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/*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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// 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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// Copyright (C) 2013, OpenCV Foundation, 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 the copyright holders 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 <math.h>
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#include "precomp.hpp" |
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namespace cv |
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{ |
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/*!
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The class implements the following algorithm: |
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"Efficient Adaptive Density Estimation per Image Pixel for the Task of Background Subtraction" |
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Z.Zivkovic, F. van der Heijden |
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Pattern Recognition Letters, vol. 27, no. 7, pages 773-780, 2006 |
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http://www.zoranz.net/Publications/zivkovicPRL2006.pdf
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*/ |
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// default parameters of gaussian background detection algorithm
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static const int defaultHistory2 = 500; // Learning rate; alpha = 1/defaultHistory2
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static const int defaultNsamples = 7; // number of samples saved in memory
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static const float defaultDist2Threshold = 20.0f*20.0f;//threshold on distance from the sample
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// additional parameters
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static const unsigned char defaultnShadowDetection2 = (unsigned char)127; // value to use in the segmentation mask for shadows, set 0 not to do shadow detection
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static const float defaultfTau = 0.5f; // Tau - shadow threshold, see the paper for explanation
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class BackgroundSubtractorKNNImpl : public BackgroundSubtractorKNN |
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{ |
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public: |
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//! the default constructor
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BackgroundSubtractorKNNImpl() |
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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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history = defaultHistory2; |
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//set parameters
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// N - the number of samples stored in memory per model
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nN = defaultNsamples; |
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//kNN - k nearest neighbour - number on NN for detecting background - default K=[0.1*nN]
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nkNN=MAX(1,cvRound(0.1*nN*3+0.40)); |
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//Tb - Threshold Tb*kernelwidth
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fTb = defaultDist2Threshold; |
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// Shadow detection
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bShadowDetection = 1;//turn on
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nShadowDetection = defaultnShadowDetection2; |
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fTau = defaultfTau;// Tau - shadow threshold
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name_ = "BackgroundSubtractor.KNN"; |
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} |
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//! the full constructor that takes the length of the history,
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// the number of gaussian mixtures, the background ratio parameter and the noise strength
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BackgroundSubtractorKNNImpl(int _history, float _dist2Threshold, bool _bShadowDetection=true) |
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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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history = _history > 0 ? _history : defaultHistory2; |
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//set parameters
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// N - the number of samples stored in memory per model
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nN = defaultNsamples; |
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//kNN - k nearest neighbour - number on NN for detcting background - default K=[0.1*nN]
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nkNN=MAX(1,cvRound(0.1*nN*3+0.40)); |
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//Tb - Threshold Tb*kernelwidth
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fTb = _dist2Threshold>0? _dist2Threshold : defaultDist2Threshold; |
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bShadowDetection = _bShadowDetection; |
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nShadowDetection = defaultnShadowDetection2; |
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fTau = defaultfTau; |
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name_ = "BackgroundSubtractor.KNN"; |
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} |
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//! the destructor
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~BackgroundSubtractorKNNImpl() {} |
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//! the update operator
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void apply(InputArray image, OutputArray fgmask, double learningRate=-1); |
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//! computes a background image which are the mean of all background gaussians
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virtual void getBackgroundImage(OutputArray backgroundImage) const; |
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//! re-initiaization method
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void 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( nchannels <= CV_CN_MAX ); |
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// Reserve memory for the model
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int size=frameSize.height*frameSize.width; |
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// for each sample of 3 speed pixel models each pixel bg model we store ...
