#@ File[] (Label="Templates", style="file") template_files #@ ImagePlus (label="Image for which to look for the template") image #@ 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 height for maxima detection relative to neighbourhood (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="Display image with found ROI") 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 : ImagePlus for the target image ie this macro search for one template (with eventual flipped/rotated version)into one target image. The target image should be already open in Fiji. 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 : - 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) ''' ## Initialise variables before import (otherwise the ROI is lost) ImageName = image.getTitle() searchRoi = image.getRoi() ## Rectangle ROI ? if searchRoi and searchRoi.getTypeAsString()=="Rectangle": Bool_SearchRoi = True else: Bool_SearchRoi = False # Define offset if Bool_SearchRoi: image = image.crop() dX = int(searchRoi.getXBase()) dY = int(searchRoi.getYBase()) else: dX = dY = 0 ## IMPORT MODULES (after retrieving ROI) from ij import IJ # Home-Made module from ROIdetection.MatchTemplate_Module import getHit_Template, CornerToCenter from ROIdetection.NonMaximaSupression_Py2 import NMS # Convert method string to the 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 list of hit before looping Hits_BeforeNMS = [] ## Loop over templates for template matching and maxima detection for temp_file in template_files: # Get ImageProcessor for the current template PathTemp = temp_file.getPath() ImpTemplate = IJ.openImage(PathTemp) # Get hits for the current template (and his flipped and/or rotated versions) List_Hit = getHit_Template(ImpTemplate, image, flipv, fliph, angles, Method, n_hit, score_threshold, tolerance) # Store the hits Hits_BeforeNMS.extend(List_Hit) ### NMS inter template print "\n-- Hits before NMS --\n" for hit in Hits_BeforeNMS: print hit # 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 ### Add offset to BBox if a search region was provided ### if Bool_SearchRoi: for hit in Hits_AfterNMS: hit['BBox'] = (hit['BBox'][0]+dX, hit['BBox'][1]+dY, hit['BBox'][2], hit['BBox'][3]) ## Outputs if show_table: from ij.measure import ResultsTable from utils import AddToTable Table = ResultsTable().getResultsTable() # allows to append to an existing table if show_roi: from ij.plugin.frame import RoiManager from ij.gui import Roi # Initialise RoiManager RM = RoiManager() rm = RM.getInstance() # Show All ROI + Associate ROI to slices rm.runCommand("Associate", "true") rm.runCommand("Show All with labels") # Loop over final hits to generate ROI, result table... for hit in Hits_AfterNMS: if show_roi: roi = Roi(*hit['BBox']) roi.setName(hit['TemplateName']) rm.addRoi(roi) if show_table: Xcorner, Ycorner = hit['BBox'][0], hit['BBox'][1] Xcenter, Ycenter = CornerToCenter(Xcorner, Ycorner, hit['BBox'][2], hit['BBox'][3]) Dico = {"Image":ImageName, '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")) ## Finally update display if show_roi: IJ.selectWindow(ImageName) if show_table: Table.show("Results")