def openjpg(imagefile, metainfo, method='IJ',
series=0):
if method == 'IJ':
# using IJ static method
imp = IJ.openImage(imagefile)
if method == 'Opener':
# Using Opener class
imp = Opener().openImage(imagefile)
if method == 'BF':
# using BioFormats library
imps = BF.openImagePlus(imagefile)
# read image data using the specified pyramid level
imp, slices, width, height, pylevel = ImageTools.getImageSeries(imps, series=series)
metainfo['Output Slices'] = slices
metainfo['Output SizeX'] = width
metainfo['Output SizeY'] = height
return imp, metainfo
def convert32to16(fullFilePath): #overwrite the file!!!
if not os.path.isfile(fullFilePath):
bPrintLog(
'\nERROR: runOneFile() did not find file: ' + fullFilePath + '\n',
0)
return 0
elif not fullFilePath.endswith('tif'):
msg = fullFilePath + " is not a deconvoluted tif file"
bPrintLog(msg, 2)
else:
bPrintLog(
time.strftime("%H:%M:%S") +
' starting runOneFile()for overwrite: ' + fullFilePath, 1)
#open and overwrite the deconvoluted file as 16 bit
imp = Opener().openImage(fullFilePath) #deconvoluted file
if imp.getBitDepth() == 32:
imp.show()
msgStr = "Converting 32 to 16-bit..."
bPrintLog(msgStr, 3)
IJ.run("16-bit")
msgStr = "Overwriting 32-bit with 16-bit File in" + fullFilePath
bPrintLog(msgStr, 3)
IJ.run(imp, "Save", "") #save as replace without asking
imp.close()
def resizeAndSave(filePaths, l):
while l.get() < min(len(filePaths), currentWrittenLayer + nTilesAtATime + 1) :
k = l.getAndIncrement()
if k < min(len(filePaths), currentWrittenLayer + nTilesAtATime):
filePath = filePaths[k]
imageName = os.path.basename(filePath)
resizedImageName = os.path.splitext(imageName)[0] + '_resized_' + factorString + os.path.splitext(imageName)[1]
imageFolderName = os.path.basename(os.path.dirname(filePath))
resizedFilePath = fc.cleanLinuxPath(os.path.join(downSampledEMFolder, imageFolderName, resizedImageName))
im = Opener().openImage(filePath)
IJ.log('Am I going to process the image: im.height = ' + str(im.height) + ' - tileHeight = ' + str(tileHeight) + ' tile number ' + str(k))
if im.height == tileHeight: # crop a few lines at the top only if it has not already been done (sometimes the pipeline gets rerun)
im = fc.crop(im,cropRoi)
im = fc.normLocalContrast(im, normLocalContrastSize, normLocalContrastSize, 3, True, True)
# IJ.run(im, 'Replace value', 'pattern=0 replacement=1') # only for final waferOverview
FileSaver(im).saveAsTiff(filePath)
if not os.path.isfile(resizedFilePath):
im = fc.resize(im, scaleFactor)
FileSaver(im).saveAsTiff(resizedFilePath)
IJ.log('Image resized to ' + resizedFilePath)
im.close()
def combinestacks(directory, height=5):
"""Combine all tiff stacks in a directory to a panel.
Args:
directory: Path to a directory containing a collection of .tiff files.
height: The height of the panel (integer). Defaults to 5. The width is spaces automatically.
Returns:
A combined stack of the input images.
"""
IJ.log("\nCombining stacks...")
files = [
f for f in sorted(os.listdir(directory))
if os.path.isfile(os.path.join(directory, f))
]
IJ.log("Number of files: {}".format(len(files)))
groups = chunks(files, height)
horiz = []
for group in groups:
h = [Opener().openImage(directory, imfile) for imfile in group]
h = _horcombine(h)
# h.show()
horiz.append(h)
montage = _vercombine(horiz)
montage.show()
def import_multiple_rois(roi_files):
rois = [f.getPath() for f in roi_files if f.exists() and f.getName().lower().endswith("roi")]
opener = Opener()
rm = RoiManager.getRoiManager()
if not rm.getInstance():
rm.reset()
[rm.addRoi(opener.openRoi(r)) for r in rois]
def get_enhance_bounds(chf_fpaths, low_theshold, high_threshold):
# initialize the pixels array
pix = []
# open 100 images max (time consuming)
if len(chf_fpaths) > 100:
chf_fpaths = random.sample(chf_fpaths, 100)
# create a for loop here
counter = 1
for image_path in chf_fpaths:
# open the image
print "Getting pixels in Image " + image_path
print str(counter) + " / " + str(len(chf_fpaths))
counter += 1
imp_orig = Opener().openImage(image_path)
# get the pixel values
image_pix = list(imp_orig.getProcessor().getPixels())
imp_orig.close()
imp_orig.flush()
# select randomly 10% of the pixels (maybe memory issues)
image_pix_sel = random.sample(image_pix, int(len(image_pix) * 0.1))
pix = pix + image_pix_sel
# get the percentile values to threshold
IJ.log('Quantifying pixel values for contrast enhancement...')
low_pix = percentile(pix, low_theshold)
high_pix = percentile(pix, high_threshold)
return low_pix, high_pix
def normalized_roi_length(roi_path, pixel_scaling, normalization_fn=None):
'''
Returns the scaled measure of the input roi performing the normalization
function if provided.
'''
opener = Opener()
roi = opener.openRoi(roi_path)
scaled_measure = roi.getLength() / pixel_scaling
if callable(normalization_fn):
return normalization_fn(scaled_measure)
return measure
def file_opener(current_title, extension):
for filename in os.listdir(dest):
i = 0
if filename.endswith(file_type):
pass
if filename != current_title:
pass
if not filename.startswith(extension) and i == 0:
O = Opener()
path = os.path.join(dest, filename)
O.open(path)
i += 1
break
def main():
indir = IJ.getDirectory("input directory")
outdir = IJ.getDirectory("output directory")
files = sorted(os.listdir(indir))
IJ.log("files: {}".format(files))
montages = []
for imfile in files:
IJ.log("File: {}/{}".format(files.index(imfile)+1, len(files)))
if imfile.endswith(".tif"):
imp = Opener().openImage(indir, imfile)
montage = makemontage(imp, hsize=6, vsize=6, increment=2)
_saveimage(montage, outdir)
def main():
indir = IJ.getDirectory("input directory")
outdir = IJ.getDirectory("output directory")
files = sorted(os.listdir(indir))
# IJ.log("files: {}".format(files))
# montages = []
for imfile in files:
IJ.log("File: {}/{}".format(files.index(imfile) + 1, len(files)))
if imfile.endswith(".tif"):
imp = Opener().openImage(indir, imfile)
channels = ChannelSplitter().split(imp)
name = outdir + imfile + "_t001_c001.tif"
IJ.run(channels[0], "Image Sequence... ",
"format=TIFF save={}".format(name))
def main():
# Get the wanted input and output directories.
indir = IJ.getDirectory("input directory")
outdir = IJ.getDirectory("output directory")
# Collect all files in the import directory and sort on file name.
files = sorted(os.listdir(indir))
# Loop through all input files.
montages = []
for imfile in files:
