python 模板匹配对比

对于形变，以下两种 相关系数的效果比较好：

cv2.TM_CCOEFF
cv2.TM_CCOEFF_NORMED

其余四种会误检。

• 平方差匹配CV_TM_SQDIFF：用两者的平方差来匹配，最好的匹配值为0
• 归一化平方差匹配CV_TM_SQDIFF_NORMED
• 相关匹配CV_TM_CCORR：用两者的乘积匹配，数值越大表明匹配程度越好
• 归一化相关匹配CV_TM_CCORR_NORMED
• 相关系数匹配CV_TM_CCOEFF：用两者的相关系数匹配，1表示完美的匹配，-1表示最差的匹配
• 归一化相关系数匹配CV_TM_CCOEFF_NORMED

  

   

    

     

    
    

     

      # -*- coding:utf-8 -*-
     
    
   

    

     

    
    

     

      __author__ = 'Microcosm'
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      import cv2
     
    
   

    

     

    
    

     

      import numpy as np
     
    
   

    

     

    
    

     

      from matplotlib import pyplot as plt
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      img = cv2.imread(r"e:/new/n3.jpg",0)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      template = cv2.imread(r"e:/new/muban3.jpg",0)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      img2 = img.copy()
     
    
   

    

     

    
    

     

      w,h = template.shape[::-1]
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # 6 中匹配效果对比算法
     
    
   

    

     

    
    

     

      methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',
     
    
   

    

     

    
    

     
 'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      for meth in methods:
     
    
   

    

     

    
    

     

       img = img2.copy()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       method = eval(meth)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       res = cv2.matchTemplate(img,template,method)
     
    
   

    

     

    
    

     

       min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
     
    
   

    

     

    
    

     

       top_left = min_loc
     
    
   

    

     

    
    

     
 else:
     
    
   

    

     

    
    

     

       top_left = max_loc
     
    
   

    

     

    
    

     

       bottom_right = (top_left[0] + w, top_left[1] + h)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       cv2.rectangle(img,top_left, bottom_right, 255, 2)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       cv2.imshow("Point",img)
     
    
   

    

     

    
    

     

       cv2.imshow("Matching Result",res)
     
    
   

    

     

    
    

     

       cv2.waitKeyEx()
     
    
   

    

     

    
    

     

       print(meth)
     
    
   

    

     

    
    

     
 # plt.subplot(221), plt.imshow(img2,cmap= "gray")
     
    
   

    

     

    
    

     
 # plt.title('Original Image'), plt.xticks([]),plt.yticks([])
     
    
   

    

     

    
    

     
 # plt.subplot(222), plt.imshow(template,cmap= "gray")
     
    
   

    

     

    
    

     
 # plt.title('template Image'),plt.xticks([]),plt.yticks([])
     
    
   

    

     

    
    

     
 # plt.subplot(121), plt.imshow(res,cmap= "gray")
     
    
   

    

     

    
    

     
 # plt.title('Matching Result'), plt.xticks([]),plt.yticks([])
     
    
   

    

     

    
    

     
 # plt.subplot(122), plt.imshow(img,cmap= "gray")
     
    
   

    

     

    
    

     
 # plt.title('Detected Point'),plt.xticks([]),plt.yticks([])
     
    
   

    

     

    
    

     
 # plt.show()
     
    
  
 

文章来源: blog.csdn.net，作者：网奇，版权归原作者所有，如需转载，请联系作者。

原文链接：blog.csdn.net/jacke121/article/details/95319446