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74 lines
2.3 KiB
Python
74 lines
2.3 KiB
Python
'''
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Feature homography
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==================
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Example of using features2d framework for interactive video homography matching.
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Keys
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----
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SPACE - set reference frame
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ESC - exit
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'''
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import numpy as np
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import cv2
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import video
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from common import draw_str
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if __name__ == '__main__':
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print __doc__
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detector = cv2.FeatureDetector_create('ORB')
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extractor = cv2.DescriptorExtractor_create('ORB')
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matcher = cv2.DescriptorMatcher_create('BruteForce-Hamming') # 'BruteForce-Hamming' # FlannBased
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ref_desc = None
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ref_kp = None
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green, red = (0, 255, 0), (0, 0, 255)
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cap = video.create_capture(0)
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while True:
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ret, img = cap.read()
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vis = img.copy()
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kp = detector.detect(img)
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for p in kp:
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x, y = np.int32(p.pt)
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r = int(0.5*p.size)
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cv2.circle(vis, (x, y), r, (0, 255, 0))
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draw_str(vis, (20, 20), 'feature_n: %d' % len(kp))
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desc = extractor.compute(img, kp)
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if ref_desc is not None:
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raw_matches = matcher.knnMatch(desc, ref_desc, 2)
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eps = 1e-5
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matches = [(m1.trainIdx, m1.queryIdx) for m1, m2 in raw_matches if (m1.distance+eps) / (m2.distance+eps) < 0.7]
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match_n = len(matches)
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inliner_n = 0
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if match_n > 10:
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p0 = np.float32( [ref_kp[i].pt for i, j in matches] )
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p1 = np.float32( [kp[j].pt for i, j in matches] )
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H, status = cv2.findHomography(p0, p1, cv2.RANSAC, 10.0)
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inlier_n = sum(status)
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if inlier_n > 10:
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for (x1, y1), (x2, y2), inlier in zip(np.int32(p0), np.int32(p1), status):
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cv2.line(vis, (x1, y1), (x2, y2), (red, green)[inlier])
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h, w = img.shape[:2]
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overlay = cv2.warpPerspective(ref_img, H, (w, h))
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vis = cv2.addWeighted(vis, 0.5, overlay, 0.5, 0.0)
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draw_str(vis, (20, 40), 'matched: %d ( %d outliers )' % (match_n, match_n-inlier_n))
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cv2.imshow('img', vis)
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ch = cv2.waitKey(1)
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if ch == ord(' '):
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ref_desc = desc
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ref_kp = kp
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ref_img = img.copy()
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if ch == 27:
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break
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