topical media & game development
lib-of-vs-addons-ofxOpenCv-libs-opencv-include-cvaux.h / h
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ifndef __CVAUX__H__
define __CVAUX__H__
include <cv.h>
ifdef __cplusplus
extern "C" {
endif
CVAPI(CvSeq*) cvSegmentImage( const CvArr* srcarr, CvArr* dstarr,
double canny_threshold,
double ffill_threshold,
CvMemStorage* storage );
**************************************************************************************\
Eigen objects *
\***************************************************************************************
typedef int (CV_CDECL * CvCallback)(int index, void* buffer, void* user_data);
typedef union
{
CvCallback callback;
void* data;
}
CvInput;
define CV_EIGOBJ_NO_CALLBACK 0
define CV_EIGOBJ_INPUT_CALLBACK 1
define CV_EIGOBJ_OUTPUT_CALLBACK 2
define CV_EIGOBJ_BOTH_CALLBACK 3
/* Calculates covariation matrix of a set of arrays */
CVAPI(void) cvCalcCovarMatrixEx( int nObjects, void* input, int ioFlags,
int ioBufSize, uchar* buffer, void* userData,
IplImage* avg, float* covarMatrix );
/* Calculates eigen values and vectors of covariation matrix of a set of
arrays */
CVAPI(void) cvCalcEigenObjects( int nObjects, void* input, void* output,
int ioFlags, int ioBufSize, void* userData,
CvTermCriteria* calcLimit, IplImage* avg,
float* eigVals );
/* Calculates dot product (obj - avg) * eigObj (i.e. projects image to eigen vector) */
CVAPI(double) cvCalcDecompCoeff( IplImage* obj, IplImage* eigObj, IplImage* avg );
/* Projects image to eigen space (finds all decomposion coefficients */
CVAPI(void) cvEigenDecomposite( IplImage* obj, int nEigObjs, void* eigInput,
int ioFlags, void* userData, IplImage* avg,
float* coeffs );
/* Projects original objects used to calculate eigen space basis to that space */
CVAPI(void) cvEigenProjection( void* eigInput, int nEigObjs, int ioFlags,
void* userData, float* coeffs, IplImage* avg,
IplImage* proj );
**************************************************************************************\
1D/2D HMM *
\***************************************************************************************
typedef struct CvImgObsInfo
{
int obs_x;
int obs_y;
int obs_size;
float* obs;//consequtive observations
int* state;/* arr of pairs superstate/state to which observation belong */
int* mix; /* number of mixture to which observation belong */
}
CvImgObsInfo;/*struct for 1 image*/
typedef CvImgObsInfo Cv1DObsInfo;
typedef struct CvEHMMState
{
int num_mix; /*number of mixtures in this state*/
float* mu; /*mean vectors corresponding to each mixture*/
float* inv_var; /* square root of inversed variances corresp. to each mixture*/
float* log_var_val; /* sum of 0.5 (LN2PI + ln(variance[i]) ) for i=1,n */
float* weight; /*array of mixture weights. Summ of all weights in state is 1. */
}
CvEHMMState;
typedef struct CvEHMM
{
int level; /* 0 - lowest(i.e its states are real states), ..... */
int num_states; /* number of HMM states */
float* transP;/*transition probab. matrices for states */
float** obsProb; /* if level == 0 - array of brob matrices corresponding to hmm
if level == 1 - martix of matrices */
union
{
CvEHMMState* state; /* if level == 0 points to real states array,
if not - points to embedded hmms */
struct CvEHMM* ehmm; /* pointer to an embedded model or NULL, if it is a leaf */
} u;
}
CvEHMM;
/*CVAPI(int) icvCreate1DHMM( CvEHMM** this_hmm,
int state_number, int* num_mix, int obs_size );
CVAPI(int) icvRelease1DHMM( CvEHMM** phmm );
CVAPI(int) icvUniform1DSegm( Cv1DObsInfo* obs_info, CvEHMM* hmm );
CVAPI(int) icvInit1DMixSegm( Cv1DObsInfo** obs_info_array, int num_img, CvEHMM* hmm);
CVAPI(int) icvEstimate1DHMMStateParams( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm);
CVAPI(int) icvEstimate1DObsProb( CvImgObsInfo* obs_info, CvEHMM* hmm );
CVAPI(int) icvEstimate1DTransProb( Cv1DObsInfo** obs_info_array,
int num_seq,
CvEHMM* hmm );
CVAPI(float) icvViterbi( Cv1DObsInfo* obs_info, CvEHMM* hmm);
CVAPI(int) icv1DMixSegmL2( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm );*/
********************************* Embedded HMMs ************************************
/* Creates 2D HMM */
CVAPI(CvEHMM*) cvCreate2DHMM( int* stateNumber, int* numMix, int obsSize );
/* Releases HMM */
CVAPI(void) cvRelease2DHMM( CvEHMM** hmm );
define CV_COUNT_OBS(roi, win, delta, numObs ) \
{ \
(numObs)->width =((roi)->width -(win)->width +(delta)->width)/(delta)->width; \
(numObs)->height =((roi)->height -(win)->height +(delta)->height)/(delta)->height;\
}
/* Creates storage for observation vectors */
CVAPI(CvImgObsInfo*) cvCreateObsInfo( CvSize numObs, int obsSize );
/* Releases storage for observation vectors */
CVAPI(void) cvReleaseObsInfo( CvImgObsInfo** obs_info );
/* The function takes an image on input and and returns the sequnce of observations
to be used with an embedded HMM; Each observation is top-left block of DCT
coefficient matrix */
CVAPI(void) cvImgToObs_DCT( const CvArr* arr, float* obs, CvSize dctSize,
CvSize obsSize, CvSize delta );
/* Uniformly segments all observation vectors extracted from image */
CVAPI(void) cvUniformImgSegm( CvImgObsInfo* obs_info, CvEHMM* ehmm );
/* Does mixture segmentation of the states of embedded HMM */
CVAPI(void) cvInitMixSegm( CvImgObsInfo** obs_info_array,
int num_img, CvEHMM* hmm );
/* Function calculates means, variances, weights of every Gaussian mixture
of every low-level state of embedded HMM */
CVAPI(void) cvEstimateHMMStateParams( CvImgObsInfo** obs_info_array,
int num_img, CvEHMM* hmm );
/* Function computes transition probability matrices of embedded HMM
given observations segmentation */
CVAPI(void) cvEstimateTransProb( CvImgObsInfo** obs_info_array,
int num_img, CvEHMM* hmm );
/* Function computes probabilities of appearing observations at any state
(i.e. computes P(obs|state) for every pair(obs,state)) */
CVAPI(void) cvEstimateObsProb( CvImgObsInfo* obs_info,
CvEHMM* hmm );
/* Runs Viterbi algorithm for embedded HMM */
CVAPI(float) cvEViterbi( CvImgObsInfo* obs_info, CvEHMM* hmm );
/* Function clusters observation vectors from several images
given observations segmentation.
