模板匹配是在一个大图里搜索和找模板图像位置的方法。OpenCV有个函数cv2.matchTemplate()来做这个。它吧模板图像在输入图像上滑动，对比模板和在模板图像下的输入图像块。它返回了一个灰度图像，每个像素表示那个像素周围和模板匹配的情况。

如果输入图像大小是WxH而模板图像大小是wxh，输出图像的大小是（W-w+1， H-h+1）。当你得到了结果，你可以用cv2.minMaxLoc()函数来找最大最小值。把它作为矩形左上角，w，h作为矩形的宽和高。矩形是你的模板区域。

注意：
如果你用cv2.TM_SQDIFF作为比较方法，最小值是最匹配。

匹配单个目标

import cv2
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
img = cv2.imread('touxiang.jpg',0)
img2 = img.copy()
template = cv2.imread('template.jpg',0)
w, h = template.shape[::-1]# All the 6 methods for comparison in a list
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)# Apply template Matchingres = cv2.matchTemplate(img,template,method)min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)# If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimumif 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)cv2.rectangle(img,top_left, bottom_right, 255, 2)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.suptitle(meth)plt.show()

匹配多个目标

import cv2
import numpy as np
from matplotlib import pyplot as pltimg_rgb = cv2.imread('touxiang.jpg')
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
template = cv2.imread('template.jpg',0)
w, h = template.shape[::-1]res = cv2.matchTemplate(img_gray,template,cv2.TM_CCOEFF_NORMED)threshold = 0.402
loc = np.where( res >= threshold)
print(loc)for pt in zip(*loc[::-1]):print(pt)cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), (0,0,255), 2)cv2.imwrite('res.png',img_rgb)

(array([92, 92, 93], dtype=int64), array([158, 159, 158], dtype=int64))
(158, 92)
(159, 92)
(158, 93)True