import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt

img = cv.imread('lena.jpg',0)
img2 = img.copy()
template = cv.imread('eyes.jpg',0)
w, h = template.shape[::-1]

# 6种比较方法
methods = ['cv.TM_CCOEFF', 'cv.TM_CCOEFF_NORMED', 'cv.TM_CCORR',
            'cv.TM_CCORR_NORMED', 'cv.TM_SQDIFF', 'cv.TM_SQDIFF_NORMED']
i = 'a'

for meth in methods:
    img = img2.copy()
    method = eval(meth)
    # 模板匹配
    res = cv.matchTemplate(img,template,method)
    min_val, max_val, min_loc, max_loc = cv.minMaxLoc(res)
    # 如果是 TM_SQDIFF 或 TM_SQDIFF_NORMED,则取最小值；反之，最大值
    if method in [cv.TM_SQDIFF, cv.TM_SQDIFF_NORMED]:
        top_left = min_loc
    else:
        top_left = max_loc
    # 目标右下角坐标
    bottom_right = (top_left[0] + w, top_left[1] + h)
    # 绘制目标矩形边界
    cv.rectangle(img,top_left, bottom_right, 255, 2)

    # 结果显示
    plt.subplot(121),plt.imshow(res,cmap = 'gray'),plt.title('Matching Result')
    plt.subplot(122),plt.imshow(img,cmap = 'gray'),plt.title('Detected Point')
    # plt.suptitle(meth)
    plt.savefig('fig8-13%s.png'%i,bbox_inches='tight')
    i=chr(ord(i)+1)
    plt.show()