I'm working with the following python opencv example:
import cv2 import numpy as np from matplotlib import pyplot as plt
img = cv2.imread(‘messi5.jpg’,0)
img2 = img.copy()
template = cv2.imread(‘template.jpg’,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 + w, top_left + 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()
The matching works pretty well on a set of chosen images, which clearly contained the template. My problem is that even in images which clearly doesn’t contain the template a rectangle was drawn. How can I fit the source code, so it can handle image which doesn’t match at all.
Thanks in advance
In the document:
It returns a grayscale image, where each pixel denotes how much does the neighbourhood of that pixel match with template
So set a threshold for the res, if there is no similarity in the image, it does nothing.
res = cv2.matchTemplate(img,template,method) if res<0.8: return ...
Just like in the Template Matching with Multiple Objects part
Your code always shows the best match, regardless of how good the match is.
You could check the value of max_val (or min_val when SQDIFF is used) and only show the match when this value exceeds a certain threshold.
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