498 lines
14 KiB
C++
498 lines
14 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 GPUs.
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#ifdef _WIN32
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#define NOMINMAX
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#include <windows.h>
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#else
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#include <pthread.h>
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#include <unistd.h>
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#endif
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#include <iostream>
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#include <iomanip>
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#include "opencv2/core.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/imgproc.hpp"
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#include "opencv2/cudastereo.hpp"
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using namespace std;
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using namespace cv;
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using namespace cv::cuda;
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///////////////////////////////////////////////////////////
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// Thread
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// OS-specific wrappers for multi-threading
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#ifdef _WIN32
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class Thread
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{
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struct UserData
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{
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void (*func)(void* userData);
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void* param;
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};
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static DWORD WINAPI WinThreadFunction(LPVOID lpParam)
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{
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UserData* userData = static_cast<UserData*>(lpParam);
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userData->func(userData->param);
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return 0;
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}
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UserData userData_;
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HANDLE thread_;
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DWORD threadId_;
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public:
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Thread(void (*func)(void* userData), void* userData)
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{
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userData_.func = func;
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userData_.param = userData;
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thread_ = CreateThread(
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NULL, // default security attributes
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0, // use default stack size
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WinThreadFunction, // thread function name
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&userData_, // argument to thread function
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0, // use default creation flags
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&threadId_); // returns the thread identifier
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}
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~Thread()
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{
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CloseHandle(thread_);
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}
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void wait()
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{
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WaitForSingleObject(thread_, INFINITE);
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}
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};
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#else
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class Thread
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{
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struct UserData
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{
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void (*func)(void* userData);
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void* param;
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};
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static void* PThreadFunction(void* lpParam)
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{
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UserData* userData = static_cast<UserData*>(lpParam);
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userData->func(userData->param);
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return 0;
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}
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pthread_t thread_;
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UserData userData_;
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public:
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Thread(void (*func)(void* userData), void* userData)
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{
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userData_.func = func;
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userData_.param = userData;
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pthread_create(&thread_, NULL, PThreadFunction, &userData_);
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}
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~Thread()
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{
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pthread_detach(thread_);
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}
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void wait()
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{
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pthread_join(thread_, NULL);
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}
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};
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#endif
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///////////////////////////////////////////////////////////
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// StereoSingleGpu
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// Run Stereo algorithm on single GPU
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class StereoSingleGpu
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{
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public:
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explicit StereoSingleGpu(int deviceId = 0);
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~StereoSingleGpu();
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void compute(const Mat& leftFrame, const Mat& rightFrame, Mat& disparity);
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private:
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int deviceId_;
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GpuMat d_leftFrame;
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GpuMat d_rightFrame;
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GpuMat d_disparity;
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Ptr<cuda::StereoBM> d_alg;
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};
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StereoSingleGpu::StereoSingleGpu(int deviceId) : deviceId_(deviceId)
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{
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cuda::setDevice(deviceId_);
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d_alg = cuda::createStereoBM(256);
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}
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StereoSingleGpu::~StereoSingleGpu()
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{
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cuda::setDevice(deviceId_);
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d_leftFrame.release();
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d_rightFrame.release();
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d_disparity.release();
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d_alg.release();
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}
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void StereoSingleGpu::compute(const Mat& leftFrame, const Mat& rightFrame, Mat& disparity)
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{
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cuda::setDevice(deviceId_);
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d_leftFrame.upload(leftFrame);
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d_rightFrame.upload(rightFrame);
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d_alg->compute(d_leftFrame, d_rightFrame, d_disparity);
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d_disparity.download(disparity);
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}
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///////////////////////////////////////////////////////////
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// StereoMultiGpuThread
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// Run Stereo algorithm on two GPUs using different host threads
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class StereoMultiGpuThread
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{
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public:
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StereoMultiGpuThread();
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~StereoMultiGpuThread();
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void compute(const Mat& leftFrame, const Mat& rightFrame, Mat& disparity);
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private:
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GpuMat d_leftFrames[2];
