I would like to implement a convolution between an image and a kernel, somewhat like MATLAB conv2(img, kernel,'same'), which indicates that the result image is the same size as the original image.
Before, I use filter2d, like:
filter2D(source, dest, img.depth(), kernel, anchor, 0, borderMode);
However, filter2D is a little bit slow when dealing with large images. That's why I think `cv::cuda::Convolution::convolve` might be a little bit faster.
I read the documentation of `cv::cuda::Convolution::convolve`
virtual void cv::cuda::Convolution::convolve ( InputArray image,
InputArray templ,
OutputArray result,
bool ccorr = false,
Stream & stream = Stream::Null()
)
I am a little bit confused, does the word 'template image' equals to kernel?
Also there seems no way to choose the size of the result image (which I would like the result image to be the same size as the original one) and border mode, unlike filter2D.
Really hope there is a cuda::filter2D version.......
So could `cv::cuda::Convolution::convolve` do what I want? If `cv::cuda::Convolution::convolve` function really can't work for my purpose, then how to use this function?
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