered pixels
filtered.append(FloatProcessor(ip.width, ip.height, _filtered, None))
# save each channels
for i in range(3):
def main():
#print (sys.version_info) # debug
#print(sys.path) # debug
data_root = r'C:\Users\dougk\Desktop\test'
# debug
output_root = r'C:\Users\dougk\Desktop\test'
#debug
#default_directory = r'C:\\Users\\Doug\\Desktop\\test';
#data_root, output_root = file_location_chooser(default_directory);
if (data_root is None) or (output_root is None):
raise IOError("File location dialogs cancelled!")
timestamp = datetime.strftime(datetime.now(), "%Y-%m-%d %H.%M.%S")
output_path = os.path.join(output_root, (timestamp + " output"))
for file_path in filterByFileType(os.listdir(data_root), '.tif'):
subfolder_name = os.path.splitext(file_path)[0]
output_subfolder = os.path.join(output_path, subfolder_name)
print(output_subfolder)
os.makedirs(output_subfolder)
imps = bf.openImagePlus(os.path.join(data_root, file_path))
imp = imps[0]
imp.show()
h = imp.height
w = imp.width
slices = imp.getNSlices()
channels = imp.getNChannels()
frames = imp.getNFrames()
# rotation step - since using multiples of 90, TransformJ.Turn is more efficient
IJ.run("Enhance Contrast", "saturated=0.35")
angleZ = 1
while ((angleZ % 90) > 0):
gd = GenericDialog("Rotate?")
gd.addMessage(
"Define rotation angle - increments of 90. Apical at top")
gd.addNumericField("Rotation angle", 0, 0)
gd.showDialog()
angleZ = int(gd.getNextNumber())
if (angleZ > 1):
IJ.run("TransformJ Turn",
"z-angle=" + str(angleZ) + " y-angle=0 x-angle=0")
imp.close()
imp = WindowManager.getCurrentImage()
imp.setTitle(file_path)
# trim time series
IJ.run("Enhance Contrast", "saturated=0.35")
imp.setDisplayMode(IJ.COLOR)
WaitForUserDialog(
"Scroll to the first frame of the period of interest and click OK"
).show()
start_frame = imp.getT()
WaitForUserDialog(
"Scroll to the last frame of the period of interest and click OK"
).show()
end_frame = imp.getT()
trim_imp = Duplicator().run(imp, 1, channels, 1, slices, start_frame,
end_frame)
imp.close()
trim_imp.show()
dup_imp = Duplicator().run(trim_imp)
# create images to process and find bounds for
dup_imps = ChannelSplitter().split(dup_imp)
myo_imp = dup_imps[1]
mem_imp = dup_imps[0]
FileSaver(myo_imp).saveAsTiffStack(
os.path.join(output_subfolder, "myosin_channel.tif"))
FileSaver(mem_imp).saveAsTiffStack(
os.path.join(output_subfolder, "membrane_channel.tif"))
# set basal bounds
myo_imp.show()
ImageConverter(myo_imp).convertToGray8()
frames = myo_imp.getNFrames()
gb = GaussianBlur()
for fridx in range(0, frames):
myo_imp.setSliceWithoutUpdate(fridx + 1)
ip = myo_imp.getProcessor()
gb.blurGaussian(ip, 5.0, 1.0, 0.02)
# assymmetrical Gaussian
IJ.run(myo_imp, "Convert to Mask",
"method=Otsu background=Dark calculate")
IJ.run("Despeckle", "stack")
title = myo_imp.getTitle()
