def measureOneFile(nucfilepath): #parent = os.path.dirname(nucfilepath) #gparent = os.path.dirname(parent) #gparentname = os.path.basename(gparent) #ggparent = os.path.dirname(gparent) iodefectflag = 0 op = Opener() filetype = op.getFileType(nucfilepath) if filetype == Opener.TIFF: imp = op.openImage(nucfilepath) imgtitle = imp.getTitle() imgstats = imp.getStatistics() intrange = imgstats.max - imgstats.min #print imgtitle, imp.getWidth(), 'x', imp.getWidth(), 'Max - Min', intrange, 'SD', imgstats.stdDev print imgtitle, imp.getWidth(), 'x', imp.getWidth(), 'Mean', imgstats.mean, 'SD', imgstats.stdDev #outfilename = gparentname + imgtitle + ".csv" #outdir = os.path.join(ggparent, 'outfiles') #if not os.path.isdir(outdir): #os.mkdir(outdir) #outpath = os.path.join(ggparent, 'outfiles', outfilename) #print outpath #outlist = ["'" + imgtitle + "'", intrange, imgstats.stdDev] outlist = ["'" + imgtitle + "'", imgstats.mean, imgstats.stdDev] else: iodefectflag = 1 filename = os.path.basename(nucfilepath) print "****** Could not open:",filename outlist = ["'" + filename + "'", 1.0, 1.0] return outlist, nucfilepath, iodefectflag
def main(): Interpreter.batchMode = True if (lambda_flat == 0) ^ (lambda_dark == 0): print ("ERROR: Both of lambda_flat and lambda_dark must be zero," " or both non-zero.") return lambda_estimate = "Automatic" if lambda_flat == 0 else "Manual" #import pdb; pdb.set_trace() print "Loading images..." filenames = enumerate_filenames(pattern) num_channels = len(filenames) num_images = len(filenames[0]) image = Opener().openImage(filenames[0][0]) width = image.width height = image.height image.close() # The internal initialization of the BaSiC code fails when we invoke it via # scripting, unless we explicitly set a the private 'noOfSlices' field. # Since it's private, we need to use Java reflection to access it. Basic_noOfSlices = Basic.getDeclaredField('noOfSlices') Basic_noOfSlices.setAccessible(True) basic = Basic() Basic_noOfSlices.setInt(basic, num_images) # Pre-allocate the output profile images, since we have all the dimensions. ff_image = IJ.createImage("Flat-field", width, height, num_channels, 32); df_image = IJ.createImage("Dark-field", width, height, num_channels, 32); print("\n\n") # BaSiC works on one channel at a time, so we only read the images from one # channel at a time to limit memory usage. for channel in range(num_channels): print "Processing channel %d/%d..." % (channel + 1, num_channels) print "===========================" stack = ImageStack(width, height, num_images) opener = Opener() for i, filename in enumerate(filenames[channel]): print "Loading image %d/%d" % (i + 1, num_images) image = opener.openImage(filename) stack.setProcessor(image.getProcessor(), i + 1) input_image = ImagePlus("input", stack) # BaSiC seems to require the input image is actually the ImageJ # "current" image, otherwise it prints an error and aborts. WindowManager.setTempCurrentImage(input_image) basic.exec( input_image, None, None, "Estimate shading profiles", "Estimate both flat-field and dark-field", lambda_estimate, lambda_flat, lambda_dark, "Ignore", "Compute shading only" ) input_image.close() # Copy the pixels from the BaSiC-generated profile images to the # corresponding channel of our output images. ff_channel = WindowManager.getImage("Flat-field:%s" % input_image.title) ff_image.slice = channel + 1 ff_image.getProcessor().insert(ff_channel.getProcessor(), 0, 0) ff_channel.close() df_channel = WindowManager.getImage("Dark-field:%s" % input_image.title) df_image.slice = channel + 1 df_image.getProcessor().insert(df_channel.getProcessor(), 0, 0) df_channel.close() print("\n\n") template = '%s/%s-%%s.tif' % (output_dir, experiment_name) ff_filename = template % 'ffp' IJ.saveAsTiff(ff_image, ff_filename) ff_image.close() df_filename = template % 'dfp' IJ.saveAsTiff(df_image, df_filename) df_image.close() print "Done!"
