├── .gitignore ├── CHANGELOG.md ├── Fiji.app ├── jars │ └── Lib │ │ ├── ImageConverter.py │ │ ├── ImageRotator.py │ │ ├── Template_Matching │ │ ├── MatchTemplate_Module.py │ │ ├── NonMaximaSupression_Py2.py │ │ ├── Version.py │ │ └── __init__.py │ │ └── utils.py └── scripts │ └── Plugins │ └── Multi-Template-Matching │ ├── 2step-TemplateMatching.ijm │ ├── Compute_ScoreMap.py │ ├── Crop_Roi_ToStack.py │ ├── Template_Matching_Folder.py │ ├── Template_Matching_Image.py │ ├── Template_Matching_ListImage.py │ └── Template_Matching_ListTemplate.py ├── Images ├── Acquifer_Logo_60k_cmyk_300dpi.png ├── EggDetected.png ├── FishRoi.JPG ├── GUI_Fiji_Annotated.png ├── HeadRoi.JPG ├── ImageInlife.png ├── MarieCurie.jpg ├── MontageEgg.png ├── Montage_Head.png ├── NMS.png ├── RoiManager.JPG ├── Screen_MatchImage.JPG ├── Screen_MatchListImage.JPG ├── TemplateMatching.png ├── TemplateMatchingFolder.JPG └── TemplateMatching_Montage.png ├── LICENSE ├── README.md └── Tuto └── 2-step-TemplateMatching ├── 2step-TemplateMatching.html ├── 2step-TemplateMatching.ijm ├── Fish.png └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | 2 | Images/TemplateMatching.pptx 3 | -------------------------------------------------------------------------------- /CHANGELOG.md: -------------------------------------------------------------------------------- 1 | # Changelog 2 | 3 | ## [1.1.6] - 2024-01-17 4 | 5 | ### Changed 6 | - Warning if template has same dimensions than the image 7 | -------------------------------------------------------------------------------- /Fiji.app/jars/Lib/ImageConverter.py: -------------------------------------------------------------------------------- 1 | ''' 2 | This module contains functions to converts from an ImagePlus (RGB, 32-bit, 16-bit...) to an opencv matrix and vice-versa 3 | The default BitType of the opencv matricx is 8-bit 4 | ''' 5 | from ijopencv.ij import ImagePlusMatConverter 6 | from ijopencv.opencv import MatImagePlusConverter 7 | 8 | ip2mat = ImagePlusMatConverter() 9 | mat2ip = MatImagePlusConverter() 10 | 11 | def ImProcToMat(ImProc, Bit=8): 12 | ''' 13 | Convert an ImageProcessor to an opencv matrix with optionnal BitDepth conversion 14 | ''' 15 | # I - Convert the image Processor to a different BitType (if necessary) 16 | if ImProc.getBitDepth() != Bit: 17 | 18 | # For the conversion from higher to lower bitType the value are scaled 19 | # Similarly RGB to Gray result in a one channel gray image thanks to the ImageProcessor method 20 | if Bit == 8: 21 | ImProc = ImProc.convertToByteProcessor() # Boolean argument doScaling = True by default ? 22 | 23 | elif Bit == 16: 24 | ImProc = ImProc.convertToShortProcessor() 25 | 26 | elif Bit == 32: 27 | ImProc = ImProc.convertToFloatProcessor() 28 | 29 | else: 30 | # left as is 31 | pass 32 | 33 | 34 | # II - Convert the image processor to an opencv matrix 35 | ImCV = ip2mat.toMat(ImProc) 36 | 37 | return ImCV 38 | 39 | 40 | 41 | def MatToImProc(Mat): 42 | '''Convert an opencv matrix to an ImageProcessor of sane BitType''' 43 | IP = mat2ip.toImageProcessor(Mat) 44 | return IP -------------------------------------------------------------------------------- /Fiji.app/jars/Lib/ImageRotator.py: -------------------------------------------------------------------------------- 1 | ''' 2 | Perform rotation of an image expanding the canvas and filling the background with the modal value of the initial value 3 | without calling IJ.run 4 | For that we create an empty canvas of the size of the final matrix (or larger : dimension of the initial image), paste the centered image and rotate it 5 | This is due to the fact that the canvas must be big enough to contain the all the rotated state of the image 6 | ''' 7 | from ij.gui import Roi 8 | from ij.plugin import RoiRotator 9 | from fiji.selection import Select_Bounding_Box 10 | from java.awt import Rectangle 11 | #from ij.plugin import CanvasResizer # does not allow to set a background value in gray level neither only rgb 12 | 13 | 14 | def Rotate(ImProc, angle): 15 | ''' 16 | This return a rotated version of the input ImageProcessor, filling the background with the modal value of the initial image 17 | For square rotation, prefer the rotateLeft/rotateRight method in ImageProcessor 18 | ''' 19 | 20 | ### Calculate width and height of rotated image ### 21 | # For this we simply use a ROI of the same size than the initial image, rotate it by the same angle and ask for the bounding box of this rotated rectangle 22 | 23 | # Get size of the initial image 24 | w = ImProc.width 25 | h = ImProc.height 26 | 27 | # Generate a ROI of this size 28 | Rect = Roi(0,0,w,h) 29 | 30 | # Rotate this ROI 31 | RectRot = RoiRotator().rotate(Rect,angle) 32 | 33 | # Compute size of the new Image 34 | # For its dimensions we take for each dimension the largest length between the initial image size and the size of the rotated bounding rectangle 35 | # otherwise the image gets cropped while rotated 36 | nw = max(w, int( RectRot.getFloatWidth() ) ) 37 | nh = max(h, int( RectRot.getFloatHeight() ) ) 38 | 39 | ''' 40 | # Previous way to compute new dimensions 41 | rangle = math.radians(angle) 42 | nw = abs(math.sin(rangle)*h) + abs(math.cos(rangle)*w) 43 | nh = abs(math.cos(rangle)*h) + abs(math.sin(rangle)*w) 44 | nw = int(round(nw)) 45 | nh = int(round(nh)) 46 | ''' 47 | 48 | # Compute offset/translation vector to center initial image in the new canvas 49 | Xoff = (nw-w)//2 50 | Yoff = (nh-h)//2 51 | 52 | # Adjust canvas of the image 53 | IPnew = ImProc.createProcessor(nw, nh) 54 | 55 | # Compute modal value of initial image 56 | Gray = ImProc.getStats().dmode 57 | #print Gray 58 | 59 | # Fill background with define gray level 60 | IPnew.setValue(Gray) # for fill 61 | IPnew.fill() 62 | 63 | # Insert previous image centered 64 | IPnew.insert(ImProc, Xoff, Yoff) 65 | IPnew.setBackgroundValue(Gray) # for rotate 66 | IPnew.rotate(angle) 67 | 68 | ### Remove the extra border that can still be there after rotation. Not always working : background might not be recognised ### 69 | Rect0 = Rectangle(nw,nh) # intialise ROI of size of the image (where to compute the background) 70 | 71 | # Get ROI 72 | BoxFinder = Select_Bounding_Box() 73 | bg = BoxFinder.guessBackground(IPnew) 74 | NewRect = BoxFinder.getBoundingBox(IPnew, Rect0, bg) 75 | #print NewRect 76 | 77 | # Crop 78 | IPnew.setRoi(NewRect) 79 | IPnew = IPnew.crop() 80 | 81 | return IPnew 82 | 83 | 84 | ''' 85 | # MAIN - Rotate the input image 86 | Rotated = Rotate(Image.getProcessor(), 60) 87 | 88 | # Display 89 | NewImage = ImagePlus("Rotated", Rotated) 90 | NewImage.show() 91 | ''' -------------------------------------------------------------------------------- /Fiji.app/jars/Lib/Template_Matching/MatchTemplate_Module.py: -------------------------------------------------------------------------------- 1 | ''' 2 | This module contains a set of functions for multi-template-matching between a template and an image/stack. 3 | The detected area will be displayed as a rectangular ROI on the image, and this ROI will be added to the ROI manager 4 | 5 | Requirements : 6 | - IJ-OpenCV (from the updater) 7 | 8 | TO DO : 9 | - Images with overlay in Stack : change to the easier ij.plugin.ImagesToStack that use a list to make the stack. No need to precise the size.. 10 | ''' 11 | # Python 12 | from __future__ import division 13 | 14 | # ImageJ 15 | from ij import IJ,ImagePlus, ImageStack 16 | from ij.plugin.filter import MaximumFinder 17 | #from ij.gui import Roi, PointRoi 18 | 19 | if IJ.getFullVersion()<"1.52o": 20 | IJ.error("Please update ImageJ to min v1.52o. Help>Update ImageJ...") 21 | 22 | # OpenCV 23 | try: 24 | from org.bytedeco.javacpp.opencv_imgproc import matchTemplate, threshold, CV_THRESH_TOZERO 25 | from org.bytedeco.javacpp.opencv_core import Mat, Scalar, Point, minMaxLoc, subtract # UNUSED normalize, NORM_MINMAX, CV_8UC1, CV_32FC1 26 | from org.bytedeco.javacpp import DoublePointer 27 | 28 | except: 29 | IJ.error("Missing OpenCV dependencies. Make sure to activate 'IJ-OpenCV plugins' update site.") 30 | 31 | 32 | # Java 33 | from java.lang import Float #used to convert BytesToFloat 34 | 35 | # Home-made module in jars/Lib sent with Acquifer update site 36 | from ImageConverter import ImProcToMat, MatToImProc 37 | from ImageRotator import Rotate 38 | 39 | 40 | def MatchTemplate(imProc_template, imProc_target, method): 41 | ''' 42 | Function that performs the matching between one template and an image 43 | imProc_template : ImageProcessor object of the template image 44 | imProc_target : ImageProcessor object of the image in which we search the template 45 | method : Integer for the template matching method (openCV) 46 | return the correlation map 47 | ''' 48 | 49 | # Check if template larger than image 50 | if imProc_template.getWidth() > imProc_target.getWidth() or imProc_template.getHeight() > imProc_target.getHeight(): 51 | raise Exception("Template is larger than the searched image (width and/or height)") 52 | 53 | # Check if template has exact same dimensions than the image 54 | if imProc_template.getWidth() == imProc_target.getWidth() and imProc_template.getHeight() == imProc_target.getHeight(): 55 | raise Exception("Template has identical dimensions (width and height) than the searched image") 56 | 57 | # Convert to image matrix, 8-bit (if both are 8-bit) or 32-bit 58 | if imProc_template.getBitDepth()==8 and imProc_target.getBitDepth()==8: 59 | imTemplate = ImProcToMat(imProc_template, Bit=8) 60 | imTargetCV = ImProcToMat(imProc_target, Bit=8) 61 | else: 62 | imTemplate = ImProcToMat(imProc_template, Bit=32) 63 | imTargetCV = ImProcToMat(imProc_target, Bit=32) 64 | 65 | # Create a correlation map object and do the matching 66 | corrMapCV = Mat() 67 | matchTemplate(imTargetCV, imTemplate, corrMapCV, method) # result directly stored in CorrMap 68 | 69 | return corrMapCV 70 | 71 | 72 | 73 | def FindMinMax(CorrMapCV, Unique=True, MinMax="Max", Score_Threshold=0.5, Tolerance=0): 74 | ''' 75 | Detect Minima(s) or Maxima(s) in the correlation map 76 | The function uses for that the MinMaxLoc from opencv for unique detection 77 | or the MaximumFinder from ImageJ for multi-detection (in this case, for min detection the correlation map is inverted) 78 | 79 | - Unique : True if we look for one global min/max, False if we want local ones 80 | - MinMax : "Min" if we look for Minima, "Max" if we look for maxima 81 | - Score_Threshold : in range [0;1] (correlation maps are normalised) 82 | - Tolerance : Parameters for flood-fill. Not used here so should be set to 0 83 | 84 | Returns List of Min/Max : [(X, Y, Coefficient), ... ] 85 | ''' 86 | #### Detect min/maxima of correlation map #### 87 | Extrema = [] 88 | 89 | ## if 1 hit expected 90 | if Unique: # Look for THE global min/max 91 | minVal = DoublePointer(1) 92 | maxVal = DoublePointer(1) 93 | minLoc = Point() 94 | maxLoc = Point() 95 | minMaxLoc(CorrMapCV, minVal, maxVal, minLoc, maxLoc, Mat()) 96 | 97 | if MinMax=="Min": # For method based on difference we look at the min value 98 | X = minLoc.x() 99 | Y = minLoc.y() 100 | Coeff = minVal.get() 101 | 102 | elif MinMax=="Max": 103 | X = maxLoc.x() 104 | Y = maxLoc.y() 105 | Coeff = maxVal.get() 106 | 107 | # Append to the output list 108 | Extrema.append( (X, Y, Coeff) ) 109 | 110 | 111 | 112 | ## if more hit expected 113 | else: # expect more than 1 template/image. Do a multi minima/maxima detection 114 | 115 | ## OLD CODE LEFT BUT DONT DO IT, NOT GOOD PREACTICE !! Normalise the correlation map to 0-1 (easier to apply the threshold) 116 | ## Rather use normalised method only for the computation of the correlation map (by default normalised method are 0-1 bound) 117 | # The normalised method are already normalised to 0-1 but it is more work to take it into account in this function 118 | # WARNING : this normalisation normalise regarding to the maxima of the given correlation map 119 | # Not good because it means the thresholding is