# assume that first frame is good quality image...
basal_edges = find_basal_edges(myo_imp)
#myo_imp.hide()
mem_imp.hide()
# draw some edges for checking
roim = RoiManager()
xs = [x for x in range(1,
trim_imp.getWidth() + 1)]
trim_imp.show()
for fridx in range(0, myo_imp.getNFrames()):
trim_imp.setPosition(2, 1, fridx + 1)
IJ.run("Enhance Contrast", "saturated=0.35")
roi = PolygonRoi(xs, basal_edges[fridx], Roi.POLYLINE)
trim_imp.setRoi(roi)
roim.addRoi(roi)
def auto_threshold(imp_in, str_thresh, bScale=False):
"""
auto_threshold_otsu(imp_in, str_thresh="Otsu", bScale=True)
Compute an autothreshold for an image. Adapted from
http://wiki.cmci.info/documents/120206pyip_cooking/python_imagej_cookbook
Parameters
----------
imp_in ImagePlus
The image to threshold
str_thresh String (Default: Default)
The threshold: Otsu, Huang, IsoData, Intermodes, Li,
MaxEntropy, Mean, MinError, Minimum, Moments, Percentile,
RenyiEntropy, Shanbhag, Triangle, Yen, or Default
bScale Boolean (Default: False)
Return
------
imp_out ImagePlus
The binary image
thr_val integer
The threshold value
"""
ti = imp_in.getShortTitle()
imp = imp_in.duplicate()
hist = imp.getProcessor().getHistogram()
if (str_thresh == "Otsu"):
lowTH = Auto_Threshold.Otsu(hist)
elif (str_thresh == "Huang"):
lowTH = Auto_Threshold.Huang(hist)
elif (str_thresh == "Intermodes"):
lowTH = Auto_Threshold.Intermodes(hist)
elif (str_thresh == "IsoData"):
lowTH = Auto_Threshold.IsoData(hist)
elif (str_thresh == "Li"):
lowTH = Auto_Threshold.Li(hist)
elif (str_thresh == "MaxEntropy"):
lowTH = Auto_Threshold.MaxEntropy(hist)
elif (str_thresh == "Mean"):
lowTH = Auto_Threshold.Mean(hist)
elif (str_thresh == "MinError"):
lowTH = Auto_Threshold.MinError(hist)
elif (str_thresh == "Minimum"):
lowTH = Auto_Threshold.Minimum(hist)
elif (str_thresh == "Moments"):
lowTH = Auto_Threshold.Moments(hist)
elif (str_thresh == "Percentile"):
lowTH = Auto_Threshold.Percentile(hist)
elif (str_thresh == "RenyiEntropy"):
lowTH = Auto_Threshold.RenyiEntropy(hist)
elif (str_thresh == "Shanbhag"):
lowTH = Auto_Threshold.Shanbhag(hist)
elif (str_thresh == "Triangle"):
lowTH = Auto_Threshold.Triangle(hist)
elif (str_thresh == "Yen"):
lowTH = Auto_Threshold.Yen(hist)
else:
lowTH = Auto_Threshold.Default(hist)
imp.getProcessor().threshold(lowTH)
imp.setDisplayRange(0, lowTH + 1)
ImageConverter.setDoScaling(bScale)
IJ.run(imp, "8-bit", "")
imp.setDisplayRange(0, 255)
imp.setTitle(ti + "-bin-" + str_thresh)
return ([imp, lowTH])
def runOneTif(tifPath, dstTifPath):
bPrintLog('=== runOneTif processing tif:' + tifPath, 3)
bPrintLog('Loading file...', 3)
imp = IJ.openImage(tifPath)
if imp is None:
bPrintLog('ERROR: could not open image from file:' + tifPath, 3)
return 0
logStr = 'done loading file: ' + str(imp.width) + ' ' + str(imp.height) + ' ' + str(imp.getNSlices())
bPrintLog(logStr, 3)
numSlices = imp.getNSlices()
if numSlices>1:
pass
else:
bPrintLog('ERROR: number of slices must be more than one, file: ' + tifPath)
return 0
bPrintLog('numSlices: ' + str(numSlices), 3)
infoStr = imp.getProperty("Info") #get all tags
#print infoStr
if infoStr is None:
infoStr = ''
infoStr += 'bAverageFrames=v0.1\n'
imp.setProperty("Info", infoStr)
imp.show()
impWin = imp.getTitle()
#
# start body
#infer type of file from
# get the actual bit depth used (e.g. ScanImage is 11 bit, Prairie is 13 bit)
header = bParseHeader(imp)
b_sequence = ''
if 'b_sequence' in header:
b_sequence = str(header['b_sequence'])
bPrintLog('b_sequence: ' + b_sequence, 3)
madeAverage = 0
# if numSlices is not divisable by gNumToAverage then chop a few slices off bottom/end
if b_sequence.startswith('TSeries'):
if globalOptions['gNumToAverage'] > 1:
numToRemove = numSlices % globalOptions['gNumToAverage']
if numToRemove > 0:
bPrintLog('Removing bottom slices: ' + str(numToRemove), 3)
# run("Slice Remover", "first=3 last=5 increment=1");
removeArgs = 'first=' + str(numSlices-numToRemove+1) + ' last=' + str(numSlices) + ' increment=1'
IJ.run('Slice Remover', removeArgs)
numSlices = imp.getNSlices()
bPrintLog('numSlices: ' + str(numSlices), 3)
#fix this: if stack is really short this will not be taken
if (numSlices > globalOptions['gNumToAverage']):
bPrintLog('Taking average of ' + str(globalOptions['gNumToAverage']) + ' slices from ' + str(numSlices), 3)
stackRegParams = 'projection=[Average Intensity] group=' + str(globalOptions['gNumToAverage'])
IJ.run('Grouped Z Project...', stackRegParams) # makes window AVG_
madeAverage = 1
avgWindow = 'AVG_' + impWin
avgImp = WindowManager.getImage(avgWindow)
avgSlices = avgImp.getNSlices()
