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# /usr/ports/pobj/OpenCV-2.2.0/OpenCV-2.2.0/modules/video/include/opencv2/video/background_segm.hpp

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    00042
    00043 #ifndef __OPENCV_BACKGROUND_SEGM_HPP__
    00044 #define __OPENCV_BACKGROUND_SEGM_HPP__
    00045
    00046 #include "opencv2/core/core.hpp"
    00047
    00048 #ifdef __cplusplus
    00049 extern "C" {
    00050 #endif
    00051
    00052 /****************************************************************************************\
    00053 *                           Background/foreground segmentation                           *
    00054 \****************************************************************************************/
    00055
    00056 /* We discriminate between foreground and background pixels
    00057  * by building and maintaining a model of the background.
    00058  * Any pixel which does not fit this model is then deemed
    00059  * to be foreground.
    00060  *
    00061  * At present we support two core background models,
    00062  * one of which has two variations:
    00063  *
    00064  *  o CV_BG_MODEL_FGD: latest and greatest algorithm, described in
    00065  *
    00066  *       Foreground Object Detection from Videos Containing Complex Background.
    00067  *       Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian.
    00068  *       ACM MM2003 9p
    00069  *
    00070  *  o CV_BG_MODEL_FGD_SIMPLE:
    00071  *       A code comment describes this as a simplified version of the above,
    00072  *       but the code is in fact currently identical
    00073  *
    00074  *  o CV_BG_MODEL_MOG: "Mixture of Gaussians", older algorithm, described in
    00075  *
    00076  *       Moving target classification and tracking from real-time video.
    00077  *       A Lipton, H Fujijoshi, R Patil
    00078  *       Proceedings IEEE Workshop on Application of Computer Vision pp 8-14 1998
    00079  *
    00080  *       Learning patterns of activity using real-time tracking
    00081  *       C Stauffer and W Grimson  August 2000
    00082  *       IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8):747-757
    00083  */
    00084
    00085
    00086 #define CV_BG_MODEL_FGD         0
    00087 #define CV_BG_MODEL_MOG         1                       /* "Mixture of Gaussians".      */
    00088 #define CV_BG_MODEL_FGD_SIMPLE  2
    00089
    00090 struct CvBGStatModel;
    00091
    00092 typedef void (CV_CDECL * CvReleaseBGStatModel)( struct CvBGStatModel** bg_model );
    00093 typedef int (CV_CDECL * CvUpdateBGStatModel)( IplImage* curr_frame, struct CvBGStatModel* bg_model,
    00094                                               double learningRate );
    00095
    00096 #define CV_BG_STAT_MODEL_FIELDS()                                                   \
    00097     int             type; /*type of BG model*/                                      \
    00098     CvReleaseBGStatModel release;                                                   \
    00099     CvUpdateBGStatModel update;                                                     \
    00100     IplImage*       background;   /*8UC3 reference background image*/               \
    00101     IplImage*       foreground;   /*8UC1 foreground image*/                         \
    00102     IplImage**      layers;       /*8UC3 reference background image, can be null */ \
    00103     int             layer_count;  /* can be zero */                                 \
    00104     CvMemStorage*   storage;      /*storage for foreground_regions*/                \
    00105     CvSeq*          foreground_regions /*foreground object contours*/
    00106
    00107 typedef struct CvBGStatModel
    00108 {
    00109     CV_BG_STAT_MODEL_FIELDS();
    00110 } CvBGStatModel;
    00111
    00112 //
    00113
    00114 // Releases memory used by BGStatModel
    00115 CVAPI(void) cvReleaseBGStatModel( CvBGStatModel** bg_model );
    00116
    00117 // Updates statistical model and returns number of found foreground regions
    00118 CVAPI(int) cvUpdateBGStatModel( IplImage* current_frame, CvBGStatModel*  bg_model,
    00119                                 double learningRate CV_DEFAULT(-1));
    00120
    00121 // Performs FG post-processing using segmentation
    00122 // (all pixels of a region will be classified as foreground if majority of pixels of the region are FG).
