def saveAllImages(self, oldImages):
try:
#this will extract all the new images generated sence oldImages was created
#and will save them all to the downloads folder
titles2Save = WindowManager.getImageTitles()
inspectorOutput = list(set(titles2Save) - set(oldImages))[0]
im2Save = WindowManager.getImage(inspectorOutput)
IJ.save(
im2Save,
join(
self.downloadsFolder, self.outputFolder +
"InspectorOutput" + self.uniqueID + ".tif"))
iterID = int(0)
for image in list(set(titles2Save) - set([inspectorOutput])):
im2Save = WindowManager.getImage(image)
IJ.save(
im2Save,
join(
self.downloadsFolder, self.uniqueID +
" speckleInspectorImage{}.tif".format(iterID)))
iterID = iterID + 1
print("saved all images.")
except:
print("failed to save all images.")
finally:
print("")
def runPoreDetection(inputImp, data, ops, display): name=inputImp.getTitle() inputDataset=Utility.getDatasetByName(data, name) detectionParameters=DetectionParams() roi=inputImp.getRoi() if (roi is None): message=name+": "+Messages.NoRoi IJ.write(message) return roi=inputImp.getRoi().clone(); header, statslist=poreDetectionTrueColor(inputImp, inputDataset, roi, ops, data, display, detectionParameters) directory, overlayname, roiname=Utility.createImageNames(inputImp) statsname=directory+'truecolor_stats.csv' IJ.save(inputImp, overlayname); IJ.saveAs(inputImp, "Selection", roiname); header.insert(0,Messages.FileName) statslist.insert(0,name) print header print statslist ExportDataFunction.exportSummaryStats(statsname, header, statslist)
def saveNewImages(self, oldImages):
try:
#this will extract all the new images generated sence oldImages was created
#and will save them all to the downloads folder
newImages = WindowManager.getImageTitles()
titles2Save = list(set(newImages) - set(oldImages))
iterID = int(0)
for image in titles2Save:
im2Save = WindowManager.getImage(image)
IJ.save(
im2Save,
join(
self.downloadsFolder,
self.outputFolder + " speckleInspectorOutput " +
self.uniqueID + "{}.tif".format(iterID)))
iterID = iterID + 1
for image in oldImages:
#this loop will kill off the save dialog box that will
#pop up for the secondary image
im2Save = WindowManager.getImage(image)
im2Save.changes = False
print("saved new images.")
except:
print("failed to save new images.")
finally:
print("")
def saveMIP(self):
curdir = os.path.split(os.getcwd())[1]
savename = self.saveDir + curdir + self.savfileName + "_MIP.tif"
print "savename = ", savename
print "saving...."
IJ.save(self.outimp, savename)
print "saved as", savename
def runPoreDetection(inputImp, data, ops, display):
name = inputImp.getTitle()
inputDataset = Utility.getDatasetByName(data, name)
detectionParameters = DetectionParams()
roi = inputImp.getRoi()
if (roi is None):
message = name + ": " + Messages.NoRoi
IJ.write(message)
return
roi = inputImp.getRoi().clone()
header, statslist = poreDetectionTrueColor(inputImp, inputDataset, roi,
ops, data, display,
detectionParameters)
directory, overlayname, roiname = Utility.createImageNames(inputImp)
statsname = directory + 'truecolor_stats.csv'
IJ.save(inputImp, overlayname)
IJ.saveAs(inputImp, "Selection", roiname)
header.insert(0, Messages.FileName)
statslist.insert(0, name)
print header
print statslist
ExportDataFunction.exportSummaryStats(statsname, header, statslist)
def scale_convertTo8Bit_montage(self):
for channel in self.channel_names:
# apply lut
try:
IJ.run(self.cropped_imps[channel], self.luts[channel], "")
except:
IJ.run(self.cropped_imps[channel], "Grays", "")
## min/max
if channel not in self.contrasts:
## do quick autocontrast based on min/max
self.contrasts[channel] = [
self.cropped_imps[channel].getProcessor().getMin(),
self.cropped_imps[channel].getProcessor().getMax(),
]
try:
c = self.contrasts[channel]
self.cropped_imps[channel].getProcessor().setMinAndMax(c[0], c[1])
except:
print "Unable to do contrast"
## save 16 bit images
directory = self.outdir
file_name = self.name
x = self.x
y = self.y
save_as = os.path.join(directory, "%s_%s_%s_%s.tif" % (file_name, channel, x, y))
IJ.save(self.cropped_imps[channel], save_as)
## save RGBs
IJ.run(self.cropped_imps[channel], "RGB Color", "")
save_as = os.path.join(directory, "%s_%s_%s_%s_RGB.tif" % (file_name, channel, x, y))
IJ.save(self.cropped_imps[channel], save_as)
self.make_montage(xy="%sx%s" % (x, y))
def run():
srcDir = DirectoryChooser("Choose!").getDirectory()
if not srcDir:
# user canceled dialog
return
for root, directories, filenames in os.walk(srcDir):
for filename in filenames:
# Skip non-TIFF files
if not filename.endswith(fileID):
continue
path = os.path.join(root, filename)
print path
imp = IJ.openImage(path)
imp.show()
if method=="contrast":
IJ.run(imp,"Enhance Contrast", "saturated=0.35");
time.sleep(0.2)
IJ.save(imp,path + "_auto.jpeg")
imp.changes = False
imp.getWindow().close()
elif method=="scale":
IJ.run(imp, "Scale...", "x=0.2 y=0.2 interpolation=None create title=small");
impScale = IJ.getImage()
time.sleep(0.2)
IJ.save(impScale,path + "_thumb.jpeg")
imp.changes = False
imp.getWindow().close()
impScale.changes = False
impScale.getWindow().close()
def getTortuosity(directory, imageName):
print(imageName)
image = IJ.openImage(directory + imageName)
skeletoniseWrapper = cs.run("org.bonej.wrapperPlugins.SkeletoniseWrapper",
False, ["inputImage", image])
skeletonInstance = skeletoniseWrapper.get()
skeleton = skeletonInstance.getOutput("skeleton")
IJ.save(
skeleton,
directory + "Skeletons/" + imageName.replace(".tif", "") + "-skeleton")
wrapper = cs.run("org.bonej.wrapperPlugins.AnalyseSkeletonWrapper", False,
["inputImage", image, "verbose", True])
wrapperInstance = wrapper.get()
table = wrapperInstance.getOutput("verboseTable")
print(table[0])
nskeletons = max(table[0])
print(nskeletons)
branchLength = table[2]
euclideanDistance = table[9]
if (len(branchLength) > nskeletons):
print("multiple branches in skeleton: returning zero tortuosity")
return [0, 0, 0]