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// values + flag (nchannels+1 values)
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bgmodel.create( 1,(nN * 3) * (nchannels+1)* size,CV_8U); |
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//index through the three circular lists
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aModelIndexShort.create(1,size,CV_8U); |
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aModelIndexMid.create(1,size,CV_8U); |
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aModelIndexLong.create(1,size,CV_8U); |
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//when to update next
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nNextShortUpdate.create(1,size,CV_8U); |
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nNextMidUpdate.create(1,size,CV_8U); |
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nNextLongUpdate.create(1,size,CV_8U); |
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//Reset counters
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nShortCounter = 0; |
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nMidCounter = 0; |
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nLongCounter = 0; |
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aModelIndexShort = Scalar::all(0);//random? //((m_nN)*rand())/(RAND_MAX+1);//0...m_nN-1
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aModelIndexMid = Scalar::all(0); |
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aModelIndexLong = Scalar::all(0); |
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nNextShortUpdate = Scalar::all(0); |
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nNextMidUpdate = Scalar::all(0); |
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nNextLongUpdate = Scalar::all(0); |
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} |
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virtual AlgorithmInfo* info() const { return 0; } |
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virtual int getHistory() const { return history; } |
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virtual void setHistory(int _nframes) { history = _nframes; } |
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virtual int getNSamples() const { return nN; } |
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virtual void setNSamples(int _nN) { nN = _nN; }//needs reinitialization!
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virtual int getkNNSamples() const { return nkNN; } |
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virtual void setkNNSamples(int _nkNN) { nkNN = _nkNN; } |
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virtual double getDist2Threshold() const { return fTb; } |
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virtual void setDist2Threshold(double _dist2Threshold) { fTb = (float)_dist2Threshold; } |
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virtual bool getDetectShadows() const { return bShadowDetection; } |
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virtual void setDetectShadows(bool detectshadows) { bShadowDetection = detectshadows; } |
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virtual int getShadowValue() const { return nShadowDetection; } |
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virtual void setShadowValue(int value) { nShadowDetection = (uchar)value; } |
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virtual double getShadowThreshold() const { return fTau; } |
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virtual void setShadowThreshold(double value) { fTau = (float)value; } |
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virtual void write(FileStorage& fs) const |
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{ |
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fs << "name" << name_ |
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<< "history" << history |
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<< "nsamples" << nN |
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<< "nKNN" << nkNN |
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<< "dist2Threshold" << fTb |
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<< "detectShadows" << (int)bShadowDetection |
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<< "shadowValue" << (int)nShadowDetection |
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<< "shadowThreshold" << fTau; |
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} |
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virtual void read(const FileNode& fn) |
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{ |
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CV_Assert( (String)fn["name"] == name_ ); |
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history = (int)fn["history"]; |
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nN = (int)fn["nsamples"]; |
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nkNN = (int)fn["nKNN"]; |
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fTb = (float)fn["dist2Threshold"]; |
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bShadowDetection = (int)fn["detectShadows"] != 0; |
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nShadowDetection = saturate_cast<uchar>((int)fn["shadowValue"]); |
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fTau = (float)fn["shadowThreshold"]; |
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} |
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protected: |
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Size frameSize; |
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int frameType; |
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int nframes; |
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/////////////////////////
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//very important parameters - things you will change
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////////////////////////
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int history; |
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//alpha=1/history - speed of update - if the time interval you want to average over is T
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//set alpha=1/history. It is also usefull at start to make T slowly increase
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//from 1 until the desired T
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float fTb; |
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//Tb - threshold on the squared distance from the sample used to decide if it is well described
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//by the background model or not. A typical value could be 2 sigma
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//and that is Tb=2*2*10*10 =400; where we take typical pixel level sigma=10
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/////////////////////////
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//less important parameters - things you might change but be carefull
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////////////////////////
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int nN;//totlal number of samples
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int nkNN;//number on NN for detcting background - default K=[0.1*nN]
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//shadow detection parameters
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bool bShadowDetection;//default 1 - do shadow detection
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unsigned char nShadowDetection;//do shadow detection - insert this value as the detection result - 127 default value
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float fTau; |
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// Tau - shadow threshold. The shadow is detected if the pixel is darker
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//version of the background. Tau is a threshold on how much darker the shadow can be.
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//Tau= 0.5 means that if pixel is more than 2 times darker then it is not shadow
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//See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003.