    00123 // parameters:
    00124 //      segments - pointer to result of segmentation (for example MeanShiftSegmentation)
    00125 //      bg_model - pointer to CvBGStatModel structure
    00126 CVAPI(void) cvRefineForegroundMaskBySegm( CvSeq* segments, CvBGStatModel*  bg_model );
    00127
    00128 /* Common use change detection function */
    00129 CVAPI(int)  cvChangeDetection( IplImage*  prev_frame,
    00130                                IplImage*  curr_frame,
    00131                                IplImage*  change_mask );
    00132
    00133 /*
    00134   Interface of ACM MM2003 algorithm
    00135 */
    00136
    00137 /* Default parameters of foreground detection algorithm: */
    00138 #define  CV_BGFG_FGD_LC              128
    00139 #define  CV_BGFG_FGD_N1C             15
    00140 #define  CV_BGFG_FGD_N2C             25
    00141
    00142 #define  CV_BGFG_FGD_LCC             64
    00143 #define  CV_BGFG_FGD_N1CC            25
    00144 #define  CV_BGFG_FGD_N2CC            40
    00145
    00146 /* Background reference image update parameter: */
    00147 #define  CV_BGFG_FGD_ALPHA_1         0.1f
    00148
    00149 /* stat model update parameter
    00150  * 0.002f ~ 1K frame(~45sec), 0.005 ~ 18sec (if 25fps and absolutely static BG)
    00151  */
    00152 #define  CV_BGFG_FGD_ALPHA_2         0.005f
    00153
    00154 /* start value for alpha parameter (to fast initiate statistic model) */
    00155 #define  CV_BGFG_FGD_ALPHA_3         0.1f
    00156
    00157 #define  CV_BGFG_FGD_DELTA           2
    00158
    00159 #define  CV_BGFG_FGD_T               0.9f
    00160
    00161 #define  CV_BGFG_FGD_MINAREA         15.f
    00162
    00163 #define  CV_BGFG_FGD_BG_UPDATE_TRESH 0.5f
    00164
    00165 /* See the above-referenced Li/Huang/Gu/Tian paper
    00166  * for a full description of these background-model
    00167  * tuning parameters.
    00168  *
    00169  * Nomenclature:  'c'  == "color", a three-component red/green/blue vector.
    00170  *                         We use histograms of these to model the range of
    00171  *                         colors we've seen at a given background pixel.
    00172  *
    00173  *                'cc' == "color co-occurrence", a six-component vector giving
    00174  *                         RGB color for both this frame and preceding frame.
    00175  *                             We use histograms of these to model the range of
    00176  *                         color CHANGES we've seen at a given background pixel.
    00177  */
    00178 typedef struct CvFGDStatModelParams
    00179 {
    00180     int    Lc;                  /* Quantized levels per 'color' component. Power of two, typically 32, 64 or 128.                               */
    00181     int    N1c;                 /* Number of color vectors used to model normal background color variation at a given pixel.                    */
    00182     int    N2c;                 /* Number of color vectors retained at given pixel.  Must be > N1c, typically ~ 5/3 of N1c.                     */
    00183                                 /* Used to allow the first N1c vectors to adapt over time to changing background.                               */
    00184
    00185     int    Lcc;                 /* Quantized levels per 'color co-occurrence' component.  Power of two, typically 16, 32 or 64.                 */
    00186     int    N1cc;                /* Number of color co-occurrence vectors used to model normal background color variation at a given pixel.      */
    00187     int    N2cc;                /* Number of color co-occurrence vectors retained at given pixel.  Must be > N1cc, typically ~ 5/3 of N1cc.     */
    00188                                 /* Used to allow the first N1cc vectors to adapt over time to changing background.                              */
    00189
    00190     int    is_obj_without_holes;/* If TRUE we ignore holes within foreground blobs. Defaults to TRUE.                                           */
    00191     int    perform_morphing;    /* Number of erode-dilate-erode foreground-blob cleanup iterations.                                             */
