def main():
# Open a .ome.tif image from the Flexoscope.
impath = IJ.getFilePath("Choose .ome.tiff file")
channels = Opener.openUsingBioFormats(impath)
# Show image
# imp.show() # straight to channels object sames memory.
# Split channels.
channels = ChannelSplitter().split(channels)
# Process channel 1.
# subtractzproject(imp, projectionMethod="Median")
channels[1] = ImagePlus()
channels.append(ImagePlus())
channels[1] = subtractzproject(channels[0])
IJ.run(channels[0], "Enhance Contrast...", "saturated=0.3 normalize process_all use")
IJ.run(channels[0], "8-bit", "")
IJ.run(channels[1], "Square", "stack")
IJ.run(channels[1], "Enhance Contrast...", "saturated=0.3 normalize process_all use")
IJ.run(channels[1], "8-bit", "")
# Merge channels.
merge = RGBStackMerge().mergeChannels(channels, True) # boolean keep
merge.show()
def rot3d(imp, axis='x'):
"""pare back Slicer to implement whole-image, +90* rotations that return ImagePlus"""
if imp.getType()==ImagePlus.COLOR_256 or imp.getType()==ImagePlus.COLOR_RGB:
raise NotImplementedError("Handling of colour images isn't implemented yet");
IJ.showStatus("Rotating around {}-axis...".format(axis))
title = imp.getTitle();
original_cal = imp.getCalibration();
new_cal = original_cal;
if imp.getNChannels > 1:
split_ch = ChannelSplitter().split(imp);
else:
split_ch = [imp];
out_imps = [];
if axis=='x':
for ch_imp in split_ch:
input_stack = ch_imp.getStack();
output_stack = rot_around_x(input_stack);
out_imps.append(ImagePlus(title, output_stack));
new_cal.pixelHeight = original_cal.pixelDepth;
new_cal.pixelDepth = original_cal.pixelHeight;
elif axis=='y' or axis=='-y':
for ch_imp in split_ch:
if axis[0]=='-':
input_stack = StackProcessor(ch_imp.getStack()).rotateLeft();
else:
input_stack = StackProcessor(ch_imp.getStack()).rotateRight();
output_stack = rot_around_x(input_stack);
if axis[0]=='-':
final_stack = StackProcessor(output_stack).rotateLeft();
else:
final_stack = StackProcessor(output_stack).rotateRight();
out_imps.append(ImagePlus(title, final_stack));
new_cal.pixelWidth = original_cal.pixelDepth;
new_cal.pixelDepth = original_cal.pixelWidth;
elif axis=='z' or axis=='-z':
for ch_imp in split_ch:
if axis[0]=='-':
output_stack = StackProcessor(ch_imp.getStack()).rotateLeft();
else:
output_stack = StackProcessor(ch_imp.getStack()).rotateRight();
out_imps.append(ImagePlus(title, output_stack));
new_cal.pixelWidth = original_cal.pixelHeight;
new_cal.pixelHeight = original_cal.pixelWidth;
else:
raise NotImplementedError("Please check which axis you've chosen - if not (x, +/-y, +/-z) then it's not implemented...");
imp.changes = False;
imp.close();
if len(out_imps) > 1:
out_imp = RGBStackMerge().mergeChannels(out_imps, False);
else:
out_imp = out_imps[0];
out_imp.setCalibration(new_cal);
return out_imp;
def makemontage(imp, hsize=5, vsize=5, increment=1):
"""Makes a montage of a multichannel ImagePlus object.
Args:
imp (ImagePlus): An ImagePlus object.
hsize (int, optional): Size of the horizontal axis. Defaults to 5.
vsize (int, optional): Size of the vertical axis. Defaults to 5.
increment (int, optional): The increment between images. Allows for dropping of e.g. every second frame. Defaults to 1.
Returns:
ImagePlus: The montage as ImagePlus object.
"""
gridsize = hsize * vsize
def _listProduct(inlist):
"""Calculates the product of all elements in a list.
Args:
inlist (list): A list of numbers.
Returns:
int or double: The product of all list elements.
"""
product = 1
for element in inlist:
if isinstance(element, (int, float)):
product = element * product
return product
def _channelmontage(_imp):
"""Makes a montage of a single channel ImagePlus object.
Args:
_imp (ImagePlus): A single channel ImagePlus object.
Returns:
ImagePlus: A montage of the one input channel.
"""
dims = _imp.getDimensions(
) # width, height, nChannels, nSlices, nFrames
frames = _listProduct(dims[2:])
if frames > gridsize: frames = gridsize
_montage = MontageMaker().makeMontage2(_imp, hsize, vsize, 1.00, 1,
frames, increment, 0, True)
return _montage
name = imp.getTitle()
channels = ChannelSplitter().split(imp)
montages = [_channelmontage(channel) for channel in channels]
montage = RGBStackMerge().mergeChannels(montages, False)
montage.setTitle(name)
return montage
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 _horcombine(imp_collection):
"""Combine a list of stacks with the same dimensions horizontally.
Args:
imp_collection: A list of stacks.
Returns:
A horizontally combined stack of the input images.
"""
comb = imp_collection[0]
comb_channels = ChannelSplitter().split(comb)
comb_channels = [i.getImageStack() for i in comb_channels]
for imp in imp_collection:
if imp == imp_collection[0]:
continue
imp_channels = ChannelSplitter().split(imp)
imp_channels = [i.getImageStack() for i in imp_channels]
comb_channels = [
StackCombiner().combineHorizontally(i, j)
for i, j in zip(comb_channels, imp_channels)
]
comb_channels = [
ImagePlus("C{}".format(i + 1), channel)
for i, channel in enumerate(comb_channels)
]
impout = RGBStackMerge().mergeChannels(comb_channels,
False) # boolean keep
return impout
def analyze(imagefile, outdir, size=10000):
"""Opens a file and creates masks."""
masks = []
image = IJ.openImage(imagefile)
imagetitle = image.getTitle().split(".")[0]
# close existing Summary window
rtframe = WindowManager.getFrame("Summary")
if rtframe is not None:
rtframe.close()
# segmentation
IJ.setAutoThreshold(image, "Default dark");
IJ.run(image, "Options...", "iterations=1 count=1 do=Nothing");
#IJ.run(mask, "Convert to Mask", "");
IJ.run(image, "Analyze Particles...", "size=0-"+str(size)+" show=Masks summarize");
masks.append(IJ.getImage())
IJ.run(image, "Analyze Particles...", "size="+str(size)+"-infinity show=Masks summarize");
masks.append(IJ.getImage())
# get ResultsTable object and save it
rt = WindowManager.getFrame("Summary").getTextPanel().getResultsTable()
rtfile = path.join(outdir, str(size)+"Summary_" + imagetitle + ".csv")
rt.save(rtfile)
# create multi-color merged mask
mergedmask = RGBStackMerge.mergeChannels(masks, False)
for m in masks:
m.close()
mergedmask.setTitle(imagetitle + "-mask.tif")
outputfile = path.join(outdir, mergedmask.getTitle())
IJ.saveAs(mergedmask, "TIFF", outputfile)
def analyse(imp, root, filename):
imp.show()
print ""
print(imp.getTitle())
print "**********************************"
channels=(1,2,3)
thresholds=(3000,1000,8000) # day...
backgrounds=thresholds #(175,0,275)
#IJ.run(imp, "Gaussian Blur...", "sigma=1 stack");
impA = []
sumIntensities = []
sumIntensitiesBW = []
for iChannel in channels:
# general issues:
# - intensity decreases along z
# measure total intensity
# - bg subtraction?
impc = extractChannel(imp, iChannel, 1)
impbw = threshold(impc, thresholds[iChannel-1])
impcBGcorr = Duplicator().run(impc)
IJ.run(impcBGcorr, "Subtract...", "value="+str(backgrounds[iChannel-1])+" stack");
sumIntensities.append(measureSumIntensity3D(impcBGcorr))
# measure number of pixels containing a signification signal
# - what is good here for thresholding and preprocessing?
#impbw = autoThreshold(impc, "Default")
#impbw.setTitle("bw"+str(iChannel))
#impbw.show()
impA.append(impbw)
sumIntensitiesBW.append(measureSumIntensity3D(impbw)/255)
print "intensities,",str(sumIntensities)[1:-1]
print "volumes,",str(sumIntensitiesBW)[1:-1]
sumIntensitiesNorm = [x / sumIntensitiesBW[1] for x in sumIntensities]
sumIntensitiesBWNorm = [x / sumIntensitiesBW[1] for x in sumIntensitiesBW]
print "intensities normalised,",str(sumIntensitiesNorm)[1:-1]
print "volumes normalised,",str(sumIntensitiesBWNorm)[1:-1]
impMerge = RGBStackMerge.mergeChannels(array(impA, ImagePlus), False)
impMerge.setDisplayMode(IJ.COLOR);
impMerge.setC(1);
IJ.run(impMerge, "Green", "");
impMerge.setC(2);
IJ.run(impMerge, "Grays", "");
impMerge.setC(3);
IJ.run(impMerge, "Blue", "");
impMerge.show()
IJ.saveAs(impMerge, "Tiff", os.path.join(root, filename + "--segmented.tif"));
def mergechannels(self, root, sortedchannels, onGPU=False):
'''
Merge sorted channels(list of titles) under a given root title.
If ColorMerger was initialized with a savefolder it will try to save img in savefolder
else will merge channels, close original images and show the resulting composite.
'''
IJ.log("\n## Merging <{0}>".format(root))
imgpaths = [
os.path.join(self.imgfolder, title) if title else None
for title in sortedchannels
]
imps = [imageloader(path) if path else None for path in imgpaths]
for img in imps:
if img:
calibration = img.getCalibration()
break
merger = RGBStackMerge()
try:
composite = merger.mergeChannels(imps, False)
except (Exception, java.lang.Exception):
t_name = current_thread().name
IJ.log(
"# {0}\t{1} images skipped as channels have different dimensions"
.format(t_name, root))
return
composite.setCalibration(calibration)
if self.savefolder:
save_string = os.path.join(self.savefolder, root)
try:
FileSaver(composite).saveAsTiff(save_string)
IJ.log("{0}".format(save_string))
except (Exception, java.lang.Exception) as e:
IJ.log(
"ij.io.FileSaver raised an {0} exception while trying to save img '{1}' as '{2}'. Skipping image."
