''' 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 (relative to the correlation score) and a relative threshold (relative to the maxima of this particular map) ''' #@PrefService prefs from fiji.util.gui import GenericDialogPlus ## Create GUI Win = GenericDialogPlus("Multiple Template Matching") Win.addImageChoice("Template", prefs.get("Template","Choice")) Win.addImageChoice("Image", prefs.get("Image", "Choice")) Win.addCheckbox("Flip_template_vertically", prefs.getInt("FlipV", False)) Win.addCheckbox("Flip_template_horizontally", prefs.getInt("FlipH", False)) Win.addStringField("Rotate template by (,-separated)", prefs.get("Angles", "")) Win.addChoice("Matching_method", ["Normalised Square Difference","Normalised cross-correlation","Normalised 0-mean cross-correlation"], prefs.get("Method","Normalised 0-mean cross-correlation")) Win.addNumericField("Number_of_templates expected", prefs.getInt("N_hit",1),0) Win.addMessage("If more than 1 template expected :") Win.addNumericField("Score_Threshold [0-1]", prefs.getFloat("Score_Threshold",0.5), 2) Win.addNumericField("Min_peak_height relative to neighborhood ([0-1], decrease to get more hits)", prefs.getFloat("Tolerance",0.1), 2) Win.addNumericField("Maximal_overlap between Bounding boxes [0-1]", prefs.getFloat("MaxOverlap",0.4), 2) Win.addMessage("Outputs") Win.addCheckbox("Add detected ROI to ROI manager", prefs.getInt("AddRoi", True)) Win.addCheckbox("Show result table", prefs.getInt("ShowTable", False)) Win.addMessage("If you use this plugin please cite : xxx") Win.addHelp("https://github.com/acquifer/RoiDetection/wiki") Win.showDialog() if Win.wasOKed(): template = Win.getNextImage() image = Win.getNextImage() flipv = Win.getNextBoolean() fliph = Win.getNextBoolean() angles = Win.getNextString() method = Win.getNextChoice() n_hit = int(Win.getNextNumber()) score_threshold = Win.getNextNumber() tolerance = Win.getNextNumber() max_overlap = Win.getNextNumber() add_roi = Win.getNextBoolean() show_table = Win.getNextBoolean() # Save for persistence ImageName = image.getTitle() prefs.put("Template", template.getTitle()) prefs.put("Image", ImageName) prefs.put("FlipV",flipv) prefs.put("FlipH",fliph) prefs.put("Angles", angles) prefs.put("Method", method) prefs.put("N_hit", n_hit) prefs.put("Score_Threshold", score_threshold) prefs.put("Tolerance", tolerance) prefs.put("MaxOverlap", max_overlap) prefs.put("AddRoi", add_roi) prefs.put("ShowTable", show_table) # Check if input are valid if n_hit<=0: raise Exception('The expected number of object should be at least 1') if score_threshold<0 or score_threshold>1: raise Exception('The score threshold should be in range [0,1]') if tolerance<0 or tolerance>1: raise Exception('Tolerance should be in range [0,1]') if max_overlap<0 or max_overlap>1: raise Exception('The max overlap should be in range [0,1]') ## 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 imageBis = image.duplicate() # If ROI is present duplicate crop it. Better than crop which act only on one slice for stacks if Bool_SearchRoi: dX = int(searchRoi.getXBase()) dY = int(searchRoi.getYBase()) else: dX = dY = 0 ## Import modules from ij import IJ from ij.gui import Roi ## Import HomeMade modules from ROIdetection.NonMaximaSupression_Py2 import NMS from ROIdetection.MatchTemplate_Module import getHit_Template, CornerToCenter ## Check that the template is smaller than the (possibly cropped) image if template.height>imageBis.height or template.width>imageBis.width: raise Exception('The template is larger in width and/or height than the searched image') ### 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 add_roi: from ij.plugin.frame import RoiManager 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 imageBis.getStackSize()==1: ListImage = [imageBis] else: from ij import ImagePlus ImageStack = imageBis.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]) # Create detected ROI roi = Roi(*hit['BBox']) roi.setName(hit['TemplateName']) roi.setPosition(i+1) # set ROI Z-position image.setSlice(i+1) image.setRoi(roi) if add_roi: rm.add(None, roi, i+1) # Trick to be able to set slice when less images than the number of 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':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 add_roi: # Show All ROI + Associate ROI to slices rm.runCommand("Associate", "true") rm.runCommand("Show All with labels") # Bring image to the front IJ.selectWindow(ImageName) # does not work always