算法思路

将目标图像在源图像中滑动，计算两个相同大小图像的距离，最后根据距离最小的位置作为匹配结果
其中计算图像距离的算法有多种：TM_SQDIFF，TM_SQDIFF_NORMED等详细见opencv官网
算法很简单，只用用在图像没有发生变形的情况下

代码

import cv2

def match_image(source, template):
# 选择匹配算法
match_method = cv2.TM_SQDIFF

result = cv2.matchTemplate(source, template, match_method)
cv2.normalize(result, result, 0, 1, cv2.NORM_MINMAX, -1)
# 概率图
cv2.imshow("rate", result)

min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result, None)
if match_method == cv2.TM_SQDIFF or match_method == cv2.TM_SQDIFF_NORMED:
match_loc = min_loc
else:
match_loc = max_loc
return match_loc

template = cv2.imread("dog.png")
img = cv2.imread("img.png")

location = match_image(img, template)
pt1 = (int(location[0]), int(location[1]))
x = location[0] + img.shape[1] / 2
y = location[1] + img.shape[0] / 2
pt2 = (int(x), int(y))
cv2.rectangle(img, pt1, pt2, (255, 0, 0), 2)
cv2.imshow("img", img)
cv2.waitKey(0)

匹配结果