# Grouped Z PRoject swaps slices for frames?
tmpSlices = avgImp.getNSlices()
tmpFrames = avgImp.getNFrames()
if tmpFrames > 1:
newSlices = tmpFrames
newFrames = tmpSlices
nChannels = 1
bPrintLog('Swaping frames for slices after grouped z',3)
bPrintLog('newSlices=' + str(newSlices) + ' newFrames='+str(newFrames), 4)
avgImp.setDimensions(nChannels, newSlices, newFrames)
infoStr += 'gNumToAverage=' + str(globalOptions['gNumToAverage']) + '\n'
# I want to adjust the framePeriod, prairie would be 'b_framePeriod'
avgImp.setProperty("Info", infoStr)
else:
avgImp = imp
avgSlices = numSlices
else:
bPrintLog('Not taking average of sequence: ' + b_sequence,3)
avgImp = imp
avgSlices = numSlices
if globalOptions['medianFilter']>0:
bPrintLog('Running median filter: ' + str(globalOptions['medianFilter']), 3)
medianArgs = 'radius=' + str(globalOptions['medianFilter']) + ' stack'
IJ.run(avgImp, "Median...", medianArgs);
infoStr += 'bMedianFilter=' + str(globalOptions['medianFilter']) + '\n'
avgImp.setProperty("Info", infoStr)
# convert to 8 bit
# 1) read bit depth from header (e.g. 2^13)
# 2) do math on image and convert to 8-bit
# run("Divide...", "value=32 stack");
if globalOptions['gConvertToEightBit']:
bPrintLog('converting to 8-bit by dividing image down and then convert to 8-bit with ImageConverter.setDoScaling(False)', 3)
bitDepth = 2^13
divideBy = bitDepth / 2^8
# divide the 13 bit image down to 8 bit
#run("Divide...", "value=32 stack");
bPrintLog('divideBy:' + str(divideBy), 3)
divideArgs = 'value=' + str(divideBy) + ' stack'
IJ.run(avgImp, "Divide...", divideArgs);
# convert to 8-bit will automatically scale, to turn this off use
# eval("script", "ImageConverter.setDoScaling(false)");
ImageConverter.setDoScaling(False)
# run("8-bit");
bPrintLog('converting to 8-bit with setDoScaling False', 3)
IJ.run(avgImp, "8-bit", '');
bPrintLog('Saving stack with ' + str(avgSlices) + ' slices:' + dstTifPath, 3)
fs = FileSaver(avgImp)
if avgSlices>1:
fs.saveAsTiffStack(dstTifPath)
else:
fs.saveAsTiff(dstTifPath)
if madeAverage:
avgImp.changes = 0
avgImp.close()
imp.changes = 0
imp.close()
# end body
#
# why was this here
#imp.changes = 0
#imp.close()
return 1
def analyse(cwd, user, imagefolder, stats, experiments, multi, Rloc2,
subfoldernames, names, statsfolderPath, cwdR):
""" Main image analysis
Gets user image analysis settings from the .csv file.
If multiple experiments have been selected by the user
(multi) each subfolder will be looped through. A nested
loop will then interate through each .tif image and
analyse. A .csv file will be produced for each folder
analysed with the name of each image and its % neurite
density and % myelination. A summary csv file will also
be produced with the average % neurite density and %
myelination for each subfolder. If statistical analysis
has been selected (stats) then MyelinJ's Rscript will be
run via the command line. If multple experiments is not
selected then all of the images within the selected
folder will be analysed together and no summary .csv will
be produced.
Independ of the analysis settings defined, a processed
myelin channel image and a processed neurite channel
image will be saved. The images can be any number of
subdirectories (folders within folders).
