33 lines
1.2 KiB
Python
33 lines
1.2 KiB
Python
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# Initiate SIFT detector
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sift = cv2.SIFT_create()
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# find the keypoints and descriptors with SIFT
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# Here img1 and img2 are grayscale images
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kp1, des1 = sift.detectAndCompute(img1,None)
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kp2, des2 = sift.detectAndCompute(img2,None)
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# FLANN parameters
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# I literally copy-pasted the defaults
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FLANN_INDEX_KDTREE = 1
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index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
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search_params = dict(checks=50) # or pass empty dictionary
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# do the matching
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flann = cv2.FlannBasedMatcher(index_params,search_params)
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matches = flann.knnMatch(des1,des2,k=2)
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## OPTIONAL - Show matches ##
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# Need to draw only good matches, so create a mask
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matchesMask = [[0,0] for i in range(len(matches))]
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# ratio test as per Lowe's paper <- this is a criterion for matches selection
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for i,(m,n) in enumerate(matches):
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if m.distance < 0.7*n.distance:
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matchesMask[i]=[1,0]
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draw_params = dict(matchColor = (0,255,0),
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singlePointColor = (255,0,0),
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matchesMask = matchesMask,
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flags = cv2.DrawMatchesFlags_DEFAULT)
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img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,matches,None, **draw_params)
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f, ax = plt.subplots(1, figsize=(15,15))
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ax.imshow(img3)
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plt.show()
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