avgBL = sum(branchLength) / float(nskeletons)
avgED = sum(euclideanDistance) / float(nskeletons)
print("branch length: ", avgBL)
print("euclidean distance: ", avgED)
tortuosity = avgED / avgBL
return [avgBL, avgED, tortuosity]
def saveMask(self, maskdir):
imp = self.mask
imp.getProcessor().invert()
filebasename = os.path.basename(self.filename)
maskfilename = os.path.join(maskdir, "mask_%s" % filebasename)
IJ.save(imp, maskfilename)
IJ.log("Mask image: %s\n" % maskfilename)
def contrastImage(): while atomicI.get() < nPaths: k = atomicI.getAndIncrement() if k < nPaths: im = IJ.openImage(toContrastPaths[k][0]) im = fc.normLocalContrast(im, normLocalContrastSize, normLocalContrastSize, 3, True, True) IJ.save(im, toContrastPaths[k][1]) im.close()
def analyzeImage(passImage, passModel, passChannel, passProbability,
passPixels, passOutput):
retResults = list()
windows = set()
# Register current window
registerWindow(passImage.title, windows)
# Extract the requested channel
IJ.run("Z Project...", "projection=[Max Intensity]")
registerWindow("MAX_" + passImage.title, windows)
IJ.run("Duplicate...", "title=temp")
registerWindow("temp", windows)
# Apply WEKA training model to image
wekaSeg = WekaSegmentation(WindowManager.getCurrentImage())
wekaSeg.loadClassifier(passModel)
wekaSeg.applyClassifier(True)
# Extract first slice of probability map
wekaImg = wekaSeg.getClassifiedImage()
wekaImg.show()
registerWindow("Probability maps", windows)
IJ.setSlice(1)
IJ.run("Duplicate...", "title=temp2")
registerWindow("temp2", windows)
# Apply threshold and save
IJ.setThreshold(passProbability, 1, "Black & White")
fileParts = passImage.getTitle().split(".")
IJ.save(
os.path.join(
passOutput, "{0}-probmap.png".format(fileParts[0],
'.'.join(fileParts[1:]))))
# Perform particle analysis and save
IJ.run("Analyze Particles...",
"size={0}-Infinity show=Outlines pixel clear".format(passPixels))
registerWindow("Drawing of temp2", windows)
IJ.save(
os.path.join(
passOutput, "{0}-particles.png".format(fileParts[0],
'.'.join(fileParts[1:]))))
# Get measurements
tableResults = ResultsTable.getResultsTable()
for rowIdx in range(tableResults.size()):
retResults.append(tableResults.getRowAsString(rowIdx).split())
retResults[-1].insert(
0,
WindowManager.getCurrentImage().getCalibration().unit)
retResults[-1].append(
float(retResults[-1][4]) / float(retResults[-1][3]))
# Close windows
closeWindows(windows)
return retResults
def run():
time_dirs = glob(os.path.join(input_dir.getPath(), 't*/'))
for time_dir in time_dirs:
log.info('\tprocessing %s' % time_dir)
# Extract information from file name
xy_files = glob(os.path.join(time_dir, '*.tif'))
match = re.search('(.*)_x\d_y\d_t(\d{1,2})\.tif',
os.path.basename(xy_files[0]))
if not match:
log.error('\tCould not extract time digits from %s\nAbort.' %
(time_dir))
return
experiment = match.group(1)
time = match.group(2)
fields = str(len(xy_files))
log.info('\t\texperiment=%s, time-point=%s, #files=%s' %
(experiment, time, fields))
# Compose the output file name
output_file = os.path.join(input_dir.getPath(),
experiment + '_t' + time + '.tif')
if os.path.exists(output_file):
log.info('\t\talreday stitched: %s)' % output_file)
continue
# Run the stitching macro
print '\t\truning stitching plugin...'
IJ.run('Grid/Collection stitching', \
'type=[Filename defined position]' + \
' order=[Defined by filename ]' + \
' grid_size_x=1' + \
' grid_size_y=' + fields + \
' tile_overlap=' + str(overlap) + \
' first_file_index_x=1' + \
' first_file_index_y=1' + \
' directory=' + time_dir + \
' file_names=12_x{x}_y{y}_t' + time + '.tif' + \
' output_textfile_name=TileConfiguration.txt' + \
' fusion_method=[Linear Blending]' + \
' regression_threshold=0.30' + \
' max/avg_displacement_threshold=2.50' + \
' absolute_displacement_threshold=3.50' + \
' compute_overlap' + \
' subpixel_accuracy' + \
' computation_parameters=[Save computation time (but use more RAM)]' + \
' image_output=[Fuse and display]')
# Save the output
imp = WindowManager.getImage("Fused")
IJ.save(imp, output_file)
log.info('\t\tsaved: %s' % (output_file))
IJ.run('Close All')
log.info('Done.')
def exportFlatForPresentations(project,outputFolder,scaleFactor,rectangle):
layerset = project.getRootLayerSet()
loader = project.getLoader()
for l,layer in enumerate(layerset.getLayers()): # import the patches
IJ.log('Exporting layer ' + str(l) + 'with rectangle ' + str(rectangle) + 'scale factor ' + str(scaleFactor))
imp = loader.getFlatImage(layer,rectangle,scaleFactor, 0x7fffffff, ImagePlus.GRAY8, Patch, layer.getAll(Patch), True, Color.black, None)
IJ.save(imp,outputFolder + os.sep + nameFromPath(outputFolder.rstrip(os.sep)) + '_' + str(l) + '.tif') #use the name of the outputFolder to name the images
IJ.log('Layer ' + str(l)+' flat exported to ' + outputFolder + os.sep + nameFromPath(outputFolder.rstrip(os.sep)) + '_' + str(l) + '.tif')
imp.close()
def saveImg(imp, nslices, label):
mask = WindowManager.getImage('Mask of ' + imp.getTitle())
IJ.save(mask, saveDir + '/' + title + '_' + label + '.tif')
labeled = mask.duplicate()
for n in range(1,nslices+1):
s = labeled.getStack().getProcessor(n);
s.setFont(Font("SansSerif", Font.BOLD, 30));
s.setColor(Color.blue);
s.drawString(title + ' Layer: ' + label + ' Slice: ' + str(n), 20, 40)
labeled.updateAndDraw()
IJ.save(labeled, saveDir + '/labeled_' + title + '_' + label + '.tif')
IJ.run("Close")
def main():
files = []
for filt in img_extensions.split(";"):
if len(filt.strip()) > 0:
files += glob.glob(os.path.join(str(input_dir), filt.strip()))
else:
files += glob.glob(os.path.join(str(input_dir), "*.*"))
break
if len(files) == 0:
IJ.showMessage("No files found in '{}'\nfor extensions '{}'".format(
str(input_dir), img_extensions))
else:
for fn in files:
try:
imp = BF.openImagePlus(fn)[0]
except:
IJ.showMessage(
"Could not open file '{}' (skipping).\nUse extension filter to filter non-image files..."
