This lib is for accelerating general computer vision algorithms, and fined-tuned.

• Windows
• Linux(Coming)

Now, it fits for Windows only, but it's easy to port to supporting Linux. My new toy jetson tx2 just didn't work, back to distributor now, I have to wait for a while.

1. Cmake 3.10+
2. OpenCV 3.x(it doesn't need to compile, just download it and extract to folder C:\Program Files\opencv
3. CUDA SDK(8.0 or later)
4. VS 2015 or later Tips: Your video card should be capable of compute capability 2.1 at least.

# get the repo, and make sure those dependencies is installed.
git clone https://github.com/Dengjianping/AcceleratedVision.git

# configure
mkdir build && cd build
cmake -G "Visual Studio 14 Win64 ..

# build, a dll and lib files will be generated at build\Release
msbuild /t:rebuild /p:Configuration=Release /p:Platform=x64 accel_vision.sln

Copy that folder named include to your project folder, as well as the dll and lib files you just compiled.

#include "include\cuda_color_converter.h"

using namespace std;
using namespace cv;

#prgram comment(lib,"accel_vision.lib")

int main(void)
{
    string path = "2028.jpg";
    Mat img = imread(path), result;

    cudaGray(img, result); // gray this image

    string title = "CUDA";
    namedWindow(title);

    imshow(title, result);
    waitKey(0);

    return 0;
}

I just test these api on my laptop(Thinkpad T420), which equips with CPU I5-2520M video card Nvidia NVS 4200M, that has only 1 SM, 48 CUDA cores, compute capability 2.1.

The size of testing image is 2048 * 2048. BTW, time cost only means the kernel executing time on gpu device, excluding data copying time on operations host-to-device and device-to-host.

More details see the excel.