mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
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432 lines
15 KiB
432 lines
15 KiB
#include <iostream> // Console I/O |
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#include <sstream> // String to number conversion |
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#include <opencv2/core.hpp> // Basic OpenCV structures |
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#include <opencv2/core/utility.hpp> |
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#include <opencv2/imgproc.hpp>// Image processing methods for the CPU |
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#include <opencv2/imgcodecs.hpp>// Read images |
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// CUDA structures and methods |
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#include <opencv2/cudaarithm.hpp> |
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#include <opencv2/cudafilters.hpp> |
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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_CUDA(const Mat& I1, const Mat& I2); // Basic CUDA versions |
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Scalar getMSSIM_CUDA( const Mat& I1, const Mat& I2); |
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struct BufferPSNR // Optimized CUDA versions |
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{ // Data allocations are very expensive on CUDA. Use a buffer to solve: allocate once reuse later. |
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cuda::GpuMat gI1, gI2, gs, t1,t2; |
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cuda::GpuMat buf; |
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}; |
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double getPSNR_CUDA_optimized(const Mat& I1, const Mat& I2, BufferPSNR& b); |
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struct BufferMSSIM // Optimized CUDA versions |
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{ // Data allocations are very expensive on CUDA. Use a buffer to solve: allocate once reuse later. |
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cuda::GpuMat gI1, gI2, gs, t1,t2; |
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cuda::GpuMat I1_2, I2_2, I1_I2; |
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vector<cuda::GpuMat> vI1, vI2; |
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cuda::GpuMat mu1, mu2; |
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cuda::GpuMat mu1_2, mu2_2, mu1_mu2; |
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cuda::GpuMat sigma1_2, sigma2_2, sigma12; |
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cuda::GpuMat t3; |
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cuda::GpuMat ssim_map; |
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cuda::GpuMat buf; |
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}; |
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Scalar getMSSIM_CUDA_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 CUDA 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 = 10; |
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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 CUDA ---------------------------------------------------- |
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time = (double)getTickCount(); |
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for (int i = 0; i < TIMES; ++i) |
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result = getPSNR_CUDA(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 CUDA (averaged for " << TIMES << " runs): " << time << " milliseconds." |
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<< " With result of: " << result << endl; |
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//------------------------------- PSNR CUDA Optimized-------------------------------------------- |
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time = (double)getTickCount(); // Initial call |
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result = getPSNR_CUDA_optimized(I1, I2, bufferPSNR); |
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time = 1000*((double)getTickCount() - time)/getTickFrequency(); |
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cout << "Initial call CUDA 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_CUDA_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 CUDA 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 CUDA ----------------------------------------------------- |
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time = (double)getTickCount(); |
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for (int i = 0; i < TIMES; ++i) |
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x = getMSSIM_CUDA(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 CUDA (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 CUDA Optimized-------------------------------------------- |
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time = (double)getTickCount(); |
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x = getMSSIM_CUDA_optimized(I1,I2, bufferMSSIM); |
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time = 1000*((double)getTickCount() - time)/getTickFrequency(); |
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cout << "Time of MSSIM CUDA 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_CUDA_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 CUDA 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_CUDA_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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cuda::absdiff(b.t1.reshape(1), b.t2.reshape(1), b.gs); |
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cuda::multiply(b.gs, b.gs, b.gs); |
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double sse = cuda::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_CUDA(const Mat& I1, const Mat& I2) |
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{ |
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cuda::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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cuda::absdiff(t1.reshape(1), t2.reshape(1), gs); |
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cuda::multiply(gs, gs, gs); |
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Scalar s = cuda::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_CUDA( 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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cuda::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<cuda::GpuMat> vI1, vI2; |
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cuda::split(tmp1, vI1); |
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cuda::split(tmp2, vI2); |
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Scalar mssim; |
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Ptr<cuda::Filter> gauss = cuda::createGaussianFilter(vI2[0].type(), -1, Size(11, 11), 1.5); |
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for( int i = 0; i < gI1.channels(); ++i ) |
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{ |
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cuda::GpuMat I2_2, I1_2, I1_I2; |
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cuda::multiply(vI2[i], vI2[i], I2_2); // I2^2 |