Parameters
----------
cwd : string
Path for current working directory (location of
MyelinJ folder in Fiji).
user: string
User name
imagefolder: string
Path to .tiff image folder(s) defined by user.
stats: boolean
Perform statistical analysing using R?
experiments: 2D list of strings
list of all the subfolders (experiments) that are in each
experimental condition.
multi: boolean
Analyse multiple experiments?
Rloc2: string
file path to Rscript location
subfoldernames: string
name of each subfolder which denoates each individual
experiment, if multple experiments are being analysed.
names: array
array of textfields for each experimental condition defined by
user. User will enter the name of each experimental condition.
statsfolderPath: string
file path to the create statsfolder.
cwdR: string
file path to MyelinJstats.R
"""
# read settings from the user name CSV
bg = False
readsettings = []
imagenames = []
neuritedensity = []
myelinoverlay = []
myelinaverage2 = []
neuriteaverage2 = []
root = cwd
filename = user
fullpath = os.path.join(root, filename)
f = open(fullpath, 'rb')
readCSV = csv.reader(f)
for row in readCSV:
readsettings.append(row[0])
readsettings.append(row[1])
readsettings.append(row[2])
readsettings.append(row[3])
readsettings.append(row[4])
readsettings.append(row[5])
readsettings.append(row[6])
f.close()
i = 0
for i in range(len(subfoldernames)):
# if multiple experimental conditions has been selected each folder is treated as a
# separate experiment and looped through separately otherwise all folders will be
# treated as one experiment this only works for sub directories within the main folder.
# Further folders will be ignored (each image can be in its own folder for example)
if multi is True:
# if multiple experiments are being analysed the file path is changed to the
# current subfolder
settings2 = os.path.join(imagefolder, subfoldernames[i])
if "Windows" in OS:
settings2 = settings2 + "\\"
elif "Mac" in OS:
settings2 = settings2 + "/"
else:
settings2 = imagefolder
# loop through all .tiff files in location
for root, dirs, files in os.walk(settings2):
for name in files:
if name.endswith((".tif")):
imagenames.append(os.path.join(name))
# open .tiff image, split channels and
# convert to 8bit grey scale.
imp = IJ.openImage(os.path.join(root, name))
g = int(readsettings[4])
r = int(readsettings[5])
imp = ChannelSplitter.split(imp)
green = imp[g]
red = imp[r]
conv = ImageConverter(red)
conv.convertToGray8()
conv = ImageConverter(green)
conv.convertToGray8()
# thresholding to select cell bodies
green2 = green.duplicate()
if (readsettings[0] != "0") or (readsettings[1] != "0"):
bg = True
IJ.setAutoThreshold(green2, readsettings[2])
IJ.setRawThreshold(green2, int(readsettings[0]),
int(readsettings[1]), None)
Prefs.blackBackground = True
IJ.run(green2, "Convert to Mask", "")
IJ.run(green2, "Invert LUT", "")
if readsettings[7] != "0":
IJ.run(green2, "Make Binary", "")
IJ.run(
green2, "Remove Outliers...", "radius=" +
readsettings[7] + " threshold=50 which=Dark")
# CLAHE and background subtraction
if readsettings[8] == "True":
mpicbg.ij.clahe.Flat.getFastInstance().run(
green, 127, 256, 3, None, False)
if readsettings[9] == "True":
calc = ImageCalculator()
green = calc.run("Subtract create", green, red)
elif readsettings[6] == "True":
IJ.run(green, "Subtract Background...", "rolling=50")
if readsettings[10] != "0":
IJ.run(green, "Subtract...",
"value=" + readsettings[10])
# run frangi vesselness
pixelwidth = str(green.getCalibration().pixelWidth)
IJ.run(
green, "Frangi Vesselness (imglib, experimental)",
"number=1 minimum=" + pixelwidth + " maximum=" +
pixelwidth)
green = IJ.getImage()
# convert frangi vesselness image to 8bit grey scale
conv = ImageConverter(green)
conv.convertToGray8()
IJ.run(green, "Convert to Mask", "")
# remove cell bodies
if bg is True:
green = ImageCalculator().run("Subtract create", green,