define relative to a correlation map : a score of 1 might not correspond to a correlation of 1 120 | # But only means that the pixel was the maximum of this particular correlation map 121 | 122 | #CorrMapNormCV = Mat() 123 | #normalize(CorrMapCV, CorrMapNormCV, 0, 1, NORM_MINMAX, CV_32FC1, None) 124 | 125 | # Visualisation for debugging 126 | #CorrMapNorm = MatToImProc(CorrMapNormCV) 127 | #CorrMapNorm = ImagePlus("Normalised", CorrMapNorm) 128 | #CorrMapNorm.show() 129 | 130 | 131 | 132 | ### Maxima/Minima detection on the correlation map 133 | 134 | ## Difference-based method : we look for min value. For that we detect maxima on an inverted correlation map 135 | if MinMax=="Min": 136 | 137 | # Invert the correlation map : min becomes maxima 138 | One = Scalar(1.0) 139 | CorrMapInvCV = subtract(One, CorrMapCV).asMat() 140 | #CorrMapInv = MatToImProc(CorrMapInvCV) 141 | #CorrMapInv = ImagePlus("Inverted", CorrMapInv) 142 | #CorrMapInv.show() 143 | 144 | 145 | # Apply a "TO ZERO" threshold on the correlation map : to compensate the fact that maxima finder does not have a threshold argument 146 | # TO ZERO : below the threshold set to 0, above left untouched 147 | # NB : 1-score_threshold since we have inverted the image : we want to look for minima of value 1-x in the inverted image 148 | CorrMapThreshCV = Mat() 149 | threshold(CorrMapInvCV, CorrMapThreshCV, 1-Score_Threshold, 0, CV_THRESH_TOZERO) 150 | CorrMapThresh = MatToImProc(CorrMapThreshCV) # Keep this conversion, not only for visualisation 151 | #CorrMapThreshImp = ImagePlus("Thresholded", CorrMapThresh) 152 | #CorrMapThreshImp.show() 153 | 154 | 155 | 156 | ## Correlation-based method : we look for maxima 157 | elif MinMax=="Max": 158 | # Apply a "TO ZERO" threshold on the correlation map : to compensate the fact that maxima finder does not have a threshold argument 159 | # TO ZERO : below the threshold set to 0, above left untouched 160 | # NB : 1-score_threshold since we have inverted the image : we want to look for minima of value 1-x in the inverted image 161 | CorrMapThreshCV = Mat() 162 | threshold(CorrMapCV, CorrMapThreshCV, Score_Threshold, 0, CV_THRESH_TOZERO) 163 | CorrMapThresh = MatToImProc(CorrMapThreshCV) # Keep this conversion, not only for visualisation 164 | #CorrMapThreshImp = ImagePlus("Thresholded", CorrMapThresh) 165 | #CorrMapThreshImp.show() 166 | 167 | 168 | 169 | ## For both cases (Multi-Min/Max-detection) detect maxima on the thresholded map 170 | # Detect local maxima 171 | excludeOnEdge = False # otherwise miss quite a lot of them 172 | Polygon = MaximumFinder().getMaxima(CorrMapThresh, Tolerance, excludeOnEdge) 173 | 174 | # Maxima as list of points 175 | #roi = PointRoi(Polygon) 176 | 177 | # Generate Hit from max coordinates 178 | #print Polygon.npoints," maxima detected in this score map" 179 | if Polygon.npoints!=0: # Check that there are some points indeed. Otherwise Polygon.xpoints and ypoints are anyway initialised with [0,0,0,0] even if Npoints=0 ! 180 | 181 | for i in range(Polygon.npoints): # dont directly loop on xpoints and ypoint since initialised with [0,0,0,0] ie less than 4 points we get some 0,0 coordinates 182 | 183 | # Get point coordinates 184 | X, Y = Polygon.xpoints[i], Polygon.ypoints[i] 185 | 186 | # Get Coeff 187 | Coeff = CorrMapThresh.getPixel(X, Y) 188 | Coeff = Float.intBitsToFloat(Coeff) # require java.lang.Float 189 | 190 | # Revert the score again if we were detecting minima initially (since found as maxima of a reverted correlation map) 191 | if MinMax=="Min": 192 | Coeff = 1-Coeff 193 | 194 | # Wrap into corresponding hit 195 | Extrema.append( (X, Y, Coeff) ) 196 | 197 | return Extrema 198 | 199 | 200 | 201 | 202 | def getHit_Template(ImpTemplate, ImpImage, FlipV=False, FlipH=False, Angles='', Method=5, N_Hit=1, Score_Threshold=0.5, Tolerance=0): 203 | ''' 204 | This function performs : 205 | 1) Preprocessing of the template (flipping and/or rotation) 206 | 2) Template matching for each of those template 207 | 4) Then for each correlation map, Min/Max detection and Non-Maxima Suppression (NMS) 208 | NB : It does not do the NMS between correlation map, only for a given correlation map (intra-NMS) 209 | The inter-NMS still remains to be done, it is not included in the function because other templates/hits might be used 210 | 211 | 212 | - ImProc_Template : ImageProcessor object of the template image 213 | - ImProc_Image : ImageProcessor object of the image in which we search the template 214 | - FlipV/H : Boolean, search for additional vertical/horizontal flipped version of the template 215 | - Angles : String like "10,20,50" - angles to search for additional rotated version of the template (both initial and flipped template are rotated) 216 | - Method : Integer for the template matching method (openCV) 217 | - N_Hit : Expected number of templates in the image 218 | - Score_Threshold : respectively Min/Max value for the detection of Maxima/Minima in the correlation map 219 | - Tolerance : peak height relative to neighbourhood for min/max detection (only used if N_hit>1). Set to 0 so that no effect (this is design for flood fill) 220 | - Max_Overlap : Max overlap (Intersection over Union, IoU) between bounding boxes used for NMS 221 | 222 | It returns a list of hit [ {TemplateName, BBox, Score}, ..., ] 223 | ''' 224 | # Get ImageProcessor for template and image 225 | ImProc_Template = ImpTemplate.getProcessor() 226 | ImProc_Image = ImpImage.getProcessor() 227 | 228 | # Get Image names for template and image 229 | TemplateName = ImpTemplate.getTitle() 230 | ImageName = ImpImage.getTitle() 231 | 232 | ListTemplate = [ {"Name":TemplateName, "ImProc":ImProc_Template} ] # initialise list with initial template 233 | 234 | ## Pre-Process template (Flip and/or Rotation) 235 | # Flip vertical 236 | if FlipV: 237 | TemplateFlipV = ImProc_Template.duplicate() 238 | TemplateFlipV.flipVertical() # duplicate previously to prevent in-place change 239 | ListTemplate.append( {"Name":TemplateName+"_Vertical_Flip", "ImProc":TemplateFlipV} ) 240 | 241 | # Flip horizontal 242 | if FlipH: 243 | TemplateFlipH = ImProc_Template.duplicate() 244 | TemplateFlipH.flipHorizontal() # duplicate previously to prevent in-place change 245 | ListTemplate.append( {"Name":TemplateName+"_Horizontal_Flip", "ImProc":TemplateFlipH}) 246 | 247 | 248 | # Rotation (initial and flipped templates) 249 | if Angles: # if different than an empty string 250 | 251 | # Recover individual angle values 252 | Angles = Angles.split(",") 253 | Angles = list(map(int, Angles)) # # convert to int. map returns a list in Py2 but an iterator in Py3 hence the list conversion for max compatibility 254 | #print Angles 255 | 256 | # copy before appending any rotated ones. ie only contains initial + flipped ones 257 | ListToRotate = ListTemplate[:] 258 | 259 | # Loop over list of angles 260 | for angle in Angles: 261 | 262 | # Loop over initial and flipped template and rotate each template in input list 263 | for template in ListToRotate: # template is a dictionnary here 264 | 265 | TemplateName = template['Name'] # it can be original or with "_flipX" 266 | 267 | # Perform the rotation 268 | Rotated = Rotate(template['ImProc'], angle) # a rotated template might be out of the search region 269 | 270 | # Append to the list of templates 271 | ListTemplate.append( {"Name": TemplateName + "_" + str(angle) + 'degrees', "ImProc":Rotated} ) 272 | 273 | 274 | ### Loop over template to do the matching #### 275 | List_Hit = [] 276 | for template in ListTemplate: # template is a dico here 277 | 278 | ## Search the current template in the image -> Correlation Map ### 279 | try : 280 | CorrMapCV = MatchTemplate(template["ImProc"], ImProc_Image, Method) 281 | except Exception as error: # template larger than image for instance 282 | IJ.log("Issue with template " + template["Name"] + " (was skipped)\n" + str(error)) 283 | continue 284 | # View map for debugging 285 | #CorrMap = MatToImProc(CorrMapCV) 286 | #CorrMap = ImagePlus("CorrMap", CorrMap) 287 | #CorrMap.show() 288 | 289 | 290 | #### Detect min/maxima of correlation map #### 291 | if N_Hit==1: 292 | Unique = True 293 | else: 294 | Unique = False 295 | 296 | if Method in [0,1]: 297 | MinMax="Min" 298 | else: 299 | MinMax="Max" 300 | 301 | List_XYCoeff = FindMinMax(CorrMapCV, Unique, MinMax, Score_Threshold, Tolerance) 302 | List_NewHit = [ { "ImageName":ImageName, 303 | "TemplateName":template["Name"], 304 | "BBox":(x, y, template['ImProc'].width, template['ImProc'].height), 305 | "Score":Coeff} for x,y,Coeff in List_XYCoeff ] 306 | 307 | # Append Hit for this particular template to the list of output hit 308 | List_Hit.extend(List_NewHit) 309 | 310 | # Return list of Hits 311 | return List_Hit 312 | 313 | 314 | 315 | def CornerToCenter(Xcorner, Ycorner, TemplateWidth, TemplateHeight): 316 | ''' Convert from top left pixel to center pixel of detected ROI''' 317 | 318 | Xcenter = Xcorner + TemplateWidth//2 319 | Ycenter = Ycorner + TemplateHeight//2 320 | 321 | return Xcenter,Ycenter -------------------------------------------------------------------------------- /Fiji.app/jars/Lib/Template_Matching/NonMaximaSupression_Py2.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | -Python2- 4 | 5 | Non maxima supression for match template 6 | 7 | Let say we have a correlation map and we want to detect the N best location in this map, while we want to make sure the detected location do not overlap too much 8 | The threshold for the overlap used is a maximal value of Intersection over Union (IoU). Large overlap will have large IoU. 9 | The IoU is conveneient as it is normalised between 0 and 1 and is a value normalised by the intial area. 10 | 11 | To keep the N best hit/bounding-boxes without overlap, we first collect all hits that pass the score threshold. If we used several tempaltes and thus have several correlation map we perform the NMS on the full set of hit 12 | Then we order all those hit by score to have the best score first 13 | We initialise the list of finally collected hit with the first best hit 14 | Then we loop over the remaining hit, and compute the IoU between the bounding box of this hit and the previously collected hit. 15 | If one of the IoU is above the threshold, then the current hit is discarded 16 | Otherwise thie means the current hit is not overlapping with any previously collected hit, therefore we can add it to the lsit of finally collected hit. 17 | The iteration continues with the next hit, until we collect the N expected hits, or until there are no more hits to test 18 | 19 | if N_Hit = 1, the NMS will not compute any IoU actually, only returns the first oen with the highest score 20 | 21 | ListOfHit contains N dictionnaries, 1 per hit : {'TemplateIdx':i, 'BBox':(x,y,width,hieght), 'Score':float} 22 | 23 | NB : remove print statements that were dramatically slowing down the execution when used as a plugin (not observed from the script interpreter) 24 | 25 | @author: Laurent Thomas 26 | """ 27 | from __future__ import division 28 | from ij.gui import Roi 29 | 30 | def computeIoU(BBox1,BBox2): 31 | ''' 32 | Compute the IoU (Intersection over Union) between 2 rectangular bounding boxes defined by the top left (Xtop,Ytop) and bottom right (Xbot, Ybot) pixel coordinates 33 | Code adapted from https://www.pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/ 34 | ''' 35 | #print 'BBox1 : ', BBox1 36 | #print 'BBox2 : ', BBox2 37 | 38 | # Unpack input (python3 - tuple input are no more supported) 39 | Xleft1, Ytop1, Width1, Height1 = BBox1 40 | Xleft2, Ytop2, Width2, Height2 = BBox2 41 | 42 | # Compute bottom coordinates 43 | Xright1 = Xleft1 + Width1 -1 # we remove -1 from the width since we start with 1 pixel already (the top one) 44 | Ybot1 = Ytop1 + Height1 -1 # idem for the height 45 | 46 | Xright2 = Xleft2 + Width2 -1 47 | Ybot2 = Ytop2 + Height2 -1 48 | 49 | # determine the (x, y)-coordinates of the top left and bottom right points of