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595 lines
20 KiB
595 lines
20 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// Intel License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2000, Intel Corporation, all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of Intel Corporation may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#include "precomp.hpp" |
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#include "gcgraph.hpp" |
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#include <limits> |
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using namespace cv; |
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namespace { |
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/* |
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This is implementation of image segmentation algorithm GrabCut described in |
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"GrabCut - Interactive Foreground Extraction using Iterated Graph Cuts". |
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Carsten Rother, Vladimir Kolmogorov, Andrew Blake. |
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*/ |
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/* |
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GMM - Gaussian Mixture Model |
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*/ |
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class GMM |
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{ |
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public: |
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static const int componentsCount = 5; |
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GMM( Mat& _model ); |
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double operator()( const Vec3d color ) const; |
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double operator()( int ci, const Vec3d color ) const; |
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int whichComponent( const Vec3d color ) const; |
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void initLearning(); |
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void addSample( int ci, const Vec3d color ); |
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void endLearning(); |
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private: |
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void calcInverseCovAndDeterm(int ci, double singularFix); |
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Mat model; |
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double* coefs; |
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double* mean; |
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double* cov; |
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double inverseCovs[componentsCount][3][3]; |
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double covDeterms[componentsCount]; |
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double sums[componentsCount][3]; |
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double prods[componentsCount][3][3]; |
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int sampleCounts[componentsCount]; |
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int totalSampleCount; |
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}; |
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GMM::GMM( Mat& _model ) |
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{ |
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const int modelSize = 3/*mean*/ + 9/*covariance*/ + 1/*component weight*/; |
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if( _model.empty() ) |
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{ |
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_model.create( 1, modelSize*componentsCount, CV_64FC1 ); |
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_model.setTo(Scalar(0)); |
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} |
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else if( (_model.type() != CV_64FC1) || (_model.rows != 1) || (_model.cols != modelSize*componentsCount) ) |
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CV_Error( CV_StsBadArg, "_model must have CV_64FC1 type, rows == 1 and cols == 13*componentsCount" ); |
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model = _model; |
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coefs = model.ptr<double>(0); |
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mean = coefs + componentsCount; |
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cov = mean + 3*componentsCount; |
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for( int ci = 0; ci < componentsCount; ci++ ) |
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if( coefs[ci] > 0 ) |
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calcInverseCovAndDeterm(ci, 0.0); |
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totalSampleCount = 0; |
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} |
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double GMM::operator()( const Vec3d color ) const |
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{ |
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double res = 0; |
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for( int ci = 0; ci < componentsCount; ci++ ) |
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res += coefs[ci] * (*this)(ci, color ); |
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return res; |
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} |
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double GMM::operator()( int ci, const Vec3d color ) const |
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{ |
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double res = 0; |
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if( coefs[ci] > 0 ) |
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{ |
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CV_Assert( covDeterms[ci] > std::numeric_limits<double>::epsilon() ); |
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Vec3d diff = color; |
