OpenCV 模板匹配

import cv2   
 
 def get_sing_loc(img, template):
        '''
        methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',
                'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']
        :return:
        '''
        # 模板匹配
        template_h, template_w, _ = template.shape
        method = cv2.TM_CCOEFF_NORMED
        res = cv2.matchTemplate(img, template, method)
        # 寻找最值
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
        if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
            top_left = min_loc
        else:
            top_left = max_loc
        bottom_right = (top_left[0] + template_w, top_left[1] + template_h)
        return top_left, bottom_right

import cv2
import numpy as np   
 
 def get_sing_loc(img, template):
        '''
        methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',
                'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']
        :return:
        '''
        # 模板匹配
        template_h, template_w, _ = template.shape
        method = cv2.TM_CCOEFF_NORMED
        res = cv2.matchTemplate(img, template, method)
        threshold = 0.95
        loc = np.where(res >= threshold)
        # np.where返回的坐标值(x,y)是(h,w)，注意h,w的顺序
        points = []
        for pt in zip(*loc[::-1]):
            points.append(pt)
        return points