Euclidean distance used for clustering vectors.
Centers of clusters are given means of every mixture */
CVAPI(void) cvMixSegmL2( CvImgObsInfo** obs_info_array,
int num_img, CvEHMM* hmm );
**************************************************************************************\
A few functions from old stereo gesture recognition demosions *
\***************************************************************************************
/* Creates hand mask image given several points on the hand */
CVAPI(void) cvCreateHandMask( CvSeq* hand_points,
IplImage *img_mask, CvRect *roi);
/* Finds hand region in range image data */
CVAPI(void) cvFindHandRegion (CvPoint3D32f* points, int count,
CvSeq* indexs,
float* line, CvSize2D32f size, int flag,
CvPoint3D32f* center,
CvMemStorage* storage, CvSeq **numbers);
/* Finds hand region in range image data (advanced version) */
CVAPI(void) cvFindHandRegionA( CvPoint3D32f* points, int count,
CvSeq* indexs,
float* line, CvSize2D32f size, int jc,
CvPoint3D32f* center,
CvMemStorage* storage, CvSeq **numbers);
**************************************************************************************\
Additional operations on Subdivisions *
\***************************************************************************************
// paints voronoi diagram: just demo function
CVAPI(void) icvDrawMosaic( CvSubdiv2D* subdiv, IplImage* src, IplImage* dst );
// checks planar subdivision for correctness. It is not an absolute check,
// but it verifies some relations between quad-edges
CVAPI(int) icvSubdiv2DCheck( CvSubdiv2D* subdiv );
// returns squared distance between two 2D points with floating-point coordinates.
CV_INLINE double icvSqDist2D32f( CvPoint2D32f pt1, CvPoint2D32f pt2 )
{
double dx = pt1.x - pt2.x;
double dy = pt1.y - pt2.y;
return dx*dx + dy*dy;
}
**************************************************************************************\
More operations on sequences *
\***************************************************************************************
**************************************************************************************
define CV_CURRENT_INT( reader ) (*((int *)(reader).ptr))
define CV_PREV_INT( reader ) (*((int *)(reader).prev_elem))
define CV_GRAPH_WEIGHTED_VERTEX_FIELDS() CV_GRAPH_VERTEX_FIELDS()\
float weight;
define CV_GRAPH_WEIGHTED_EDGE_FIELDS() CV_GRAPH_EDGE_FIELDS()
typedef struct CvGraphWeightedVtx
{
CV_GRAPH_WEIGHTED_VERTEX_FIELDS()
}
CvGraphWeightedVtx;
typedef struct CvGraphWeightedEdge
{
CV_GRAPH_WEIGHTED_EDGE_FIELDS()
}
CvGraphWeightedEdge;
typedef enum CvGraphWeightType
{
CV_NOT_WEIGHTED,
CV_WEIGHTED_VTX,
CV_WEIGHTED_EDGE,
CV_WEIGHTED_ALL
} CvGraphWeightType;
**************************************************************************************
*****************************Stereo correspondence************************************
typedef struct CvCliqueFinder
{
CvGraph* graph;
int** adj_matr;
int N; //graph size
// stacks, counters etc/
int k; //stack size
int* current_comp;
int** All;
int* ne;
int* ce;
int* fixp; //node with minimal disconnections
int* nod;
int* s; //for selected candidate
int status;
int best_score;
int weighted;
int weighted_edges;
float best_weight;
float* edge_weights;
float* vertex_weights;
float* cur_weight;
float* cand_weight;
} CvCliqueFinder;
define CLIQUE_TIME_OFF 2
define CLIQUE_FOUND 1
define CLIQUE_END 0
/*CVAPI(void) cvStartFindCliques( CvGraph* graph, CvCliqueFinder* finder, int reverse,
int weighted CV_DEFAULT(0), int weighted_edges CV_DEFAULT(0));
CVAPI(int) cvFindNextMaximalClique( CvCliqueFinder* finder, int* clock_rest CV_DEFAULT(0) );
CVAPI(void) cvEndFindCliques( CvCliqueFinder* finder );
CVAPI(void) cvBronKerbosch( CvGraph* graph );*/
/*F/////////////////////////////////////////////////////////////////////////////////
//
// Name: cvSubgraphWeight
// Purpose: finds weight of subgraph in a graph
// Context:
// Parameters:
// graph - input graph.
// subgraph - sequence of pairwise different ints. These are indices of vertices of subgraph.
// weight_type - describes the way we measure weight.
// one of the following:
// CV_NOT_WEIGHTED - weight of a clique is simply its size
// CV_WEIGHTED_VTX - weight of a clique is the sum of weights of its vertices
// CV_WEIGHTED_EDGE - the same but edges
// CV_WEIGHTED_ALL - the same but both edges and vertices
// weight_vtx - optional vector of floats, with size = graph->total.