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GpuMat d_rightFrames[2];
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GpuMat d_disparities[2];
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Ptr<cuda::StereoBM> d_algs[2];
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struct StereoLaunchData
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{
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int deviceId;
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Mat leftFrame;
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Mat rightFrame;
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Mat disparity;
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GpuMat* d_leftFrame;
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GpuMat* d_rightFrame;
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GpuMat* d_disparity;
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Ptr<cuda::StereoBM> d_alg;
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};
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static void launchGpuStereoAlg(void* userData);
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};
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StereoMultiGpuThread::StereoMultiGpuThread()
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{
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cuda::setDevice(0);
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d_algs[0] = cuda::createStereoBM(256);
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cuda::setDevice(1);
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d_algs[1] = cuda::createStereoBM(256);
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}
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StereoMultiGpuThread::~StereoMultiGpuThread()
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{
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cuda::setDevice(0);
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d_leftFrames[0].release();
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d_rightFrames[0].release();
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d_disparities[0].release();
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d_algs[0].release();
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cuda::setDevice(1);
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d_leftFrames[1].release();
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d_rightFrames[1].release();
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d_disparities[1].release();
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d_algs[1].release();
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}
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void StereoMultiGpuThread::compute(const Mat& leftFrame, const Mat& rightFrame, Mat& disparity)
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{
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disparity.create(leftFrame.size(), CV_8UC1);
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// Split input data onto two parts for each GPUs.
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// We add small border for each part,
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// because original algorithm doesn't calculate disparity on image borders.
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// With such padding we will get output in the middle of final result.
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StereoLaunchData launchDatas[2];
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launchDatas[0].deviceId = 0;
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launchDatas[0].leftFrame = leftFrame.rowRange(0, leftFrame.rows / 2 + 32);
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launchDatas[0].rightFrame = rightFrame.rowRange(0, rightFrame.rows / 2 + 32);
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launchDatas[0].disparity = disparity.rowRange(0, leftFrame.rows / 2);
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launchDatas[0].d_leftFrame = &d_leftFrames[0];
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launchDatas[0].d_rightFrame = &d_rightFrames[0];
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launchDatas[0].d_disparity = &d_disparities[0];
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launchDatas[0].d_alg = d_algs[0];
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launchDatas[1].deviceId = 1;
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launchDatas[1].leftFrame = leftFrame.rowRange(leftFrame.rows / 2 - 32, leftFrame.rows);
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launchDatas[1].rightFrame = rightFrame.rowRange(leftFrame.rows / 2 - 32, leftFrame.rows);
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launchDatas[1].disparity = disparity.rowRange(leftFrame.rows / 2, leftFrame.rows);
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launchDatas[1].d_leftFrame = &d_leftFrames[1];
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launchDatas[1].d_rightFrame = &d_rightFrames[1];
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launchDatas[1].d_disparity = &d_disparities[1];
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launchDatas[1].d_alg = d_algs[1];
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Thread thread0(launchGpuStereoAlg, &launchDatas[0]);
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Thread thread1(launchGpuStereoAlg, &launchDatas[1]);
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thread0.wait();
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thread1.wait();
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}
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void StereoMultiGpuThread::launchGpuStereoAlg(void* userData)
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{
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StereoLaunchData* data = static_cast<StereoLaunchData*>(userData);
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cuda::setDevice(data->deviceId);
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data->d_leftFrame->upload(data->leftFrame);
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data->d_rightFrame->upload(data->rightFrame);
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data->d_alg->compute(*data->d_leftFrame, *data->d_rightFrame, *data->d_disparity);
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if (data->deviceId == 0)
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data->d_disparity->rowRange(0, data->d_disparity->rows - 32).download(data->disparity);
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else
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data->d_disparity->rowRange(32, data->d_disparity->rows).download(data->disparity);
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}
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///////////////////////////////////////////////////////////
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// StereoMultiGpuStream
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// Run Stereo algorithm on two GPUs from single host thread using async API
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class StereoMultiGpuStream
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{
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public:
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StereoMultiGpuStream();
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~StereoMultiGpuStream();
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void compute(const HostMem& leftFrame, const HostMem& rightFrame, HostMem& disparity);
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private:
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GpuMat d_leftFrames[2];
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GpuMat d_rightFrames[2];
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GpuMat d_disparities[2];
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Ptr<cuda::StereoBM> d_algs[2];
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Ptr<Stream> streams[2];
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};
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StereoMultiGpuStream::StereoMultiGpuStream()
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{
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cuda::setDevice(0);
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d_algs[0] = cuda::createStereoBM(256);
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streams[0] = makePtr<Stream>();
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cuda::setDevice(1);
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d_algs[1] = cuda::createStereoBM(256);
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streams[1] = makePtr<Stream>();
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}
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StereoMultiGpuStream::~StereoMultiGpuStream()
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{
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cuda::setDevice(0);
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d_leftFrames[0].release();
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d_rightFrames[0].release();
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d_disparities[0].release();
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d_algs[0].release();
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streams[0].release();
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cuda::setDevice(1);
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d_leftFrames[1].release();
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d_rightFrames[1].release();
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d_disparities[1].release();
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d_algs[1].release();
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streams[1].release();
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}
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void StereoMultiGpuStream::compute(const HostMem& leftFrame, const HostMem& rightFrame, HostMem& disparity)
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{
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disparity.create(leftFrame.size(), CV_8UC1);
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// Split input data onto two parts for each GPUs.