the intersection rectangle 50 | Xleft = max(Xleft1, Xleft2) 51 | Ytop = max(Ytop1, Ytop2) 52 | Xright = min(Xright1, Xright2) 53 | Ybot = min(Ybot1, Ybot2) 54 | 55 | # Generate a Roi for the BBox just to be able to check that the BBox are not contained within another 56 | Roi1 = Roi(Xleft1, Ytop1, Width1, Height1) 57 | Roi2 = Roi(Xleft2, Ytop2, Width2, Height2) 58 | 59 | # Get boolean if BBox within another one (simply test contain with each corner point) 60 | Roi1_in_Roi2 = Roi2.contains(Xleft1, Ytop1) and Roi2.contains(Xright1, Ytop1) and Roi2.contains(Xleft1, Ybot1) and Roi2.contains(Xright1, Ybot1) 61 | Roi2_in_Roi1 = Roi1.contains(Xleft2, Ytop2) and Roi1.contains(Xright2, Ytop2) and Roi1.contains(Xleft2, Ybot2) and Roi1.contains(Xright2, Ybot2) 62 | 63 | if Roi1_in_Roi2 or Roi2_in_Roi1: 64 | #print '1 BBox is included within the other' 65 | IoU = 1 # otherwise using the formula below we have value below 1 eventhough the bbox are included 66 | 67 | elif Xright=scoreThreshold] 129 | 130 | elif not sortDescending : # We keep hit below the threshold 131 | List_ThreshHit = [dico for dico in List_Hit if dico['Score']<=scoreThreshold] 132 | 133 | 134 | # Sort score to have best predictions first (important as we loop testing the best boxes against the other boxes) 135 | if sortDescending: 136 | List_ThreshHit.sort(key=lambda dico: dico['Score'], reverse=True) # Hit = [list of (x,y),score] - sort according to descending (best = high correlation) 137 | else: 138 | List_ThreshHit.sort(key=lambda dico: dico['Score']) # sort according to ascending score (best = small difference) 139 | #print "Sorted List", List_ThreshHit 140 | 141 | # Split the inital pool into Final Hit that are kept and restHit that can be tested 142 | # Initialisation : 1st keep is kept for sure, restHit is the rest of the list 143 | #print "\nInitialise final hit list with first best hit" 144 | FinalHit = [List_ThreshHit[0]] 145 | restHit = List_ThreshHit[1:] 146 | 147 | #print "-> Final hit list" 148 | #for hit in FinalHit: print hit 149 | 150 | #print "\n-> Remaining hit list" 151 | #for hit in restHit: print hit 152 | 153 | 154 | 155 | # Loop to compute overlap 156 | while len(FinalHit) Final hit list" 162 | #for hit in FinalHit: print hit 163 | 164 | #print "\n-> Remaining hit list" 165 | #for hit in restHit: print hit 166 | 167 | # pick the next best peak in the rest of peak 168 | test_hit = restHit[0] 169 | test_bbox = test_hit['BBox'] 170 | #print "\nTest BBox:{} for overlap against higher score bboxes".format(test_bbox) 171 | 172 | # Loop over hit in FinalHit to compute successively overlap with test_peak 173 | for hit in FinalHit: 174 | 175 | # Recover Bbox from hit 176 | bbox2 = hit['BBox'] 177 | 178 | # Compute the Intersection over Union between test_peak and current peak 179 | IoU = computeIoU(test_bbox, bbox2) 180 | 181 | # Initialise the boolean value to true before test of overlap 182 | ToAppend = True 183 | 184 | if IoU>maxOverlap: 185 | ToAppend = False 186 | #print "IoU above threshold\n" 187 | break # no need to test overlap with the other peaks 188 | 189 | else: 190 | #print "IoU below threshold\n" 191 | # no overlap for this particular (test_peak,peak) pair, keep looping to test the other (test_peak,peak) 192 | continue 193 | 194 | 195 | # After testing against all peaks (for loop is over), append or not the peak to final 196 | if ToAppend: 197 | # Move the test_hit from restHit to FinalHit 198 | #print "Append {} to list of final hits and remove it from Remaining hit list".format(test_hit) 199 | FinalHit.append(test_hit) 200 | restHit.remove(test_hit) 201 | 202 | else: 203 | # only remove the test_peak from restHit 204 | #print "Remove {} from Remaining hit list".format(test_hit) 205 | restHit.remove(test_hit) 206 | 207 | 208 | # Once function execution is done, return list of hit without overlap 209 | #print "\nCollected N expected hit, or no hit left to test" 210 | 211 | 212 | #print "NMS over\n" 213 | return FinalHit 214 | 215 | 216 | 217 | 218 | if __name__ == "__main__": 219 | Hit1 = {'TemplateName':"Initial",'BBox':(780, 350, 700, 480), 'Score':0.8} 220 | Hit2 = {'TemplateName':"Initial",'BBox':(806, 416, 716, 442), 'Score':0.6} 221 | Hit3 = {'TemplateName':"Initial",'BBox':(1074, 530, 680, 390), 'Score':0.4} 222 | 223 | ListHit = [Hit1, Hit2, Hit3] 224 | 225 | ListFinalHit = NMS(ListHit) 226 | 227 | print ListFinalHit 228 | -------------------------------------------------------------------------------- /Fiji.app/jars/Lib/Template_Matching/Version.py: -------------------------------------------------------------------------------- 1 | version = "Version 1.1.6" -------------------------------------------------------------------------------- /Fiji.app/jars/Lib/Template_Matching/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/multi-template-matching/MultiTemplateMatching-Fiji/47b1600f02c6bacf72c1250c9c22eb3e32809592/Fiji.app/jars/Lib/Template_Matching/__init__.py -------------------------------------------------------------------------------- /Fiji.app/jars/Lib/utils.py: -------------------------------------------------------------------------------- 1 | def AddToTable(Table, Dico, Order=None, Label=""): 2 | ''' 3 | Append a row to an existing result table 4 | Dico : {'column1':value1, 'column2':value2...} - NB: The column header of the dictionnary should match the one of the existing table 5 | Order : ('column1', 'column2',...), if one wants to specify an order for the column 6 | Label : (optional) String to use as row index, if empty then only columns in the table 7 | ''' 8 | # add new row 9 | Table.incrementCounter() 10 | 11 | # Define the Label for that row = row index 12 | if Label: Table.addLabel(Label) # otherwise there are only columns 13 | 14 | if Order and set(Order)==set(Dico.keys()): # check that the content of order is indeed equal to the content of dico keys 15 | for key in Order: # we loop over order to have the keys ordonned 16 | Table.addValue(key, Dico[key]) 17 | 18 | else: # When the content of order is not equal to the content of the dico. Then just iterate over the dictionnary without a specific order 19 | for key, value in Dico.iteritems(): 20 | Table.addValue(key, value) 21 | 22 | 23 | 24 | if __name__ in ['__main__', '__builtin__']: 25 | from ij.measure import ResultsTable 26 | 27 | Table = ResultsTable() 28 | 29 | AddToTable(Table, {'Column1':5, 'Column2':41}, Order=("Column2", "Column1"), Label='Line1') 30 | AddToTable(Table, {'Column1':5, 'Column2':41}, Order=("Column2", "Column1"), Label='Line2') 31 | 32 | Table.show("Results") -------------------------------------------------------------------------------- /Fiji.app/scripts/Plugins/Multi-Template-Matching/2step-TemplateMatching.ijm: -------------------------------------------------------------------------------- 1 | /* 2 | * This macro allows to do a 2-step template matching 3 | * The first template allows to find an object and the second template finds a sub-object within the previously found object 4 | * Therefore the second template must be smaller than the first one 5 | * Both template images and the image to search should be open in fiji. 6 | * Both detection (1st and 2nd template) are returned in the ROI manager. 7 | * 8 | * NB : Make sure the ROI Manager is empty before running this macro 9 | * 10 | */ 11 | #@ImagePlus (Label="Template1") temp1 12 | #@ImagePlus (Label="Template2") temp2 13 | #@ImagePlus (Label="Image") image 14 | 15 | // Show all ROI and associate with Slices 16 | //roiManager("Show All"); 17 | //roiManager("Associate", "true"); 18 | 19 | // Get image names 20 | selectImage(temp1); 21 | Temp1_title = getTitle(); 22 | //print(Temp1_title); 23 | 24 | selectImage(temp2); 25 | Temp2_title = getTitle(); 26 | //print(Temp2_title); 27 | 28 | selectImage(image); 29 | Image_title = getTitle(); 30 | //print(Image_title); 31 | 32 | // Call 1st template matching 33 | //Start_Temp1 = getTime(); 34 | run("Template Matching Image", "template=" + Temp1_title + " image=" + Image_title + " rotate=[] matching_method=[Normalised 0-mean cross-correlation] number_of_objects=1 score_threshold=0.50 maximal_overlap=0.25 add_roi"); 35 | //Stop_Temp1 = getTime(); 36 | //AverageTimeTemp1 = (Stop_Temp1 - Start_Temp1)/96; 37 | // Because we want to have the average time per image, and the first template matching is a batch-operation (one call for all slices) we divide by the number of images 38 | 39 | // Loop over stack 40 | //print("Average time per image(ms)"); 41 | setBatchMode(true); // do not open extracted slices 42 | selectImage(image); 43 | Roi.remove; 44 | n = nSlices; 45 | nRoi_old = roiManager("count"); // Roi of the 1st template matching 46 | for (i=1; i<=n; i++) { 47 | 48 | 49 | //Start_Temp2 = getTime(); 50 | 51 | // Isolate slice from stack (to perform the second template matching with a custom search ROI for that slice) 52 | selectImage(image); //important here to select back the image when entering a new iteration, during looping the slice is selected 53 | setSlice(i); 54 | Roi.remove; 55 | run("Duplicate...", "title=Slice_"+i); // The isolated slice has for title "Slice_i" 56 | 57 | // Set search ROI on isolated slice 58 | roiManager("select", i-1); // i-1 since ROI manager starts at 0 59 | Roi.getBounds(x, y, width, height); 60 | makeRectangle(x, y, width, height); 61 | //print(x,y,width,height); 62 | 63 | // Run template matching on slice with search ROI 64 | run("Template Matching Image", "template=" + Temp2_title + " image=Slice_" + i +" flip_template_vertically rotate=[] matching_method=[Normalised 0-mean cross-correlation] number_of_objects=2 score_threshold=0.50 maximal_overlap=0.25 add_roi show_result"); 65 | 66 | // Close hidden Slice image 67 | //selectImage("Slice_"+i); 68 | //close(); 69 | 70 | // Rename and Set Z-position of the last found ROI 71 | nRoi_new = roiManager("count"); 72 | 73 | for (j=1; j<=nRoi_new-nRoi_old; j++) { 74 | 75 | roiManager("select", nRoi_new-j); 76 | run("Properties... ", "position="+i); // Set slice position 77 | InitName = call("ij.plugin.frame.RoiManager.getName", nRoi_new-j); 78 | roiManager("rename", i + substring(InitName, 1)); 79 | } 80 | 81 | // Update the count 82 | nRoi_old = nRoi_new; 83 | 84 | //Stop_Temp2 = getTime(); 85 | //AverageTimeTemp2 = Stop_Temp2 - Start_Temp2; 86 | //print(AverageTimeTemp1+AverageTimeTemp2); 87 | } 88 | 89 | // Again make sure that all ROI are displayed and associated to the slices 90 | roiManager("Show All"); 91 | roiManager("Associate", "true"); 92 | -------------------------------------------------------------------------------- /Fiji.app/scripts/Plugins/Multi-Template-Matching/Compute_ScoreMap.py: -------------------------------------------------------------------------------- 1 | #@ ImagePlus ("Label=Template") template 2 | #@ ImagePlus ("Label=Image") image 3 | #@ String (Label="Matching method",choices={"Normalised Square Difference", "Normalised cross-correlation", "Normalised 0-mean cross-correlation"}, value="Normalised 0-mean cross-correlation") method 4 | #@ Float ("Score Threshold", style="slider", min=0, max=1, stepSize=0.05) score_threshold 5 | ''' 6 | Compute correlation map between a template and an image 7 | currently only the normalsied corss correlation used 8 | ''' 9 | from Template_Matching.MatchTemplate_Module import MatchTemplate 10 | from org.bytedeco.javacpp.opencv_imgproc import threshold, CV_THRESH_TOZERO 11 | from org.bytedeco.javacpp.opencv_core import Mat, Scalar, subtract 12 | from ImageConverter import MatToImProc 13 | from ij import ImagePlus 14 | from ij.plugin.filter import MaximumFinder 15 | from ij.gui import Roi, PointRoi 16 | 17 | # Convert method string to the opencv corresponding index 18 | 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} 19 | Method = Dico_Method[method] 20 | 21 | # Convert to ImageProcessor 22 | TempProc = template.getProcessor() 23 | ImProc = image.getProcessor() 24 | 25 | # Do matching 26 | CorrMapCV = MatchTemplate(TempProc, ImProc, Method) 27 | CorrMap = MatToImProc(CorrMapCV) 28 | CorrMapImp = ImagePlus("Score Map", CorrMap) 29 | CorrMapImp.show() 30 | 31 | 32 | # Threshold the corrmap (below threshold 0, above left untouched) 33 | if Method==1: 34 | # Invert the correlation map : min becomes maxima 35 | One = Scalar(1.0) 36 | CorrMapInvCV = subtract(One, CorrMapCV).asMat() 37 | #CorrMapInv = MatToImProc(CorrMapInvCV) 38 | #CorrMapInv = ImagePlus("Inverted", CorrMapInv) 39 | #CorrMapInv.show() 40 | 41 | 42 | # Apply a "TO ZERO" threshold on the correlation map : to compensate the fact that maxima finder does not have a threshold argument 43 | # TO ZERO : below the threshold set to 0, above left untouched 44 | # NB : 1-score_threshold since we have inverted the image : we want to look for minima of value 1-x in the inverted image 45 | CorrMapThreshCV = Mat() 46 | threshold(CorrMapInvCV, CorrMapThreshCV, 1-score_threshold, 0, CV_THRESH_TOZERO) 47 | #CorrMapThreshImp = ImagePlus("Thresholded", CorrMapThresh) 48 | #CorrMapThreshImp.show() 49 | 50 | else: 51 | CorrMapThreshCV = Mat() 52 | threshold(CorrMapCV, CorrMapThreshCV, score_threshold, 0, CV_THRESH_TOZERO) 53 | 54 | 55 | # Display 56 | CorrMapThresh = MatToImProc(CorrMapThreshCV) # Keep this conversion, not only for visualisation 57 | CorrMapThreshImp = ImagePlus("Score Map - thresholded", CorrMapThresh) 58 | CorrMapThreshImp.show() 59 | 60 | ## For both cases (Multi-Min/Max-detection) detect maxima on the thresholded map 61 | excludeOnEdge = False # otherwise miss quite a lot of them 62 | tolerance = 0 63 | Polygon = MaximumFinder().getMaxima(CorrMapThresh, tolerance, excludeOnEdge) 64 | roi = PointRoi(Polygon) 65 | CorrMapThreshImp.setRoi(roi) -------------------------------------------------------------------------------- /Fiji.app/scripts/Plugins/Multi-Template-Matching/Crop_Roi_ToStack.py: -------------------------------------------------------------------------------- 1 | #@ImagePlus imp 2 | ''' 3 | From a list of rectangular ROI associated to some slices of a stack in Fiji 4 | Crop each Roi from the image slice and append them to a stack 5 | The size of the stack is read from the first ROI 6 | 7 | NB : if using with MultiTemplate Matching, using a non square template, the rotation yield different dimensiosn that do not fit in the stack 8 | ''' 9 | from ij.plugin.frame import RoiManager 10 | from ij import ImageStack, ImagePlus 11 | 12 | RM = RoiManager.getRoiManager() 13 | n = RM.getCount() 14 | 15 | # get ROI dimension by reading first one 16 | Roi1 = RM.getRoi(0) 17 | Width = int(Roi1.getFloatWidth()) 18 | Height = int(Roi1.getFloatHeight()) 19 | Stack = ImageStack(Width, Height) 20 | 21 | for i in range(n): 22 | roi = RM.getRoi(i) 23 | ImIndex = roi.getPosition() 24 | #print ImIndex 25 | 26 | imp.setSlice(ImIndex) 27 | imp.setRoi(roi) 28 | Crop = imp.crop() 29 | Stack.addSlice( Crop.getProcessor() ) 30 | 31 | StackImp = ImagePlus("Stack",Stack) 32 | StackImp.show() 33 | 34 | -------------------------------------------------------------------------------- /Fiji.app/scripts/Plugins/Multi-Template-Matching/Template_Matching_Folder.py: -------------------------------------------------------------------------------- 1 | ''' 2 | Object recognition using one or multiple template images 3 | this plugin searches for some templates (with eventual flipped/rotated versions) into some target image(s). 4 | 5 | input : 6 | - template : Path to template image or folder of template images 7 | - image : Path to image or folder of images in which to look for the template 8 | 9 | First, additionnal versions of the template are generated (flip+rotation) if selected 10 | Then each template is searched in the target image. This yield as set of correlation maps 11 | 12 | Minima/maxima in the correlation maps are detected, followed by Non-Maxima Supression when several object are explected in the target image 13 | - matchTemplate Method limited to normalised method to have correlation map in range 0-1 : easier to apply a treshold. 14 | 15 | The search region can be limited to a rectangular ROI, that is provided as a .roi file 16 | 17 | Requirements: 18 | - IJ-OpenCV update site 19 | ''' 20 | 21 | ## Import modules 22 | #@PrefService prefs 23 | from fiji.util.gui import GenericDialogPlus 24 | from ij import IJ 25 | from ij.io import Opener 26 | from ij.gui import Roi 27 | from os import listdir 28 | from os.path import join, isfile, isdir, basename 29 | from org.scijava.io.location import FileLocation 30 | 31 | #import time 32 | 33 | ## Home-Made module 34 | from Template_Matching.MatchTemplate_Module import getHit_Template, CornerToCenter 35 | from Template_Matching.Version import version 36 | from Template_Matching.NonMaximaSupression_Py2 import NMS 37 | 38 | 39 | def isImageFile(filePath): 40 | """Return false for filepath corresponding to directories or to non-image file.""" 41 | 42 | if basename(filePath).startswith(".") : 43 | return False 44 | 45 | opener = Opener() 46 | type = opener.getFileType(filePath) 47 | 48 | if type in [Opener.UNKNOWN, Opener.JAVA_OR_TEXT, Opener.ROI, Opener.TEXT] : 49 | return False 50 | 51 | return True 52 | 53 | 54 | 55 | ## Create GUI 56 | Win = GenericDialogPlus("Multiple Template Matching") 57 | Win.addDirectoryOrFileField("Template file or templates folder", prefs.get("TemplatePath", "template(s)")) 58 | Win.addDirectoryOrFileField("Image file or images folder", prefs.get("ImagePath", "image(s)")) 59 | Win.addFileField("Rectangular_search_ROI (optional)", prefs.get("RoiPath","searchRoi")) 60 | 61 | # Template pre-processing 62 | Win.addMessage("# Template(s) pre-processing") 63 | Win.addCheckbox("Flip_template_vertically", prefs.getInt("FlipV", False)) 64 | Win.addCheckbox("Flip_template_horizontally", prefs.getInt("FlipH", False)) 65 | Win.addStringField("Rotate template by ..(comma-separated)", prefs.get("Angles", "")) 66 | 67 | # Template matching parameters 68 | Win.addMessage("# Detection parameters") 69 | Win.addChoice("Matching_method", ["Normalised Square Difference","Normalised cross-correlation","Normalised 0-mean cross-correlation"], prefs.get("Method","Normalised 0-mean cross-correlation")) 70 | Win.addNumericField("Number_of_objects expected", prefs.getInt("N_hit",1),0) 71 | 72 | # Non-Maxima Suppression 73 | Win.addMessage("# Non-Maxima Suppression (if Nobjects > 1)") 74 | Win.addSlider("Score_Threshold", 0, 1, prefs.getFloat("Score_Threshold",0.5), 0.1) 75 | #Win.addNumericField("Min_peak_height relative to neighborhood ([0-1], decrease to get more hits)", prefs.getFloat("Tolerance",0.1), 2) 76 | Win.addSlider("Maximal_overlap between Bounding boxes", 0, 1, prefs.getFloat("MaxOverlap",0.4), 0.1) 77 | 78 | # Outputs 79 | Win.addMessage("# Outputs") 80 | Win.addCheckbox("Open_images as a stack (must have identical sizes)", prefs.getInt("ShowImages", True)) 81 | Win.addCheckbox("Add_ROI detected to ROI Manager", prefs.getInt("AddRoi", True)) 82 | Win.addCheckbox("Show_result table", prefs.getInt("ShowTable", False)) 83 | Win.addMessage(version) 84 | Win.addMessage("""If you use this plugin please cite : 85 | Thomas, L.S.V., Gehrig, J. 86 | Multi-template matching: a versatile tool for object-localization in microscopy images. 87 | BMC Bioinformatics 21, 44 (2020). https://doi.org/10.1186/s12859-020-3363-7""") 88 | Win.addHelp("https://github.com/multi-template-matching/MultiTemplateMatching-Fiji/wiki") 89 | 90 | Win.showDialog() 91 | 92 | if Win.wasOKed(): 93 | TemplatePath = Win.getNextString() 94 | ImagePath = Win.getNextString() 95 | RoiPath = Win.getNextString() 96 | flipv = Win.getNextBoolean() 97 | fliph = Win.getNextBoolean() 98 | angles = Win.getNextString() 99 | method = Win.getNextChoice() 100 | n_hit = int(Win.getNextNumber()) 101 | score_threshold = Win.getNextNumber() 102 | #tolerance = Win.getNextNumber() 103 | tolerance = 0 104 | max_overlap = Win.getNextNumber() 105 | show_images = Win.getNextBoolean() 106 | add_roi = Win.getNextBoolean() 107 | show_table = Win.getNextBoolean() 108 | 109 | # Save for persistence 110 | prefs.put("TemplatePath", TemplatePath) 111 | prefs.put("ImagePath", ImagePath) 112 | prefs.put("RoiPath", RoiPath) 113 | prefs.put("FlipV",flipv) 114 | prefs.put("FlipH",fliph) 115 | prefs.put("Angles", angles) 116 | prefs.put("Method", method) 117 | prefs.put("N_hit", n_hit) 118 | prefs.put("Score_Threshold", score_threshold) 119 | #prefs.put("Tolerance", tolerance) 120 | prefs.put("MaxOverlap", max_overlap) 121 | prefs.put("ShowImages", show_images) 122 | prefs.put("AddRoi", add_roi) 123 | prefs.put("ShowTable", show_table) 124 | 125 | # Convert method string to the opencv corresponding index 126 | 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} 127 | Method = Dico_Method[method] 128 | 129 | if show_images: 130 | from ij import ImagePlus, ImageStack 131 | Stack_Image = ImageStack() 132 | Stack_Image_ImP = ImagePlus() 133 | 134 | if add_roi: 135 | from ij.plugin.frame import RoiManager 136 | from ij.gui import Roi 137 | RM = RoiManager() 138 | rm = RM.getInstance() 139 | 140 | if show_table: 141 | from ij.measure import ResultsTable 142 | from utils import AddToTable 143 | Table = ResultsTable().getResultsTable() # allows to append to an existing table 144 | 145 | 146 | ## Check if input are valid 147 | if n_hit<=0: 148 | raise Exception('The expected number of object should be at least 1') 149 | 150 | if score_threshold<0 or score_threshold>1: 151 | raise Exception('The score threshold should be in range [0,1]') 152 | 153 | #if tolerance<0 or tolerance>1: 154 | # raise Exception('Tolerance should be in range [0,1]') 155 | 156 | if max_overlap<0 or max_overlap>1: 157 | raise Exception('The max overlap should be in range [0,1]') 158 | 159 | 160 | ### Search ROI ? ### 161 | # Check if there is a searchRoi 162 | if RoiPath: 163 | from ij.io import RoiDecoder 164 | searchRoi = RoiDecoder.open(RoiPath) 165 | else: 166 | searchRoi = None 167 | 168 | # Check if it is a rectangular one 169 | if searchRoi and searchRoi.getTypeAsString()=="Rectangle": 170 | Bool_SearchRoi = True 171 | dX = int(searchRoi.getXBase()) 172 | dY = int(searchRoi.getYBase()) 173 | 174 | else: 175 | Bool_SearchRoi = False 176 | dX = dY = 0 177 | 178 | 179 | 180 | ## File or Folder 181 | # Template(s) 182 | if isfile(TemplatePath): # single template file 183 | 184 | if isImageFile(TemplatePath): 185 | ListPathTemplate = [TemplatePath] 186 | 187 | else: 188 | ListPathTemplate = [] 189 | IJ.error("No image found for template.") 