// This file is part of OpenCV project. // It is subject to the license terms in the LICENSE file found in the top-level directory // of this distribution and at http://opencv.org/license.html. #include "precomp.hpp" #include "opencv2/quality/qualityssim.hpp" #include "opencv2/imgproc.hpp" // GaussianBlur #include "opencv2/quality/quality_utils.hpp" namespace { using namespace cv; using namespace cv::quality; using _mat_type = UMat; using _quality_map_type = _mat_type; // SSIM blur function _mat_type blur(const _mat_type& mat) { _mat_type result = {}; cv::GaussianBlur( mat, result, cv::Size(11, 11), 1.5 ); return result; } } // ns QualitySSIM::_mat_data::_mat_data( const _mat_type& mat ) { this->I = mat; cv::multiply(this->I, this->I, this->I_2); this->mu = ::blur(this->I); cv::multiply(this->mu, this->mu, this->mu_2); this->sigma_2 = ::blur(this->I_2); // blur the squared img, subtract blurred_squared cv::subtract(this->sigma_2, this->mu_2, this->sigma_2); } QualitySSIM::_mat_data::_mat_data(InputArray arr ) : _mat_data( quality_utils::expand_mat<mat_type>(arr) ) // delegate {} // static Ptr<QualitySSIM> QualitySSIM::create( InputArray ref ) { return Ptr<QualitySSIM>(new QualitySSIM( _mat_data( ref ))); } // static cv::Scalar QualitySSIM::compute( InputArray ref, InputArray cmp, OutputArray qualityMap ) { auto result = _mat_data::compute( _mat_data(ref), _mat_data(cmp) ); if (qualityMap.needed()) qualityMap.assign(result.second); return result.first; } cv::Scalar QualitySSIM::compute( InputArray cmp ) { auto result = _mat_data::compute( this->_refImgData , _mat_data(cmp) ); OutputArray(this->_qualityMap).assign(result.second); return result.first; } // static. computes ssim and quality map for single frame // based on https://docs.opencv.org/2.4/doc/tutorials/highgui/video-input-psnr-ssim/video-input-psnr-ssim.html std::pair<cv::Scalar, _mat_type> QualitySSIM::_mat_data::compute(const _mat_data& lhs, const _mat_data& rhs) { const double C1 = 6.5025 , C2 = 58.5225 ; mat_type I1_I2 , mu1_mu2 , t1 , t2 , t3 , sigma12 ; cv::multiply(lhs.I, rhs.I, I1_I2); cv::multiply(lhs.mu, rhs.mu, mu1_mu2); cv::subtract(::blur(I1_I2), mu1_mu2, sigma12); // t3 = ((2*mu1_mu2 + C1).*(2*sigma12 + C2)) cv::multiply(mu1_mu2, 2., t1); cv::add(t1, C1, t1);// t1 += C1 cv::multiply(sigma12, 2., t2); cv::add(t2, C2, t2);// t2 += C2 // t3 = t1 * t2 cv::multiply(t1, t2, t3); // t1 =((mu1_2 + mu2_2 + C1).*(sigma1_2 + sigma2_2 + C2)) cv::add(lhs.mu_2, rhs.mu_2, t1); cv::add(t1, C1, t1); cv::add(lhs.sigma_2, rhs.sigma_2, t2); cv::add(t2, C2, t2); // t1 *= t2 cv::multiply(t1, t2, t1); // quality map: t3 /= t1 cv::divide(t3, t1, t3); return { cv::mean(t3) , std::move(t3) }; } // compute