1.配置成功能，编译
https://blog.csdn.net/heyongfu20052005/article/details/102382034
2，编译也可以使用（release | X64也可以编译通过）
3.生成文件后，配置项目
代码：（ opencv gpu-basics-similarity）
#include "stdafx.h"
#include <iostream> // Console I/O
#include <sstream> // String to number conversion
#include <opencv2/core.hpp> // Basic OpenCV structures
#include <opencv2/core/utility.hpp>
#include <opencv2/imgproc.hpp>// Image processing methods for the CPU
#include <opencv2/imgcodecs.hpp>// Read images
// CUDA structures and methods
#include <opencv2/cudaarithm.hpp>
#include <opencv2/cudafilters.hpp>
using namespace std;
using namespace cv;
double getPSNR(const Mat& I1, const Mat& I2); // CPU versions
Scalar getMSSIM(const Mat& I1, const Mat& I2);
double getPSNR_CUDA(const Mat& I1, const Mat& I2); // Basic CUDA versions
Scalar getMSSIM_CUDA(const Mat& I1, const Mat& I2);
//! [psnr]
struct BufferPSNR // Optimized CUDA versions
{ // Data allocations are very expensive on CUDA. Use a buffer to solve: allocate once reuse later.
cuda::GpuMat gI1, gI2, gs, t1, t2;
cuda::GpuMat buf;
};
//! [psnr]
double getPSNR_CUDA_optimized(const Mat& I1, const Mat& I2, BufferPSNR& b);
//! [ssim]
struct BufferMSSIM // Optimized CUDA versions
{ // Data allocations are very expensive on CUDA. Use a buffer to solve: allocate once reuse later.
cuda::GpuMat gI1, gI2, gs, t1, t2;
cuda::GpuMat I1_2, I2_2, I1_I2;
vector<cuda::GpuMat> vI1, vI2;
cuda::GpuMat mu1, mu2;
cuda::GpuMat mu1_2, mu2_2, mu1_mu2;
cuda::GpuMat sigma1_2, sigma2_2, sigma12;
cuda::GpuMat t3;
cuda::GpuMat ssim_map;
cuda::GpuMat buf;
};
//! [ssim]
Scalar getMSSIM_CUDA_optimized(const Mat& i1, const Mat& i2, BufferMSSIM& b);
static void help()
{
cout
<< "\n--------------------------------------------------------------------------" << endl
<< "This program shows how to port your CPU code to CUDA or write that from scratch." << endl
<< "You can see the performance improvement for the similarity check methods (PSNR and SSIM)." << endl
<< "Usage:" << endl
<< "./gpu-basics-similarity referenceImage comparedImage numberOfTimesToRunTest(like 10)." << endl
<< "--------------------------------------------------------------------------" << endl
<< endl;
}
int main(int, char* argv[])
{
help();
Mat I1 = imread("E://opencv//VS_demo//opencvdemo//data//lena.jpg"); // Read the two images
Mat I2 = imread("E://opencv//VS_demo//opencvdemo//data//lena_tmpl.jpg");
if (!I1.data || !I2.data) // Check for success
{
cout << "Couldn't read the image";
return 0;
}
BufferPSNR bufferPSNR;
BufferMSSIM bufferMSSIM;
int TIMES = 1000;
/*stringstream sstr(argv[3]);
sstr >> TIMES;*/
double time, result = 0;
//------------------------------- PSNR CPU ----------------------------------------------------
time = (double)getTickCount();
for (int i = 0; i < TIMES; ++i)
result = getPSNR(I1, I2);
time = 1000 * ((double)getTickCount() - time) / getTickFrequency();
time /= TIMES;
cout << "Time of PSNR CPU (averaged for " << TIMES << " runs): " << time << " milliseconds."
<< " With result of: " << result << endl;
//------------------------------- PSNR CUDA ----------------------------------------------------
time = (double)getTickCount();
for (int i = 0; i < TIMES; ++i)
result = getPSNR_CUDA(I1, I2);
time = 1000 * ((double)getTickCount() - time) / getTickFrequency();
time /= TIMES;
cout << "Time of PSNR CUDA (averaged for " << TIMES << " runs): " << time << " milliseconds."
<< " With result of: " << result << endl;
//------------------------------- PSNR CUDA Optimized--------------------------------------------
time = (double)getTickCount(); // Initial call
result = getPSNR_CUDA_optimized(I1, I2, bufferPSNR);
time = 1000 * ((double)getTickCount() - time) / getTickFrequency();
cout << "Initial call CUDA optimized: " << time << " milliseconds."