190 | 191 | elif isdir(TemplatePath): # template folder 192 | ListPathTemplate = [] 193 | 194 | # Check extension 195 | for name in listdir(TemplatePath): 196 | 197 | FullPathTem = join(TemplatePath, name) 198 | 199 | if isImageFile(FullPathTem) : 200 | ListPathTemplate.append(FullPathTem) 201 | 202 | else: 203 | raise Exception("Template path does not exist") 204 | 205 | #print ListPathTemplate 206 | 207 | # Initialise list of templates (rather than opening them for every image iteration) 208 | List_Template = [ IJ.openImage(PathTemp) for PathTemp in ListPathTemplate ] 209 | 210 | 211 | 212 | 213 | # Image(s) 214 | if isfile(ImagePath): # single image path 215 | 216 | if isImageFile(ImagePath): 217 | ListPathImage = [ImagePath] 218 | 219 | else: 220 | ListPathImage = [] 221 | IJ.error("Selected file for image is not a recognized image file.") 222 | 223 | 224 | elif isdir(ImagePath): # image folder 225 | 226 | ListPathImage = [] # initialise 227 | 228 | for name in listdir(ImagePath): 229 | 230 | FullPathIm = join(ImagePath,name) 231 | 232 | if isImageFile(FullPathIm) : 233 | ListPathImage.append(FullPathIm) 234 | 235 | else: # neither a file path nor a folder path (ie non existing) 236 | raise Exception("Image path does not exist") 237 | 238 | ## Initialise Result table for time 239 | #TimeTable = ResultsTable() 240 | 241 | ## Loop over images for template matching and maxima detection 242 | for i, PathIm in enumerate(ListPathImage): 243 | 244 | if IJ.escapePressed(): 245 | IJ.resetEscape() # for next call 246 | raise KeyboardInterrupt("Escape was pressed") 247 | 248 | # Get the current image 249 | ImpImage = IJ.openImage(PathIm) 250 | if not ImpImage: continue # skip for certain file formats such as xml which are scijava compatible but not images 251 | ImName = ImpImage.getTitle() 252 | ImProc = ImpImage.getProcessor().duplicate() 253 | 254 | # Crop Image if searchRoi 255 | if Bool_SearchRoi: 256 | ImpImage.setRoi(searchRoi) 257 | ImpImage = ImpImage.crop() 258 | 259 | ## Start Timer here (dont count opening of the image) 260 | #Start = time.clock() 261 | 262 | # Initialise list before looping over templates 263 | Hits_BeforeNMS = [] 264 | 265 | ## Loop over template for matching against current image 266 | for ImpTemplate in List_Template: 267 | 268 | # Check that template is smaller than the searched image or ROI 269 | if Bool_SearchRoi and (ImpTemplate.height>searchRoi.getFloatHeight() or ImpTemplate.width>searchRoi.getFloatWidth()): 270 | IJ.log("The template "+ ImpTemplate.getTitle() +" is larger in width and/or height than the search region -> skipped") 271 | continue # go directly to the next for iteration 272 | 273 | elif ImpTemplate.width>ImpImage.width or ImpTemplate.height>ImpImage.height: 274 | IJ.log("The template "+ ImpTemplate.getTitle() + " is larger in width and/or height than the searched image-> skipped") 275 | continue # go directly to the next for iteration 276 | 277 | # Get hits for the current template (and his flipped and/or rotated versions) 278 | 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 279 | 280 | # Store the hits 281 | Hits_BeforeNMS.extend(List_Hit) 282 | 283 | 284 | 285 | ### NMS ### 286 | #print "\n-- Hits before NMS --\n", 287 | #for hit in Hits_BeforeNMS: print hit 288 | 289 | # InterHit NMS if more than one hit 290 | if Method in [0,1]: 291 | Hits_AfterNMS = NMS(Hits_BeforeNMS, N=n_hit, maxOverlap=max_overlap, sortDescending=False) # only difference is the sorting 292 | 293 | else: 294 | Hits_AfterNMS = NMS(Hits_BeforeNMS, N=n_hit, maxOverlap=max_overlap, sortDescending=True) 295 | 296 | #print "\n-- Hits after NMS --\n" 297 | #for hit in Hits_AfterNMS : print hit 298 | 299 | 300 | 301 | 302 | ## Loop over final hits to generate ROI ## 303 | for hit in Hits_AfterNMS: 304 | 305 | #print hit 306 | 307 | if Bool_SearchRoi: # Add offset of search ROI 308 | hit['BBox'] = (hit['BBox'][0]+dX, hit['BBox'][1]+dY, hit['BBox'][2], hit['BBox'][3]) 309 | 310 | # Create detected ROI 311 | roi = Roi(*hit['BBox']) 312 | roi.setName(hit['TemplateName']) 313 | roi.setProperty("Score", str(hit["Score"]) ) 314 | roi.setPosition(i+1) # set ROI Z-position 315 | 316 | if add_roi: 317 | rm.add(None, roi, i+1) # Trick to be able to set Z-position when less images than the number of ROI. Here i is an digit index before the Roi Name 318 | 319 | if show_table: 320 | Xcorner, Ycorner = hit['BBox'][0], hit['BBox'][1] 321 | Xcenter, Ycenter = CornerToCenter(Xcorner, Ycorner, hit['BBox'][2], hit['BBox'][3]) 322 | 323 | Dico = {'Image':ImName, 'Template':hit['TemplateName'] ,'Xcorner':Xcorner, 'Ycorner':Ycorner, 'Xcenter':Xcenter, 'Ycenter':Ycenter, 'Score':hit['Score']} 324 | if add_roi: 325 | Dico['Roi Index'] = rm.getCount() 326 | AddToTable(Table, Dico, Order=("Image", "Template", "Score", "Roi Index", "Xcorner", "Ycorner", "Xcenter", "Ycenter")) 327 | else: 328 | AddToTable(Table, Dico, Order=('Image', 'Template', 'Score', 'Xcorner', 'Ycorner', 'Xcenter', 'Ycenter')) 329 | 330 | 331 | ## Display outputs 332 | if show_images: 333 | 334 | # Initialise a stack of proper size if not the case before 335 | if Stack_Image.getSize()==0: 336 | Stack_Image = ImageStack(ImProc.width, ImProc.height) # instead of using ImagePlus.getStack otherwise we loose the slice title for the 1st image 337 | 338 | # Add images to Stack 339 | Stack_Image.addSlice(ImName, ImProc) 340 | Stack_Image_ImP.setStack("Result", Stack_Image) 341 | 342 | # Update display 343 | Stack_Image_ImP.setSlice(i) 344 | Stack_Image_ImP.show() 345 | 346 | if add_roi: # Show All ROI + Associate ROI to slices 347 | rm.runCommand("Associate", "true") 348 | rm.runCommand("Show All with labels") 349 | 350 | 351 | ## Stop time here 352 | #Stop = time.clock() 353 | #Elapsed = Stop - Start # in seconds 354 | #TimeTable.incrementCounter() 355 | #TimeTable.addValue('Time(s)', Elapsed) 356 | 357 | 358 | # At the end, display result table 359 | if show_table: 360 | Table.show("Results") 361 | 362 | #TimeTable.show("BenchmarkTime") -------------------------------------------------------------------------------- /Fiji.app/scripts/Plugins/Multi-Template-Matching/Template_Matching_Image.py: -------------------------------------------------------------------------------- 1 | ''' 2 | Object recognition using one or multiple template images 3 | this plugin search for one template (with eventual flipped/rotated version) into one target image. 4 | The 2 images should be already opened in Fiji. 5 | 6 | input : 7 | - template : ImagePlus of the object to search in the target image 8 | - image : ImagePlus or Stack 9 | 10 | First, additionnal versions of the template are generated (flip+rotation) if selected 11 | Then each template is searched in the target image. This yield as set of correlation maps 12 | 13 | Minima/maxima in the correlation maps are detected, followed by Non-Maxima Supression when several object are explected in the target image 14 | - matchTemplate Method limited to normalised method to have correlation map in range 0-1 : easier to apply a treshold. 15 | 16 | The search region can be limited to a rectangular ROI, that is drawn on the image/stack before execution of the plugin. 17 | 18 | Requirements: 19 | - IJ-OpenCV update site 20 | ''' 21 | #import time 22 | #from timeit import default_timer as timer 23 | 24 | #@PrefService prefs 25 | from Template_Matching.Version import version 26 | from fiji.util.gui import GenericDialogPlus 27 | # rest of imports below on purpose (otherwise searchRoi lost) 28 | 29 | ## Create GUI 30 | Win = GenericDialogPlus("Multiple Template Matching") 31 | Win.addImageChoice("Template", prefs.get("Template","Choice")) 32 | Win.addImageChoice("Image", prefs.get("Image", "Choice")) 33 | Win.addMessage("# Template pre-processing") 34 | Win.addCheckbox("Flip_template_vertically", prefs.getInt("FlipV", False)) 35 | Win.addCheckbox("Flip_template_horizontally", prefs.getInt("FlipH", False)) 36 | Win.addStringField("Rotate template by ..(comma-separated)", prefs.get("Angles", "")) 37 | Win.addMessage("# Detection parameters") 38 | Win.addChoice("Matching_method", ["Normalised Square Difference","Normalised cross-correlation","Normalised 0-mean cross-correlation"], prefs.get("Method","Normalised 0-mean cross-correlation")) 39 | Win.addNumericField("Number_of_objects expected", prefs.getInt("N_hit",1),0) 40 | Win.addMessage("# Non-Maxima Suppression (if N_object > 1)") 41 | Win.addSlider("Score_Threshold", 0, 1, prefs.getFloat("Score_Threshold",0.5), 0.1) 42 | #Win.addNumericField("Min_peak_height relative to neighborhood ([0-1], decrease to get more hits)", prefs.getFloat("Tolerance",0.1), 2) 43 | Win.addSlider("Maximal_overlap between Bounding boxes", 0, 1, prefs.getFloat("MaxOverlap",0.4), 0.1) 44 | Win.addMessage("# Outputs") 45 | Win.addCheckbox("Add_ROI detected to ROI manager", prefs.getInt("AddRoi", True)) 46 | Win.addCheckbox("Show_result table", prefs.getInt("ShowTable", False)) 47 | Win.addMessage(version) 48 | Win.addMessage("""If you use this plugin please cite : 49 | Thomas, L.S.V., Gehrig, J. 50 | Multi-template matching: a versatile tool for object-localization in microscopy images. 51 | BMC Bioinformatics 21, 44 (2020). https://doi.org/10.1186/s12859-020-3363-7""") 52 | Win.addHelp("https://github.com/multi-template-matching/MultiTemplateMatching-Fiji/wiki") 53 | 54 | Win.showDialog() 55 | 56 | if Win.wasOKed(): 57 | template = Win.getNextImage() 58 | image = Win.getNextImage() 59 | flipv = Win.getNextBoolean() 60 | fliph = Win.getNextBoolean() 61 | angles = Win.getNextString() 62 | method = Win.getNextChoice() 63 | n_hit = int(Win.getNextNumber()) 64 | score_threshold = Win.getNextNumber() 65 | #tolerance = Win.getNextNumber() 66 | tolerance = 0 67 | max_overlap = Win.getNextNumber() 68 | add_roi = Win.getNextBoolean() 69 | show_table = Win.getNextBoolean() 70 | 71 | # Save for persistence 72 | ImageName = image.getTitle() 73 | prefs.put("Template", template.getTitle()) 74 | prefs.put("Image", ImageName) 75 | prefs.put("FlipV",flipv) 76 | prefs.put("FlipH",fliph) 77 | prefs.put("Angles", angles) 78 | prefs.put("Method", method) 79 | prefs.put("N_hit", n_hit) 80 | prefs.put("Score_Threshold", score_threshold) 81 | #prefs.put("Tolerance", tolerance) 82 | prefs.put("MaxOverlap", max_overlap) 83 | prefs.put("AddRoi", add_roi) 84 | prefs.put("ShowTable", show_table) 85 | 86 | # Check if input are valid 87 | if n_hit<=0: 88 | raise Exception('The expected number of object should be at least 1') 89 | 90 | if score_threshold<0 or score_threshold>1: 91 | raise Exception('The score threshold should be in range [0,1]') 92 | 93 | #if tolerance<0 or tolerance>1: 94 | # raise Exception('Tolerance should be in range [0,1]') 95 | 96 | if max_overlap<0 or max_overlap>1: 97 | raise Exception('The max overlap should be in range [0,1]') 98 | 99 | 100 | ## Initialise variables before import (otherwise the ROI is lost) 101 | searchRoi = image.getRoi() 102 | 103 | ## Rectangle ROI ? 104 | if searchRoi and searchRoi.getTypeAsString()=="Rectangle": 105 | Bool_SearchRoi = True 106 | else: 107 | Bool_SearchRoi = False 108 | 109 | # Define offset 110 | if Bool_SearchRoi: 111 | dX = int(searchRoi.getXBase()) 112 | dY = int(searchRoi.getYBase()) 113 | else: 114 | dX = dY = 0 115 | 116 | 117 | ## Import modules 118 | from ij import IJ, ImagePlus 119 | from ij.gui import Roi 120 | 121 | ## Import HomeMade modules 122 | from Template_Matching.NonMaximaSupression_Py2 import NMS 123 | from Template_Matching.MatchTemplate_Module import getHit_Template, CornerToCenter 124 | 125 | 126 | ## Check that the template is smaller than the (possibly cropped) image 127 | if Bool_SearchRoi and (template.height>searchRoi.getFloatHeight() or template.width>searchRoi.getFloatWidth()): 128 | raise Exception('The template is larger in width and/or height than the searched Roi') 129 | 130 | elif template.height>image.height or template.width>image.width: 131 | raise Exception('The template is larger in width and/or height than the searched image') 132 | 133 | ### Initialize outputs ### 134 | if show_table: 135 | from ij.measure import ResultsTable 136 | from utils import AddToTable 137 | Table = ResultsTable().getResultsTable() # allows to append to an existing table 138 | 139 | if add_roi: 140 | from ij.plugin.frame import RoiManager 141 | RM = RoiManager() 142 | rm = RM.getInstance() 143 | 144 | 145 | # Convert method string to the opencv corresponding index 146 | 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} 147 | Method = Dico_Method[method] 148 | 149 | 150 | # Loop over the images in the stack (or directly process if unique) 151 | imageStack = image.getStack() 152 | nSlice = image.getStackSize() 153 | 154 | for i in xrange(1,nSlice+1): 155 | 156 | if IJ.escapePressed(): 157 | IJ.resetEscape() # for next call 158 | raise KeyboardInterrupt("Escape was pressed") 159 | 160 | searchedImage = imageStack.getProcessor(i) # of slice i 161 | 162 | if Bool_SearchRoi: 163 | searchedImage.setRoi(searchRoi) 164 | searchedImage = searchedImage.crop() 165 | 166 | 167 | # Fix the name for searchImage 168 | if nSlice>1: 169 | SliceLabel = imageStack.getSliceLabel(i) 170 | 171 | if SliceLabel: # sometimes the slicelabel is none 172 | Title = SliceLabel.split('\n',1)[0] # split otherwise we get some unecessary information 173 | else: 174 | Title = image.getTitle() 175 | 176 | else: 177 | Title = image.getTitle() 178 | 179 | # Do the template(s) matching 