基于opencv的gpu与cpu对比程序，代码来自opencv的文档中

 

原文链接：

http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/gpu/gpu-basics-similarity/gpu-basics-similarity.html

 

代码中有错误，关于GpuMat OpenCV代码中没有对其进行操作符运算的重载，所有编译的时候有错误。对于GpuMat的运算只能调用相关函数才行，后面我嫌麻烦就没有重写

 

 

 

<span style="font-size:18px;">// PSNR.cpp : 定义控制台应用程序的入口点。
//#include "stdafx.h"#include <iostream>                   // Console I/O
#include <sstream>                    // String to number conversion#include <opencv2/core/core.hpp>      // Basic OpenCV structures
#include <opencv2/imgproc/imgproc.hpp>// Image processing methods for the CPU
#include <opencv2/highgui/highgui.hpp>// Read images
#include <opencv2/gpu/gpu.hpp>        // GPU structures and methodsusing 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_GPU(const Mat& I1, const Mat& I2);  // Basic GPU versions
Scalar getMSSIM_GPU( const Mat& I1, const Mat& I2);struct BufferPSNR                                     // Optimized GPU versions
{   // Data allocations are very expensive on GPU. Use a buffer to solve: allocate once reuse later.gpu::GpuMat gI1, gI2, gs, t1,t2;gpu::GpuMat buf;
};
double getPSNR_GPU_optimized(const Mat& I1, const Mat& I2, BufferPSNR& b);struct BufferMSSIM                                     // Optimized GPU versions
{   // Data allocations are very expensive on GPU. Use a buffer to solve: allocate once reuse later.gpu::GpuMat gI1, gI2, gs, t1,t2;gpu::GpuMat I1_2, I2_2, I1_I2;vector<gpu::GpuMat> vI1, vI2;gpu::GpuMat mu1, mu2; gpu::GpuMat mu1_2, mu2_2, mu1_mu2; gpu::GpuMat sigma1_2, sigma2_2, sigma12; gpu::GpuMat t3; gpu::GpuMat ssim_map;gpu::GpuMat buf;
};
Scalar getMSSIM_GPU_optimized( const Mat& i1, const Mat& i2, BufferMSSIM& b);void help()
{cout<< "\n--------------------------------------------------------------------------" << endl<< "This program shows how to port your CPU code to GPU 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 argc, char *argv[])
{help(); Mat I1 = imread("swan1.jpg",1);           // Read the two imagesMat I2 = imread("swan2.jpg",1);if (!I1.data || !I2.data)           // Check for success{cout << "Couldn't read the image";return 0;}BufferPSNR bufferPSNR;BufferMSSIM bufferMSSIM;int TIMES; stringstream sstr("500"); sstr >> TIMES;double time, result;//------------------------------- 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 GPU ----------------------------------------------------time = (double)getTickCount();    for (int i = 0; i < TIMES; ++i)result = getPSNR_GPU(I1,I2);time = 1000*((double)getTickCount() - time)/getTickFrequency();time /= TIMES;cout << "Time of PSNR GPU (averaged for " << TIMES << " runs): " << time << " milliseconds."<< " With result of: " <<  result << endl; 
/*//------------------------------- PSNR GPU Optimized--------------------------------------------time = (double)getTickCount();                                  // Initial callresult = getPSNR_GPU_optimized(I1, I2, bufferPSNR);time = 1000*((double)getTickCount() - time)/getTickFrequency();cout << "Initial call GPU optimized:              " << time  <<" milliseconds."<< " With result of: " << result << endl;time = (double)getTickCount();    for (int i = 0; i < TIMES; ++i)result = getPSNR_GPU_optimized(I1, I2, bufferPSNR);time = 1000*((double)getTickCount() - time)/getTickFrequency();time /= TIMES;cout << "Time of PSNR GPU 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 GPU -----------------------------------------------------time = (double)getTickCount();    for (int i = 0; i < TIMES; ++i)x = getMSSIM_GPU(I1,I2);time = 1000*((double)getTickCount() - time)/getTickFrequency();time /= TIMES;cout << "Time of MSSIM GPU (averaged for " << TIMES << " runs): " << time << " milliseconds."<< " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl; //------------------------------- SSIM GPU Optimized--------------------------------------------time = (double)getTickCount();    x = getMSSIM_GPU_optimized(I1,I2, bufferMSSIM);time = 1000*((double)getTickCount() - time)/getTickFrequency();cout << "Time of MSSIM GPU 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_GPU_optimized(I1,I2, bufferMSSIM);time = 1000*((double)getTickCount() - time)/getTickFrequency();time /= TIMES;cout << "Time of MSSIM GPU OPTIMIZED ( / " << TIMES << " runs): " << time << " milliseconds."<< " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl << endl; return 0;*/getchar();
}double getPSNR(const Mat& I1, const Mat& I2)
{Mat s1; absdiff(I1, I2, s1);       // |I1 - I2|s1.convertTo(s1, CV_32F);  // cannot make a square on 8 bitss1 = s1.mul(s1);           // |I1 - I2|^2Scalar s = sum(s1);         // sum elements per channeldouble sse = s.val[0] + s.val[1] + s.val[2]; // sum channelsif( sse <= 1e-10) // for small values return zeroreturn 0;else{double  mse =sse /(double)(I1.channels() * I1.total());double psnr = 10.0*log10((255*255)/mse);return psnr;}
}double getPSNR_GPU_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);gpu::absdiff(b.t1.reshape(1), b.t2.reshape(1), b.gs);gpu::multiply(b.gs, b.gs, b.gs);double sse = gpu::sum(b.gs, b.buf)[0];if( sse <= 1e-10) // for small values return zeroreturn 0;else{double mse = sse /(double)(I1.channels() * I1.total());double psnr = 10.0*log10((255*255)/mse);return psnr;}
}double getPSNR_GPU(const Mat& I1, const Mat& I2)
{gpu::GpuMat gI1, gI2, gs, t1,t2; gI1.upload(I1);gI2.upload(I2);gI1.convertTo(t1, CV_32F);gI2.convertTo(t2, CV_32F);gpu::absdiff(t1.reshape(1), t2.reshape(1), gs); gpu::multiply(gs, gs, gs);Scalar s = gpu::sum(gs);double sse = s.val[0] + s.val[1] + s.val[2];if( sse <= 1e-10) // for small values return zeroreturn 0;else{double  mse =sse /(double)(gI1.channels() * I1.total());double psnr = 10.0*log10((255*255)/mse);return psnr;}
}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 valuesi2.convertTo(I2, d); Mat I2_2   = I2.mul(I2);        // I2^2Mat I1_2   = I1.mul(I1);        // I1^2Mat I1_I2  = I1.mul(I2);        // I1 * I2/*************************** END INITS **********************************/Mat mu1, mu2;   // PRELIMINARY COMPUTINGGaussianBlur(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 mapreturn mssim; 
}Scalar getMSSIM_GPU( const Mat& i1, const Mat& i2)
{ const float C1 = 6.5025f, C2 = 58.5225f;/***************************** INITS **********************************/gpu::GpuMat gI1, gI2, gs1, t1,t2; gI1.upload(i1);gI2.upload(i2);gI1.convertTo(t1, CV_MAKE_TYPE(CV_32F, gI1.channels()));gI2.convertTo(t2, CV_MAKE_TYPE(CV_32F, gI2.channels()));vector<gpu::GpuMat> vI1, vI2; gpu::split(t1, vI1);gpu::split(t2, vI2);Scalar mssim;for( int i = 0; i < gI1.channels(); ++i ){gpu::GpuMat I2_2, I1_2, I1_I2; gpu::multiply(vI2[i], vI2[i], I2_2);        // I2^2gpu::multiply(vI1[i], vI1[i], I1_2);        // I1^2gpu::multiply(vI1[i], vI2[i], I1_I2);       // I1 * I2/*************************** END INITS **********************************/gpu::GpuMat mu1, mu2;   // PRELIMINARY COMPUTINGgpu::GaussianBlur(vI1[i], mu1, Size(11, 11), 1.5);gpu::GaussianBlur(vI2[i], mu2, Size(11, 11), 1.5);gpu::GpuMat mu1_2, mu2_2, mu1_mu2; gpu::multiply(mu1, mu1, mu1_2);   gpu::multiply(mu2, mu2, mu2_2);   gpu::multiply(mu1, mu2, mu1_mu2);   gpu::GpuMat sigma1_2, sigma2_2, sigma12; gpu::GaussianBlur(I1_2, sigma1_2, Size(11, 11), 1.5);//sigma1_2 = sigma1_2 - mu1_2;gpu::subtract(sigma1_2,mu1_2,sigma1_2);gpu::GaussianBlur(I2_2, sigma2_2, Size(11, 11), 1.5);//sigma2_2 = sigma2_2 - mu2_2;gpu::GaussianBlur(I1_I2, sigma12, Size(11, 11), 1.5);(Mat)sigma12 =(Mat)sigma12 - (Mat)mu1_mu2;//sigma12 = sigma12 - mu1_mu2/ FORMULA gpu::GpuMat t1, t2, t3; // 		t1 = 2 * mu1_mu2 + C1; 
