422 lines
15 KiB
C++
422 lines
15 KiB
C++
/*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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//
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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 <float.h>
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// to make sure we can use these short names
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#undef K
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#undef L
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#undef T
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// This is based on the "An Improved Adaptive Background Mixture Model for
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// Real-time Tracking with Shadow Detection" by P. KaewTraKulPong and R. Bowden
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// http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
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//
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// The windowing method is used, but not the shadow detection. I make some of my
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// own modifications which make more sense. There are some errors in some of their
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// equations.
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//
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namespace cv
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{
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BackgroundSubtractor::~BackgroundSubtractor() {}
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void BackgroundSubtractor::operator()(InputArray, OutputArray, double)
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{
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}
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void BackgroundSubtractor::getBackgroundImage(OutputArray) const
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{
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}
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static const int defaultNMixtures = 5;
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static const int defaultHistory = 200;
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static const double defaultBackgroundRatio = 0.7;
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static const double defaultVarThreshold = 2.5*2.5;
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static const double defaultNoiseSigma = 30*0.5;
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static const double defaultInitialWeight = 0.05;
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BackgroundSubtractorMOG::BackgroundSubtractorMOG()
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{
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frameSize = Size(0,0);
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frameType = 0;
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nframes = 0;
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nmixtures = defaultNMixtures;
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history = defaultHistory;
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varThreshold = defaultVarThreshold;
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backgroundRatio = defaultBackgroundRatio;
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noiseSigma = defaultNoiseSigma;
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}
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BackgroundSubtractorMOG::BackgroundSubtractorMOG(int _history, int _nmixtures,
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double _backgroundRatio,
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double _noiseSigma)
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{
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frameSize = Size(0,0);
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frameType = 0;
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nframes = 0;
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nmixtures = min(_nmixtures > 0 ? _nmixtures : defaultNMixtures, 8);
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history = _history > 0 ? _history : defaultHistory;
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varThreshold = defaultVarThreshold;
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backgroundRatio = min(_backgroundRatio > 0 ? _backgroundRatio : 0.95, 1.);
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noiseSigma = _noiseSigma <= 0 ? defaultNoiseSigma : _noiseSigma;
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}
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BackgroundSubtractorMOG::~BackgroundSubtractorMOG()
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{
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}
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void BackgroundSubtractorMOG::initialize(Size _frameSize, int _frameType)
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{
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frameSize = _frameSize;
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frameType = _frameType;
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nframes = 0;
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int nchannels = CV_MAT_CN(frameType);
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CV_Assert( CV_MAT_DEPTH(frameType) == CV_8U );
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// for each gaussian mixture of each pixel bg model we store ...
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// the mixture sort key (w/sum_of_variances), the mixture weight (w),
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// the mean (nchannels values) and
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// the diagonal covariance matrix (another nchannels values)
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bgmodel.create( 1, frameSize.height*frameSize.width*nmixtures*(2 + 2*nchannels), CV_32F );
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bgmodel = Scalar::all(0);
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}
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template<typename VT> struct MixData
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{
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float sortKey;
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float weight;
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VT mean;
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VT var;
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};
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static void process8uC1( const Mat& image, Mat& fgmask, double learningRate,
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Mat& bgmodel, int nmixtures, double backgroundRatio,
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double varThreshold, double noiseSigma )
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{
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int x, y, k, k1, rows = image.rows, cols = image.cols;
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float alpha = (float)learningRate, T = (float)backgroundRatio, vT = (float)varThreshold;
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int K = nmixtures;
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MixData<float>* mptr = (MixData<float>*)bgmodel.data;
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const float w0 = (float)defaultInitialWeight;
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const float sk0 = (float)(w0/(defaultNoiseSigma*2));
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const float var0 = (float)(defaultNoiseSigma*defaultNoiseSigma*4);
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const float minVar = (float)(noiseSigma*noiseSigma);
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for( y = 0; y < rows; y++ )
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{
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const uchar* src = image.ptr<uchar>(y);
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uchar* dst = fgmask.ptr<uchar>(y);
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if( alpha > 0 )
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{
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for( x = 0; x < cols; x++, mptr += K )
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{
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float wsum = 0;
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float pix = src[x];
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int kHit = -1, kForeground = -1;