    00192                                 /* These erase one-pixel junk blobs and merge almost-touching blobs. Default value is 1.                        */
    00193
    00194     float  alpha1;              /* How quickly we forget old background pixel values seen.  Typically set to 0.1                                */
    00195     float  alpha2;              /* "Controls speed of feature learning". Depends on T. Typical value circa 0.005.                               */
    00196     float  alpha3;              /* Alternate to alpha2, used (e.g.) for quicker initial convergence. Typical value 0.1.                         */
    00197
    00198     float  delta;               /* Affects color and color co-occurrence quantization, typically set to 2.                                      */
    00199     float  T;                   /* "A percentage value which determines when new features can be recognized as new background." (Typically 0.9).*/
    00200     float  minArea;             /* Discard foreground blobs whose bounding box is smaller than this threshold.                                  */
    00201 } CvFGDStatModelParams;
    00202
    00203 typedef struct CvBGPixelCStatTable
    00204 {
    00205     float          Pv, Pvb;
    00206     uchar          v[3];
    00207 } CvBGPixelCStatTable;
    00208
    00209 typedef struct CvBGPixelCCStatTable
    00210 {
    00211     float          Pv, Pvb;
    00212     uchar          v[6];
    00213 } CvBGPixelCCStatTable;
    00214
    00215 typedef struct CvBGPixelStat
    00216 {
    00217     float                 Pbc;
    00218     float                 Pbcc;
    00219     CvBGPixelCStatTable*  ctable;
    00220     CvBGPixelCCStatTable* cctable;
    00221     uchar                 is_trained_st_model;
    00222     uchar                 is_trained_dyn_model;
    00223 } CvBGPixelStat;
    00224
    00225
    00226 typedef struct CvFGDStatModel
    00227 {
    00228     CV_BG_STAT_MODEL_FIELDS();
    00229     CvBGPixelStat*         pixel_stat;
    00230     IplImage*              Ftd;
    00231     IplImage*              Fbd;
    00232     IplImage*              prev_frame;
    00233     CvFGDStatModelParams   params;
    00234 } CvFGDStatModel;
    00235
    00236 /* Creates FGD model */
    00237 CVAPI(CvBGStatModel*) cvCreateFGDStatModel( IplImage* first_frame,
    00238                     CvFGDStatModelParams* parameters CV_DEFAULT(NULL));
    00239
    00240 /*
    00241    Interface of Gaussian mixture algorithm
    00242
    00243    "An improved adaptive background mixture model for real-time tracking with shadow detection"
    00244    P. KadewTraKuPong and R. Bowden,
    00245    Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001."
    00246    http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
    00247 */
    00248
    00249 /* Note:  "MOG" == "Mixture Of Gaussians": */
    00250
    00251 #define CV_BGFG_MOG_MAX_NGAUSSIANS 500
    00252
    00253 /* default parameters of gaussian background detection algorithm */
    00254 #define CV_BGFG_MOG_BACKGROUND_THRESHOLD     0.7     /* threshold sum of weights for background test */
    00255 #define CV_BGFG_MOG_STD_THRESHOLD            2.5     /* lambda=2.5 is 99% */
    00256 #define CV_BGFG_MOG_WINDOW_SIZE              200     /* Learning rate; alpha = 1/CV_GBG_WINDOW_SIZE */
    00257 #define CV_BGFG_MOG_NGAUSSIANS               5       /* = K = number of Gaussians in mixture */
    00258 #define CV_BGFG_MOG_WEIGHT_INIT              0.05
    00259 #define CV_BGFG_MOG_SIGMA_INIT               30
    00260 #define CV_BGFG_MOG_MINAREA                  15.f
    00261
    00262
    00263 #define CV_BGFG_MOG_NCOLORS                  3
    00264
    00265 typedef struct CvGaussBGStatModelParams
    00266 {
    00267     int     win_size;               /* = 1/alpha */
    00268     int     n_gauss;
    00269     double  bg_threshold, std_threshold, minArea;
    00270     double  weight_init, variance_init;
    00271 }CvGaussBGStatModelParams;
    00272
    00273 typedef struct CvGaussBGValues
