Open Source Computer Vision Library https://opencv.org/
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#include <iomanip>
#include "performance.h"
using namespace std;
using namespace cv;
void Test::gen(Mat& mat, int rows, int cols, int type)
{
mat.create(rows, cols, type);
Mat mat8u(rows, cols * mat.elemSize(), CV_8U, mat.data, mat.step);
RNG rng(0);
rng.fill(mat, RNG::UNIFORM, Scalar(0), Scalar(256));
}
void Test::gen(Mat& mat, int rows, int cols, int type, double low, double high)
{
mat.create(rows, cols, type);
RNG rng(0);
rng.fill(mat, RNG::UNIFORM, Scalar::all(low), Scalar::all(high));
}
void TestSystem::run()
{
cout << setiosflags(ios_base::left);
cout << " " << setw(10) << "CPU, ms" << setw(10) << "GPU, ms"
<< setw(10) << "SPEEDUP" << "DESCRIPTION\n";
cout << resetiosflags(ios_base::left);
vector<Test*>::iterator it = tests_.begin();
for (; it != tests_.end(); ++it)
{
Test* test = *it;
cout << endl << test->name() << ":\n";
test->run();
flush();
}
cout << setiosflags(ios_base::fixed | ios_base::left);
cout << "\nAverage GPU Speedup: x" << setprecision(3)
<< speedup_total_ / num_subtests_called_ << endl;
cout << resetiosflags(ios_base::fixed | ios_base::left);
}
void TestSystem::flush()
{
if (!can_flush_)
return;
int cpu_time = static_cast<int>(cpu_elapsed_ / getTickFrequency() * 1000.0);
int gpu_time = static_cast<int>(gpu_elapsed_ / getTickFrequency() * 1000.0);
cpu_elapsed_ = 0;
gpu_elapsed_ = 0;
double speedup = static_cast<double>(cpu_time) / std::max(1, gpu_time);
speedup_total_ += speedup;
cout << " " << setiosflags(ios_base::fixed | ios_base::left);
stringstream stream;
stream << cpu_time;
cout << setw(10) << stream.str();
stream.str("");
stream << gpu_time;
cout << setw(10) << stream.str();
stream.str("");
stream << "x" << setprecision(3) << speedup;
cout << setw(10) << stream.str();
cout << description_.str();
description_.str("");
cout << resetiosflags(ios_base::fixed | ios_base::left) << endl;
can_flush_ = false;
num_subtests_called_++;
}
int main()
{
TestSystem::instance()->run();
return 0;
}