.format(e, root, save_string))
else:
composite.setTitle(root)
[imp.close() for imp in imps if imp]
composite.show()
def main():
# Open a .ome.tif image from the Flexoscope.
impath = IJ.getFilePath("Choose .ome.tiff file")
channels = Opener.openUsingBioFormats(impath)
cal = channels.getCalibration()
# Show image
# imp.show() # straight to channels object sames memory.
# Split channels.
channels = ChannelSplitter().split(channels)
# Process channel 1.
# subtractzproject(imp, projectionMethod="Median")
channels[0] = subtractzproject(channels[0])
IJ.run(channels[0], "8-bit", "")
# Process channel 2.
# glidingprojection(imp, startframe=1, stopframe=None, glidingFlag=True, no_frames_per_integral=3, projectionmethod="Median")
channels[1] = glidingprojection(channels[1])
IJ.run(channels[1], "8-bit", "")
# [Optional] Process channel 3, 4, etc.
# subtractzproject(channels[3], projectionMethod="Median")
# glidingprojection(channels[3], startframe=1, stopframe=None, glidingFlag=True, no_frames_per_integral=3, projectionmethod="Median")
# IJ.run(channels[3], "8-bit", "")
# Merge channels.
merge = RGBStackMerge().mergeChannels(channels, True) # boolean keep
merge.setCalibration(cal)
merge.show()
def merge_kymographs(kym1_imp, kym2_imp, params):
"""Merge two kymographs"""
mrg_imp = RGBStackMerge().mergeChannels([kym1_imp, kym2_imp], True)
mrg_imp.setTitle("Merged " + params.labeled_species +
" intensity and curvature kymograph")
mrg_imp.show()
return mrg_imp
def downsample_for_isotropy(imp, extra_downsample_factor=2.0, info=None):
"""downsample x, y pixel directions to get ~cubic voxels"""
title = imp.getTitle();
cal = imp.getCalibration();
if info is None:
pix_w = cal.pixelWidth;
pix_h = cal.pixelHeight;
pix_d = cal.pixelDepth;
else:
pix_w = info.get_xy_pixel_size_um();
pix_h = pix_w;
pix_d = info.get_z_plane_spacing_um();
im_w = imp.getWidth();
im_h = imp.getHeight();
im_d = imp.getNSlices();
print("original pixel whd = ({}, {}, {})".format(pix_w, pix_h, pix_d));
print("original image whd = ({}, {}, {})".format(im_w, im_h, im_d));
im_nch = imp.getNChannels();
if im_nch > 1:
split_ch = ChannelSplitter().split(imp);
else:
split_ch = [imp];
print("downsampling {} and making isotropic...".format(title));
IJ.showStatus("Downsampling and making ~isotropic...");
xy_scale = pix_h / (pix_d * extra_downsample_factor);
xy_scaled_h = int(xy_scale * im_h);
xy_scaled_w = int(xy_scale * im_w);
z_scale = 1/ extra_downsample_factor;
z_scaled_h = int(z_scale * im_d);
out_imps = [];
for ch_imp in split_ch:
print(ch_imp.getTitle());
sp = StackProcessor(ch_imp.getStack());
print((xy_scaled_w, xy_scaled_h));
stack = sp.resize(xy_scaled_w, xy_scaled_h, True);
xz_stack = rot_around_x(stack);
xz_sp = StackProcessor(xz_stack);
xz_stack = xz_sp.resize(xy_scaled_w, z_scaled_h, True);
out_stack = rot_around_x(xz_stack);
out_imps.append(ImagePlus("Isotropic downsampled {}".format(title), out_stack));
cal.setUnit('um');
cal.pixelWidth = im_w/xy_scaled_w * pix_w;
cal.pixelHeight = im_h/xy_scaled_h * pix_h;
cal.pixelDepth = im_d/z_scaled_h * pix_d;
print("new pixel whd = ({}, {}, {})".format(cal.pixelWidth, cal.pixelHeight, cal.pixelDepth));
imp.changes = False;
imp.close();
for ch_imp in split_ch:
ch_imp.close();
if len(out_imps) > 1:
out_imp = RGBStackMerge().mergeChannels(out_imps, False);
else:
out_imp = out_imps[0];
out_imp.setCalibration(cal);
print("new image whd = ({}, {}, {})".format(out_imp.getWidth(), out_imp.getHeight(), out_imp.getNSlices()));
print("...done downsampling {} and making isotropic. ".format(title));
IJ.showStatus("...done downsampling and making ~isotropic. ");
return out_imp;
def previewImage(self, imp):
roi = imp.getRoi()
splitter = ChannelSplitter()
channels = []
for c in range(1, imp.getNChannels() + 1):
channel = ImagePlus("Channel %i" % c, splitter.getChannel(imp, c))
projector = ZProjector(channel)
projector.setMethod(ZProjector.MAX_METHOD)
projector.doProjection()
channels.append(projector.getProjection())
image = RGBStackMerge.mergeChannels(channels, False)
image.title = imp.title + " MAX Intensity"
image.luts = imp.luts
imp.setRoi(roi)
return image
def getPreview(image):
enhancer = ContrastEnhancer()
projector = ZProjector()
splitter = ChannelSplitter()
imp1 = ImagePlus("CH1", )
width, height, channels, slices, frames = image.getDimensions()
chimps = []
for ch in range(1, channels + 1):
projector = ZProjector(ImagePlus("C%i" % ch, splitter.getChannel(image, ch)))
projector.setMethod(ZProjector.MAX_METHOD)
projector.doProjection()
proj = projector.getProjection()
enhancer.equalize(proj)
chimps.append(proj)
return RGBStackMerge.mergeChannels(chimps, False)
def preprocess(cell):
print cell.getRawImageFilePath()
imp = Opener().openImage(cell.getRawImageFilePath()) # open raw Image
#if imp.getBitDepth() != 8: # converting to 8 bit if
# ImageConverter(imp).convertToGray8()
roi = imp.roi
imps = CS.split(imp)
p = Preprocessor()
for aimp in imps:
p.setImp(aimp)
p.run()
if roi != None:
aimp.setRoi(roi)
for n in range(1, aimp.getImageStackSize()+1):
aimp.getImageStack().getProcessor(n).fillOutside(roi)
aimp.killRoi()
final = StackMerge.mergeChannels(imps, False)
final.copyScale(imp) # copyscale from .copyscale
return final
def analyze(image, outdir, index, size=10000):
"""Opens a file and creates masks."""
masks = []
imagetitle = image.getTitle().split(".")[0]
# segmentation
IJ.setAutoThreshold(image, "Default dark");
IJ.run(image, "Options...", "iterations=1 count=1 do=Nothing");
#IJ.run(mask, "Convert to Mask", "");
IJ.run(image, "Analyze Particles...", "size=0-"+str(size)+" show=Masks summarize display");
masks.append(IJ.getImage())
IJ.run(image, "Analyze Particles...", "size="+str(size)+"-infinity show=Masks summarize display");
masks.append(IJ.getImage())
# create multi-color merged mask
mergedmask = RGBStackMerge.mergeChannels(masks, False)
for m in masks:
m.close()
mergedmask.setTitle(imagetitle + "-"+str(index)+"-mask.tif")
outputfile = path.join(outdir, mergedmask.getTitle())
IJ.saveAs(mergedmask, "TIFF", outputfile)
print 'yellow image file: ' + green_id
g_imp = ImagePlus(img_dir+'/'+green_id)
imps_for_comp[1] = g_imp
if red:
red_id = color_sublists['rfp'][stage_pos]
print 'red image file: ' + red_id
r_imp = ImagePlus(img_dir+'/'+red_id)
imps_for_comp[5] = r_imp
if brightfield:
bf_id = color_sublists['bf'][stage_pos]
print 'brightfield image file: ' + bf_id
bf_imp = ImagePlus(img_dir+'/'+bf_id)
print ''
if 'w' in colors:
if len(colors) > 2:
composite = RGBStackMerge.mergeChannels(imps_for_comp, True)
IJ.saveAsTiff(composite, merge_output_dir + '/' + stage_pos)
composite.close()
else:
if len(colors) > 1:
composite = RGBStackMerge.mergeChannels(imps_for_comp, True)
IJ.saveAsTiff(composite, merge_output_dir + '/' + stage_pos)
composite.close()
imps_for_z_comp = [None, None, None, None, None, None, None]
if blue:
z_cyan = maxZprojection(c_imp)
IJ.saveAsTiff(z_cyan, z_proj_dir+'/blue/'+stage_pos)
imps_for_z_comp[4] = z_cyan
c_imp.close()
if cyan:
z_cyan = maxZprojection(c_imp)
k = k + 1
stack.addSlice(
str(k),
FloatProcessor(h, w, doutarray[ioffset:(ioffset + planesize)]))
ioffset = ioffset + planesize
#outds1 = ijmService.getDataset(doutMNarray)
imp.setStack(stack, c, z, 1)
if c == 2:
outds1 = extractChannel(imp, 1, 2)
outds2 = extractChannel(imp, 2, 2)
# outds1 = createChannel(doutarray,h,w,z,1)
# outds2 = createChannel(doutarray,h,w,z,2)
outds1.setColor(java.awt.Color.RED)
outds2.setColor(java.awt.Color.GREEN)
outds = RGBStackMerge.mergeChannels(array([outds1, outds2], ImagePlus), 0)
else:
if c > 2:
outds1 = extractChannel(imp, 1, 3)
outds2 = extractChannel(imp, 2, 3)
outds3 = extractChannel(imp, 3, 3)
outds1.setColor(java.awt.Color.RED)
outds2.setColor(java.awt.Color.GREEN)
outds3.setColor(java.awt.Color.BLUE)
outds = RGBStackMerge.mergeChannels(
array([outds1, outds2, outds3], ImagePlus), 0)
else:
outds = imp
outds.show()
IJ.run(
"Multichannel ZT-axis Profile",
_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):
IJ.save(ImagePlus("filtered_%d" % i, filtered[i]),
dstpath[:-4] + '_%d' % i)
# add slices to new stack
stack_new = ImageStack(imp.width, imp.height)
for i in range(3):
stack_new.addSlice(None, filtered[i])
# new image (stack)
imp_new = ImagePlus("new", stack_new)
imp_new.show()
IJ.save(imp_new, dstpath[:-4] + '_stack.tif')
# new image (color)
mergeimp = RGBStackMerge.mergeChannels([
ImagePlus(None, filtered[0]),
ImagePlus(None, filtered[1]),
ImagePlus(None, filtered[2]), None, None, None, None
], False)
mergeimp.title = "filtered"
RGBStackConverter.convertToRGB(mergeimp)
mergeimp.show()
IJ.save(mergeimp, dstpath)
imRed=IJ.openImage(rFn)
imTL=IJ.openImage(tlFn)
# imGreen.show()
# imRed.show()
# imTL.show()
# stack=ij.ImageStack(imRed.width, imRed.height)
# stack.addSlice("red", imRed.getProcessor())
# stack.addSlice("green", imGreen.getProcessor())
# stack.addSlice("tl", imTL.getProcessor())
# imstack=ij.ImagePlus("Stack", stack)
imMerge=RGBStackMerge.mergeChannels([imRed, imGreen, None, imTL], True)
#The merged image is a composite with three seperate channels. For now, we just want to convert the thing to RGB and save it out.