.format(str(fn)))
continue
img = IJF.wrap(imp)
cali = imp.getCalibration().copy()
if pixel_width > 0:
cali.pixelWidth = pixel_width
cali.pixelHeight = pixel_width
cali.setUnit("micron")
if frame_interval > 0:
cali.frameInterval = frame_interval
cali.setTimeUnit("sec.")
imp_out = smooth_temporal_gradient(ops, img, sigma_xy, sigma_t,
frame_start, frame_end,
normalize_output)
imp_out.setCalibration(cali)
channels = ChannelSplitter.split(imp_out)
fn_basename = os.path.splitext(fn)[0]
IJ.save(channels[0], "{}_shrinkage.tiff".format(fn_basename))
IJ.save(channels[1], "{}_growth.tiff".format(fn_basename))
print("{} processed".format(fn_basename))
IJ.showMessage(
"Growth/shrinkage extraction of {} inputs finsihed.".format(
len(files)))
def main(argv):
"""Convert .dm3 files to tifs."""
inputdir = 'INPUTDIR'
outputdir = 'OUTPUTDIR'
output_postfix = 'OUTPUT_POSTFIX'
infiles = glob.glob(os.path.join(inputdir, '*.dm3'))
for infile in infiles:
imp = BF.openImagePlus(infile)
tail = os.path.split(infile)[1]
filename = os.path.splitext(tail)[0][-4:] + output_postfix + '.tif'
outpath = os.path.join(outputdir, filename)
IJ.save(imp[0], outpath)
def exportFlatCloseFiji(project,outputFolder,scaleFactor):
#todo: check whether the output file already exists. If yes, skip
for l,layer in enumerate(layerset.getLayers()):
savePath = outputFolder + os.sep + nameFromPath(outputFolder.rstrip(os.sep)) + '_' + str(l) + '.tif'
savePathNext = outputFolder + os.sep + nameFromPath(outputFolder.rstrip(os.sep)) + '_' + str(l+1) + '.tif'
if os.isfile(savePathNext):
IJ.log('Skipping layer ' + str(l) + ': already processed')
else:
IJ.log('Exporting layer ' + str(layer) + '; layer number ' + str(l))
layerset = project.getRootLayerSet()
loader = project.getLoader()
imp = loader.getFlatImage(layer,layerset.get2DBounds(),scaleFactor, 0x7fffffff, ImagePlus.GRAY8, Patch, layer.getAll(Patch), True, Color.black, None)
IJ.save(imp, savePath)
IJ.log('Layer ' + str(layerCurrent)+' flat exported to ' + savePath)
imp.close()
IJ.log('exportFlatCloseFiji has reached the end')
def saveIndividualTimePoints(inputImp, outputFolder):
imp = inputImp
width, height, nChannels, nSlices, nFrames = imp.getDimensions()
print nSlices
for f in range(nFrames):
print "frame: ", f
minSlice = (f*nSlices)+1
maxSlice = (f+1)*(nSlices)
slices = str(minSlice) + "-" + str(maxSlice)
print "Isolating slices ",slices
subStack = SubstackMaker().makeSubstack(imp, slices)
name = "t"+ str(f) + ".tif"
IJ.save(subStack, path.join(outputFolder, name) )
imp.close()
def runPoreDetection(inputImp, data, ops, display):
# in this step get the image in the "imagej2" dataset format
# this way we can take advantage of some of the new imagej2 functionality
name = inputImp.getTitle()
inputDataset = Utility.getDatasetByName(data, name)
# this structure keeps track of detection parameters
detectionParameters = DetectionParams()
roi = inputImp.getRoi()
# check if there is an roi on the image. If not return an error
if (roi is None):
message = name + ": " + Messages.NoRoi
IJ.write(message)
return
# clone the roi
roi = inputImp.getRoi().clone()
# call the function that processes the UV image
roilist, statslist, statsheader = poreDetectionUV(inputImp, inputDataset,
roi, ops, data, display,
detectionParameters)
# get the names for the images that will be saved
directory, overlayname, roiname = Utility.createImageNames(inputImp)
# name of file to save summary stats
statsname = os.path.join(directory, 'stats.csv')
# save the image with overlay
IJ.save(inputImp, overlayname)
# save the roi
IJ.saveAs(inputImp, "Selection", roiname)
statsheader.insert(0, Messages.FileName)
statslist.insert(0, name)
ExportDataFunction.exportSummaryStats(statsname, statsheader, statslist)
print statsname
print statsheader
print statslist
def runPoreDetection(inputImp, data, ops, display): # in this step get the image in the "imagej2" dataset format # this way we can take advantage of some of the new imagej2 functionality name=inputImp.getTitle() inputDataset=Utility.getDatasetByName(data, name) # this structure keeps track of detection parameters detectionParameters=DetectionParams() roi=inputImp.getRoi() # check if there is an roi on the image. If not return an error if (roi is None): message=name+": "+Messages.NoRoi IJ.write(message) return # clone the roi roi=inputImp.getRoi().clone(); # call the function that processes the UV image roilist, statslist, statsheader=poreDetectionUV(inputImp, inputDataset, roi, ops, data, display, detectionParameters) # get the names for the images that will be saved directory, overlayname, roiname=Utility.createImageNames(inputImp) # name of file to save summary stats statsname=os.path.join(directory, 'stats.csv') # save the image with overlay IJ.save(inputImp, overlayname); # save the roi IJ.saveAs(inputImp, "Selection", roiname); statsheader.insert(0,Messages.FileName) statslist.insert(0,name) ExportDataFunction.exportSummaryStats(statsname, statsheader, statslist) print statsname print statsheader print statslist
def analyzeImage(passImage, passModel, passProbability, passPixels,
passOutput):
retResults = list()
# Apply WEKA training model to image
wekaSeg = WekaSegmentation(passImage)
wekaSeg.loadClassifier(passModel)
wekaSeg.applyClassifier(True)
# Extract first slice of probability map
wekaImg = wekaSeg.getClassifiedImage()
wekaImg.show()
IJ.selectWindow("Probability maps")
IJ.setSlice(1)
IJ.run("Duplicate...", "title=temp")
# Apply threshold and save
IJ.setThreshold(passProbability, 1, "Black & White")
fileParts = passImage.getTitle().split(".")