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cuda::multiply(vI1[i], vI1[i], I1_2); // I1^2 |
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cuda::multiply(vI1[i], vI2[i], I1_I2); // I1 * I2 |
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/*************************** END INITS **********************************/ |
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cuda::GpuMat mu1, mu2; // PRELIMINARY COMPUTING |
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gauss->apply(vI1[i], mu1); |
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gauss->apply(vI2[i], mu2); |
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cuda::GpuMat mu1_2, mu2_2, mu1_mu2; |
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cuda::multiply(mu1, mu1, mu1_2); |
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cuda::multiply(mu2, mu2, mu2_2); |
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cuda::multiply(mu1, mu2, mu1_mu2); |
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cuda::GpuMat sigma1_2, sigma2_2, sigma12; |
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gauss->apply(I1_2, sigma1_2); |
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cuda::subtract(sigma1_2, mu1_2, sigma1_2); // sigma1_2 -= mu1_2; |
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gauss->apply(I2_2, sigma2_2); |
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cuda::subtract(sigma2_2, mu2_2, sigma2_2); // sigma2_2 -= mu2_2; |
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gauss->apply(I1_I2, sigma12); |
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cuda::subtract(sigma12, mu1_mu2, sigma12); // sigma12 -= mu1_mu2; |
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///////////////////////////////// FORMULA //////////////////////////////// |
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cuda::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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cuda::multiply(t1, t2, t3); // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2)) |
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cuda::addWeighted(mu1_2, 1.0, mu2_2, 1.0, C1, t1); // t1 = mu1_2 + mu2_2 + C1; |
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cuda::addWeighted(sigma1_2, 1.0, sigma2_2, 1.0, C2, t2); // t2 = sigma1_2 + sigma2_2 + C2; |
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cuda::multiply(t1, t2, t1); // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2)) |
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cuda::GpuMat ssim_map; |
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cuda::divide(t3, t1, ssim_map); // ssim_map = t3./t1; |
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Scalar s = cuda::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_CUDA_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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cuda::Stream stream; |
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b.gI1.convertTo(b.t1, CV_32F, stream); |
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b.gI2.convertTo(b.t2, CV_32F, stream); |
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cuda::split(b.t1, b.vI1, stream); |
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cuda::split(b.t2, b.vI2, stream); |
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Scalar mssim; |
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Ptr<cuda::Filter> gauss = cuda::createGaussianFilter(b.vI1[0].type(), -1, Size(11, 11), 1.5); |
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for( int i = 0; i < b.gI1.channels(); ++i ) |
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{ |
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cuda::multiply(b.vI2[i], b.vI2[i], b.I2_2, 1, -1, stream); // I2^2 |
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cuda::multiply(b.vI1[i], b.vI1[i], b.I1_2, 1, -1, stream); // I1^2 |
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cuda::multiply(b.vI1[i], b.vI2[i], b.I1_I2, 1, -1, stream); // I1 * I2 |
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gauss->apply(b.vI1[i], b.mu1, stream); |
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gauss->apply(b.vI2[i], b.mu2, stream); |
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cuda::multiply(b.mu1, b.mu1, b.mu1_2, 1, -1, stream); |
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cuda::multiply(b.mu2, b.mu2, b.mu2_2, 1, -1, stream); |
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cuda::multiply(b.mu1, b.mu2, b.mu1_mu2, 1, -1, stream); |
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gauss->apply(b.I1_2, b.sigma1_2, stream); |
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cuda::subtract(b.sigma1_2, b.mu1_2, b.sigma1_2, cuda::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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gauss->apply(b.I2_2, b.sigma2_2, stream); |
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cuda::subtract(b.sigma2_2, b.mu2_2, b.sigma2_2, cuda::GpuMat(), -1, stream); |
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//b.sigma2_2 -= b.mu2_2; |
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gauss->apply(b.I1_I2, b.sigma12, stream); |
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cuda::subtract(b.sigma12, b.mu1_mu2, b.sigma12, cuda::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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cuda::multiply(b.mu1_mu2, 2, b.t1, 1, -1, stream); //b.t1 = 2 * b.mu1_mu2 + C1; |
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cuda::add(b.t1, C1, b.t1, cuda::GpuMat(), -1, stream); |
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cuda::multiply(b.sigma12, 2, b.t2, 1, -1, stream); //b.t2 = 2 * b.sigma12 + C2; |
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cuda::add(b.t2, C2, b.t2, cuda::GpuMat(), -12, stream); |
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cuda::multiply(b.t1, b.t2, b.t3, 1, -1, stream); // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2)) |
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cuda::add(b.mu1_2, b.mu2_2, b.t1, cuda::GpuMat(), -1, stream); |
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cuda::add(b.t1, C1, b.t1, cuda::GpuMat(), -1, stream); |
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cuda::add(b.sigma1_2, b.sigma2_2, b.t2, cuda::GpuMat(), -1, stream); |
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cuda::add(b.t2, C2, b.t2, cuda::GpuMat(), -1, stream); |
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cuda::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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cuda::divide(b.t3, b.t1, b.ssim_map, 1, -1, stream); // ssim_map = t3./t1; |
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stream.waitForCompletion(); |
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Scalar s = cuda::sum(b.ssim_map, b.buf); |
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mssim.val[i] = s.val[0] / (b.ssim_map.rows * b.ssim_map.cols); |
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} |
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return mssim; |
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}
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