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