import cv2 as cv

import numpy as np

"""

matchTemplate():

参数image:待搜索的图像(大图)

参数temple:搜索模板,需要和原图一样的数据类型且尺寸不能大于源图像

参数result:比较结果的映射图像,其必须为单通道,32位浮点型图像,如果原图(待搜索图像)尺寸为W*H,而temple尺寸为w*h,则result尺寸一定是

    (W-w+1)*(H-h+1)

参数method:指定匹配方法,有如下几种:

    CV_TM_SQDIFF:平方差匹配法

    CV_TM_SQDIFF_NORMED:归一化平方差匹配法

    CV_TM_CCORR:相关匹配法

    CV_TM_CCORR_NORMED:归一化相关匹配法

    CV_TM_CCOEFF:系数匹配法

    CV_TM_CCOEFF_NORMED:化相关系数匹配法

"""

"""

minMaxLoc()函数

作用:一维数组当作向量,寻找矩阵中最小值和最大值位置

"""

def match_image():

    target = cv.imread(r"C:\Users\lenovo\Desktop\test\2.jpg")

    temple = cv.imread(r"C:\Users\lenovo\Desktop\test\1.png")

    # shape是获取矩阵的长度

    print(temple.shape)

    # 获取到小图的尺寸

    th, tw = temple.shape[:2]

    result = cv.matchTemplate(target, temple, cv.TM_SQDIFF_NORMED)

    # 返回匹配的最小坐标

    min_val, max_val, min_loc, max_loc = cv.minMaxLoc(result)

    tl=min_loc

    print(tl)

    br = (int(tl[0]) + tw, int(tl[1]) + th)

    print('br==',br)

    cv.rectangle(target, tl, br, [0, 255, 0])

    cv.imshow("匹配结果" + np.str(cv.TM_SQDIFF_NORMED), target)

match_image()

cv.waitKey(0)

cv.destroyAllWindows()