RGBStackConverter.convertToRGB(imMerge)
saveFn=os.path.join(analysisPath, tlFn[-9:-7], 'Merge.jpg'
#imMerge.show()
if stack==None:
stack=Stack("MergedChannels", imMerge)
else:fiji
stack+=imMerge
FileSaver(imMerge).saveAsJpeg(saveFn)
def processFile():
# start logging
IJ.log("\n______________________________\n\n\t\tOlympus DM correction\n\t\tVersion " + pluginVersion +"\n______________________________\n")
# ask user for file
ofd = OpenDialog("Choose a file", None)
filename = ofd.getFileName()
if filename is None:
IJ.log("User canceled the dialog!\nImage processing canceled!\n")
return
directory = ofd.getDirectory()
filepath = directory + filename
IJ.log("File path: " + filepath)
if not filename.endswith(".oir"):
IJ.log("Not an Olympus (.oir) file.\nNo image to process.\n")
return
filenameExExt = os.path.splitext(filename)[0]
# parse metadata
reader = ImageReader()
omeMeta = MetadataTools.createOMEXMLMetadata()
reader.setMetadataStore(omeMeta)
reader.setId(filepath)
numChannels = reader.getSizeC()
numSlices = reader.getSizeZ()
numFrames = reader.getSizeT()
seriesCount = reader.getSeriesCount()
globalMetadata = reader.getGlobalMetadata()
seriesMetadata = reader.getSeriesMetadata()
objLensName = globalMetadata['- Objective Lens name #1']
areaRotation = float(seriesMetadata['area rotation #1'])
acquisitionValueRotation = float(seriesMetadata['acquisitionValue rotation #1'])
if 'regionInfo rotation #1' in seriesMetadata:
regionInfoRotation = float(seriesMetadata['regionInfo rotation #1'])
else:
regionInfoRotation = float(0)
totalRotation = areaRotation + regionInfoRotation
physSizeX = omeMeta.getPixelsPhysicalSizeX(0)
physSizeY = omeMeta.getPixelsPhysicalSizeY(0)
pxSizeX = physSizeX.value(UNITS.MICROM)
pxSizeY = physSizeY.value(UNITS.MICROM)
# log metadata
IJ.log("\nMETADATA")
#IJ.log("Filename: " + filepath)
IJ.log("Number of series: " + str(seriesCount))
IJ.log("Number of channels: " + str(numChannels))
IJ.log("Number of frames: " + str(numFrames))
IJ.log("Number of slices: " + str(numSlices))
IJ.log("Objective lens: " + objLensName)
IJ.log("FOV rotation: " + str(areaRotation))
IJ.log("ROI rotation: " + str(regionInfoRotation))
IJ.log("Total rotation: " + str(totalRotation))
IJ.log("Pixel size:")
IJ.log("\t\tX = " + str(physSizeX.value()) + " " + physSizeX.unit().getSymbol())
IJ.log("\t\tY = " + str(physSizeY.value()) + " " + physSizeY.unit().getSymbol())
# ask user to identify dichroic mirror used for each channel
gdDM = GenericDialog("Dichroic mirrors")
DMs = ["DM1", "DM2", "DM3", "DM4", "DM5"]
for i in range(numChannels):
gdDM.addChoice("Channel " + str(i+1), DMs, DMs[0])
gdDM.addCheckbox("Merge channels", False)
gdDM.showDialog()
if gdDM.wasCanceled():
IJ.log("User canceled the dialog!\nImage processing canceled!\n")
return
dichroics = []
for i in range(numChannels):
dichroics.append(gdDM.getNextChoice())
merge = gdDM.getNextBoolean()
IJ.log("\nUser selected dichroic mirrors")
for i in range(numChannels):
IJ.log("\t\tChannel " + str(i+1) + ": " + dichroics[i])
if merge:
channels = []
chDict = {}
for i in range(numChannels):
chName = "Channel"+str(i+1)
channels.append(chName)
chDict[chName] = i
channels.append("NONE")
colourChoices = ["red", "green", "blue", "gray", "cyan", "magenta", "yellow"]
gdMerge = GenericDialog("Merge channels")
for c in colourChoices:
gdMerge.addChoice(c + ":", channels, channels[numChannels])
gdMerge.showDialog()
if gdMerge.wasCanceled():
IJ.log("User canceled the dialog!\nImage processing canceled!\n")
return
IJ.log("\nUser selected channel colours")
mergeList = []
for i in range(len(colourChoices)):
ch = gdMerge.getNextChoice()
if ch == "NONE":
mergeList.append(None)
else:
mergeList.append(chDict[ch])
IJ.log("\t\t" + colourChoices[i] + ": " + ch)
# ask user for an output directory
dc = DirectoryChooser("Choose folder for output")
od = dc.getDirectory()
if od is None:
IJ.log("User canceled the dialog!\nImage processing canceled!\n")
return
if merge:
tifDir = od + "." + str(datetime.now()).replace(" ", "").replace(":", "") + "/"
if not os.path.exists(tifDir):
os.makedirs(tifDir)
IJ.log("\nCreated temporary folder: " + tifDir + "\n")
else:
IJ.log("Unable to create temporary folder!\n")
else:
tifDir = od + filenameExExt + "/"
if not os.path.exists(tifDir):
os.makedirs(tifDir)
IJ.log("\nCreated subfolder: " + tifDir + "\n")
else:
IJ.log("\nSubfolder " + tifDir + " already exists")
# correct images
tifFilePaths = []
for i in range(numChannels):
ip = extractChannel(oirFile=filepath, ch=i)
if dichroics[i] == "DM1":
IJ.log("Channel " + str(i+1) + " was imaged using DM1, so no correction required.")
else:
offsets = getOffset(obj=objLensName,dm=dichroicDict[dichroics[i]])
xom = offsets['x']
yom = offsets['y']
if abs(totalRotation) > 0.1:
rotOff = rotateOffset(x=xom, y=yom, angle=-totalRotation)
xom = rotOff['x']
yom = rotOff['y']
xop = int(round(xom/pxSizeX))
yop = int(round(yom/pxSizeY))
IJ.log("Channel " + str(i+1) + " offsets")
IJ.log("\t\tMicrometres")
IJ.log("\t\t\t\tx = " + str(xom))
IJ.log("\t\t\t\ty = " + str(yom))
IJ.log("\t\tPixels")
IJ.log("\t\t\t\tx = " + str(xop))
IJ.log("\t\t\t\ty = " + str(yop))
IJ.run(ip, "Translate...", "x=" + str(-xop) + " y=" + str(-yop) + " interpolation=None stack")
tifFilePath = tifDir + filenameExExt + "_ch_"+str(i+1)+".tif"
tifFilePaths.append(tifFilePath)
if os.path.exists(tifFilePath):
IJ.log("\nOutput file exists: " + tifFilePath)
IJ.log("Rerun plugin choosing a different output folder")
IJ.log("or delete file and then rerun plugin.")
IJ.log("Image processing terminated!\n")
return
FileSaver(ip).saveAsTiff(tifFilePath)
if merge:
for i in range(len(mergeList)):
if mergeList[i] != None:
mergeList[i] = readSingleChannelImg(tifFilePaths[mergeList[i]])
merged = RGBStackMerge.mergeChannels(mergeList, False)
mergedChannelFilepath = od + filenameExExt + ".tif"
if os.path.exists(mergedChannelFilepath):
IJ.log("\nOutput file exists: " + mergedChannelFilepath)
IJ.log("Rerun plugin choosing a different output folder")
IJ.log("or delete file and then rerun plugin.")