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// We add small border for each part,
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// because original algorithm doesn't calculate disparity on image borders.
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// With such padding we will get output in the middle of final result.
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Mat leftFrameHdr = leftFrame.createMatHeader();
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Mat rightFrameHdr = rightFrame.createMatHeader();
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Mat disparityHdr = disparity.createMatHeader();
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Mat disparityPart0 = disparityHdr.rowRange(0, leftFrame.rows / 2);
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Mat disparityPart1 = disparityHdr.rowRange(leftFrame.rows / 2, leftFrame.rows);
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cuda::setDevice(0);
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d_leftFrames[0].upload(leftFrameHdr.rowRange(0, leftFrame.rows / 2 + 32), *streams[0]);
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d_rightFrames[0].upload(rightFrameHdr.rowRange(0, leftFrame.rows / 2 + 32), *streams[0]);
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d_algs[0]->compute(d_leftFrames[0], d_rightFrames[0], d_disparities[0], *streams[0]);
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d_disparities[0].rowRange(0, leftFrame.rows / 2).download(disparityPart0, *streams[0]);
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cuda::setDevice(1);
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d_leftFrames[1].upload(leftFrameHdr.rowRange(leftFrame.rows / 2 - 32, leftFrame.rows), *streams[1]);
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d_rightFrames[1].upload(rightFrameHdr.rowRange(leftFrame.rows / 2 - 32, leftFrame.rows), *streams[1]);
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d_algs[1]->compute(d_leftFrames[1], d_rightFrames[1], d_disparities[1], *streams[1]);
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d_disparities[1].rowRange(32, d_disparities[1].rows).download(disparityPart1, *streams[1]);
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cuda::setDevice(0);
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streams[0]->waitForCompletion();
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cuda::setDevice(1);
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streams[1]->waitForCompletion();
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}
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///////////////////////////////////////////////////////////
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// main
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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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cerr << "Usage: stereo_multi <left_video> <right_video>" << endl;
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return -1;
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}
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const int numDevices = getCudaEnabledDeviceCount();
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if (numDevices != 2)
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{
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cerr << "Two GPUs are required" << endl;
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return -1;
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}
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for (int i = 0; i < numDevices; ++i)
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{
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DeviceInfo devInfo(i);
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if (!devInfo.isCompatible())
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{
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cerr << "CUDA module wasn't built for GPU #" << i << " ("
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<< devInfo.name() << ", CC " << devInfo.majorVersion()
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<< devInfo.minorVersion() << endl;
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return -1;
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}
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printShortCudaDeviceInfo(i);
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}
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VideoCapture leftVideo(argv[1]);
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VideoCapture rightVideo(argv[2]);
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if (!leftVideo.isOpened())
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{
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cerr << "Can't open " << argv[1] << " video file" << endl;
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return -1;
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}
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if (!rightVideo.isOpened())
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{
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cerr << "Can't open " << argv[2] << " video file" << endl;
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return -1;
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}
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cout << endl;
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cout << "This sample demonstrates working on one piece of data using two GPUs." << endl;
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cout << "It splits input into two parts and processes them separately on different GPUs." << endl;
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cout << endl;
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Mat leftFrame, rightFrame;
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HostMem leftGrayFrame, rightGrayFrame;
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StereoSingleGpu gpu0Alg(0);
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StereoSingleGpu gpu1Alg(1);
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StereoMultiGpuThread multiThreadAlg;
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StereoMultiGpuStream multiStreamAlg;