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double* m = mean + 3*ci; |
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diff[0] -= m[0]; diff[1] -= m[1]; diff[2] -= m[2]; |
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double mult = diff[0]*(diff[0]*inverseCovs[ci][0][0] + diff[1]*inverseCovs[ci][1][0] + diff[2]*inverseCovs[ci][2][0]) |
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+ diff[1]*(diff[0]*inverseCovs[ci][0][1] + diff[1]*inverseCovs[ci][1][1] + diff[2]*inverseCovs[ci][2][1]) |
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+ diff[2]*(diff[0]*inverseCovs[ci][0][2] + diff[1]*inverseCovs[ci][1][2] + diff[2]*inverseCovs[ci][2][2]); |
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res = 1.0f/sqrt(covDeterms[ci]) * exp(-0.5f*mult); |
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} |
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return res; |
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} |
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int GMM::whichComponent( const Vec3d color ) const |
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{ |
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int k = 0; |
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double max = 0; |
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for( int ci = 0; ci < componentsCount; ci++ ) |
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{ |
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double p = (*this)( ci, color ); |
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if( p > max ) |
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{ |
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k = ci; |
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max = p; |
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} |
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} |
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return k; |
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} |
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void GMM::initLearning() |
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{ |
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for( int ci = 0; ci < componentsCount; ci++) |
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{ |
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sums[ci][0] = sums[ci][1] = sums[ci][2] = 0; |
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prods[ci][0][0] = prods[ci][0][1] = prods[ci][0][2] = 0; |
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prods[ci][1][0] = prods[ci][1][1] = prods[ci][1][2] = 0; |
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prods[ci][2][0] = prods[ci][2][1] = prods[ci][2][2] = 0; |
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sampleCounts[ci] = 0; |
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} |
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totalSampleCount = 0; |
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} |
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void GMM::addSample( int ci, const Vec3d color ) |
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{ |
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sums[ci][0] += color[0]; sums[ci][1] += color[1]; sums[ci][2] += color[2]; |
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prods[ci][0][0] += color[0]*color[0]; prods[ci][0][1] += color[0]*color[1]; prods[ci][0][2] += color[0]*color[2]; |
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prods[ci][1][0] += color[1]*color[0]; prods[ci][1][1] += color[1]*color[1]; prods[ci][1][2] += color[1]*color[2]; |
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prods[ci][2][0] += color[2]*color[0]; prods[ci][2][1] += color[2]*color[1]; prods[ci][2][2] += color[2]*color[2]; |
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sampleCounts[ci]++; |
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totalSampleCount++; |
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} |
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void GMM::endLearning() |
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{ |
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for( int ci = 0; ci < componentsCount; ci++ ) |
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{ |
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int n = sampleCounts[ci]; |
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if( n == 0 ) |
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coefs[ci] = 0; |
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else |
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{ |
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CV_Assert(totalSampleCount > 0); |
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double inv_n = 1.0 / n; |
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coefs[ci] = (double)n/totalSampleCount; |
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double* m = mean + 3*ci; |
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m[0] = sums[ci][0] * inv_n; m[1] = sums[ci][1] * inv_n; m[2] = sums[ci][2] * inv_n; |
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double* c = cov + 9*ci; |
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c[0] = prods[ci][0][0] * inv_n - m[0]*m[0]; c[1] = prods[ci][0][1] * inv_n - m[0]*m[1]; c[2] = prods[ci][0][2] * inv_n - m[0]*m[2]; |
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c[3] = prods[ci][1][0] * inv_n - m[1]*m[0]; c[4] = prods[ci][1][1] * inv_n - m[1]*m[1]; c[5] = prods[ci][1][2] * inv_n - m[1]*m[2]; |
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c[6] = prods[ci][2][0] * inv_n - m[2]*m[0]; c[7] = prods[ci][2][1] * inv_n - m[2]*m[1]; c[8] = prods[ci][2][2] * inv_n - m[2]*m[2]; |
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calcInverseCovAndDeterm(ci, 0.01); |
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} |
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} |
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} |
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void GMM::calcInverseCovAndDeterm(int ci, const double singularFix) |
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{ |
