/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to this license. // If you do not agree to this license, do not download, install, // copy or use the software. // // // License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000-2008, Intel Corporation, all rights reserved. // Copyright (C) 2009, Willow Garage Inc., all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // // * The name of the copyright holders may not be used to endorse or promote products // derived from this software without specific prior written permission. // // This software is provided by the copyright holders and contributors "as is" and // any express or implied warranties, including, but not limited to, the implied // warranties of merchantability and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or contributors be liable for any direct, // indirect, incidental, special, exemplary, or consequential damages // (including, but not limited to, procurement of substitute goods or services; // loss of use, data, or profits; or business interruption) however caused // and on any theory of liability, whether in contract, strict liability, // or tort (including negligence or otherwise) arising in any way out of // the use of this software, even if advised of the possibility of such damage. // //M*/ #ifndef __OPENCV_BACKGROUND_SEGM_HPP__ #define __OPENCV_BACKGROUND_SEGM_HPP__ #include "opencv2/core/core.hpp" #ifdef __cplusplus extern "C" { #endif /****************************************************************************************\ * Background/foreground segmentation * \****************************************************************************************/ /* We discriminate between foreground and background pixels * by building and maintaining a model of the background. * Any pixel which does not fit this model is then deemed * to be foreground. * * At present we support two core background models, * one of which has two variations: * * o CV_BG_MODEL_FGD: latest and greatest algorithm, described in * * Foreground Object Detection from Videos Containing Complex Background. * Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian. * ACM MM2003 9p * * o CV_BG_MODEL_FGD_SIMPLE: * A code comment describes this as a simplified version of the above, * but the code is in fact currently identical * * o CV_BG_MODEL_MOG: "Mixture of Gaussians", older algorithm, described in * * Moving target classification and tracking from real-time video. * A Lipton, H Fujijoshi, R Patil * Proceedings IEEE Workshop on Application of Computer Vision pp 8-14 1998 * * Learning patterns of activity using real-time tracking * C Stauffer and W Grimson August 2000 * IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8):747-757 */ #define CV_BG_MODEL_FGD 0 #define CV_BG_MODEL_MOG 1 /* "Mixture of Gaussians". */ #define CV_BG_MODEL_FGD_SIMPLE 2 struct CvBGStatModel; typedef void (CV_CDECL * CvReleaseBGStatModel)( struct CvBGStatModel** bg_model ); typedef int (CV_CDECL * CvUpdateBGStatModel)( IplImage* curr_frame, struct CvBGStatModel* bg_model, double learningRate ); #define CV_BG_STAT_MODEL_FIELDS() \ int type; /*type of BG model*/ \ CvReleaseBGStatModel release; \ CvUpdateBGStatModel update; \ IplImage* background; /*8UC3 reference background image*/ \ IplImage* foreground; /*8UC1 foreground image*/ \ IplImage** layers; /*8UC3 reference background image, can be null */ \ int layer_count; /* can be zero */ \ CvMemStorage* storage; /*storage for foreground_regions*/ \ CvSeq* foreground_regions /*foreground object contours*/ typedef struct CvBGStatModel { CV_BG_STAT_MODEL_FIELDS(); } CvBGStatModel; // // Releases memory used by BGStatModel CVAPI(void) cvReleaseBGStatModel( CvBGStatModel** bg_model ); // Updates statistical model and returns number of found foreground regions CVAPI(int) cvUpdateBGStatModel( IplImage* current_frame, CvBGStatModel* bg_model, double learningRate CV_DEFAULT(-1)); // Performs FG post-processing using segmentation // (all pixels of a region will be classified as foreground if majority