IJ.log("Image processing terminated!\n")
FileSaver(merged).saveAsTiff(mergedChannelFilepath)
for tf in tifFilePaths:
os.remove(tf)
os.rmdir(tifDir)
IJ.log("\nFinished processing file:\n" + filepath + "\n")
if merge:
IJ.log("Image file with channels aligned:\n" + od + filenameExExt + ".tif\n")
else:
IJ.log("Aligned images (one tiff file for each channel) can be found in:\n" + tifDir + "\n")
def processFile(filename, inDir, outDir, dichroics, mergeList):
if mergeList is None:
merge = False
else:
merge = True
filenameExExt = os.path.splitext(filename)[0]
filepath = inDir + filename
# parse metadata
reader = ImageReader()
omeMeta = MetadataTools.createOMEXMLMetadata()
reader.setMetadataStore(omeMeta)
reader.setId(filepath)
numChannels = reader.getSizeC()
numSlices = reader.getSizeZ()
numFrames = reader.getSizeT()
seriesCount = reader.getSeriesCount()
globalMetadata = reader.getGlobalMetadata()
seriesMetadata = reader.getSeriesMetadata()
objLensName = globalMetadata['- Objective Lens name #1']
areaRotation = float(seriesMetadata['area rotation #1'])
acquisitionValueRotation = float(seriesMetadata['acquisitionValue rotation #1'])
if 'regionInfo rotation #1' in seriesMetadata:
regionInfoRotation = float(seriesMetadata['regionInfo rotation #1'])
else:
regionInfoRotation = float(0)
totalRotation = areaRotation + regionInfoRotation
physSizeX = omeMeta.getPixelsPhysicalSizeX(0)
physSizeY = omeMeta.getPixelsPhysicalSizeY(0)
pxSizeX = physSizeX.value(UNITS.MICROM)
pxSizeY = physSizeY.value(UNITS.MICROM)
# log metadata
IJ.log("\nMETADATA")
#IJ.log("Filename: " + filepath)
IJ.log("Number of series: " + str(seriesCount))
IJ.log("Number of channels: " + str(numChannels))
IJ.log("Number of frames: " + str(numFrames))
IJ.log("Number of slices: " + str(numSlices))
IJ.log("Objective lens: " + objLensName)
IJ.log("FOV rotation: " + str(areaRotation))
IJ.log("ROI rotation: " + str(regionInfoRotation))
IJ.log("Total rotation: " + str(totalRotation))
IJ.log("Pixel size:")
IJ.log("\t\tX = " + str(physSizeX.value()) + " " + physSizeX.unit().getSymbol())
IJ.log("\t\tY = " + str(physSizeY.value()) + " " + physSizeY.unit().getSymbol())
if merge:
tifDir = outDir + "." + str(datetime.now()).replace(" ", "").replace(":", "") + "/"
if not os.path.exists(tifDir):
os.makedirs(tifDir)
IJ.log("\nCreated temporary folder: " + tifDir + "\n")
else:
IJ.log("Unable to create temporary folder!\n")
else:
tifDir = outDir + filenameExExt + "/"
if not os.path.exists(tifDir):
os.makedirs(tifDir)
IJ.log("\nCreated subfolder: " + tifDir + "\n")
else:
IJ.log("\nSubfolder " + tifDir + " already exists.\n")
# correct images
tifFilePaths = []
for i in range(numChannels):
ip = extractChannel(oirFile=filepath, ch=i)
if dichroics[i] == "DM1":
IJ.log("Channel " + str(i+1) + " was imaged using DM1, so no correction required.")
else:
offsets = getOffset(obj=objLensName,dm=dichroicDict[dichroics[i]])
xom = offsets['x']
yom = offsets['y']
if abs(totalRotation) > 0.1:
rotOff = rotateOffset(x=xom, y=yom, angle=-totalRotation)
xom = rotOff['x']
yom = rotOff['y']
xop = int(round(xom/pxSizeX))
yop = int(round(yom/pxSizeY))
IJ.log("Channel " + str(i+1) + " offsets")
IJ.log("\t\tMicrometres")
IJ.log("\t\t\t\tx = " + str(xom))
IJ.log("\t\t\t\ty = " + str(yom))
IJ.log("\t\tPixels")
IJ.log("\t\t\t\tx = " + str(xop))
IJ.log("\t\t\t\ty = " + str(yop))
IJ.run(ip, "Translate...", "x=" + str(-xop) + " y=" + str(-yop) + " interpolation=None stack")
tifFilePath = tifDir + filenameExExt + "_ch_"+str(i+1)+".tif"
tifFilePaths.append(tifFilePath)
if os.path.exists(tifFilePath):
IJ.log("\nOutput file exists: " + tifFilePath)
IJ.log("Rerun plugin choosing a different output folder")
IJ.log("or delete file and then rerun plugin.")
IJ.log("Image processing terminated!\n")
return
FileSaver(ip).saveAsTiff(tifFilePath)
if merge:
max_list = []
for i in range(len(mergeList)):
if mergeList[i] != None:
mergeList[i] = readSingleChannelImg(tifFilePaths[mergeList[i]])
channel = mergeList[i]#https://python.hotexamples.com/examples/ij.plugin/RGBStackMerge/mergeChannels/python-rgbstackmerge-mergechannels-method-examples.html
projector = ZProjector(channel)
projector.setMethod(ZProjector.MAX_METHOD)
projector.doProjection()
max_list.append(projector.getProjection())
merged = RGBStackMerge.mergeChannels(mergeList, False)
merged_max = RGBStackMerge.mergeChannels(max_list, False)
mergedChannelFilepath = outDir + filenameExExt + ".tif"
maxMergedChannelFilepath = outDir + filenameExExt + "_max.tif"
if os.path.exists(mergedChannelFilepath):
IJ.log("\nOutput file exists: " + mergedChannelFilepath)
IJ.log("Rerun plugin choosing a different output folder")
IJ.log("or delete file and then rerun plugin.")
IJ.log("Image processing terminated!\n")
FileSaver(merged).saveAsTiff(mergedChannelFilepath)
FileSaver(merged_max).saveAsTiff(maxMergedChannelFilepath)
for tf in tifFilePaths:
os.remove(tf)
os.rmdir(tifDir)
IJ.log("\nFinished processing file:\n" + filepath + "\n")
if merge:
IJ.log("Image file with channels aligned:\n" + outDir + filenameExExt + ".tif\n")
else:
IJ.log("Aligned images (one tiff file for each channel) can be found in:\n" + tifDir + "\n")
def do_tubefitting(im_path=im_test_path,
metadata_path=metadata_test_path,
output_path=output_path,
save_output=False):
# todo: fix things so that all operations use a consistent definition of background rather than changing Prefs on the fly...
Prefs.blackBackground = False
info = PrescreenInfo()
info.load_info_from_json(metadata_path)
z_xy_ratio = abs(
info.get_z_plane_spacing_um()) / info.get_xy_pixel_size_um()
#z_xy_ratio = 1.0;
bfimp = bf.openImagePlus(im_path)
imp = bfimp[0]
imp.show()
IJ.run(imp, "Set Scale...", "distance=0 known=0 pixel=1 unit=pixel")
imp = utils.downsample_for_isotropy(imp,
extra_downsample_factor=1.0,
info=info)
rot_seg_imp, rot_proj_imp, egfp_mch_imps = split_and_rotate(imp, info)
depth = rot_seg_imp.getNSlices() if rot_seg_imp.getNSlices(
) > rot_seg_imp.getNFrames() else rot_seg_imp.getNFrames()
width = rot_seg_imp.getWidth()
height = int(round(rot_seg_imp.getHeight() * z_xy_ratio))
# Apply 3d MEDIAN FILTER to denoise and emphasise vessel-associated voxels
fit_basis_imp = threshold_and_binarise(rot_seg_imp, z_xy_ratio)
fit_basis_imp.setTitle("fit_basis_imp")
fit_basis_imp.show()
# plane-wise, use binary-outline
# say the non-zero points then make up basis for fitting to be performed per http://nicky.vanforeest.com/misc/fitEllipse/fitEllipse.html
rois = []
centres = []
major_axes = []
roi_imp = IJ.createImage("rois", width, height, depth, 32)
pts_stack = ImageStack(width, height + 1)
IJ.run(imp, "Line Width...", "line=3")
for zidx in range(fit_basis_imp.getNSlices()):
fit_basis_imp.setZ(zidx + 1)
IJ.run(fit_basis_imp, "Outline", "slice")
IJ.run(fit_basis_imp, "Create Selection", "")
roi = fit_basis_imp.getRoi()
fit_basis_imp.killRoi()
pts = [(pt.x, pt.y) for pt in roi.getContainedPoints()]
clean_pts = convex_hull_pts(pts)
clean_pts = [(x, z_xy_ratio * y) for (x, y) in clean_pts]
# make a stack of clean points...
ip = FloatProcessor(width, height + 1)
pix = ip.getPixels()
for pt in clean_pts:
pix[int(pt[1]) * width + int(pt[0])] = 128
pts_stack.addSlice(ip)
centre, angle, axl = ellipse_fitting.fit_ellipse(clean_pts)
major_axes.append(max(axl))
centres.append(centre)
rot_seg_imp.setZ(zidx + 1)
ellipse_roi = ellipse_fitting.generate_ellipse_roi(centre, angle, axl)
rois.append(ellipse_roi)
IJ.run(imp, "Line Width...", "line=1")
cal = imp.getCalibration()
smooth_centres, tangent_vecs = generate_smoothed_vessel_axis(
centres, pixel_size_um=cal.pixelDepth)
for zidx in range(fit_basis_imp.getNSlices()):
centre = smooth_centres[zidx]
major_axis = major_axes[zidx]
ellipse_roi = EllipseRoi(centre[0] - 2, centre[1], centre[0] + 2,
centre[1], 1.0)
roi_imp.setZ(zidx + 1)
roi_imp.setRoi(ellipse_roi)
IJ.run(roi_imp, "Set...",
"value=" + str(roi_imp.getProcessor().maxValue()) + " slice")
pts_stack_imp = ImagePlus("Cleaned points", pts_stack)
pts_stack_imp.setTitle("pts_stack_imp")
pts_stack_imp.show()
rot_seg_imp.changes = False
rot_seg_imp.close()
egfp_imp = egfp_mch_imps[0]
mch_imp = egfp_mch_imps[1]
imps_to_combine = [egfp_mch_imps[1], egfp_mch_imps[0], roi_imp]
egfp_imp.show()
mch_imp.show()
roi_imp.show()
print("box height um = " +
str(roi_imp.getNSlices() * info.get_xy_pixel_size_um()))
IJ.run(
egfp_imp, "Size...", "width=" + str(width) + " height=" + str(height) +
" depth=" + str(depth) + " average interpolation=Bilinear")
IJ.run(
mch_imp, "Size...", "width=" + str(width) + " height=" + str(height) +
" depth=" + str(depth) + " average interpolation=Bilinear")
#IJ.run("Merge Channels...", "c1=[" + mch_imp.getTitle() +
# "] c2=[" + egfp_imp.getTitle() +
# "] c7=[" + roi_imp.getTitle() + "] create keep");
composite_imp = RGBStackMerge().mergeChannels(imps_to_combine, False)
print(composite_imp)
composite_imp.show()
print("end of vessel centerline id step, image dims = ({}x{}x{})".format(
composite_imp.getWidth(), composite_imp.getHeight(),
composite_imp.getNSlices()))
WaitForUserDialog("pause").show()
