#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(argv[1]); // Read the two images Mat I2 = imread(argv[2]); if (!I1.data || !I2.data) // Check for success { cout << "Couldn't read the image"; return 0; } BufferPSNR bufferPSNR; BufferMSSIM bufferMSSIM; int TIMES = 10; 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) { 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]