多高斯背景差分，非常吃cpu，特别是多路视屏，所以想用gpu做检测 后面的跟踪一系列的规则判断用cpu
opencv+cuda+stl做了个测试
代码：
// MTTestCudaMog.cpp : Defines the entry point for the console application.
//
#include "stdafx.h"
#include "opencv.hpp"
#include <thread>
#include <iostream>
#include <mutex>
#include "cudacodec.hpp"
#include "cudabgsegm.hpp"
#include "cudaobjdetect.hpp"
#include "Timer.h"
using namespace std;
std::mutex m;
void f1()
{
cv::VideoCapture cap("D:\\testvideo\\22.mp4");
cv::Mat mat;
cv::Ptr<cv::cuda::BackgroundSubtractorMOG2> mog = cv::cuda::createBackgroundSubtractorMOG2(0.0002, 16,false);
cv::cuda::GpuMat gpuMat;
cv::cuda::GpuMat gpuMsk;
cv::Mat matMsk;
CTimer timeTester;
double t = 0.0;
while (true)
{
m.lock();
cap>>mat;
timeTester.Start();
gpuMat.upload(mat);
mog->apply( gpuMat, gpuMsk );
gpuMat.download( matMsk );
t = timeTester.End();
cout << "Intial ThreadID : " << std::this_thread::get_id() << ":" << t<< endl;
m.unlock();
}
};
int _tmain(int argc, _TCHAR* argv[])
{
int ichannel = 9;
vector<thread> vThread;
vThread.resize(ichannel);
for( int i=0; i<ichannel; i++ )
{
vThread[i] = std::thread(f1);
}
for( int i=0; i<ichannel; i++ )
{
vThread[i].join();
}
cout<<"Main Thread"<<endl;
return 0;
}
gpu负载率在36%左右 cpu使用25%左右包括读入视屏的占用
下面这幅图是每一帧gpu的多高斯检测时间包括数据上载gpu和下载到内存的时间
