def main():
#opens the image and gets its name
# imp = open_test_img()
# imp.show()
imp = IJ.getImage()
title = imp.getTitle()
basename, extension = splitext(title)
# does a minimum projection and makes a mask
imp_min = ZProjector.run(imp, 'min')
ip = imp_min.getProcessor()
ip.setThreshold(1, 9999999999, 1)
mask = ip.createMask()
mask = ImagePlus("mask", mask)
# fills in the holes in the mask
IJ.run(mask, "Invert", "")
IJ.run(mask, "Fill Holes", "")
IJ.run(mask, "Invert", "")
# converts to list and then finds the crop coordinates
px = mask.getProcessor().getPixels()
px = [-int(_) for _ in px] #for some reason the 1s are -1 here
px = reshape(px, (imp.height, imp.width))
crop_top_left, crop_width, crop_height = find_largest_rectangle_2D(px)
# crops the original image
imp.setRoi(crop_top_left[0], crop_top_left[1], crop_width, crop_height)
imp_cropped = imp.resize(crop_width, crop_height, "bilinear")
imp_cropped.setTitle(basename + "_autocrop" + extension)
imp_cropped.show()
def perform_cropping(imp, info, output_folder, default_path):
imp.show()
imp.setC(info.get_mosaic_labeled_ch())
IJ.run("Enhance Contrast", "saturated=0.35")
zcrop_imp, z_lims = z_crop(imp)
info.set_z_crop_frames(z_lims)
info.set_mosaic_labeled_ch(imp.getC())
IJ.setTool("rect")
zcrop_imp.hide()
imp2 = ZProjector.run(zcrop_imp, "max")
imp2.show()
imp2.setC(info.get_mosaic_labeled_ch())
crop_roi = None
while crop_roi is None:
WaitForUserDialog("Select XY region to crop...").show()
crop_roi = imp2.getRoi()
if crop_roi.getType():
info.set_xy_crop_rect(
str([(x, y) for x, y in zip(crop_roi.getPolygon().xpoints,
crop_roi.getPolygon().ypoints)]))
else:
info.set_xy_crop_rect(crop_roi.getBounds().toString())
imp2.close()
zcrop_imp.show()
zcrop_imp.setRoi(crop_roi)
IJ.run("Crop", "")
if crop_roi.getType():
IJ.run(zcrop_imp, "Make Inverse", "")
inv_roi = zcrop_imp.getRoi()
IJ.run(zcrop_imp, "Set...", "value=0 stack")
IJ.run(zcrop_imp, "Make Inverse", "")
# zcrop_imp = z_crop_finetune(zcrop_imp, info);
return imp, zcrop_imp, info, info.get_input_file_path()
def images_to_frames_stack(imgdir, savestack=False):
'''
Method for preparing a stack of frames using images inside the input folder.
When savestack is true it saves the resulting stack to the images folder while opening the stack.
'''
title = "tracing_stack.tiff" # Title of the final image stack.
imgdir = imgdir.getPath()
imglist = buildList(imgdir, ext)
if not imglist:
raise IOError("No {0} were found in {0}.".format(ext, imgdir))
method = "max"
implist = []
for img in imglist:
imp = ImagePlus(img)
if imp.isStack():
imp = ZProjector.run(imp, method)
implist.append(imp)
stack = ImageStack(
implist[0].getWidth(),
implist[0].getHeight())
[stack.addSlice(img.getProcessor()) for img in implist]
stackimp = ImagePlus(title, stack)
IJ.run(stackimp, "Properties...", "slices=1 frames={0}".format(stackimp.getStackSize()))
if savestack:
IJ.log("# Saving tracing stack...")
save_string = os.path.join(imgdir, title)
fs = ThreadedFileSaver.ThreadedFileSaver(stackimp, save_string, "saveAsTiff")
fs.start()
stackimp.show()
def stackprocessor(path, nChannels=4, nSlices=1, nFrames=1):
imp = ImagePlus(path)
imp = HyperStackConverter().toHyperStack(imp,
nChannels, # channels
nSlices, # slices
nFrames) # frames
imp = ZProjector.run(imp, "max")
return imp
def create_and_save(imp, projections, path, filename, export_format):
"""Wrapper to create one or more projections and export the results.
Parameters
----------
imp : ij.ImagePlus
The image stack to create the projections from.
projections : list(str)
A list of projection types to be done, valid options are 'Average',
'Maximum' and 'Sum'.
path : str
The path to store the results in. Existing files will be overwritten.
filename : str
The original file name to derive the results name from.
export_format : str
The suffix to be given to Bio-Formats, determining the storage format.
Returns
-------
bool
True in case projections were created, False otherwise (e.g. if the
given ImagePlus is not a Z-stack).
"""
if not projections:
log.debug("No projection type requested, skipping...")
return False
if imp.getDimensions()[3] < 2:
log.error("ImagePlus is not a z-stack, not creating any projections!")
return False
command = {
"Average": "avg",
"Maximum": "max",
"Sum": "sum",
}
for projection in projections:
log.debug("Creating '%s' projection...", projection)
proj = ZProjector.run(imp, command[projection])
export_using_orig_name(
proj,
path,
filename,
"-%s" % command[projection],
export_format,
overwrite=True,
)
proj.close()
return True
def average(imp):
"""Create an average intensity projection.
Parameters
----------
imp : ij.ImagePlus
The input stack to be projected.
Returns
-------
ij.ImagePlus
The result of the projection.