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if( coefs[ci] > 0 ) |
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{ |
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double *c = cov + 9*ci; |
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double dtrm = c[0]*(c[4]*c[8]-c[5]*c[7]) - c[1]*(c[3]*c[8]-c[5]*c[6]) + c[2]*(c[3]*c[7]-c[4]*c[6]); |
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if (dtrm <= 1e-6 && singularFix > 0) |
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{ |
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// Adds the white noise to avoid singular covariance matrix. |
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c[0] += singularFix; |
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c[4] += singularFix; |
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c[8] += singularFix; |
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dtrm = c[0] * (c[4] * c[8] - c[5] * c[7]) - c[1] * (c[3] * c[8] - c[5] * c[6]) + c[2] * (c[3] * c[7] - c[4] * c[6]); |
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} |
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covDeterms[ci] = dtrm; |
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CV_Assert( dtrm > std::numeric_limits<double>::epsilon() ); |
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double inv_dtrm = 1.0 / dtrm; |
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inverseCovs[ci][0][0] = (c[4]*c[8] - c[5]*c[7]) * inv_dtrm; |
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inverseCovs[ci][1][0] = -(c[3]*c[8] - c[5]*c[6]) * inv_dtrm; |
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inverseCovs[ci][2][0] = (c[3]*c[7] - c[4]*c[6]) * inv_dtrm; |
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inverseCovs[ci][0][1] = -(c[1]*c[8] - c[2]*c[7]) * inv_dtrm; |
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inverseCovs[ci][1][1] = (c[0]*c[8] - c[2]*c[6]) * inv_dtrm; |
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inverseCovs[ci][2][1] = -(c[0]*c[7] - c[1]*c[6]) * inv_dtrm; |
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inverseCovs[ci][0][2] = (c[1]*c[5] - c[2]*c[4]) * inv_dtrm; |
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inverseCovs[ci][1][2] = -(c[0]*c[5] - c[2]*c[3]) * inv_dtrm; |
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inverseCovs[ci][2][2] = (c[0]*c[4] - c[1]*c[3]) * inv_dtrm; |
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} |
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} |
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} // namespace |
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/* |
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Calculate beta - parameter of GrabCut algorithm. |
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beta = 1/(2*avg(sqr(||color[i] - color[j]||))) |
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*/ |
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static double calcBeta( const Mat& img ) |
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{ |
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double beta = 0; |
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for( int y = 0; y < img.rows; y++ ) |
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{ |
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for( int x = 0; x < img.cols; x++ ) |
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{ |
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Vec3d color = img.at<Vec3b>(y,x); |
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if( x>0 ) // left |
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{ |
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Vec3d diff = color - (Vec3d)img.at<Vec3b>(y,x-1); |
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beta += diff.dot(diff); |
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} |
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if( y>0 && x>0 ) // upleft |
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{ |
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Vec3d diff = color - (Vec3d)img.at<Vec3b>(y-1,x-1); |
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beta += diff.dot(diff); |
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} |
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if( y>0 ) // up |
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{ |
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Vec3d diff = color - (Vec3d)img.at<Vec3b>(y-1,x); |
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beta += diff.dot(diff); |
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} |
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if( y>0 && x<img.cols-1) // upright |
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{ |
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Vec3d diff = color - (Vec3d)img.at<Vec3b>(y-1,x+1); |
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beta += diff.dot(diff); |
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} |
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} |
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} |
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if( beta <= std::numeric_limits<double>::epsilon() ) |
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beta = 0; |
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else |
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beta = 1.f / (2 * beta/(4*img.cols*img.rows - 3*img.cols - 3*img.rows + 2) ); |
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return beta; |
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} |
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/* |
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Calculate weights of noterminal vertices of graph. |
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beta and gamma - parameters of GrabCut algorithm. |
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*/ |
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static void calcNWeights( const Mat& img, Mat& leftW, Mat& upleftW, Mat& upW, Mat& uprightW, double beta, double gamma ) |