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for( k = 0; k < K; k++ )
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{
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float w = mptr[k].weight;
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wsum += w;
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if( w < FLT_EPSILON )
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break;
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float mu = mptr[k].mean;
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float var = mptr[k].var;
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float diff = pix - mu;
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float d2 = diff*diff;
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if( d2 < vT*var )
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{
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wsum -= w;
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float dw = alpha*(1.f - w);
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mptr[k].weight = w + dw;
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mptr[k].mean = mu + alpha*diff;
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var = max(var + alpha*(d2 - var), minVar);
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mptr[k].var = var;
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mptr[k].sortKey = w/sqrt(var);
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for( k1 = k-1; k1 >= 0; k1-- )
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{
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if( mptr[k1].sortKey >= mptr[k1+1].sortKey )
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break;
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std::swap( mptr[k1], mptr[k1+1] );
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}
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kHit = k1+1;
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break;
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}
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}
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if( kHit < 0 ) // no appropriate gaussian mixture found at all, remove the weakest mixture and create a new one
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{
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kHit = k = min(k, K-1);
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wsum += w0 - mptr[k].weight;
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mptr[k].weight = w0;
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mptr[k].mean = pix;
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mptr[k].var = var0;
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mptr[k].sortKey = sk0;
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}
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else
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for( ; k < K; k++ )
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wsum += mptr[k].weight;
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float wscale = 1.f/wsum;
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wsum = 0;
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for( k = 0; k < K; k++ )
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{
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wsum += mptr[k].weight *= wscale;
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mptr[k].sortKey *= wscale;
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if( wsum > T && kForeground < 0 )
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kForeground = k+1;
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}
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dst[x] = (uchar)(-(kHit >= kForeground));
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}
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}
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else
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{
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for( x = 0; x < cols; x++, mptr += K )
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{
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float pix = src[x];
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int kHit = -1, kForeground = -1;
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for( k = 0; k < K; k++ )
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{
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if( mptr[k].weight < FLT_EPSILON )
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break;
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float mu = mptr[k].mean;
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float var = mptr[k].var;
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float diff = pix - mu;
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float d2 = diff*diff;
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if( d2 < vT*var )
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{
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kHit = k;
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break;
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}
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}
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if( kHit >= 0 )
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{
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float wsum = 0;
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for( k = 0; k < K; k++ )
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{
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wsum += mptr[k].weight;
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if( wsum > T )
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{
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kForeground = k+1;
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break;
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}
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}
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}
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dst[x] = (uchar)(kHit < 0 || kHit >= kForeground ? 255 : 0);
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}
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}
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}
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}
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static void process8uC3( const Mat& image, Mat& fgmask, double learningRate,
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Mat& bgmodel, int nmixtures, double backgroundRatio,
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double varThreshold, double noiseSigma )
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{
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int x, y, k, k1, rows = image.rows, cols = image.cols;
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float alpha = (float)learningRate, T = (float)backgroundRatio, vT = (float)varThreshold;
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int K = nmixtures;
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const float w0 = (float)defaultInitialWeight;
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const float sk0 = (float)(w0/(defaultNoiseSigma*2*sqrt(3.)));
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const float var0 = (float)(defaultNoiseSigma*defaultNoiseSigma*4);
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const float minVar = (float)(noiseSigma*noiseSigma);
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MixData<Vec3f>* mptr = (MixData<Vec3f>*)bgmodel.data;
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for( y = 0; y < rows; y++ )
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{
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const uchar* src = image.ptr<uchar>(y);
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uchar* dst = fgmask.ptr<uchar>(y);
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if( alpha > 0 )
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{
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for( x = 0; x < cols; x++, mptr += K )
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{
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float wsum = 0;
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Vec3f pix(src[x*3], src[x*3+1], src[x*3+2]);
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int kHit = -1, kForeground = -1;
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for( k = 0; k < K; k++ )
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{
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float w = mptr[k].weight;
