7bbc42127e
- handle bad matching case - use BFMatcher and FlannBasedMatcher (and thus fixing a bug: L2^2 metric was used for flann)
96 lines
3.3 KiB
Python
96 lines
3.3 KiB
Python
'''
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Feature-based image matching sample.
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USAGE
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find_obj.py [ <image1> <image2> ]
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'''
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import numpy as np
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import cv2
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FLANN_INDEX_KDTREE = 1 # bug: flann enums are missing
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flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
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def draw_match(img1, img2, p1, p2, status = None, H = None):
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h1, w1 = img1.shape[:2]
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h2, w2 = img2.shape[:2]
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vis = np.zeros((max(h1, h2), w1+w2), np.uint8)
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vis[:h1, :w1] = img1
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vis[:h2, w1:w1+w2] = img2
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vis = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR)
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if H is not None:
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corners = np.float32([[0, 0], [w1, 0], [w1, h1], [0, h1]])
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corners = np.int32( cv2.perspectiveTransform(corners.reshape(1, -1, 2), H).reshape(-1, 2) + (w1, 0) )
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cv2.polylines(vis, [corners], True, (255, 255, 255))
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if status is None:
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status = np.ones(len(p1), np.bool_)
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green = (0, 255, 0)
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red = (0, 0, 255)
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for (x1, y1), (x2, y2), inlier in zip(np.int32(p1), np.int32(p2), status):
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col = [red, green][inlier]
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if inlier:
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cv2.line(vis, (x1, y1), (x2+w1, y2), col)
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cv2.circle(vis, (x1, y1), 2, col, -1)
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cv2.circle(vis, (x2+w1, y2), 2, col, -1)
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else:
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r = 2
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thickness = 3
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cv2.line(vis, (x1-r, y1-r), (x1+r, y1+r), col, thickness)
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cv2.line(vis, (x1-r, y1+r), (x1+r, y1-r), col, thickness)
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cv2.line(vis, (x2+w1-r, y2-r), (x2+w1+r, y2+r), col, thickness)
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cv2.line(vis, (x2+w1-r, y2+r), (x2+w1+r, y2-r), col, thickness)
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return vis
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if __name__ == '__main__':
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print __doc__
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import sys
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try: fn1, fn2 = sys.argv[1:3]
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except:
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fn1 = '../c/box.png'
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fn2 = '../c/box_in_scene.png'
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img1 = cv2.imread(fn1, 0)
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img2 = cv2.imread(fn2, 0)
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detector = cv2.SIFT()
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kp1, desc1 = detector.detectAndCompute(img1, None)
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kp2, desc2 = detector.detectAndCompute(img2, None)
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print 'img1 - %d features, img2 - %d features' % (len(kp1), len(kp2))
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bf_matcher = cv2.BFMatcher(cv2.NORM_L2)
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flann_matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
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def match_and_draw(matcher, r_threshold = 0.75):
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raw_matches = matcher.knnMatch(desc1, trainDescriptors = desc2, k = 2)
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p1, p2 = [], []
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for m in raw_matches:
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if len(m) == 2 and m[0].distance < m[1].distance * r_threshold:
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m = m[0]
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p1.append( kp1[m.queryIdx].pt )
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p2.append( kp2[m.trainIdx].pt )
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p1, p2 = np.float32((p1, p2))
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if len(p1) >= 4:
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H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 2.0)
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print '%d / %d inliers/matched' % (np.sum(status), len(status))
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else:
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H, status = None, None
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print '%d matches found, not enough for homography estimation' % len(p1)
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vis = draw_match(img1, img2, p1, p2, status, H)
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return vis
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print 'bruteforce match:',
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vis_brute = match_and_draw( bf_matcher )
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print 'flann match:',
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vis_flann = match_and_draw( flann_matcher )
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cv2.imshow('find_obj', vis_brute)
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cv2.imshow('find_obj flann', vis_flann)
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0xFF & cv2.waitKey()
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cv2.destroyAllWindows()
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