180 | #Start = time.clock() 181 | #start = timer() 182 | Hits_BeforeNMS = getHit_Template(template, 183 | ImagePlus(Title, searchedImage), 184 | flipv, fliph, 185 | angles, 186 | Method, 187 | n_hit, 188 | score_threshold, 189 | tolerance) # template and image as ImagePlus (to get the name together with the image matrix) 190 | #Stop = time.clock() 191 | #IJ.log("getHit_Template took " + str(Stop-Start) + " seconds") 192 | 193 | ### NMS ### 194 | #IJ.log(str(len(Hits_BeforeNMS)) + " hit before NMS") 195 | 196 | #print "\n-- Hits before NMS --\n", 197 | #for hit in Hits_BeforeNMS : print hit 198 | 199 | # 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 200 | #start_NMS = time.clock() 201 | if len(Hits_BeforeNMS)==1: 202 | Hits_AfterNMS = Hits_BeforeNMS 203 | 204 | elif Method in [0,1]: 205 | Hits_AfterNMS = NMS(Hits_BeforeNMS, N=n_hit, maxOverlap=max_overlap, sortDescending=False) # only difference is the sorting 206 | 207 | else: 208 | Hits_AfterNMS = NMS(Hits_BeforeNMS, N=n_hit, maxOverlap=max_overlap, sortDescending=True) 209 | 210 | 211 | #stop = timer() 212 | #print stop-start 213 | 214 | #stop_NMS = time.clock() 215 | #IJ.log("NMS duration(s): " + str(stop_NMS-start_NMS)) 216 | #print "\n-- Hits after NMS --\n" 217 | #for hit in Hits_AfterNMS : print hit 218 | 219 | # NB : Hits coordinates have not been corrected for cropping here ! Done in the next for loop 220 | 221 | 222 | # Loop over final hits to generate ROI 223 | for hit in Hits_AfterNMS: 224 | 225 | #print hit 226 | 227 | if Bool_SearchRoi: # Add offset of search ROI 228 | hit['BBox'] = (hit['BBox'][0]+dX, hit['BBox'][1]+dY, hit['BBox'][2], hit['BBox'][3]) 229 | 230 | # Create detected ROI 231 | roi = Roi(*hit['BBox']) 232 | roi.setName(hit['TemplateName']) 233 | roi.setProperty("Score", str(hit["Score"]) ) 234 | roi.setPosition(i) # set ROI Z-position 235 | #roi.setProperty("class", hit["TemplateName"]) 236 | 237 | image.setSlice(i) 238 | image.setRoi(roi) 239 | 240 | if add_roi: 241 | rm.add(None, roi, i) # Trick to be able to set Z-position when less images than the number of ROI. Here i is an digit index before the Roi Name 242 | 243 | # Show All ROI + Associate ROI to slices 244 | rm.runCommand("Associate", "true") 245 | rm.runCommand("Show All with labels") 246 | IJ.selectWindow(ImageName) # does not work always 247 | 248 | if show_table: 249 | Xcorner, Ycorner = hit['BBox'][0], hit['BBox'][1] 250 | Xcenter, Ycenter = CornerToCenter(Xcorner, Ycorner, hit['BBox'][2], hit['BBox'][3]) 251 | 252 | Dico = {'Image':hit['ImageName'],'Slice':i, 'Template':hit['TemplateName'] ,'Xcorner':Xcorner, 'Ycorner':Ycorner, 'Xcenter':Xcenter, 'Ycenter':Ycenter, 'Score':hit['Score']} # column order is defined below 253 | 254 | if add_roi: 255 | # Add ROI index to the result table 256 | Dico['Roi Index'] = rm.getCount() 257 | AddToTable(Table, Dico, Order=("Image", "Slice", "Template", "Score", "Roi Index", "Xcorner", "Ycorner", "Xcenter", "Ycenter")) 258 | else: 259 | AddToTable(Table, Dico, Order=("Image", "Slice", "Template", "Score", "Xcorner", "Ycorner", "Xcenter", "Ycenter")) 260 | 261 | 262 | 263 | # Display result table 264 | if show_table: 265 | Table.show("Results") -------------------------------------------------------------------------------- /Fiji.app/scripts/Plugins/Multi-Template-Matching/Template_Matching_ListImage.py: -------------------------------------------------------------------------------- 1 | #@ File[] (Label="Templates", style="file") template_files 2 | #@ File[] (label="Image for which to look for the template") image_files 3 | #@ File (label="Region of Interest (ROI) for search (optional)", required=false) roi_file 4 | #@ Boolean (Label="Flip template vertically") flipv 5 | #@ Boolean (Label="Flip template horizontally") fliph 6 | #@ String (Label="Additional rotation angles separated by ," ,required=False) angles 7 | #@ String (Label="Matching method",choices={"Normalised Square Difference", "Normalised cross-correlation", "Normalised 0-mean cross-correlation"}, value="Normalised 0-mean cross-correlation") method 8 | #@ Integer (Label="Expected number of objects", min=1) n_hit 9 | #@ String (visibility="MESSAGE", value="The parameters below are used only if more than 1 template are expected in the image") doc 10 | #@ Float (Label="Score Threshold", min=0, max=1, value=0.5, stepSize=0.1, style="slider") score_threshold 11 | #@ Float (Label="Maximal overlap between Bounding boxes", min=0, max=1, value=0.4, stepSize=0.1, style="slider") max_overlap 12 | #@ String (visibility="MESSAGE", value="Output") out 13 | #@ Boolean (Label="Open images (as stack ie images must have identical dimensions)") show_images 14 | #@ Boolean (Label="Add ROI to ROI Manager") add_roi 15 | #@ Boolean (Label="Show result table") show_table 16 | #@ String (visibility="MESSAGE", value="
If you use this plugin please cite:Multi-template matching: a versatile tool for object-localization in microscopy images.
BMC Bioinformatics 21, 44 (2020)
https://doi.org/10.1186/s12859-020-3363-7
") disclaimer 17 | ''' 18 | previous field : 19 | 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 20 | Boolean (Label="Display correlation map(s)") show_map 21 | 22 | Object recognition using one or multiple template images 23 | ie this macro search for N templates (with eventual flipped/rotated version) into L target images. 24 | 25 | input : 26 | - template_files : list of template path 27 | - image_files : list of image path for which we want to search a template 28 | 29 | First, additionnal versions of the template are generated (flip+rotation) if selected 30 | Then each template is searched in the target image. This yield as set of correlation maps 31 | 32 | Minima/maxima in the correlation maps are detected, followed by Non-Maxima Supression when several object are explected in the target image 33 | - matchTemplate Method limited to normalised method to have correlation map in range 0-1 : easier to apply a treshold. 34 | 35 | The search region can be limited to a rectangular ROI, that is provided as a .roi file. 36 | 37 | Requirements: 38 | - IJ-OpenCV update site 39 | 40 | 41 | ## TO DO : 42 | - order of the column in result table 43 | 44 | ## NB : 45 | - If show_images is true, the images must have the same dimensions 46 | 47 | - The multifile input is not yet macro recordable. An alternative is to use the folder input version 48 | 49 | - Method limited to normalised method to have correlation map in range 0-1 : easier to apply a treshold. 50 | Otherwise normalising relative to maxima of each correlation map is not good since this result in having the global maxima to always be one, 51 | eventhough its correlation value was not one. 52 | ''' 53 | ### IMPORT ### 54 | import time # for benchmark 55 | from ij import IJ # ImageJ1 import 56 | 57 | ## Home-Made module 58 | from Template_Matching.MatchTemplate_Module import getHit_Template, CornerToCenter 59 | from Template_Matching.NonMaximaSupression_Py2 import NMS 60 | 61 | if show_images: 62 | from ij import ImagePlus, ImageStack 63 | Stack_Image = ImageStack() 64 | Stack_Image_ImP = ImagePlus() 65 | 66 | if add_roi: 67 | from ij.plugin.frame import RoiManager 68 | from ij.gui import Roi 69 | RM = RoiManager() 70 | rm = RM.getInstance() 71 | 72 | if show_table: 73 | from ij.measure import ResultsTable 74 | from utils import AddToTable 75 | Table = ResultsTable().getResultsTable() # allows to append to an existing table 76 | 77 | 78 | # Convert method string to the opencv corresponding index 79 | 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} 80 | Method = Dico_Method[method] 81 | 82 | # Initialise time 83 | ListTime = [] 84 | 85 | # Initialise list of templates (rather than opening them for every image iteration) 86 | List_Template = [ IJ.openImage( templ_file.getPath() ) for templ_file in template_files ] 87 | 88 | 89 | 90 | ### Search ROI ? ### 91 | # Check if there is a searchRoi 92 | if roi_file: 93 | from ij.io import RoiDecoder 94 | searchRoi = RoiDecoder.open(roi_file.getPath()) 95 | else: 96 | searchRoi = None 97 | 98 | # Check if it is a rectangular one 99 | if searchRoi and searchRoi.getTypeAsString()=="Rectangle": 100 | Bool_SearchRoi = True 101 | else: 102 | Bool_SearchRoi = False 103 | 104 | # Define Offset for BBox if proper searchRoi 105 | if Bool_SearchRoi: 106 | dX = searchRoi.getXBase() 107 | dY = searchRoi.getYBase() 108 | else: 109 | dX = dY = 0 110 | 111 | 112 | 113 | 114 | ## Loop over templates for template matching and maxima detection 115 | tolerance = 0 # deactivate the flood fill of the max detector 116 | for i, im_file in enumerate(image_files): 117 | 118 | if IJ.escapePressed(): 119 | IJ.resetEscape() # for next call 120 | raise KeyboardInterrupt("Escape was pressed") 121 | 122 | # Get the current image 123 | PathIm = im_file.getPath() 124 | ImpImage = IJ.openImage(PathIm) 125 | ImName = ImpImage.getTitle() 126 | ImProc = ImpImage.getProcessor().duplicate() 127 | 128 | # Crop Image if searchRoi 129 | if Bool_SearchRoi: 130 | ImpImage.setRoi(searchRoi) 131 | ImpImage = ImpImage.crop() 132 | 133 | ## Start Timer here (dont count opening of the image) 134 | #Start = time.clock() 135 | 136 | # Initialise list before looping over templates 137 | Hits_BeforeNMS = [] 138 | 139 | ## Loop over template for matching against current image 140 | for ImpTemplate in List_Template: 141 | 142 | # Check that template is smaller than the searched image 143 | if Bool_SearchRoi and (ImpTemplate.height>searchRoi.getFloatHeight() or ImpTemplate.width>searchRoi.getFloatWidth()): 144 | IJ.log("The template "+ ImpTemplate.getTitle() +" is larger in width and/or height than the search region -> skipped") 145 | continue # go directly to the next for iteration 146 | 147 | elif ImpTemplate.width>ImpImage.width or ImpTemplate.height>ImpImage.height: 148 | IJ.log("The template "+ ImpTemplate.getTitle() + " is larger in width and/or height than the searched image-> skipped") 149 | continue # go directly to the next for iteration 150 | 151 | # Get hits for the current template (and his flipped and/or rotated versions) 152 | 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 153 | 154 | # Store the hits 155 | Hits_BeforeNMS.extend(List_Hit) 156 | 157 | 158 | 159 | ### NMS ### 160 | #print "\n-- Hits before NMS --\n", 161 | #for hit in Hits_BeforeNMS: print hit 162 | 163 | # InterHit NMS if more than one hit 164 | if Method in [0,1]: 165 | Hits_AfterNMS = NMS(Hits_BeforeNMS, N=n_hit, maxOverlap=max_overlap, sortDescending=False) # only difference is the sorting 166 | 167 | else: 168 | Hits_AfterNMS = NMS(Hits_BeforeNMS, N=n_hit, maxOverlap=max_overlap, sortDescending=True) 169 | 170 | #print "\n-- Hits after NMS --\n" 171 | #for hit in Hits_AfterNMS : print hit 172 | 173 | 174 | 175 | ## Stop time here (we dont benchmark display time) 176 | #Stop = time.clock() 177 | #Elapsed = Stop - Start # in seconds 178 | #ListTime.append(Elapsed) 179 | 180 | 181 | 182 | ## Loop over final hits to generate ROI, result table... ### 183 | for hit in Hits_AfterNMS: 184 | 185 | if Bool_SearchRoi: # Add the offset of the search ROI 186 | hit['BBox'] = (hit['BBox'][0]+dX, hit['BBox'][1]+dY, hit['BBox'][2], hit['BBox'][3]) 187 | 188 | if add_roi: 189 | roi = Roi(*hit['BBox']) 190 | roi.setName(hit['TemplateName']) 191 | roi.setProperty("Score", str(hit["Score"]) ) 192 | roi.setPosition(i+1) # set slice position 193 | 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 194 | 195 | if show_table: 196 | Xcorner, Ycorner = hit['BBox'][0], hit['BBox'][1] 197 | Xcenter, Ycenter = CornerToCenter(Xcorner, Ycorner, hit['BBox'][2], hit['BBox'][3]) 198 | 199 | Dico = {'Image':ImName, 'Template':hit['TemplateName'] ,'Xcorner':Xcorner, 'Ycorner':Ycorner, 'Xcenter':Xcenter, 'Ycenter':Ycenter, 'Score':hit['Score']} 200 | if add_roi: 201 | Dico['Roi Index'] = rm.getCount() 202 | AddToTable(Table, Dico, Order=("Image", "Template", "Score", "Roi Index", "Xcorner", "Ycorner", "Xcenter", "Ycenter")) 