<< " With result of: " << result << endl;
time = (double)getTickCount();
for (int i = 0; i < TIMES; ++i)
result = getPSNR_CUDA_optimized(I1, I2, bufferPSNR);
time = 1000 * ((double)getTickCount() - time) / getTickFrequency();
time /= TIMES;
cout << "Time of PSNR CUDA OPTIMIZED ( / " << TIMES << " runs): " << time
<< " milliseconds." << " With result of: " << result << endl << endl;
//------------------------------- SSIM CPU -----------------------------------------------------
Scalar x;
time = (double)getTickCount();
for (int i = 0; i < TIMES; ++i)
x = getMSSIM(I1, I2);
time = 1000 * ((double)getTickCount() - time) / getTickFrequency();
time /= TIMES;
cout << "Time of MSSIM CPU (averaged for " << TIMES << " runs): " << time << " milliseconds."
<< " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl;
//------------------------------- SSIM CUDA -----------------------------------------------------
time = (double)getTickCount();
for (int i = 0; i < TIMES; ++i)
x = getMSSIM_CUDA(I1, I2);
time = 1000 * ((double)getTickCount() - time) / getTickFrequency();
time /= TIMES;
cout << "Time of MSSIM CUDA (averaged for " << TIMES << " runs): " << time << " milliseconds."
<< " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl;
//------------------------------- SSIM CUDA Optimized--------------------------------------------
time = (double)getTickCount();
x = getMSSIM_CUDA_optimized(I1, I2, bufferMSSIM);
time = 1000 * ((double)getTickCount() - time) / getTickFrequency();
cout << "Time of MSSIM CUDA Initial Call " << time << " milliseconds."
<< " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl;
time = (double)getTickCount();
for (int i = 0; i < TIMES; ++i)
x = getMSSIM_CUDA_optimized(I1, I2, bufferMSSIM);
time = 1000 * ((double)getTickCount() - time) / getTickFrequency();
time /= TIMES;
cout << "Time of MSSIM CUDA OPTIMIZED ( / " << TIMES << " runs): " << time << " milliseconds."
<< " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl << endl;
return 0;
}
//! [getpsnr]
double getPSNR(const Mat& I1, const Mat& I2) //峰值信噪比
{
Mat s1;
absdiff(I1, I2, s1); // |I1 - I2|
s1.convertTo(s1, CV_32F); // cannot make a square on 8 bits
s1 = s1.mul(s1); // |I1 - I2|^2
Scalar s = sum(s1); // sum elements per channel
double sse = s.val[0] + s.val[1] + s.val[2]; // sum channels
if (sse <= 1e-10) // for small values return zero
return 0;
else
{
double mse = sse / (double)(I1.channels() * I1.total());
double psnr = 10.0 * log10((255 * 255) / mse);
return psnr;
}
}
//! [getpsnr]
//! [getpsnropt]
double getPSNR_CUDA_optimized(const Mat& I1, const Mat& I2, BufferPSNR& b)
{
b.gI1.upload(I1);
b.gI2.upload(I2);
b.gI1.convertTo(b.t1, CV_32F);
b.gI2.convertTo(b.t2, CV_32F);
cuda::absdiff(b.t1.reshape(1), b.t2.reshape(1), b.gs);
cuda::multiply(b.gs, b.gs, b.gs);
double sse = cuda::sum(b.gs, b.buf)[0];
if (sse <= 1e-10) // for small values return zero
return 0;
else
{
double mse = sse / (double)(I1.channels() * I1.total());
double psnr = 10.0 * log10((255 * 255) / mse);
return psnr;
}
}
//! [getpsnropt]
//! [getpsnrcuda]
double getPSNR_CUDA(const Mat& I1, const Mat& I2)
{
cuda::GpuMat gI1, gI2, gs, t1, t2;
gI1.upload(I1);
gI2.upload(I2);
gI1.convertTo(t1, CV_32F);
gI2.convertTo(t2, CV_32F);