green2)
# run grey scale morphology filter from MorpholibJ
if readsettings[11] != "0":
green = green.getProcessor()
algo = BoxDiagonalOpeningQueue()
algo.setConnectivity(4)
result = algo.process(green, int(readsettings[11]))
green = ImagePlus("result", result)
IJ.run(green, "Invert LUT", "")
if len(readsettings) > 14:
# sparse neurite image analysis
if readsettings[15] == "True":
IJ.run(
red, "Enhance Local Contrast (CLAHE)",
"blocksize=127 histogram=256 maximum=3 mask=*None* fast_(less_accurate)"
)
if readsettings[14] == "True":
IJ.run(red, "Subtract Background...", "rolling=50")
IJ.setAutoThreshold(red, readsettings[16])
IJ.setRawThreshold(red, int(readsettings[17]),
int(readsettings[18]), None)
IJ.run(red, "Convert to Mask", "")
IJ.run(red, "Invert LUT", "")
else:
# dense neurite image analysis
IJ.run(
red, "Normalize Local Contrast",
"block_radius_x=40 block_radius_y=40 standard_deviations="
+ readsettings[12] + " center stretch")
IJ.run(red, "Auto Threshold", "method=Default white")
IJ.run(red, "Invert LUT", "")
if readsettings[3] == "True":
IJ.run(red, "Despeckle", "")
IJ.saveAs(red, "Jpeg", settings2 + name + "neurites")
# get number of neurite pixels
# get number of neurite pixels
statsneurite = red.getProcessor()
statsneurite = statsneurite.getHistogram()
neuritedensity.append(statsneurite[255])
IJ.saveAs(green, "Jpeg", settings2 + name + "myelinFinal")
# get number of myelin pixels
statsmyelin = green.getProcessor()
statsmyelin = statsmyelin.getHistogram()
myelinoverlay.append(statsmyelin[255])
closeallimages()
# get pixel total of image
whitepixels = (statsneurite[0])
blackpixels = (statsneurite[255])
totalpixels = whitepixels + blackpixels
totalpixels = [totalpixels] * len(neuritedensity)
# for each image calculate % myelination as number of myelin pixels
# divided by the number of neurite pixels * 100
myelinoverlay = [
x1 / x2 * 100 for (x1, x2) in zip(myelinoverlay, neuritedensity)
]
myelinaverage = sum(myelinoverlay) / len(myelinoverlay)
myelinaverage2.append(myelinaverage)
# for each image calculate % neurite density as neurite pixels divided
# by the total number of pixels in the image * 100.
neuritedensity = [
x1 / x2 * 100 for (x1, x2) in zip(neuritedensity, totalpixels)
]
neuriteaverage = sum(neuritedensity) / len(neuritedensity)
neuriteaverage2.append(neuriteaverage)
name = "Image names"
green = "% myelination"
red = "% neurite density"
imagenames = [name] + imagenames
neuritedensity = [red] + neuritedensity
myelinoverlay = [green] + myelinoverlay
result = []
result.append(imagenames)
result.append(neuritedensity)
result.append(myelinoverlay)
root = settings2
filename = "Results.csv"
fullpath = os.path.join(root, filename)
f = open(fullpath, 'wb')
writer = csv.writer(f)
for d in range(len(result)):
row = [result[d]]
writer.writerows(row)
f.close()
# must be reset to 0 for each iteration.
y = 0
r = 0
# if statistical analysis is being performed the results .csv file
# is also saved to a subfolder within the statistical analysis folder
# which denotes the experimental condition the results belong to.
if stats is True:
# nested for loop to identify correct experimental condition
# for the current subfolder being analysed.
for y in range(0, len(experiments)):
for r in range(0, len(experiments[0])):
if experiments[y][r] == subfoldernames[i]:
if "Windows" in OS:
root = imagefolder + "\\statistical analysis\\" + names[
y].getText()
elif "Mac" in OS:
root = imagefolder + "/statistical analysis/" + names[
y].getText()
filename = subfoldernames[i] + ".csv"
fullpath = os.path.join(root, filename)
f = open(fullpath, 'wb')
writer = csv.writer(f)
for e in range(len(result)):
row = [result[e]]
writer.writerows(row)
f.close()
break
cwd2 = os.getcwd()
for files in os.listdir(cwd2):
if files.endswith(".csv"):
os.remove(os.path.join(cwd2, files))
imagenames = []
myelinoverlay = []
neuritedensity = []