of pixels of the region are FG). // parameters: // segments - pointer to result of segmentation (for example MeanShiftSegmentation) // bg_model - pointer to CvBGStatModel structure CVAPI(void) cvRefineForegroundMaskBySegm( CvSeq* segments, CvBGStatModel* bg_model ); /* Common use change detection function */ CVAPI(int) cvChangeDetection( IplImage* prev_frame, IplImage* curr_frame, IplImage* change_mask ); /* Interface of ACM MM2003 algorithm */ /* Default parameters of foreground detection algorithm: */ #define CV_BGFG_FGD_LC 128 #define CV_BGFG_FGD_N1C 15 #define CV_BGFG_FGD_N2C 25 #define CV_BGFG_FGD_LCC 64 #define CV_BGFG_FGD_N1CC 25 #define CV_BGFG_FGD_N2CC 40 /* Background reference image update parameter: */ #define CV_BGFG_FGD_ALPHA_1 0.1f /* stat model update parameter * 0.002f ~ 1K frame(~45sec), 0.005 ~ 18sec (if 25fps and absolutely static BG) */ #define CV_BGFG_FGD_ALPHA_2 0.005f /* start value for alpha parameter (to fast initiate statistic model) */ #define CV_BGFG_FGD_ALPHA_3 0.1f #define CV_BGFG_FGD_DELTA 2 #define CV_BGFG_FGD_T 0.9f #define CV_BGFG_FGD_MINAREA 15.f #define CV_BGFG_FGD_BG_UPDATE_TRESH 0.5f /* See the above-referenced Li/Huang/Gu/Tian paper * for a full description of these background-model * tuning parameters. * * Nomenclature: 'c' == "color", a three-component red/green/blue vector. * We use histograms of these to model the range of * colors we've seen at a given background pixel. * * 'cc' == "color co-occurrence", a six-component vector giving * RGB color for both this frame and preceding frame. * We use histograms of these to model the range of * color CHANGES we've seen at a given background pixel. */ typedef struct CvFGDStatModelParams { int Lc; /* Quantized levels per 'color' component. Power of two, typically 32, 64 or 128. */ int N1c; /* Number of color vectors used to model normal background color variation at a given pixel. */ int N2c; /* Number of color vectors retained at given pixel. Must be > N1c, typically ~ 5/3 of N1c. */ /* Used to allow the first N1c vectors to adapt over time to changing background. */ int Lcc; /* Quantized levels per 'color co-occurrence' component. Power of two, typically 16, 32 or 64. */ int N1cc; /* Number of color co-occurrence vectors used to model normal background color variation at a given pixel. */ int N2cc; /* Number of color co-occurrence vectors retained at given pixel. Must be > N1cc, typically ~ 5/3 of N1cc. */ /* Used to allow the first N1cc vectors to adapt over time to changing background. */ int is_obj_without_holes;/* If TRUE we ignore holes within foreground blobs. Defaults to TRUE. */ int perform_morphing; /* Number of erode-dilate-erode foreground-blob cleanup iterations. */ /* These erase one-pixel junk blobs and merge almost-touching blobs. Default value is 1. */ float alpha1; /* How quickly we forget old background pixel values seen. Typically set to 0.1 */ float alpha2; /* "Controls speed of feature learning". Depends on T. Typical value circa 0.005. */ float alpha3; /* Alternate to alpha2, used (e.g.) for quicker initial convergence. Typical value 0.1. */ float delta; /* Affects color and color co-occurrence quantization, typically set to 2. */ float T; /* "A percentage value which determines when new features can be recognized as new background." (Typically 0.9).*/ float minArea; /* Discard foreground blobs whose bounding box is smaller than this threshold. */ } CvFGDStatModelParams; typedef struct CvBGPixelCStatTable { float Pv, Pvb; uchar v[3]; } CvBGPixelCStatTable; typedef struct CvBGPixelCCStatTable { float Pv, Pvb; uchar v[6]; } CvBGPixelCCStatTable; typedef struct CvBGPixelStat { float Pbc; float Pbcc; CvBGPixelCStatTable* ctable; CvBGPixelCCStatTable* cctable; uchar is_trained_st_model; uchar is_trained_dyn_model; } CvBGPixelStat; typedef struct CvFGDStatModel { CV_BG_STAT_MODEL_FIELDS(); CvBGPixelStat* pixel_stat; IplImage* Ftd; IplImage* Fbd; IplImage* prev_frame; CvFGDStatModelParams params; } CvFGDStatModel; /* Creates FGD model */ CVAPI(CvBGStatModel*) cvCreateFGDStatModel( IplImage* first_frame, CvFGDStatModelParams* parameters CV_DEFAULT(NULL)); /* Interface of Gaussian mixture algorithm "An improved adaptive background mixture model for real-time tracking with shadow detection" P. KadewTraKuPong and R. Bowden, Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001." http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf */ /* Note: "MOG" == "Mixture Of Gaussians": */ #define CV_BGFG_MOG_MAX_NGAUSSIANS 500 /* default parameters of gaussian background detection algorithm */ #define CV_BGFG_MOG_BACKGROUND_THRESHOLD 0.7 /* threshold sum of weights for background test */ #define CV_BGFG_MOG_STD_THRESHOLD 2.5 /* lambda=2.5 is 99% */ #define CV_BGFG_MOG_WINDOW_SIZE 200 /* Learning rate; alpha = 1/CV_GBG_WINDOW_SIZE */ #define CV_BGFG_MOG_NGAUSSIANS 5 /* = K = number of Gaussians in mixture */ #define CV_BGFG_MOG_WEIGHT_INIT 0.05 #define CV_BGFG_MOG_SIGMA_INIT 30 #define CV_BGFG_MOG_MINAREA 15.f #define CV_BGFG_MOG_NCOLORS 3 typedef struct CvGaussBGStatModelParams { int win_size; /* = 1/alpha */ int n_gauss; double bg_threshold, std_threshold, minArea; double weight_init, variance_init; }CvGaussBGStatModelParams; typedef struct CvGaussBGValues { int match_sum; double weight; double variance[CV_BGFG_MOG_NCOLORS]; double mean[CV_BGFG_MOG_NCOLORS]; } CvGaussBGValues; typedef struct CvGaussBGPoint { CvGaussBGValues* g_values; } CvGaussBGPoint; typedef struct CvGaussBGModel { CV_BG_STAT_MODEL_FIELDS(); CvGaussBGStatModelParams params; CvGaussBGPoint* g_point; int countFrames; } CvGaussBGModel; /* Creates Gaussian mixture background model */ CVAPI(CvBGStatModel*) cvCreateGaussianBGModel( IplImage* first_frame, CvGaussBGStatModelParams* parameters CV_DEFAULT(NULL)); typedef struct CvBGCodeBookElem { struct CvBGCodeBookElem* next; int tLastUpdate; int stale; uchar boxMin[3]; uchar boxMax[3]; uchar learnMin[3]; uchar learnMax[3]; } CvBGCodeBookElem; typedef struct CvBGCodeBookModel { CvSize size; int t; uchar cbBounds[3]; uchar modMin[3]; uchar modMax[3]; CvBGCodeBookElem** cbmap; CvMemStorage* storage; CvBGCodeBookElem* freeList; } CvBGCodeBookModel; CVAPI(CvBGCodeBookModel*) cvCreateBGCodeBookModel(); CVAPI(void) cvReleaseBGCodeBookModel( CvBGCodeBookModel** model ); CVAPI(void) cvBGCodeBookUpdate( CvBGCodeBookModel* model, const CvArr* image, CvRect roi CV_DEFAULT(cvRect(0,0,0,0)), const CvArr* mask CV_DEFAULT(0) ); CVAPI(int) cvBGCodeBookDiff( const CvBGCodeBookModel* model, const CvArr* image, CvArr* fgmask, CvRect roi CV_DEFAULT(cvRect(0,0,0,0)) ); CVAPI(void) cvBGCodeBookClearStale( CvBGCodeBookModel* model, int staleThresh, CvRect roi CV_DEFAULT(cvRect(0,0,0,0)), const CvArr* mask CV_DEFAULT(0) ); CVAPI(CvSeq*) cvSegmentFGMask( CvArr *fgmask, int poly1Hull0 CV_DEFAULT(1), float perimScale CV_DEFAULT(4.f), CvMemStorage* storage CV_DEFAULT(0), CvPoint offset CV_DEFAULT(cvPoint(0,0))); #ifdef __cplusplus } namespace cv { /*! The Base Class for Background/Foreground Segmentation The class is only used to define the common interface for the whole family of background/foreground segmentation algorithms. */ class CV_EXPORTS_W BackgroundSubtractor { public: //! the virtual destructor virtual ~BackgroundSubtractor(); //! the update operator that takes the next video frame and returns the current foreground mask as 8-bit binary image. CV_WRAP_AS(apply) virtual void operator()(InputArray image, OutputArray fgmask, double learningRate=0); //! computes a background image virtual void getBackgroundImage(OutputArray backgroundImage) const; }; /*! Gaussian Mixture-based Backbround/Foreground Segmentation Algorithm The class implements the following algorithm: "An improved adaptive background mixture model for real-time tracking with shadow detection" P. KadewTraKuPong and R. Bowden, Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001." http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf */ class CV_EXPORTS_W BackgroundSubtractorMOG : public BackgroundSubtractor { public: //! the default constructor CV_WRAP BackgroundSubtractorMOG(); //! the