// 		t2 = 2 * sigma12 + C2; 
// 		gpu::multiply(t1, t2, t3);     // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
// 
// 		t1 = mu1_2 + mu2_2 + C1; 
// 		t2 = sigma1_2 + sigma2_2 + C2;     
// 		gpu::multiply(t1, t2, t1);     // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))gpu::GpuMat ssim_map;gpu::divide(t3, t1, ssim_map);      // ssim_map =  t3./t1;Scalar s = gpu::sum(ssim_map);    mssim.val[i] = s.val[0] / (ssim_map.rows * ssim_map.cols);}return mssim; 
}Scalar getMSSIM_GPU_optimized( const Mat& i1, const Mat& i2, BufferMSSIM& b)
{ int cn = i1.channels();const float C1 = 6.5025f, C2 = 58.5225f;/***************************** INITS **********************************/b.gI1.upload(i1);b.gI2.upload(i2);gpu::Stream stream;stream.enqueueConvert(b.gI1, b.t1, CV_32F);stream.enqueueConvert(b.gI2, b.t2, CV_32F);      gpu::split(b.t1, b.vI1, stream);gpu::split(b.t2, b.vI2, stream);Scalar mssim;for( int i = 0; i < b.gI1.channels(); ++i ){        gpu::multiply(b.vI2[i], b.vI2[i], b.I2_2, stream);        // I2^2gpu::multiply(b.vI1[i], b.vI1[i], b.I1_2, stream);        // I1^2gpu::multiply(b.vI1[i], b.vI2[i], b.I1_I2, stream);       // I1 * I2//gpu::GaussianBlur(b.vI1[i], b.mu1, Size(11, 11), 1.5, 0, BORDER_DEFAULT, -1, stream);//gpu::GaussianBlur(b.vI2[i], b.mu2, Size(11, 11), 1.5, 0, BORDER_DEFAULT, -1, stream);gpu::multiply(b.mu1, b.mu1, b.mu1_2, stream);   gpu::multiply(b.mu2, b.mu2, b.mu2_2, stream);   gpu::multiply(b.mu1, b.mu2, b.mu1_mu2, stream);   //gpu::GaussianBlur(b.I1_2, b.sigma1_2, Size(11, 11), 1.5, 0, BORDER_DEFAULT, -1, stream);//gpu::subtract(b.sigma1_2, b.mu1_2, b.sigma1_2, stream);//b.sigma1_2 -= b.mu1_2;  - This would result in an extra data transfer operation//gpu::GaussianBlur(b.I2_2, b.sigma2_2, Size(11, 11), 1.5, 0, BORDER_DEFAULT, -1, stream);//gpu::subtract(b.sigma2_2, b.mu2_2, b.sigma2_2, stream);//b.sigma2_2 -= b.mu2_2;//gpu::GaussianBlur(b.I1_I2, b.sigma12, Size(11, 11), 1.5, 0, BORDER_DEFAULT, -1, stream);//gpu::subtract(b.sigma12, b.mu1_mu2, b.sigma12, stream);//b.sigma12 -= b.mu1_mu2;//here too it would be an extra data transfer due to call of operator*(Scalar, Mat)gpu::multiply(b.mu1_mu2, 2, b.t1, stream); //b.t1 = 2 * b.mu1_mu2 + C1; //gpu::add(b.t1, C1, b.t1, stream);gpu::multiply(b.sigma12, 2, b.t2, stream); //b.t2 = 2 * b.sigma12 + C2; //gpu::add(b.t2, C2, b.t2, stream);     gpu::multiply(b.t1, b.t2, b.t3, stream);     // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))//gpu::add(b.mu1_2, b.mu2_2, b.t1, stream);//gpu::add(b.t1, C1, b.t1, stream);//gpu::add(b.sigma1_2, b.sigma2_2, b.t2, stream);//gpu::add(b.t2, C2, b.t2, stream);gpu::multiply(b.t1, b.t2, b.t1, stream);     // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))        gpu::divide(b.t3, b.t1, b.ssim_map, stream);      // ssim_map =  t3./t1;stream.waitForCompletion();Scalar s = gpu::sum(b.ssim_map, b.buf);    mssim.val[i] = s.val[0] / (b.ssim_map.rows * b.ssim_map.cols);}return mssim; 
}</span>


 

 

实现效果：

转载于:https://www.cnblogs.com/wuyida/p/6301427.html

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