// If weight_type is either CV_WEIGHTED_VTX or CV_WEIGHTED_ALL
// weights of vertices must be provided. If weight_vtx not zero
// these weights considered to be here, otherwise function assumes
// that vertices of graph are inherited from CvGraphWeightedVtx.
// weight_edge - optional matrix of floats, of width and height = graph->total.
// If weight_type is either CV_WEIGHTED_EDGE or CV_WEIGHTED_ALL
// weights of edges ought to be supplied. If weight_edge is not zero
// function finds them here, otherwise function expects
// edges of graph to be inherited from CvGraphWeightedEdge.
// If this parameter is not zero structure of the graph is determined from matrix
// rather than from CvGraphEdge's. In particular, elements corresponding to
// absent edges should be zero.
// Returns:
// weight of subgraph.
// Notes:
//F*/
/*CVAPI(float) cvSubgraphWeight( CvGraph *graph, CvSeq *subgraph,
CvGraphWeightType weight_type CV_DEFAULT(CV_NOT_WEIGHTED),
CvVect32f weight_vtx CV_DEFAULT(0),
CvMatr32f weight_edge CV_DEFAULT(0) );*/
/*F/////////////////////////////////////////////////////////////////////////////////
//
// Name: cvFindCliqueEx
// Purpose: tries to find clique with maximum possible weight in a graph
// Context:
// Parameters:
// graph - input graph.
// storage - memory storage to be used by the result.
// is_complementary - optional flag showing whether function should seek for clique
// in complementary graph.
// weight_type - describes our notion about weight.
// one of the following:
// CV_NOT_WEIGHTED - weight of a clique is simply its size
// CV_WEIGHTED_VTX - weight of a clique is the sum of weights of its vertices
// CV_WEIGHTED_EDGE - the same but edges
// CV_WEIGHTED_ALL - the same but both edges and vertices
// weight_vtx - optional vector of floats, with size = graph->total.
// If weight_type is either CV_WEIGHTED_VTX or CV_WEIGHTED_ALL
// weights of vertices must be provided. If weight_vtx not zero
// these weights considered to be here, otherwise function assumes
// that vertices of graph are inherited from CvGraphWeightedVtx.
// weight_edge - optional matrix of floats, of width and height = graph->total.
// If weight_type is either CV_WEIGHTED_EDGE or CV_WEIGHTED_ALL
// weights of edges ought to be supplied. If weight_edge is not zero
// function finds them here, otherwise function expects
// edges of graph to be inherited from CvGraphWeightedEdge.
// Note that in case of CV_WEIGHTED_EDGE or CV_WEIGHTED_ALL
// nonzero is_complementary implies nonzero weight_edge.
// start_clique - optional sequence of pairwise different ints. They are indices of
// vertices that shall be present in the output clique.
// subgraph_of_ban - optional sequence of (maybe equal) ints. They are indices of
// vertices that shall not be present in the output clique.
// clique_weight_ptr - optional output parameter. Weight of found clique stored here.
// num_generations - optional number of generations in evolutionary part of algorithm,
// zero forces to return first found clique.
// quality - optional parameter determining degree of required quality/speed tradeoff.
// Must be in the range from 0 to 9.
// 0 is fast and dirty, 9 is slow but hopefully yields good clique.
// Returns:
// sequence of pairwise different ints.
// These are indices of vertices that form found clique.
// Notes:
// in cases of CV_WEIGHTED_EDGE and CV_WEIGHTED_ALL weights should be nonnegative.
// start_clique has a priority over subgraph_of_ban.
//F*/
/*CVAPI(CvSeq*) cvFindCliqueEx( CvGraph *graph, CvMemStorage *storage,
int is_complementary CV_DEFAULT(0),
CvGraphWeightType weight_type CV_DEFAULT(CV_NOT_WEIGHTED),
CvVect32f weight_vtx CV_DEFAULT(0),
CvMatr32f weight_edge CV_DEFAULT(0),
CvSeq *start_clique CV_DEFAULT(0),
CvSeq *subgraph_of_ban CV_DEFAULT(0),
float *clique_weight_ptr CV_DEFAULT(0),
int num_generations CV_DEFAULT(3),
int quality CV_DEFAULT(2) );*/
define CV_UNDEF_SC_PARAM 12345 //default value of parameters
define CV_IDP_BIRCHFIELD_PARAM1 25
define CV_IDP_BIRCHFIELD_PARAM2 5
define CV_IDP_BIRCHFIELD_PARAM3 12
define CV_IDP_BIRCHFIELD_PARAM4 15
define CV_IDP_BIRCHFIELD_PARAM5 25
define CV_DISPARITY_BIRCHFIELD 0
/*F/////////////////////////////////////////////////////////////////////
//
// Name: cvFindStereoCorrespondence
// Purpose: find stereo correspondence on stereo-pair
// Context:
// Parameters:
// leftImage - left image of stereo-pair (format 8uC1).
// rightImage - right image of stereo-pair (format 8uC1).
// mode - mode of correspondence retrieval (now CV_DISPARITY_BIRCHFIELD only)
// dispImage - destination disparity image
// maxDisparity - maximal disparity
// param1, param2, param3, param4, param5 - parameters of algorithm
// Returns:
// Notes:
// Images must be rectified.
// All images must have format 8uC1.
