template_matcher.py

import cv2
import numpy as np
import matplotlib.pyplot as plt
 
# Read the main image
img_rgb = cv2.imread('analog_full2.jpeg')
 
# Convert it to grayscale
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
 
# Read the template
template = cv2.imread('analog_template2.jpeg',0)
 
# Store width and heigth of template in w and h
w, h = template.shape[::-1]
 
# Perform match operations.
res = cv2.matchTemplate(img_gray,template,cv2.TM_CCOEFF_NORMED)
 
# Specify a threshold
threshold = 0.8
 
# Store the coordinates of matched area in a numpy array
loc = np.where( res >= threshold) 
 
# Draw a rectangle around the matched region.
for pt in zip(*loc[::-1]):
    cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), (0,255,255), 2)
 
# Show the final image with the matched area.
plt.imshow(img_rgb)
plt.show()





















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

# img = cv2.imread('analog2.jpg',0)
# img2 = img.copy()
# template = cv2.imread('analog_template.jpeg',0)
# w, h = template.shape[::-1]
# # All the 6 methods for comparison in a list
# 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)
#     cv2.rectangle(img,top_left, bottom_right, 255, 2)
#     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.suptitle(meth)
#     plt.show()