IJ.save(
os.path.join(
passOutput, "{0}-probmap.png".format(fileParts[0],
'.'.join(fileParts[1:]))))
# Perform particle analysis and save
IJ.run("Analyze Particles...",
"size={0}-Infinity show=Outlines pixel clear".format(passPixels))
IJ.selectWindow("Drawing of temp")
IJ.save(
os.path.join(
passOutput, "{0}-particles.png".format(fileParts[0],
'.'.join(fileParts[1:]))))
# Get measurements (skip final row, this will correspond to legend)
tableResults = ResultsTable.getResultsTable()
for rowIdx in range(tableResults.size() - 1):
retResults.append(tableResults.getRowAsString(rowIdx).split())
# Close interim windows
IJ.run("Close")
IJ.selectWindow("temp")
IJ.run("Close")
IJ.selectWindow("Probability maps")
IJ.run("Close")
return retResults
def exportFlat(project,outputFolder,scaleFactor, baseName = '', bitDepth = 8, layers = [], roi = ''):
layerset = project.getRootLayerSet()
loader = project.getLoader()
for l,layer in enumerate(layerset.getLayers()):
if (layers ==[] ) or (l in layers):
IJ.log('Exporting layer ' + str(l))
if roi == '':
roiExport = layerset.get2DBounds()
else:
roiExport = roi
if bitDepth == 8:
imp = loader.getFlatImage(layer,roiExport,scaleFactor, 0x7fffffff, ImagePlus.GRAY8, Patch, layer.getAll(Patch), True, Color.black, None)
elif bitDepth == 16:
imp = loader.getFlatImage(layer,roiExport,scaleFactor, 0x7fffffff, ImagePlus.GRAY16, Patch, layer.getAll(Patch), True, Color.black, None)
savePath = os.path.join(outputFolder, baseName + '_' + str(l).zfill(4) + '.tif')
IJ.save(imp, savePath)
IJ.log('Layer ' + str(l) +' flat exported to ' + savePath)
imp.close()
def find_folders(inpt_folder):
#get list of folders and open folder one after the other
for idx, i in enumerate(os.listdir(inpt_folder)):
if os.path.isdir(os.path.join(inpt_folder, i)):
comp_img, timepoints = open_images(os.path.join(inpt_folder, i))
corrected_img, corrected_img_bleach = pre_process_images(
comp_img, timepoints)
create_folder(os.path.join(inpt_folder, i, "Corrected"))
file_name = i + "_Red_corrected.tif"
folder_file = os.path.join(inpt_folder, i, "Corrected", file_name)
IJ.save(corrected_img, folder_file)
create_folder(os.path.join(inpt_folder, i, "Corrected_bleach"))
file_name = i + "_Red_corrected_bleach.tif"
folder_file = os.path.join(inpt_folder, i, "Corrected_bleach",
file_name)
IJ.save(corrected_img_bleach, folder_file)
def process(srcDir, currentDir, fileName, table, numberOfWidthMeasurements):
if "control-images" in currentDir:
return
print "Processing:"
# Opening the image
print "Open image file", fileName
imp = IJ.openImage(os.path.join(currentDir, fileName))
# Put your processing commands here!
imp.show()
processCurrentImage(table);
impIn = WindowManager.getCurrentImage()
path = os.path.join(srcDir, 'control-images')
if not os.path.exists(path):
os.makedirs(path)
IJ.save(os.path.join(path, fileName))
impIn.changes = False
impIn.close()
def exportFlatRoi(project, scaleFactor, x, y, w, h, layer, saveName):
loader = project.getLoader()
rectangle = Rectangle(x-int(w/2),y-int(h/2),w,h)
patches = layer.find(Patch, x, y)
print patches
# IJ.log('patches' + str(patches))
for p, patch in enumerate(patches):
visible = patch.visible
patch.visible = True
tiles = ArrayList()
tiles.add(patch)
print 'tiles',tiles
print 'rectangle',rectangle
IJ.log('Patch ' + str(patch) + ' cropped with rectangle ' + str(rectangle) )
imp = loader.getFlatImage(layer, rectangle, scaleFactor, 0x7fffffff, ImagePlus.GRAY8, Patch, tiles, True, Color.black, None)
exportName = saveName + '_' + str(int(p)) + '.tif'
IJ.save(imp, exportName)
patch.visible = visible
def make_montage(self, xy="", montage_name=""):
if self.channels_to_montage == None:
channels_to_montage = self.channel_names
else:
channels_to_montage = self.channels_to_montage
w = self.cropped_imps[self.channel_names[0]].width
h = self.cropped_imps[self.channel_names[0]].height
new_stack = ImageStack(w, h)
# print self.cropped_imps.keys()
for channel in channels_to_montage:
img = self.cropped_imps[channel].getProcessor()
new_stack.addSlice(img)
tomontage = ImagePlus("to montage", new_stack)
montager = MontageMaker()
self.montage = montager.makeMontage2(
tomontage, len(channels_to_montage), 1, 1, 1, len(channels_to_montage), 1, 0, 0
)
directory = self.outdir
file_name = self.name
save_as = os.path.join(directory, "%s_%s_montage.tif" % (file_name, xy))
IJ.save(self.montage, save_as)
def saveNewImage(self, oldImages):
try:
#this will extract one image
newImages = WindowManager.getImageTitles()
title2Save = list(set(newImages) - set(oldImages))
im2Save = WindowManager.getImage(title2Save[0])
IJ.save(
im2Save,
join(
self.downloadsFolder, self.outputFolder +
" speckleInspectorOutput " + self.uniqueID + ".tif"))
for image in list(set(newImages) - set([title2Save[0]])):
#this loop will kill off the save dialog box that will
#pop up for other images bescides the Inspector output
im2Save = WindowManager.getImage(image)
im2Save.changes = False
print("saved new image.")
except:
print("failed to save image.")
