150 lines
3.6 KiB
C++
150 lines
3.6 KiB
C++
/* This sample demonstrates working on one piece of data using two GPUs.
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It splits input into two parts and processes them separately on different
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GPUs. */
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// Disable some warnings which are caused with CUDA headers
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#if defined(_MSC_VER)
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#pragma warning(disable: 4201 4408 4100)
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#endif
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#include <iostream>
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#include "cvconfig.h"
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#include "opencv2/core/core.hpp"
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#include "opencv2/highgui/highgui.hpp"
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#include "opencv2/gpu/gpu.hpp"
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#if !defined(HAVE_CUDA) || !defined(HAVE_TBB)
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int main()
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{
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#if !defined(HAVE_CUDA)
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std::cout << "CUDA support is required (CMake key 'WITH_CUDA' must be true).\n";
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#endif
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#if !defined(HAVE_TBB)
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std::cout << "TBB support is required (CMake key 'WITH_TBB' must be true).\n";
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#endif
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return 0;
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}
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#else
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#include "opencv2/core/internal.hpp" // For TBB wrappers
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using namespace std;
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using namespace cv;
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using namespace cv::gpu;
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struct Worker { void operator()(int device_id) const; };
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// GPUs data
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GpuMat d_left[2];
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GpuMat d_right[2];
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StereoBM_GPU* bm[2];
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GpuMat d_result[2];
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static void printHelp()
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{
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std::cout << "Usage: stereo_multi_gpu --left <image> --right <image>\n";
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}
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int main(int argc, char** argv)
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{
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if (argc < 5)
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{
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printHelp();
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return -1;
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}
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int num_devices = getCudaEnabledDeviceCount();
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if (num_devices < 2)
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{
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std::cout << "Two or more GPUs are required\n";
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return -1;
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}
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for (int i = 0; i < num_devices; ++i)
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{
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cv::gpu::printShortCudaDeviceInfo(i);
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DeviceInfo dev_info(i);
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if (!dev_info.isCompatible())
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{
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std::cout << "GPU module isn't built for GPU #" << i << " ("
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<< dev_info.name() << ", CC " << dev_info.majorVersion()
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<< dev_info.minorVersion() << "\n";
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return -1;
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}
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}
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// Load input data
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Mat left, right;
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for (int i = 1; i < argc; ++i)
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{
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if (string(argv[i]) == "--left")
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{
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left = imread(argv[++i], CV_LOAD_IMAGE_GRAYSCALE);
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CV_Assert(!left.empty());
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}
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else if (string(argv[i]) == "--right")
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{
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right = imread(argv[++i], CV_LOAD_IMAGE_GRAYSCALE);
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CV_Assert(!right.empty());
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}
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else if (string(argv[i]) == "--help")
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{
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printHelp();
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return -1;
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}
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}
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// Split source images for processing on the GPU #0
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setDevice(0);
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d_left[0].upload(left.rowRange(0, left.rows / 2));
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d_right[0].upload(right.rowRange(0, right.rows / 2));
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bm[0] = new StereoBM_GPU();
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// Split source images for processing on the GPU #1
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setDevice(1);
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d_left[1].upload(left.rowRange(left.rows / 2, left.rows));
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d_right[1].upload(right.rowRange(right.rows / 2, right.rows));
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bm[1] = new StereoBM_GPU();
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// Execute calculation in two threads using two GPUs
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int devices[] = {0, 1};
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parallel_do(devices, devices + 2, Worker());
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// Release the first GPU resources
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setDevice(0);
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imshow("GPU #0 result", Mat(d_result[0]));
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d_left[0].release();
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d_right[0].release();
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d_result[0].release();
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delete bm[0];
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// Release the second GPU resources
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setDevice(1);
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imshow("GPU #1 result", Mat(d_result[1]));
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d_left[1].release();
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d_right[1].release();
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d_result[1].release();
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delete bm[1];
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waitKey();
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return 0;
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}
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void Worker::operator()(int device_id) const
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{
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setDevice(device_id);
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bm[device_id]->operator()(d_left[device_id], d_right[device_id],
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d_result[device_id]);
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std::cout << "GPU #" << device_id << " (" << DeviceInfo().name()
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<< "): finished\n";
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}
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#endif
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