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You could also use the output mask that findHomography creates when you use ransac or lmeds inside of it. Thats what opencv says about it: The best subset is then used to produce the initial estimate of the homography matrix and the mask of inliers/outliers.

So you could take this mask and count the inliers and outliers. Then you would check for example: if inliers > 50 and outliers/inliers > 0.5, then object found. Those numbers are totally random, you need to find them on your own through testing.

You could also use the output mask that findHomography creates when you use ransac or lmeds inside of it. Thats what opencv says about it: The best subset is then used to produce the initial estimate of the homography matrix and the mask of inliers/outliers.

So you could take this mask and count the inliers and outliers. Then you would check for example: if inliers > 50 and outliers/inliers > < 0.5, then object found. Those numbers are totally random, you need to find them on your own through testing.