203 | else: 204 | AddToTable(Table, Dico, Order=('Image', 'Template', 'Score', 'Xcorner', 'Ycorner', 'Xcenter', 'Ycenter')) 205 | 206 | Table.show("Results") 207 | 208 | 209 | 210 | ## Display outputs 211 | if show_images: 212 | 213 | # Initialise a stack of proper size if not the case before 214 | if Stack_Image.getSize()==0: 215 | Stack_Image = ImageStack(ImProc.width, ImProc.height) # instead of using ImagePlus.getStack otherwise we loose the slice title for the 1st image 216 | 217 | # Add images to Stack 218 | Stack_Image.addSlice(ImName, ImProc) 219 | Stack_Image_ImP.setStack("Result", Stack_Image) 220 | 221 | # Update display 222 | Stack_Image_ImP.setSlice(i) 223 | Stack_Image_ImP.show() 224 | 225 | if add_roi: 226 | # Show All ROI + Associate ROI to slices 227 | rm.runCommand("Associate", "true") 228 | rm.runCommand("Show All with labels") 229 | 230 | #for i in ListTime: 231 | #print i -------------------------------------------------------------------------------- /Fiji.app/scripts/Plugins/Multi-Template-Matching/Template_Matching_ListTemplate.py: -------------------------------------------------------------------------------- 1 | #@ File[] (Label="Templates", style="file") template_files 2 | #@ ImagePlus (label="Image for which to look for the template") image 3 | #@ Boolean (Label="Flip template vertically") flipv 4 | #@ Boolean (Label="Flip template horizontally") fliph 5 | #@ String (Label="Additional rotation angles separated by ," ,required=False) angles 6 | #@ String (Label="Matching method",choices={"Normalised Square Difference", "Normalised cross-correlation", "Normalised 0-mean cross-correlation"}, value="Normalised 0-mean cross-correlation") method 7 | #@ int (Label="Expected number of templates", min=1) n_hit 8 | #@ String (visibility="MESSAGE", value="The parameters below are used only if more than 1 template are expected in the image") doc 9 | #@ Float (Label="Score Threshold (0-1)", min=0, max=1, value=0.5, stepSize=0.1) score_threshold 10 | #@ 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 11 | #@ Float (Label="Maximal overlap between Bounding boxes (0-1)",min=0, max=1, value=0.4, stepSize=0.1) max_overlap 12 | #@ String (visibility="MESSAGE", value="Output") out 13 | #@ Boolean (Label="Add ROI to ROI manager") show_roi 14 | #@ Boolean (Label="Show result table") show_table 15 | ''' 16 | previous field : Boolean (Label="Display correlation map(s)") show_map 17 | 18 | Requires ImageJ 1.52i to have the possibility to fill the background while rotating for 16-bit images 19 | 20 | FIJI macro to do template matching 21 | input : 22 | - template_files : list of template path 23 | - image : ImagePlus for the target image 24 | ie this macro search for one template (with eventual flipped/rotated version)into one target image. 25 | The target image should be already open in Fiji. 26 | 27 | First of all, additionnal versions of the template are generated (flip+rotation) 28 | For the resulting list of templates the search is carried out and results in a list of correlation maps 29 | 30 | Minima/maxima in the correlation map are detected, followed by Non-Maxima Supression in case of multiple correlation map/templates 31 | 32 | 33 | TO DO : 34 | - order of the column in result table 35 | - use steerable tempalte matching see steerable detector BIG Lausanne 36 | 37 | NB : 38 | - 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) 39 | 40 | - (currently no search ROi so not applicable) Delete the previous ROI for every new Run otherwise 1st ROI is used to limit the search 41 | 42 | - Method limited to normalised method to have correlation map in range 0-1 : easier to apply a treshold. 43 | Otherwise normalising relative to maxima of each correlation map is not good since this result in having the global maxima to always be one, 44 | eventhough its correlation value was not one. 45 | 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) 46 | 47 | 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) 48 | ''' 49 | ## Initialise variables before import (otherwise the ROI is lost) 50 | ImageName = image.getTitle() 51 | searchRoi = image.getRoi() 52 | 53 | ## Rectangle ROI ? 54 | if searchRoi and searchRoi.getTypeAsString()=="Rectangle": 55 | Bool_SearchRoi = True 56 | else: 57 | Bool_SearchRoi = False 58 | 59 | # Define offset 60 | if Bool_SearchRoi: 61 | image = image.crop() 62 | dX = int(searchRoi.getXBase()) 63 | dY = int(searchRoi.getYBase()) 64 | else: 65 | dX = dY = 0 66 | 67 | 68 | ### IMPORT MODULES (after retrieving ROI) ### 69 | from ij import IJ 70 | 71 | # Home-Made module 72 | from Template_Matching.MatchTemplate_module import getHit_Template, CornerToCenter 73 | from Template_Matching.NonMaximaSupression_Py2 import NMS 74 | 75 | 76 | # Convert method string to the index 77 | 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} 78 | Method = Dico_Method[method] 79 | 80 | 81 | # Initialise list of hit before looping 82 | Hits_BeforeNMS = [] 83 | 84 | ## Loop over templates for template matching and maxima detection 85 | for temp_file in template_files: 86 | 87 | # Get ImageProcessor for the current template 88 | PathTemp = temp_file.getPath() 89 | ImpTemplate = IJ.openImage(PathTemp) 90 | 91 | # Check that template is smaller than the searched image 92 | if ImpTemplate.width>image.width or ImpTemplate.height>image.height: 93 | raise Exception('The current template is larger in width and/or height than the searched image') 94 | 95 | # Get hits for the current template (and his flipped and/or rotated versions) 96 | List_Hit = getHit_Template(ImpTemplate, image, flipv, fliph, angles, Method, n_hit, score_threshold, tolerance) 97 | 98 | # Store the hits 99 | Hits_BeforeNMS.extend(List_Hit) 100 | 101 | 102 | 103 | ### NMS inter template ### 104 | print "\n-- Hits before NMS --\n" 105 | for hit in Hits_BeforeNMS: print hit 106 | 107 | # NMS if more than one hit 108 | if Method in [0,1]: 109 | Hits_AfterNMS = NMS(Hits_BeforeNMS, N=n_hit, maxOverlap=max_overlap, sortDescending=False) # only difference is the sorting 110 | 111 | else: 112 | Hits_AfterNMS = NMS(Hits_BeforeNMS, N=n_hit, maxOverlap=max_overlap, sortDescending=True) 113 | 114 | print "\n-- Hits after NMS --\n" 115 | for hit in Hits_AfterNMS: print hit 116 | 117 | 118 | 119 | 120 | ### Outputs ### 121 | if show_table: 122 | from ij.measure import ResultsTable 123 | from utils import AddToTable 124 | 125 | Table = ResultsTable().getResultsTable() # allows to append to an existing table 126 | 127 | if show_roi: 128 | from ij.plugin.frame import RoiManager 129 | from ij.gui import Roi 130 | 131 | # Initialise RoiManager 132 | RM = RoiManager() 133 | rm = RM.getInstance() 134 | 135 | # Show All ROI + Associate ROI to slices 136 | rm.runCommand("Associate", "true") 137 | rm.runCommand("Show All with labels") 138 | 139 | 140 | # Loop over final hits to generate ROI, result table... 141 | for hit in Hits_AfterNMS: 142 | 143 | if Bool_SearchRoi: # Add offset of searchRoi 144 | hit['BBox'] = (hit['BBox'][0]+dX, hit['BBox'][1]+dY, hit['BBox'][2], hit['BBox'][3]) 145 | 146 | if show_roi: 147 | roi = Roi(*hit['BBox']) 148 | roi.setName(hit['TemplateName']) 149 | rm.addRoi(roi) 150 | 151 | if show_table: 152 | Xcorner, Ycorner = hit['BBox'][0], hit['BBox'][1] 153 | Xcenter, Ycenter = CornerToCenter(Xcorner, Ycorner, hit['BBox'][2], hit['BBox'][3]) 154 | Dico = {"Image":ImageName, 'Template':hit['TemplateName'] ,'Xcorner':Xcorner, 'Ycorner':Ycorner, 'Xcenter':Xcenter, 'Ycenter':Ycenter, 'Score':hit['Score']} 155 | AddToTable(Table, Dico, Order=("Image", "Template", "Score", "Xcorner", "Ycorner", "Xcenter", "Ycenter")) 156 | 157 | 158 | ## Finally update display 159 | if 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Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [![DOI](https://zenodo.org/badge/DOI/10.1186/s12859-020-3363-7.svg)](https://doi.org/10.1186/s12859-020-3363-7) 2 | ![Twitter Follow](https://img.shields.io/twitter/follow/LauLauThom?style=social) 3 | 4 | # Installation 5 | 6 | ## Via the Fiji updater 7 | Tick the __Multi-Template-Matching__ AND __IJ-OpenCV__ update site of the Fiji udpater. 8 | A new entry will show up in the Plugin Menu (all the way down) after restarting Fiji. 9 | See how to [activate an update site](https://imagej.net/How_to_follow_a_3rd_party_update_site). 10 | 11 | ## Manual installation 12 | You can also do a manual installation by copying the files in the right place. 13 | This can be useful if you would like to use a previous version that is not available via the update site, but which is archived in the releases tab. 14 | You still need to tick the IJ-OpenCV update site in the Fiji updater to install the dependencies. 15 | 16 | Then you can download the files either on the main page above this readme, by clicking the green button with the arrow pointing down *Code* then *Download ZIP*. 17 | You can download the zip of previous versions on the [release tab](https://github.com/multi-template-matching/MultiTemplateMatching-Fiji/releases), below asset select *Source code (zip)*. 18 | Unzip the zip file, and on Windows, drag the folder Fiji.app from the unzipped directory, and drop it on the directory Fiji.app of your existing Fiji installation. 19 | The idea is to merge both Fiji.app directories, in Windows this drag and dropping will automatically copy the files in the corresponding subdirectories. 20 | Restart Fiji then. 21 | If the drag and dropping does not work for you, you just have to copy the files from the unzziped directory to the corresponding directories in your Fiji installation. 22 | If some directories (ex: Lib in Fiji.app/jars) does not exist, just create them. 23 | 24 | # Documentation 25 | Template matching is an algorithm that can be used for object-detections in images. 26 | The algorithm computes the probability to find one (or several) template images provided by the user. 27 | See the [wiki section](https://github.com/multi-template-matching/MultiTemplateMatching-Fiji/wiki/) for the documentation, or the [website](https://multi-template-matching.github.io/Multi-Template-Matching/) of the project. 28 | 29 | You can find a similar implementation in: 30 | - [Python](https://github.com/multi-template-matching/MultiTemplateMatching-Python) 31 | - [KNIME](https://github.com/multi-template-matching/MultipleTemplateMatching-KNIME) relying on the python implementation 32 | 33 | ## Video tutorial 34 | Playlist on [YouTube](https://www.youtube.com/playlist?list=PLHZOgc1s26MJ8QjYau7NcG5k0zh9SjHpo) 35 | [![Tuto](https://img.youtube.com/vi/KlzIqSG5XBU/0.jpg)](https://youtu.be/KlzIqSG5XBU) 36 | 37 | 38 | # Citation 39 | If you use this implementation for your research, please cite: 40 | 41 | Thomas, L.S.V., Gehrig, J. _Multi-template matching: a versatile tool for object-localization in microscopy images._ 42 | BMC Bioinformatics 21, 44 (2020). https://doi.org/10.1186/s12859-020-3363-7 43 | 44 | Download the citation as a .ris file from the journal website, [here](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3363-7.ris). 45 | 46 | # Related resources 47 | ## IJ-OpenCV 48 | This plugin is using OpenCV in Fiji thanks to [IJ-OpenCV](https://github.com/joheras/IJ-OpenCV). 49 | 50 | see also: 51 | _Domínguez, César, Jónathan Heras, and Vico Pascual. "IJ-OpenCV: Combining ImageJ and OpenCV for processing images in biomedicine." Computers in biology and medicine 84 (2017): 189-194._ 52 | 53 | 54 | # Licence 55 | Creative Commons License
The content of this wiki (including illustrations and videos) is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. 56 | 57 | As a derived work of IJ-OpenCV, the source codes are licensed under GPL-3. 58 | 59 | # Origin of the work 60 | This work has been part of the PhD project of **Laurent Thomas** under supervision of **Dr. Jochen Gehrig** at ACQUIFER. 61 | 62 | Fish 63 | 64 | # Funding 65 | This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 721537 ImageInLife. 66 | 67 |