//F*/
CVAPI(void)
cvFindStereoCorrespondence(
const CvArr* leftImage, const CvArr* rightImage,
int mode,
CvArr* dispImage,
int maxDisparity,
double param1 CV_DEFAULT(CV_UNDEF_SC_PARAM),
double param2 CV_DEFAULT(CV_UNDEF_SC_PARAM),
double param3 CV_DEFAULT(CV_UNDEF_SC_PARAM),
double param4 CV_DEFAULT(CV_UNDEF_SC_PARAM),
double param5 CV_DEFAULT(CV_UNDEF_SC_PARAM) );
**************************************************************************************
********** Epiline functions ******************
typedef struct CvStereoLineCoeff
{
double Xcoef;
double XcoefA;
double XcoefB;
double XcoefAB;
double Ycoef;
double YcoefA;
double YcoefB;
double YcoefAB;
double Zcoef;
double ZcoefA;
double ZcoefB;
double ZcoefAB;
}CvStereoLineCoeff;
typedef struct CvCamera
{
float imgSize[2]; /* size of the camera view, used during calibration */
float matrix[9]; /* intinsic camera parameters: [ fx 0 cx; 0 fy cy; 0 0 1 ] */
float distortion[4]; /* distortion coefficients - two coefficients for radial distortion
and another two for tangential: [ k1 k2 p1 p2 ] */
float rotMatr[9];
float transVect[3]; /* rotation matrix and transition vector relatively
to some reference point in the space. */
}
CvCamera;
typedef struct CvStereoCamera
{
CvCamera* camera[2]; /* two individual camera parameters */
float fundMatr[9]; /* fundamental matrix */
/* New part for stereo */
CvPoint3D32f epipole[2];
CvPoint2D32f quad[2][4]; /* coordinates of destination quadrangle after
epipolar geometry rectification */
double coeffs[2][3][3];/* coefficients for transformation */
CvPoint2D32f border[2][4];
CvSize warpSize;
CvStereoLineCoeff* lineCoeffs;
int needSwapCameras;/* flag set to 1 if need to swap cameras for good reconstruction */
float rotMatrix[9];
float transVector[3];
}
CvStereoCamera;
typedef struct CvContourOrientation
{
float egvals[2];
float egvects[4];
float max, min; // minimum and maximum projections
int imax, imin;
} CvContourOrientation;
define CV_CAMERA_TO_WARP 1
define CV_WARP_TO_CAMERA 2
CVAPI(int) icvConvertWarpCoordinates(double coeffs[3][3],
CvPoint2D32f* cameraPoint,
CvPoint2D32f* warpPoint,
int direction);
CVAPI(int) icvGetSymPoint3D( CvPoint3D64f pointCorner,
CvPoint3D64f point1,
CvPoint3D64f point2,
CvPoint3D64f *pointSym2);
CVAPI(void) icvGetPieceLength3D(CvPoint3D64f point1,CvPoint3D64f point2,double* dist);
CVAPI(int) icvCompute3DPoint( double alpha,double betta,
CvStereoLineCoeff* coeffs,
CvPoint3D64f* point);
CVAPI(int) icvCreateConvertMatrVect( CvMatr64d rotMatr1,
CvMatr64d transVect1,
CvMatr64d rotMatr2,
CvMatr64d transVect2,
CvMatr64d convRotMatr,
CvMatr64d convTransVect);
CVAPI(int) icvConvertPointSystem(CvPoint3D64f M2,
CvPoint3D64f* M1,
CvMatr64d rotMatr,
CvMatr64d transVect
);
CVAPI(int) icvComputeCoeffForStereo( CvStereoCamera* stereoCamera);
CVAPI(int) icvGetCrossPieceVector(CvPoint2D32f p1_start,CvPoint2D32f p1_end,CvPoint2D32f v2_start,CvPoint2D32f v2_end,CvPoint2D32f *cross);
CVAPI(int) icvGetCrossLineDirect(CvPoint2D32f p1,CvPoint2D32f p2,float a,float b,float c,CvPoint2D32f* cross);
CVAPI(float) icvDefinePointPosition(CvPoint2D32f point1,CvPoint2D32f point2,CvPoint2D32f point);
CVAPI(int) icvStereoCalibration( int numImages,
int* nums,
CvSize imageSize,
CvPoint2D32f* imagePoints1,
CvPoint2D32f* imagePoints2,
CvPoint3D32f* objectPoints,
CvStereoCamera* stereoparams
);
CVAPI(int) icvComputeRestStereoParams(CvStereoCamera *stereoparams);
CVAPI(void) cvComputePerspectiveMap( const double coeffs[3][3], CvArr* rectMapX, CvArr* rectMapY );
CVAPI(int) icvComCoeffForLine( CvPoint2D64f point1,
CvPoint2D64f point2,
CvPoint2D64f point3,
CvPoint2D64f point4,
CvMatr64d camMatr1,
CvMatr64d rotMatr1,
CvMatr64d transVect1,
CvMatr64d camMatr2,
CvMatr64d rotMatr2,
CvMatr64d transVect2,
CvStereoLineCoeff* coeffs,
int* needSwapCameras);
CVAPI(int) icvGetDirectionForPoint( CvPoint2D64f point,
CvMatr64d camMatr,
CvPoint3D64f* direct);
CVAPI(int) icvGetCrossLines(CvPoint3D64f point11,CvPoint3D64f point12,
CvPoint3D64f point21,CvPoint3D64f point22,
CvPoint3D64f* midPoint);
CVAPI(int) icvComputeStereoLineCoeffs( CvPoint3D64f pointA,
CvPoint3D64f pointB,
CvPoint3D64f pointCam1,
double gamma,
CvStereoLineCoeff* coeffs);
/*CVAPI(int) icvComputeFundMatrEpipoles ( CvMatr64d camMatr1,
CvMatr64d rotMatr1,
CvVect64d transVect1,
CvMatr64d camMatr2,
CvMatr64d rotMatr2,