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{ |
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const double gammaDivSqrt2 = gamma / std::sqrt(2.0f); |
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leftW.create( img.rows, img.cols, CV_64FC1 ); |
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upleftW.create( img.rows, img.cols, CV_64FC1 ); |
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upW.create( img.rows, img.cols, CV_64FC1 ); |
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uprightW.create( img.rows, img.cols, CV_64FC1 ); |
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for( int y = 0; y < img.rows; y++ ) |
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{ |
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for( int x = 0; x < img.cols; x++ ) |
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{ |
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Vec3d color = img.at<Vec3b>(y,x); |
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if( x-1>=0 ) // left |
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{ |
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Vec3d diff = color - (Vec3d)img.at<Vec3b>(y,x-1); |
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leftW.at<double>(y,x) = gamma * exp(-beta*diff.dot(diff)); |
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} |
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else |
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leftW.at<double>(y,x) = 0; |
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if( x-1>=0 && y-1>=0 ) // upleft |
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{ |
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Vec3d diff = color - (Vec3d)img.at<Vec3b>(y-1,x-1); |
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upleftW.at<double>(y,x) = gammaDivSqrt2 * exp(-beta*diff.dot(diff)); |
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} |
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else |
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upleftW.at<double>(y,x) = 0; |
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if( y-1>=0 ) // up |
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{ |
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Vec3d diff = color - (Vec3d)img.at<Vec3b>(y-1,x); |
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upW.at<double>(y,x) = gamma * exp(-beta*diff.dot(diff)); |
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} |
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else |
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upW.at<double>(y,x) = 0; |
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if( x+1<img.cols && y-1>=0 ) // upright |
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{ |
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Vec3d diff = color - (Vec3d)img.at<Vec3b>(y-1,x+1); |
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uprightW.at<double>(y,x) = gammaDivSqrt2 * exp(-beta*diff.dot(diff)); |
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} |
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else |
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uprightW.at<double>(y,x) = 0; |
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} |
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} |
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} |
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/* |
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Check size, type and element values of mask matrix. |
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*/ |
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static void checkMask( const Mat& img, const Mat& mask ) |
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{ |
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if( mask.empty() ) |
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CV_Error( CV_StsBadArg, "mask is empty" ); |
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if( mask.type() != CV_8UC1 ) |
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CV_Error( CV_StsBadArg, "mask must have CV_8UC1 type" ); |
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if( mask.cols != img.cols || mask.rows != img.rows ) |
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CV_Error( CV_StsBadArg, "mask must have as many rows and cols as img" ); |
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for( int y = 0; y < mask.rows; y++ ) |
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{ |
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for( int x = 0; x < mask.cols; x++ ) |
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{ |
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uchar val = mask.at<uchar>(y,x); |
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if( val!=GC_BGD && val!=GC_FGD && val!=GC_PR_BGD && val!=GC_PR_FGD ) |
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CV_Error( CV_StsBadArg, "mask element value must be equal " |
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"GC_BGD or GC_FGD or GC_PR_BGD or GC_PR_FGD" ); |
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} |
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} |
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} |
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/* |
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Initialize mask using rectangular. |
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*/ |
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static void initMaskWithRect( Mat& mask, Size imgSize, Rect rect ) |
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{ |
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mask.create( imgSize, CV_8UC1 ); |
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mask.setTo( GC_BGD ); |
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rect.x = std::max(0, rect.x); |
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rect.y = std::max(0, rect.y); |
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rect.width = std::min(rect.width, imgSize.width-rect.x); |