finally:
print("")
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 main():
root = myDir.getPath() # get the root out the java file object
print(root)
import os, glob
# set up bioformats
from loci.plugins import BF
from loci.plugins.in import ImporterOptions
options = ImporterOptions()
options.setColorMode(ImporterOptions.COLOR_MODE_COMPOSITE)
options.setGroupFiles(True) # if the files are named logically if will group them into a stack
# this will do the maximum intensity projection
from ij.plugin import ZProjector
Zproj = ZProjector()
for path, subdirs, files in os.walk(root):
print(path)
# just get the one of the files that matches your image pattern
flist = glob.glob(os.path.join(path,"*.tif"))
print(len(flist))
if( flist ):
file = flist[0]
print("Processing {}".format(file))
options.setId(file)
imp = BF.openImagePlus(options)[0]
# show the image if you want to see it
IJ.run(imp, "Grays", "")
imp.show()
imp_max = Zproj.run(imp,'max')
IJ.run(imp_max, "Grays", "")
imp_max.show()
# save the Z projection
IJ.save(imp_max,file.rsplit('_',1)[0]+'_processed.tif')
def prepareImage(passDir, passFile, passOutput, passPixels, passBlur, passThreshold):
# Attempt to open as image, exit if not
fullPath = os.path.join(passDir, passFile)
retImage = IJ.openImage(fullPath)
if not retImage: return None
retImage.show()
# Resize canvas to prepare for fast rotations, select original
imgWidth = retImage.getWidth()
imgHeight = retImage.getHeight()
maxDim = math.sqrt(imgWidth ** 2 + imgHeight ** 2)
borderX = (maxDim - imgWidth)/2
borderY = (maxDim - imgHeight)/2
IJ.run("Canvas Size...", "width={0} height={0} position=Center".format(maxDim))
IJ.makeRectangle(borderX, borderY, imgWidth, imgHeight)
# Set scale to pixels (will manually calculate later)
IJ.run("Set Scale...", "distance=0 known=0 pixel=1 unit=pixel");
# Blur "overhanging" projections
IJ.run("Gaussian Blur...", "sigma={0}".format(passBlur))
# Set threshold
IJ.setAutoThreshold(retImage, "{0} dark apply".format(passThreshold))
IJ.makeRectangle(0, 0, retImage.getWidth(), retImage.getHeight())
# Analyze particles and obtain outline image
IJ.run("Analyze Particles...", "size={0}-Infinity show=Outlines pixel clear".format(passPixels));
IJ.selectWindow("Drawing of {0}".format(retImage.getTitle()))
fileParts = passFile.split(".")
IJ.save(os.path.join(passOutput, "{0}-outline-{1}.{2}".format(fileParts[0], passThreshold, '.'.join(fileParts[1:]))))
IJ.run("Close")
return retImage
def cropAndSave(name, row):
# for checking bounds and making Regions of Interest (ROI, or aka rectangles)
# 4.818 comes from converting micron to pixels
global prolif_counter
global iffy_counter
global senescent_counter
global curr
global length
global height
global batch_folder
# checking for out of bounds
if float(row[4]) < 152.0 or float(row[5]) < 152.0 or float(row[4]) + float(
row[6]) > 27000 or float(row[5]) + float(row[7]) > 27000:
IJ.log("outta bounds")
return
else:
topLeft_x = int(float(row[2]) * 4.818 - (length / 2))
topLeft_y = int(float(row[3]) * 4.818 - (height / 2))
c3.setRoi(topLeft_x, topLeft_y, length, height)
temp_image = c3.crop() # note: does not render, so saves memory
#temp_image.show()
IJ.log("Mean: " + row[1])
if float(row[1]) < 10:
IJ.save(
temp_image, "D:/Stem_Cells/Batches/%s/Senescent/cell%i.tiff" %
(batch_folder, senescent_counter))
senescent_counter += 1
IJ.log("senescent saved")
elif float(row[1]) > 30:
IJ.save(
temp_image, "D:/Stem_Cells/Batches/%s/Prolif/cell%i.tiff" %
(batch_folder, prolif_counter))
#IJ.save(temp_image,"D:/Stem_Cells/A3_Exp/cell%i.tif" %prolif_counter)
#IJ.save(temp_image,"D:/Stem_Cells/A3_Exp/sub_exp/cells/test/Proliferative/cell%i.tif" %prolif_counter)
prolif_counter += 1
IJ.log("prolif saved")
else:
IJ.save(
temp_image, "D:/Stem_Cells/Batches/%s/Iffy/cell%i.tif" %
(batch_folder, iffy_counter))
iffy_counter += 1
IJ.log("iffy saved")
capturer.execute(trackmate)
#---------------
# Export results
#---------------
dir = File(settings.imageFolder + '/Tracked')
dir.mkdir()
if not batchmode:
cap = WindowManager.getImage("TrackMate capture")
if not WindowManager.checkForDuplicateName:
capTitle = 'Captured_' + settings.imageFileName
capFile = File(dir, '/' + capTitle)
print 'Saving capture to ' + capFile.getPath()
IJ.save(cap, capFile.getPath())
cap.setTitle(capTitle)
else:
print 'Warning: Capture not saved because there are duplicate ImagePlus names'
loglist.append ('Warning: Capture not saved because there are duplicate ImagePlus names')
model.getLogger().log('Warning: Capture not saved because there are duplicate ImagePlus names')
if batchmode:
batchtracks = File(dir, 'Tracked_Batch_' + settings.imageFileName + '.xml')
ExportTracksToXML.export(model, settings, batchtracks)
else:
tracks = File(dir, 'Tracked_TracksOnly.xml')
ExportTracksToXML.export(model, settings, tracks)
file = File(dir, 'Tracked_Complete.xml')
print 'Preparing to save complete xml to ' + file.getPath()
def runVesselDistance(path):
"""
path: full path to original oir file
"""
#
# PREPROCESS (mean, threshold, closing)
# pre-process ch1
ch1_AnalysisOptions = getDefaultAnalysisDict(1)
ch1_AnalysisOptions, ch1_Final_Mask_Title, impCh1 = myPreProcess(
path, ch1_AnalysisOptions)
if ch1_AnalysisOptions is None:
# there was an error in myPreProcess
return None
# pre-process ch1
ch2_AnalysisOptions = getDefaultAnalysisDict(2)
ch2_AnalysisOptions['removeBlobsSmallerThan'] = 100 # units ???