"""
if imp.getDimensions()[3] < 2:
log.warn("ImagePlus is not a z-stack, not creating a projection!")
return imp
log.debug("Creating average projection...")
proj = ZProjector.run(imp, "avg")
return proj
def extract_frame_process_roi(imp, frame, channel, process, background, roi, z_min, z_max, correct_only_xy):
# extract frame and channel
imp_frame = ImagePlus("", extract_frame(imp, frame, channel, z_min, z_max)).duplicate()
# check for roi and crop
if roi != None:
#print roi.getBounds()
imp_frame.setRoi(roi)
IJ.run(imp_frame, "Crop", "")
# subtract background
if background > 0:
#IJ.log("Subtracting "+str(background));
IJ.run(imp_frame, "Subtract...", "value="+str(background)+" stack");
# enhance edges
if process:
IJ.run(imp_frame, "Mean 3D...", "x=1 y=1 z=0");
IJ.run(imp_frame, "Find Edges", "stack");
# project into 2D
if imp_frame.getNSlices() > 1:
if correct_only_xy:
imp_frame = ZProjector.run( imp_frame, "avg");
# return
return imp_frame
def extract_frame_process_roi(imp, frame, roi, options):
# extract frame and channel
imp_frame = ImagePlus("", extract_frame(imp, frame, options['channel'], options['z_min'], options['z_max'])).duplicate()
# check for roi and crop
if roi != None:
#print roi.getBounds()
imp_frame.setRoi(roi)
IJ.run(imp_frame, "Crop", "")
# subtract background
if options['background'] > 0:
#IJ.log("Subtracting "+str(background));
IJ.run(imp_frame, "Subtract...", "value="+str(options['background'])+" stack");
# enhance edges
if options['process']:
IJ.run(imp_frame, "Mean 3D...", "x=1 y=1 z=0");
IJ.run(imp_frame, "Find Edges", "stack");
# project into 2D if we only want to correct the drift in x and y
if imp_frame.getNSlices() > 1:
if options['correct_only_xy']:
imp_frame = ZProjector.run(imp_frame, "avg");
# return
return imp_frame
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 doprojection(self, title, onGPU=False):
'''
Run ij.plugin.Zprojector on image referenced by title using self.method as
projection method and saves the projection on self.savefolder.
'''
IJ.log("# Processing {0}...".format(title))
imp = self.load_image(title)
if onGPU:
projection = self.CLIJ2_max_projection(imp)
else:
projection = ZProjector.run(imp, self.method)
if self.savefolder:
save_string = os.path.join(self.savefolder, title)
try:
ij.io.FileSaver(projection).saveAsTiff(save_string)
IJ.log("{0}".format(save_string))
except:
IJ.log(
"ij.io.FileSaver raised an exception while trying to save img '{0}' as '{1}'.Skipping image."
.format(title, save_string))
else:
imp.close()
projection.show()
def main():
check_dependencies()
# setup
Prefs.blackBackground = True
params = Parameters()
params.load_last_params()
# select folders
if params.last_input_path is not None:
DirectoryChooser.setDefaultDirectory(params.last_input_path)
dc = DirectoryChooser("Choose root folder containing data for analysis")
input_folder = dc.getDirectory()
params.last_input_path = input_folder
if input_folder is None:
print("Run canceled")
return
if params.last_output_path is not None:
DirectoryChooser.setDefaultDirectory(
os.path.dirname(params.last_output_path))
dc = DirectoryChooser("choose location to save output")
output_folder = dc.getDirectory()
if output_folder is None:
print("Run canceled")
return
timestamp = datetime.strftime(datetime.now(), '%Y-%m-%d %H-%M-%S')
output_folder = os.path.join(output_folder, (timestamp + ' output'))
params.last_output_path = output_folder
os.mkdir(output_folder)
analysis_mode, threshold_method, minimum_cell_area_um2 = choose_analysis_mode(
params)
if analysis_mode is None:
print("Run canceled")
return
params.last_analysis_mode = analysis_mode
do_manual_qc = "+ manual correction" in analysis_mode
params.last_threshold_method = threshold_method
params.last_minimum_cell_area_um2 = minimum_cell_area_um2
params.persist_parameters()
# load image(s):
files_lst = [
f for f in os.listdir(input_folder) if os.path.splitext(f)[1] == '.tif'
]
out_statses = []
for f in files_lst:
print("Working on image {}...".format(os.path.splitext(f)[0]))
imp = IJ.openImage(os.path.join(input_folder, f))
metadata = import_iq3_metadata(
os.path.join(input_folder,
os.path.splitext(f)[0] + '.txt'))
n_channels = int(metadata['n_channels'])
gfp_channel_number = [
("488" in ch) for ch in metadata['channel_list']
].index(True) + 1 if any(
[("488" in ch) for ch in metadata['channel_list']]) else None
dapi_channel_number = [
("405" in ch) for ch in metadata['channel_list']
].index(True) + 1 if any(
[("405" in ch) for ch in metadata['channel_list']]) else None
red_channel_number = [
("561" in ch) for ch in metadata['channel_list']
].index(True) + 1 if any(
[("561" in ch) for ch in metadata['channel_list']]) else None
imp = HyperStackConverter.toHyperStack(imp, n_channels,
imp.getNSlices() // n_channels,
1, "Color")
imp = ZProjector.run(imp, "max")
if dapi_channel_number is not None:
imp.setC(dapi_channel_number)
IJ.run(imp, "Blue", "")
IJ.run(imp, "Enhance Contrast", "saturated=0.35")
else:
raise NotImplementedError(
"Nuclear staining channel doesn't seem to exist!")
