''' Code to process the live-view image in GMS, producing a binary display. The live image is thresholded and several morphological operations applied to produce a binary display. Then the largest contiguous bright region is found and circled. A plot of the position of the center of mass of that bright region is also updated live. 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 The code also works with an IS video played back with the IS player To stop calculation, delete the ROI. Frames are processed as often as possible. Lines of code between #XXXXXXXX... lines are specific to thresholding and finding the largest region. All other lines of code are general, and can be re-used to produce other kinds of processed images from a live-view image #Code written by Ben Miller. Last Updated Apr 2022 ''' import time import numpy as np if not DM.IsScriptOnMainThread(): print('Scipy scripts cannot be run on Background Thread.'); exit() import scipy.ndimage.filters as sfilt from skimage.filters import threshold_otsu import skimage.morphology as morph import skimage.measure as measure import traceback #User editable variables are set here #XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX filter_extent = 2 #(Default 2) Number defining the pixel radius to use for filter kernels small_area_minimum = 200 #(Default 200) Number defining the min number of pixels below which dark/light patches of pixels are removed. plot_data_for_N_frames = 200 #(Default 200) Number of points in the plots of measured parameters print_timing = True # (Default True) Select whether to output the time it takes to compute each frame #XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 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(n) 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 #XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX #Function to process the data (numpy array) from the ROI area, returning a #binary image as well as a centroid location and convex area of the largest bright region def ROI_process(self,image_data): #pre-process the data with a gaussian blur image_data = sfilt.gaussian_filter(image_data, filter_extent) #threshold the data via the otsu method thresh = threshold_otsu(image=image_data) result = image_data < thresh #apply morphological operations to remove small dark and bright regions result = morph.remove_small_objects(result,min_size=small_area_minimum) result = morph.remove_small_holes(result,area_threshold=small_area_minimum) #find and measure all contiguous regions in the binary image thresh_labels = measure.label(result, connectivity=1, background=0).astype('uint32') region_props = sorted(measure.regionprops(thresh_labels), key=lambda r: r.area, reverse=True) return (result.astype("int8"),region_props[0].centroid,region_props[0].convex_area) #Funtion to add a circular ROI to an image (deleting existing ones if present) def add_circle_ROI(self,image,center,radius): #get the image display of image im_disp = image.GetImageDisplay(0) #if there is an exising ROI produced by this script, delete it try: im_disp.DeleteROI(self.my_roi) except: # Circular ROI not found. pass #Create new Cirlular ROI self.my_roi=DM.NewROI() self.my_roi.SetCircle(center[1],center[0],radius) #Set the color of the ROI self.my_roi.SetColor(1,1,0) #Add the ROI to the image display im_disp.AddROI(self.my_roi) #Function to show an image as a lineplot with better defaults def display_DM_lineplot(self, image, name=None, scale_y=1, origin_y = 0, scale_unit_y='a.u.', scale_x=1, origin_x = 0, scale_unit_x='', draw_style=1, legend=True): #Set image name (and thus the lineplot window title) if name is None: name = "Python LinePlot of "+image.GetName() image.SetName(name) #set calibration/scale image.SetIntensityScale(scale_x) image.SetIntensityOrigin(origin_y) image.SetIntensityUnitString(scale_unit_y) image.SetDimensionCalibration(0,origin_x,scale_x,scale_unit_x,0) #Get lineplot display image_document = DM.NewImageDocument("") image_display = image_document.AddImageDisplay(image,3) lineplot_display = DM.GetLinePlotImageDisplay(image_display) #Set a few more lineplot parameters lineplot_display.SetSliceDrawingStyle(0,draw_style) lineplot_display.SetLegendShown(legend) #Display Lineplot in DM image_document.Show() #XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX #Initialization Function def __init__(self,img): try: #Create an index that is incremented each time data is processed. self.i = 0 #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)) 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, scale_unit = self.imgref.GetDimensionCalibration(1, 0) if scale_unit == b'\xb5m': scale_unit = 'um' #scale unit of microns causes problems for python in DM #Create a new image to contain the results of processing self.result_image = DM.CreateImage(self.ROI_process(self.data)[0]) #Create a new image for the lineplot with 2 plots self.plot_image = DM.CreateImage(np.zeros((2,plot_data_for_N_frames))) #Show the lineplot via function defined above self.display_DM_lineplot(self.plot_image,name="Measured Shifts",scale_y=x_scale,scale_unit_y=scale_unit,scale_x=-1,scale_unit_x='frames') #Set the lineplot names for the legend #This is not possible via Python script, so I use a short DM script, which is passed as a string to DM.ExecuteScriptString() dm_legend_names = ('ImageDisplay disp = GetFrontImage().ImageGetImageDisplay( 0 )\n' 'disp.ImageDisplaySetSliceLabelByID(disp.ImageDisplayGetSliceIDByIndex( 0 ),"X-shift")\n' 'disp.ImageDisplaySetSliceLabelByID(disp.ImageDisplayGetSliceIDByIndex( 1 ),"Y-shift")') DM.ExecuteScriptString(dm_legend_names) #Get the numpy array of the plot image so I can directly change the data values later self.plot_data = self.plot_image.GetNumArray() #Set the calibration based on the original data self.result_image.SetDimensionCalibration(0,origin,x_scale,scale_unit,0) self.result_image.SetDimensionCalibration(1,origin,x_scale,scale_unit,0) #Get the numpy array of the result image so I can directly change the data values later self.result_data=self.result_image.GetNumArray() #Display the result image in GMS self.result_image.ShowImage() #Set the image name which will be displayed in the image window's title bar self.result_image.SetName("Binary Map of "+img.GetName()) DM.Py_ScriptObject.__init__(self) self.stop = 0 except: print(traceback.format_exc()) #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() #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)] #Process the data and place in the result array (note that the [:] here is essential) result = self.ROI_process(self.data) self.result_data[:]=result[0] #Add a circular ROI to the result image self.add_circle_ROI(self.result_image,result[1],np.sqrt(result[2]/np.pi)) #Update Plot #place centroid result into plot data self.plot_data[:] = np.roll(self.plot_data,1) self.plot_data[:,0] = result[1] #after a few values are available, set blank values to mean instead of 0 #this makes the autosurvey limits for the plot more reasonable if self.i == 4: self.plot_data[0,5:] = np.mean(self.plot_data[0,:5]) self.plot_data[1,5:] = np.mean(self.plot_data[1,:5]) #update plot calibrations, so the x axis stays in sync self.plot_image.SetDimensionCalibration(0,self.i,-1,'frames',0) self.plot_image.UpdateImage() #XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX #Update the result image display self.result_image.UpdateImage() #end timing and output time to process this frame end=time.perf_counter() if print_timing: 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 Delete Image Listener def __del__(self): print("Listener Deleted") DM.Py_ScriptObject.__del__(self) #Function to end processing by deleting or unregistering listener def RemoveListeners(self): try: if not self.stop: self.stop = 1 DM.DoEvents() global listener #DM 3.5.2 and higher have new function for unregistering listeners. #DM 3.4.3 and lower should delete the listener instead #DM 3.5.0 and 3.5.1 have a fatal flaw regarding listeners, so this script is not compatible with those versions if (get_DM_version()[1][0] == "4" and get_DM_version()[0] == "3"): del listener else: listener.UnregisterAllListeners() print("Live Processing Script Ended") except: print(traceback.format_exc()) #Remove listeners if source image window is closed def HandleWindowClosedEvent(self, event_flags, window): print("Window Closed") self.RemoveListeners() #Remove listeners if the ROI is deleted def HandleROIRemovedEvent(self, img_disp_event_flags, img_disp, roi_change_flag, roi_disp_change_flags, roi): print("ROI Removed") self.RemoveListeners() #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')) and get_DM_version()[0] == "3"): 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.