ch2_AnalysisOptions, ch2_Final_Mask_Title, impCh2 = myPreProcess(
path, ch2_AnalysisOptions)
if ch2_AnalysisOptions is None:
# there was an error in myPreProcess
return None
##
## Calculate (EDT, EVF)
## in general, we want pixel values from EDT but only for pixels in EVF in range [0.0, 1.0]
## todo: I am not going to get to this right now
##
doDistanceMap = False
if doDistanceMap:
# this is using '#D ImageJ Suite
# see: https://imagejdocu.tudor.lu/doku.php?id=plugin:stacks:3d_ij_suite:start
#myThreshold = 1
#paramStr = 'map=Both image=' + ch2_Final_Mask_Title + ' mask=' + ch1_Final_Mask_Title + ' threshold=' + str(myThreshold) + ' inverse'
paramStr = 'map=Both image={0} mask={1} threshold={2} inverse'.format(
ch2_Final_Mask_Title, ch1_Final_Mask_Title, myThreshold)
print('3D Distance Map paramStr:', paramStr)
print(
' . calculating (EDT, EVF) ... this will make new windows (EDT, EVF) ...please wait'
)
IJ.run("3D Distance Map", paramStr)
IJ.run("Fire", "") # set last window, evf, to fire
# shrink (EDT, EVF) windows
'''
Both (EDT, EVF) are 32-bit images
EDT has:
pixel values set to the micrometer distance to the vascular mask
EVF has pixel values:
-1: pixels that were in the ch2 vascular mask
0: pixels outside the ch1 HCN1 mask
[0..1]: distance between pixels in HCN1 mask and vascular map (these are normalized)
todo:
for each value in SVF between [0..1], grab value in EDT (in microns)
'''
##
# save results
##
#
# make output folder
folderPath, filename = os.path.split(path)
fileNameNoExtension, fileExtension = filename.split('.')
outputPath = os.path.join(folderPath, 'distanceAnalysis')
if not os.path.exists(outputPath):
os.makedirs(outputPath)
# append to this to get final output fil path ('_ch1.tif', '_ch2.tif', '_ch1_mask.tif', ...)
outBasePath = os.path.join(outputPath, fileNameNoExtension)
# ch1
ch1SavePath = outBasePath + '_ch1.tif'
print(' . saving ch1:', ch1SavePath)
IJ.save(impCh1, ch1SavePath)
# ch2
ch2SavePath = outBasePath + '_ch2.tif'
print(' . saving ch2:', ch2SavePath)
IJ.save(impCh2, ch2SavePath)
# ch1 mask
impCh1Mask = WindowManager.getImage(ch1_Final_Mask_Title)
ch1MaskSavePath = outBasePath + '_ch1_mask.tif'
print(' . saving ch1 mask:', ch1MaskSavePath)
IJ.save(impCh1Mask, ch1MaskSavePath)
# ch2 mask
impCh2Mask = WindowManager.getImage(ch2_Final_Mask_Title)
ch2MaskSavePath = outBasePath + '_ch2_mask.tif'
print(' . saving ch2 mask:', ch2MaskSavePath)
IJ.save(impCh2Mask, ch2MaskSavePath)
if doDistanceMap:
impEDT = WindowManager.getImage('EDT')
edtSavePath = outBasePath + '_edt.tif'
print(' . saving EDT:', edtSavePath)
IJ.save(impEDT, edtSavePath)
impEVF = WindowManager.getImage('EVF')
evfSavePath = outBasePath + '_evf.tif'
print(' . saving EVF:', evfSavePath)
IJ.save(impEVF, evfSavePath)
##
# merge results into composite (merge EVF and vessel mask)
##
'''
IJ.selectWindow(edmFile);
IJ.run("Enhance Contrast", "saturated=0.35"); # not sure how to calculate auto, 0.35 will be different !!!
IJ.run("Apply LUT", "stack");
# merge into composite
print(' . === merging into composite')
#impOriginalWindowTitle was assigned right at start
maskWindowTitle = imp.getTitle() # (mean, threshold) was done in place on copy
edmWindowTitle = edmImp.getTitle()
# run("Merge Channels...", "c1=20200420_distalHEAD__ch2.tif c2=20200420_distalHEAD__ch2_mask.tif c3=20200420_distalHEAD__ch2_edm.tif create keep ignore");
paramStr = 'c1=' + impOriginalWindowTitle + ' c2=' + maskWindowTitle + ' c3=' + edmWindowTitle + ' create keep ignore'
IJ.run("Merge Channels...", paramStr)
'''
# tile all the windows
IJ.run('Tile', '')
#
print('closing all windows')
#closeWorked = WindowManager.closeAllWindows()
return True
#!/bin/sh
''''exec "$(dirname "$0")"/../fiji --jython "$0" "$@" # (call again with fiji)'''
import sys
from ij import IJ, ImageJ
if len(sys.argv) < 2:
print 'Need an output file'
sys.exit(1)
window = ImageJ()
window.hide()
IJ.run("Capture Screen ")
IJ.save(sys.argv[1])
sys.exit(0)
import os
from ij import IJ, ImagePlus
srcDir = "/home/albert/jo/casa/Cambridge/Fulbrooke Road/Fulbrooke Road/HR/"
tgtDir = "/home/albert/jo/casa/Cambridge/Fulbrooke Road/Fulbrooke Road/HR-0.5/"
if not os.path.exists(tgtDir):
os.makedirs(tgtDir)
for filename in os.listdir(srcDir):
if filename.endswith(".jpg"):
imp = IJ.openImage(os.path.join(srcDir, filename))
ip = imp.getProcessor().resize(imp.getWidth() / 2)
IJ.save(ImagePlus(imp.getTitle(), ip), os.path.join(tgtDir, filename))
import sys from ij import IJ # Fiji Jython interpreter implements Python 2.5 which does not # provide support for argparse. title = sys.argv[ -6 ] width = int( sys.argv[ -5 ] ) height = int( sys.argv[ -4 ] ) depth = int( sys.argv[ -3 ] ) type = sys.argv[ -2 ].replace( '_', ' ' ) tmp_image_path = sys.argv[ -1 ] imp = IJ.newImage( title, type, width, height, depth ) IJ.save( imp, "%s" % tmp_image_path )
zscale = calib.pixelDepth
cred = Color(255, 0, 0)
cblue = Color(0, 0, 255)
xoffset = imp.getWidth()
yoffset = imp.getHeight()
ip = out.getStack().getProcessor(1)
for d in data:
frame = int(d[1])
x1 = int(round(float(d[2]) / xscale))
y1 = int(round(float(d[3]) / yscale))
z1 = int(round(float(d[4]) / xscale**2 * zscale))
x2 = int(round(float(d[5]) / xscale))
y2 = int(round(float(d[6]) / yscale))
z2 = int(round(float(d[7]) / xscale**2 * zscale))
direction = float(d[8])
if direction <= 0:
ip.setColor(Color(255, 100, 100))
else:
ip.setColor(Color(100, 100, 255))
ip.setLineWidth(1)
ip.drawLine(x1, y1, x2, y2)
ip.drawLine(x1, yoffset+ z1, x2, yoffset+z2)
ip.drawLine(xoffset+z1, y1, xoffset+z2, y2)
#out.updateAndDraw()
# plot
#out.show()
outimp = ImagePlus(os.path.basename(filename)+'_Out.tif', ip)
#outimp.show()
IJ.save(outimp, newfilepath)
IJ.log("File saved:" + newfilepath)
from ij import IJ
from ij.io import DirectoryChooser
import os
inputFolder = DirectoryChooser('Set input directory.').getDirectory()
outputFolder = DirectoryChooser('Set output directory.').getDirectory()
for filename in os.listdir(inputFolder):
image = IJ.openImage(inputFolder + '/' + filename);
print image.getImageStackSize()
if image.getStackSize() > 1:
IJ.run(image, 'Make Composite', '')
image.show()
IJ.run(image, 'Stack to RGB','')
image.show()
image.close()
newImage = IJ.getImage()
newImage.show()
IJ.save(newImage, outputFolder + '/' + filename);
newImage.close()
else:
IJ.run(image, 'RGB Color','')
IJ.save(image, outputFolder + '/' + filename);
image.close()
f.close()
font = Font("SanSerif", Font.PLAIN, 12)
overlay = ij.gui.Overlay()
for (i,t) in enumerate(ts):
roi = ij.gui.TextRoi(10, 5, "%s" % t, font)
roi.setPosition(0,0,i+1) # order: (channel, slice, frame), 0 means all.