# create .csv summary sheet with average % neurite density
# and average % myelination for each subfolder (experiment).
if multi is True:
name = "Folder name"
imagenames = [name] + subfoldernames
neuritedensity = [red] + neuriteaverage2
myelinoverlay = [green] + myelinaverage2
result = []
result.append(imagenames)
result.append(neuritedensity)
result.append(myelinoverlay)
if "Windows" in OS:
root = imagefolder + "\\"
elif "Mac" in OS:
root = imagefolder + "/"
filename = "Result-Summary.csv"
fullpath = os.path.join(root, filename)
f = open(fullpath, 'wb')
writer = csv.writer(f)
for p in range(len(result)):
row = [result[p]]
writer.writerows(row)
f.close()
imagenames = []
myelinoverlay = []
neuritedensity = []
# Run Rscript for statistical analysis via the command line
if stats is True:
cmd = Rloc2 + " " + cwdR + " " + statsfolderPath
Runtime.getRuntime().exec(cmd)
Finished()
def runOneTif(tifPath, dstTifPath):
bPrintLog('=== runOneTif processing tif:' + tifPath, 3)
bPrintLog('Loading file...', 3)
imp = IJ.openImage(tifPath)
if imp is None:
bPrintLog('ERROR: could not open image from file:' + tifPath, 3)
return 0
logStr = 'done loading file: ' + str(imp.width) + ' ' + str(
imp.height) + ' ' + str(imp.getNSlices())
bPrintLog(logStr, 3)
numSlices = imp.getNSlices()
bPrintLog('numSlices: ' + str(numSlices), 3)
infoStr = imp.getProperty("Info") #get all tags
if infoStr is None:
infoStr = ''
infoStr += 'bMaxProject=v0.1\n'
imp.setProperty("Info", infoStr)
imp.show()
impWin = imp.getTitle()
# BODY
# get the bit depth form opened imp
impBitDepth = imp.getBitDepth()
bPrintLog('image bit depth:' + str(impBitDepth), 3)
# get the actual bit depth used (e.g. ScanImage is 11 bit, Prairie is 13 bit)
header = bParseHeader(imp)
actualBitDepth = impBitDepth
if 'b_bitDepth' in header:
actualBitDepth = int(header['b_bitDepth'])
bPrintLog('actual bit depth:' + str(actualBitDepth), 3)
made8bit = 0
if impBitDepth == 8:
made8bit = 1
else:
made8bit = 1
if 0:
divideBy = math.pow(2, actualBitDepth) / math.pow(
2, 8) # divide the 13 bit or 11 bit image down to 8 bit
bPrintLog('diving by:' + str(divideBy), 3)
bPrintLog(
'converting to 8-bit by dividing image by ' + str(divideBy) +
' and then convert to 8-bit with ImageConverter.setDoScaling(False)',
3)
#run("Divide...", "value=32 stack");
divideArgs = 'value=' + str(divideBy) + ' stack'
IJ.run(imp, "Divide...", divideArgs)
# convert to 8-bit will automatically scale, to turn this off use
# eval("script", "ImageConverter.setDoScaling(false)");
# 20170810 was this
#ImageConverter.setDoScaling(False)
ImageConverter.setDoScaling(True)
# run("8-bit");
bPrintLog('converting to 8-bit with setDoScaling False', 3)
IJ.run(imp, "8-bit", '')
#does this in place, no new window
# save
bPrintLog('Saving stack with ' + str(numSlices) + ' slices:' + dstTifPath,
3)
fs = FileSaver(imp)
if numSlices > 1:
fs.saveAsTiffStack(dstTifPath)
else:
fs.saveAsTiff(dstTifPath)
# END BODY
# close original
imp.changes = 0
imp.close()
from ij.plugin import RGBStackMerge
from loci.plugins import BF
from loci.plugins.in import ImporterOptions
import os,sys
path = '/Users/justin/UTS/Research/microscopy/'
filetab = '%s/ijfiles' %path
for l in open(filetab):
f = l.strip().split()
out = f[0]
ch1 = '%s/%s' %(path,f[1])
ch2 = '%s/%s' %(path,f[2])
print('%s: opening %s and %s' %(out,ch1,ch2))
ch1 = ij.IJ.openImage(ch1)
conv = ImageConverter(ch1)
conv.convertToGray16()
# ch1.show()
ch2 = ij.IJ.openImage(ch2)
conv = ImageConverter(ch2)
conv.convertToGray16()
# ch2.show()
# ch1.close()
# ch2.close()
mrg = RGBStackMerge.mergeChannels([ch1, ch2], False)
# mrg.show()
mrgf = '%s/%s.merged.tiff' %(path,out)
ij.IJ.saveAs(mrg,"Tiff",mrgf)
mrg.close()
opts = ImporterOptions()
opts.setId(mrgf)
