python opencv -模板匹配

模板匹配就是，我们现有一个模板和一个图片，然后，在这个图片中寻找和模板近似的部分。

在opencv 中主要通过cv2.matchTemplate这个函数去实现。

下面我们先看一下，模板图片和需要匹配的图片：
模板：

需要匹配的图片：

下面来看代码：

import cv2
import copy
import math
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import ospath=r'D:\learn\photo\cv\lena.jpg'
path2=r'D:\learn\photo\cv\face.jpg'img=cv2.imread(path,1)img_gray=cv2.imread(path,0)img_template=cv2.imread(path2,1)img_gray_template=cv2.imread(path2,0)def cv_show(name,img):cv2.imshow(name,img)#cv2.waitKey(0),接收0，表示窗口暂停cv2.waitKey(0)#销毁所有窗口cv2.destroyAllWindows()print(img.shape)
print(img_template.shape)
h, w = img_template.shape[:2]
"""
- TM_SQDIFF：计算平方不同，计算出来的值越小，越相关       
- TM_SQDIFF_NORMED：计算归一化平方不同，计算出来的值越接近0，越相关 
- TM_CCORR：计算相关性，计算出来的值越大，越相关
- TM_CCOEFF：计算相关系数，计算出来的值越大，越相关
- TM_CCORR_NORMED：计算归一化相关性，计算出来的值越接近1，越相关
- TM_CCOEFF_NORMED：计算归一化相关系数，计算出来的值越接近1，越相关
链接：https://docs.opencv.org/3.3.1/df/dfb/group__imgproc__object.html#ga3a7850640f1fe1f58fe91a2d7583695d
"""methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR','cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']res = cv2.matchTemplate(img, img_template, cv2.TM_SQDIFF)
print(res.shape)
# exit()min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
print(min_val, max_val, min_loc, max_loc)for meth in methods:img2 = img.copy()# 匹配方法的真值method = eval(meth)print(method)res = cv2.matchTemplate(img, img_template, method)min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)# 如果是平方差匹配TM_SQDIFF或归一化平方差匹配TM_SQDIFF_NORMED，取最小值if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:top_left = min_locelse:top_left = max_locbottom_right = (top_left[0] + w, top_left[1] + h)# 画矩形cv2.rectangle(img2, top_left, bottom_right, 255, 2)plt.subplot(121), plt.imshow(res,'gray')plt.xticks([]), plt.yticks([])  # 隐藏坐标轴plt.subplot(122), plt.imshow(img2[:,:,::-1])plt.xticks([]), plt.yticks([])plt.suptitle(meth)plt.show()

运行结果如下：