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//model data
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int nLongCounter;//circular counter
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int nMidCounter; |
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int nShortCounter; |
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Mat bgmodel; // model data pixel values
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Mat aModelIndexShort;// index into the models
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Mat aModelIndexMid; |
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Mat aModelIndexLong; |
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Mat nNextShortUpdate;//random update points per model
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Mat nNextMidUpdate; |
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Mat nNextLongUpdate; |
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String name_; |
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}; |
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//{ to do - paralelization ...
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//struct KNNInvoker....
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CV_INLINE void |
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_cvUpdatePixelBackgroundNP( long pixel,const uchar* data, int nchannels, int m_nN, |
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uchar* m_aModel, |
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uchar* m_nNextLongUpdate, |
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uchar* m_nNextMidUpdate, |
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uchar* m_nNextShortUpdate, |
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uchar* m_aModelIndexLong, |
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uchar* m_aModelIndexMid, |
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uchar* m_aModelIndexShort, |
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int m_nLongCounter, |
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int m_nMidCounter, |
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int m_nShortCounter, |
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int m_nLongUpdate, |
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int m_nMidUpdate, |
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int m_nShortUpdate, |
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uchar include |
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) |
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{ |
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// hold the offset
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int ndata=1+nchannels; |
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long offsetLong = ndata * (pixel * m_nN * 3 + m_aModelIndexLong[pixel] + m_nN * 2); |
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long offsetMid = ndata * (pixel * m_nN * 3 + m_aModelIndexMid[pixel] + m_nN * 1); |
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long offsetShort = ndata * (pixel * m_nN * 3 + m_aModelIndexShort[pixel]); |
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// Long update?
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if (m_nNextLongUpdate[pixel] == m_nLongCounter) |
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{ |
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// add the oldest pixel from Mid to the list of values (for each color)
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memcpy(&m_aModel[offsetLong],&m_aModel[offsetMid],ndata*sizeof(unsigned char)); |
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// increase the index
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m_aModelIndexLong[pixel] = (m_aModelIndexLong[pixel] >= (m_nN-1)) ? 0 : (m_aModelIndexLong[pixel] + 1); |
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}; |
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if (m_nLongCounter == (m_nLongUpdate-1)) |
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{ |
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//m_nNextLongUpdate[pixel] = (uchar)(((m_nLongUpdate)*(rand()-1))/RAND_MAX);//0,...m_nLongUpdate-1;
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m_nNextLongUpdate[pixel] = (uchar)( rand() % m_nLongUpdate );//0,...m_nLongUpdate-1;
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}; |
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// Mid update?
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if (m_nNextMidUpdate[pixel] == m_nMidCounter) |
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{ |
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// add this pixel to the list of values (for each color)
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memcpy(&m_aModel[offsetMid],&m_aModel[offsetShort],ndata*sizeof(unsigned char)); |
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// increase the index
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m_aModelIndexMid[pixel] = (m_aModelIndexMid[pixel] >= (m_nN-1)) ? 0 : (m_aModelIndexMid[pixel] + 1); |
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}; |
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if (m_nMidCounter == (m_nMidUpdate-1)) |
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{ |
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m_nNextMidUpdate[pixel] = (uchar)( rand() % m_nMidUpdate ); |
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}; |
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// Short update?
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if (m_nNextShortUpdate[pixel] == m_nShortCounter) |
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{ |
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// add this pixel to the list of values (for each color)
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memcpy(&m_aModel[offsetShort],data,ndata*sizeof(unsigned char)); |
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//set the include flag
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m_aModel[offsetShort+nchannels]=include; |
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// increase the index
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m_aModelIndexShort[pixel] = (m_aModelIndexShort[pixel] >= (m_nN-1)) ? 0 : (m_aModelIndexShort[pixel] + 1); |
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}; |
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if (m_nShortCounter == (m_nShortUpdate-1)) |
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{ |
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m_nNextShortUpdate[pixel] = (uchar)( rand() % m_nShortUpdate ); |
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}; |
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}; |
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CV_INLINE int |
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_cvCheckPixelBackgroundNP(long pixel, |
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const uchar* data, int nchannels, |
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int m_nN, |
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uchar* m_aModel, |
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float m_fTb, |
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int m_nkNN, |
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float tau, |
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int m_nShadowDetection, |
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uchar& include) |
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{ |
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int Pbf = 0; // the total probability that this pixel is background
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int Pb = 0; //background model probability
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float dData[CV_CN_MAX]; |
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//uchar& include=data[nchannels];
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include=0;//do we include this pixel into background model?
