#@ ImagePlus (Label="Template image") template #@ ImagePlus (Label="Target image") 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="0-mean normalised cross-correlation") method #@ int (Label="Expected number of templates", min=1) n_hit #@ 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 #@ Boolean (Label="Add detected ROI to ROI manager") show_roi #@ Boolean (Label="Show result table") show_table ''' previous parameter removed : Boolean (Label="Display correlation map(s)") show_map # Complicated, showing several correlation map with each variation of the template At the gaps above, removed because it was displayed when macro recorded String (visibility="MESSAGE", value="The parameters below are used only if more than 1 template are expected in the image") doc String (visibility="MESSAGE", value="Output") out Requires ImageJ 1.52i to have the possibilityy to fill the background while rotating for 16-bit images FIJI macro to do template matching input : - template : ImagePlus for the template - image : ImagePlus for the target image ie this macro search for one template (with eventual flipped/rotated version)into one target image. The 2 images 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 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) TO DO : - order of the column in result table - use steerable tempalte matching see steerable detector BIG Lausanne NB : - 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) ''' ## Initialise variables before import (otherwise the ROI is lost) 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 from ij import IJ # Import HomeMade modules from ROIdetection.NonMaximaSupression_Py2 import NMS from ROIdetection.MatchTemplate_Module import getHit_Template, CornerToCenter ### Initialise 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 RM = RoiManager() rm = RM.getInstance() # 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] # Generate the list of images if image.getStackSize()==1: ListImage = [image] else: from ij import ImagePlus ImageStack = image.getStack() ListImage = [ ImagePlus(ImageStack.getSliceLabel(i).split('\n',1)[0], ImageStack.getProcessor(i) ) for i in xrange(1,ImageStack.getSize()+1) ] # Loop over the images in the stack (or directly process if unique) for i,ImpImage in enumerate(ListImage): # Do the template matching Hits_BeforeNMS = getHit_Template(template, ImpImage, flipv, fliph, angles, Method, n_hit, score_threshold, tolerance) # template and image as ImagePlus (to get the name together with the image matrix) ### NMS ### print "\n-- Hits before NMS --\n", for hit in Hits_BeforeNMS : print hit # NMS if more than one hit before NMS. For n_hit=1 the NMS does not actually compute the IoU it will just take the best score if len(Hits_BeforeNMS)==1: Hits_AfterNMS = Hits_BeforeNMS elif 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 # NB : Hits coordinates have not been corrected for cropping here ! Done in the next for loop # Loop over final hits to generate ROI for hit in Hits_AfterNMS: print hit if Bool_SearchRoi: # Add offset of 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 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 image.setSlice(i+1) if show_table: Xcorner, Ycorner = hit['BBox'][0], hit['BBox'][1] Xcenter, Ycenter = CornerToCenter(Xcorner, Ycorner, hit['BBox'][2], hit['BBox'][3]) Dico = {'Image':hit['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")) # Display result table if show_table: Table.show("Results") if show_roi: # Show All ROI + Associate ROI to slices rm.runCommand("Associate", "true") rm.runCommand("Show All with labels") # Bring image to the front ImageName = image.getTitle() IJ.selectWindow(ImageName)