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654 lines
22 KiB
C++
Executable File
654 lines
22 KiB
C++
Executable File
/*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 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 nchannels = CV_MAT_CN(_src.type());
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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
|
|
long i = 0;
|
|
for (long y = 0; y < _src.rows; y++)
|
|
{
|
|
for (long x = 0; x < _src.cols; x++)
|
|
{
|
|
const uchar* data = _src.ptr((int)y, (int)x);
|
|
|
|
//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
|
|
*_dst.ptr((int)y, (int)x) = 255;
|
|
break;
|
|
case 1:
|
|
//background
|
|
*_dst.ptr((int)y, (int)x) = 0;
|
|
break;
|
|
case 2:
|
|
//shadow
|
|
*_dst.ptr((int)y, (int)x) = nShadowDetection;
|
|
break;
|
|
}
|
|
i++;
|
|
}
|
|
}
|
|
};
|
|
|
|
|
|
|
|
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);
|
|
}
|
|
|
|
}
|
|
|
|
/* End of file. */
|