cuda::absdiff(t1.reshape(1), t2.reshape(1), gs);
cuda::multiply(gs, gs, gs);
Scalar s = cuda::sum(gs);
double sse = s.val[0] + s.val[1] + s.val[2];
if (sse <= 1e-10) // for small values return zero
return 0;
else
{
double mse = sse / (double)(gI1.channels() * I1.total());
double psnr = 10.0 * log10((255 * 255) / mse);
return psnr;
}
}
//! [getpsnrcuda]
//! [getssim]
Scalar getMSSIM(const Mat& i1, const Mat& i2)
{
const double C1 = 6.5025, C2 = 58.5225;
/***************************** INITS **********************************/
int d = CV_32F;
Mat I1, I2;
i1.convertTo(I1, d); // cannot calculate on one byte large values
i2.convertTo(I2, d);
Mat I2_2 = I2.mul(I2); // I2^2
Mat I1_2 = I1.mul(I1); // I1^2
Mat I1_I2 = I1.mul(I2); // I1 * I2
/*************************** END INITS **********************************/
Mat mu1, mu2; // PRELIMINARY COMPUTING
GaussianBlur(I1, mu1, Size(11, 11), 1.5);
GaussianBlur(I2, mu2, Size(11, 11), 1.5);
Mat mu1_2 = mu1.mul(mu1);
Mat mu2_2 = mu2.mul(mu2);
Mat mu1_mu2 = mu1.mul(mu2);
Mat sigma1_2, sigma2_2, sigma12;
GaussianBlur(I1_2, sigma1_2, Size(11, 11), 1.5);
sigma1_2 -= mu1_2;
GaussianBlur(I2_2, sigma2_2, Size(11, 11), 1.5);
sigma2_2 -= mu2_2;
GaussianBlur(I1_I2, sigma12, Size(11, 11), 1.5);
sigma12 -= mu1_mu2;
///////////////////////////////// FORMULA ////////////////////////////////
Mat t1, t2, t3;
t1 = 2 * mu1_mu2 + C1;
t2 = 2 * sigma12 + C2;
t3 = t1.mul(t2); // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
t1 = mu1_2 + mu2_2 + C1;
t2 = sigma1_2 + sigma2_2 + C2;
t1 = t1.mul(t2); // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))
Mat ssim_map;
divide(t3, t1, ssim_map); // ssim_map = t3./t1;
Scalar mssim = mean(ssim_map); // mssim = average of ssim map
return mssim;
}
//! [getssim]
//! [getssimcuda]
Scalar getMSSIM_CUDA(const Mat& i1, const Mat& i2)
{
const float C1 = 6.5025f, C2 = 58.5225f;
/***************************** INITS **********************************/
cuda::GpuMat gI1, gI2, gs1, tmp1, tmp2;
gI1.upload(i1);
gI2.upload(i2);
gI1.convertTo(tmp1, CV_MAKE_TYPE(CV_32F, gI1.channels()));
gI2.convertTo(tmp2, CV_MAKE_TYPE(CV_32F, gI2.channels()));
vector<cuda::GpuMat> vI1, vI2;
cuda::split(tmp1, vI1);
cuda::split(tmp2, vI2);
Scalar mssim;
Ptr<cuda::Filter> gauss = cuda::createGaussianFilter(vI2[0].type(), -1, Size(11, 11), 1.5);
for (int i = 0; i < gI1.channels(); ++i)
{
cuda::GpuMat I2_2, I1_2, I1_I2;
cuda::multiply(vI2[i], vI2[i], I2_2); // I2^2
cuda::multiply(vI1[i], vI1[i], I1_2); // I1^2
cuda::multiply(vI1[i], vI2[i], I1_I2); // I1 * I2
/*************************** END INITS **********************************/
cuda::GpuMat mu1, mu2; // PRELIMINARY COMPUTING
gauss->apply(vI1[i], mu1);
gauss->apply(vI2[i], mu2);
cuda::GpuMat mu1_2, mu2_2, mu1_mu2;
cuda::multiply(mu1, mu1, mu1_2);
cuda::multiply(mu2, mu2, mu2_2);
cuda::multiply(mu1, mu2, mu1_mu2);
cuda::GpuMat sigma1_2, sigma2_2, sigma12;
gauss->apply(I1_2, sigma1_2);
cuda::subtract(sigma1_2, mu1_2, sigma1_2); // sigma1_2 -= mu1_2;
gauss->apply(I2_2, sigma2_2);
cuda::subtract(sigma2_2, mu2_2, sigma2_2); // sigma2_2 -= mu2_2;
gauss->apply(I1_I2, sigma12);
cuda::subtract(sigma12, mu1_mu2, sigma12); // sigma12 -= mu1_mu2;
///////////////////////////////// FORMULA ////////////////////////////////