CvVect64d transVect2,
CvPoint2D64f* epipole1,
CvPoint2D64f* epipole2,
CvMatr64d fundMatr);*/
CVAPI(int) icvGetAngleLine( CvPoint2D64f startPoint, CvSize imageSize,CvPoint2D64f *point1,CvPoint2D64f *point2);
CVAPI(void) icvGetCoefForPiece( CvPoint2D64f p_start,CvPoint2D64f p_end,
double *a,double *b,double *c,
int* result);
/*CVAPI(void) icvGetCommonArea( CvSize imageSize,
CvPoint2D64f epipole1,CvPoint2D64f epipole2,
CvMatr64d fundMatr,
CvVect64d coeff11,CvVect64d coeff12,
CvVect64d coeff21,CvVect64d coeff22,
int* result);*/
CVAPI(void) icvComputeeInfiniteProject1(CvMatr64d rotMatr,
CvMatr64d camMatr1,
CvMatr64d camMatr2,
CvPoint2D32f point1,
CvPoint2D32f *point2);
CVAPI(void) icvComputeeInfiniteProject2(CvMatr64d rotMatr,
CvMatr64d camMatr1,
CvMatr64d camMatr2,
CvPoint2D32f* point1,
CvPoint2D32f point2);
CVAPI(void) icvGetCrossDirectDirect( CvVect64d direct1,CvVect64d direct2,
CvPoint2D64f *cross,int* result);
CVAPI(void) icvGetCrossPieceDirect( CvPoint2D64f p_start,CvPoint2D64f p_end,
double a,double b,double c,
CvPoint2D64f *cross,int* result);
CVAPI(void) icvGetCrossPiecePiece( CvPoint2D64f p1_start,CvPoint2D64f p1_end,
CvPoint2D64f p2_start,CvPoint2D64f p2_end,
CvPoint2D64f* cross,
int* result);
CVAPI(void) icvGetPieceLength(CvPoint2D64f point1,CvPoint2D64f point2,double* dist);
CVAPI(void) icvGetCrossRectDirect( CvSize imageSize,
double a,double b,double c,
CvPoint2D64f *start,CvPoint2D64f *end,
int* result);
CVAPI(void) icvProjectPointToImage( CvPoint3D64f point,
CvMatr64d camMatr,CvMatr64d rotMatr,CvVect64d transVect,
CvPoint2D64f* projPoint);
CVAPI(void) icvGetQuadsTransform( CvSize imageSize,
CvMatr64d camMatr1,
CvMatr64d rotMatr1,
CvVect64d transVect1,
CvMatr64d camMatr2,
CvMatr64d rotMatr2,
CvVect64d transVect2,
CvSize* warpSize,
double quad1[4][2],
double quad2[4][2],
CvMatr64d fundMatr,
CvPoint3D64f* epipole1,
CvPoint3D64f* epipole2
);
CVAPI(void) icvGetQuadsTransformStruct( CvStereoCamera* stereoCamera);
CVAPI(void) icvComputeStereoParamsForCameras(CvStereoCamera* stereoCamera);
CVAPI(void) icvGetCutPiece( CvVect64d areaLineCoef1,CvVect64d areaLineCoef2,
CvPoint2D64f epipole,
CvSize imageSize,
CvPoint2D64f* point11,CvPoint2D64f* point12,
CvPoint2D64f* point21,CvPoint2D64f* point22,
int* result);
CVAPI(void) icvGetMiddleAnglePoint( CvPoint2D64f basePoint,
CvPoint2D64f point1,CvPoint2D64f point2,
CvPoint2D64f* midPoint);
CVAPI(void) icvGetNormalDirect(CvVect64d direct,CvPoint2D64f point,CvVect64d normDirect);
CVAPI(double) icvGetVect(CvPoint2D64f basePoint,CvPoint2D64f point1,CvPoint2D64f point2);
CVAPI(void) icvProjectPointToDirect( CvPoint2D64f point,CvVect64d lineCoeff,
CvPoint2D64f* projectPoint);
CVAPI(void) icvGetDistanceFromPointToDirect( CvPoint2D64f point,CvVect64d lineCoef,double*dist);
CVAPI(IplImage*) icvCreateIsometricImage( IplImage* src, IplImage* dst,
int desired_depth, int desired_num_channels );
CVAPI(void) cvDeInterlace( const CvArr* frame, CvArr* fieldEven, CvArr* fieldOdd );
/*CVAPI(int) icvSelectBestRt( int numImages,
int* numPoints,
CvSize imageSize,
CvPoint2D32f* imagePoints1,
CvPoint2D32f* imagePoints2,
CvPoint3D32f* objectPoints,
CvMatr32f cameraMatrix1,
CvVect32f distortion1,
CvMatr32f rotMatrs1,
CvVect32f transVects1,
CvMatr32f cameraMatrix2,
CvVect32f distortion2,
CvMatr32f rotMatrs2,
CvVect32f transVects2,
CvMatr32f bestRotMatr,
CvVect32f bestTransVect
);*/
**************************************************************************************\
Contour Morphing *
\***************************************************************************************
/* finds correspondence between two contours */
CvSeq* cvCalcContoursCorrespondence( const CvSeq* contour1,
const CvSeq* contour2,
CvMemStorage* storage);
/* morphs contours using the pre-calculated correspondence:
alpha=0 ~ contour1, alpha=1 ~ contour2 */
CvSeq* cvMorphContours( const CvSeq* contour1, const CvSeq* contour2,
CvSeq* corr, double alpha,
CvMemStorage* storage );
**************************************************************************************\
Texture Descriptors *
\***************************************************************************************
define CV_GLCM_OPTIMIZATION_NONE -2
define CV_GLCM_OPTIMIZATION_LUT -1
define CV_GLCM_OPTIMIZATION_HISTOGRAM 0
define CV_GLCMDESC_OPTIMIZATION_ALLOWDOUBLENEST 10
