428 lines
15 KiB
C++
428 lines
15 KiB
C++
#include <iostream> // Console I/O
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#include <sstream> // String to number conversion
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#include <opencv2/core/core.hpp> // Basic OpenCV structures
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#include <opencv2/imgproc/imgproc.hpp>// Image processing methods for the CPU
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#include <opencv2/highgui/highgui.hpp>// Read images
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#include <opencv2/gpu/gpu.hpp> // GPU structures and methods
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using namespace std;
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using namespace cv;
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double getPSNR(const Mat& I1, const Mat& I2); // CPU versions
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Scalar getMSSIM( const Mat& I1, const Mat& I2);
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double getPSNR_GPU(const Mat& I1, const Mat& I2); // Basic GPU versions
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Scalar getMSSIM_GPU( const Mat& I1, const Mat& I2);
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struct BufferPSNR // Optimized GPU versions
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{ // Data allocations are very expensive on GPU. Use a buffer to solve: allocate once reuse later.
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gpu::GpuMat gI1, gI2, gs, t1,t2;
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gpu::GpuMat buf;
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};
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double getPSNR_GPU_optimized(const Mat& I1, const Mat& I2, BufferPSNR& b);
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struct BufferMSSIM // Optimized GPU versions
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{ // Data allocations are very expensive on GPU. Use a buffer to solve: allocate once reuse later.
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gpu::GpuMat gI1, gI2, gs, t1,t2;
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gpu::GpuMat I1_2, I2_2, I1_I2;
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vector<gpu::GpuMat> vI1, vI2;
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gpu::GpuMat mu1, mu2;
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gpu::GpuMat mu1_2, mu2_2, mu1_mu2;
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gpu::GpuMat sigma1_2, sigma2_2, sigma12;
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gpu::GpuMat t3;
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gpu::GpuMat ssim_map;
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gpu::GpuMat buf;
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};
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Scalar getMSSIM_GPU_optimized( const Mat& i1, const Mat& i2, BufferMSSIM& b);
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static void help()
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{
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cout
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<< "\n--------------------------------------------------------------------------" << endl
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<< "This program shows how to port your CPU code to GPU or write that from scratch." << endl
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<< "You can see the performance improvement for the similarity check methods (PSNR and SSIM)." << endl
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<< "Usage:" << endl
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<< "./gpu-basics-similarity referenceImage comparedImage numberOfTimesToRunTest(like 10)." << endl
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<< "--------------------------------------------------------------------------" << endl
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<< endl;
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}
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int main(int, char *argv[])
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{
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help();
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Mat I1 = imread(argv[1]); // Read the two images
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Mat I2 = imread(argv[2]);
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if (!I1.data || !I2.data) // Check for success
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{
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cout << "Couldn't read the image";
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return 0;
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}
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BufferPSNR bufferPSNR;
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BufferMSSIM bufferMSSIM;
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int TIMES;
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stringstream sstr(argv[3]);
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sstr >> TIMES;
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double time, result = 0;
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//------------------------------- PSNR CPU ----------------------------------------------------
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time = (double)getTickCount();
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for (int i = 0; i < TIMES; ++i)
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result = getPSNR(I1,I2);
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time = 1000*((double)getTickCount() - time)/getTickFrequency();
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time /= TIMES;
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cout << "Time of PSNR CPU (averaged for " << TIMES << " runs): " << time << " milliseconds."
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<< " With result of: " << result << endl;
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//------------------------------- PSNR GPU ----------------------------------------------------
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time = (double)getTickCount();
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for (int i = 0; i < TIMES; ++i)
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result = getPSNR_GPU(I1,I2);
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time = 1000*((double)getTickCount() - time)/getTickFrequency();
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time /= TIMES;
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cout << "Time of PSNR GPU (averaged for " << TIMES << " runs): " << time << " milliseconds."
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<< " With result of: " << result << endl;
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//------------------------------- PSNR GPU Optimized--------------------------------------------
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time = (double)getTickCount(); // Initial call
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result = getPSNR_GPU_optimized(I1, I2, bufferPSNR);
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time = 1000*((double)getTickCount() - time)/getTickFrequency();
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cout << "Initial call GPU optimized: " << time <<" milliseconds."