    00274 {
    00275     int         match_sum;
    00276     double      weight;
    00277     double      variance[CV_BGFG_MOG_NCOLORS];
    00278     double      mean[CV_BGFG_MOG_NCOLORS];
    00279 } CvGaussBGValues;
    00280
    00281 typedef struct CvGaussBGPoint
    00282 {
    00283     CvGaussBGValues* g_values;
    00284 } CvGaussBGPoint;
    00285
    00286
    00287 typedef struct CvGaussBGModel
    00288 {
    00289     CV_BG_STAT_MODEL_FIELDS();
    00290     CvGaussBGStatModelParams   params;
    00291     CvGaussBGPoint*            g_point;
    00292     int                        countFrames;
    00293 } CvGaussBGModel;
    00294
    00295
    00296 /* Creates Gaussian mixture background model */
    00297 CVAPI(CvBGStatModel*) cvCreateGaussianBGModel( IplImage* first_frame,
    00298                 CvGaussBGStatModelParams* parameters CV_DEFAULT(NULL));
    00299
    00300
    00301 typedef struct CvBGCodeBookElem
    00302 {
    00303     struct CvBGCodeBookElem* next;
    00304     int tLastUpdate;
    00305     int stale;
    00306     uchar boxMin[3];
    00307     uchar boxMax[3];
    00308     uchar learnMin[3];
    00309     uchar learnMax[3];
    00310 } CvBGCodeBookElem;
    00311
    00312 typedef struct CvBGCodeBookModel
    00313 {
    00314     CvSize size;
    00315     int t;
    00316     uchar cbBounds[3];
    00317     uchar modMin[3];
    00318     uchar modMax[3];
    00319     CvBGCodeBookElem** cbmap;
    00320     CvMemStorage* storage;
    00321     CvBGCodeBookElem* freeList;
    00322 } CvBGCodeBookModel;
    00323
    00324 CVAPI(CvBGCodeBookModel*) cvCreateBGCodeBookModel();
    00325 CVAPI(void) cvReleaseBGCodeBookModel( CvBGCodeBookModel** model );
    00326
    00327 CVAPI(void) cvBGCodeBookUpdate( CvBGCodeBookModel* model, const CvArr* image,
    00328                                 CvRect roi CV_DEFAULT(cvRect(0,0,0,0)),
    00329                                 const CvArr* mask CV_DEFAULT(0) );
    00330
    00331 CVAPI(int) cvBGCodeBookDiff( const CvBGCodeBookModel* model, const CvArr* image,
    00332                              CvArr* fgmask, CvRect roi CV_DEFAULT(cvRect(0,0,0,0)) );
    00333
    00334 CVAPI(void) cvBGCodeBookClearStale( CvBGCodeBookModel* model, int staleThresh,
    00335                                     CvRect roi CV_DEFAULT(cvRect(0,0,0,0)),
    00336                                     const CvArr* mask CV_DEFAULT(0) );
    00337
    00338 CVAPI(CvSeq*) cvSegmentFGMask( CvArr *fgmask, int poly1Hull0 CV_DEFAULT(1),
    00339                                float perimScale CV_DEFAULT(4.f),
    00340                                CvMemStorage* storage CV_DEFAULT(0),
    00341                                CvPoint offset CV_DEFAULT(cvPoint(0,0)));
    00342
    00343 #ifdef __cplusplus
    00344 }
    00345
    00346 namespace cv
    00347 {
    00348
    00355 class CV_EXPORTS_W BackgroundSubtractor
    00356 {
    00357 public:
    00359     virtual ~BackgroundSubtractor();
    00361     CV_WRAP_AS(apply) virtual void operator()(const Mat& image, CV_OUT Mat& fgmask,
    00362                                               double learningRate=0);
    00363 };
    00364
    00365
    00376 class CV_EXPORTS_W BackgroundSubtractorMOG : public BackgroundSubtractor
    00377 {
    00378 public:
    00380     CV_WRAP BackgroundSubtractorMOG();
    00382     CV_WRAP BackgroundSubtractorMOG(int history, int nmixtures, double backgroundRatio, double noiseSigma=0);
    00384     virtual ~BackgroundSubtractorMOG();
    00386     virtual void operator()(const Mat& image, Mat& fgmask, double learningRate=0);
    00387
    00389     virtual void initialize(Size frameSize, int frameType);
    00390
    00391     Size frameSize;
    00392     int frameType;
    00393     Mat bgmodel;
    00394     int nframes;
    00395     int history;
    00396     int nmixtures;
    00397     double varThreshold;
    00398     double backgroundRatio;
    00399     double noiseSigma;
    00400 };
    00401
    00402 }
    00403 #endif
    00404
    00405 #endif

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