''' Code to compute radial-max profiles of either: The FFT of an HRTEM image Diffraction pattern Profiles can be computed from either: Camera live-view image in DigitalMicrograph In-situ camera data played back via the In-Situ Player The result is a 2D display of these profiles over time Since the data is computed as fast as possible, which could vary over time, the time axis in the resulting image may be somewhat non-linear, so it is not calibrated. WARNING: Due to a bug in GMS 3.5.0 and 3.5.1, this script will not run in those versions Run the code with the live-view image, containing a rectangular ROI, front-most in GMS For diffraction data, be sure to center the ROI precisely over the diffraction pattern center. The code also works with an IS video played back with the IS player To stop calculation, delete the ROI. Profiles are computed as often as possible. Lines of code between #XXXXXXXX... lines are specific to computing a radial-max profile All other lines of code are general, and can be re-used to produce other kinds of profiles from a live-view image Code written by Ben Miller. Last Updated Oct 2024 ''' import time import numpy as np import traceback if not DM.IsScriptOnMainThread(): print('Scipy scripts cannot be run on Background Thread.'); exit() import scipy from scipy import ndimage from scipy import signal from scipy import fftpack from scipy.ndimage.interpolation import geometric_transform import sys from tkinter import * sys.argv.extend(['-a', ' ']) from numpy.lib.stride_tricks import as_strided #User editable variables are set here #XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX Diff_not_FFT = False #Set to True to calculate profiles from a live diffraction pattern, rather than from the FFT of an HRTEM image. median = 0 #set to 1 to apply median filter to the FFT prior to profile creation (slows calculation, especially for large input images) initial_result_image_width = 200 #how many profiles can be displayed in the intial result window (window is automatically expanded as needed) profile_result_length_ratio = 2 #Set to some integer 2^N, N=>0. Smaller N will make calculation slower. Default: 4 profile_angular_sampling_resolution = 256 #set how many samples are taken around the circumference of the radial profile mask_center_lines = True #Set to True to mask the vertical and horizontal center lines (often useful for FFTs) ask_about_diffraction = True #Set to True to always ask to confirm whether to process diffraction patterns or HRTEM images FFT_bin = 2 #Set to an integer number >=1 to bin the diffraction pattern or diffractogram prior to computing the radial profile. Smaller N will make the calculation slower. #XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX #XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX change = True if ask_about_diffraction: if Diff_not_FFT: message = "Do you want to process a series of diffraction patterns? \nSelect OK to continue or cancel to instead process HRTEM images." else: message = "Do you want to process a series of HRTEM images? \nSelect OK to continue or cancel to instead process diffraction patterns." change = not DM.OkCancelDialog(message) if (change and ask_about_diffraction): Diff_not_FFT = not Diff_not_FFT #XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX def DM_interaction_wait_dialog(instructions_str, title_str='DM_Interaction_Dialog',button1_str='Continue',button2_str='Cancel' ): ''' Function to Create a tkinter dialog that enables the user to interact with GMS while the script is paused, and then resume the script after they are done Accepts: instructions_str string giving instructions to the user about what actions are expected in GMS title_str optional string for the dialog window title button1_str optional string to change the continue button label text button1_str optional string to change the cancel button label text Returns: Nothing ''' root = Tk() root.attributes('-topmost', 'true') root.title(title_str) def go_on(): root.destroy() def cancel(): print("Script Aborted by User") root.destroy() sys.exit() Label(root, text=instructions_str).grid(row=0, sticky=W) Button(root, text=button1_str, command=go_on).grid(column=0, row=1, sticky=W, pady=4) Button(root, text=button2_str, command=cancel).grid(column=1, row=1, sticky=W, pady=4) mainloop() def strided_binning2D(array,binning=(1,1)): """ Function to Bin 2D Data Accepts: array 2D numpy array to be binned binning 2 element tuple of integer binning amounts (bin_x, bin_y) Returns: A binned 2D array """ bin=np.flip(binning) nh = (array.shape[0]-bin[0])//bin[0]+1 nw = (array.shape[1]-bin[1])//bin[1]+1 strides = (array.strides[0],array.strides[1],array.strides[0]*bin[0],array.strides[1]*bin[1]) strides = (array.strides[0],array.strides[1],array.strides[0]*bin[0],array.strides[1]*bin[1]) shape = (bin[0],bin[1],nh,nw) virtual_datacube = as_strided(array,shape=shape,strides=strides) result = np.sum(virtual_datacube,axis=(0,1)) return result class CListen(DM.Py_ScriptObject): #Function to find an ROI placed on