# Some optional feedback if you're impatient.
IJ.log("File: {}/{}".format(files.index(imfile) + 1, len(files)))
# Make montage of every .tiff file and save as .tiff in the output directory.
if imfile.endswith(".tif"):
imp = Opener().openImage(indir, imfile)
montage = makemontage(imp, hsize=6, vsize=6, increment=2)
saveimage(montage, outdir)
def FindThreholds(mainpath, listOfImages, quantArray):
#initialize the pixels array
pix = []
#create a for loop here
for image in listOfImages:
#open the image
IJ.log('Getting pixels in Image ' + image)
imp_orig = Opener().openImage(mainpath + image)
imp_GB = blurImage(imp_orig)
imp_orig.close()
imp_GB.hide()
#get the pixel values
pix = getPixelValues(imp_GB, pix)
imp_GB.close()
#get the percentile values to threshold
IJ.log('Quantifying thresholds...')
percs = []
for q in quantArray:
percs.append(percentile(pix, q))
return percs
def batch_profile_from_threshold(batch_parameters):
'''
Thresholds the desired channel using the threshold method specified and saves the
roi profile in the csv_output.
Rois will be set to the width in pixels specified by stroke_width.
Provide a batch parameters dict to operate on images.
WIP:
PUT A BETTER ALGO IN PLACE TO DETERMINE THE CORRECT ROI TO USE.
'''
from ij.io import Opener
from IBPlib.ij.Utils.Files import buildList
opener = Opener()
rois_folder, csvs_folder = setup_output_folders(batch_parameters.get("output_folder"))
IJ.log("Plotting profiles...")
for i, img in enumerate(batch_parameters.get("analysed_images")):
progress = "{0}/{1}".format(i+1, len(batch_parameters.get("analysed_images")))
IJ.log("\n# Progress: {0}\n".format(progress))
imp = imageloader(img)
#title = os.path.basename(os.path.splitext(img)[0])
title = os.path.basename(img)
IJ.log("\nMeasuring -> {0}".format(title))
rois_path_list = buildList(rois_folder, extension=".roi")
rois = [opener.openRoi(roi) for roi in rois_path_list if roi.find(title) > 0]
if not rois:
IJ.log("## No rois found for {0}".format(title))
if not profile_from_threshold(imp, batch_parameters.get("analysis_ch"),
rois, batch_parameters.get("stroke_width"),
batch_parameters.get("th_method"), csvs_folder):
IJ.log("Batch run canceled.")
return
IJ.log("Done ...")
IJ.log("Results stored in '{0}'".format(csvs_folder))
def runOneFile(fullFilePath):
global gNumChannels
global gAlignBatchVersion
if not os.path.isfile(fullFilePath):
bPrintLog(
'\nERROR: runOneFile() did not find file: ' + fullFilePath + '\n',
0)
return 0
bPrintLog(
time.strftime("%H:%M:%S") + ' starting runOneFile(): ' + fullFilePath,
1)
enclosingPath = os.path.dirname(fullFilePath)
head, tail = os.path.split(enclosingPath)
enclosingPath += '/'
#make output folders
destFolder = enclosingPath + tail + '_channels/'
if not os.path.isdir(destFolder):
os.makedirs(destFolder)
destMaxFolder = destFolder + 'max/'
if not os.path.isdir(destMaxFolder):
os.makedirs(destMaxFolder)
if gDoAlign:
destAlignmentFolder = destFolder + 'alignment/'
if not os.path.isdir(destAlignmentFolder):
os.makedirs(destAlignmentFolder)
if gSave8bit:
eightBitFolder = destFolder + 'channels8/'
if not os.path.isdir(eightBitFolder):
os.makedirs(eightBitFolder)
eightBitMaxFolder = eightBitFolder + 'max/'
if not os.path.isdir(eightBitMaxFolder):
os.makedirs(eightBitMaxFolder)
# open image
imp = Opener().openImage(fullFilePath)
# get parameters of image
(width, height, nChannels, nSlices, nFrames) = imp.getDimensions()
bitDepth = imp.getBitDepth()
infoStr = imp.getProperty("Info") #get all .tif tags
if not infoStr:
infoStr = ''
infoStr += 'bAlignBatch_Version=' + str(gAlignBatchVersion) + '\n'
infoStr += 'bAlignBatch_Time=' + time.strftime(
"%Y%m%d") + '_' + time.strftime("%H%M%S") + '\n'
msgStr = 'w:' + str(width) + ' h:' + str(height) + ' slices:' + str(nSlices) \
+ ' channels:' + str(nChannels) + ' frames:' + str(nFrames) + ' bitDepth:' + str(bitDepth)
bPrintLog(msgStr, 1)
path, filename = os.path.split(fullFilePath)
shortName, fileExtension = os.path.splitext(filename)
#
# look for num channels in ScanImage infoStr
if gGetNumChanFromScanImage:
for line in infoStr.split('\n'):
#scanimage.SI4.channelsSave = [1;2]
scanimage4 = find(line, 'scanimage.SI4.channelsSave =') == 0
#state.acq.numberOfChannelsSave=2
scanimage3 = find(line, 'state.acq.numberOfChannelsSave=') == 0
if scanimage3:
#print 'line:', line
equalIdx = find(line, '=')
line2 = line[equalIdx + 1:]
if gGetNumChanFromScanImage:
gNumChannels = int(line2)
bPrintLog(
'over-riding gNumChannels with: ' + str(gNumChannels),
2)
if scanimage4:
#print ' we have a scanimage 4 file ... now i need to exptract the number of channel'
#print 'line:', line
equalIdx = find(line, '=')
line2 = line[equalIdx + 1:]
for delim in ';[]':
line2 = line2.replace(delim, ' ')
if gGetNumChanFromScanImage:
gNumChannels = len(line2.split())
bPrintLog(
'over-riding gNumChannels with: ' + str(gNumChannels),
2)
# show
imp.show()
