#/usr/bin/env python ''' Feature-based image matching sample. USAGE find_obj.py [--feature=[-flann]] [ ] --feature - Feature to use. Can be sift, surf of orb. Append '-flann' to feature name to use Flann-based matcher instead bruteforce. Press left mouse button on a feature point to see its mathcing point. ''' import numpy as np import cv2 from common import anorm, getsize FLANN_INDEX_KDTREE = 1 # bug: flann enums are missing FLANN_INDEX_LSH = 6 def init_feature(name): chunks = name.split('-') if chunks[0] == 'sift': detector = cv2.SIFT() norm = cv2.NORM_L2 elif chunks[0] == 'surf': detector = cv2.SURF(800) norm = cv2.NORM_L2 elif chunks[0] == 'orb': detector = cv2.ORB(400) norm = cv2.NORM_HAMMING else: return None, None if 'flann' in chunks: if norm == cv2.NORM_L2: flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) else: flann_params= dict(algorithm = FLANN_INDEX_LSH, table_number = 6, # 12 key_size = 12, # 20 multi_probe_level = 1) #2 matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329) else: matcher = cv2.BFMatcher(norm) return detector, matcher def filter_matches(kp1, kp2, matches, ratio = 0.75): mkp1, mkp2 = [], [] for m in matches: if len(m) == 2 and m[0].distance < m[1].distance * ratio: m = m[0] mkp1.append( kp1[m.queryIdx] ) mkp2.append( kp2[m.trainIdx] ) p1 = np.float32([kp.pt for kp in mkp1]) p2 = np.float32([kp.pt for kp in mkp2]) kp_pairs = zip(mkp1, mkp2) return p1, p2, kp_pairs def explore_match(win, img1, img2, kp_pairs, status = None, H = None): h1, w1 = img1.shape[:2] h2, w2 = img2.shape[:2] vis = np.zeros((max(h1, h2), w1+w2), np.uint8) vis[:h1, :w1] = img1 vis[:h2, w1:w1+w2] = img2 vis = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR) if H is not None: corners = np.float32([[0, 0], [w1, 0], [w1, h1], [0, h1]]) corners = np.int32( cv2.perspectiveTransform(corners.reshape(1, -1, 2), H).reshape(-1, 2) + (w1, 0) ) cv2.polylines(vis, [corners], True, (255, 255, 255)) if status is None: status = np.ones(len(kp_pairs), np.bool_) p1 = np.int32([kpp[0].pt for kpp in kp_pairs]) p2 = np.int32([kpp[1].pt for kpp in kp_pairs]) + (w1, 0) green = (0, 255, 0) red = (0, 0, 255) white = (255, 255, 255) kp_color = (51, 103, 236) for (x1, y1), (x2, y2), inlier in zip(p1, p2, status): if inlier: col = green cv2.circle(vis, (x1, y1), 2, col, -1) cv2.circle(vis, (x2, y2), 2, col, -1) else: col = red r = 2 thickness = 3 cv2.line(vis, (x1-r, y1-r), (x1+r, y1+r), col, thickness) cv2.line(vis, (x1-r, y1+r), (x1+r, y1-r), col, thickness) cv2.line(vis, (x2-r, y2-r), (x2+r, y2+r), col, thickness) cv2.line(vis, (x2-r, y2+r), (x2+r, y2-r), col, thickness) vis0 = vis.copy() for (x1, y1), (x2, y2), inlier in zip(p1, p2, status): if inlier: cv2.line(vis, (x1, y1), (x2, y2), green) cv2.imshow(win, vis) def onmouse(event, x, y, flags, param): cur_vis = vis if flags & cv2.EVENT_FLAG_LBUTTON: cur_vis = vis0.copy() r = 8 m = (anorm(p1 - (x, y)) < r) | (anorm(p2 - (x, y)) < r) idxs = np.where(m)[0] kp1s, kp2s = [], [] for i in idxs: (x1, y1), (x2, y2) = p1[i], p2[i] col = (red, green)[status[i]] cv2.line(cur_vis, (x1, y1), (x2, y2), col) kp1, kp2 = kp_pairs[i] kp1s.append(kp1) kp2s.append(kp2) cur_vis = cv2.drawKeypoints(cur_vis, kp1s, flags=4, color=kp_color) cur_vis[:,w1:] = cv2.drawKeypoints(cur_vis[:,w1:], kp2s, flags=4, color=kp_color) cv2.imshow(win, cur_vis) cv2.setMouseCallback(win, onmouse) return vis if __name__ == '__main__': print __doc__ import sys, getopt opts, args = getopt.getopt(sys.argv[1:], '', ['feature=']) opts = dict(opts) feature_name = opts.get('--feature', 'sift') try: fn1, fn2 = args except: fn1 = '../c/box.png' fn2 = '../c/box_in_scene.png' img1 = cv2.imread(fn1, 0) img2 = cv2.imread(fn2, 0) detector, matcher = init_feature(feature_name) if detector != None: print 'using', feature_name else: print 'unknown feature:', feature_name sys.exit(1) kp1, desc1 = detector.detectAndCompute(img1, None) kp2, desc2 = detector.detectAndCompute(img2, None) print 'img1 - %d features, img2 - %d features' % (len(kp1), len(kp2)) def match_and_draw(win): print 'matching...' raw_matches = matcher.knnMatch(desc1, trainDescriptors = desc2, k = 2) #2 p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches) if len(p1) >= 4: H, status = cv2.findHomography(p1, p2, cv2.RANSAC, 5.0) print '%d / %d inliers/matched' % (np.sum(status), len(status)) else: H, status = None, None print '%d matches found, not enough for homography estimation' % len(p1) vis = explore_match(win, img1, img2, kp_pairs, status, H) match_and_draw('find_obj') cv2.waitKey() cv2.destroyAllWindows()