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Open Source Computer Vision Library
https://opencv.org/
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79 lines
2.7 KiB
79 lines
2.7 KiB
import numpy as np |
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import cv2 |
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from common import anorm |
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help_message = '''SURF image match |
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USAGE: findobj.py [ <image1> <image2> ] |
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''' |
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def match(desc1, desc2, r_threshold = 0.75): |
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res = [] |
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for i in xrange(len(desc1)): |
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dist = anorm( desc2 - desc1[i] ) |
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n1, n2 = dist.argsort()[:2] |
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r = dist[n1] / dist[n2] |
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if r < r_threshold: |
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res.append((i, n1)) |
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return np.array(res) |
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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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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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print help_message |
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img1 = cv2.imread(fn1, 0) |
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img2 = cv2.imread(fn2, 0) |
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surf = cv2.SURF(1000) |
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kp1, desc1 = surf.detect(img1, None, False) |
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kp2, desc2 = surf.detect(img2, None, False) |
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desc1.shape = (-1, surf.descriptorSize()) |
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desc2.shape = (-1, surf.descriptorSize()) |
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print 'img1 - %d features, img2 - %d features' % (len(kp1), len(kp2)) |
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m = match(desc1, desc2) |
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matched_p1 = np.array([kp1[i].pt for i, j in m]) |
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matched_p2 = np.array([kp2[j].pt for i, j in m]) |
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H, status = cv2.findHomography(matched_p1, matched_p2, cv2.RANSAC, 10.0) |
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print '%d / %d inliers/matched' % (np.sum(status), len(status)) |
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vis = draw_match(img1, img2, matched_p1, matched_p2, status, H) |
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cv2.imshow('find_obj SURF', vis) |
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cv2.waitKey() |