/* 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 #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; }; // 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 \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; } // Split source images for processing on the GPU #0 setDevice(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(); // Split source images for processing on the GPU #1 setDevice(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(); // Execute calculation in two threads using two GPUs int devices[] = {0, 1}; parallel_do(devices, devices + 2, Worker()); // Release the first GPU resources setDevice(0); imshow("GPU #0 result", Mat(d_result[0])); d_left[0].release(); d_right[0].release(); d_result[0].release(); delete bm[0]; // Release the second GPU resources setDevice(1); imshow("GPU #1 result", Mat(d_result[1])); d_left[1].release(); d_right[1].release(); d_result[1].release(); delete bm[1]; waitKey(); return 0; } void Worker::operator()(int device_id) const { setDevice(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"; } #endif