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 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 iter_rois_fwhm(roi_path, img_path):
roi = readRois(roi_path)
img = Opener.openUsingBioFormats(img_path)
title = str(img.getShortTitle())
all_out = []
for i in roi.getIndexes():
roi.select(img, i)
x, y = getXY(img)
fwhm, r2, params = fit_curve(x, y)
all_out.append({
"fwhm": fwhm,
"r2": r2,
"roi_id": i,
"ch_n": 2,
"img_name": title,
"ch_name": "NaV1.6",
})
roi.close()
img.close()
return all_out
for filename in filenames: match = re.search(pattern, filename) if match is not None: #print filename, match.group(3) GRlist.append(match.group(3)) print srcDir print 'files: ', len(GRlist) GRlist = sorted(GRlist) timeseries = [] for timepoint in GRlist: thisfile = basename + '_R' + repetition + '_GR' + timepoint + '_B' + block + '_L' + location + '.lsm' print thisfile imp = Opener.openUsingBioFormats(os.path.join(srcDir, thisfile)) imp.setOpenAsHyperStack(False) timeseries.append(imp) newname = basename + '_R' + repetition + '_B' + block + '_L' + location + '.lsm' calib = timeseries[0].getCalibration() dimA = timeseries[0].getDimensions() jaimp = array(timeseries, ImagePlus) ccc = Concatenator() #allimp = ccc.concatenateHyperstacks(jaimp, newname, False) allimp = ccc.concatenate(jaimp, False) allimp.setDimensions(dimA[2], dimA[3], len(GRlist)) allimp.setCalibration(calib) allimp.setOpenAsHyperStack(True) allimp.show()
IJ.run("Collect Garbage");
#inputDir1 = IJ.getDirectory("Choose image directory! ")
inputDir1 = "/home/mt/Downloads/1/"
fileList1 = os.listdir(inputDir1);
###tilestring=getString("Which Tilescan", "1")
RGB=os.path.join(inputDir1,'RGB.png')
FileList=sorted(os.listdir(inputDir1))
print(FileList)
stitchedFiles=[x for x in FileList if "stitched.TIF" in x]
stitchedNames=[x.split('_')[0] for x in stitchedFiles]
mergedFiles=[x for x in FileList if "merged.TIF" in x]
imp = Opener.openUsingBioFormats(os.path.join(inputDir1,mergedFiles[0]))
xsize=imp.getWidth()
ysize=imp.getHeight()
imp.close()
print(xsize,ysize)
for im in stitchedFiles:
if not "hoechst" in im.lower():
IJ.open(inputDir1+im)
imp = IJ.getImage()
imp.setTitle(im.split('_')[0])
IJ.run("Images to Stack", "name=Stack title=[] use")
imp = IJ.getImage()
#IJ.run("Multiply...", "value=2.2")
IJ.beep()
image_basenames = list(set(image_basenames))
print(image_basenames)
ImageJ()
for i in image_basenames:
print(i)
cur_images = []
for j in image_list:
m = re.search(i, j)
if m:
cur_images.append(j)
# imp_c1 = IJ.openImage(cur_images[0])
# imp_c2 = IJ.openImage(cur_images[1])
image_path = image_directory + cur_images[2]
imp_c3 = Opener.openUsingBioFormats(image_path)
imp_c3.show()
save_name = image_directory + '/Stacks/' + i + 'merged.tif'
IJ.saveAsTiff(imp_c3, save_name)
# merge_string = "c1=" + cur_images[0] + " c2=" + cur_images[1] + " create";
# IJ.run("Merge Channels...", merge_string)
IJ.run('Z Project...', 'projection=[Max Intensity] all')
IJ.run('Enhance Contrast...', 'saturated=0.05')
max_im = IJ.getImage()
save_name = image_directory + '/MaxZProj/' + i + 'MaxZProj.tif'
IJ.saveAsTiff(max_im, save_name)
IJ.run('Close All')
gc.collect()
IJ.run('Close All')