# split channels if necc. and grab the original window names
if gNumChannels == 1:
origImpWinStr = imp.getTitle() #use this when only one channel
origImpWin = WindowManager.getWindow(
origImpWinStr) #returns java.awt.Window
if gNumChannels == 2:
winTitle = imp.getTitle()
bPrintLog('Deinterleaving 2 channels...', 1)
IJ.run('Deinterleave',
'how=2 keep') #makes ' #1' and ' #2', with ' #2' frontmost
origCh1WinStr = winTitle + ' #1'
origCh2WinStr = winTitle + ' #2'
origCh1Imp = WindowManager.getImage(origCh1WinStr)
origCh2Imp = WindowManager.getImage(origCh2WinStr)
origCh1File = destFolder + shortName + '_ch1.tif'
origCh2File = destFolder + shortName + '_ch2.tif'
# work on a copy, mostly for alignment with cropping
copy = Duplicator().run(imp)
#copy.copyAttributes(imp) #don't copy attributes, it copies the name (which we do not want)
copy.show()
#
# crop (on copy)
if gDoCrop:
bPrintLog('making cropping rectangle (left,top,width,height) ', 1)
bPrintLog(
str(gCropLeft) + ' ' + str(gCropTop) + ' ' + str(gCropWidth) +
' ' + str(gCropHeight), 2)
roi = Roi(gCropLeft, gCropTop, gCropWidth,
gCropHeight) #left,top,width,height
copy.setRoi(roi)
time.sleep(
0.5
) # otherwise, crop SOMETIMES failes. WHAT THE F**K FIJI DEVELOPERS, REALLY, WHAT THE F**K
#bPrintLog('cropping', 1)
IJ.run('Crop')
infoStr += 'bCropping=' + str(gCropLeft) + ',' + str(
gCropTop) + ',' + str(gCropWidth) + ',' + str(gCropHeight) + '\n'
#
# remove calibration ( on original)
if gRemoveCalibration:
cal = imp.getCalibration()
calCoeff = cal.getCoefficients()
if calCoeff:
msgStr = 'Calibration is y=a+bx' + ' a=' + str(
calCoeff[0]) + ' b=' + str(calCoeff[1])
bPrintLog(msgStr, 1)
#remove calibration
bPrintLog('\tRemoving Calibration', 2)
imp.setCalibration(None)
#without these, 8-bit conversion goes to all 0 !!! what the f**k !!!
#bPrintLog('calling imp.resetStack() and imp.resetDisplayRange()', 2)
imp.resetStack()
imp.resetDisplayRange()
#get and print out min/max
origMin = StackStatistics(imp).min
origMax = StackStatistics(imp).max
msgStr = '\torig min=' + str(origMin) + ' max=' + str(origMax)
bPrintLog(msgStr, 2)
# 20150723, 'shift everybody over by linear calibration intercept calCoeff[0] - (magic number)
if 1:
# [1] was this
#msgStr = 'Subtracting original min '+str(origMin) + ' from stack.'
#bPrintLog(msgStr, 2)
#subArgVal = 'value=%s stack' % (origMin,)
#IJ.run('Subtract...', subArgVal)
# [2] now this
#msgStr = 'Adding calCoeff[0] '+str(calCoeff[0]) + ' from stack.'
#bPrintLog(msgStr, 2)
#addArgVal = 'value=%s stack' % (int(calCoeff[0]),)
#IJ.run('Add...', addArgVal)
# [3] subtract a magic number 2^15-2^7 = 32768 - 128
magicNumber = gLinearShift #2^15 - 128
msgStr = 'Subtracting a magic number (linear shift) ' + str(
magicNumber) + ' from stack.'
bPrintLog(msgStr, 2)
infoStr += 'bLinearShift=' + str(gLinearShift) + '\n'
subArgVal = 'value=%s stack' % (gLinearShift, )
IJ.run(imp, 'Subtract...', subArgVal)
# 20150701, set any pixel <0 to 0
if 0:
ip = imp.getProcessor() # returns a reference
pixels = ip.getPixels() # returns a reference
msgStr = '\tSet all pixels <0 to 0. This was added 20150701 ...'
bPrintLog(msgStr, 2)
pixels = map(lambda x: 0 if x < 0 else x, pixels)
bPrintLog('\t\t... done', 2)
#get and print out min/max
newMin = StackStatistics(imp).min
newMax = StackStatistics(imp).max
msgStr = '\tnew min=' + str(newMin) + ' max=' + str(newMax)
bPrintLog(msgStr, 2)
#append calibration to info string
infoStr += 'bCalibCoeff_a = ' + str(calCoeff[0]) + '\n'
infoStr += 'bCalibCoeff_b = ' + str(calCoeff[1]) + '\n'
infoStr += 'bNewMin = ' + str(newMin) + '\n'
infoStr += 'bNewMax = ' + str(newMax) + '\n'
#
# set up
if gNumChannels == 1:
impWinStr = copy.getTitle() #use this when only one channel
impWin = WindowManager.getWindow(impWinStr) #returns java.awt.Window
if gNumChannels == 2:
winTitle = copy.getTitle()
bPrintLog('Deinterleaving 2 channels...', 1)
IJ.run('Deinterleave',
'how=2 keep') #makes ' #1' and ' #2', with ' #2' frontmost
ch1WinStr = winTitle + ' #1'
ch2WinStr = winTitle + ' #2'
ch1Imp = WindowManager.getImage(ch1WinStr)
ch2Imp = WindowManager.getImage(ch2WinStr)
ch1File = destFolder + shortName + '_ch1.tif'
ch2File = destFolder + shortName + '_ch2.tif'
#
# alignment
if gDoAlign and gNumChannels == 1 and copy.getNSlices() > 1:
infoStr += 'AlignOnChannel=1' + '\n'
#snap to middle slice
if gAlignOnMiddleSlice:
middleSlice = int(
math.floor(copy.getNSlices() /
2)) #int() is necc., python is f*****g picky
else:
middleSlice = gAlignOnThisSlice
copy.setSlice(middleSlice)
transformationFile = destAlignmentFolder + shortName + '.txt'
bPrintLog('MultiStackReg aligning:' + impWinStr, 1)
stackRegParams = 'stack_1=[%s] action_1=Align file_1=[%s] stack_2=None action_2=Ignore file_2=[] transformation=[Rigid Body] save' % (
impWin, transformationFile)
IJ.run('MultiStackReg', stackRegParams)
infoStr += 'AlignOnSlice=' + str(middleSlice) + '\n'