overlay.add(roi)
out.setOverlay(overlay)
# === Saving
IJ.log("Saving.")
#print type(dc_st), dc_st
if not os.path.isfile(of):
IJ.save(out, of)
#if dc_st is None:
# f=open(of, 'w')
# f.write("Only one frame\n")
# f.close()
#else:
# IJ.save(out, of)
# === Save shifts (if needed)
if run_DC:
f=open(of+'.shifts', "w")
f.write("frame,x,y,z\n")
for (i,s) in enumerate(dc_st[0]):
f.write("%i, %f, %f, %f\n" % (i,s.x,s.y, s.z))
f.close()
IJ.run("Quit")
# http://fiji.sc/wiki/index.php/Enhance_Local_Contrast_(CLAHE)
# http://fiji.sc/cgi-bin/gitweb.cgi?p=mpicbg.git;a=blob;f=mpicbg/ij/clahe/PlugIn.java;h=663153764493547de560c08ee11f2e6b1e7e1a32;hb=HEAD
dir = "/usr/people/tmacrina/seungmount/research/Julimaps/datasets/AIBS_pilot_v1/0_raw/"
blocksize = 63
histogram_bins = 255
maximum_slope = 3
mask = "*None*"
composite = False
mask = None
# files = os.listdir(dir)
# files.sort()
# for file in files:
# if file.endswith(".tif")
fn = os.path.join(dir, 'original.tif')
imp = IJ.openImage(fn)
output_fn = os.path.splitext(fn)[0] + "_CLAHE_8bit.tif"
imp = IJ.openImage(fn)
Flat.getFastInstance().run( imp,
blocksize,
histogram_bins,
maximum_slope,
mask,
composite )
ImageConverter(imp).convertToGray8()
IJ.save(imp, output_fn)
for cutIndex in cutIndices:
IJ.log('Working on short time course around cut at frame ' + str(cutIndex))
imStack = ij.ImageStack(512,512)
for fileIndex in range(-1,21):
filename = "%06d_mix.tif" % (cutIndex+fileIndex)
#print filename
imp = IJ.openImage(os.path.join(srcDir, folder, filename))
ip = imp.getProcessor()
#print(ip)
imStack.addSlice(filename, ip)
newFileName = "%s_E%sC%d.tif" % (date, embryoNumber, 1+cutIndices.index(cutIndex))
newImp = ImagePlus(newFileName, imStack)
newImp.show()
IJ.log('Saving data to ' + os.path.join(outputDir, newFileName))
IJ.save(newImp, os.path.join(outputDir, newFileName))
newImp.close()
# Also save whole lot as an image stack...
IJ.log('Working on complete time course - this might take a while...')
images = [f for f in os.listdir(os.path.join(srcDir, folder))
if f.endswith('mix.tif')]
imStack = ij.ImageStack(512,512)
for image in images:
imp = IJ.openImage(os.path.join(srcDir, folder, image))
ip = imp.getProcessor()
imStack.addSlice(image, ip)
newFileName = "%s_E%s complete data.tif" % (date, embryoNumber)
newImp = ImagePlus(newFileName, imStack)
import sys
from ij import IJ
# Fiji Jython interpreter implements Python 2.5 which does not
# provide support for argparse.
title = sys.argv[-6]
width = int(sys.argv[-5])
height = int(sys.argv[-4])
depth = int(sys.argv[-3])
type = sys.argv[-2].replace('_', ' ')
tmp_image_path = sys.argv[-1]
imp = IJ.newImage(title, type, width, height, depth)
IJ.save(imp, "%s" % tmp_image_path)
def write(self, patch=None):
""" Write the patch to a file.
"""
if self.save:
IJ.save(patch, os.path.join(self.datapath, patch.getTitle()+".tif"))
#!/bin/sh
''''exec "$(dirname "$0")"/../fiji --jython "$0" "$@" # (call again with fiji)'''
import sys
from ij import IJ, ImageJ
if len(sys.argv) < 2:
print 'Need an output file'
sys.exit(1)
window = ImageJ()
window.hide()
IJ.run("Capture Screen ")
IJ.save(sys.argv[1])
sys.exit(0)
def crop_project_and_montage(self, roi=None):
## Check for settings
## Dont proceed if no roi is give and no channel is set
if self.roi_channel is None and roi is None:
print "Please set the channel to display the ROI"