def tethered_cell(image_path, frame_number=100, frame_rate=100.0, CCW=1): """ parameter setting; frame rate (frame/sec) CCW = 1 : the motor rotation direction and the cell rotation direction on the image are same CCW = -1: the motor rotation direction and the cell rotation direction on the image are different """ opener = Opener() imp = opener.openImage(image_path) image_slice_number = imp.getNSlices() rm = RoiManager().getInstance() if image_slice_number < frame_number: # too short movie IJ.log('Number of frame of the movie is fewer than the number of frame that you selected') return False # create result directory result_path = image_path + '_tethered_cell_result' if os.path.lexists(result_path) is False: os.mkdir(result_path) #z projection; standard deviation, tethered cell shorws circle IJ.run(imp, 'Subtract Background...', 'rolling=5 light stack') IJ.run(imp, 'Median...', 'radius=2 stack') IJ.run(imp, 'Z Project...', 'stop=500 projection=[Standard Deviation]') zimp = IJ.getImage() IJ.saveAs(zimp, 'bmp', os.path.join(result_path,'STD_DEV.bmp')) # pick up tethered cell IJ.setAutoThreshold(zimp, 'MaxEntropy dark') IJ.run(zimp, 'Convert to Mask', '') IJ.run('Set Measurements...', "area centroid bounding shape feret's limit redirect=None decimal=3") IJ.run(zimp, 'Analyze Particles...', 'size=30-Infinity circularity=0.88-1.00 show=Nothing display exclude clear include') zrt = ResultsTable.getResultsTable() IJ.saveAs('Results', os.path.join(result_path,'RoiInfo.csv')) #tcX and tcY are xy coordinates of tethered cell, tcdia is outer diameter of rotating tethered cell #add ROI into stack image for i in range(zrt.getCounter()): tcX = zrt.getValue('X', i) tcY = zrt.getValue('Y', i) tcdia = zrt.getValue('Feret', i) rm.add(imp, OvalRoi(tcX - tcdia/2.0, tcY - tcdia/2.0, tcdia + 1, tcdia + 1), i) #calculate rotation speed by ellipse fitting IJ.setAutoThreshold(imp, 'Li') for roi_number in range(rm.getCount()): t = [] XM = [] YM = [] theta = [] rotation_speed = [] area = [] imp.setRoi(rm.getRoi(roi_number)) cropped_imp = Duplicator().run(imp) IJ.run('Set Measurements...', 'area mean center fit limit redirect=None decimal=3') rm.select(roi_number) rt = rm.multiMeasure(imp) # check cell is present while analysis. Don't a cell gose anywhare? for i in range(frame_number): area.append(rt.getValue('Area1', i)) if 0 in area: continue for i in range(frame_number): t.append((1/frame_rate)*i) XM.append(rt.getValue('XM1', i)) YM.append(rt.getValue('YM1', i)) theta.append(rt.getValue('Angle1', i)/180.0*math.pi) # convert to radian if i == 0: rotation_speed.append(0) else: # phase treatment, theta should be -pi ~ pi temp_rotation_speed = [theta[i] - theta[i-1], theta[i] - theta[i-1] + math.pi, theta[i] - theta[i-1] - math.pi, theta[i] - theta[i-1] + 2*math.pi, theta[i] - theta[i-1] - 2*math.pi] temp_rotation_speed = sorted(temp_rotation_speed, key = lambda x :abs(x) )[0] rotation_speed.append(CCW*temp_rotation_speed/(2.0*math.pi)*frame_rate) # write csv # earch columns indicate 1:index, 2:time(sec), 3:X-coordinate of center of mass(pixel), 4:Y-coordinate of center of mass (pixel), 5:Angle(Radian), 6:Rotation Speed(Hz) with open(os.path.join(result_path,'Roi' + str(roi_number) + '.csv'), 'w') as f: writer = csv.writer(f) writer.writerow(['Index', 'time(s)', 'X', 'Y', 'Angle(rad)', 'Rotation Speed(Hz)']) for i in range(len(t)): writer.writerow([i, t[i], XM[i], YM[i], theta[i], rotation_speed[i]]) # plot x-y, t-x, t-y, t-rotation speed, save plot as bmp plotRotation(roi_number, result_path, t, XM, YM, rotation_speed) IJ.saveAs(cropped_imp, 'tiff', os.path.join(result_path,'Roi' + str(roi_number) + '.tiff')) rt.reset() # get analysis date and time dt = datetime.datetime.today() dtstr = dt.strftime('%Y-%m-%d %H:%M:%S') # wtite analysis setting with open(os.path.join(result_path,'analysis_setting.csv'), 'w') as f: writer = csv.writer(f) writer.writerow(['Analysis Date','frame number','frame rate','CCW direction', 'Method','Auto threshold', 'Subtruct Background', 'Median filter']) writer.writerow([dtstr, frame_number, frame_rate, CCW, 'Ellipse', 'Li', '5.0', '2']) # save roi if rm.getCount() != 0: rm.runCommand('Save', os.path.join(result_path, 'Roi.zip')) zimp.close() imp.close() rm.close() zrt.reset()