#20150723, we just aligned on a cropped copy, apply alignment to original imp
origImpTitle = imp.getTitle()
stackRegParams = 'stack_1=[%s] action_1=[Load Transformation File] file_1=[%s] stack_2=None action_2=Ignore file_2=[] transformation=[Rigid Body]' % (
origImpTitle, transformationFile)
IJ.run('MultiStackReg', stackRegParams)
if gDoAlign and gNumChannels == 2 and ch1Imp.getNSlices(
) > 1 and ch2Imp.getNSlices() > 1:
#apply to gAlignThisChannel
alignThisWindow = ''
applyAlignmentToThisWindow = ''
if gAlignThisChannel == 1:
infoStr += 'AlignOnChannel=1' + '\n'
transformationFile = destAlignmentFolder + shortName + '_ch1.txt'
alignThisWindow = ch1WinStr
applyAlignmentToThisWindow = ch2WinStr
else:
infoStr += 'AlignOnChannel=2' + '\n'
transformationFile = destAlignmentFolder + shortName + '_ch2.txt'
alignThisWindow = ch2WinStr
applyAlignmentToThisWindow = ch1WinStr
alignThisImp = WindowManager.getImage(alignThisWindow)
#snap to middle slice
if gAlignOnMiddleSlice:
middleSlice = int(
math.floor(alignThisImp.getNSlices() /
2)) #int() is necc., python is f*****g picky
else:
middleSlice = gAlignOnThisSlice
alignThisImp.setSlice(middleSlice)
infoStr += 'bAlignOnSlice=' + str(middleSlice) + '\n'
bPrintLog('MultiStackReg aligning:' + alignThisWindow, 1)
stackRegParams = 'stack_1=[%s] action_1=Align file_1=[%s] stack_2=None action_2=Ignore file_2=[] transformation=[Rigid Body] save' % (
alignThisWindow, transformationFile)
IJ.run('MultiStackReg', stackRegParams)
# 20150723, we just aligned on a copy, apply alignment to both channels of original
# ch1
bPrintLog('MultiStackReg applying alignment to:' + origCh1WinStr, 1)
stackRegParams = 'stack_1=[%s] action_1=[Load Transformation File] file_1=[%s] stack_2=None action_2=Ignore file_2=[] transformation=[Rigid Body]' % (
origCh1WinStr, transformationFile)
IJ.run('MultiStackReg', stackRegParams)
# ch2
bPrintLog('MultiStackReg applying alignment to:' + origCh2WinStr, 1)
stackRegParams = 'stack_1=[%s] action_1=[Load Transformation File] file_1=[%s] stack_2=None action_2=Ignore file_2=[] transformation=[Rigid Body]' % (
origCh2WinStr, transformationFile)
IJ.run('MultiStackReg', stackRegParams)
#apply alignment to other window
#bPrintLog('MultiStackReg applying alignment to:' + applyAlignmentToThisWindow, 1)
#applyAlignThisImp = WindowManager.getImage(applyAlignmentToThisWindow)
#stackRegParams = 'stack_1=[%s] action_1=[Load Transformation File] file_1=[%s] stack_2=None action_2=Ignore file_2=[] transformation=[Rigid Body]' %(applyAlignmentToThisWindow,transformationFile)
#IJ.run('MultiStackReg', stackRegParams)
elif gDoAlign:
bPrintLog('Skipping alignment, there may be only one slice?', 3)
#
# save
if gNumChannels == 1:
imp.setProperty("Info", infoStr)
impFile = destFolder + shortName + '.tif'
#bPrintLog('Saving:' + impFile, 1)
bSaveStack(imp, impFile)
#max project
bSaveZProject(imp, destMaxFolder, shortName)
if gNumChannels == 2:
#ch1
origCh1Imp.setProperty("Info", infoStr)
#bPrintLog('Saving:' + ch1File, 1)
bSaveStack(origCh1Imp, ch1File)
#max project
bSaveZProject(origCh1Imp, destMaxFolder, shortName + '_ch1')
#ch2
origCh2Imp.setProperty("Info", infoStr)
#bPrintLog('Saving:' + ch2File, 1)
bSaveStack(origCh2Imp, ch2File)
#max project
bSaveZProject(origCh2Imp, destMaxFolder, shortName + '_ch2')
#
# post convert to 8-bit and save
if gSave8bit:
if bitDepth == 16:
if gNumChannels == 1:
bPrintLog('Converting to 8-bit:' + impWinStr, 1)
IJ.selectWindow(impWinStr)
#IJ.run('resetMinAndMax()')
IJ.run("8-bit")
impFile = eightBitFolder + shortName + '.tif'
bPrintLog('Saving 8-bit:' + impFile, 2)
bSaveStack(imp, impFile)
#max project
bSaveZProject(imp, eightBitMaxFolder, shortName)
if gNumChannels == 2:
#
bPrintLog('Converting to 8-bit:' + origCh1WinStr, 1)
IJ.selectWindow(origCh1WinStr)
IJ.run("8-bit")
impFile = eightBitFolder + shortName + '_ch1.tif'
bPrintLog('Saving 8-bit:' + impFile, 2)
bSaveStack(origCh1Imp, impFile)
#max project
bSaveZProject(origCh1Imp, eightBitMaxFolder,
shortName + '_ch1')
#
bPrintLog('Converting to 8-bit:' + origCh2WinStr, 1)
IJ.selectWindow(origCh2WinStr)
#IJ.run('resetMinAndMax()')
IJ.run("8-bit")
impFile = eightBitFolder + shortName + '_ch2.tif'
bPrintLog('Saving 8-bit:' + impFile, 2)
bSaveStack(origCh2Imp, impFile)
#max project
bSaveZProject(origCh2Imp, eightBitMaxFolder,
shortName + '_ch2')
#
# close original window
imp.changes = 0
imp.close()
#copy
copy.changes = 0
copy.close()
#
# close ch1/ch2
if gNumChannels == 2:
#original
origCh1Imp.changes = 0
origCh1Imp.close()
origCh2Imp.changes = 0
origCh2Imp.close()
#copy
ch1Imp.changes = 0
ch1Imp.close()
ch2Imp.changes = 0
ch2Imp.close()
bPrintLog(
time.strftime("%H:%M:%S") + ' finished runOneFile(): ' + fullFilePath,
1)
def main():
Interpreter.batchMode = True
if (lambda_flat == 0) ^ (lambda_dark == 0):
print ("ERROR: Both of lambda_flat and lambda_dark must be zero,"
" or both non-zero.")