an image by the user, returning the ROI ID. #If no ROI found, create a new one covering the entire image. def find_ROI(self,image): imageDisplay = image.GetImageDisplay(0) numROIs = imageDisplay.CountROIs() id = None for n in range(numROIs): roi = imageDisplay.GetROI(0) if roi.IsRectangle(): roi.SetVolatile(False) roi.SetResizable(False) id = roi.GetID() break if id is None: #If No ROI is found, create one that covers the whole image. print("\nRectangular ROI not found... using whole image") data_shape = image.GetNumArray().shape roi=DM.NewROI() roi.SetRectangle(0, 0, data_shape[0], data_shape[1]) imageDisplay.AddROI(roi) roi.SetVolatile(False) roi.SetResizable(False) id = roi.GetID() return id #XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX #Function to Determine Result Profile Length def profile_length(self,sx,sf): return int(sx/sf/FFT_bin) #Function to Set Result Image Calibration def calibrate_result(self,scale,sx,sf,ustr,diff_not_fft=False): if diff_not_fft: diff_scale = scale*sf*FFT_bin unit_str = unit_str else: diff_scale = FFT_bin*sf/scale/sx/2 unit_str = ustr+"-1" return(diff_scale,unit_str) #Funtion to convert cartesian-coordinate image to polar-coordinate image def topolar(self, img, r_size, theta_size, order=1): sx, sy = img.shape max_radius = int(sx/2) #define transform def transform(coords): theta = 2.0*np.pi*coords[1] / (theta_size - 1.) radius = max_radius * coords[0] / r_size i = int(sx/2) - radius*np.sin(theta) j = radius*np.cos(theta) + int(sx/2) return i,j #perform transform polar = geometric_transform(img, transform, output_shape=(r_size,theta_size), order=order,mode='constant',cval=1.0,prefilter=False) return polar #Function to calculate radial profile of a diffraction pattern or of FFT from image def radial_profile(self, image_o, profile_ang_res,length_ratio,do_median, FFT=False): if FFT: image_o = np.absolute(scipy.fftpack.fftshift(np.fft.fft2(image_o))) #determine profile size sx, sy = image_o.shape if mask_center_lines: image_o[:,sy//2-1:sy//2-1+1] = 0 image_o[sx//2-1:sx//2-1+1,:] = 0 image_o = strided_binning2D(image_o,binning=(FFT_bin,FFT_bin)) sx, sy = image_o.shape #Median-Filter FFT to remove single-pixel outliers if do_median: image_o_median = scipy.ndimage.median_filter(image_o, size=3) else: image_o_median = image_o if mask_center_lines: image_o_median[:,sx//2] = 0 image_o_median[sx//2,:] = 0 profile_size = int(sx/length_ratio) #convert FFT image to polar coordinates polar_im = self.topolar(image_o_median, profile_size, profile_ang_res, order=1) #compute radial mean and maximum profiles radial_max=np.amax(polar_im,1) radial_mean=np.mean(polar_im,1) #median-filter the radial mean profile to smooth this further radial_mean_median = scipy.signal.medfilt(radial_mean) #radial profile is radial-max minus radial-mean radial_profile = np.atleast_2d(radial_max-radial_mean_median) return radial_profile #XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX #Initialization Function def __init__(self, img): try: #Create an index that is incremented each time data is processed. (This index is not utilized in this script.) self.i = 0 self.name = "ImageO" #get the original image and assign it to self.imgref self.imgref = img #Get the data from the region within an ROI self.roi = DM.GetROIFromID(self.find_ROI(self.imgref)) if Diff_not_FFT: self.roi.SetVolatile(True) self.roi.SetResizable(True) DM_interaction_wait_dialog("Move the ROI in your dataset so it is exactly centered on the diffraction pattern center, and includes as much of the pattern as you want to analyze.", title_str='DM_Interaction_Dialog',button1_str='Continue',button2_str='Cancel' ) self.roi = DM.GetROIFromID(self.find_ROI(self.imgref)) val, val2, val3, val4 = self.roi.GetRectangle() self.data = self.imgref.GetNumArray()[int(val):int(val3),int(val2):int(val4)] #get the shape and calibration of the original image (input_sizex, input_sizey) = self.data.shape origin, x_scale_orig, scale_unit_orig = self.imgref.GetDimensionCalibration(1, 0) if scale_unit_orig == b'\xb5m': scale_unit_orig = 'um' #scale unit of microns causes problems for python in DM #XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX #Set the size and calibration of the result image (self.scale,self.unit_string) = self.calibrate_result(x_scale_orig,input_sizex,profile_result_length_ratio,scale_unit_orig) r_img_size=self.profile_length(input_sizex,profile_result_length_ratio) #XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX #create empty set for result images self.result_images = {} #create 1st result image and set calibration self.result_images[self.name] = DM.CreateImage(np.zeros((r_img_size,initial_result_image_width))) self.result_images[self.name].SetDimensionCalibration(1,0,self.scale,self.unit_string,0) self.result_images[self.name].ShowImage() #get numpy array from result image self.result_array = self.result_images[self.name].GetNumArray() #Set the image name which will be displayed in the image window's title bar self.result_images[self.name].SetName("Radial Max Profiles of "+img.GetName()) DM.Py_ScriptObject.