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<< " With result of: " << result << endl;
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time = (double)getTickCount();
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for (int i = 0; i < TIMES; ++i)
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result = getPSNR_GPU_optimized(I1, I2, bufferPSNR);
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time = 1000*((double)getTickCount() - time)/getTickFrequency();
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time /= TIMES;
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cout << "Time of PSNR GPU OPTIMIZED ( / " << TIMES << " runs): " << time
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<< " milliseconds." << " With result of: " << result << endl << endl;
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//------------------------------- SSIM CPU -----------------------------------------------------
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Scalar x;
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time = (double)getTickCount();
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for (int i = 0; i < TIMES; ++i)
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x = getMSSIM(I1,I2);
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time = 1000*((double)getTickCount() - time)/getTickFrequency();
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time /= TIMES;
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cout << "Time of MSSIM CPU (averaged for " << TIMES << " runs): " << time << " milliseconds."
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<< " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl;
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//------------------------------- SSIM GPU -----------------------------------------------------
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time = (double)getTickCount();
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for (int i = 0; i < TIMES; ++i)
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x = getMSSIM_GPU(I1,I2);
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time = 1000*((double)getTickCount() - time)/getTickFrequency();
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time /= TIMES;
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cout << "Time of MSSIM GPU (averaged for " << TIMES << " runs): " << time << " milliseconds."
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<< " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl;
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//------------------------------- SSIM GPU Optimized--------------------------------------------
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time = (double)getTickCount();
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x = getMSSIM_GPU_optimized(I1,I2, bufferMSSIM);
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time = 1000*((double)getTickCount() - time)/getTickFrequency();
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cout << "Time of MSSIM GPU Initial Call " << time << " milliseconds."
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<< " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl;
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time = (double)getTickCount();
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for (int i = 0; i < TIMES; ++i)
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x = getMSSIM_GPU_optimized(I1,I2, bufferMSSIM);
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time = 1000*((double)getTickCount() - time)/getTickFrequency();
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time /= TIMES;
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cout << "Time of MSSIM GPU OPTIMIZED ( / " << TIMES << " runs): " << time << " milliseconds."
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<< " With result of B" << x.val[0] << " G" << x.val[1] << " R" << x.val[2] << endl << endl;
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return 0;
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}
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double getPSNR(const Mat& I1, const Mat& I2)
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{
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Mat s1;
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absdiff(I1, I2, s1); // |I1 - I2|
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s1.convertTo(s1, CV_32F); // cannot make a square on 8 bits
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s1 = s1.mul(s1); // |I1 - I2|^2
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Scalar s = sum(s1); // sum elements per channel
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double sse = s.val[0] + s.val[1] + s.val[2]; // sum channels
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if( sse <= 1e-10) // for small values return zero
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return 0;
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else
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{
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double mse =sse /(double)(I1.channels() * I1.total());
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double psnr = 10.0*log10((255*255)/mse);
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return psnr;
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}
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}
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double getPSNR_GPU_optimized(const Mat& I1, const Mat& I2, BufferPSNR& b)
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{
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b.gI1.upload(I1);
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b.gI2.upload(I2);
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b.gI1.convertTo(b.t1, CV_32F);
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b.gI2.convertTo(b.t2, CV_32F);
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gpu::absdiff(b.t1.reshape(1), b.t2.reshape(1), b.gs);
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gpu::multiply(b.gs, b.gs, b.gs);
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double sse = gpu::sum(b.gs, b.buf)[0];
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if( sse <= 1e-10) // for small values return zero
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return 0;
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else
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{
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double mse = sse /(double)(I1.channels() * I1.total());
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double psnr = 10.0*log10((255*255)/mse);
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return psnr;
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}
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}
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double getPSNR_GPU(const Mat& I1, const Mat& I2)
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{
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gpu::GpuMat gI1, gI2, gs, t1,t2;
