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')