return None
## Dont proceed if no name is set. How will you name the files?
if self.name == None:
print "Please set the name of the stack"
return None
if self.outdir == None:
print "Please set where to save the processed images"
return None
self.make_all_projections()
## grab the ROI if no ROI is passed to this function
if roi is None:
roi = self.stack_imps[self.roi_channel].getRoi()
## set images to use
self.to_crop = {}
for channel in self.channel_names:
if channel in self.projections and channel in self.do_z:
self.to_crop[channel] = self.projections[channel]
else:
self.select_slice_to_crop(channel)
## Crop down
x = str(roi.getXBase())[:-2] ## get updated size of Roi
y = str(roi.getYBase())[:-2]
self.processors_to_crop = {}
self.cropped_imps = {}
self.cropped_processors = {}
for channel in self.channel_names:
self.processors_to_crop[channel] = self.to_crop[channel].getProcessor()
self.processors_to_crop[channel].setRoi(roi)
self.cropped_processors[channel] = self.processors_to_crop[channel].crop()
self.cropped_imps[channel] = ImagePlus("Cropped_%s" % channel, self.cropped_processors[channel])
## apply lut
try:
IJ.run(self.cropped_imps[channel], self.luts[channel], "")
except:
IJ.run(self.cropped_imps[channel], "Grays", "")
## min/max
if channel not in self.contrasts:
## do quick autocontrast based on min/max
self.contrasts[channel] = [
self.cropped_imps[channel].getProcessor().getMin(),
self.cropped_imps[channel].getProcessor().getMax(),
]
try:
c = self.contrasts[channel]
self.cropped_imps[channel].getProcessor().setMinAndMax(c[0], c[1])
except:
"Print unable to do contrast"
## save 16 bit images
directory = self.outdir
file_name = self.name
save_as = os.path.join(directory, "%s_%s_%s_%s.tif" % (file_name, channel, x, y))
IJ.save(self.cropped_imps[channel], save_as)
## save RGBs
IJ.run(self.cropped_imps[channel], "RGB Color", "")
save_as = os.path.join(directory, "%s_%s_%s_%s_RGB.tif" % (file_name, channel, x, y))
IJ.save(self.cropped_imps[channel], save_as)
self.make_montage(xy="%sx%s" % (x, y))
BS.rollingBallBackground(dapi_ip,
*[a[1] for a in bkgd_sub])
if SHOW_IMAGES:
dapi.updateAndDraw()
# IJ.setAutoThreshold("Otsu");
# IJ.run("Convert to Mask");
dapi_ip.autoThreshold()
dapi.setProcessor(dapi_ip.convertToByteProcessor(False))
dapi_ip = dapi.getProcessor()
# IJ.run("Watershed");
EDM().toWatershed(dapi_ip)
dapi_ip.invert()
# IJ.run("Analyze Particles...", "size=30-400 show=Masks");
pa_options = particle_analyzer(dapi.getCalibration().pixelWidth)
PA = ParticleAnalyzer(*[a[1] for a in pa_options])
PA.analyze(dapi, dapi_ip)
if SHOW_IMAGES:
dapi.updateAndDraw()
IJ.save(dapi, savename(stack))
dapi.close()
rename_file(progress_name, done_name)
print 'done'
from ij import IJ, ImagePlus, ImageStack, WindowManager
imp = WindowManager.getCurrentImage()
fileinfo = imp.getOriginalFileInfo()
filename = fileinfo.fileName
directory = fileinfo.directory
def extractFrames(imp, interval, offset, nFrames):
""" Extract a stack containing a subset of frames from a stack """
stack = imp.getImageStack()
man = ImageStack(imp.width, imp.height)
for i in range(0, nFrames):
index = imp.getStackIndex(0, 1, i * interval + offset)
man.addSlice(stack.getProcessor(index))
return ImagePlus("Manual_" + filename, man)
mimp = extractFrames(imp, 45, 3, 4)
IJ.save(mimp, directory + '/Manual/Manual.tif')
def run_combine(filePath):
basePath,baseName = os.path.split(filePath)
imagePrefix = os.path.splitext(baseName)[0]
resizeName = basePath + "/" + baseName + "_tiles/resized/"
checkName = basePath + "/" + baseName + "_tiles/resized/" + imagePrefix + "_seq/"
activeName = basePath + "/" + baseName + "_tiles"
outputName = basePath + "/stitched/" + imagePrefix + "/"
IJ.log("Combining " + filePath + "...")
if (not os.path.isdir(outputName)):
try:
os.makedirs(outputName)
if (not os.path.isdir(checkName)):
raise ValueError("Stitched image directory not found")
for dirpath, dirnames, files in os.walk(checkName):
if not files:
raise ValueError("Stitched image directory is empty")
IJ.run("Bio-Formats Importer", "open=["+ activeName +"/tile_1.ome.tif] color_mode=Default view=Hyperstack stack_order=XYCZT");
tileImage = WindowManager.getImage("tile_1.ome.tif")
nChannels = tileImage.getNChannels()
tileImage.close()
options = "open=[" + checkName + "] starting=1 increment=1 scale=100 file=[] sort"
IJ.run("Image Sequence...", options)
theImage = IJ.getImage()
for i in range(1,nChannels+1):
params = "slices=" + str(i) + "-" + str(theImage.getNSlices()) + "-" + str(nChannels)
IJ.run(theImage,"Make Substack...",params)
theImage.close()
wList = WindowManager.getIDList()
for i in range(len(wList)):
currImage = WindowManager.getImage(wList[i])
currImage.setTitle("image"+str(i+1))
IJ.save(currImage,outputName+imagePrefix+"_ch"+str(i+1)+".tif")
if nChannels > 1 and not os.path.isfile(outputName+imagePrefix+"_composite.tif"):
if nChannels == 2:
params = ("c1=" + WindowManager.getImage(wList[0]).getTitle() +
" c4=" + WindowManager.getImage(wList[1]).getTitle() + " create ignore")
elif nChannels == 3:
params = ("c1=" + WindowManager.getImage(wList[1]).getTitle() +
" c2=" + WindowManager.getImage(wList[0]).getTitle() +
" c4=" + WindowManager.getImage(wList[2]).getTitle() + " create ignore")
elif nChannels == 4:
params = ("c1=" + WindowManager.getImage(wList[1]).getTitle() +
" c2=" + WindowManager.getImage(wList[2]).getTitle() +
" c3=" + WindowManager.getImage(wList[0]).getTitle() +
" c4=" + WindowManager.getImage(wList[3]).getTitle() + " create ignore")
elif nChannels == 5:
params = ("c1=" + WindowManager.getImage(wList[1]).getTitle() +
" c2=" + WindowManager.getImage(wList[2]).getTitle() +
" c3=" + WindowManager.getImage(wList[0]).getTitle() +
" c4=" + WindowManager.getImage(wList[4]).getTitle() +
" c7=" + WindowManager.getImage(wList[3]).getTitle() +
" create ignore")
else:
IJ.log("No composite image created due to excess channels (>4)")
IJ.run("Merge Channels...", params)
compositeImage = IJ.getImage()
IJ.save(compositeImage,outputName+imagePrefix+"_composite.tif")
wList = WindowManager.getIDList()
for i in range(len(wList)):
currImage = WindowManager.getImage(wList[i])
currImage.close()
IJ.log("succeeded")
returnVal = 0
except:
IJ.log("failed")
os.rmdir(outputName)
wList = WindowManager.getIDList()
for i in range(len(wList)):
currImage = WindowManager.getImage(wList[i])
currImage.close()
returnVal = 1
else:
IJ.log("skipped")
returnVal = 2
return returnVal