return
lambda_estimate = "Automatic" if lambda_flat == 0 else "Manual"
#import pdb; pdb.set_trace()
print "Loading images..."
filenames = enumerate_filenames(pattern)
num_channels = len(filenames)
num_images = len(filenames[0])
image = Opener().openImage(filenames[0][0])
width = image.width
height = image.height
image.close()
# The internal initialization of the BaSiC code fails when we invoke it via
# scripting, unless we explicitly set a the private 'noOfSlices' field.
# Since it's private, we need to use Java reflection to access it.
Basic_noOfSlices = Basic.getDeclaredField('noOfSlices')
Basic_noOfSlices.setAccessible(True)
basic = Basic()
Basic_noOfSlices.setInt(basic, num_images)
# Pre-allocate the output profile images, since we have all the dimensions.
ff_image = IJ.createImage("Flat-field", width, height, num_channels, 32);
df_image = IJ.createImage("Dark-field", width, height, num_channels, 32);
print("\n\n")
# BaSiC works on one channel at a time, so we only read the images from one
# channel at a time to limit memory usage.
for channel in range(num_channels):
print "Processing channel %d/%d..." % (channel + 1, num_channels)
print "==========================="
stack = ImageStack(width, height, num_images)
opener = Opener()
for i, filename in enumerate(filenames[channel]):
print "Loading image %d/%d" % (i + 1, num_images)
image = opener.openImage(filename)
stack.setProcessor(image.getProcessor(), i + 1)
input_image = ImagePlus("input", stack)
# BaSiC seems to require the input image is actually the ImageJ
# "current" image, otherwise it prints an error and aborts.
WindowManager.setTempCurrentImage(input_image)
basic.exec(
input_image, None, None,
"Estimate shading profiles", "Estimate both flat-field and dark-field",
lambda_estimate, lambda_flat, lambda_dark,
"Ignore", "Compute shading only"
)
input_image.close()
# Copy the pixels from the BaSiC-generated profile images to the
# corresponding channel of our output images.
ff_channel = WindowManager.getImage("Flat-field:%s" % input_image.title)
ff_image.slice = channel + 1
ff_image.getProcessor().insert(ff_channel.getProcessor(), 0, 0)
ff_channel.close()
df_channel = WindowManager.getImage("Dark-field:%s" % input_image.title)
df_image.slice = channel + 1
df_image.getProcessor().insert(df_channel.getProcessor(), 0, 0)
df_channel.close()
print("\n\n")
template = '%s/%s-%%s.tif' % (output_dir, experiment_name)
ff_filename = template % 'ffp'
IJ.saveAsTiff(ff_image, ff_filename)
ff_image.close()
df_filename = template % 'dfp'
IJ.saveAsTiff(df_image, df_filename)
df_image.close()
print "Done!"
def tethered_cell(image_path, frame_number=100, frame_rate=100.0, CCW=1):
"""
parameter setting; frame rate (frame/sec)
CCW = 1 : the motor rotation direction and the cell rotation direction on the image are same
CCW = -1: the motor rotation direction and the cell rotation direction on the image are different
"""
opener = Opener()
imp = opener.openImage(image_path)
image_slice_number = imp.getNSlices()
rm = RoiManager().getInstance()
if image_slice_number < frame_number: # too short movie
IJ.log('Number of frame of the movie is fewer than the number of frame that you selected')
return False
# create result directory
result_path = image_path + '_tethered_cell_result'
if os.path.lexists(result_path) is False:
os.mkdir(result_path)
#z projection; standard deviation, tethered cell shorws circle
IJ.run(imp, 'Subtract Background...', 'rolling=5 light stack')
IJ.run(imp, 'Median...', 'radius=2 stack')
IJ.run(imp, 'Z Project...', 'stop=500 projection=[Standard Deviation]')
zimp = IJ.getImage()
IJ.saveAs(zimp, 'bmp', os.path.join(result_path,'STD_DEV.bmp'))
# pick up tethered cell
IJ.setAutoThreshold(zimp, 'MaxEntropy dark')
IJ.run(zimp, 'Convert to Mask', '')
IJ.run('Set Measurements...', "area centroid bounding shape feret's limit redirect=None decimal=3")
IJ.run(zimp, 'Analyze Particles...', 'size=30-Infinity circularity=0.88-1.00 show=Nothing display exclude clear include')
zrt = ResultsTable.getResultsTable()
IJ.saveAs('Results', os.path.join(result_path,'RoiInfo.csv'))
#tcX and tcY are xy coordinates of tethered cell, tcdia is outer diameter of rotating tethered cell
#add ROI into stack image
for i in range(zrt.getCounter()):
tcX = zrt.getValue('X', i)
tcY = zrt.getValue('Y', i)
tcdia = zrt.getValue('Feret', i)
rm.add(imp, OvalRoi(tcX - tcdia/2.0, tcY - tcdia/2.0, tcdia + 1, tcdia + 1), i)
#calculate rotation speed by ellipse fitting
IJ.setAutoThreshold(imp, 'Li')
for roi_number in range(rm.getCount()):
t = []
XM = []
YM = []
theta = []
rotation_speed = []
area = []
imp.setRoi(rm.getRoi(roi_number))
cropped_imp = Duplicator().run(imp)
IJ.run('Set Measurements...', 'area mean center fit limit redirect=None decimal=3')
rm.select(roi_number)
rt = rm.multiMeasure(imp)
