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a LOT of obsolete stuff has been moved to the legacy module.

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This commit is contained in:
Vadim Pisarevsky
2012-03-30 12:19:25 +00:00
parent 7e5726e251
commit beb7fc3c92
42 changed files with 3711 additions and 1960 deletions
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299
modules/video/include/opencv2/video/background_segm.hpp
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@@ -45,304 +45,6 @@
#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
{
@@ -483,6 +185,5 @@ public:
};
}
#endif
#endif

15
modules/video/include/opencv2/video/tracking.hpp
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@@ -60,21 +60,6 @@ extern "C" {
/************************************ optical flow ***************************************/
/* Calculates optical flow for 2 images using classical Lucas & Kanade algorithm */
CVAPI(void) cvCalcOpticalFlowLK( const CvArr* prev, const CvArr* curr,
CvSize win_size, CvArr* velx, CvArr* vely );
/* Calculates optical flow for 2 images using block matching algorithm */
CVAPI(void) cvCalcOpticalFlowBM( const CvArr* prev, const CvArr* curr,
CvSize block_size, CvSize shift_size,
CvSize max_range, int use_previous,
CvArr* velx, CvArr* vely );
/* Calculates Optical flow for 2 images using Horn & Schunck algorithm */
CVAPI(void) cvCalcOpticalFlowHS( const CvArr* prev, const CvArr* curr,
int use_previous, CvArr* velx, CvArr* vely,
double lambda, CvTermCriteria criteria );
#define CV_LKFLOW_PYR_A_READY 1
#define CV_LKFLOW_PYR_B_READY 2
#define CV_LKFLOW_INITIAL_GUESSES 4