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int ndata=nchannels+1; |
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long posPixel = pixel * ndata * m_nN * 3; |
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// float k;
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// now increase the probability for each pixel
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for (int n = 0; n < m_nN*3; n++) |
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{ |
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uchar* mean_m = &m_aModel[posPixel + n*ndata]; |
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//calculate difference and distance
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float dist2; |
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if( nchannels == 3 ) |
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{ |
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dData[0] = (float)mean_m[0] - data[0]; |
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dData[1] = (float)mean_m[1] - data[1]; |
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dData[2] = (float)mean_m[2] - data[2]; |
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dist2 = dData[0]*dData[0] + dData[1]*dData[1] + dData[2]*dData[2]; |
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} |
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else |
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{ |
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dist2 = 0.f; |
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for( int c = 0; c < nchannels; c++ ) |
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{ |
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dData[c] = (float)mean_m[c] - data[c]; |
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dist2 += dData[c]*dData[c]; |
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} |
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} |
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if (dist2<m_fTb) |
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{ |
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Pbf++;//all
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//background only
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//if(m_aModel[subPosPixel + nchannels])//indicator
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if(mean_m[nchannels])//indicator
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{ |
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Pb++; |
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if (Pb >= m_nkNN)//Tb
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{ |
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include=1;//include
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return 1;//background ->exit
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}; |
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} |
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}; |
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}; |
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//include?
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if (Pbf>=m_nkNN)//m_nTbf)
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{ |
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include=1; |
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} |
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int Ps = 0; // the total probability that this pixel is background shadow
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// Detected as moving object, perform shadow detection
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if (m_nShadowDetection) |
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{ |
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for (int n = 0; n < m_nN*3; n++) |
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{ |
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//long subPosPixel = posPixel + n*ndata;
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uchar* mean_m = &m_aModel[posPixel + n*ndata]; |
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if(mean_m[nchannels])//check only background
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{ |
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float numerator = 0.0f; |
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float denominator = 0.0f; |
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for( int c = 0; c < nchannels; c++ ) |
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{ |
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numerator += (float)data[c] * mean_m[c]; |
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denominator += (float)mean_m[c] * mean_m[c]; |
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} |
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// no division by zero allowed
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if( denominator == 0 ) |