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Mat disparityGpu0;
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Mat disparityGpu1;
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Mat disparityMultiThread;
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HostMem disparityMultiStream;
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Mat disparityGpu0Show;
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Mat disparityGpu1Show;
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Mat disparityMultiThreadShow;
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Mat disparityMultiStreamShow;
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TickMeter tm;
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cout << "-------------------------------------------------------------------" << endl;
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cout << "| Frame | GPU 0 ms | GPU 1 ms | Multi Thread ms | Multi Stream ms |" << endl;
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cout << "-------------------------------------------------------------------" << endl;
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for (int i = 0;; ++i)
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{
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leftVideo >> leftFrame;
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rightVideo >> rightFrame;
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if (leftFrame.empty() || rightFrame.empty())
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break;
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if (leftFrame.size() != rightFrame.size())
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{
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cerr << "Frames have different sizes" << endl;
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return -1;
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}
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leftGrayFrame.create(leftFrame.size(), CV_8UC1);
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rightGrayFrame.create(leftFrame.size(), CV_8UC1);
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cvtColor(leftFrame, leftGrayFrame.createMatHeader(), COLOR_BGR2GRAY);
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cvtColor(rightFrame, rightGrayFrame.createMatHeader(), COLOR_BGR2GRAY);
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tm.reset(); tm.start();
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gpu0Alg.compute(leftGrayFrame.createMatHeader(), rightGrayFrame.createMatHeader(),
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disparityGpu0);
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tm.stop();
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const double gpu0Time = tm.getTimeMilli();
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tm.reset(); tm.start();
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gpu1Alg.compute(leftGrayFrame.createMatHeader(), rightGrayFrame.createMatHeader(),
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disparityGpu1);
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tm.stop();
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const double gpu1Time = tm.getTimeMilli();
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tm.reset(); tm.start();
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multiThreadAlg.compute(leftGrayFrame.createMatHeader(), rightGrayFrame.createMatHeader(),
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disparityMultiThread);
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tm.stop();
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const double multiThreadTime = tm.getTimeMilli();
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tm.reset(); tm.start();
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multiStreamAlg.compute(leftGrayFrame, rightGrayFrame, disparityMultiStream);
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tm.stop();
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const double multiStreamTime = tm.getTimeMilli();
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cout << "| " << setw(5) << i << " | "
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<< setw(8) << setprecision(1) << fixed << gpu0Time << " | "
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<< setw(8) << setprecision(1) << fixed << gpu1Time << " | "
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<< setw(15) << setprecision(1) << fixed << multiThreadTime << " | "
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<< setw(15) << setprecision(1) << fixed << multiStreamTime << " |" << endl;
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resize(disparityGpu0, disparityGpu0Show, Size(1024, 768), 0, 0, INTER_AREA);
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resize(disparityGpu1, disparityGpu1Show, Size(1024, 768), 0, 0, INTER_AREA);
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resize(disparityMultiThread, disparityMultiThreadShow, Size(1024, 768), 0, 0, INTER_AREA);
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resize(disparityMultiStream.createMatHeader(), disparityMultiStreamShow, Size(1024, 768), 0, 0, INTER_AREA);
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imshow("disparityGpu0", disparityGpu0Show);
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imshow("disparityGpu1", disparityGpu1Show);
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imshow("disparityMultiThread", disparityMultiThreadShow);
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imshow("disparityMultiStream", disparityMultiStreamShow);
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const int key = waitKey(30) & 0xff;
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if (key == 27)
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break;
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}
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cout << "-------------------------------------------------------------------" << endl;
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return 0;
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}
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