if red_channel_number is not None:
imp.setC(red_channel_number)
IJ.run(imp, "Red", "")
IJ.run(imp, "Enhance Contrast", "saturated=0.35")
if gfp_channel_number is not None:
imp.setC(gfp_channel_number)
IJ.run(imp, "Green", "")
IJ.run(imp, "Enhance Contrast", "saturated=0.35")
imp.setC(gfp_channel_number)
imp.show()
imp.setDisplayMode(IJ.COMPOSITE)
cal = imp.getCalibration()
cal.setUnit(metadata["y_unit"])
cal.pixelWidth = metadata["x_physical_size"]
cal.pixelHeight = metadata["y_physical_size"]
imp.setCalibration(cal)
min_size_pix = minimum_cell_area_um2 / (cal.pixelHeight *
cal.pixelWidth)
if "GFP intensity" in analysis_mode:
out_stats = gfp_analysis(imp,
f,
output_folder,
gfp_channel_number=gfp_channel_number,
dapi_channel_number=dapi_channel_number,
threshold_method=threshold_method,
do_manual_qc=do_manual_qc,
min_size_pix=min_size_pix)
elif analysis_mode == "Manual":
important_channel = gfp_channel_number if gfp_channel_number is not None else dapi_channel_number
out_stats = manual_analysis(
imp,
f,
output_folder,
gfp_channel_number=gfp_channel_number,
dapi_channel_number=dapi_channel_number,
important_channel=important_channel)
elif "E-cadherin watershed" in analysis_mode:
out_stats = ecad_analysis(imp,
f,
output_folder,
gfp_channel_number=gfp_channel_number,
red_channel_number=red_channel_number,
dapi_channel_number=dapi_channel_number,
do_manual_qc=do_manual_qc)
imp.close()
out_statses.extend(out_stats)
print("Current total number of cells identified: {}".format(
len(out_statses)))
# get # nuclei per "cell"
params.save_parameters_to_json(
os.path.join(output_folder, "parameters used.json"))
WaitForUserDialog(
"Done", "Done, having analysed {} cells in total!".format(
len(out_statses))).show()
return
def main():
# Prepare directory tree for output.
indir = IJ.getDirectory("input directory")
outdir = IJ.getDirectory(".csv output directory")
c1dir = os.path.join(outdir, "Channel1")
c2dir = os.path.join(outdir, "Channel2")
c3dir = os.path.join(outdir, "Channel3")
c4dir = os.path.join(outdir, "Channel4")
channelsdir = os.path.join(outdir, "Channels")
if not os.path.isdir(c1dir):
os.mkdir(c1dir)
if not os.path.isdir(c2dir):
os.mkdir(c2dir)
if not os.path.isdir(c3dir):
os.mkdir(c3dir)
if not os.path.isdir(c4dir):
os.mkdir(c4dir)
if not os.path.isdir(channelsdir):
os.mkdir(channelsdir)
# Collect all file paths in the input directory
files = readdirfiles(indir)
# Initialize the results tables.
c1Results = ResultsTable()
c2Results = ResultsTable()
c3Results = ResultsTable()
c4Results = ResultsTable()
for file in files:
IJ.log("File: {}/{}".format(files.index(file) + 1, len(files)))
if file.endswith('.tif'):
# Open .tiff file as ImagePlus.
imp = Opener().openImage(file)
imp = ZProjector.run(imp, "max")
# imp = stackprocessor(file,
# nChannels=4,
# nSlices=7,
# nFrames=1)
channels = ChannelSplitter.split(imp)
name = imp.getTitle()
# For every channel, save the inverted channel in grayscale as .jpg.
for channel in channels:
IJ.run(channel, "Grays", "")
IJ.run(channel, "Invert", "")
jpgname = channel.getShortTitle()
jpgoutfile = os.path.join(channelsdir,
"{}.jpg".format(jpgname))
IJ.saveAs(channel.flatten(), "Jpeg", jpgoutfile)
IJ.run(channel, "Invert", "")
# OPTIONAL - Perform any other operations (e.g. crossexcitation compensation tasks) before object count.
c2name = channels[2].getTitle()
cal = channels[2].getCalibration()
channels[2] = ImagePlus(
c2name,
ImageCalculator().run("divide create 32-bit", channels[2],
channels[3]).getProcessor(
) # This removes AF647 bleed-through
)
channels[2].setCalibration(cal)
# Settings for channel1 threshold.
c1 = countobjects(channels[0],
c1Results,
threshMethod="Triangle",
subtractBackground=True,
watershed=True,
minSize=0.00,
maxSize=100,
minCirc=0.00,
maxCirc=1.00)
# Settings for channel2 threshold.
c2 = countobjects(channels[1],
c2Results,
threshMethod="RenyiEntropy",
subtractBackground=True,
watershed=False,
minSize=0.00,
maxSize=30.00,
minCirc=0.00,
maxCirc=1.00)
# Settings for channel3 threshold.
c3 = countobjects(channels[2],
c3Results,
threshMethod="RenyiEntropy",
subtractBackground=True,
watershed=False,
minSize=0.00,
maxSize=30.00,
minCirc=0.00,
maxCirc=1.00)
# Settings for channel4 threshold.
c4 = countobjects(channels[3],
c4Results,
threshMethod="RenyiEntropy",
subtractBackground=True,
watershed=False,
minSize=0.20,
maxSize=100.00,
minCirc=0.00,
maxCirc=1.00)
# Format filenames for thresholded .tiff files.
outfileC1 = os.path.join(c1dir, "threshold_c1_{}".format(name))
outfileC2 = os.path.join(c2dir, "threshold_c2_{}".format(name))
outfileC3 = os.path.join(c3dir, "threshold_c3_{}".format(name))
outfileC4 = os.path.join(c4dir, "threshold_c4_{}".format(name))
# Save thresholded .tiff files.
IJ.saveAs(c1.flatten(), "Tiff", outfileC1)
IJ.saveAs(c2.flatten(), "Tiff", outfileC2)
IJ.saveAs(c3.flatten(), "Tiff", outfileC3)
IJ.saveAs(c4.flatten(), "Tiff", outfileC4)
# Show results tables.
# c1Results.show("channel1")
# c2Results.show("channel2")
# c3Results.show("channel3")
# c4Results.show("channel4")
# Prepare results table filenames.
c1out = os.path.join(outdir, "channel1.csv")
c2out = os.path.join(outdir, "channel2.csv")
c3out = os.path.join(outdir, "channel3.csv")
c4out = os.path.join(outdir, "channel4.csv")