# do qc here?
#WM.getImage("Composite").addImageListener(UpdateRoiImageListener(rois));
IJ.run(roi_imp, "8-bit", "")
if save_output:
FileSaver(composite_imp).saveAsTiffStack(
os.path.join(output_path, "segmentation result.tif"))
print(roi_imp)
FileSaver(roi_imp).saveAsTiff(
os.path.join(output_path, "vessel axis.tif"))
egfp_imp.changes = False
mch_imp.changes = False
roi_imp.changes = False
fit_basis_imp.changes = False
pts_stack_imp.changes = False
egfp_imp.close()
mch_imp.close()
#roi_imp.close();
fit_basis_imp.close()
pts_stack_imp.close()
zcoords = [i for i in range(composite_imp.getNSlices())]
xyz_smooth_centres = [(x, y, z)
for ((x, y), z) in zip(smooth_centres, zcoords)]
composite_imp2 = straighten_vessel(composite_imp,
xyz_smooth_centres,
save_output=True)
composite_imp3 = straighten_vessel(composite_imp2,
xyz_smooth_centres,
it=2,
save_output=True)
return composite_imp3
def processImage(filename):
# many zeiss images have a pyramid format. In order to speed up the evaluation, we chose only the second highest resolution (=series_2).
# depending on your computer/workstation this parameter can be optimized (i.e. chose series_1 if you have a fast computer or series_3 for slow ones)
IJ.run(
"Bio-Formats", "open=" + filename +
" color_mode=Composite rois_import=[ROI manager] view=Hyperstack stack_order=XYCZT series_2"
)
imp = IJ.getImage()
imp.show()
if (not entertain):
imp.show()
# preparing memory for saving the results
original1 = NewImage.createFloatImage("Original 1",
imp.getWidth() / tileSize + 1,
imp.getHeight() / tileSize + 1, 1,
NewImage.FILL_BLACK)
original1Map = original1.getProcessor()
original2 = NewImage.createFloatImage("Original 2",
imp.getWidth() / tileSize + 1,
imp.getHeight() / tileSize + 1, 1,
NewImage.FILL_BLACK)
original2Map = original2.getProcessor()
spotCount1 = NewImage.createFloatImage("Spot count 1",
imp.getWidth() / tileSize + 1,
imp.getHeight() / tileSize + 1, 1,
NewImage.FILL_BLACK)
spotCount1Map = spotCount1.getProcessor()
spotCount2 = NewImage.createFloatImage("Spot count 2",
imp.getWidth() / tileSize + 1,
imp.getHeight() / tileSize + 1, 1,
NewImage.FILL_BLACK)
spotCount2Map = spotCount2.getProcessor()
ratio = NewImage.createFloatImage("Ratio",
imp.getWidth() / tileSize + 1,
imp.getHeight() / tileSize + 1, 1,
NewImage.FILL_BLACK)
ratioMap = ratio.getProcessor()
if (entertain):
ratio.show()
# go through all tiles
for x in range(0, ratioMap.getWidth() - 1):
for y in range(0, ratioMap.getHeight() - 1):
# crop out the tile from the original images
imp.setRoi(Roi(x * tileSize, y * tileSize, tileSize, tileSize))
channel1 = Duplicator().run(imp, 1, 1, 1, 1, 1, 1)
channel2 = Duplicator().run(imp, 2, 2, 1, 1, 1, 1)
# spot detection
spots1 = detectSpots(channel1,
spotDetection_GaussianBlur_sigma_GFP,
spotDetection_findMaxima_noise_GFP)
spots2 = detectSpots(channel2,
spotDetection_GaussianBlur_sigma_DAPI,
spotDetection_findMaxima_noise_DAPI)
# pixel statistics
statistics1 = channel1.getStatistics()
statistics2 = channel2.getStatistics()
# calculate ratio if spots were found
s1 = 0
s2 = 0
r = 0
if (spots1 is not None and spots2 is not None):
fp1 = spots1.getFloatPolygon()
fp2 = spots2.getFloatPolygon()
s1 = fp1.npoints
s2 = fp2.npoints
if (s2 > cutOff_DAPI and s1 > cutOff_GFP):
r = 1.0 * s1 / s2
# fill result memory
original1Map.setf(x, y, statistics1.mean)
original2Map.setf(x, y, statistics2.mean)
spotCount1Map.setf(x, y, s1)
spotCount2Map.setf(x, y, s2)
ratioMap.setf(x, y, r)
if (entertain):
# show current result image
ratio.updateAndDraw()
IJ.run(ratio, "Enhance Contrast", "saturated=0.35")
# put all results image channels together to one image
images = []
images.append(original1)
images.append(original2)
images.append(spotCount1)
images.append(spotCount2)
images.append(ratio)
resultMap = RGBStackMerge.mergeChannels(images, False)
# fix pixel size
# factor is multiplied by 2 because ImageJ seems to have a problem when using .czi file series of lower resolution (i.e. series_2); please check for individual cases!
factor = (imp.getWidth() / resultMap.getWidth()) * 2
IJ.run(
resultMap, "Properties...", "channels=5 slices=1 frames=1 unit=" +
imp.getCalibration().getUnit() + " pixel_width=" +
str(imp.getCalibration().pixelWidth * factor) + " pixel_height=" +
str(imp.getCalibration().pixelHeight * factor) + " voxel_depth=1.0000")
IJ.run(
ratio, "Properties...", "channels=1 slices=1 frames=1 unit=" +
imp.getCalibration().getUnit() + " pixel_width=" +
str(imp.getCalibration().pixelWidth * factor) + " pixel_height=" +
str(imp.getCalibration().pixelHeight * factor) + " voxel_depth=1.0000")
# visualisation
resultMap.setC(1)
IJ.run(resultMap, "Green", "")
IJ.run(resultMap, "Enhance Contrast", "saturated=0.35")
resultMap.setC(2)
IJ.run(resultMap, "Blue", "")
IJ.run(resultMap, "Enhance Contrast", "saturated=0.35")
resultMap.setC(3)
IJ.run(resultMap, "mpl-inferno", "")
IJ.run(resultMap, "Enhance Contrast", "saturated=0.35")
resultMap.setC(4)
IJ.run(resultMap, "mpl-inferno", "")
IJ.run(resultMap, "Enhance Contrast", "saturated=0.35")
resultMap.setC(5)
IJ.run(resultMap, "mpl-inferno", "")
resultMap.show()
IJ.resetMinAndMax(resultMap)
resultMap.setDisplayMode(IJ.COLOR)
IJ.run(ratio, "mpl-inferno", "")
IJ.setMinAndMax(ratio, 0, 1)
# save result
IJ.saveAsTiff(resultMap, filename + "_suitable-name_map.tif")
IJ.saveAsTiff(ratio, filename + "_suitable-name_ratio.tif")
IJ.run("Close All", "")
thr1, thrimp1 = calculateThreshold(imp1, roi, methods[0])
thr2, thrimp2 = calculateThreshold(imp2, roi, methods[1])
cursor = TwinCursor(img1.randomAccess(), img2.randomAccess(), Views.iterable(mask).localizingCursor())
rtype = img1.randomAccess().get().createVariable()
raw = manders.calculateMandersCorrelation(cursor, rtype)
rthr1 = rtype.copy()
rthr2 = rtype.copy()
rthr1.set(thr1)
rthr2.set(thr2)
cursor.reset()
thrd = manders.calculateMandersCorrelation(cursor, rthr1, rthr2, ThresholdMode.Above)
print "Results are: %f %f %f %f" % (raw.m1, raw.m2, thrd.m1, thrd.m2)
results.incrementCounter()
rowno = results.getCounter() - 1
results.setValue("Cell", rowno, int(rowno))
results.setValue("Threshold 1", rowno, int(thr1))
results.setValue("Threshold 2", rowno, int(thr2))
results.setValue("M1 raw", rowno, float(raw.m1))
results.setValue("M2 raw", rowno, float(raw.m2))
results.setValue("M1 thrd", rowno, float(thrd.m1))
results.setValue("M2 thrd", rowno, float(thrd.m2))
thrimp = RGBStackMerge.mergeChannels([thrimp1, thrimp2], False)
saver = FileSaver(thrimp)
saver.saveAsTiffStack(outputDir + "Cell_%i-" % results.getCounter() + title + ".tif")
thrimp.close()
results.show("Colocalization results")
def batch_open_images(pathImage,
pathMask,
file_typeImage=None,
name_filterImage=None,
recursive=False):
'''Open all files in the given folder.
:param path: The path from were to open the images. String and java.io.File are allowed.
:param file_type: Only accept files with the given extension (default: None).
:param name_filter: Reject files that contain the given string (default: wild characters).
:param recursive: Process directories recursively (default: False).
'''
# Converting a File object to a string.
if isinstance(pathImage, File):
pathImage = pathImage.getAbsolutePath()
def check_type(string):
'''This function is used to check the file type.
It is possible to use a single string or a list/tuple of strings as filter.
This function can access the variables of the surrounding function.
:param string: The filename to perform the check on.
'''
if file_typeImage:
# The first branch is used if file_type is a list or a tuple.
if isinstance(file_typeImage, (list, tuple)):
for file_type_ in file_typeImage:
if string.endswith(file_type_):
# Exit the function with True.
return True
else:
# Next iteration of the for loop.
continue
# The second branch is used if file_type is a string.
elif isinstance(file_typeImage, string):
if string.endswith(file_typeImage):
return True
else:
return False
return False
# Accept all files if file_type is None.
else:
return True
def check_filter(string):
'''This function is used to check for a given filter.
It is possible to use a single string or a list/tuple of strings as filter.
This function can access the variables of the surrounding function.
:param string: The filename to perform the filtering on.
