23_template_matching.py

import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread('scenic_background.jpg', 0)
img2 = img.copy()

template = cv2.imread('scenic_background_cut.jpg', 0)
w, h = template.shape[::-1]

# all 6 comparison methods
methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR', 
           'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']

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

    # apply template matching
    res = cv2.matchTemplate(img, template, method)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)

    # if the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
    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)

    # draw bounding box
    cv2.rectangle(img, top_left, bottom_right, (255, 0, 0), 2)

    # show images
    plt.subplot(121)
    plt.imshow(res, cmap='gray')
    plt.title('Matching Result')
    plt.xticks([]), plt.yticks([])

    plt.subplot(122)
    plt.imshow(img, cmap='gray')
    plt.title('Detected Point')
    plt.xticks([]), plt.yticks([])

    plt.show()