#@ File[] (Label="Templates", style="file") template_files #@ File[] (label="Image for which to look for the template") image_files #@ File (label="Region of Interest (ROI) for search (optional)", required=false) roi_file #@ Boolean (Label="Flip template vertically") flipv #@ Boolean (Label="Flip template horizontally") fliph #@ String (Label="Additional rotation angles separated by ," ,required=False) angles #@ String (Label="Matching method",choices={"Normalised Square Difference", "Normalised cross-correlation", "Normalised 0-mean cross-correlation"}, value="Normalised 0-mean cross-correlation") method #@ int (Label="Expected number of templates", min=1) n_hit #@ String (visibility="MESSAGE", value="The parameters below are used only if more than 1 template are expected in the image") doc #@ Float (Label="Score Threshold (0-1)", min=0, max=1, value=0.5, stepSize=0.1) score_threshold #@ Float (Label="Min peak height relative to neighborhood (0-1, decrease to get more hits)", min=0, max=1, value=0.1, stepSize=0.1) tolerance #@ Float (Label="Maximal overlap between Bounding boxes (0-1)",min=0, max=1, value=0.4, stepSize=0.1) max_overlap #@ String (visibility="MESSAGE", value="Output") out #@ Boolean (Label="Open images (as stack ie images must have identical dimensions)") show_images #@ Boolean (Label="Add ROI to ROI Manager") show_roi #@ Boolean (Label="Show result table") show_table ''' previous field : Boolean (Label="Display correlation map(s)") show_map Requires ImageJ 1.52i to have the possibility to fill the background while rotating for 16-bit images FIJI macro to do template matching input : - template_files : list of template path - image_files : list of image path for which we want to search a template ie this macro search for N templates (with eventual flipped/rotated version) into L target images. First of all, additionnal versions of the template are generated (flip+rotation) For the resulting list of templates the search is carried out and results in a list of correlation maps Minima/maxima in the correlation map are detected, followed by Non-Maxima Supression in case of multiple correlation map/templates ## TO DO : - order of the column in result table - use steerable tempalte matching see steerable detector BIG Lausanne ## NB : - If open_images is true, the images must have the same dimensions - The multifile input is not yet macro recordable. An alternative is to use a folder input and to process the content of the folder (but not as flexible) - (currently no search ROi so not applicable) Delete the previous ROI for every new Run otherwise 1st ROI is used to limit the search - Method limited to normalised method to have correlation map in range 0-1 : easier to apply a treshold. Otherwise normalising relative to maxima of each correlation map is not good since this result in having the global maxima to always be one, eventhough its correlation value was not one. Another possibility would be to have an absolute threshold (realtive to the correlation score) and a relative threshold (relative to the maxima of this particular map) The multifile input is not yet macro recordable. An alternative is to use a folder input and to process the content of the folder (but not as flexible) ''' ### IMPORT ### import time # for benchmark from ij import IJ # ImageJ1 import ## Home-Made module from ROIdetection.MatchTemplate_Module import getHit_Template, CornerToCenter from ROIdetection.NonMaximaSupression_Py2 import NMS if show_images: from ij import ImagePlus, ImageStack Stack_Image = ImageStack() Stack_Image_ImP = ImagePlus() if show_roi: from ij.plugin.frame import RoiManager from ij.gui import Roi RM = RoiManager() rm = RM.getInstance() if show_table: from ij.measure import ResultsTable from utils import AddToTable Table = ResultsTable().getResultsTable() # allows to append to an existing table # Convert method string to the opencv corresponding index Dico_Method = {"Square difference":0,"Normalised Square