OpenCV模板

import cv2;
import numpy;
import pandas;
import matplotlib.pyplot as plt;

# 模板匹配
img = cv2.imread('D:\\pythonDemon\\opencv\\lena.jpg', 0)
template = cv2.imread('D:\\pythonDemon\\opencv\\face.jpg', 0)
h, w = template.shape[:2] 

res = cv2.matchTemplate(img, template,cv2.TM_SQDIFF)
#print(res)

min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
print(min_val, max_val, min_loc, max_loc)

# - TM_SQDIFF：计算平方不同，计算出来的值越小，越相关        
# - TM_CCORR：计算相关性，计算出来的值越大，越相关
# - TM_CCOEFF：计算相关系数，计算出来的值越大，越相关
# - TM_SQDIFF_NORMED：计算归一化平方不同，计算出来的值越接近0，越相关
# - TM_CCORR_NORMED：计算归一化相关性，计算出来的值越接近1，越相关
# - TM_CCOEFF_NORMED：计算归一化相关系数，计算出来的值越接近1，越相关

methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',
           'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']

    # 如果是平方差匹配TM_SQDIFF或归一化平方差匹配TM_SQDIFF_NORMED，取最小值

for meth in methods:
    img2 = img.copy()

    # 匹配方法的真值
    method = eval(meth)
    print (method)
    res = cv2.matchTemplate(img, template, method)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)

    # 如果是平方差匹配TM_SQDIFF或归一化平方差匹配TM_SQDIFF_NORMED，取最小值
    if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
        top_left = min_loc
    else:
        top_left = max_loc
    bottom_right = (top_left[0] + w, top_left[1] + h)

    # 画矩形
    cv2.rectangle(img2, top_left, bottom_right, 255, 2)

    plt.subplot(121), plt.imshow(res, cmap='gray')
    plt.xticks([]), plt.yticks([])  # 隐藏坐标轴
    plt.subplot(122), plt.imshow(img2, cmap='gray')
    plt.xticks([]), plt.yticks([])
    plt.suptitle(meth)
    plt.show()