From 8f01dabb66f6e34554b7d0a830772862c7272e21 Mon Sep 17 00:00:00 2001 From: = <=> Date: Tue, 26 Mar 2019 21:58:38 +0100 Subject: [PATCH] Now using FLANN feature matching instead. 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cv2.COLOR_BGR2GRAY) MAX_FEATURES = 500 -GOOD_MATCH_PERCENT = 0.002 +GOOD_MATCH_PERCENT = 0.0005 cv2.imwrite('pls_lasse.jpg', img) @@ -57,6 +57,8 @@ img_tmp = img.copy() gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) sift = cv2.xfeatures2d.SIFT_create() +#sift = cv2.ORB_create(MAX_FEATURES) +#sift = cv2.xfeatures2d.SURF_create() kp = sift.detect(gray_2, None) kp2 = sift.detect(gray_3, None) @@ -66,11 +68,68 @@ kp2, des2 = sift.compute(gray_3, kp2) cv2.drawKeypoints(img_tmp_tmp, keypoints=kp, outImage=img_tmp_tmp) cv2.imwrite('keypoints_img.jpg', img_tmp_tmp) -matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_SL2) -matches = matcher.match(des, des2, None) +# FLANN parameters +FLANN_INDEX_KDTREE = 0 +index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 8) +search_params = dict(checks=100) # or pass empty dictionary + + +#matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING) +#matches = matcher.match(des, des2, None) + + +flann = cv2.FlannBasedMatcher(index_params,search_params) +matches = flann.knnMatch(des, des2, k=2) + +# Need to draw only good matches, so create a mask +matchesMask = [[0,0] for i in range(len(matches))] + + + + +good_matches = [] +# ratio test as per Lowe's paper +for i,(m,n) in enumerate(matches): + if m.distance < 0.5*n.distance: + matchesMask[i]=[1,0] + good_matches.append([m,n]) + +draw_params = dict(matchColor = (0,255,0), + singlePointColor = (255,0,0), + matchesMask = matchesMask, + flags = 0) + +img3 = cv2.drawMatchesKnn(img_tmp_tmp, kp, img2, kp2, matches, None, **draw_params) +cv2.imwrite("matches.jpg", img3) + + +matches.sort(key=lambda x: x[0].distance, reverse=False) +# Remove poor matches +numGoodMatches = int(len(matches) * GOOD_MATCH_PERCENT) +#good_matches = matches[:numGoodMatches] + +# Extract location of good matches +points1 = np.zeros((len(good_matches), 2), dtype=np.float32) +points2 = np.zeros((len(good_matches), 2), dtype=np.float32) + +for i, (m, n) in enumerate(good_matches): + points1[i, :] = kp[m.queryIdx].pt + points2[i, :] = kp2[m.trainIdx].pt + +print(points1) +print(len(points2)) + +h, mask = cv2.findHomography(points1, points2, cv2.RANSAC) + +height, width, channels = img2.shape +im1Reg = cv2.warpPerspective(img_tmp_tmp, h, (width, height)) + +cv2.imwrite('homo_pls_fuck.jpg', im1Reg) + +''' # Sort matches by score -matches.sort(key=lambda x: x.distance, reverse=False) +matches.sort(key=lambda x: x[0].distance, reverse=False) # Remove poor matches numGoodMatches = int(len(matches) * GOOD_MATCH_PERCENT) @@ -88,13 +147,18 @@ for i, match in enumerate(matches): points1[i, :] = kp[match.queryIdx].pt points2[i, :] = kp2[match.trainIdx].pt +print(len(points1)) +print(len(points2)) +''' +''' h, mask = cv2.findHomography(points1, points2, cv2.RANSAC) height, width, channels = img2.shape im1Reg = cv2.warpPerspective(img_tmp_tmp, h, (width, height)) cv2.imwrite('homo_pls_fuck.jpg', im1Reg) +''' '''

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