def main(): Interpreter.batchMode = True if (lambda_flat == 0) ^ (lambda_dark == 0): print ("ERROR: Both of lambda_flat and lambda_dark must be zero," " or both non-zero.") return lambda_estimate = "Automatic" if lambda_flat == 0 else "Manual" print "Loading images..." filenames = enumerate_filenames(pattern) if len(filenames) == 0: return # This is the number of channels inferred from the filenames. The number # of channels in an individual image file will be determined below. num_channels = len(filenames) num_images = len(filenames[0]) image = Opener().openImage(filenames[0][0]) if image.getNDimensions() > 3: print "ERROR: Can't handle images with more than 3 dimensions." (width, height, channels, slices, frames) = image.getDimensions() # The third dimension could be any of these three, but the other two are # guaranteed to be equal to 1 since we know NDimensions is <= 3. image_channels = max((channels, slices, frames)) image.close() if num_channels > 1 and image_channels > 1: print ( "ERROR: Can only handle single-channel images with {channel} in" " the pattern, or multi-channel images without {channel}. The" " filename patterns imply %d channels and the images themselves" " have %d channels." % (num_channels, image_channels) ) return if image_channels == 1: multi_channel = False else: print ( "Detected multi-channel image files with %d channels" % image_channels ) multi_channel = True num_channels = image_channels # Clone the filename list across all channels. We will handle reading # the individual image planes for each channel below. filenames = filenames * num_channels # The internal initialization of the BaSiC code fails when we invoke it via # scripting, unless we explicitly set a the private 'noOfSlices' field. # Since it's private, we need to use Java reflection to access it. Basic_noOfSlices = Basic.getDeclaredField('noOfSlices') Basic_noOfSlices.setAccessible(True) basic = Basic() Basic_noOfSlices.setInt(basic, num_images) # Pre-allocate the output profile images, since we have all the dimensions. ff_image = IJ.createImage("Flat-field", width, height, num_channels, 32); df_image = IJ.createImage("Dark-field", width, height, num_channels, 32); print("\n\n") # BaSiC works on one channel at a time, so we only read the images from one # channel at a time to limit memory usage. for channel in range(num_channels): print "Processing channel %d/%d..." % (channel + 1, num_channels) print "===========================" stack = ImageStack(width, height, num_images) opener = Opener() for i, filename in enumerate(filenames[channel]): print "Loading image %d/%d" % (i + 1, num_images) # For multi-channel images the channel determines the plane to read. args = [channel + 1] if multi_channel else [] image = opener.openImage(filename, *args) stack.setProcessor(image.getProcessor(), i + 1) input_image = ImagePlus("input", stack) # BaSiC seems to require the input image is actually the ImageJ # "current" image, otherwise it prints an error and aborts. WindowManager.setTempCurrentImage(input_image) basic.exec( input_image, None, None, "Estimate shading profiles", "Estimate both flat-field and dark-field", lambda_estimate, lambda_flat, lambda_dark, "Ignore", "Compute shading only" ) input_image.close() # Copy the pixels from the BaSiC-generated profile images to the # corresponding channel of our output images. ff_channel = WindowManager.getImage("Flat-field:%s" % input_image.title) ff_image.slice = channel + 1 ff_image.getProcessor().insert(ff_channel.getProcessor(), 0, 0) ff_channel.close() df_channel = WindowManager.getImage("Dark-field:%s" % input_image.title) df_image.slice = channel + 1 df_image.getProcessor().insert(df_channel.getProcessor(), 0, 0) df_channel.close() print("\n\n") template = '%s/%s-%%s.tif' % (output_dir, experiment_name) ff_filename = template % 'ffp' IJ.saveAsTiff(ff_image, ff_filename) ff_image.close() df_filename = template % 'dfp' IJ.saveAsTiff(df_image, df_filename) df_image.close() print "Done!"