added init() function into MultiGpuMgr, added samples
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@ -131,15 +131,16 @@ namespace cv
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/////////////////////////// Multi GPU Manager //////////////////////////////
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// Provides functionality for working with many GPUs. Object of this
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// class must be created before any OpenCV GPU call and no call must
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// be done after its destruction.
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// Provides functionality for working with many GPUs
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class CV_EXPORTS MultiGpuMgr
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{
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public:
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MultiGpuMgr();
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~MultiGpuMgr();
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// Must be called before any other GPU calls
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void init();
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// Makes the given GPU active
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void gpuOn(int gpu_id);
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@ -48,6 +48,7 @@ namespace cv { namespace gpu {
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class MultiGpuMgr::Impl {};
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MultiGpuMgr::MultiGpuMgr() { throw_nogpu(); }
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void MultiGpuMgr::init() { throw_nogpu(); }
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void MultiGpuMgr::gpuOn(int) { throw_nogpu(); }
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void MultiGpuMgr::gpuOff() { throw_nogpu(); }
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@ -79,6 +80,8 @@ public:
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void gpuOn(int gpu_id)
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{
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if (gpu_id < 0 || gpu_id >= num_devices_)
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CV_Error(CV_StsBadArg, "MultiGpuMgr::gpuOn: GPU ID is out of range");
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cuSafeCall(cuCtxPushCurrent(contexts_[gpu_id]));
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}
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@ -105,6 +108,8 @@ MultiGpuMgr::Impl::Impl(): num_devices_(0)
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num_devices_ = getCudaEnabledDeviceCount();
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contexts_.resize(num_devices_);
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cuSafeCall(cuInit(0));
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CUdevice device;
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CUcontext prev_context;
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for (int i = 0; i < num_devices_; ++i)
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@ -116,18 +121,28 @@ MultiGpuMgr::Impl::Impl(): num_devices_(0)
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}
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MultiGpuMgr::MultiGpuMgr(): impl_(new Impl()) {}
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MultiGpuMgr::MultiGpuMgr() {}
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MultiGpuMgr::~MultiGpuMgr() {}
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void MultiGpuMgr::init()
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{
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impl_ = Ptr<Impl>(new Impl());
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}
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void MultiGpuMgr::gpuOn(int gpu_id)
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{
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if (impl_.empty())
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CV_Error(CV_StsNullPtr, "MultiGpuMgr::gpuOn: must be initialized before any calls");
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impl_->gpuOn(gpu_id);
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}
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void MultiGpuMgr::gpuOff()
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{
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if (impl_.empty())
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CV_Error(CV_StsNullPtr, "MultiGpuMgr::gpuOff: must be initialized before any calls");
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impl_->gpuOff();
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}
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@ -120,25 +120,25 @@ int main(int argc, char** argv)
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// Init CUDA Driver API
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safeCall(cuInit(0));
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// Create context for the first GPU
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// Create context for GPU #0
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CUdevice device;
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safeCall(cuDeviceGet(&device, 0));
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safeCall(cuCtxCreate(&contexts[0], 0, device));
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contextOff();
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// Create context for the second GPU
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// Create context for GPU #1
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safeCall(cuDeviceGet(&device, 1));
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safeCall(cuCtxCreate(&contexts[1], 0, device));
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contextOff();
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// Split source images for processing on the first GPU
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// Split source images for processing on GPU #0
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contextOn(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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contextOff();
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// Split source images for processing on the second GPU
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// Split source images for processing on the GPU #1
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contextOn(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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102
samples/gpu/multi.cpp
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102
samples/gpu/multi.cpp
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@ -0,0 +1,102 @@
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/* This sample demonstrates the way you can perform independed tasks
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on the different 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/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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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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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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MultiGpuMgr multi_gpu_mgr;
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int main()
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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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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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DeviceInfo dev_info(i);
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if (!dev_info.isCompatible())
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{
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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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multi_gpu_mgr.init();
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// Execute calculation in two threads using two GPUs
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int devices[] = {0, 2};
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parallel_do(devices, devices + 2, Worker());
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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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multi_gpu_mgr.gpuOn(device_id);
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Mat src(1000, 1000, CV_32F);
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Mat dst;
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RNG rng(0);
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rng.fill(src, RNG::UNIFORM, 0, 1);
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// CPU works
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transpose(src, dst);
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// GPU works
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GpuMat d_src(src);
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GpuMat d_dst;
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transpose(d_src, d_dst);
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// Check results
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bool passed = norm(dst - Mat(d_dst), NORM_INF) < 1e-3;
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cout << "GPU #" << device_id << " (" << DeviceInfo().name() << "): "
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<< (passed ? "passed" : "FAILED") << endl;
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// Deallocate data here, otherwise deallocation will be performed
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// after context is extracted from the stack
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d_src.release();
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d_dst.release();
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multi_gpu_mgr.gpuOff();
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}
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#endif
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samples/gpu/stereo_multi.cpp
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148
samples/gpu/stereo_multi.cpp
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@ -0,0 +1,148 @@
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/* 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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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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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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MultiGpuMgr multi_gpu_mgr;
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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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// CPU result
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Mat result;
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int main(int argc, char** argv)
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{
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if (argc < 3)
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{
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cout << "Usage: stereo_multi_gpu <left_image> <right_image>\n";
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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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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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DeviceInfo dev_info(i);
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if (!dev_info.isCompatible())
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{
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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 = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
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Mat right = imread(argv[2], CV_LOAD_IMAGE_GRAYSCALE);
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if (left.empty())
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{
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cout << "Cannot open '" << argv[1] << "'\n";
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return -1;
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}
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if (right.empty())
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{
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cout << "Cannot open '" << argv[2] << "'\n";
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return -1;
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}
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multi_gpu_mgr.init();
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// Split source images for processing on the GPU #0
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multi_gpu_mgr.gpuOn(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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multi_gpu_mgr.gpuOff();
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// Split source images for processing on the GPU #1
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multi_gpu_mgr.gpuOn(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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multi_gpu_mgr.gpuOff();
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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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multi_gpu_mgr.gpuOn(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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multi_gpu_mgr.gpuOff();
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// Release the second GPU resources
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multi_gpu_mgr.gpuOn(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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multi_gpu_mgr.gpuOff();
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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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multi_gpu_mgr.gpuOn(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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cout << "GPU #" << device_id << " (" << DeviceInfo().name()
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<< "): finished\n";
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multi_gpu_mgr.gpuOff();
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}
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#endif
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