一个图像要与另外一个图像配准
我们先对两个图像提取各自的sift特征点集合，然后再将分布于图像上的特征点一一配对。
这里假设相互配对特征点，其位置从一个集合，变换到新的集合时，是符合刚体变换的。
这个刚体变换就可以用于求取一一配对的匹配点的过程中。
asift.py
”’
Affine invariant feature-based image matching sample.
图像配准例子代码–基于仿射不变特征
这个代码与find_obj.py类似，但是用了仿射变换空间采样技术，命名为ASIFT。这里提取特征仍然用的是原来的提取特征方法SIFT，所以这里可以用SURF,或者ORB来替代。
Homograph RANSAC用来剔除异常点。
多线程用来快速地进行仿射采样。
[1]
USAGE
asift.py [–feature=<sift|surf|orb|brisk>[-flann]] [ <image1> <image2> ]
–feature – 选择提取feature的方法 . 可以是 sift, surf, orb or brisk. ‘-flann’
特指用 Flann-based 匹配器而不使用bruteforce.
在特征点上点击鼠标左键，可以查看它对应的匹配点。
imag1,和image2是需要配准的两张图片
”’
使用ASIFT方法图像配准
打开链接
源代码：
1 #!/usr/bin/env python
2
3 ”’
4 Affine invariant feature-based image matching sample.
5
6 This sample is similar to find_obj.py, but uses the affine transformation
7 space sampling technique, called ASIFT [1]. While the original implementation
8 is based on SIFT, you can try to use SURF or ORB detectors instead. Homography RANSAC
9 is used to reject outliers. Threading is used for faster affine sampling.
10
11 [1]
12
13 USAGE
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
23 from __future__ import print_function
24
25 import numpy as np
26 import cv2 as cv
27
28 # built-in modules
29 import itertools as it
30 from multiprocessing.pool import ThreadPool
31
32 # local modules
33 from common import Timer
34 from find_obj import init_feature, filter_matches, explore_match
35
36
37 def affine_skew(tilt, phi, img, mask=None):
38 ”’
39 affine_skew(tilt, phi, img, mask=None) -> skew_img, skew_mask,
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
69 def 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 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 / %dr’ % (i+1, len(params)), end=”)
103 keypoints.extend(k)
104 descrs.extend(d)
105
106 print()
107 return keypoints, np.array(descrs)
108
109
110 def 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
165 if __name__ == ‘__main__’:
166 print(__doc__)
167 main()
168 cv.destroyAllWindows()