cuda::GpuMat t1, t2, t3;
mu1_mu2.convertTo(t1, -1, 2, C1); // t1 = 2 * mu1_mu2 + C1;
sigma12.convertTo(t2, -1, 2, C2); // t2 = 2 * sigma12 + C2;
cuda::multiply(t1, t2, t3); // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
cuda::addWeighted(mu1_2, 1.0, mu2_2, 1.0, C1, t1); // t1 = mu1_2 + mu2_2 + C1;
cuda::addWeighted(sigma1_2, 1.0, sigma2_2, 1.0, C2, t2); // t2 = sigma1_2 + sigma2_2 + C2;
cuda::multiply(t1, t2, t1); // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))
cuda::GpuMat ssim_map;
cuda::divide(t3, t1, ssim_map); // ssim_map = t3./t1;
Scalar s = cuda::sum(ssim_map);
mssim.val[i] = s.val[0] / (ssim_map.rows * ssim_map.cols);
}
return mssim;
}
//! [getssimcuda]
//! [getssimopt]
Scalar getMSSIM_CUDA_optimized(const Mat& i1, const Mat& i2, BufferMSSIM& b)
{
const float C1 = 6.5025f, C2 = 58.5225f;
/***************************** INITS **********************************/
b.gI1.upload(i1);
b.gI2.upload(i2);
cuda::Stream stream;
b.gI1.convertTo(b.t1, CV_32F, stream);
b.gI2.convertTo(b.t2, CV_32F, stream);
cuda::split(b.t1, b.vI1, stream);
cuda::split(b.t2, b.vI2, stream);
Scalar mssim;
Ptr<cuda::Filter> gauss = cuda::createGaussianFilter(b.vI1[0].type(), -1, Size(11, 11), 1.5);
for (int i = 0; i < b.gI1.channels(); ++i)
{
cuda::multiply(b.vI2[i], b.vI2[i], b.I2_2, 1, -1, stream); // I2^2
cuda::multiply(b.vI1[i], b.vI1[i], b.I1_2, 1, -1, stream); // I1^2
cuda::multiply(b.vI1[i], b.vI2[i], b.I1_I2, 1, -1, stream); // I1 * I2
gauss->apply(b.vI1[i], b.mu1, stream);
gauss->apply(b.vI2[i], b.mu2, stream);
cuda::multiply(b.mu1, b.mu1, b.mu1_2, 1, -1, stream);
cuda::multiply(b.mu2, b.mu2, b.mu2_2, 1, -1, stream);
cuda::multiply(b.mu1, b.mu2, b.mu1_mu2, 1, -1, stream);
gauss->apply(b.I1_2, b.sigma1_2, stream);
cuda::subtract(b.sigma1_2, b.mu1_2, b.sigma1_2, cuda::GpuMat(), -1, stream);
//b.sigma1_2 -= b.mu1_2; - This would result in an extra data transfer operation
gauss->apply(b.I2_2, b.sigma2_2, stream);
cuda::subtract(b.sigma2_2, b.mu2_2, b.sigma2_2, cuda::GpuMat(), -1, stream);
//b.sigma2_2 -= b.mu2_2;
gauss->apply(b.I1_I2, b.sigma12, stream);
cuda::subtract(b.sigma12, b.mu1_mu2, b.sigma12, cuda::GpuMat(), -1, stream);
//b.sigma12 -= b.mu1_mu2;
//here too it would be an extra data transfer due to call of operator*(Scalar, Mat)
cuda::multiply(b.mu1_mu2, 2, b.t1, 1, -1, stream); //b.t1 = 2 * b.mu1_mu2 + C1;
cuda::add(b.t1, C1, b.t1, cuda::GpuMat(), -1, stream);
cuda::multiply(b.sigma12, 2, b.t2, 1, -1, stream); //b.t2 = 2 * b.sigma12 + C2;
cuda::add(b.t2, C2, b.t2, cuda::GpuMat(), -12, stream);
cuda::multiply(b.t1, b.t2, b.t3, 1, -1, stream); // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
cuda::add(b.mu1_2, b.mu2_2, b.t1, cuda::GpuMat(), -1, stream);
cuda::add(b.t1, C1, b.t1, cuda::GpuMat(), -1, stream);
cuda::add(b.sigma1_2, b.sigma2_2, b.t2, cuda::GpuMat(), -1, stream);
cuda::add(b.t2, C2, b.t2, cuda::GpuMat(), -1, stream);
cuda::multiply(b.t1, b.t2, b.t1, 1, -1, stream); // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))
cuda::divide(b.t3, b.t1, b.ssim_map, 1, -1, stream); // ssim_map = t3./t1;
stream.waitForCompletion();
Scalar s = cuda::sum(b.ssim_map, b.buf);
mssim.val[i] = s.val[0] / (b.ssim_map.rows * b.ssim_map.cols);
}
return mssim;
}
//! [getssimopt]
运行：
