In the template matching by OpenCV it's better at the first get canny edge from image then smooth this edge image then in the source image you can fill your mask region by zero.
In the template matching ,if you reduce your features size for correlation process.In the other words، the response will be more than distinct from massive state(with pattern).So you can find all objects by use of the specific threshold in the result.
Hi i agree with Mostafa Satak's sugguestion,here is an example: http://samwize.com/2013/06/09/using-opencv-to-match-template-multiple-times/
If you're using simple cross correlation, you could use a mask to set those excluded areas to 0, which would prevent them from contributing to the cross correlation sum. This tactic isn't going to work with any sort of normalization, and probably not with coefficient correlation, as they use a mean of all template pixels - which is going to be affected by "0" value pixels.
I'm working on a warpPerspective -> matchTemplate setup, and was about ready to try and rewrite matchTemplate to be able to handle the quadrilaterals I was getting from warpPerspective (inscribed in rectangles with padded 0's), until I figured out I could just use the inverse transform matrix to warpPerspective of the larger image rather than the template. (I'm more interested in the center of the image, so I can assume the match will be somewhere in the interior of the image)
Have you done any more with this? I'm using the normalized cross correlation and would like to mask away parts of my template. I'm thinking about rewriting matchTemplate to handle masks, but I'm not sure that I have the time and knowledge needed...
Well, my approach for WarpPerspective & MatchTemplate failed (Warping the target instead of the template uses a TON of memory - much more than I have available on Android phones), so I am probably going to deep dive into a new/modified version of matchTemplate in April. That is, unless someone else beats me to it (and saves me a bunch of work)
Actually I ended up using a workaround. Starting from a warped image quadrilateral inscribed in a rectangle, I pick a smaller rectangle inscribed (and thus containing only "active pixels") in the quadrilateral. I discard everything else and use this rectangle with matchTemplate - thus no "empty" zero-value pixels to influence averages and weights in the matching algorithms. As of yet, results are inconclusive - I think it might work because the warps I am dealing with are "gentle" - maybe 5, 10 degree Euler angle rotations.
Hi guys, I ended up writing my own function from the matchTemplate() source code, with the CV_SQDIFF_NORMED method; it takes a transparent PNG and creates a vector with the coordinates of every point in the mask (every point that has an alpha >0) then it uses this point to access every point in the image for a given offset, instead of using the integral rectangle trick as it is actually done in the original function. It' s surely slower but it seems to work.
Hope it helps
Asked: 2013-01-01 15:50:49 -0600
Seen: 15,416 times
Last updated: Apr 09 '14