full constructor that takes the length of the history, the number of gaussian mixtures, the background ratio parameter and the noise strength CV_WRAP BackgroundSubtractorMOG(int history, int nmixtures, double backgroundRatio, double noiseSigma=0); //! the destructor virtual ~BackgroundSubtractorMOG(); //! the update operator virtual void operator()(InputArray image, OutputArray fgmask, double learningRate=0); //! re-initiaization method virtual void initialize(Size frameSize, int frameType); Size frameSize; int frameType; Mat bgmodel; int nframes; int history; int nmixtures; double varThreshold; double backgroundRatio; double noiseSigma; }; class CV_EXPORTS BackgroundSubtractorMOG2 : public BackgroundSubtractor { public: //! the default constructor BackgroundSubtractorMOG2(); //! the full constructor that takes the length of the history, the number of gaussian mixtures, the background ratio parameter and the noise strength BackgroundSubtractorMOG2(int history, float varThreshold, bool bShadowDetection=1); //! the destructor virtual ~BackgroundSubtractorMOG2(); //! the update operator virtual void operator()(InputArray image, OutputArray fgmask, double learningRate=-1); //! computes a background image which are the mean of all background gaussians virtual void getBackgroundImage(OutputArray backgroundImage) const; //! re-initiaization method virtual void initialize(Size frameSize, int frameType); Size frameSize; int frameType; Mat bgmodel; Mat bgmodelUsedModes;//keep track of number of modes per pixel int nframes; int history; int nmixtures; //! here it is the maximum allowed number of mixture comonents. //! Actual number is determined dynamically per pixel float varThreshold; // threshold on the squared Mahalan. dist. to decide if it is well described //by the background model or not. Related to Cthr from the paper. //This does not influence the update of the background. A typical value could be 4 sigma //and that is varThreshold=4*4=16; Corresponds to Tb in the paper. ///////////////////////// //less important parameters - things you might change but be carefull //////////////////////// float backgroundRatio; //corresponds to fTB=1-cf from the paper //TB - threshold when the component becomes significant enough to be included into //the background model. It is the TB=1-cf from the paper. So I use cf=0.1 => TB=0. //For alpha=0.001 it means that the mode should exist for approximately 105 frames before //it is considered foreground //float noiseSigma; float varThresholdGen; //correspondts to Tg - threshold on the squared Mahalan. dist. to decide //when a sample is close to the existing components. If it is not close //to any a new component will be generated. I use 3 sigma => Tg=3*3=9. //Smaller Tg leads to more generated components and higher Tg might make //lead to small number of components but they can grow too large float fVarInit; float fVarMin; float fVarMax; //initial variance for the newly generated components. //It will will influence the speed of adaptation. A good guess should be made. //A simple way is to estimate the typical standard deviation from the images. //I used here 10 as a reasonable value // min and max can be used to further control the variance float fCT;//CT - complexity reduction prior //this is related to the number of samples needed to accept that a component //actually exists. We use CT=0.05 of all the samples. By setting CT=0 you get //the standard Stauffer&Grimson algorithm (maybe not exact but very similar) //shadow detection parameters bool bShadowDetection;//default 1 - do shadow detection unsigned char nShadowDetection;//do shadow detection - insert this value as the detection result - 127 default value float fTau; // Tau - shadow threshold. The shadow is detected if the pixel is darker //version of the background. Tau is a threshold on how much darker the shadow can be. //Tau= 0.5 means that if pixel is more than 2 times darker then it is not shadow //See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003. }; } #endif #endif