'''
if name_filterImage:
# The first branch is used if name_filter is a list or a tuple.
if isinstance(name_filterImage, (list, tuple)):
for name_filter_ in name_filterImage:
if name_filter_ in string:
# Exit the function with True.
return True
else:
# Next iteration of the for loop.
continue
# The second branch is used if name_filter is a string.
elif isinstance(name_filterImage, string):
if name_filterImage in string:
return True
else:
return False
return False
else:
# Accept all files if name_filter is None.
return True
# We collect all files to open in a list.
path_to_Image = []
# Replacing some abbreviations (e.g. $HOME on Linux).
path = os.path.expanduser(pathImage)
path = os.path.expandvars(pathImage)
# If we don't want a recursive search, we can use os.listdir().
if not recursive:
for file_name in os.listdir(pathImage):
full_path = os.path.join(pathImage, file_name)
if os.path.isfile(full_path):
if check_type(file_name):
if check_filter(file_name):
path_to_Image.append(full_path)
# For a recursive search os.walk() is used.
else:
# os.walk() is iterable.
# Each iteration of the for loop processes a different directory.
# the first return value represents the current directory.
# The second return value is a list of included directories.
# The third return value is a list of included files.
for directory, dir_names, file_names in os.walk(pathImage):
# We are only interested in files.
for file_name in file_names:
# The list contains only the file names.
# The full path needs to be reconstructed.
full_path = os.path.join(directory, file_name)
# Both checks are performed to filter the files.
if check_type(file_name):
if check_filter(file_name) is False:
# Add the file to the list of images to open.
path_to_Image.append([
full_path,
os.path.basename(os.path.splitext(full_path)[0])
])
# Create the list that will be returned by this function.
Images = []
Masks = []
for img_path, file_name in path_to_Image:
# IJ.openImage() returns an ImagePlus object or None.
imp = IJ.openImage(img_path)
imp.show()
IJ.run("8-bit")
print(img_path)
if check_filter(file_name):
continue
else:
# An object equals True and None equals False.
MaskName = str(pathMask) + '/' + "Mask" + file_name + '.tif'
Mask = IJ.openImage(MaskName)
Mask.show()
IJ.run("8-bit")
IJ.run("Find Edges")
IJ.selectWindow(file_name + ".tif")
IJ.selectWindow("Mask" + file_name + ".tif")
Mask.changes = False
imp.changes = False
impResult = RGBStackMerge.mergeChannels([imp, Mask], False)
impResult.show()
IJ.saveAs(impResult, '.tif',
str(pathMask) + "/" + file_name)
imp.close()
Mask.close()
#print(img_path, RoiName)
Images.append(imp)
return Images
# overwrite EM stack with transformed EM image
fs = FileSaver(EM)
fs.saveAsTiff(saveEMfilepath)
# open EM image as ImagePlus for merging channel
EM = ImagePlus(saveEMfilepath)
# split rLM image in different channels
saverLMpath = os.path.join(transformation_output, "rLM.tif")
rLM = IJ.openImage(saverLMpath)
R, G, B = ChannelSplitter.split(rLM)
# Merge channels
overlay = RGBStackMerge.mergeChannels(
[None, None, B, EM, None, None, None],
True) # Change R,G, B if the chromatin channel is a different one.
# If you want a different color for the chromatin in the overlay image, change the position of the letter R,G,B,
# e.g. [B, None, None, EM, None, None, None] for displaying chromatin in the red channel.
RGBStackConverter.convertToRGB(overlay)
# save overlay
saveoverlaypath = os.path.join(workdir, "overlay_EM_Chromatin.tif")
fs = FileSaver(overlay)
fs.saveAsTiff(saveoverlaypath)
print("(----- )overlay EM and rLM image done")
############################################ clean up
# copy important files into the working directory
def saveMultichannelImage(self, title, channels, luts): tmp = RGBStackMerge.mergeChannels(channels, False) tmp.luts = luts saver = FileSaver(tmp) saver.saveAsTiffStack(self.outputDir + title + ".tif") tmp.close()
---------- --- ----------------------------------------------------
2019-06-16 JRM Initial adaptation. Use the GIT_HOME environment
variable to get the start of the path
"""
import os
from ij import IJ, ImagePlus
from ij.plugin import RGBStackMerge, RGBStackConverter
# use a path relative to the GIT_HOME environment variable
git_home = os.getenv("GIT_HOME")
print(git_home)
# start clean
IJ.run("Close All")
# Load the R, G, and B color separation images. These were created
# using make_mandrill_256.py
impc1 = ImagePlus(git_home + "/tips/ImageJ/tif/mandrill_256_red.tif")
impc2 = ImagePlus(git_home + "/tips/ImageJ/tif/mandrill_256_green.tif")
impc3 = ImagePlus(git_home + "/tips/ImageJ/tif/mandrill_256_blue.tif")
# merge my three images
imp_merge = RGBStackMerge.mergeChannels([impc1, impc2, impc3], True)
# convert the composite image to the RGB image
RGBStackConverter.convertToRGB(imp_merge)
imp_merge.show()
IJ.setThreshold(green_coloc,summary["Green" + "-threshold-used"], 255);
IJ.run(green_coloc, "Convert to Mask", "");
pa_green.analyze(green_coloc)
# green_coloc.show()
rt4 = ResultsTable()
ta_coloc2=Analyzer(green_coloc,Measurements.INTEGRATED_DENSITY ,rt4);
ta_coloc2.measure();
greenIntensity=(rt4.getColumnAsDoubles(rt4.getColumnIndex("IntDen")))[0];
ic_coloc =ImageCalculator();
coloc_img=ic_coloc.run("Multiply create",red_coloc,green_coloc);
rt5 = ResultsTable()
ta_coloc3=Analyzer(coloc_img,Measurements.INTEGRATED_DENSITY ,rt5);
ta_coloc3.measure();
totalIntensity=(rt5.getColumnAsDoubles(rt5.getColumnIndex("IntDen")))[0];
rgb=RGBStackMerge();
composite=rgb.mergeChannels([red_coloc,green_coloc],False);
composite.show();
fs=FileSaver(composite);
fs.saveAsJpeg(outputDirectory + '/' + "coloc_"+filename);
composite.close();
if redIntensity == 0:
summary["Red-Green-Coloc-%"]= "NaN"
else:
summary["Red-Green-Coloc-%"]= float (totalIntensity*100/redIntensity)
if greenIntensity == 0:
summary["Green-Green-Coloc-%"]= "NaN"
else:
summary["Green-Red-Coloc-%"]= float (totalIntensity*100/greenIntensity)
if gd.wasOKed():
amplifyChannel = gd.getNextChoiceIndex() + 1
refChannel = gd.getNextChoiceIndex() + 1
ampFactor = gd.getNextNumber()
doRemoveBleedthrough = gd.getNextBoolean()
doCorrectRefraction = gd.getNextBoolean()
chImages = ChannelSplitter.split(theImage)
## After this step, the image to operate on is "nextStepImage"
if doRemoveBleedthrough:
params = ("bleeding_channel=" + str(refChannel) + " bloodied_channel=" + str(amplifyChannel) + " " +
"allowable_saturation_percent=1.0 rsquare_threshold=0.50")
IJ.run("Remove Bleedthrough (automatic)", params)
unbledImage = WindowManager.getImage("Corrected_ch" + str(amplifyChannel))
mergingImages = [unbledImage,chImages[refChannel-1].duplicate()]
nextStepImage = RGBStackMerge.mergeChannels(mergingImages,True)
#nextStepImage.show()
unbledImage.close()
for img in mergingImages:
img.close()
else:
mergingImages = [chImages[amplifyChannel-1].duplicate(),chImages[refChannel-1].duplicate()]
nextStepImage = RGBStackMerge.mergeChannels(mergingImages,True)
#nextStepImage.show()
for img in mergingImages:
img.close()
for img in chImages:
img.close()
# theImage.close()
## After this step, the image to operate on is "next2StepImage"
def main():
DEBUG = False
CN = [c.strip() for c in channel_names.split(",")]
if DEBUG:
imp1, imp2, imp3 = open_test()
else:
imp1, imp2, imp3 = open_msr(str(msr_fn))
msr_fn_base = os.path.basename(str(msr_fn))
signed2unsigned16(imp1)
signed2unsigned16(imp2)
signed2unsigned16(imp3)
imp1.updateAndDraw()
imp2.updateAndDraw()
imp3.updateAndDraw()
IJ.run(imp1, "Enhance Contrast", "saturated=0.35")
IJ.run(imp2, "Enhance Contrast", "saturated=0.35")
IJ.run(imp3, "Enhance Contrast", "saturated=0.35")
cal = imp1.getCalibration()
IMAGE_AREA_UM = cal.pixelWidth * imp1.getWidth() * cal.pixelHeight * imp1.getHeight()
t1 = apply_mask(imp1, sigma, CN[0], is_auto_thresh)
t2 = apply_mask(imp2, sigma, CN[1], is_auto_thresh)
t3 = apply_mask(imp3, sigma, CN[2], is_auto_thresh)
results = ResultsTable()
results.setHeading(0, "Channel")
results.setHeading(1, "Count")
results.setHeading(2, "Surface area (um)")
results.setHeading(3, "Surface area (%)")
results.setHeading(4, "Surface signal")
#results.setHeading(5, "Threshold used")
def add_to_table(channel, c, a, s):
results.incrementCounter()
results.setValue(0, results.getCounter()-1, channel)
results.addValue(1, c)
results.addValue(2, a)
results.addValue(3, 100 * a /IMAGE_AREA_UM)
results.addValue(4, s)