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rect.height = std::min(rect.height, imgSize.height-rect.y); |
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(mask(rect)).setTo( Scalar(GC_PR_FGD) ); |
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} |
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/* |
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Initialize GMM background and foreground models using kmeans algorithm. |
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*/ |
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static void initGMMs( const Mat& img, const Mat& mask, GMM& bgdGMM, GMM& fgdGMM ) |
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{ |
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const int kMeansItCount = 10; |
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const int kMeansType = KMEANS_PP_CENTERS; |
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Mat bgdLabels, fgdLabels; |
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std::vector<Vec3f> bgdSamples, fgdSamples; |
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Point p; |
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for( p.y = 0; p.y < img.rows; p.y++ ) |
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{ |
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for( p.x = 0; p.x < img.cols; p.x++ ) |
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{ |
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if( mask.at<uchar>(p) == GC_BGD || mask.at<uchar>(p) == GC_PR_BGD ) |
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bgdSamples.push_back( (Vec3f)img.at<Vec3b>(p) ); |
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else // GC_FGD | GC_PR_FGD |
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fgdSamples.push_back( (Vec3f)img.at<Vec3b>(p) ); |
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} |
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} |
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CV_Assert( !bgdSamples.empty() && !fgdSamples.empty() ); |
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{ |
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Mat _bgdSamples( (int)bgdSamples.size(), 3, CV_32FC1, &bgdSamples[0][0] ); |
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int num_clusters = GMM::componentsCount; |
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num_clusters = std::min(num_clusters, (int)bgdSamples.size()); |
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kmeans( _bgdSamples, num_clusters, bgdLabels, |
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TermCriteria( CV_TERMCRIT_ITER, kMeansItCount, 0.0), 0, kMeansType ); |
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} |
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{ |
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Mat _fgdSamples( (int)fgdSamples.size(), 3, CV_32FC1, &fgdSamples[0][0] ); |
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int num_clusters = GMM::componentsCount; |
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num_clusters = std::min(num_clusters, (int)fgdSamples.size()); |
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kmeans( _fgdSamples, num_clusters, fgdLabels, |
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TermCriteria( CV_TERMCRIT_ITER, kMeansItCount, 0.0), 0, kMeansType ); |
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} |
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bgdGMM.initLearning(); |
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for( int i = 0; i < (int)bgdSamples.size(); i++ ) |
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bgdGMM.addSample( bgdLabels.at<int>(i,0), bgdSamples[i] ); |
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bgdGMM.endLearning(); |
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fgdGMM.initLearning(); |
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for( int i = 0; i < (int)fgdSamples.size(); i++ ) |
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fgdGMM.addSample( fgdLabels.at<int>(i,0), fgdSamples[i] ); |
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fgdGMM.endLearning(); |
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} |
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/* |
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Assign GMMs components for each pixel. |
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*/ |
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static void assignGMMsComponents( const Mat& img, const Mat& mask, const GMM& bgdGMM, const GMM& fgdGMM, Mat& compIdxs ) |
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{ |
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Point p; |
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for( p.y = 0; p.y < img.rows; p.y++ ) |
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{ |
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for( p.x = 0; p.x < img.cols; p.x++ ) |
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{ |
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Vec3d color = img.at<Vec3b>(p); |
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compIdxs.at<int>(p) = mask.at<uchar>(p) == GC_BGD || mask.at<uchar>(p) == GC_PR_BGD ? |
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bgdGMM.whichComponent(color) : fgdGMM.whichComponent(color); |
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} |
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} |
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} |
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/* |
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Learn GMMs parameters. |
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*/ |
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static void learnGMMs( const Mat& img, const Mat& mask, const Mat& compIdxs, GMM& bgdGMM, GMM& fgdGMM ) |
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{ |
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bgdGMM.initLearning(); |
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fgdGMM.initLearning(); |
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Point p; |