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wsum += w;
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if( w < FLT_EPSILON )
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break;
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Vec3f mu = mptr[k].mean;
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Vec3f var = mptr[k].var;
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Vec3f diff = pix - mu;
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float d2 = diff.dot(diff);
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if( d2 < vT*(var[0] + var[1] + var[2]) )
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{
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wsum -= w;
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float dw = alpha*(1.f - w);
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mptr[k].weight = w + dw;
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mptr[k].mean = mu + alpha*diff;
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var = Vec3f(max(var[0] + alpha*(diff[0]*diff[0] - var[0]), minVar),
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max(var[1] + alpha*(diff[1]*diff[1] - var[1]), minVar),
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max(var[2] + alpha*(diff[2]*diff[2] - var[2]), minVar));
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mptr[k].var = var;
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mptr[k].sortKey = w/sqrt(var[0] + var[1] + var[2]);
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for( k1 = k-1; k1 >= 0; k1-- )
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{
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if( mptr[k1].sortKey >= mptr[k1+1].sortKey )
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break;
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std::swap( mptr[k1], mptr[k1+1] );
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}
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kHit = k1+1;
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break;
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}
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}
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if( kHit < 0 ) // no appropriate gaussian mixture found at all, remove the weakest mixture and create a new one
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{
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kHit = k = min(k, K-1);
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wsum += w0 - mptr[k].weight;
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mptr[k].weight = w0;
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mptr[k].mean = pix;
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mptr[k].var = Vec3f(var0, var0, var0);
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mptr[k].sortKey = sk0;
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}
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else
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for( ; k < K; k++ )
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wsum += mptr[k].weight;
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float wscale = 1.f/wsum;
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wsum = 0;
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for( k = 0; k < K; k++ )
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{
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wsum += mptr[k].weight *= wscale;
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mptr[k].sortKey *= wscale;
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if( wsum > T && kForeground < 0 )
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kForeground = k+1;
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}
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dst[x] = (uchar)(-(kHit >= kForeground));
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}
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}
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else
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{
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for( x = 0; x < cols; x++, mptr += K )
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{
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Vec3f pix(src[x*3], src[x*3+1], src[x*3+2]);
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int kHit = -1, kForeground = -1;
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for( k = 0; k < K; k++ )
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{
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if( mptr[k].weight < FLT_EPSILON )
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break;
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Vec3f mu = mptr[k].mean;
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Vec3f var = mptr[k].var;
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Vec3f diff = pix - mu;
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float d2 = diff.dot(diff);
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if( d2 < vT*(var[0] + var[1] + var[2]) )
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{
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kHit = k;
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break;
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}
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}
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if( kHit >= 0 )
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{
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float wsum = 0;
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for( k = 0; k < K; k++ )
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{
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wsum += mptr[k].weight;
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if( wsum > T )
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{
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kForeground = k+1;
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break;
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}
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}
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}
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dst[x] = (uchar)(kHit < 0 || kHit >= kForeground ? 255 : 0);
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}
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}
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}
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}
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void BackgroundSubtractorMOG::operator()(InputArray _image, OutputArray _fgmask, double learningRate)
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{
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Mat image = _image.getMat();
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bool needToInitialize = nframes == 0 || learningRate >= 1 || image.size() != frameSize || image.type() != frameType;
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if( needToInitialize )
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initialize(image.size(), image.type());
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CV_Assert( image.depth() == CV_8U );
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_fgmask.create( image.size(), CV_8U );
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Mat fgmask = _fgmask.getMat();
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++nframes;
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learningRate = learningRate >= 0 && nframes > 1 ? learningRate : 1./min( nframes, history );
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CV_Assert(learningRate >= 0);
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if( image.type() == CV_8UC1 )
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process8uC1( image, fgmask, learningRate, bgmodel, nmixtures, backgroundRatio, varThreshold, noiseSigma );
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else if( image.type() == CV_8UC3 )
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process8uC3( image, fgmask, learningRate, bgmodel, nmixtures, backgroundRatio, varThreshold, noiseSigma );
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else
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CV_Error( CV_StsUnsupportedFormat, "Only 1- and 3-channel 8-bit images are supported in BackgroundSubtractorMOG" );
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}
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}
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/* End of file. */
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