# Save results tables.
ResultsTable.save(c1Results, c1out)
ResultsTable.save(c2Results, c2out)
ResultsTable.save(c3Results, c3out)
ResultsTable.save(c4Results, c4out)
# manual roi copied from z-projected version of this resliced image (add roi from z-proj to ROI manager, then add
# to resliced image)
imp.killRoi();
IJ.run(imp, "Reslice [/]...", "output=1.000 start=Top avoid");
rot_imp = IJ.getImage();
crop_roi = Roi(10, 0, rot_imp.getWidth()-20, rot_imp.getHeight());
rot_imp.setRoi(crop_roi);
rot_imp.crop();
rot_imp.show();
WaitForUserDialog("pause").show();
split_ch = ChannelSplitter().split(rot_imp);
mch_imp = split_ch[0];
egfp_imp = split_ch[1];
roi_imp = split_ch[2];
zproj_imp = ZProjector.run(roi_imp, "max");
IJ.setRawThreshold(zproj_imp, 33153, 65535, None);
IJ.run(zproj_imp, "Make Binary", "")
zproj_imp.show();
raise error;
IJ.run(zproj_imp, "Skeletonize", "");
IJ.run(zproj_imp, "Create Selection", "");
roi = zproj_imp.getRoi();
floatpoly = roi.getContainedFloatPoints();
roi = PolygonRoi(floatpoly, Roi.FREELINE);
zproj_imp.setRoi(roi);
WaitForUserDialog("pause").show();
#IJ.setTool("freeline");
#WaitForUserDialog("Draw a line").show();
#IJ.run(zproj_imp, "Fit Spline", "");
def main():
# setup
Prefs.blackBackground = True
params = Parameters()
params.load_last_params()
# select folders
if params.last_input_path is not None:
DirectoryChooser.setDefaultDirectory(params.last_input_path)
dc = DirectoryChooser("Choose root folder containing data for analysis")
input_folder = dc.getDirectory()
params.last_input_path = input_folder
if input_folder is None:
raise KeyboardInterrupt("Run canceled")
if params.last_output_path is not None:
DirectoryChooser.setDefaultDirectory(
os.path.dirname(params.last_output_path))
dc = DirectoryChooser("choose location to save output")
output_folder = dc.getDirectory()
timestamp = datetime.strftime(datetime.now(), '%Y-%m-%d %H-%M-%S')
output_folder = os.path.join(output_folder, (timestamp + ' output'))
params.last_output_path = output_folder
os.mkdir(output_folder)
analysis_mode = choose_analysis_mode(params)
params.last_analysis_mode = analysis_mode
params.persist_parameters()
# load image(s):
files_lst = [
f for f in os.listdir(input_folder) if os.path.splitext(f)[1] == '.tif'
]
out_statses = []
for f in files_lst:
print("Working on image {}...".format(os.path.splitext(f)[0]))
imp = IJ.openImage(os.path.join(input_folder, f))
metadata = import_iq3_metadata(
os.path.join(input_folder,
os.path.splitext(f)[0] + '.txt'))
imp = HyperStackConverter.toHyperStack(imp, 3,
imp.getNSlices() // 3, 1,
"Color")
imp = ZProjector.run(imp, "max")
imp.setC(3)
IJ.run(imp, "Blue", "")
IJ.run(imp, "Enhance Contrast", "saturated=0.35")
imp.setC(2)
IJ.run(imp, "Red", "")
IJ.run(imp, "Enhance Contrast", "saturated=0.35")
imp.setC(1)
IJ.run(imp, "Green", "")
IJ.run(imp, "Enhance Contrast", "saturated=0.35")
imp.show()
imp.setDisplayMode(IJ.COMPOSITE)
cal = imp.getCalibration()
cal.setUnit(metadata["y_unit"])
cal.pixelWidth = metadata["x_physical_size"]
cal.pixelHeight = metadata["y_physical_size"]
imp.setCalibration(cal)
if analysis_mode == "GFP intensity":
out_stats = gfp_analysis(imp, f, output_folder)
elif analysis_mode == "Manual":
out_stats = manual_analysis(imp, f, output_folder)
out_statses.extend(out_stats)
print("Current total number of cells identified: {}".format(
len(out_statses)))
# get # nuclei per "cell"
params.save_parameters_to_json(
os.path.join(output_folder, "parameters used.json"))
WaitForUserDialog(
"Done", "Done, having analysed {} cells in total!".format(
len(out_statses))).show()
return
def make_projection(imp, method, slices):
z_prjor = ZProjector(imp)
prj_imp = z_prjor.run(imp, method, slices[0], slices[1])
return prj_imp
def project(img, method):
zp = ZProjector.run(img, method)
return zp
def run():
IJ.run("Close All", "")
IJ.log("\\Clear")
IJ.log("Find_close_peaks")
imp = IJ.run("Bio-Formats Importer")
imp = IJ.getImage()
Channel_1, Channel_2, radius_background, sigmaSmaller, sigmaLarger, minPeakValue, min_dist = getOptions()
IJ.log("option used:" \
+ "\n" + "channel 1:" + str(Channel_1) \
+ "\n" + "channel 2:"+ str(Channel_2) \
+ "\n" + "Radius Background:"+ str(radius_background) \
+ "\n" + "Smaller Sigma:"+ str(sigmaSmaller) \
+ "\n" + "Larger Sigma:"+str(sigmaLarger) \
+ "\n" + "Min Peak Value:"+str(minPeakValue) \
+ "\n" + "Min dist between peaks:"+str(min_dist))
IJ.log("Computing Max Intensity Projection")
if imp.getDimensions()[3] > 1:
imp_max = ZProjector.run(imp,"max")
#imp_max = IJ.run("Z Project...", "projection=[Max Intensity]")
#imp_max = IJ.getImage()
else:
imp_max = imp