#results.setLabel(msr_fn_base, results.getCounter()-1)
imp1_mask, c,a,s = analyze(imp1, min_area)
add_to_table(CN[0], c, a, s)
#results.addValue(4, t1)
imp2_mask, c,a,s = analyze(imp2, min_area)
add_to_table(CN[1], c, a,s)
#results.addValue(4, t2)
imp3_mask, c,a,s = analyze(imp3, min_area)
add_to_table(CN[2], c, a,s)
#results.addValue(4, t3)
# IJ.run(imp1, "Enhance Contrast", "saturated=0.35")
# IJ.run(imp2, "Enhance Contrast", "saturated=0.35")
# IJ.run(imp3, "Enhance Contrast", "saturated=0.35")
imp_raw = RGBStackMerge.mergeChannels([imp1, imp2, imp3], True)
luts = list(imp_raw.getLuts())
imp_raw.setLuts([luts[2], luts[0], luts[1]] )
imp_raw.setTitle(msr_fn_base )
imp_raw.show()
if save_raw:
save_base_fn = os.path.splitext(str(msr_fn))[0]
IJ.save(imp_raw, save_base_fn + ".tif")
IJ.run(imp1, "Convert to Mask", "")
IJ.run(imp2, "Convert to Mask", "")
IJ.run(imp3, "Convert to Mask", "")
ic = ImageCalculator()
imp12 = ic.run("Muliply create", imp1, imp2)
imp13 = ic.run("Muliply create", imp1, imp3)
imp23 = ic.run("Muliply create", imp2, imp3)
imp123 = ic.run("Muliply create", imp12, imp3)
imp12_mask, c,a,s = analyze(imp12, min_area)
add_to_table("{}+{}".format(CN[0],CN[1]), c, a, -1)
imp13_mask, c,a,s = analyze(imp13, min_area)
add_to_table("{}+{}".format(CN[0],CN[2]), c, a, -1)
imp23_mask,c,a,s = analyze(imp23, min_area)
add_to_table("{}+{}".format(CN[1],CN[2]), c, a, -1)
imp123_mask,c,a,s = analyze(imp123, min_area)
add_to_table("{}+{}+{}".format(CN[0],CN[1], CN[2]), c, a, -1)
title = "Coloco3surf {}".format(msr_fn_base)
results.show(title)
imp_merge = RGBStackMerge.mergeChannels([imp1_mask, imp2_mask, imp3_mask, imp12_mask, imp13_mask, imp23_mask, imp123_mask], False)
luts = list(imp_merge.getLuts())
imp_merge.setLuts([luts[2], luts[0], luts[1]] + luts[3:])
imp_merge.setTitle(title)
imp_merge.show()
if save_surf:
save_base_fn = os.path.splitext(str(msr_fn))[0]
IJ.save(imp_merge, save_base_fn + "_surfaces.tif")
IJ.log("Done")
for f in image_list:
if (w_re.search(f) != None and
int(w_re.search(f).group()[0:3]) != 405): #Leave out DAPI
wave_lengths.append(int(w_re.search(f).group()[0:3]))
break
channel_no = len(wave_lengths)
print 'Detected ' + str(
channel_no) + ' channel(s) with wavelength(s) ' + str(wave_lengths)
return wave_lengths, channel_no
oc = OverlayCommands()
rt = ResultsTable()
measured = rt.getResultsTable()
cs = ChannelSplitter()
cm = RGBStackMerge()
zp = ZProjector()
zp.setMethod(ZProjector.AVG_METHOD)
IJ.run("Close All")
#Find images and wavelengths
imagelist = [
f for f in os.listdir(str(inputpath))
if (('.tif' in f.lower()) and ('thumb' not in f.lower()))
]
wavelengths, nChannels = get_apical_channels(imagelist, 6)
tm_wavelength = [x for x in wavelengths if not x == ref_wavelength][0]
#Sort image files
regexDetect = re.compile(
# for each channel.
channels = {}
for image_file in image_files:
channel = re.search('channel(\d+)_', image_file).group(1)
if channel in channels:
channels[channel] += 1
else:
channels[channel] = 1
if len(channels) > 7:
print 'Images with more than 7 channels are not currently supported by the Merge Channels operation.'
sys.exit(1)
# Open an image sequence for each channel.
stacks = []
for channel in channels:
num_frames = channels[channel]
IJ.run('Image Sequence...', 'open=%s file=channel%s sort' % (image_files[0], channel))
stack = IJ.getImage()
stack.setTitle(channel)
stack.setDimensions(1, 1, num_frames) # TODO Don't hardcode the number of slices
stacks.append(stack)
merged_image = RGBStackMerge().mergeChannels(stacks, False)
merged_image.setTitle(dir_name)
# TODO Alternatively, we could upload directly to OMERO. But that probably
# would not work in headless mode.
IJ.run(merged_image, 'OME-TIFF...', 'save=%s.ome.tif compression=Uncompressed' % image_dir_path)
rt.setValue("Ch2_TotalIntensity", ct, stats.area * stats.mean)
rt.setValue("Ch2_MeanIntensity", ct, stats.mean)
rt.setValue("Ch3_TotalIntensity", ct, statsch3.area * statsch3.mean)
rt.setValue("Ch3_meanIntensity", ct, statsch3.mean)
ct += 1
rt.show("Dot Intensity")
#AREA, AREA_FRACTION, CENTER_OF_MASS, CENTROID, CIRCULARITY, ELLIPSE, FERET,
#INTEGRATED_DENSITY, INVERT_Y, KURTOSIS, LABELS, LIMIT, MAX_STANDARDS, MEAN,
#MEDIAN, MIN_MAX, MODE, PERIMETER, RECT, SCIENTIFIC_NOTATION, SHAPE_DESCRIPTORS,
#SKEWNESS, SLICE, STACK_POSITION, STD_DEV
# preparing merged stack with detected dots.
merge = RGBStackMerge()
#stacks = Array()
#stacks[0] = imp2.getImageStack()
#stacks[1] = imp.getImageStack()
#imgconv = ImageConverter(imp)
#imgconv.setDoScaling(True)
#imgconv.convertToGray8()
IJ.run(impch2, "Enhance Contrast", "saturated=0.02 use")
IJ.run(impch2, "8-bit", "")
IJ.run(impch3, "Enhance Contrast", "saturated=0.02 use")
IJ.run(impch3, "8-bit", "")
stacks = array(ImageStack, [imp2.getImageStack(), impch2.getImageStack(), impch3.getImageStack()])
impmerged = merge.createComposite(imp2.getWidth(), imp2.getHeight(), imp2.getStackSize(), stacks, True)
impmerged.show()
impch1.changes = False
def cellSegmentation(srcDir, dstDir, currentDir, filename, keepDirectories):
print "Processing:"
# Opening the image
print "Open image file", filename
imp = IJ.openImage(os.path.join(currentDir, dstDir))
# Put your processing commands here!
localinput=srcDir.replace("/", "\\")
saveDir = localinput.replace(srcDir, dstDir)
string="."
dotIndex=filename.find(string)
localfile= filename[0:dotIndex]
print(localfile)
IJ.run("New... ", "name="+f+" type=Table")
print(f,"\\Headings:Cell\tarea\tCirc\tAR\tRoundness\tMaximum")
IJ.run("Bio-Formats", "open=[" + localinput + os.path.sep + filename +"] autoscale color_mode=Default rois_import=[ROI manager] view=Hyperstack stack_order=XYCZT")
IJ.open()
idd= WM.getIDList();
imageID= idd[0];
IJ.run("Clear Results")
WM.getImage(imageID)
IJ.run("Duplicate...", "duplicate channels="+str(x)+"") #Nucleus channel #took away x
IJ.run("Z Project...", "projection=[Standard Deviation]");#picture for frame detection
IJ.run("8-bit");
IJ.run("Duplicate...", "title=IMAGE");#frame
IJ.run("Duplicate...", "title=SUBTRACT");#Background subtraction mask (for frame and watershed)
imp=IJ.getImage()
pixelWidth=imp.getWidth()
pixelWidth=pixelWidth/1647.89
pixelHeight= imp.getHeight()
#create subtraction mask, applying constraining maximum (step I)
IJ.selectWindow("SUBTRACT")
nResults=imp.getStatistics()
row = nResults
rt_exist = WM.getWindow("Results")
if rt_exist==None:
rt= ResultsTable()
else:
rt = rt_exist.getTextPanel().getOrCreateResultsTable()
rt.setValue("Max ", 0, row.max) #text file
rt.show("Results")
u=math.floor(row.mean*3)
IJ.run("Max...","value="+str(u)) #constraining maximum of 3-fold mean to reduce effect of extreme values during subtraction
#gaussian blurring (step II)
IJ.run("Gaussian Blur...", "sigma=100 scaled") #blurring for subtraction mask
IJ.selectWindow("IMAGE")
pxrollrad = cellradius/pixelWidth; #rolling ball radius in pixels needed (= predefined cell radius[µm]/pixelsize[µm/px])
IJ.run("Subtract Background...", "rolling="+str(pxrollrad)+"")
IJ.run("Gaussian Blur...", "sigma=2 scaled") #reduces punctate character of grayscale image '
IM=IJ.selectWindow("IMAGE")
SUB=IJ.selectWindow("SUBTRACT")
ic().run("SUBTRACT", IM, SUB) #just subtracts two images
IJ.selectWindow("IMAGE") #see how to call
IJ.run("Duplicate...", "title=AND")#watershed
IJ.run("Duplicate...", "title=CHECK")#for checking if maxima exist within selection later
#Apply threshold to get binary image of cell borders (step IV)
IJ.selectWindow("IMAGE")
imp = IJ.getImage() # the current image
imp.getProcessor().setThreshold(1, 255, ImageProcessor.NO_LUT_UPDATE)
IJ.run("Subtract Background...","...")
IJ.run("Convert to Mask", "method=Default background=Dark only black")
IJ.run("Fill Holes")
#Create watershed line image (step V)
IJ.selectWindow("AND")
IJ.run("Gaussian Blur...", "sigma=2 scaled")
imp=IJ.getImage()
pixelWidth=imp.getWidth()
pixelWidth=pixelWidth/1647.89
pixelHeight= imp.getHeight()
# Saving the image
nResults=imp.getStatistics()
row = nResults
rt.setValue("Max ", 1, row.max) #text file
nBins = 256
Hist = HistogramWindow("Histogram",imp,nBins)
Table = Hist.getResultsTable()
Counts = Table.getColumn(1)
#mean gray value of pixels belonging to cells needed (i.e. mean of ONLY non-zero pixel)
Sum = 0 #all counts
CV = 0 #weighed counts (= counts * intensity)
for i in range(0, len(Counts)): #starting with 1 instead of 0. -> 0 intensity values are not considered.