define CV_GLCMDESC_OPTIMIZATION_ALLOWTRIPLENEST 11
define CV_GLCMDESC_OPTIMIZATION_HISTOGRAM 4
define CV_GLCMDESC_ENTROPY 0
define CV_GLCMDESC_ENERGY 1
define CV_GLCMDESC_HOMOGENITY 2
define CV_GLCMDESC_CONTRAST 3
define CV_GLCMDESC_CLUSTERTENDENCY 4
define CV_GLCMDESC_CLUSTERSHADE 5
define CV_GLCMDESC_CORRELATION 6
define CV_GLCMDESC_CORRELATIONINFO1 7
define CV_GLCMDESC_CORRELATIONINFO2 8
define CV_GLCMDESC_MAXIMUMPROBABILITY 9
define CV_GLCM_ALL 0
define CV_GLCM_GLCM 1
define CV_GLCM_DESC 2
typedef struct CvGLCM CvGLCM;
CVAPI(CvGLCM*) cvCreateGLCM( const IplImage* srcImage,
int stepMagnitude,
const int* stepDirections CV_DEFAULT(0),
int numStepDirections CV_DEFAULT(0),
int optimizationType CV_DEFAULT(CV_GLCM_OPTIMIZATION_NONE));
CVAPI(void) cvReleaseGLCM( CvGLCM** GLCM, int flag CV_DEFAULT(CV_GLCM_ALL));
CVAPI(void) cvCreateGLCMDescriptors( CvGLCM* destGLCM,
int descriptorOptimizationType
CV_DEFAULT(CV_GLCMDESC_OPTIMIZATION_ALLOWDOUBLENEST));
CVAPI(double) cvGetGLCMDescriptor( CvGLCM* GLCM, int step, int descriptor );
CVAPI(void) cvGetGLCMDescriptorStatistics( CvGLCM* GLCM, int descriptor,
double* average, double* standardDeviation );
CVAPI(IplImage*) cvCreateGLCMImage( CvGLCM* GLCM, int step );
**************************************************************************************\
Face eyes&mouth tracking *
\***************************************************************************************
typedef struct CvFaceTracker CvFaceTracker;
define CV_NUM_FACE_ELEMENTS 3
enum CV_FACE_ELEMENTS
{
CV_FACE_MOUTH = 0,
CV_FACE_LEFT_EYE = 1,
CV_FACE_RIGHT_EYE = 2
};
CVAPI(CvFaceTracker*) cvInitFaceTracker(CvFaceTracker* pFaceTracking, const IplImage* imgGray,
CvRect* pRects, int nRects);
CVAPI(int) cvTrackFace( CvFaceTracker* pFaceTracker, IplImage* imgGray,
CvRect* pRects, int nRects,
CvPoint* ptRotate, double* dbAngleRotate);
CVAPI(void) cvReleaseFaceTracker(CvFaceTracker** ppFaceTracker);
typedef struct CvFace
{
CvRect MouthRect;
CvRect LeftEyeRect;
CvRect RightEyeRect;
} CvFaceData;
CvSeq * cvFindFace(IplImage * Image,CvMemStorage* storage);
CvSeq * cvPostBoostingFindFace(IplImage * Image,CvMemStorage* storage);
**************************************************************************************\
3D Tracker *
\***************************************************************************************
typedef unsigned char CvBool;
typedef struct
{
int id;
CvPoint2D32f p; // pgruebele: So we do not loose precision, this needs to be float
} Cv3dTracker2dTrackedObject;
CV_INLINE Cv3dTracker2dTrackedObject cv3dTracker2dTrackedObject(int id, CvPoint2D32f p)
{
Cv3dTracker2dTrackedObject r;
r.id = id;
r.p = p;
return r;
}
typedef struct
{
int id;
CvPoint3D32f p; // location of the tracked object
} Cv3dTrackerTrackedObject;
CV_INLINE Cv3dTrackerTrackedObject cv3dTrackerTrackedObject(int id, CvPoint3D32f p)
{
Cv3dTrackerTrackedObject r;
r.id = id;
r.p = p;
return r;
}
typedef struct
{
CvBool valid;
float mat[4][4]; /* maps camera coordinates to world coordinates */
CvPoint2D32f principal_point; /* copied from intrinsics so this structure */
/* has all the info we need */
} Cv3dTrackerCameraInfo;
typedef struct
{
CvPoint2D32f principal_point;
float focal_length[2];
float distortion[4];
} Cv3dTrackerCameraIntrinsics;
CVAPI(CvBool) cv3dTrackerCalibrateCameras(int num_cameras,
const Cv3dTrackerCameraIntrinsics camera_intrinsics[], /* size is num_cameras */
CvSize etalon_size,
float square_size,
IplImage *samples[], /* size is num_cameras */
Cv3dTrackerCameraInfo camera_info[]); /* size is num_cameras */
CVAPI(int) cv3dTrackerLocateObjects(int num_cameras, int num_objects,
const Cv3dTrackerCameraInfo camera_info[], /* size is num_cameras */
const Cv3dTracker2dTrackedObject tracking_info[], /* size is num_objects*num_cameras */
Cv3dTrackerTrackedObject tracked_objects[]); /* size is num_objects */
**************************************************************************************
tracking_info is a rectangular array; one row per camera, num_objects elements per row.
The id field of any unused slots must be -1. Ids need not be ordered or consecutive. On
completion, the return value is the number of objects located; i.e., the number of objects
visible by more than one camera. The id field of any unused slots in tracked objects is
set to -1.