68 | ImageInLife 69 | MarieCurie 70 |

71 | 72 | 73 | # Examples 74 | 75 | ## Zebrafish head detection 76 | Fish 77 | MontageHead 78 | 79 | Dataset available on Zenodo 80 | [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2650162.svg)](https://doi.org/10.5281/zenodo.2650162) 81 | 82 | 83 | ## Medaka larvae detections 84 | MedakaEgg 85 | MontageEgg 86 | 87 | Image courtesy Jakob Gierten (COS, Heidelberg) 88 | Dataset available on Zenodo 89 | [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2650147.svg)](https://doi.org/10.5281/zenodo.2650147) 90 | -------------------------------------------------------------------------------- /Tuto/2-step-TemplateMatching/2step-TemplateMatching.html: -------------------------------------------------------------------------------- 1 | 2 | 16 | 17 | 18 |

This macro allows to do a 2-step template matching, the 2 templates and the image should be opend in Fiji already.
19 | The first template allows to find an object and the second template finds a sub-object within the previously found object.
20 | Therefore the second template must be smaller than the first one.
21 | Both detection (1st and 2nd template) are returned in the ROI manager.

22 |

NB : Make sure the ROI Manager is empty before running this macro

23 | 

 24 | 
 25 | 
 26 |  
 27 |
28 |

Get image names

29 | 
selectImage(temp1); 
 30 | Temp1_title = getTitle(); 
 31 | //print(Temp1_title); 
 32 |  
 33 | selectImage(temp2); 
 34 | Temp2_title = getTitle(); 
 35 | //print(Temp2_title); 
 36 |  
 37 | selectImage(image); 
 38 | Image_title = getTitle(); 
 39 | //print(Image_title); 
 40 |  
 41 |
42 |

Call 1st template matching

43 | 
run("Template Matching Image", "template=" + Temp1_title + " image=" + Image_title + " rotate=[] matching_method=[Normalised 0-mean cross-correlation] number_of_objects=1 score_threshold=0.50 maximal_overlap=0.25 add_roi"); 
 44 | 
 45 | 
 46 |
47 |

Loop over stack of ROI

48 | 
setBatchMode(true); // do not open extracted slices 
 49 | selectImage(image); 
 50 | Roi.remove; 
 51 | n = nSlices; 
 52 | nRoi_old = roiManager("count"); // Roi of the 1st template matching 
 53 | for (i=1; i<=n; i++) { 
 54 |  	 
 55 | 	// Isolate slice from stack (to perform the second template matching with a custom search ROI for that slice) 
 56 | 	selectImage(image); //important here to select back the image when entering a new iteration, during looping the slice is selected 
 57 | 	setSlice(i); 
 58 | 	Roi.remove; 
 59 | 	run("Duplicate...", "title=Slice_"+i); // The isolated slice has for title "Slice_i" 
 60 | 	 
 61 | 	// Set search ROI on isolated slice 
 62 | 	roiManager("select", i-1); // i-1 since ROI manager starts at 0 
 63 | 	Roi.getBounds(x, y, width, height); 
 64 | 	makeRectangle(x, y, width, height); 
 65 | 	//print(x,y,width,height); 
 66 |  
 67 | 	// Run template matching on slice with search ROI 
 68 | 	run("Template Matching Image", "template=" + Temp2_title + " image=Slice_" + i +" flip_template_vertically rotate=[] matching_method=[Normalised 0-mean cross-correlation] number_of_objects=2 score_threshold=0.50 maximal_overlap=0.25 add_roi show_result"); 
 69 | 	 
 70 | 	// Close hidden Slice image 
 71 | 	//selectImage("Slice_"+i); 
 72 | 	//close(); 
 73 | 	 
 74 | 	// Rename and Set Z-position of the last found ROI 
 75 | 	nRoi_new = roiManager("count"); 
 76 | 	 
 77 | 	for (j=1; j<=nRoi_new-nRoi_old; j++) { 
 78 |  
 79 | 		roiManager("select", nRoi_new-j); 
 80 | 		run("Properties... ", "position="+i); // Set slice position 
 81 | 		InitName = call("ij.plugin.frame.RoiManager.getName", nRoi_new-j); 
 82 | 		roiManager("rename", i + substring(InitName, 1)); 
 83 | 	} 
 84 | 	 
 85 | 	// Update the count 
 86 | 	nRoi_old = nRoi_new; 
 87 |  
 88 | } 
 89 |  
 90 |
91 |

Image

92 | 93 | 94 | 95 | 96 |
ImageSliceTemplateScoreRoi IndexXcornerYcornerXcenterYcenter
Slice_11temp21.0002651897712939
Slice_11temp2_Vertical_Flip0.794365710017181043
97 |

Again make sure that all ROI are displayed and associated to the slices

98 | 
roiManager("Show All"); 
 99 | roiManager("Associate", "true"); 
100 |
101 | 102 | 103 | 104 | -------------------------------------------------------------------------------- /Tuto/2-step-TemplateMatching/2step-TemplateMatching.ijm: -------------------------------------------------------------------------------- 1 | /* 2 | This macro allows to do a 2-step template matching, the 2 templates and the image should be opend in Fiji already. 3 | The first template allows to find an object and the second template finds a sub-object within the previously found object. 4 | Therefore the second template must be smaller than the first one. 5 | Both detection (1st and 2nd template) are returned in the ROI manager. 6 | 7 | NB : Make sure the ROI Manager is empty before running this macro 8 | 9 | */ 10 | #@ImagePlus (Label="Template1") temp1 11 | #@ImagePlus (Label="Template2") temp2 12 | #@ImagePlus (Label="Image") image 13 | 14 | /* # Get image names */ 15 | selectImage(temp1); 16 | Temp1_title = getTitle(); 17 | //print(Temp1_title); 18 | 19 | selectImage(temp2); 20 | Temp2_title = getTitle(); 21 | //print(Temp2_title); 22 | 23 | selectImage(image); 24 | Image_title = getTitle(); 25 | //print(Image_title); 26 | 27 | /* # Call 1st template matching */ 28 | run("Template Matching Image", "template=" + Temp1_title + " image=" + Image_title + " rotate=[] matching_method=[Normalised 0-mean cross-correlation] number_of_objects=1 score_threshold=0.50 maximal_overlap=0.25 add_roi"); 29 | 30 | 31 | /* # Loop over stack of ROI */ 32 | setBatchMode(true); // do not open extracted slices 33 | selectImage(image); 34 | Roi.remove; 35 | n = nSlices; 36 | nRoi_old = roiManager("count"); // Roi of the 1st template matching 37 | for (i=1; i<=n; i++) { 38 | 39 | // Isolate slice from stack (to perform the second template matching with a custom search ROI for that slice) 40 | selectImage(image); //important here to select back the image when entering a new iteration, during looping the slice is selected 41 | setSlice(i); 42 | Roi.remove; 43 | run("Duplicate...", "title=Slice_"+i); // The isolated slice has for title "Slice_i" 44 | 45 | // Set search ROI on isolated slice 46 | roiManager("select", i-1); // i-1 since ROI manager starts at 0 47 | Roi.getBounds(x, y, width, height); 48 | makeRectangle(x, y, width, height); 49 | //print(x,y,width,height); 50 | 51 | // Run template matching on slice with search ROI 52 | run("Template Matching Image", "template=" + Temp2_title + " image=Slice_" + i +" flip_template_vertically rotate=[] matching_method=[Normalised 0-mean cross-correlation] number_of_objects=2 score_threshold=0.50 maximal_overlap=0.25 add_roi show_result"); 53 | 54 | // Close hidden Slice image 55 | //selectImage("Slice_"+i); 56 | //close(); 57 | 58 | // Rename and Set Z-position of the last found ROI 59 | nRoi_new = roiManager("count"); 60 | 61 | for (j=1; j<=nRoi_new-nRoi_old; j++) { 62 | 63 | roiManager("select", nRoi_new-j); 64 | run("Properties... ", "position="+i); // Set slice position 65 | InitName = call("ij.plugin.frame.RoiManager.getName", nRoi_new-j); 66 | roiManager("rename", i + substring(InitName, 1)); 67 | } 68 | 69 | // Update the count 70 | nRoi_old = nRoi_new; 71 | 72 | } 73 | 74 | /* # Again make sure that all ROI are displayed and associated to the slices */ 75 | roiManager("Show All"); 76 | roiManager("Associate", "true"); -------------------------------------------------------------------------------- /Tuto/2-step-TemplateMatching/Fish.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/multi-template-matching/MultiTemplateMatching-Fiji/47b1600f02c6bacf72c1250c9c22eb3e32809592/Tuto/2-step-TemplateMatching/Fish.png -------------------------------------------------------------------------------- /Tuto/2-step-TemplateMatching/README.md: -------------------------------------------------------------------------------- 1 | This tutorial was generated with the ImageJ-Macro-Markdown extension provided by Robert Haase 2 | See https://github.com/haesleinhuepf/imagejmacromarkdown 3 | 4 | This macro allows to do a 2-step template matching, the 2 templates and the image should be opend in Fiji already. 5 | The first template allows to find an object and the second template finds a sub-object within the previously found object. 6 | Therefore the second template must be smaller than the first one. 7 | Both detection (1st and 2nd template) are returned in the ROI manager. 8 | 9 | NB : Make sure the ROI Manager is empty before running this macro 10 | 11 | # Get image names 12 | ```java 13 | selectImage(temp1); 14 | Temp1_title = getTitle(); 15 | //print(Temp1_title); 16 | 17 | selectImage(temp2); 18 | Temp2_title = getTitle(); 19 | //print(Temp2_title); 20 | 21 | selectImage(image); 22 | Image_title = getTitle(); 23 | //print(Image_title); 24 | 25 | ``` 26 | # Call 1st template matching 27 | ```java 28 | run("Template Matching Image", "template=" + Temp1_title + " image=" + Image_title + " rotate=[] matching_method=[Normalised 0-mean cross-correlation] number_of_objects=1 score_threshold=0.50 maximal_overlap=0.25 add_roi"); 29 | 30 | 31 | ``` 32 | # Loop over stack of ROI 33 | ```java 34 | setBatchMode(true); // do not open extracted slices 35 | selectImage(image); 36 | Roi.remove; 37 | n = nSlices; 38 | nRoi_old = roiManager("count"); // Roi of the 1st template matching 39 | for (i=1; i<=n; i++) { 40 | 41 | // Isolate slice from stack (to perform the second template matching with a custom search ROI for that slice) 42 | selectImage(image); //important here to select back the image when entering a new iteration, during looping the slice is selected 43 | setSlice(i); 44 | Roi.remove; 45 | run("Duplicate...", "title=Slice_"+i); // The isolated slice has for title "Slice_i" 46 | 47 | // Set search ROI on isolated slice 48 | roiManager("select", i-1); // i-1 since ROI manager starts at 0 49 | Roi.getBounds(x, y, width, height); 50 | makeRectangle(x, y, width, height); 51 | //print(x,y,width,height); 52 | 53 | // Run template matching on slice with search ROI 54 | run("Template Matching Image", "template=" + Temp2_title + " image=Slice_" + i +" flip_template_vertically rotate=[] matching_method=[Normalised 0-mean cross-correlation] number_of_objects=2 score_threshold=0.50 maximal_overlap=0.25 add_roi show_result"); 55 | 56 | // Close hidden Slice image 57 | //selectImage("Slice_"+i); 58 | //close(); 59 | 60 | // Rename and Set Z-position of the last found ROI 61 | nRoi_new = roiManager("count"); 62 | 63 | for (j=1; j<=nRoi_new-nRoi_old; j++) { 64 | 65 | roiManager("select", nRoi_new-j); 66 | run("Properties... ", "position="+i); // Set slice position 67 | InitName = call("ij.plugin.frame.RoiManager.getName", nRoi_new-j); 68 | roiManager("rename", i + substring(InitName, 1)); 69 | } 70 | 71 | // Update the count 72 | nRoi_old = nRoi_new; 73 | 74 | } 75 | 76 | ``` 77 | ![Image](Fish.png) 78 | 79 | 80 | 81 | 82 |
ImageSliceTemplateScoreRoi IndexXcornerYcornerXcenterYcenter
Slice_11temp21.0002651897712939
Slice_11temp2_Vertical_Flip0.794365710017181043
83 | 84 | # Again make sure that all ROI are displayed and associated to the slices 85 | ```java 86 | roiManager("Show All"); 87 | roiManager("Associate", "true"); 88 | ``` 89 | --------------------------------------------------------------------------------