Sum += Counts[i]
CV += Counts[i]*i
m = (CV/Sum)
m=math.floor(m)
l = math.floor(2*m) #Maxima need to be at least twice the intensity of cellular mean intensity
IJ.run("Find Maxima...", "noise="+str(l)+" output=[Segmented Particles] exclude") #watershedding
#Combine watershed lines and cell frame (step VI)
IJ.selectWindow("IMAGE")
imp=IJ.getImage()
imp.getProcessor().setThreshold(1, 255, ImageProcessor.NO_LUT_UPDATE)
IJ.run(imp, "Watershed", "") #useful
imp = IJ.getImage()
ip = imp.getProcessor()
segip = MaximumFinder().findMaxima( ip, 1, ImageProcessor.NO_THRESHOLD, MaximumFinder.SEGMENTED , False, False)
segip.invert()
segimp = ImagePlus("seg", segip)
segimp.show()
mergeimp = RGBStackMerge.mergeChannels(array([segimp, None, None, imp, None, None, None], ImagePlus), True)
mergeimp.show()
pa_exist = WM.getWindow("Results for PA")
if pa_exist==None:
pa_rt= ResultsTable()
else:
pa_rt = pa_exist.getTextPanel().getOrCreateResultsTable()
ParticleAnalyzer.setResultsTable(pa_rt)
IJ.run("Set Measurements...", "area mean perimeter shape decimal=3")
IJ.run("Analyze Particles...", "size=" + str(cellradius) + "-Infinity circularity=0.1-1.00 add"); #Cell bodies detected
pa_rt.show("Results for PA ")
save_all(srcDir, dstDir, filename, localfile, keepDirectories, imageID)
if flat == True:
for i in range(0, channels):
imageProcessor = ShortProcessor(sx*4, sy*4)
imageStack = ImageStack(sx*4, sy*4)
for sphere in spheres:
fnorm = int(round((sphere['f'][i] - fmin[i]) / (fmax[i] - fmin[i]) * 65536.0))
x = sphere['cx'] * 4 - sphere['r']
y = sphere['cy'] * 4 - sphere['r']
d = sphere['r'] * 2
imageProcessor.setValue(fnorm)
imageProcessor.fillOval(x, y, d, d)
imageStack.addSlice(imageProcessor)
channelImages.append(ImagePlus("Rendering C" + "%i" % (i + 1), imageStack))
imageO = RGBStackMerge.mergeChannels(channelImages, False)
elif pixels == True:
for i in range(0, channels):
imageProcessor = ShortProcessor(width, height)
imageStack = ImageStack(width, height)
for sphere in spheres:
fnorm = int(round((sphere['f'][i] - fmin[i]) / (fmax[i] - fmin[i]) * 65536.0))
x = sphere['cx']
y = sphere['cy']
d = sphere['r'] * 2
imageProcessor.putPixel(x, y, fnorm)
imageStack.addSlice(imageProcessor)
channelImages.append(ImagePlus("Rendering C" + "%i" % (i + 1), imageStack))
imageO = RGBStackMerge.mergeChannels(channelImages, False)
else:
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)
opts.setColorMode(ImporterOptions.COLOR_MODE_COMPOSITE)
bio = BF.openImagePlus(opts)[0]
# bio.show()
ij.IJ.saveAs(bio.flatten(), "Tiff", '%s/%s.flat.tiff' %(path,out))
# bio.close()
# while(True): continue
for image in moFileList:
print "Processing cell " + image.group() + " (" + str(moFileList.index(image)+1) + "/" + str(len(moFileList)) + ")"
IJ.log("Processing cell " + image.group() + " (" + str(moFileList.index(image)+1) + "/" + str(len(moFileList)) + ")")
imp = Opener().openImage(path + image.group()) # open Image
#if imp.getBitDepth() != 8: # converting to 8 bit if
# ImageConverter(imp).convertToGray8()
roi = imp.roi
imps = CS.split(imp)
ppc = PPC()
for aimp in imps:
ppc.setImp(aimp)
ppc.run()
if roi != None:
aimp.setRoi(roi)
for n in range(1, aimp.getImageStackSize()+1):
aimp.getImageStack().getProcessor(n).fillOutside(roi)
aimp.killRoi()
final = StackMerge.mergeChannels(imps, False)
final.copyScale(imp) # copyscale from .copyscale
if not pth.exists(saveFolder):
makedirs(saveFolder)
fileName = G_saveFilePrefix + image.group('prefix')
IJ.saveAs(final, ".tiff", pth.join(saveFolder, fileName) ) # saveAs(ImagePlus imp, java.lang.String format, java.lang.String path)
print "Successfully saved", G_saveFilePrefix + image.group('prefix')
IJ.log("Successfully saved " + G_saveFilePrefix + image.group('prefix') + ".tif")
for win in WindowManager.getIDList():
imp = WindowManager.getImage(win)
imp.close()
print "Finished."
IJ.log("Finished pre-processing.")
def analyse(imp, root, filename, thresholds):
imp.show()
print ""
print(imp.getTitle())
print "**********************************"
channels=(1,2,3)
backgrounds=thresholds #(175,0,275)
#IJ.run(imp, "Gaussian Blur...", "sigma=1 stack");
impA = []
sumIntensities = []
sumIntensitiesBW = []
for iChannel in channels:
# general issues:
# - intensity decreases along z
# measure total intensity
# - bg subtraction?
impc = extractChannel(imp, iChannel, 1)
impbw = threshold(impc, thresholds[iChannel-1])
impcBGcorr = Duplicator().run(impc)
IJ.run(impcBGcorr, "Subtract...", "value="+str(backgrounds[iChannel-1])+" stack");
sumIntensities.append(measureSumIntensity3D(impcBGcorr))
# measure number of pixels containing a signification signal
# - what is good here for thresholding and preprocessing?
#impbw = autoThreshold(impc, "Default")
#impbw.setTitle("bw"+str(iChannel))
#impbw.show()
impA.append(impbw)
sumIntensitiesBW.append(measureSumIntensity3D(impbw)/255)
# measure how many pixels are above threshold in either of the images (2015-09-21)
impbw = ImageCalculator().run("OR create stack", impA[0], impA[1])
impbw = ImageCalculator().run("OR create stack", impbw, impA[2])
impA.append(impbw)
sumIntensitiesBW.append(measureSumIntensity3D(impbw)/255) # volume of pixels that are above threshold in either of the images
sumIntensities.append(0.0)
print "intensities,",str(sumIntensities)[1:-1]
print "volumes,",str(sumIntensitiesBW)[1:-1]
# normalise to the volume of the 2nd channel (which is the AF channel) (removed on 2015-09-21)
#sumIntensitiesNorm = [x / sumIntensitiesBW[1] for x in sumIntensities]
#sumIntensitiesBWNorm = [x / sumIntensitiesBW[1] for x in sumIntensitiesBW]
# normalise to the volume of the 4th channel (which is the OR channel) (added on 2015-09-21)
sumIntensitiesNorm = [x / sumIntensitiesBW[3] for x in sumIntensities]
sumIntensitiesBWNorm = [x / sumIntensitiesBW[3] for x in sumIntensitiesBW]
# print all the results without the brackets (that's why the 1:-1 and not :)
print "intensities normalised,",str(sumIntensitiesNorm)[1:-1]
print "volumes normalised,",str(sumIntensitiesBWNorm)[1:-1]
print "thresholds,",str(thresholds)[1:-1]
impMerge = RGBStackMerge.mergeChannels(array(impA, ImagePlus), False)
impMerge.setDisplayMode(IJ.COLOR);
impMerge.setC(1);
IJ.run(impMerge, "Green", "");
impMerge.setC(2);
IJ.run(impMerge, "Grays", "");
impMerge.setC(3);
IJ.run(impMerge, "Blue", "");
impMerge.setC(4);
IJ.run(impMerge, "Red", "");
impMerge.show()
IJ.saveAs(impMerge, "Tiff", os.path.join(root, filename + "--segmented.tif"));
import os
print(folder)
images = [];
# load all images, collect them in a list
for root, directories, filenames in os.walk(folder.getAbsolutePath()):
filenames.sort();
for filename in filenames:
print(filename)
if (filename.endswith(".raw")):
IJ.run("Raw...", "open=[" + os.path.join(folder.getAbsolutePath(), filename) + "] image=[16-bit Signed] width=" + str(imageWidth) + " height=" + str(imageHeight) + " number=" + str(imageDepth) + " little-endian");
imp = IJ.getImage();
images.append(imp);
imp.hide();
# merge all images as channels
merged = RGBStackMerge.mergeChannels(images, False);
merged.show();
# switch channels and frames
IJ.run(merged,"Re-order Hyperstack ...", "channels=[Frames (t)] slices=[Slices (z)] frames=[Channels (c)]");
merged = IJ.getImage()
# fix Lookup table
IJ.run(merged, "Grays", "");
z boolean
c char
b byte
h short
i int
l long
f float
d double
"""
ja = jarray.array([0, 1, 2, 3], 'i')
print(ja)
"""
What if you want to make a java array of a specific class?
You could then name the class as the second argument.
For example
"""
img_dir = git_home + "/tips/ImageJ/"
imp_blobs_1 = IJ.openImage("http://imagej.nih.gov/ij/images/blobs.gif")
IJ.run(imp_blobs_1, "Red", "")
imp_blobs_2 = imp_blobs_1.duplicate()
IJ.run(imp_blobs_2, "Green", "")
imp_blobs_3 = imp_blobs_1.duplicate()
IJ.run(imp_blobs_3, "Blue", "")
img_array = jarray.array([imp_blobs_1, imp_blobs_2, imp_blobs_3], ImagePlus)
gray_stack = RGBStackMerge().mergeHyperstacks(img_array, True)
gray_stack.show()
gray_comp = RGBStackMerge().mergeChannels(img_array, False)
gray_comp.show()