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gI1.upload(I1);
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gI2.upload(I2);
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gI1.convertTo(t1, CV_32F);
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gI2.convertTo(t2, CV_32F);
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gpu::absdiff(t1.reshape(1), t2.reshape(1), gs);
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gpu::multiply(gs, gs, gs);
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Scalar s = gpu::sum(gs);
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double sse = s.val[0] + s.val[1] + s.val[2];
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if( sse <= 1e-10) // for small values return zero
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return 0;
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else
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{
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double mse =sse /(double)(gI1.channels() * I1.total());
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double psnr = 10.0*log10((255*255)/mse);
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return psnr;
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}
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}
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Scalar getMSSIM( const Mat& i1, const Mat& i2)
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{
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const double C1 = 6.5025, C2 = 58.5225;
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/***************************** INITS **********************************/
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int d = CV_32F;
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Mat I1, I2;
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i1.convertTo(I1, d); // cannot calculate on one byte large values
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i2.convertTo(I2, d);
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Mat I2_2 = I2.mul(I2); // I2^2
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Mat I1_2 = I1.mul(I1); // I1^2
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Mat I1_I2 = I1.mul(I2); // I1 * I2
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/*************************** END INITS **********************************/
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Mat mu1, mu2; // PRELIMINARY COMPUTING
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GaussianBlur(I1, mu1, Size(11, 11), 1.5);
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GaussianBlur(I2, mu2, Size(11, 11), 1.5);
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Mat mu1_2 = mu1.mul(mu1);
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Mat mu2_2 = mu2.mul(mu2);
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Mat mu1_mu2 = mu1.mul(mu2);
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Mat sigma1_2, sigma2_2, sigma12;
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GaussianBlur(I1_2, sigma1_2, Size(11, 11), 1.5);
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sigma1_2 -= mu1_2;
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GaussianBlur(I2_2, sigma2_2, Size(11, 11), 1.5);
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sigma2_2 -= mu2_2;
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GaussianBlur(I1_I2, sigma12, Size(11, 11), 1.5);
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sigma12 -= mu1_mu2;
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///////////////////////////////// FORMULA ////////////////////////////////
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Mat t1, t2, t3;
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t1 = 2 * mu1_mu2 + C1;
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t2 = 2 * sigma12 + C2;
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t3 = t1.mul(t2); // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
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t1 = mu1_2 + mu2_2 + C1;
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t2 = sigma1_2 + sigma2_2 + C2;
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t1 = t1.mul(t2); // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))
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Mat ssim_map;
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divide(t3, t1, ssim_map); // ssim_map = t3./t1;
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Scalar mssim = mean( ssim_map ); // mssim = average of ssim map
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return mssim;
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}
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Scalar getMSSIM_GPU( const Mat& i1, const Mat& i2)
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{
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const float C1 = 6.5025f, C2 = 58.5225f;
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/***************************** INITS **********************************/
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gpu::GpuMat gI1, gI2, gs1, tmp1,tmp2;
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gI1.upload(i1);
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gI2.upload(i2);
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gI1.convertTo(tmp1, CV_MAKE_TYPE(CV_32F, gI1.channels()));
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gI2.convertTo(tmp2, CV_MAKE_TYPE(CV_32F, gI2.channels()));
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vector<gpu::GpuMat> vI1, vI2;
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gpu::split(tmp1, vI1);
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gpu::split(tmp2, vI2);
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Scalar mssim;
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for( int i = 0; i < gI1.channels(); ++i )
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{
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gpu::GpuMat I2_2, I1_2, I1_I2;
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gpu::multiply(vI2[i], vI2[i], I2_2); // I2^2
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gpu::multiply(vI1[i], vI1[i], I1_2); // I1^2
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gpu::multiply(vI1[i], vI2[i], I1_I2); // I1 * I2
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/*************************** END INITS **********************************/
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gpu::GpuMat mu1, mu2; // PRELIMINARY COMPUTING
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gpu::GaussianBlur(vI1[i], mu1, Size(11, 11), 1.5);
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gpu::GaussianBlur(vI2[i], mu2, Size(11, 11), 1.5);
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gpu::GpuMat mu1_2, mu2_2, mu1_mu2;
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gpu::multiply(mu1, mu1, mu1_2);
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gpu::multiply(mu2, mu2, mu2_2);
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gpu::multiply(mu1, mu2, mu1_mu2);
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gpu::GpuMat sigma1_2, sigma2_2, sigma12;