ip1, ip2 = extract_channel(imp_max, Channel_1, Channel_2)
imp1, imp2 = back_substraction(ip1, ip2, radius_background)
imp1.show()
imp2.show()
IJ.log("Finding Peaks")
ip1_1, ip2_1, peaks_1, peaks_2 = find_peaks(imp1, imp2, sigmaSmaller, sigmaLarger, minPeakValue)
# Create a PointRoi from the DoG peaks, for visualization
roi_1 = PointRoi(0, 0)
roi_2 = PointRoi(0, 0)
roi_3 = PointRoi(0, 0)
roi_4 = PointRoi(0, 0)
# A temporary array of integers, one per dimension the image has
p_1 = zeros(ip1_1.numDimensions(), 'i')
p_2 = zeros(ip2_1.numDimensions(), 'i')
# Load every peak as a point in the PointRoi
for peak in peaks_1:
# Read peak coordinates into an array of integers
peak.localize(p_1)
roi_1.addPoint(imp1, p_1[0], p_1[1])
for peak in peaks_2:
# Read peak coordinates into an array of integers
peak.localize(p_2)
roi_2.addPoint(imp2, p_2[0], p_2[1])
# Chose minimum distance in pixel
#min_dist = 20
for peak_1 in peaks_1:
peak_1.localize(p_1)
for peak_2 in peaks_2:
peak_2.localize(p_2)
d1 = distance(p_1, p_2)
if d1 < min_dist:
roi_3.addPoint(imp1, p_2[0], p_2[1])
break
for peak_2 in peaks_2:
peak_2.localize(p_2)
for peak_1 in peaks_1:
peak_1.localize(p_1)
d2 = distance(p_2, p_1)
if d2 < min_dist:
roi_4.addPoint(imp1, p_2[0], p_2[1])
break
rm = RoiManager.getInstance()
if not rm:
rm = RoiManager()
rm.reset()
rm.addRoi(roi_1)
rm.addRoi(roi_2)
rm.addRoi(roi_3)
rm.addRoi(roi_4)
rm.select(0)
rm.rename(0, "ROI neuron")
rm.runCommand("Set Color", "yellow")
rm.select(1)
rm.rename(1, "ROI glioma")
rm.runCommand("Set Color", "blue")
rm.select(2)
rm.rename(2, "ROI glioma touching neurons")
rm.runCommand("Set Color", "red")
rm.select(3)
rm.rename(3, "ROI neurons touching glioma")
rm.runCommand("Set Color", "green")
rm.runCommand(imp1, "Show All")
#Change distance to be in um
cal = imp.getCalibration()
min_distance = str(round((cal.pixelWidth * min_dist),1))
table = ResultsTable()
table.incrementCounter()
table.addValue("Numbers of Neuron Markers", roi_1.getCount(0))
table.addValue("Numbers of Glioma Markers", roi_2.getCount(0))
table.addValue("Numbers of Glioma within %s um of Neurons" %(min_distance), roi_3.getCount(0))
table.addValue("Numbers of Neurons within %s um of Glioma" %(min_distance), roi_4.getCount(0))
table.show("Results Analysis")
stack.size() + 1): #loop over all images in the stack
if i < 20:
ip = stack.getProcessor(i)
avgI.append(ip.getStats().mean) #get average intensity
else:
avgI.append(0)
if len(avgI) == dims[3]:
apicalZ = avgI.index(
max(avgI)) #get position of apical surface
if apicalZ == False:
print('Selecting default apical slice...')
apicalZ = 9
#Get apical average projection of apical surface and find cell positions
try:
apical_detect_imp = zp.run(detect_imp, 'avg', apicalZ - 4,
apicalZ + 4)
apical_measure_imp = zp.run(measure_imp, 'avg', apicalZ - 4,
apicalZ + 4)
except:
print('Projecting full apical part of stack...')
apical_detect_imp = zp.run(detect_imp, 'avg', 5, stack.size())
apical_measure_imp = zp.run(measure_imp, 'avg', 5, stack.size())
apical_detect_imp.show()
measured.reset()
IJ.run("Find Maxima...",
"noise=" + str(noise_threshold) + " output=List exclude")
measured = rt.getResultsTable()
centroidX = measured.getColumn(measured.getColumnIndex('X'))
centroidY = measured.getColumn(measured.getColumnIndex('Y'))
print 'Detected ' + str(len(centroidX)) + ' cells'
name = imp.getTitle()
width = imp.width
height = imp.height
num_slices = imp.getNSlices()
num_channels = imp.getNChannels()
num_frames = imp.getNFrames()
max_cycle_length = int(max_cycle_length)
assert width_sub < width and width_sub < height, "Size of square must be less than dimensions of movie"
assert num_channels == 1, "Image can only have one channel"
#imp = process.ImageConverter.convertToGray32(original_imp)
avg_img = ZProjector.run(imp,"avg");
avg_img.show()
imp2 = ImageCalculator().run("Subtract create stack", imp, avg_img)
imp2.show()
#WindowManager.getImage("AVG_Pos0")
num_window_x = int(round(width/width_sub)) #int(width-width_sub+1)
num_window_y = int(round(height/width_sub)) #int(height-width_sub+1)
center_px = int(round(width_sub/2))
new_rois = []
for i in range(0,num_window_x):
for j in range(0,num_window_y):
new_roi = Roi(i*width_sub, j*width_sub, width_sub, width_sub)
new_rois.append(new_roi)
def main():
# ensure consistent preference settings
Prefs.blackBackground = False;
# get locations for previous and new outputs
params = Parameters();
params.loadLastParams();
output_folder_old, output_folder = mbio.rerun_location_chooser(params.input_image_path);
params.loadParametersFromJson(os.path.join(output_folder_old, 'parameters used.json'));
params.setOutputPath(output_folder);
