OpenCV模板匹配算法详解

1 理论介绍

模板匹配是在一幅图像中寻找一个特定目标的方法之一，这种方法的原理非常简单，遍历图像中的每一个可能的位置，比较各处与模板是否“相似”，当相似度足够高时，就认为找到了我们的目标。OpenCV提供了6种模板匹配算法：

平方差匹配法CV_TM_SQDIFF
归一化平方差匹配法CV_TM_SQDIFF_NORMED
相关匹配法CV_TM_CCORR
归一化相关匹配法CV_TM_CCORR_NORMED
相关系数匹配法CV_TM_CCOEFF
归一化相关系数匹配法CV_TM_CCOEFF_NORMED

用T表示模板图像，I表示待匹配图像，切模板图像的宽为w高为h，用R表示匹配结果，匹配过程如下图所示：

OpenCV模板匹配算法详解

上述6中匹配方法可用以下公式进行描述：

OpenCV模板匹配算法详解

2 示例代码

下面给出方法6的python代码

 1 import numpy as np
 2 import cv2
 3 
 4 def EM(pModel, width, height):
 5     sum = np.double(0.0)
 6     for i in range(0,height):
 7         for j in range(0,width):
 8             sum += pModel[i][j]
 9     return sum
10 
11 def EM2(pModel, width, height):
12     sum = np.double(0.0)
13     for i in range(0,height):
14         for j in range(0,width):
15             sum += pModel[i][j]*1.0*pModel[i][j]
16     return sum
17 
18 def EI(pToSearch, l, h, u, v, pModel, width, height):
19     sum = np.double(0.0)
20     roi = pToSearch[v:v+height, u:u+width]
21     for i in range(0,height):
22         for j in range(0,width):
23             sum += roi[i][j]
24     return sum
25 
26 def EI2(pToSearch, l, h, u, v, pModel, width, height):
27     sum = np.double(0.0)
28     roi = pToSearch[v:v+height, u:u+width]
29     for i in range(0,height):
30         for j in range(0,width):
31             sum += roi[i][j]*1.0*roi[i][j]
32     return sum
33 
34 def EIM(pToSearch, l, h, u, v, pModel, width, height):
35     sum = np.double(0.0)
36     roi = pToSearch[v:v+height, u:u+width]
37     for i in range(0,height):
38         for j in range(0,width):
39             sum += pModel[i][j]*1.0*roi[i][j]
40     return sum
41 
42 def Match(pToSearch, l, h, pModel, width, height):
43     uMax = l-width
44     vMax = h-height
45     N = width*height
46     len = (uMax+1)*(vMax+1)
47     MatchRec = [0.0 for x in range(0, len)]
48     k = 0
49 
50     M = EM(pModel,width,height)
51     M2 = EM2(pModel,width,height)
52     for p in range(0, uMax+1):
53         for q in range(0, vMax+1):
54             I = EI(pToSearch,l,h,p,q,pModel,width,height)
55             I2 = EI2(pToSearch,l,h,p,q,pModel,width,height)
56             IM = EIM(pToSearch,l,h,p,q,pModel,width,height)
57 
58             numerator=(N*IM-I*M)*(N*IM-I*M)
59             denominator=(N*I2-I*I)*(N*M2-M*M)
60 
61             ret = numerator/denominator
62             MatchRec[k]=ret
63             k+=1
64 
65     val = 0
66     k = 0
67     x = y = 0
68     for p in range(0, uMax+1):
69         for q in range(0, vMax+1):
70             if MatchRec[k] > val:
71                 val = MatchRec[k]
72                 x = p
73                 y = q
74             k+=1
75     print "val: %f"%val
76     return (x, y)
77 
78 def main():
79     img = cv2.imread('niu.jpg', cv2.IMREAD_GRAYSCALE)
80     temp = cv2.imread('temp.png', cv2.IMREAD_GRAYSCALE)
81 
82     print temp.shape
83     imgHt, imgWd = img.shape
84     tempHt, tempWd = temp.shape
85     #print EM(temp, tempWd, tempHt)
86     (x, y) = Match(img, imgWd, imgHt, temp, tempWd, tempHt)
87     cv2.rectangle(img, (x, y), (x+tempWd, y+tempHt), (0,0,0), 2)
88     cv2.imshow("temp", temp)
89     cv2.imshow("result", img)
90     cv2.waitKey(0)
91     cv2.destroyAllWindows()
92 
93 if __name__ == '__main__':
94     main()

归一化相关系数匹配法