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197 lines
6.3 KiB
Python
Executable File
197 lines
6.3 KiB
Python
Executable File
#!/usr/bin/env python
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'''
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Feature-based image matching sample.
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Note, that you will need the https://github.com/opencv/opencv_contrib repo for SIFT and SURF
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USAGE
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find_obj.py [--feature=<sift|surf|orb|akaze|brisk>[-flann]] [ <image1> <image2> ]
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--feature - Feature to use. Can be sift, surf, orb or brisk. Append '-flann'
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to feature name to use Flann-based matcher instead bruteforce.
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Press left mouse button on a feature point to see its matching point.
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'''
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# Python 2/3 compatibility
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from __future__ import print_function
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import numpy as np
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import cv2 as cv
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from common import anorm, getsize
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FLANN_INDEX_KDTREE = 1 # bug: flann enums are missing
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FLANN_INDEX_LSH = 6
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def init_feature(name):
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chunks = name.split('-')
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if chunks[0] == 'sift':
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detector = cv.SIFT_create()
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norm = cv.NORM_L2
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elif chunks[0] == 'surf':
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detector = cv.xfeatures2d.SURF_create(800)
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norm = cv.NORM_L2
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elif chunks[0] == 'orb':
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detector = cv.ORB_create(400)
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norm = cv.NORM_HAMMING
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elif chunks[0] == 'akaze':
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detector = cv.AKAZE_create()
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norm = cv.NORM_HAMMING
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elif chunks[0] == 'brisk':
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detector = cv.BRISK_create()
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norm = cv.NORM_HAMMING
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else:
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return None, None
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if 'flann' in chunks:
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if norm == cv.NORM_L2:
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flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
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else:
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flann_params= dict(algorithm = FLANN_INDEX_LSH,
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table_number = 6, # 12
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key_size = 12, # 20
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multi_probe_level = 1) #2
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matcher = cv.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
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else:
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matcher = cv.BFMatcher(norm)
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return detector, matcher
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def filter_matches(kp1, kp2, matches, ratio = 0.75):
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mkp1, mkp2 = [], []
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for m in matches:
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if len(m) == 2 and m[0].distance < m[1].distance * ratio:
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m = m[0]
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mkp1.append( kp1[m.queryIdx] )
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mkp2.append( kp2[m.trainIdx] )
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p1 = np.float32([kp.pt for kp in mkp1])
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p2 = np.float32([kp.pt for kp in mkp2])
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kp_pairs = zip(mkp1, mkp2)
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return p1, p2, list(kp_pairs)
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def explore_match(win, img1, img2, kp_pairs, 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 = cv.cvtColor(vis, cv.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( cv.perspectiveTransform(corners.reshape(1, -1, 2), H).reshape(-1, 2) + (w1, 0) )
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cv.polylines(vis, [corners], True, (255, 255, 255))
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if status is None:
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status = np.ones(len(kp_pairs), np.bool_)
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p1, p2 = [], [] # python 2 / python 3 change of zip unpacking
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for kpp in kp_pairs:
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p1.append(np.int32(kpp[0].pt))
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p2.append(np.int32(np.array(kpp[1].pt) + [w1, 0]))
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green = (0, 255, 0)
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red = (0, 0, 255)
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kp_color = (51, 103, 236)
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for (x1, y1), (x2, y2), inlier in zip(p1, p2, status):
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if inlier:
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col = green
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cv.circle(vis, (x1, y1), 2, col, -1)
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cv.circle(vis, (x2, y2), 2, col, -1)
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else:
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col = red
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r = 2
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thickness = 3
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cv.line(vis, (x1-r, y1-r), (x1+r, y1+r), col, thickness)
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cv.line(vis, (x1-r, y1+r), (x1+r, y1-r), col, thickness)
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cv.line(vis, (x2-r, y2-r), (x2+r, y2+r), col, thickness)
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cv.line(vis, (x2-r, y2+r), (x2+r, y2-r), col, thickness)
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vis0 = vis.copy()
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for (x1, y1), (x2, y2), inlier in zip(p1, p2, status):
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if inlier:
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cv.line(vis, (x1, y1), (x2, y2), green)
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cv.imshow(win, vis)
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def onmouse(event, x, y, flags, param):
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cur_vis = vis
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if flags & cv.EVENT_FLAG_LBUTTON:
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cur_vis = vis0.copy()
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r = 8
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m = (anorm(np.array(p1) - (x, y)) < r) | (anorm(np.array(p2) - (x, y)) < r)
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idxs = np.where(m)[0]
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kp1s, kp2s = [], []
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for i in idxs:
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(x1, y1), (x2, y2) = p1[i], p2[i]
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col = (red, green)[status[i][0]]
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cv.line(cur_vis, (x1, y1), (x2, y2), col)
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kp1, kp2 = kp_pairs[i]
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kp1s.append(kp1)
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kp2s.append(kp2)
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cur_vis = cv.drawKeypoints(cur_vis, kp1s, None, flags=4, color=kp_color)
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cur_vis[:,w1:] = cv.drawKeypoints(cur_vis[:,w1:], kp2s, None, flags=4, color=kp_color)
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cv.imshow(win, cur_vis)
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cv.setMouseCallback(win, onmouse)
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return vis
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def main():
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import sys, getopt
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opts, args = getopt.getopt(sys.argv[1:], '', ['feature='])
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opts = dict(opts)
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feature_name = opts.get('--feature', 'brisk')
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try:
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fn1, fn2 = args
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except:
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fn1 = 'box.png'
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fn2 = 'box_in_scene.png'
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img1 = cv.imread(cv.samples.findFile(fn1), cv.IMREAD_GRAYSCALE)
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img2 = cv.imread(cv.samples.findFile(fn2), cv.IMREAD_GRAYSCALE)
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detector, matcher = init_feature(feature_name)
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if img1 is None:
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print('Failed to load fn1:', fn1)
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sys.exit(1)
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if img2 is None:
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print('Failed to load fn2:', fn2)
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sys.exit(1)
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if detector is None:
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print('unknown feature:', feature_name)
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sys.exit(1)
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print('using', feature_name)
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kp1, desc1 = detector.detectAndCompute(img1, None)
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kp2, desc2 = detector.detectAndCompute(img2, None)
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print('img1 - %d features, img2 - %d features' % (len(kp1), len(kp2)))
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def match_and_draw(win):
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print('matching...')
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raw_matches = matcher.knnMatch(desc1, trainDescriptors = desc2, k = 2) #2
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p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches)
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if len(p1) >= 4:
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H, status = cv.findHomography(p1, p2, cv.RANSAC, 5.0)
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print('%d / %d inliers/matched' % (np.sum(status), len(status)))
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else:
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H, status = None, None
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print('%d matches found, not enough for homography estimation' % len(p1))
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_vis = explore_match(win, img1, img2, kp_pairs, status, H)
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match_and_draw('find_obj')
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cv.waitKey()
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print('Done')
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if __name__ == '__main__':
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print(__doc__)
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main()
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cv.destroyAllWindows()
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