# get original image file (for overlays etc.)
if not os.path.isfile(params.input_image_path):
mbui.warning_dialog(["The original data can't be found at the location specified in saved parameters. ",
"(Possibly something as simple as a change in network drive mapping has occurred)",
"Please specify the location of the original image file..."]);
params.setInputImagePath(mbio.input_file_location_chooser(params.output_path));
import_opts, params = mbui.choose_series(params.input_image_path, params);
imps = bf.openImagePlus(import_opts);
imp = imps[0];
if imp.getNSlices() > 1:
mbui.warning_dialog(["More than one Z plane detected.",
"I will do a maximum projection before proceeding",
"Continue?"]);
imp = ZProjector.run(imp,"max all");
# prompt user to select ROI
original_imp = Duplicator().run(imp);
_, crop_params = mbui.crop_to_ROI(imp, params);
imp.show();
if crop_params is not None:
params.perform_spatial_crop = True;
mbui.autoset_zoom(imp);
imp.updateAndDraw();
review_crop = mb.check_cropping(output_folder_old, params);
keep_crop = not review_crop;
if review_crop:
keep_crop = mbui.crop_review();
if not keep_crop:
imp.changes = False;
imp.close();
imp = original_imp;
else:
original_imp.close();
else:
original_imp.close();
# prompt user to do time cropping
imp, start_end_tuple = mbui.time_crop(imp, params);
params.setTimeCropStartEnd(start_end_tuple);
params = mbio.get_metadata(params);
params.setCurvatureLengthUm(round(params.curvature_length_um / params.pixel_physical_size) * params.pixel_physical_size);
params.persistParameters();
IJ.run(imp, "Set Scale...", "distance=0 known=0 pixel=1 unit=pixel");
imp.show();
if imp.getNChannels() > 1:
imp.setPosition(params.membrane_channel_number, 1, 1);
mbui.autoset_zoom(imp);
IJ.run("Enhance Contrast", "saturated=0.35");
split_channels = mbfs.split_image_plus(imp, params);
membrane_test_channel_imp = Duplicator().run(split_channels[0]);
segmentation_channel_imp = split_channels[-1];
# import edges
imp.hide();
membrane_edges = mbio.load_qcd_edges2(os.path.join(output_folder_old, "user_defined_edges.zip"));
dummy_anchors = [params.manual_anchor_positions for _ in membrane_edges]
membrane_edges, fixed_anchors = mbui.perform_user_qc(membrane_test_channel_imp, membrane_edges, membrane_edges, dummy_anchors, params);
imp.show();
rgbstack = ImageStack(imp.getWidth(), imp.getHeight());
for tidx in range(imp.getNFrames()):
imp.setT(tidx+1);
ip = imp.getProcessor();
rgbip = ip.convertToRGB();
rgbstack.addSlice(rgbip);
rgbimp = ImagePlus(("RGB " + imp.getTitle()), rgbstack);
imp.close();
rgbimp.show();
IJ.run("Colors...", "foreground=yellow background=white selection=yellow");
for tidx in range(rgbimp.getNSlices()):
rgbimp.setSlice(tidx+1);
rgbimp.setRoi(membrane_edges[tidx]);
IJ.run(rgbimp, "Draw", "slice");
IJ.run("Colors...", "foreground=red background=white selection=yellow");
for tidx in range(rgbimp.getNSlices()):
rgbimp.setSlice(tidx+1);
for anchor in params.manual_anchor_positions:
rgbimp.setRoi(PointRoi(anchor[0], anchor[1]));
IJ.run(rgbimp, "Draw", "slice");
params.saveParametersToJson(os.path.join(params.output_path, "parameters used.json"));
params.persistParameters();
rgbimp.changes = False;
from loci.plugins import BF
from net.imglib2.img.display.imagej import ImageJFunctions
from net.imglib2.view import Views
from net.imglib2.roi import Regions, Masks, BinaryMaskRegionOfInterest
from net.imglib2.type.logic import BitType
from net.imglib2.type.numeric.real import DoubleType
from ij.plugin import Duplicator
image, = BF.openImagePlus(inputFile.getAbsolutePath())
t = image.getTitle()
### SELECT REGION OF INTEREST
# Z Projection
from ij.plugin import ZProjector
image_mip = ZProjector.run(image, "max")
# ROI CREATION
image_mip.show()
image_mip.setRoi(image_mip.width / 2, image_mip.height / 2, regionSize,
regionSize)
from ij.gui import WaitForUserDialog
myWait = WaitForUserDialog("Choose ROI",
"Please position the ROI.. Press 'OK' when done.")
myWait.show()
# CROP
image.setRoi(image_mip.getRoi())
from ij import IJ
image = Duplicator().run(image)
def process(srcDir, dstDir, currentDir, fileName, keepDirectories, Channel_1, Channel_2, radius_background, sigmaSmaller, sigmaLarger, minPeakValue, min_dist):
IJ.run("Close All", "")
# Opening the image
IJ.log("Open image file:" + fileName)
#imp = IJ.openImage(os.path.join(currentDir, fileName))
#imp = IJ.getImage()
imp = BF.openImagePlus(os.path.join(currentDir, fileName))
imp = imp[0]
# getDimensions(width, height, channels, slices, frames)
IJ.log("Computing Max Intensity Projection")
if imp.getDimensions()[3] > 1:
imp_max = ZProjector.run(imp,"max")
else:
imp_max = imp
ip1, ip2 = extract_channel(imp_max, Channel_1, Channel_2)
IJ.log("Substract background")
imp1, imp2 = back_substraction(ip1, ip2, radius_background)
IJ.log("Finding Peaks")
ip1_1, ip2_1, peaks_1, peaks_2 = find_peaks(imp1, imp2, sigmaSmaller, sigmaLarger, minPeakValue)
# Create a PointRoi from the DoG peaks, for visualization
roi_1 = PointRoi(0, 0)
roi_2 = PointRoi(0, 0)
roi_3 = PointRoi(0, 0)
roi_4 = PointRoi(0, 0)
# A temporary array of integers, one per dimension the image has
p_1 = zeros(ip1_1.numDimensions(), 'i')
p_2 = zeros(ip2_1.numDimensions(), 'i')
# Load every peak as a point in the PointRoi
for peak in peaks_1:
# Read peak coordinates into an array of integers
peak.localize(p_1)
roi_1.addPoint(imp1, p_1[0], p_1[1])
for peak in peaks_2:
# Read peak coordinates into an array of integers
peak.localize(p_2)
roi_2.addPoint(imp2, p_2[0], p_2[1])
# Chose minimum distance in pixel
#min_dist = 20
for peak_1 in peaks_1:
peak_1.localize(p_1)
for peak_2 in peaks_2:
peak_2.localize(p_2)
d1 = distance(p_1, p_2)
if d1 < min_dist:
roi_3.addPoint(imp1, p_2[0], p_2[1])
break
for peak_2 in peaks_2:
peak_2.localize(p_2)
for peak_1 in peaks_1:
peak_1.localize(p_1)
d2 = distance(p_2, p_1)
