accelerate OpenCV functions on gpu through CUDA

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Why not use the Python API directly instead of translating to C++? By accelerate OpenCv functions using CUDA do you mean, how can you write CUDA kernel's? Is there anyway you can overlap processing on the GPU with functions on the host which don't have OpenCV CUDA implementations?

Since opencv is implemented in c and c++, its easier for me to implement my whole code in thoses languages in case I have to modify some opencv functions. Using opencv functions using cuda means that I use functions from cv::cuda, giving them GpuMat in arguments. I could try to overlap, but the host functions are like 6 or 7 times slower than the GPU ones, so overlaping corresponds to being restricted by the CPUs

OK so what is your question, how to implement cv::undistort in CUDA?

how to implement non accelerated opencv functions on CUDA ?

I was hoping that wasn't going to be your question. My answer unfortunately is that:

1. You would need a basic understanding of the Single Instruction Multiple Thread (SIMT) execution model used by CUDA to decide whether the undistort function can be/is worth accelerating on the GPU.
2. To do the implementation you would need to learn how to program in CUDA.

Then including in OpenCV should be relatively straight forward as all the API's are there to access and manipulate the GPU's memory.

Thank you I think I have a lot of work now