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return 0; |
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// if tau < a < 1 then also check the color distortion
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if( numerator <= denominator && numerator >= tau*denominator ) |
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{ |
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float a = numerator / denominator; |
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float dist2a = 0.0f; |
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for( int c = 0; c < nchannels; c++ ) |
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{ |
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float dD= a*mean_m[c] - data[c]; |
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dist2a += dD*dD; |
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} |
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if (dist2a<m_fTb*a*a) |
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{ |
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Ps++; |
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if (Ps >= m_nkNN)//shadow
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return 2; |
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}; |
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}; |
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}; |
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}; |
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} |
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return 0; |
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}; |
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CV_INLINE void |
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icvUpdatePixelBackgroundNP(const Mat& _src, Mat& _dst, |
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Mat& _bgmodel, |
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Mat& _nNextLongUpdate, |
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Mat& _nNextMidUpdate, |
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Mat& _nNextShortUpdate, |
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Mat& _aModelIndexLong, |
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Mat& _aModelIndexMid, |
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Mat& _aModelIndexShort, |
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int& _nLongCounter, |
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int& _nMidCounter, |
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int& _nShortCounter, |
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int _nN, |
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float _fAlphaT, |
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float _fTb, |
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int _nkNN, |
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float _fTau, |
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int _bShadowDetection, |
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uchar nShadowDetection |
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) |
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{ |
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int size=_src.rows*_src.cols; |
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int nchannels = CV_MAT_CN(_src.type()); |
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const uchar* pDataCurrent=_src.ptr(0); |
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uchar* pDataOutput=_dst.ptr(0); |
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//model
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uchar* m_aModel=_bgmodel.ptr(0); |
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uchar* m_nNextLongUpdate=_nNextLongUpdate.ptr(0); |
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uchar* m_nNextMidUpdate=_nNextMidUpdate.ptr(0); |
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uchar* m_nNextShortUpdate=_nNextShortUpdate.ptr(0); |
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uchar* m_aModelIndexLong=_aModelIndexLong.ptr(0); |
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uchar* m_aModelIndexMid=_aModelIndexMid.ptr(0); |
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uchar* m_aModelIndexShort=_aModelIndexShort.ptr(0); |
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//some constants
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int m_nN=_nN; |
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float m_fAlphaT=_fAlphaT; |
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float m_fTb=_fTb;//Tb - threshold on the distance
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float m_fTau=_fTau; |
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int m_nkNN=_nkNN; |
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int m_bShadowDetection=_bShadowDetection; |
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//recalculate update rates - in case alpha is changed