# check cell is present while analysis. Don't a cell gose anywhare?
for i in range(frame_number):
area.append(rt.getValue('Area1', i))
if 0 in area:
continue
for i in range(frame_number):
t.append((1/frame_rate)*i)
XM.append(rt.getValue('XM1', i))
YM.append(rt.getValue('YM1', i))
theta.append(rt.getValue('Angle1', i)/180.0*math.pi) # convert to radian
if i == 0:
rotation_speed.append(0)
else:
# phase treatment, theta should be -pi ~ pi
temp_rotation_speed = [theta[i] - theta[i-1],
theta[i] - theta[i-1] + math.pi,
theta[i] - theta[i-1] - math.pi,
theta[i] - theta[i-1] + 2*math.pi,
theta[i] - theta[i-1] - 2*math.pi]
temp_rotation_speed = sorted(temp_rotation_speed, key = lambda x :abs(x) )[0]
rotation_speed.append(CCW*temp_rotation_speed/(2.0*math.pi)*frame_rate)
# write csv
# earch columns indicate 1:index, 2:time(sec), 3:X-coordinate of center of mass(pixel), 4:Y-coordinate of center of mass (pixel), 5:Angle(Radian), 6:Rotation Speed(Hz)
with open(os.path.join(result_path,'Roi' + str(roi_number) + '.csv'), 'w') as f:
writer = csv.writer(f)
writer.writerow(['Index', 'time(s)', 'X', 'Y', 'Angle(rad)', 'Rotation Speed(Hz)'])
for i in range(len(t)):
writer.writerow([i, t[i], XM[i], YM[i], theta[i], rotation_speed[i]])
# plot x-y, t-x, t-y, t-rotation speed, save plot as bmp
plotRotation(roi_number, result_path, t, XM, YM, rotation_speed)
IJ.saveAs(cropped_imp, 'tiff', os.path.join(result_path,'Roi' + str(roi_number) + '.tiff'))
rt.reset()
# get analysis date and time
dt = datetime.datetime.today()
dtstr = dt.strftime('%Y-%m-%d %H:%M:%S')
# wtite analysis setting
with open(os.path.join(result_path,'analysis_setting.csv'), 'w') as f:
writer = csv.writer(f)
writer.writerow(['Analysis Date','frame number','frame rate','CCW direction', 'Method','Auto threshold', 'Subtruct Background', 'Median filter'])
writer.writerow([dtstr, frame_number, frame_rate, CCW, 'Ellipse', 'Li', '5.0', '2'])
# save roi
if rm.getCount() != 0:
rm.runCommand('Save', os.path.join(result_path, 'Roi.zip'))
zimp.close()
imp.close()
rm.close()
zrt.reset()
def main():
# Prepare directory tree for output.
indir = IJ.getDirectory("input directory")
outdir = IJ.getDirectory(".csv output directory")
c1dir = os.path.join(outdir, "Channel1")
c2dir = os.path.join(outdir, "Channel2")
c3dir = os.path.join(outdir, "Channel3")
c4dir = os.path.join(outdir, "Channel4")
channelsdir = os.path.join(outdir, "Channels")
if not os.path.isdir(c1dir):
os.mkdir(c1dir)
if not os.path.isdir(c2dir):
os.mkdir(c2dir)
if not os.path.isdir(c3dir):
os.mkdir(c3dir)
if not os.path.isdir(c4dir):
os.mkdir(c4dir)
if not os.path.isdir(channelsdir):
os.mkdir(channelsdir)
# Collect all file paths in the input directory
files = readdirfiles(indir)
# Initialize the results tables.
c1Results = ResultsTable()
c2Results = ResultsTable()
c3Results = ResultsTable()
c4Results = ResultsTable()
for file in files:
IJ.log("File: {}/{}".format(files.index(file) + 1, len(files)))
if file.endswith('.tif'):
# Open .tiff file as ImagePlus.
imp = Opener().openImage(file)
imp = ZProjector.run(imp, "max")
# imp = stackprocessor(file,
# nChannels=4,
# nSlices=7,
# nFrames=1)
channels = ChannelSplitter.split(imp)
name = imp.getTitle()
# For every channel, save the inverted channel in grayscale as .jpg.
for channel in channels:
IJ.run(channel, "Grays", "")
IJ.run(channel, "Invert", "")
jpgname = channel.getShortTitle()
jpgoutfile = os.path.join(channelsdir,
"{}.jpg".format(jpgname))
IJ.saveAs(channel.flatten(), "Jpeg", jpgoutfile)
IJ.run(channel, "Invert", "")
# OPTIONAL - Perform any other operations (e.g. crossexcitation compensation tasks) before object count.
c2name = channels[2].getTitle()
cal = channels[2].getCalibration()
channels[2] = ImagePlus(
c2name,
ImageCalculator().run("divide create 32-bit", channels[2],
channels[3]).getProcessor(
) # This removes AF647 bleed-through
)
channels[2].setCalibration(cal)
# Settings for channel1 threshold.
c1 = countobjects(channels[0],
c1Results,
threshMethod="Triangle",
subtractBackground=True,
watershed=True,
minSize=0.00,
maxSize=100,
minCirc=0.00,
maxCirc=1.00)
# Settings for channel2 threshold.
c2 = countobjects(channels[1],
c2Results,
threshMethod="RenyiEntropy",
subtractBackground=True,
watershed=False,
minSize=0.00,
maxSize=30.00,
minCirc=0.00,
maxCirc=1.00)
# Settings for channel3 threshold.
c3 = countobjects(channels[2],
c3Results,
threshMethod="RenyiEntropy",
subtractBackground=True,
watershed=False,
minSize=0.00,
maxSize=30.00,
minCirc=0.00,
maxCirc=1.00)
# Settings for channel4 threshold.
c4 = countobjects(channels[3],
c4Results,
threshMethod="RenyiEntropy",
subtractBackground=True,
watershed=False,
minSize=0.20,
maxSize=100.00,
minCirc=0.00,
maxCirc=1.00)
# Format filenames for thresholded .tiff files.
outfileC1 = os.path.join(c1dir, "threshold_c1_{}".format(name))
outfileC2 = os.path.join(c2dir, "threshold_c2_{}".format(name))
outfileC3 = os.path.join(c3dir, "threshold_c3_{}".format(name))
outfileC4 = os.path.join(c4dir, "threshold_c4_{}".format(name))
# Save thresholded .tiff files.
IJ.saveAs(c1.flatten(), "Tiff", outfileC1)
IJ.saveAs(c2.flatten(), "Tiff", outfileC2)
IJ.saveAs(c3.flatten(), "Tiff", outfileC3)
IJ.saveAs(c4.flatten(), "Tiff", outfileC4)
# Show results tables.
# c1Results.show("channel1")
# c2Results.show("channel2")
# c3Results.show("channel3")
# c4Results.show("channel4")
# Prepare results table filenames.
c1out = os.path.join(outdir, "channel1.csv")
c2out = os.path.join(outdir, "channel2.csv")
c3out = os.path.join(outdir, "channel3.csv")
c4out = os.path.join(outdir, "channel4.csv")
