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104 lines
3.7 KiB
Python
104 lines
3.7 KiB
Python
import numpy as np
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import cv2
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from common import anorm
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from functools import partial
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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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FLANN_INDEX_KDTREE = 1 # bug: flann enums are missing
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flann_params = dict(algorithm = FLANN_INDEX_KDTREE,
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trees = 4)
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def match_bruteforce(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 match_flann(desc1, desc2, r_threshold = 0.6):
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flann = cv2.flann_Index(desc2, flann_params)
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idx2, dist = flann.knnSearch(desc1, 2, params = {}) # bug: need to provide empty dict
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mask = dist[:,0] / dist[:,1] < r_threshold
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idx1 = np.arange(len(desc1))
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pairs = np.int32( zip(idx1, idx2[:,0]) )
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return pairs[mask]
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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.detectAndCompute(img1, None)
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kp2, desc2 = surf.detectAndCompute(img2, None)
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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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def match_and_draw(match, r_threshold):
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m = match(desc1, desc2, r_threshold)
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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, 5.0)
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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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return vis
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print 'bruteforce match:',
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vis_brute = match_and_draw( match_bruteforce, 0.75 )
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print 'flann match:',
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vis_flann = match_and_draw( match_flann, 0.6 ) # flann tends to find more distant second
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# neighbours, so r_threshold is decreased
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cv2.imshow('find_obj SURF', vis_brute)
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cv2.imshow('find_obj SURF flann', vis_flann)
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0xFF & cv2.waitKey()
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cv2.destroyAllWindows()
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