**************************************************************************************
**************************************************************************************\
Skeletons and Linear-Contour Models *
\***************************************************************************************
typedef enum CvLeeParameters
{
CV_LEE_INT = 0,
CV_LEE_FLOAT = 1,
CV_LEE_DOUBLE = 2,
CV_LEE_AUTO = -1,
CV_LEE_ERODE = 0,
CV_LEE_ZOOM = 1,
CV_LEE_NON = 2
} CvLeeParameters;
define CV_NEXT_VORONOISITE2D( SITE ) ((SITE)->edge[0]->site[((SITE)->edge[0]->site[0] == (SITE))])
define CV_PREV_VORONOISITE2D( SITE ) ((SITE)->edge[1]->site[((SITE)->edge[1]->site[0] == (SITE))])
define CV_FIRST_VORONOIEDGE2D( SITE ) ((SITE)->edge[0])
define CV_LAST_VORONOIEDGE2D( SITE ) ((SITE)->edge[1])
define CV_NEXT_VORONOIEDGE2D( EDGE, SITE ) ((EDGE)->next[(EDGE)->site[0] != (SITE)])
define CV_PREV_VORONOIEDGE2D( EDGE, SITE ) ((EDGE)->next[2 + ((EDGE)->site[0] != (SITE))])
define CV_VORONOIEDGE2D_BEGINNODE( EDGE, SITE ) ((EDGE)->node[((EDGE)->site[0] != (SITE))])
define CV_VORONOIEDGE2D_ENDNODE( EDGE, SITE ) ((EDGE)->node[((EDGE)->site[0] == (SITE))])
define CV_TWIN_VORONOISITE2D( SITE, EDGE ) ( (EDGE)->site[((EDGE)->site[0] == (SITE))])
define CV_VORONOISITE2D_FIELDS() \
struct CvVoronoiNode2D *node[2]; \
struct CvVoronoiEdge2D *edge[2];
typedef struct CvVoronoiSite2D
{
CV_VORONOISITE2D_FIELDS()
struct CvVoronoiSite2D *next[2];
} CvVoronoiSite2D;
define CV_VORONOIEDGE2D_FIELDS() \
struct CvVoronoiNode2D *node[2]; \
struct CvVoronoiSite2D *site[2]; \
struct CvVoronoiEdge2D *next[4];
typedef struct CvVoronoiEdge2D
{
CV_VORONOIEDGE2D_FIELDS()
} CvVoronoiEdge2D;
define CV_VORONOINODE2D_FIELDS() \
CV_SET_ELEM_FIELDS(CvVoronoiNode2D) \
CvPoint2D32f pt; \
float radius;
typedef struct CvVoronoiNode2D
{
CV_VORONOINODE2D_FIELDS()
} CvVoronoiNode2D;
define CV_VORONOIDIAGRAM2D_FIELDS() \
CV_GRAPH_FIELDS() \
CvSet *sites;
typedef struct CvVoronoiDiagram2D
{
CV_VORONOIDIAGRAM2D_FIELDS()
} CvVoronoiDiagram2D;
/* Computes Voronoi Diagram for given polygons with holes */
CVAPI(int) cvVoronoiDiagramFromContour(CvSeq* ContourSeq,
CvVoronoiDiagram2D** VoronoiDiagram,
CvMemStorage* VoronoiStorage,
CvLeeParameters contour_type CV_DEFAULT(CV_LEE_INT),
int contour_orientation CV_DEFAULT(-1),
int attempt_number CV_DEFAULT(10));
/* Computes Voronoi Diagram for domains in given image */
CVAPI(int) cvVoronoiDiagramFromImage(IplImage* pImage,
CvSeq** ContourSeq,
CvVoronoiDiagram2D** VoronoiDiagram,
CvMemStorage* VoronoiStorage,
CvLeeParameters regularization_method CV_DEFAULT(CV_LEE_NON),
float approx_precision CV_DEFAULT(CV_LEE_AUTO));
/* Deallocates the storage */
CVAPI(void) cvReleaseVoronoiStorage(CvVoronoiDiagram2D* VoronoiDiagram,
CvMemStorage** pVoronoiStorage);
********************* Linear-Contour Model ***************************
struct CvLCMEdge;
struct CvLCMNode;
typedef struct CvLCMEdge
{
CV_GRAPH_EDGE_FIELDS()
CvSeq* chain;
float width;
int index1;
int index2;
} CvLCMEdge;
typedef struct CvLCMNode
{
CV_GRAPH_VERTEX_FIELDS()
CvContour* contour;
} CvLCMNode;
/* Computes hybrid model from Voronoi Diagram */
CVAPI(CvGraph*) cvLinearContorModelFromVoronoiDiagram(CvVoronoiDiagram2D* VoronoiDiagram,
float maxWidth);
/* Releases hybrid model storage */
CVAPI(int) cvReleaseLinearContorModelStorage(CvGraph** Graph);
/* two stereo-related functions */
CVAPI(void) cvInitPerspectiveTransform( CvSize size, const CvPoint2D32f vertex[4], double matrix[3][3],
CvArr* rectMap );
/*CVAPI(void) cvInitStereoRectification( CvStereoCamera* params,
CvArr* rectMap1, CvArr* rectMap2,
int do_undistortion );*/
************************* View Morphing Functions ***********************
/* The order of the function corresponds to the order they should appear in
the view morphing pipeline */
/* Finds ending points of scanlines on left and right images of stereo-pair */
CVAPI(void) cvMakeScanlines( const CvMatrix3* matrix, CvSize img_size,
int* scanlines1, int* scanlines2,
int* lengths1, int* lengths2,
int* line_count );
/* Grab pixel values from scanlines and stores them sequentially
(some sort of perspective image transform) */
CVAPI(void) cvPreWarpImage( int line_count,
IplImage* img,
uchar* dst,
int* dst_nums,
int* scanlines);
/* Approximate each grabbed scanline by a sequence of runs
(lossy run-length compression) */
CVAPI(void) cvFindRuns( int line_count,
uchar* prewarp1,
uchar* prewarp2,
int* line_lengths1,
int* line_lengths2,
int* runs1,
int* runs2,
int* num_runs1,
int* num_runs2);
/* Compares two sets of compressed scanlines */
CVAPI(void) cvDynamicCorrespondMulti( int line_count,
int* first,
int* first_runs,
int* second,
int* second_runs,
int* first_corr,
int* second_corr);
/* Finds scanline ending coordinates for some intermediate "virtual" camera position */
CVAPI(void) cvMakeAlphaScanlines( int* scanlines1,
int* scanlines2,
int* scanlinesA,
int* lengths,
int line_count,
float alpha);
/* Blends data of the left and right image scanlines to get
pixel values of "virtual" image scanlines */
CVAPI(void) cvMorphEpilinesMulti( int line_count,
uchar* first_pix,
int* first_num,
uchar* second_pix,
int* second_num,
uchar* dst_pix,
int* dst_num,
float alpha,
int* first,
int* first_runs,
int* second,
int* second_runs,
int* first_corr,
int* second_corr);
/* Does reverse warping of the morphing result to make
it fill the destination image rectangle */
CVAPI(void) cvPostWarpImage( int line_count,
uchar* src,
int* src_nums,
IplImage* img,
int* scanlines);
/* Deletes Moire (missed pixels that appear due to discretization) */
CVAPI(void) cvDeleteMoire( IplImage* img );
**************************************************************************************\
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 );
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
CV_INLINE void cvReleaseBGStatModel( CvBGStatModel** bg_model )
{
if( bg_model && *bg_model && (*bg_model)->release )
(*bg_model)->release( bg_model );
}
// Updates statistical model and returns number of found foreground regions
CV_INLINE int cvUpdateBGStatModel( IplImage* current_frame, CvBGStatModel* bg_model )
{
return bg_model && bg_model->update ? bg_model->update( current_frame, bg_model ) : 0;
}
// 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
}
endif
ifdef __cplusplus
**************************************************************************************\
Calibration engine *
\***************************************************************************************
typedef enum CvCalibEtalonType
{
CV_CALIB_ETALON_USER = -1,
CV_CALIB_ETALON_CHESSBOARD = 0,
CV_CALIB_ETALON_CHECKERBOARD = CV_CALIB_ETALON_CHESSBOARD
}
CvCalibEtalonType;
class CV_EXPORTS CvCalibFilter
{
public:
/* Constructor & destructor */
CvCalibFilter();
virtual ~CvCalibFilter();
/* Sets etalon type - one for all cameras.