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// calculate update parameters (using alpha)
|
||||
int Kshort,Kmid,Klong; |
||||
//approximate exponential learning curve
|
||||
Kshort=(int)(log(0.7)/log(1-m_fAlphaT))+1;//Kshort
|
||||
Kmid=(int)(log(0.4)/log(1-m_fAlphaT))-Kshort+1;//Kmid
|
||||
Klong=(int)(log(0.1)/log(1-m_fAlphaT))-Kshort-Kmid+1;//Klong
|
||||
|
||||
//refresh rates
|
||||
int m_nShortUpdate = (Kshort/m_nN)+1; |
||||
int m_nMidUpdate = (Kmid/m_nN)+1; |
||||
int m_nLongUpdate = (Klong/m_nN)+1; |
||||
|
||||
//int m_nShortUpdate = MAX((Kshort/m_nN),m_nN);
|
||||
//int m_nMidUpdate = MAX((Kmid/m_nN),m_nN);
|
||||
//int m_nLongUpdate = MAX((Klong/m_nN),m_nN);
|
||||
|
||||
//update counters for the refresh rate
|
||||
int m_nLongCounter=_nLongCounter; |
||||
int m_nMidCounter=_nMidCounter; |
||||
int m_nShortCounter=_nShortCounter; |
||||
|
||||
_nShortCounter++;//0,1,...,m_nShortUpdate-1
|
||||
_nMidCounter++; |
||||
_nLongCounter++; |
||||
if (_nShortCounter >= m_nShortUpdate) _nShortCounter = 0; |
||||
if (_nMidCounter >= m_nMidUpdate) _nMidCounter = 0; |
||||
if (_nLongCounter >= m_nLongUpdate) _nLongCounter = 0; |
||||
|
||||
//go through the image
|
||||
for (long i=0;i<size;i++) |
||||
{ |
||||
const uchar* data=pDataCurrent; |
||||
pDataCurrent=pDataCurrent+nchannels; |
||||
|
||||
//update model+ background subtract
|
||||
uchar include=0; |
||||
int result= _cvCheckPixelBackgroundNP(i, data, nchannels, |
||||
m_nN, m_aModel, m_fTb,m_nkNN, m_fTau,m_bShadowDetection,include); |
||||
|
||||
_cvUpdatePixelBackgroundNP(i,data,nchannels, |
||||
m_nN, m_aModel, |
||||
m_nNextLongUpdate, |
||||
m_nNextMidUpdate, |
||||
m_nNextShortUpdate, |
||||
m_aModelIndexLong, |
||||
m_aModelIndexMid, |
||||
m_aModelIndexShort, |
||||
m_nLongCounter, |
||||
m_nMidCounter, |
||||
m_nShortCounter, |
||||
m_nLongUpdate, |
||||
m_nMidUpdate, |
||||
m_nShortUpdate, |
||||
include |
||||
); |
||||
switch (result) |
||||
{ |
||||
case 0: |
||||
//foreground
|
||||
(* pDataOutput)=255; |
||||
break; |
||||
case 1: |
||||
//background
|
||||
(* pDataOutput)=0; |
||||
break; |
||||
case 2: |
||||
//shadow
|
||||
(* pDataOutput)=nShadowDetection; |
||||
break; |
||||
} |
||||
pDataOutput++; |
||||
} |
||||
}; |
||||
|
||||
|
||||
|
||||
void BackgroundSubtractorKNNImpl::apply(InputArray _image, OutputArray _fgmask, double learningRate) |
||||
{ |
||||
Mat image = _image.getMat(); |
||||
bool needToInitialize = nframes == 0 || learningRate >= 1 || image.size() != frameSize || image.type() != frameType; |
||||
|
||||
if( needToInitialize ) |
||||
initialize(image.size(), image.type()); |
||||
|
||||
_fgmask.create( image.size(), CV_8U ); |
||||
Mat fgmask = _fgmask.getMat(); |
||||
|
||||
++nframes; |
||||
learningRate = learningRate >= 0 && nframes > 1 ? learningRate : 1./std::min( 2*nframes, history ); |
||||
CV_Assert(learningRate >= 0); |
||||
|
||||
//parallel_for_(Range(0, image.rows),
|
||||
// KNNInvoker(image, fgmask,
|
||||
icvUpdatePixelBackgroundNP(image, fgmask, |
||||
bgmodel, |
||||
nNextLongUpdate, |
||||
nNextMidUpdate, |
||||
nNextShortUpdate, |
||||
aModelIndexLong, |
||||
aModelIndexMid, |
||||
aModelIndexShort, |
||||
nLongCounter, |
||||
nMidCounter, |
||||
nShortCounter, |
||||
nN, |
||||
(float)learningRate, |
||||
fTb, |
||||
nkNN, |
||||
fTau, |
||||
bShadowDetection, |
||||
nShadowDetection |
||||
); |
||||
}; |
||||
|
||||
void BackgroundSubtractorKNNImpl::getBackgroundImage(OutputArray backgroundImage) const |
||||
{ |
||||
int nchannels = CV_MAT_CN(frameType); |
||||
//CV_Assert( nchannels == 3 );
|
||||
Mat meanBackground(frameSize, CV_8UC3, Scalar::all(0)); |
||||
|
||||
int ndata=nchannels+1; |
||||
int modelstep=(ndata * nN * 3); |
||||
|
||||
const uchar* pbgmodel=bgmodel.ptr(0); |
||||
for(int row=0; row<meanBackground.rows; row++) |
||||
{ |
||||
for(int col=0; col<meanBackground.cols; col++) |
||||
{ |
||||
for (int n = 0; n < nN*3; n++) |
||||
{ |
||||
const uchar* mean_m = &pbgmodel[n*ndata]; |
||||
if (mean_m[nchannels]) |
||||
{ |
||||
meanBackground.at<Vec3b>(row, col) = Vec3b(mean_m); |
||||
break; |
||||
} |
||||
} |
||||
pbgmodel=pbgmodel+modelstep; |
||||
} |
||||
} |
||||
|
||||
switch(CV_MAT_CN(frameType)) |
||||
{ |
||||
case 1: |
||||
{ |
||||
std::vector<Mat> channels; |
||||
split(meanBackground, channels); |
||||
channels[0].copyTo(backgroundImage); |
||||
break; |
||||
} |
||||
case 3: |
||||
{ |
||||
meanBackground.copyTo(backgroundImage); |
||||
break; |
||||
} |
||||
default: |
||||
CV_Error(Error::StsUnsupportedFormat, ""); |
||||
} |
||||
}; |
||||
|
||||
|
||||
Ptr<BackgroundSubtractorKNN> createBackgroundSubtractorKNN(int _history, double _threshold2,bool _bShadowDetection) |
||||
{ |
||||
return makePtr<BackgroundSubtractorKNNImpl>(_history, (float)_threshold2, _bShadowDetection); |
||||
}; |
||||
|
||||
};//namespace cv
|
||||
Loading…
Reference in new issue