CODEWITHSUNDEEP
pythonopencvimage-processingopencv3.0template-matching
Cherry
Cherry

Reputation: 71

How to use the cv2.minMaxLoc() in template matching

Here is the code I used for template matching and what do min_val, max_val, min_loc, max_loc mean? what are they used for?

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

img = cv2.imread('C:\\machineLearning\\positive\\1.jpg', 0)
img2 = img.copy()
template = cv2.imread('C:\\machineLearning\\positive\\1_.jpg', 0)
w, h = template.shape[::-1]

img = img2.copy()
method = eval('cv2.TM_SQDIFF')

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

top_left = min_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('cv2.TM_SQDIFF')

plt.show()

Upvotes: 3

Views: 17816

Answers (1)

ZdaR
ZdaR

Reputation: 22954

If you go through cv2.matchTemplate() docs, the function returns a fuzzy single channel matrix with the matching score of template and input image segments. For cv2.TM_CCOEFF method the point with the highest score would be the brightest, but in case of cv2.TM_SQDIFF_NORMED method, the point with highest score would be darkest

cv2.TM_CCOEFF Results:

cv2.TM_CCOEFF

cv2.TM_SQDIFF_NORMED Results:

enter image description here

So depending upon the various methods available, you may sometimes need to get the brightest spot or the darkest spot in the output matrix. cv2.minMaxLoc() is just a unification of these two generic operations, when you are using minMaxLoc, you can ignore min attributes for your use case.

Upvotes: 7

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