if d2 < min_dist:
roi_4.addPoint(imp1, p_2[0], p_2[1])
break
cal = imp.getCalibration()
min_distance = str(round((cal.pixelWidth * min_dist),1))
table = ResultsTable()
table.incrementCounter()
table.addValue("Numbers of Neuron Markers", roi_1.getCount(0))
table.addValue("Numbers of Glioma Markers", roi_2.getCount(0))
table.addValue("Numbers of Glioma within %s um of Neurons" %(min_distance), roi_3.getCount(0))
table.addValue("Numbers of Neurons within %s um of Glioma" %(min_distance), roi_4.getCount(0))
#table.show("Results Analysis")
saveDir = currentDir.replace(srcDir, dstDir) if keepDirectories else dstDir
if not os.path.exists(saveDir):
os.makedirs(saveDir)
IJ.log("Saving to" + saveDir)
table.save(os.path.join(saveDir, fileName + ".csv"))
IJ.selectWindow("Log")
IJ.saveAs("Text", os.path.join(saveDir, fileName + ".csv"));
def main():
### debug ###############################################################
#print (sys.version_info);
#print(sys.path);
#file_path = "D:\\data\\Inverse blebbing\\MAX_2dpf marcksl1b-EGFP inj_TgLifeact-mCh_movie e4_split-bleb1.tif";
#output_root = "D:\\data\\Inverse blebbing\\output";
#from datetime import datetime
#timestamp = datetime.strftime(datetime.now(), '%Y-%m-%d %H-%M-%S');
#output_folder = os.path.join(output_root, (timestamp + ' output'));
#os.mkdir(output_folder);
########################################################################
# ensure consistent preference settings
Prefs.blackBackground = False;
# prompt user for input parameters
params = mbui.analysis_parameters_gui();
# prompt user for file locations
file_path, output_folder = mbio.file_location_chooser(params.input_image_path);
params.setInputImagePath(file_path);
params.setOutputPath(output_folder);
# get image file
import_opts, params = mbui.choose_series(file_path, params);
imps = bf.openImagePlus(import_opts);
imp = imps[0];
# catch unexpected image dimensions - for now, project in Z...:
if imp.getNSlices() > 1:
mbui.warning_dialog(["More than one Z plane detected.",
"I will do a maximum projection before proceeding",
"Continue?"]);
imp = ZProjector.run(imp,"max all");
params = mbio.get_metadata(params);
params.setCurvatureLengthUm(round(params.curvature_length_um / params.pixel_physical_size) * params.pixel_physical_size);
params.persistParameters();
IJ.run(imp, "Set Scale...", "distance=0 known=0 pixel=1 unit=pixel");
imp.show();
if imp.getNChannels() > 1:
imp.setPosition(params.membrane_channel_number, 1, 1);
mbui.autoset_zoom(imp);
IJ.run("Enhance Contrast", "saturated=0.35");
# prompt user to select ROI
original_imp, crop_params = mbui.crop_to_ROI(imp, params);
if crop_params is not None:
params.perform_spatial_crop = True;
mbui.autoset_zoom(imp);
else:
params.perform_spatial_crop = False;
# prompt user to do time cropping
imp, start_end_tuple = mbui.time_crop(imp, params);
params.setTimeCropStartEnd(start_end_tuple);
repeats = 1;
inner_outer_comparisons = None;
if params.inner_outer_comparison:
inner_outer_comparisons = InnerOuterComparisonData();
repeats = 2;
IJ.selectWindow(imp.getTitle());
IJ.run("Enhance Contrast", "saturated=0.35");
for r in range(0, repeats):
calculated_objects = CalculatedObjects();
if params.time_crop_start_end[0] is not None:
calculated_objects.timelist = [idx * params.frame_interval for idx in range(params.time_crop_start_end[0], params.time_crop_start_end[1]+1)]
else:
calculated_objects.timelist = [idx * params.frame_interval for idx in range(imp.getNFrames())];
calculated_objects, params, split_channels = mbfs.generate_edges(imp, params, calculated_objects, (r+1)/repeats);
membrane_channel_imp = split_channels[0];
actin_channel_imp = split_channels[1];
segmentation_channel_imp = split_channels[2];
calculated_objects = mbfs.calculate_outputs(params,
calculated_objects,
split_channels,
repeat_fraction=(r+1)/repeats);
# output colormapped images and kymographs
fig_imp_list = mbfs.generate_and_save_figures(imp, calculated_objects, params, membrane_channel_imp, segmentation_channel_imp);
mbfs.save_csvs(calculated_objects, params);
params.saveParametersToJson(os.path.join(params.output_path, "parameters used.json"));
imp.changes = False;
IJ.setTool("zoom");
if params.close_on_completion or (params.inner_outer_comparison and r!=(repeats-1)):
for fig_imp in fig_imp_list:
fig_imp.close();
elif params.close_on_completion and (r==(repeats-1)):
imp.close();
if params.inner_outer_comparison:
tname = "Time, " + params.interval_unit;
output_folder = os.path.dirname(params.output_path);
profile = [[((inner, outer), (float(outer)/inner)) for inner, outer in zip(calculated_objects.inner_outer_data.inner_means, calculated_objects.inner_outer_data.outer_means)]];
mbio.save_profile_as_csv(profile,
os.path.join(output_folder, "Intensity ratios.csv"),
"outer/inner",
xname="inner",
yname="outer",
tname=tname,
time_list=calculated_objects.timelist);
sd_profile = [[((inner, outer), (float(outer)/inner)) for inner, outer in zip(calculated_objects.inner_outer_data.inner_sds, calculated_objects.inner_outer_data.outer_sds)]];
mbio.save_profile_as_csv(profile,
os.path.join(output_folder, "Intensity standard deviation ratios.csv"),
"outer sd/inner sd",
xname="inner sd",
yname="outer sd",
tname=tname,
time_list=calculated_objects.timelist);
return;
def main():
# ensure consistent preference settings
Prefs.blackBackground = False
# get locations for previous and new outputs
params = Parameters()
params.loadLastParams()
output_folder_old, output_folder = mbio.rerun_location_chooser(
params.input_image_path)
params.loadParametersFromJson(
os.path.join(output_folder_old, 'parameters used.json'))
params.setOutputPath(output_folder)
# present user with the familiar setup UI, with options that don't make sense/aren't yet implemented disabled
params = mbui.analysis_parameters_gui(rerun_analysis=True, params=params)
