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()
