Using Template Matching on different sizes efficiently

Question

Is there a more efficient way to use Template Matching with images of different sizes? Here is my current Script:

import cv2
import numpy as np

img_bgr = cv2.imread('./full.jpg')
img_gray = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)

template = cv2.imread('./template.jpg', 0)

w, h = template.shape[::-1]

res = cv2.matchTemplate(img_gray, template, 
cv2.TM_CCOEFF_NORMED)
threshold = 0.8
loc = np.where(res >= threshold)

for pt in zip(*loc[::-1]):
  cv2.rectangle(img_bgr, pt, (pt[0]+w, pt[1]+h), (0,255,255), 2)

cv2.imshow('detected', img_bgr)
cv2.waitKey(0)
cv2.destroyAllWindows()

This is my Template: Template

And I have these images, the first one works and the second one does not because of the size:

Works!

Fails

At first thought, I'm thinking it's failing because of the size of the template vs image

So I tried using this tutorial: Multi Scale Matching

But this seems really slow and bulky especially since I intend to use this in videos when I get it working. Is there a better way to handle this

Also, eventually I would also only like to check the top right of the image, I know that's a completely different question, but if you have any ideas since we're talking about scaling :)

Answer 1

The easiest way is to use feature matching instead of template matching. Feature matching is exactly meant for this kind of applications. It can also detect if the image is rotated .. etc

Have a lock at this https://docs.opencv.org/master/dc/dc3/tutorial_py_matcher.html