我正在编写一个 c++ 代码,用于比较使用 CPU 和 GPU 版本的 opencv 的morphologyEx 方法的性能。这是我的代码:
#include <opencv2/opencv.hpp>
#include <opencv2/gpu/gpu.hpp>
#include <sys/time.h>
#include <ctime>
using namespace cv;
using namespace std;
double start_timer()
{
double start_time = (double) getTickCount();
return start_time;
}
double end_timer(double start_time,int num_tests)
{
double time = (1000 * ((double) getTickCount() - start_time)/ getTickFrequency());
cout << "Average time of " << num_tests << " frames is: " << time/num_tests << " ms" << endl;
return time;
}
int main()
{
Mat cpuSrc;
cv::gpu::GpuMat src_gpu, dst_gpu;
Mat dst;
Mat element;
int element_shape = MORPH_RECT;
element = getStructuringElement(element_shape, Size(10, 10 ), Point(-1, -1) );
cpuSrc = imread("images.jpeg",CV_LOAD_IMAGE_ANYDEPTH);
if (!cpuSrc.data)
{
cerr << "Cannot read the data" << endl;
return -1;
}
cout << "Starting calculating time for CPU ....." << endl;
double start_time = start_timer();
int d = 0;
while(d<100)
{
cv::morphologyEx(cpuSrc, dst, CV_MOP_OPEN, element,Point(-1,-1),1);
}
double total_time_cpu = end_timer(start_time,d);
//--------------------------------------------------------------
cout << "Starting calculating time for GPU ....." << endl;
d = 0;
cv::gpu::GpuMat buf1, buf2;
gpu::Stream stream;
double start_time_1 = start_timer();
while(d<100)
{
stream.enqueueUpload(cpuSrc, src_gpu);
cv::gpu::morphologyEx(src_gpu,dst_gpu,CV_MOP_OPEN,element,
buf1,buf2,Point(-1,-1),1,stream);
stream.enqueueDownload(dst_gpu, dst);
}
stream.waitForCompletion();
double total_time_gpu = end_timer(start_time_1,d);
cout << "Gain is: " << total_time_cpu / total_time_gpu << endl;
return 0;
}
我正在使用循环,就好像我正在模拟包含 100 帧的视频一样。我正在使用 NVIDIA Corporation GF110 [GeForce GTX 570] 和 Intel Corporation Xeon E5/Core i7 DMI2。此外,我测试了上传和下载的时间,第一帧非常大,但之后上传可以忽略不计,每帧为0.02ms,下载为0.1ms,主要时间消耗是morphologyEx操作.
本次模拟的时间结果如下:
CPU形态版本,100帧平均时间为::0.027349 ms,GPU版本为::18.0128 ms
你能帮我弄清楚这种意想不到的表现可能是什么原因吗?!
非常感谢你。