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Open Source Computer Vision Library
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73 lines
2.3 KiB
73 lines
2.3 KiB
''' |
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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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