# get original image file (for overlays etc.)
if not os.path.isfile(params.input_image_path):
mbui.warning_dialog([
"The original data can't be found at the location specified in saved parameters. ",
"(Possibly something as simple as a change in network drive mapping has occurred)",
"Please specify the location of the original image file..."
])
params.setInputImagePath(
mbio.input_file_location_chooser(params.output_path))
import_opts, params = mbui.choose_series(params.input_image_path, params)
imps = bf.openImagePlus(import_opts)
imp = imps[0]
if imp.getNSlices() > 1:
mbui.warning_dialog([
"More than one Z plane detected.",
"I will do a maximum projection before proceeding", "Continue?"
])
imp = ZProjector.run(imp, "max all")
params = mbio.get_metadata(params)
params.setCurvatureLengthUm(
round(params.curvature_length_um / params.pixel_physical_size) *
params.pixel_physical_size)
params.persistParameters()
IJ.run(imp, "Set Scale...", "distance=0 known=0 pixel=1 unit=pixel")
imp.show()
if imp.getNChannels() > 1:
imp.setPosition(params.membrane_channel_number, 1, 1)
mbui.autoset_zoom(imp)
IJ.run("Enhance Contrast", "saturated=0.35")
# prompt user to select ROI
original_imp = Duplicator().run(imp)
_, crop_params = mbui.crop_to_ROI(imp, params)
imp.show()
if crop_params is not None:
params.perform_spatial_crop = True
mbui.autoset_zoom(imp)
imp.updateAndDraw()
review_crop = mb.check_cropping(output_folder_old, params)
keep_crop = not review_crop
if review_crop:
keep_crop = mbui.crop_review()
if not keep_crop:
imp.changes = False
imp.close()
imp = original_imp
else:
original_imp.close()
else:
original_imp.close()
# prompt user to do time cropping
imp, start_end_tuple = mbui.time_crop(imp, params)
params.setTimeCropStartEnd(start_end_tuple)
# import edges
membrane_edges = mbio.load_qcd_edges2(
os.path.join(output_folder_old, "user_defined_edges.zip"))
mbio.save_qcd_edges2(membrane_edges, params.output_path)
calculated_objects = CalculatedObjects()
calculated_objects.membrane_edges = membrane_edges
if params.time_crop_start_end[0] is not None:
calculated_objects.timelist = [
idx * params.frame_interval
for idx in range(params.time_crop_start_end[0],
params.time_crop_start_end[1] + 1)
]
else:
calculated_objects.timelist = [
idx * params.frame_interval for idx in range(imp.getNFrames())
]
split_channels = mbfs.split_image_plus(imp, params)
membrane_channel_imp = split_channels[0]
actin_channel_imp = split_channels[1]
segmentation_channel_imp = None
if params.photobleaching_correction:
if os.path.isfile(
os.path.join(output_folder_old, 'binary_membrane_stack.tif')):
segmentation_binary_path = os.path.join(
output_folder_old, 'binary_membrane_stack.tif')
segmentation_channel_imp = IJ.openImage(segmentation_binary_path)
else:
segmentation_channel_imp = split_channels[2]
segmentation_channel_imp = mb.make_and_clean_binary(
segmentation_channel_imp, params.threshold_method)
split_channels[2] = segmentation_channel_imp
calculated_objects = mbfs.calculate_outputs(params, calculated_objects,
split_channels)
# output colormapped images and kymographs
fig_imp_list = mbfs.generate_and_save_figures(imp, calculated_objects,
params, membrane_channel_imp,
segmentation_channel_imp)
mbfs.save_csvs(calculated_objects, params)
params.saveParametersToJson(
os.path.join(params.output_path, "parameters used.json"))
imp.changes = False
IJ.setTool("zoom")
if params.close_on_completion:
for fig_imp in fig_imp_list:
fig_imp.close()
imp.close()
return
#imp2 = HyperStackConverter.toHyperStack(imp, int(metadata['n_channels']), int(metadata['z_pixels']), 1, "Composite");
print(metadata)
IJ.run(
imp, "Properties...", "channels=" + str(int(metadata['n_channels'])) +
" slices=" + str(int(metadata['z_pixels'])) + " frames=1 unit=" +
str(metadata['x_unit']) + " pixel_width=" +
str(metadata['x_physical_size']) + " pixel_height=" +
str(metadata['y_physical_size']) + " voxel_depth=" +
str(metadata['z_extent'] / metadata['z_pixels']))
imp.show()
imp, z_lims = z_crop(imp)
use_ch = imp.getC()
print(use_ch)
IJ.setTool("rect")
imp.hide()
imp2 = ZProjector.run(imp, "max")
imp2.show()
imp2.setC(use_ch + 1)
WaitForUserDialog("Select XY region to crop...").show()
crop_roi = imp2.getRoi()
imp2.close()
imp.show()
imp.setRoi(crop_roi)
IJ.run("Crop", "")
frames = imp.getNFrames()
slices = imp.getNSlices()
imp = Duplicator().run(imp, use_ch + 1, use_ch + 1, 1, slices, 1, frames)
imp.show()
# now do 3d segmentation....
#histo = StackStatistics(imp).histogram;