# Save results tables.
ResultsTable.save(c1Results, c1out)
ResultsTable.save(c2Results, c2out)
ResultsTable.save(c3Results, c3out)
ResultsTable.save(c4Results, c4out)
def runOneFile(fullFilePath):
global gFileType
global fileIndex
if not os.path.isfile(fullFilePath):
bPrintLog(
'\nERROR: runOneFile() did not find file: ' + fullFilePath + '\n',
0)
return 0
bPrintLog(
time.strftime("%H:%M:%S") + ' starting runOneFile(): ' + fullFilePath,
1)
bPrintLog('inputfile is:' + fullFilePath, 1)
enclosingPath = os.path.dirname(fullFilePath)
head, tail = os.path.split(
enclosingPath) #tail is name of enclosing folder
enclosingPath += '/'
# make output folders
destFolder = enclosingPath + tail + '_short/'
if not os.path.isdir(destFolder):
os.makedirs(destFolder)
# open
if gFileType == 'tif':
# open .tif image
imp = Opener().openImage(fullFilePath)
else:
# open .lsm
cmdStr = 'open=%s autoscale color_mode=Default view=Hyperstack stack_order=XYCZT' % (
fullFilePath, )
IJ.run('Bio-Formats Importer', cmdStr)
lsmpath, lsmfilename = os.path.split(fullFilePath)
lsWindow = lsmfilename
imp = WindowManager.getImage(lsWindow)
# get parameters of image
(width, height, nChannels, nSlices, nFrames) = imp.getDimensions()
bitDepth = imp.getBitDepth()
infoStr = imp.getProperty("Info") #get all .tif tags
#print 'original infoStr:', infoStr
if not infoStr:
infoStr = ''
infoStr += 'ShortenNames_Version=' + str(gShortenVersion) + '\n'
infoStr += 'ShortenNames_Time=' + time.strftime(
"%Y%m%d") + '_' + time.strftime("%H%M%S") + '\n'
msgStr = 'w:' + str(width) + ' h:' + str(height) + ' slices:' + str(nSlices) \
+ ' channels:' + str(nChannels) + ' frames:' + str(nFrames) + ' bitDepth:' + str(bitDepth)
bPrintLog(msgStr, 1)
path, filename = os.path.split(fullFilePath)
shortName, fileExtension = os.path.splitext(filename)
#output file name
outFile = destFolder + tail + '_' + str(fileIndex) + '.tif'
fileIndex += 1
bPrintLog('output file is:' + outFile, 1)
# put original name in header
infoStr += 'ShortenNames_OriginalFile=' + fullFilePath + '\n'
# put scanimage header back in
imp.setProperty("Info", infoStr)
#save
bSaveStack(imp, outFile)
#
# close original window
imp.changes = 0
imp.close()
straight_length, distance_from_stem_apical, angle,
convexfull_area)
col_name = [
"file_path", "exec_times", "roi_id", "types", "curve_length",
"straight_length", "distance_from_stem_apical", "angle", "convexfull_area"
]
csv_path = os.path.join(get_file_info()[0],
get_file_info()[1] + "_analyze.csv")
# ファイルに内容がある場合は何もせず, 内容がないときだけカラムを書き込む
with open(csv_path, "a") as f1:
with open(csv_path, "r") as f2:
s = f2.read()
if s == "":
with open(csv_path, "w") as f3:
writer = csv.writer(f3)
writer.writerow(col_name)
# 上書きモードで値を書き込む
with open(csv_path, "a") as f:
writer = csv.writer(f)
writer.writerows(for_csv_row)
# saveしたあとにoverlayに書き込んで線を青色に変える。
save_roi_set(imp=IJ.getImage())
# table上に表示する
op = Opener()
op.openTable(csv_path)
def main():
Interpreter.batchMode = True
if (lambda_flat == 0) ^ (lambda_dark == 0):
print ("ERROR: Both of lambda_flat and lambda_dark must be zero,"
" or both non-zero.")
return
lambda_estimate = "Automatic" if lambda_flat == 0 else "Manual"
print "Loading images..."
filenames = enumerate_filenames(pattern)
if len(filenames) == 0:
return
# This is the number of channels inferred from the filenames. The number
# of channels in an individual image file will be determined below.
num_channels = len(filenames)
num_images = len(filenames[0])
image = Opener().openImage(filenames[0][0])
if image.getNDimensions() > 3:
print "ERROR: Can't handle images with more than 3 dimensions."
(width, height, channels, slices, frames) = image.getDimensions()
# The third dimension could be any of these three, but the other two are
# guaranteed to be equal to 1 since we know NDimensions is <= 3.
image_channels = max((channels, slices, frames))
image.close()
if num_channels > 1 and image_channels > 1:
print (
"ERROR: Can only handle single-channel images with {channel} in"
" the pattern, or multi-channel images without {channel}. The"
" filename patterns imply %d channels and the images themselves"
" have %d channels." % (num_channels, image_channels)
)
return
if image_channels == 1:
multi_channel = False
else:
print (
"Detected multi-channel image files with %d channels"
% image_channels
)
multi_channel = True
num_channels = image_channels
# Clone the filename list across all channels. We will handle reading
# the individual image planes for each channel below.
filenames = filenames * num_channels
# The internal initialization of the BaSiC code fails when we invoke it via
# scripting, unless we explicitly set a the private 'noOfSlices' field.
# Since it's private, we need to use Java reflection to access it.
Basic_noOfSlices = Basic.getDeclaredField('noOfSlices')
Basic_noOfSlices.setAccessible(True)
basic = Basic()
Basic_noOfSlices.setInt(basic, num_images)
# Pre-allocate the output profile images, since we have all the dimensions.
ff_image = IJ.createImage("Flat-field", width, height, num_channels, 32);
df_image = IJ.createImage("Dark-field", width, height, num_channels, 32);
print("\n\n")
# BaSiC works on one channel at a time, so we only read the images from one
# channel at a time to limit memory usage.
for channel in range(num_channels):
print "Processing channel %d/%d..." % (channel + 1, num_channels)
print "==========================="
stack = ImageStack(width, height, num_images)
opener = Opener()
for i, filename in enumerate(filenames[channel]):
print "Loading image %d/%d" % (i + 1, num_images)
# For multi-channel images the channel determines the plane to read.
args = [channel + 1] if multi_channel else []
image = opener.openImage(filename, *args)
stack.setProcessor(image.getProcessor(), i + 1)
input_image = ImagePlus("input", stack)