Difference":1,"Cross-Correlation":2,"Normalised cross-correlation":3,"0-mean cross-correlation":4,"Normalised 0-mean cross-correlation":5} Method = Dico_Method[method] # Initialise time ListTime = [] # Initialise list of templates (rather than opening them for every image iteration) List_Template = [ IJ.openImage( templ_file.getPath() ) for templ_file in template_files ] ### Search ROI ? ### # Check if there is a searchRoi if roi_file: from ij.io import RoiDecoder searchRoi = RoiDecoder.open(roi_file.getPath()) else: searchRoi = None # Check if it is a rectangular one if searchRoi and searchRoi.getTypeAsString()=="Rectangle": Bool_SearchRoi = True else: Bool_SearchRoi = False # Define Offset for BBox if proper searchRoi if Bool_SearchRoi: dX = searchRoi.getXBase() dY = searchRoi.getYBase() else: dX = dY = 0 #nROI = 0 ## Loop over templates for template matching and maxima detection for i, im_file in enumerate(image_files): # Get the current image PathIm = im_file.getPath() ImpImage = IJ.openImage(PathIm) ImName = ImpImage.getTitle() ImProc = ImpImage.getProcessor().duplicate() # Crop Image if searchRoi if Bool_SearchRoi: ImpImage.setRoi(searchRoi) ImpImage = ImpImage.crop() ## Start Timer here (dont count opening of the image) Start = time.clock() # Initialise list before looping over templates Hits_BeforeNMS = [] ## Loop over template for matching against current image for ImpTemplate in List_Template: # Check that template is smaller than the searched image if ImpTemplate.width>ImpImage.width or ImpTemplate.height>ImpImage.height: raise Exception('The current template is larger in width and/or height than the searched image') # Get hits for the current template (and his flipped and/or rotated versions) List_Hit = getHit_Template(ImpTemplate, ImpImage, flipv, fliph, angles, Method, n_hit, score_threshold, tolerance) # raher use ImagePlus as input to get the name of the template used # Store the hits Hits_BeforeNMS.extend(List_Hit) ### NMS ### print "\n-- Hits before NMS --\n", for hit in Hits_BeforeNMS: print hit # InterHit NMS if more than one hit if Method in [0,1]: Hits_AfterNMS = NMS(Hits_BeforeNMS, N=n_hit, maxOverlap=max_overlap, sortDescending=False) # only difference is the sorting else: Hits_AfterNMS = NMS(Hits_BeforeNMS, N=n_hit, maxOverlap=max_overlap, sortDescending=True) print "\n-- Hits after NMS --\n" for hit in Hits_AfterNMS : print hit ## Stop time here (we dont benchmark display time) Stop = time.clock() Elapsed = Stop - Start # in seconds ListTime.append(Elapsed) ## Loop over final hits to generate ROI, result table... ### for hit in Hits_AfterNMS: if Bool_SearchRoi: # Add the offset of the search ROI hit['BBox'] = (hit['BBox'][0]+dX, hit['BBox'][1]+dY, hit['BBox'][2], hit['BBox'][3]) if show_roi: roi = Roi(*hit['BBox']) roi.setName(hit['TemplateName']) roi.setPosition(i+1) # set slice position #nROI+=1 rm.add(None, roi, i+1) # Trick to be able to set slice when less images than ROI. Here i is an digit index before the Roi Name if show_table: Xcorner, Ycorner = hit['BBox'][0], hit['BBox'][1] Xcenter, Ycenter = CornerToCenter(Xcorner, Ycorner, hit['BBox'][2], hit['BBox'][3]) Dico = {'Image':ImName, 'Template':hit['TemplateName'] ,'Xcorner':Xcorner, 'Ycorner':Ycorner, 'Xcenter':Xcenter, 'Ycenter':Ycenter, 'Score':hit['Score']} AddToTable(Table, Dico, Order=('Image', 'Template', 'Score', 'Xcorner', 'Ycorner', 'Xcenter', 'Ycenter')) Table.show("Results") ## Display outputs if show_images: # Initialise a stack of proper size if not the case before if Stack_Image.getSize()==0: Stack_Image = ImageStack(ImProc.width, ImProc.height) # instead of using ImagePlus.getStack otherwise we loose the slice title for the 1st image # Add images to Stack Stack_Image.addSlice(ImName, ImProc) Stack_Image_ImP.setStack("Result", Stack_Image) # Update display Stack_Image_ImP.setSlice(i) Stack_Image_ImP.show() if show_roi: # Show All ROI + Associate ROI to slices rm.runCommand("Associate", "true") rm.runCommand("Show All with labels") for i in ListTime: print i