1#!/usr/bin/env python 2 3''' 4Affine invariant feature-based image matching sample. 5 6This sample is similar to find_obj.py, but uses the affine transformation 7space sampling technique, called ASIFT [1]. While the original implementation 8is based on SIFT, you can try to use SURF or ORB detectors instead. Homography RANSAC 9is used to reject outliers. Threading is used for faster affine sampling. 10 11[1] http://www.ipol.im/pub/algo/my_affine_sift/ 12 13USAGE 14 asift.py [--feature=<sift|surf|orb|brisk>[-flann]] [ <image1> <image2> ] 15 16 --feature - Feature to use. Can be sift, surf, orb or brisk. Append '-flann' 17 to feature name to use Flann-based matcher instead bruteforce. 18 19 Press left mouse button on a feature point to see its matching point. 20''' 21 22# Python 2/3 compatibility 23from __future__ import print_function 24 25import numpy as np 26import cv2 as cv 27 28# built-in modules 29import itertools as it 30from multiprocessing.pool import ThreadPool 31 32# local modules 33from common import Timer 34from find_obj import init_feature, filter_matches, explore_match 35 36 37def affine_skew(tilt, phi, img, mask=None): 38 ''' 39 affine_skew(tilt, phi, img, mask=None) -> skew_img, skew_mask, Ai 40 41 Ai - is an affine transform matrix from skew_img to img 42 ''' 43 h, w = img.shape[:2] 44 if mask is None: 45 mask = np.zeros((h, w), np.uint8) 46 mask[:] = 255 47 A = np.float32([[1, 0, 0], [0, 1, 0]]) 48 if phi != 0.0: 49 phi = np.deg2rad(phi) 50 s, c = np.sin(phi), np.cos(phi) 51 A = np.float32([[c,-s], [ s, c]]) 52 corners = [[0, 0], [w, 0], [w, h], [0, h]] 53 tcorners = np.int32( np.dot(corners, A.T) ) 54 x, y, w, h = cv.boundingRect(tcorners.reshape(1,-1,2)) 55 A = np.hstack([A, [[-x], [-y]]]) 56 img = cv.warpAffine(img, A, (w, h), flags=cv.INTER_LINEAR, borderMode=cv.BORDER_REPLICATE) 57 if tilt != 1.0: 58 s = 0.8*np.sqrt(tilt*tilt-1) 59 img = cv.GaussianBlur(img, (0, 0), sigmaX=s, sigmaY=0.01) 60 img = cv.resize(img, (0, 0), fx=1.0/tilt, fy=1.0, interpolation=cv.INTER_NEAREST) 61 A[0] /= tilt 62 if phi != 0.0 or tilt != 1.0: 63 h, w = img.shape[:2] 64 mask = cv.warpAffine(mask, A, (w, h), flags=cv.INTER_NEAREST) 65 Ai = cv.invertAffineTransform(A) 66 return img, mask, Ai 67 68 69def affine_detect(detector, img, mask=None, pool=None): 70 ''' 71 affine_detect(detector, img, mask=None, pool=None) -> keypoints, descrs 72 73 Apply a set of affine transformations to the image, detect keypoints and 74 reproject them into initial image coordinates. 75 See http://www.ipol.im/pub/algo/my_affine_sift/ for the details. 76 77 ThreadPool object may be passed to speedup the computation. 78 ''' 79 params = [(1.0, 0.0)] 80 for t in 2**(0.5*np.arange(1,6)): 81 for phi in np.arange(0, 180, 72.0 / t): 82 params.append((t, phi)) 83 84 def f(p): 85 t, phi = p 86 timg, tmask, Ai = affine_skew(t, phi, img) 87 keypoints, descrs = detector.detectAndCompute(timg, tmask) 88 for kp in keypoints: 89 x, y = kp.pt 90 kp.pt = tuple( np.dot(Ai, (x, y, 1)) ) 91 if descrs is None: 92 descrs = [] 93 return keypoints, descrs 94 95 keypoints, descrs = [], [] 96 if pool is None: 97 ires = it.imap(f, params) 98 else: 99 ires = pool.imap(f, params) 100 101 for i, (k, d) in enumerate(ires): 102 print('affine sampling: %d / %d\r' % (i+1, len(params)), end='') 103 keypoints.extend(k) 104 descrs.extend(d) 105 106 print() 107 return keypoints, np.array(descrs) 108 109 110def main(): 111 import sys, getopt 112 opts, args = getopt.getopt(sys.argv[1:], '', ['feature=']) 113 opts = dict(opts) 114 feature_name = opts.get('--feature', 'brisk-flann') 115 try: 116 fn1, fn2 = args 117 except: 118 fn1 = 'aero1.jpg' 119 fn2 = 'aero3.jpg' 120 121 img1 = cv.imread(cv.samples.findFile(fn1), cv.IMREAD_GRAYSCALE) 122 img2 = cv.imread(cv.samples.findFile(fn2), cv.IMREAD_GRAYSCALE) 123 detector, matcher = init_feature(feature_name) 124 125 if img1 is None: 126 print('Failed to load fn1:', fn1) 127 sys.exit(1) 128 129 if img2 is None: 130 print('Failed to load fn2:', fn2) 131 sys.exit(1) 132 133 if detector is None: 134 print('unknown feature:', feature_name) 135 sys.exit(1) 136 137 print('using', feature_name) 138 139 pool=ThreadPool(processes = cv.getNumberOfCPUs()) 140 kp1, desc1 = affine_detect(detector, img1, pool=pool) 141 kp2, desc2 = affine_detect(detector, img2, pool=pool) 142 print('img1 - %d features, img2 - %d features' % (len(kp1), len(kp2))) 143 144 def match_and_draw(win): 145 with Timer('matching'): 146 raw_matches = matcher.knnMatch(desc1, trainDescriptors = desc2, k = 2) #2 147 p1, p2, kp_pairs = filter_matches(kp1, kp2, raw_matches) 148 if len(p1) >= 4: 149 H, status = cv.findHomography(p1, p2, cv.RANSAC, 5.0) 150 print('%d / %d inliers/matched' % (np.sum(status), len(status))) 151 # do not draw outliers (there will be a lot of them) 152 kp_pairs = [kpp for kpp, flag in zip(kp_pairs, status) if flag] 153 else: 154 H, status = None, None 155 print('%d matches found, not enough for homography estimation' % len(p1)) 156 157 explore_match(win, img1, img2, kp_pairs, None, H) 158 159 160 match_and_draw('affine find_obj') 161 cv.waitKey() 162 print('Done') 163 164 165if __name__ == '__main__': 166 print(__doc__) 167 main() 168 cv.destroyAllWindows() 169