149 lines
3.8 KiB
C++
149 lines
3.8 KiB
C++
/* This sample demonstrates working on one piece of data using two GPUs.
|
|
It splits input into two parts and processes them separately on 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 <cvconfig.h>
|
|
#include <opencv2/core/core.hpp>
|
|
#include <opencv2/highgui/highgui.hpp>
|
|
#include <opencv2/gpu/gpu.hpp>
|
|
|
|
#if !defined(HAVE_CUDA) || !defined(HAVE_TBB)
|
|
|
|
int main()
|
|
{
|
|
#if !defined(HAVE_CUDA)
|
|
std::cout << "CUDA support is required (CMake key 'WITH_CUDA' must be true).\n";
|
|
#endif
|
|
|
|
#if !defined(HAVE_TBB)
|
|
std::cout << "TBB support is required (CMake key 'WITH_TBB' must be true).\n";
|
|
#endif
|
|
|
|
return 0;
|
|
}
|
|
|
|
#else
|
|
|
|
#include "opencv2/core/internal.hpp" // For TBB wrappers
|
|
|
|
using namespace std;
|
|
using namespace cv;
|
|
using namespace cv::gpu;
|
|
|
|
struct Worker { void operator()(int device_id) const; };
|
|
|
|
MultiGpuManager multi_gpu_mgr;
|
|
|
|
// GPUs data
|
|
GpuMat d_left[2];
|
|
GpuMat d_right[2];
|
|
StereoBM_GPU* bm[2];
|
|
GpuMat d_result[2];
|
|
|
|
// CPU result
|
|
Mat result;
|
|
|
|
int main(int argc, char** argv)
|
|
{
|
|
if (argc < 3)
|
|
{
|
|
std::cout << "Usage: stereo_multi_gpu <left_image> <right_image>\n";
|
|
return -1;
|
|
}
|
|
|
|
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)
|
|
{
|
|
DeviceInfo dev_info(i);
|
|
if (!dev_info.isCompatible())
|
|
{
|
|
std::cout << "GPU module isn't built for GPU #" << i << " ("
|
|
<< dev_info.name() << ", CC " << dev_info.majorVersion()
|
|
<< dev_info.minorVersion() << "\n";
|
|
return -1;
|
|
}
|
|
}
|
|
|
|
// Load input data
|
|
Mat left = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
|
|
Mat right = imread(argv[2], CV_LOAD_IMAGE_GRAYSCALE);
|
|
if (left.empty())
|
|
{
|
|
std::cout << "Cannot open '" << argv[1] << "'\n";
|
|
return -1;
|
|
}
|
|
if (right.empty())
|
|
{
|
|
std::cout << "Cannot open '" << argv[2] << "'\n";
|
|
return -1;
|
|
}
|
|
|
|
multi_gpu_mgr.init();
|
|
|
|
// Split source images for processing on the GPU #0
|
|
multi_gpu_mgr.gpuOn(0);
|
|
d_left[0].upload(left.rowRange(0, left.rows / 2));
|
|
d_right[0].upload(right.rowRange(0, right.rows / 2));
|
|
bm[0] = new StereoBM_GPU();
|
|
multi_gpu_mgr.gpuOff();
|
|
|
|
// Split source images for processing on the GPU #1
|
|
multi_gpu_mgr.gpuOn(1);
|
|
d_left[1].upload(left.rowRange(left.rows / 2, left.rows));
|
|
d_right[1].upload(right.rowRange(right.rows / 2, right.rows));
|
|
bm[1] = new StereoBM_GPU();
|
|
multi_gpu_mgr.gpuOff();
|
|
|
|
// Execute calculation in two threads using two GPUs
|
|
int devices[] = {0, 1};
|
|
parallel_do(devices, devices + 2, Worker());
|
|
|
|
// Release the first GPU resources
|
|
multi_gpu_mgr.gpuOn(0);
|
|
imshow("GPU #0 result", Mat(d_result[0]));
|
|
d_left[0].release();
|
|
d_right[0].release();
|
|
d_result[0].release();
|
|
delete bm[0];
|
|
multi_gpu_mgr.gpuOff();
|
|
|
|
// Release the second GPU resources
|
|
multi_gpu_mgr.gpuOn(1);
|
|
imshow("GPU #1 result", Mat(d_result[1]));
|
|
d_left[1].release();
|
|
d_right[1].release();
|
|
d_result[1].release();
|
|
delete bm[1];
|
|
multi_gpu_mgr.gpuOff();
|
|
|
|
waitKey();
|
|
return 0;
|
|
}
|
|
|
|
|
|
void Worker::operator()(int device_id) const
|
|
{
|
|
multi_gpu_mgr.gpuOn(device_id);
|
|
|
|
bm[device_id]->operator()(d_left[device_id], d_right[device_id],
|
|
d_result[device_id]);
|
|
|
|
std::cout << "GPU #" << device_id << " (" << DeviceInfo().name()
|
|
<< "): finished\n";
|
|
|
|
multi_gpu_mgr.gpuOff();
|
|
}
|
|
|
|
#endif
|