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/* This sample demonstrates the way you can perform independent tasks
on the different GPUs */
// Disable some warnings which are caused with CUDA headers
#if defined(_MSC_VER)
#pragma warning(disable: 4201 4408 4100)
#endif
#include <iostream>
#include "opencv2/core.hpp"
#include "opencv2/cudaarithm.hpp"
#if !defined(HAVE_CUDA)
int main()
{
std::cout << "CUDA support is required (OpenCV CMake parameter 'WITH_CUDA' must be true)." << std::endl;
return 0;
}
#else
using namespace std;
using namespace cv;
using namespace cv::cuda;
struct Worker : public cv::ParallelLoopBody
{
void operator()(const Range& r) const CV_OVERRIDE
{
for (int i = r.start; i < r.end; ++i) { this->operator()(i); }
}
void operator()(int device_id) const;
};
int main()
{
int num_devices = getCudaEnabledDeviceCount();
if (num_devices < 2)
{
std::cout << "Two or more GPUs are required\n";
return -1;
}
for (int i = 0; i < num_devices; ++i)
{
cv::cuda::printShortCudaDeviceInfo(i);
DeviceInfo dev_info(i);
if (!dev_info.isCompatible())
{
std::cout << "CUDA module isn't built for GPU #" << i << " ("
<< dev_info.name() << ", CC " << dev_info.majorVersion()
<< dev_info.minorVersion() << "\n";
return -1;
}
}
// Execute calculation in two threads using two GPUs
cv::Range devices(0, 2);
cv::parallel_for_(devices, Worker(), devices.size());
return 0;
}
void Worker::operator()(int device_id) const
{
setDevice(device_id);
Mat src(1000, 1000, CV_32F);
Mat dst;
RNG rng(0);
rng.fill(src, RNG::UNIFORM, 0, 1);
// CPU works
cv::transpose(src, dst);
// GPU works
GpuMat d_src(src);
GpuMat d_dst;
cuda::transpose(d_src, d_dst);
// Check results
bool passed = cv::norm(dst - Mat(d_dst), NORM_INF) < 1e-3;
std::cout << "GPU #" << device_id << " (" << DeviceInfo().name() << "): "
<< (passed ? "passed" : "FAILED") << endl;
// Deallocate data here, otherwise deallocation will be performed
// after context is extracted from the stack
d_src.release();
d_dst.release();
}
#endif
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