cuda

CUDA

In order to use the GoCV cuda package, the CUDA toolkit from nvidia needs to be installed on the host system.

The CUDA backend for DNN module requires CC (Compute Capability) 5.3 or higher. Check your GPU https://developer.nvidia.com/cuda-gpus

Please see https://docs.nvidia.com/cuda/index.html for more information.

Furthermore opencv must be compiled with CUDA support.

GoCV also supports using CUDA as a backend for the OpenCV deep neural network (DNN) module.

How to use

Deep Neural Network (DNN) module

This code loads a Caffe model, and then uses CUDA to prepare it for execution on the GPU:

net := gocv.ReadNet("/path/to/your/model.caffemodel", "/path/to/your/config.proto")
if net.Empty() {
    fmt.Println("Error reading network model")
    return
}

net.SetPreferableBackend(gocv.NetBackendType(gocv.NetBackendCUDA))
net.SetPreferableTarget(gocv.NetTargetType(gocv.NetTargetCUDA))

OpenCV CUDA modules

You can use calls directly to the OpenCV CUDA wrappers, both the synchronous and asynchronous versions.

Here is an example that uses the synchronous CUDA calls:

    cimg, mimg, dimg := NewGpuMat(), NewGpuMat(), NewGpuMat()
    defer cimg.Close()
    defer mimg.Close()
    defer dimg.Close()

    canny := NewCannyEdgeDetector(50, 100)
    defer canny.Close()

    detector := NewHoughSegmentDetector(1, math.Pi/180, 150, 50)
    defer detector.Close()

    dest := gocv.NewMat()
    defer dest.Close()

    // after each call, CPU thread is blocked until GPU operation is completed.
    cimg.Upload(src)
    canny.Detect(cimg, &mimg)
    detector.Detect(mimg, &dimg)
    dimg.Download(&dest)

Here is an example that uses the Stream type for calling CUDA using the asynchronous interface:

    cimg, mimg, dimg := NewGpuMat(), NewGpuMat(), NewGpuMat()
    defer cimg.Close()
    defer mimg.Close()
    defer dimg.Close()

    stream := NewStream()
    defer stream.Close()

    canny := NewCannyEdgeDetector(50, 100)
    defer canny.Close()

    detector := NewHoughSegmentDetector(1, math.Pi/180, 150, 50)
    defer detector.Close()

    dest := gocv.NewMat()
    defer dest.Close()

    // all calls return immediately to CPU, work is scheduled to be done on GPU.
    cimg.UploadWithStream(src, stream)
    canny.DetectWithStream(cimg, &mimg, stream)
    detector.DetectWithStream(mimg, &dimg, stream)
    dimg.DownloadWithStream(&dest, stream)

    // CPU thread blocks until all GPU calls have completed.
    stream.WaitForCompletion()

Installing CUDA

Download and install packages from https://developer.nvidia.com/cuda-downloads

For example, download 'cuda_10.2.89_440.33.01_linux.run'

Download and install packages from https://developer.nvidia.com/rdp/cudnn-archive

For example the 'cuDNN Runtime Library for Ubuntu18.04 (Deb)' and 'cuDNN Developer Library for Ubuntu18.04 (Deb)'

Compiling OpenCV with CUDA

We have included the make target install_cuda that compiles OpenCV with CUDA support. (For more details on the compilation process please see the Makefile)

Run the command make install_cuda and you should be good to go.

If you need static opencv libraries

make install_cuda BUILD_SHARED_LIBS=OFF

Then finally verify that it is all working

cd $GOPATH/src/gocv.io/x/gocv
go run ./cmd/cuda/main.go

You should see something along the lines of:

gocv version: 0.25.0
cuda information:
  Device 0:  "GeForce MX150"  2003Mb, sm_61, Driver/Runtime ver.10.0/10.0