etalonParams is used in case of pre-defined etalons (such as chessboard).
Number of elements in etalonParams is determined by etalonType.
E.g., if etalon type is CV_ETALON_TYPE_CHESSBOARD then:
etalonParams[0] is number of squares per one side of etalon
etalonParams[1] is number of squares per another side of etalon
etalonParams[2] is linear size of squares in the board in arbitrary units.
pointCount & points are used in case of
CV_CALIB_ETALON_USER (user-defined) etalon. */
virtual bool
SetEtalon( CvCalibEtalonType etalonType, double* etalonParams,
int pointCount = 0, CvPoint2D32f* points = 0 );
/* Retrieves etalon parameters/or and points */
virtual CvCalibEtalonType
GetEtalon( int* paramCount = 0, const double** etalonParams = 0,
int* pointCount = 0, const CvPoint2D32f** etalonPoints = 0 ) const;
/* Sets number of cameras calibrated simultaneously. It is equal to 1 initially */
virtual void SetCameraCount( int cameraCount );
/* Retrieves number of cameras */
int GetCameraCount() const { return cameraCount; }
/* Starts cameras calibration */
virtual bool SetFrames( int totalFrames );
/* Stops cameras calibration */
virtual void Stop( bool calibrate = false );
/* Retrieves number of cameras */
bool IsCalibrated() const { return isCalibrated; }
/* Feeds another serie of snapshots (one per each camera) to filter.
Etalon points on these images are found automatically.
If the function can't locate points, it returns false */
virtual bool FindEtalon( IplImage** imgs );
/* The same but takes matrices */
virtual bool FindEtalon( CvMat** imgs );
/* Lower-level function for feeding filter with already found etalon points.
Array of point arrays for each camera is passed. */
virtual bool Push( const CvPoint2D32f** points = 0 );
/* Returns total number of accepted frames and, optionally,
total number of frames to collect */
virtual int GetFrameCount( int* framesTotal = 0 ) const;
/* Retrieves camera parameters for specified camera.
If camera is not calibrated the function returns 0 */
virtual const CvCamera* GetCameraParams( int idx = 0 ) const;
virtual const CvStereoCamera* GetStereoParams() const;
/* Sets camera parameters for all cameras */
virtual bool SetCameraParams( CvCamera* params );
/* Saves all camera parameters to file */
virtual bool SaveCameraParams( const char* filename );
/* Loads all camera parameters from file */
virtual bool LoadCameraParams( const char* filename );
/* Undistorts images using camera parameters. Some of src pointers can be NULL. */
virtual bool Undistort( IplImage** src, IplImage** dst );
/* Undistorts images using camera parameters. Some of src pointers can be NULL. */
virtual bool Undistort( CvMat** src, CvMat** dst );
/* Returns array of etalon points detected/partally detected
on the latest frame for idx-th camera */
virtual bool GetLatestPoints( int idx, CvPoint2D32f** pts,
int* count, bool* found );
/* Draw the latest detected/partially detected etalon */
virtual void DrawPoints( IplImage** dst );
/* Draw the latest detected/partially detected etalon */
virtual void DrawPoints( CvMat** dst );
virtual bool Rectify( IplImage** srcarr, IplImage** dstarr );
virtual bool Rectify( CvMat** srcarr, CvMat** dstarr );
protected:
enum { MAX_CAMERAS = 3 };
/* etalon data */
CvCalibEtalonType etalonType;
int etalonParamCount;
double* etalonParams;
int etalonPointCount;
CvPoint2D32f* etalonPoints;
CvSize imgSize;
CvMat* grayImg;
CvMat* tempImg;
CvMemStorage* storage;
/* camera data */
int cameraCount;
CvCamera cameraParams[MAX_CAMERAS];
CvStereoCamera stereo;
CvPoint2D32f* points[MAX_CAMERAS];
CvMat* undistMap[MAX_CAMERAS][2];
CvMat* undistImg;
int latestCounts[MAX_CAMERAS];
CvPoint2D32f* latestPoints[MAX_CAMERAS];
CvMat* rectMap[MAX_CAMERAS][2];
/* Added by Valery */
//CvStereoCamera stereoParams;
int maxPoints;
int framesTotal;
int framesAccepted;
bool isCalibrated;
};
include <cvaux.hpp>
include <cvvidsurv.hpp>
/*#include <cvmat.hpp>*/
endif
endif
/* End of file. */
(C) Æliens
04/09/2009
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