![morphologyEx]( "morphologyEx")
本文介绍了gpu :: morphologyEx比CPU中的morphologyEx慢？的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我编写一个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<< 平均时间< num_tests<< frames是：< time / num_tests<< ms< endl;
return time;
}
int main（）
{
Mat cpuSrc;
cv :: gpu :: GpuMat src_gpu，dst_gpu;
Mat dst;
Mat元素;
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< 无法读取数据< endl;
return -1;
}
cout<< 开始计算CPU的时间.....< endl;
double start_time = start_timer（）;
int d = 0;
while（d {
cv :: morphologyEx（cpuSrc，dst，CV_MOP_OPEN，element，Point（-1，-1），1）;
}
double total_time_cpu = end_timer（start_time，d）;
// -------------------------------- ------------------------------
cout<< 开始计算GPU时间...< endl;
d = 0;
cv :: gpu :: GpuMat buf1，buf2;
gpu ::流流;
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<< 增益是：< total_time_cpu / total_time_gpu<< endl;
return 0;
}
此模拟的时间结果为如下：
解决方案
在初始化时你应该调用：
cv :: gpu :: setDevice ;
它将加速初始化。
I am writing a c++ code for comparing the performance of morphologyEx method of opencv using the CPU and GPU versions. Here is my code:
#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;
}
The time results for this simulations are as follows:
Thank you so much in advance.
解决方案
In the initialization you should call:
cv::gpu::setDevice(0);
It will speed up initialization.
这篇关于gpu :: morphologyEx比CPU中的morphologyEx慢？的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！