Once you have your data up in the GPU memory you may call GPU enabled functions of OpenCV. Most of the functions keep the same name just as on the CPU, with the difference that they only accept GpuMat inputs. Another thing to keep in mind is that not for all channel numbers you can make efficient algorithms on the GPU.
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Lightweight class encapsulating pitched memory on a GPU and passed to nvcc-compiled code (CUDA kernels). Typically, it is used internally by OpenCV and by users who write device code. You can call its members from both host and device code.
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GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance, ␍ ␊ 1281: const GpuMat& maskCollection, Stream& stream = Stream::Null()); ␍ ␊ 1282 ␍ ␊ 1283 // Download trainIdx, imgIdx and distance and convert it to vector with DMatch ␍ ␊ 1284 Sep 15, 2020 · If you have been working with OpenCV for some time, you should have noticed that in most scenarios OpenCV utilizes CPU, which doesn’t always guarantee you the desired performance. To tackle this problem, in 2010 a new module that provides GPU acceleration using CUDA was added to OpenCV.