__init__(self) self.stop = 0 except: print(traceback.format_exc()) #Function to end Image Listener def __del__(self): print("Script Ended") DM.Py_ScriptObject.__del__(self) #This function is run each time the image changes def HandleDataChangedEvent(self, flags, image): try: if not self.stop: #start timing start=time.perf_counter() (result_sizey, result_sizex) = self.result_array.shape #if the result image is nearly full, make it 2x larger if self.i > result_sizex-2: #create a new numpy array 2x larger self.result_array_temp = np.append(self.result_array, np.zeros_like(self.result_array), axis=1) #close the old results image in DM DM.DeleteImage(self.result_images[self.name]) #create a new results image and calibrate it self.name="Image{0}".format(self.i) self.result_images[self.name] = DM.CreateImage(np.copy((self.result_array_temp))) self.result_images[self.name].SetDimensionCalibration(1,0,self.scale,self.unit_string,0) #Set the image name which will be displayed in the image window's title bar self.result_images[self.name].SetName("Radial Max Profiles of "+self.imgref.GetName()) #display new result image in DM self.result_images[self.name].ShowImage() #get numpy array from new result image self.result_array = (self.result_images[self.name].GetNumArray()) #XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX #Get an (updated) ROI position val, val2, val3, val4 = self.roi.GetRectangle() #Get the data from the ROI area as a numpy array self.data = self.imgref.GetNumArray()[int(val):int(val3),int(val2):int(val4)] #compute radial profile from the diffraction pattern or radial FFT profile from the image, and place this profile into results image self.result_array[:,self.i] = self.radial_profile(self.data,profile_angular_sampling_resolution,profile_result_length_ratio, median, FFT=(not Diff_not_FFT)) #XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX #Update the result image display self.result_images[self.name].UpdateImage() #end timing and output time to process this frame end=time.perf_counter() print("Processed Image "+str(self.i)+" Processing Time= "+str(end-start)) #Increment an index each time data is processed. self.i = self.i+1 except: print(traceback.format_exc()) #Function to end script if source image window is closed def HandleWindowClosedEvent(self, event_flags, window): if not self.stop: self.stop = 1 global listener print("Window Closed") DM.DoEvents() #Unregister all listeners listener.UnregisterAllListeners() print("Live Processing Script Ended") #Function to end script if the ROI is deleted def HandleROIRemovedEvent(self, img_disp_event_flags, img_disp, roi_change_flag, roi_disp_change_flags, roi): if not self.stop: self.stop = 1 global listener print("ROI Removed") DM.DoEvents() #Unregister all listeners listener.UnregisterAllListeners() print("Live Processing Script Ended") #Function to get the currently used version of DigitalMicrograph def get_DM_version(): #No Python script command exists to get the DM version, #so we first run a DM script to put the values in the global tags dm_script = ('number minor, major, bugVersion\n' 'GetApplicationVersion(major, minor, bugVersion)\n' 'GetPersistentTagGroup().TagGroupSetTagAsLong("Python_Temp:DM_Version_Major",major)\n' 'GetPersistentTagGroup().TagGroupSetTagAsLong("Python_Temp:DM_Version_Minor",minor)\n' 'GetPersistentTagGroup().TagGroupSetTagAsLong("Python_Temp:DM_Version_bugVersion",bugVersion)') DM.ExecuteScriptString(dm_script) #Now get the information stored in the global tags by the DM script version = [0,0,0] _,version[0] = DM.GetPersistentTagGroup().GetTagAsString("Python_Temp:DM_Version_Major") _,version[1] = DM.GetPersistentTagGroup().GetTagAsString("Python_Temp:DM_Version_Minor") _,version[2] = DM.GetPersistentTagGroup().GetTagAsString("Python_Temp:DM_Version_bugVersion") return version #Main Code Starts Here #Check that we are not running 3.5.0 or 3.5.1 which have a known bug affecting this script. if ((get_DM_version()[1] == '51') or (get_DM_version()[1] == '50')): DM.OkDialog("Due to a bug in DigitalMicrograph 3.5.0 and 3.5.1, this script would cause DM to crash in those versions. \n\nScript Aborted.") exit() #Get front image in GMS img1 = DM.GetFrontImage() #Get the image window, so we can check if it gets closed imageDoc = DM.GetFrontImageDocument() imDocWin = imageDoc.GetWindow() #Get the image display, for the ROI-removed listener imageDisplay = img1.GetImageDisplay(0) #Listeners are started here #initiate the image listener listener = CListen(img1) #check if the source window closes WindowClosedListenerID = listener.WindowHandleWindowClosedEvent(imDocWin, 'pythonplugin') #check if the ROI has been deleted ROIRemovedListenerID = listener.ImageDisplayHandleROIRemovedEvent(imageDisplay,'pythonplugin') #check if the source image changes DataChangedListenerID = listener.ImageHandleDataChangedEvent(img1, 'pythonplugin') #IDs are not used in this script, but could be used to unregister individual listeners in DM 3.5.2 and higher.