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for( int ci = 0; ci < GMM::componentsCount; ci++ ) |
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{ |
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for( p.y = 0; p.y < img.rows; p.y++ ) |
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{ |
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for( p.x = 0; p.x < img.cols; p.x++ ) |
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{ |
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if( compIdxs.at<int>(p) == ci ) |
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{ |
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if( mask.at<uchar>(p) == GC_BGD || mask.at<uchar>(p) == GC_PR_BGD ) |
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bgdGMM.addSample( ci, img.at<Vec3b>(p) ); |
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else |
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fgdGMM.addSample( ci, img.at<Vec3b>(p) ); |
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} |
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} |
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} |
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} |
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bgdGMM.endLearning(); |
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fgdGMM.endLearning(); |
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} |
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/* |
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Construct GCGraph |
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*/ |
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static void constructGCGraph( const Mat& img, const Mat& mask, const GMM& bgdGMM, const GMM& fgdGMM, double lambda, |
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const Mat& leftW, const Mat& upleftW, const Mat& upW, const Mat& uprightW, |
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GCGraph<double>& graph ) |
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{ |
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int vtxCount = img.cols*img.rows, |
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edgeCount = 2*(4*img.cols*img.rows - 3*(img.cols + img.rows) + 2); |
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graph.create(vtxCount, edgeCount); |
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Point p; |
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for( p.y = 0; p.y < img.rows; p.y++ ) |
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{ |
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for( p.x = 0; p.x < img.cols; p.x++) |
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{ |
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// add node |
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int vtxIdx = graph.addVtx(); |
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Vec3b color = img.at<Vec3b>(p); |
|
|
|
// set t-weights |
|
double fromSource, toSink; |
|
if( mask.at<uchar>(p) == GC_PR_BGD || mask.at<uchar>(p) == GC_PR_FGD ) |
|
{ |
|
fromSource = -log( bgdGMM(color) ); |
|
toSink = -log( fgdGMM(color) ); |
|
} |
|
else if( mask.at<uchar>(p) == GC_BGD ) |
|
{ |
|
fromSource = 0; |
|
toSink = lambda; |
|
} |
|
else // GC_FGD |
|
{ |
|
fromSource = lambda; |
|
toSink = 0; |
|
} |
|
graph.addTermWeights( vtxIdx, fromSource, toSink ); |
|
|
|
// set n-weights |
|
if( p.x>0 ) |
|
{ |
|
double w = leftW.at<double>(p); |
|
graph.addEdges( vtxIdx, vtxIdx-1, w, w ); |
|
} |
|
if( p.x>0 && p.y>0 ) |
|
{ |
|
double w = upleftW.at<double>(p); |
|
graph.addEdges( vtxIdx, vtxIdx-img.cols-1, w, w ); |
|
} |
|
if( p.y>0 ) |
|
{ |
|
double w = upW.at<double>(p); |
|
graph.addEdges( vtxIdx, vtxIdx-img.cols, w, w ); |
|
} |
|
if( p.x<img.cols-1 && p.y>0 ) |
|
{ |
|
double w = uprightW.at<double>(p); |
|
graph.addEdges( vtxIdx, vtxIdx-img.cols+1, w, w ); |
|
} |
|
} |
|
} |
|
} |
|
|
|
/* |
|
Estimate segmentation using MaxFlow algorithm |
|
*/ |
|
static void estimateSegmentation( GCGraph<double>& graph, Mat& mask ) |
|
{ |
|
graph.maxFlow(); |
|
Point p; |
|
for( p.y = 0; p.y < mask.rows; p.y++ ) |
|
{ |
|
for( p.x = 0; p.x < mask.cols; p.x++ ) |
|
{ |
|
if( mask.at<uchar>(p) == GC_PR_BGD || mask.at<uchar>(p) == GC_PR_FGD ) |
|
{ |
|
if( graph.inSourceSegment( p.y*mask.cols+p.x /*vertex index*/ ) ) |
|
mask.at<uchar>(p) = GC_PR_FGD; |
|
else |
|
mask.at<uchar>(p) = GC_PR_BGD; |
|
} |
|
} |
|
} |
|
} |
|
|
|
void cv::grabCut( InputArray _img, InputOutputArray _mask, Rect rect, |
|
InputOutputArray _bgdModel, InputOutputArray _fgdModel, |
|
int iterCount, int mode ) |
|
{ |
|
CV_INSTRUMENT_REGION(); |
|
|
|
Mat img = _img.getMat(); |
|
Mat& mask = _mask.getMatRef(); |
|
Mat& bgdModel = _bgdModel.getMatRef(); |
|
Mat& fgdModel = _fgdModel.getMatRef(); |
|
|
|
if( img.empty() ) |
|
CV_Error( CV_StsBadArg, "image is empty" ); |
|
if( img.type() != CV_8UC3 ) |
|
CV_Error( CV_StsBadArg, "image must have CV_8UC3 type" ); |
|
|
|
GMM bgdGMM( bgdModel ), fgdGMM( fgdModel ); |
|
Mat compIdxs( img.size(), CV_32SC1 ); |
|
|
|
if( mode == GC_INIT_WITH_RECT || mode == GC_INIT_WITH_MASK ) |
|
{ |
|
if( mode == GC_INIT_WITH_RECT ) |
|
initMaskWithRect( mask, img.size(), rect ); |
|
else // flag == GC_INIT_WITH_MASK |
|
checkMask( img, mask ); |
|
initGMMs( img, mask, bgdGMM, fgdGMM ); |
|
} |
|
|
|
if( iterCount <= 0) |
|
return; |
|
|
|
if( mode == GC_EVAL_FREEZE_MODEL ) |
|
iterCount = 1; |
|
|
|
if( mode == GC_EVAL || mode == GC_EVAL_FREEZE_MODEL ) |
|
checkMask( img, mask ); |
|
|
|
const double gamma = 50; |
|
const double lambda = 9*gamma; |
|
const double beta = calcBeta( img ); |
|
|
|
Mat leftW, upleftW, upW, uprightW; |
|
calcNWeights( img, leftW, upleftW, upW, uprightW, beta, gamma ); |
|
|
|
for( int i = 0; i < iterCount; i++ ) |
|
{ |
|
GCGraph<double> graph; |
|
assignGMMsComponents( img, mask, bgdGMM, fgdGMM, compIdxs ); |
|
if( mode != GC_EVAL_FREEZE_MODEL ) |
|
learnGMMs( img, mask, compIdxs, bgdGMM, fgdGMM ); |
|
constructGCGraph(img, mask, bgdGMM, fgdGMM, lambda, leftW, upleftW, upW, uprightW, graph ); |
|
estimateSegmentation( graph, mask ); |
|
} |
|
}
|
|
|