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I'm trying to compare a image to a list of other images and return a selection of images (like Google search images) of this list with up to 70% of similarity. I get this code in this post and change for my context # Load the images img =cv2.imread(MEDIA_ROOT + "/uploads/imagerecognize/armchair.jpg") # Convert them to grayscale imgg =cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) # SURF extraction surf = cv2.FeatureDetector_create("SURF") surfDescriptorExtractor = cv2.DescriptorExtractor_create("SURF") kp = surf.detect(imgg) kp, descritors = surfDescriptorExtractor.compute(imgg,kp) # Setting up samples and responses for kNN samples = np.array(descritors) responses = np.arange(len(kp),dtype = np.float32) # kNN training knn = cv2.KNearest() knn.train(samples,responses) modelImages = [MEDIA_ROOT + "/uploads/imagerecognize/1.jpg", MEDIA_ROOT + "/uploads/imagerecognize/2.jpg", MEDIA_ROOT + "/uploads/imagerecognize/3.jpg"] for modelImage in modelImages: # Now loading a template image and searching for similar keypoints template = cv2.imread(modelImage) templateg= cv2.cvtColor(template,cv2.COLOR_BGR2GRAY) keys = surf.detect(templateg) keys,desc = surfDescriptorExtractor.compute(templateg, keys) for h,des in enumerate(desc): des = np.array(des,np.float32).reshape((1,128)) retval, results, neigh_resp, dists = knn.find_nearest(des,1) res,dist = int(results[0][0]),dists[0][0] if dist<0.1: # draw matched keypoints in red color color = (0,0,255) else: # draw unmatched in blue color #print dist color = (255,0,0) #Draw matched key points on original image x,y = kp[res].pt center = (int(x),int(y)) cv2.circle(img,center,2,color,-1) #Draw matched key points on template image x,

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