# BaSiC seems to require the input image is actually the ImageJ
# "current" image, otherwise it prints an error and aborts.
WindowManager.setTempCurrentImage(input_image)
basic.exec(
input_image, None, None,
"Estimate shading profiles", "Estimate both flat-field and dark-field",
lambda_estimate, lambda_flat, lambda_dark,
"Ignore", "Compute shading only"
)
input_image.close()
# Copy the pixels from the BaSiC-generated profile images to the
# corresponding channel of our output images.
ff_channel = WindowManager.getImage("Flat-field:%s" % input_image.title)
ff_image.slice = channel + 1
ff_image.getProcessor().insert(ff_channel.getProcessor(), 0, 0)
ff_channel.close()
df_channel = WindowManager.getImage("Dark-field:%s" % input_image.title)
df_image.slice = channel + 1
df_image.getProcessor().insert(df_channel.getProcessor(), 0, 0)
df_channel.close()
print("\n\n")
template = '%s/%s-%%s.tif' % (output_dir, experiment_name)
ff_filename = template % 'ffp'
IJ.saveAsTiff(ff_image, ff_filename)
ff_image.close()
df_filename = template % 'dfp'
IJ.saveAsTiff(df_image, df_filename)
df_image.close()
print "Done!"
str(MouseID) + " contains " + str(number_of_channels_in_mouse) +
" distinct channels")
# run the function for each of the channels
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')
"analyzeCmStack": analyzeCmStack,
"rowNo": rowNo,
"colNo": colNo,
"nucMinSize": nucMinSize,
"cmMinSize": cmMinSize,
"allFileNames": allFileNames}
with open("savedSettings.json", "w+") as f:
json.dump(jsonStoreDict, f)
reList = []
for fileName in allFileNames:
match = re.match(formatString, fileName)
if match is None: continue
matchDict = match.groupdict()
matchDict['fileName'] = match.string
reList.append(matchDict)
opener = Opener()
bs = BackgroundSubtracter()
for outerPairs, group in getCombos(reList, groupBy).iteritems():
print(group)
nucIp, cmIp = openStitched(group, rowNo, colNo) if stitched else openUnstitched(group)
if nucIp is None or cmIp is None: continue
nucStack, cmStack = nucIp.getStack(), cmIp.getStack()
nucIpMedian = calcMedian(nucIp)
IJ.run(nucIp, "Subtract...", "value=" + str(nucIpMedian) + " stack")
nucIpForMeasure = nucIp.duplicate()
nucMaskIp = generateNucleusMask(nucIp, nucMethod, analyzeNucStack, nucMinSize)
cmIp, cmMaskIp = generateCardiomyocyteMask(cmIp, cmMethod, analyzeCmStack, cmMinSize, brightfield)
nucMaskIp.show()
nucIpForMeasure.show()
rm = RoiManager.getRoiManager()
rm.runCommand("Associate", "true")
def __init__(self, filepath):
"""
Load an image or stack from filepath.
Args:
filepath (str): Full path to an image file. Can be .tif, .lsm, .czi, etc
"""
if not os.path.isfile(filepath):
bPrintLog('ERROR: bImp() did not find file: ' + filepath,0)
return 0
self.filepath = filepath
folderpath, filename = os.path.split(filepath)
self.filename = filename
self.enclosingPath = folderpath
self.enclosingfolder = os.path.split(folderpath)[1]
self.dateStr = ''
self.timeStr = ''
self.imp = None
tmpBaseName, extension = os.path.splitext(filename)
isZeiss = extension in ['.czi', '.lsm']
self.islsm = extension == '.lsm'
self.isczi = extension == '.czi'
istif = extension == '.tif'
if istif:
# scanimage3 comes in with dimensions: [512, 512, 1, 52, 1]) = [width, height, numChannels, numSlices, numFrames]
self.imp = Opener().openImage(filepath)
self.imp.show()
elif isZeiss:
#open lsm using LOCI Bio-Formats
options = ImporterOptions()
#options.setColorMode(ImporterOptions.COLOR_MODE_GRAYSCALE)
options.setId(filepath)
imps = BF.openImagePlus(options)
for imp in imps:
self.imp = imp #WindowManager.getImage(self.windowname)
imp.show()
if not self.imp:
bPrintLog('ERROR: bImp() was not able to open file: '+ filepath,0)
self.windowname = filename
#self.imp = WindowManager.getImage(self.windowname)
# numChannels is not correct for scanimage, corrected in readTiffHeader()
(width, height, numChannels, numSlices, numFrames) = self.imp.getDimensions()
self.width = width # pixelsPerLine
self.height = height # linesPerFrame
self.numChannels = numChannels
self.numSlices = numSlices
self.numFrames = numFrames
self.infoStr = self.imp.getProperty("Info") #get all tags
self.voxelx = 1
self.voxely = 1
self.voxelz = 1
#self.numChannels = 1
#self.bitsPerPixel = 8
self.zoom = 1
self.motorx = None
self.motory = None
self.motorz = None
self.scanImageVersion = ''
self.msPerLine = None
self.dwellTime = None
# read file headers (date, time, voxel size)
if isZeiss:
self.readZeissHeader(self.infoStr)
elif istif:
self.readTiffHeader(self.infoStr)
self.updateInfoStr()
self.channelWindows = []
self.channelImp = []
if self.numChannels == 1:
self.channelWindows.append(self.windowname)
self.channelImp.append(self.imp)
else:
self.deinterleave()
#find the threshold values using the quantiles
percentiles = [98.5, 99.5, 99.9]
thresholds = FindThreholds(In_dir, MouseIDcFos, percentiles)
IJ.log('Thresholds for percentiles ' + str(percentiles) +
' selected to ' + str(thresholds))
#threshold and save images for each threshold value
for i, threshold in enumerate(thresholds):
# create directory
Perc_Out_dir = Out_dir + "percentile_" + str(percentiles[i]) + "/"
if not os.path.exists(Perc_Out_dir):
os.makedirs(Perc_Out_dir)
IJ.log('Processing ' + MouseID + ' for percentile ' +
str(percentiles[i]))
for image in MouseIDcFos:
#open image
imp_orig = Opener().openImage(In_dir + image)
#gaussian blur
imp_GB = blurImage(imp_orig)
imp_orig.close()
#threshold
imp_GB.getProcessor().threshold(threshold)
#save
newname = image.split('.')[0] + '_GPT_' + str(
percentiles[i]) + '.tif'
IJ.saveAsTiff(imp_GB, Perc_Out_dir + newname)
imp_GB.close()
IJ.log('Mouse ' + MouseID + ' processed')
print "DONE, find your results in " + Out_dir