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gpu::GaussianBlur(I1_2, sigma1_2, Size(11, 11), 1.5);
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gpu::subtract(sigma1_2, mu1_2, sigma1_2); // sigma1_2 -= mu1_2;
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gpu::GaussianBlur(I2_2, sigma2_2, Size(11, 11), 1.5);
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gpu::subtract(sigma2_2, mu2_2, sigma2_2); // sigma2_2 -= mu2_2;
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gpu::GaussianBlur(I1_I2, sigma12, Size(11, 11), 1.5);
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gpu::subtract(sigma12, mu1_mu2, sigma12); // sigma12 -= mu1_mu2;
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///////////////////////////////// FORMULA ////////////////////////////////
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gpu::GpuMat t1, t2, t3;
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mu1_mu2.convertTo(t1, -1, 2, C1); // t1 = 2 * mu1_mu2 + C1;
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sigma12.convertTo(t2, -1, 2, C2); // t2 = 2 * sigma12 + C2;
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gpu::multiply(t1, t2, t3); // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
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gpu::addWeighted(mu1_2, 1.0, mu2_2, 1.0, C1, t1); // t1 = mu1_2 + mu2_2 + C1;
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gpu::addWeighted(sigma1_2, 1.0, sigma2_2, 1.0, C2, t2); // t2 = sigma1_2 + sigma2_2 + C2;
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gpu::multiply(t1, t2, t1); // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))
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gpu::GpuMat ssim_map;
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gpu::divide(t3, t1, ssim_map); // ssim_map = t3./t1;
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Scalar s = gpu::sum(ssim_map);
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mssim.val[i] = s.val[0] / (ssim_map.rows * ssim_map.cols);
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}
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return mssim;
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}
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Scalar getMSSIM_GPU_optimized( const Mat& i1, const Mat& i2, BufferMSSIM& b)
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{
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const float C1 = 6.5025f, C2 = 58.5225f;
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/***************************** INITS **********************************/
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b.gI1.upload(i1);
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b.gI2.upload(i2);
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gpu::Stream stream;
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stream.enqueueConvert(b.gI1, b.t1, CV_32F);
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stream.enqueueConvert(b.gI2, b.t2, CV_32F);
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gpu::split(b.t1, b.vI1, stream);
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gpu::split(b.t2, b.vI2, stream);
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Scalar mssim;
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gpu::GpuMat buf;
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for( int i = 0; i < b.gI1.channels(); ++i )
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{
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gpu::multiply(b.vI2[i], b.vI2[i], b.I2_2, stream); // I2^2
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gpu::multiply(b.vI1[i], b.vI1[i], b.I1_2, stream); // I1^2
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gpu::multiply(b.vI1[i], b.vI2[i], b.I1_I2, stream); // I1 * I2
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gpu::GaussianBlur(b.vI1[i], b.mu1, Size(11, 11), buf, 1.5, 0, BORDER_DEFAULT, -1, stream);
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gpu::GaussianBlur(b.vI2[i], b.mu2, Size(11, 11), buf, 1.5, 0, BORDER_DEFAULT, -1, stream);
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gpu::multiply(b.mu1, b.mu1, b.mu1_2, stream);
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gpu::multiply(b.mu2, b.mu2, b.mu2_2, stream);
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gpu::multiply(b.mu1, b.mu2, b.mu1_mu2, stream);
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gpu::GaussianBlur(b.I1_2, b.sigma1_2, Size(11, 11), buf, 1.5, 0, BORDER_DEFAULT, -1, stream);
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gpu::subtract(b.sigma1_2, b.mu1_2, b.sigma1_2, gpu::GpuMat(), -1, stream);
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//b.sigma1_2 -= b.mu1_2; - This would result in an extra data transfer operation
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gpu::GaussianBlur(b.I2_2, b.sigma2_2, Size(11, 11), buf, 1.5, 0, BORDER_DEFAULT, -1, stream);
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gpu::subtract(b.sigma2_2, b.mu2_2, b.sigma2_2, gpu::GpuMat(), -1, stream);
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//b.sigma2_2 -= b.mu2_2;
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gpu::GaussianBlur(b.I1_I2, b.sigma12, Size(11, 11), buf, 1.5, 0, BORDER_DEFAULT, -1, stream);
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gpu::subtract(b.sigma12, b.mu1_mu2, b.sigma12, gpu::GpuMat(), -1, stream);
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//b.sigma12 -= b.mu1_mu2;
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//here too it would be an extra data transfer due to call of operator*(Scalar, Mat)
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gpu::multiply(b.mu1_mu2, 2, b.t1, 1, -1, stream); //b.t1 = 2 * b.mu1_mu2 + C1;
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gpu::add(b.t1, C1, b.t1, gpu::GpuMat(), -1, stream);
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gpu::multiply(b.sigma12, 2, b.t2, 1, -1, stream); //b.t2 = 2 * b.sigma12 + C2;
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gpu::add(b.t2, C2, b.t2, gpu::GpuMat(), -12, stream);
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gpu::multiply(b.t1, b.t2, b.t3, 1, -1, stream); // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))
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gpu::add(b.mu1_2, b.mu2_2, b.t1, gpu::GpuMat(), -1, stream);
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gpu::add(b.t1, C1, b.t1, gpu::GpuMat(), -1, stream);
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gpu::add(b.sigma1_2, b.sigma2_2, b.t2, gpu::GpuMat(), -1, stream);
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gpu::add(b.t2, C2, b.t2, gpu::GpuMat(), -1, stream);
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gpu::multiply(b.t1, b.t2, b.t1, 1, -1, stream); // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2))
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gpu::divide(b.t3, b.t1, b.ssim_map, 1, -1, stream); // ssim_map = t3./t1;
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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;
|
|
}
|
|
|