Give

template_detection.py

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

def template_detection(image, template):

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


    fig, axs = plt.subplots(2,3, figsize=(15, 6))
    axs = axs.ravel()
    fig.subplots_adjust(hspace = .5, wspace=.001)

    _,w, h = template.shape[::-1]
    
    for i,meth in zip(range(len(methods)), methods):
        img = image.copy()
        method = eval(meth)

        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)
        cv2.rectangle(img,top_left, bottom_right, (0,0,0), 4)
        axs[i].imshow(img)
        axs[i].set_title(f'Template Matching using \n{meth}')


    plt.show()