Question

I am trying to implement computer vision algorithm on my NVidia GPU with openCV. I am using openCV 2.4 and I am currently writing very simple programs to get accustomed to openCV. I wrote a simple code of transposing a matrix and also to implementing canny edge detection on GPU. The program is running perfectly but I need to deallocate the memory in both the CPU and GPU. So I am posting my code below :

int main(int argc,char *argv[])
{
  int k;
  cv::Mat src;
  cv::Mat dest;
  cv::Mat dest_1;
  cv::gpu::GpuMat im_source;
  cv::gpu::GpuMat im_dest;
  cv::gpu::GpuMat im_dest_1;
  cv::gpu::Stream::Null;

  k = cv::gpu :: getCudaEnabledDeviceCount();
  printf("%d\n",k);

  src = cv::imread("lena.jpg",0);
  cv::imshow("lena_org",src);
  im_source.upload(src);

  cv::gpu::transpose(im_source,im_dest);

   im_dest.download(dest);

   cv::imshow("lena_trans",dest);

   cv::gpu::Canny(im_source,im_dest_1,100,100,3,false);

   im_dest_1.download(dest_1);
   cv::imshow("lena_edge",dest_1);
   cv::waitKey();

} 

So from the code above I believe the memory is not getting freed in both the CPU and GPU. I was searching the internet a bit and I came across with cv::Mat::Release for cpu and cv::gpu::GpuMat::Release for the GPU side. But I am not getting how to use them or how I should use this functions in my code so that I could free bot my CPU and GPU memories. It would be very much helpful if someone could guide me through correct usage of the Release apis so that I could free the memory successfully. Thanks for all your support.

Answer 1

The destructor for cv::Mat objects automatically frees the memory, making calls to the release function you describe. At the level of your code, you shouldn’t have to worry about that. Once the matrix leaves scope, it gets destroyed.

If you want to manually destroy your reference to the data, you can call, for example, src.release(). There is a good tutorial on memory management in the OpenCV documentation, available here