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opencv/modules/legacy/include/opencv2/legacy.hpp
Andrey Kamaev ad5cddc007 Main module headers are moved 1 level up 
e.g. <opencv2/core/core.hpp> become <opencv2/core.hpp>

Also renamed <opencv2/core/opengl_interop.hpp> to <opencv2/core/opengl.hpp>
2013-03-13 17:26:56 +04:00

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/*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.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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_LEGACY_HPP__
#define __OPENCV_LEGACY_HPP__
#include "opencv2/imgproc.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/features2d.hpp"
#include "opencv2/calib3d.hpp"
#include "opencv2/ml.hpp"
#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);
/* Calculates the cooficients of the homography matrix */
CVAPI(void) cvCalcImageHomography( float* line, CvPoint3D32f* center,
float* intrinsic, float* homography );
/****************************************************************************************\
* 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;
/* Calculates histogram of a contour */
CVAPI(void) cvCalcPGH( const CvSeq* contour, CvHistogram* hist );
#define CV_DOMINANT_IPAN 1
/* Finds high-curvature points of the contour */
CVAPI(CvSeq*) cvFindDominantPoints( CvSeq* contour, CvMemStorage* storage,
int method CV_DEFAULT(CV_DOMINANT_IPAN),
double parameter1 CV_DEFAULT(0),
double parameter2 CV_DEFAULT(0),
double parameter3 CV_DEFAULT(0),
double parameter4 CV_DEFAULT(0));
/*****************************************************************************************/
/*******************************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( double* rotMatr1,
double* transVect1,
double* rotMatr2,
double* transVect2,
double* convRotMatr,
double* convTransVect);
CVAPI(int) icvConvertPointSystem(CvPoint3D64f M2,
CvPoint3D64f* M1,
double* rotMatr,
double* 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,
double* camMatr1,
double* rotMatr1,
double* transVect1,
double* camMatr2,
double* rotMatr2,
double* transVect2,
CvStereoLineCoeff* coeffs,
int* needSwapCameras);
CVAPI(int) icvGetDirectionForPoint( CvPoint2D64f point,
double* 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 ( double* camMatr1,
double* rotMatr1,
double* transVect1,
double* camMatr2,
double* rotMatr2,
double* transVect2,
CvPoint2D64f* epipole1,
CvPoint2D64f* epipole2,
double* 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,
double* fundMatr,
double* coeff11,double* coeff12,
double* coeff21,double* coeff22,
int* result);*/
CVAPI(void) icvComputeeInfiniteProject1(double* rotMatr,
double* camMatr1,
double* camMatr2,
CvPoint2D32f point1,
CvPoint2D32f *point2);
CVAPI(void) icvComputeeInfiniteProject2(double* rotMatr,
double* camMatr1,
double* camMatr2,
CvPoint2D32f* point1,
CvPoint2D32f point2);
CVAPI(void) icvGetCrossDirectDirect( double* direct1,double* 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,
double* camMatr,double* rotMatr,double* transVect,
CvPoint2D64f* projPoint);
CVAPI(void) icvGetQuadsTransform( CvSize imageSize,
double* camMatr1,
double* rotMatr1,
double* transVect1,
double* camMatr2,
double* rotMatr2,
double* transVect2,
CvSize* warpSize,
double quad1[4][2],
double quad2[4][2],
double* fundMatr,
CvPoint3D64f* epipole1,
CvPoint3D64f* epipole2
);
CVAPI(void) icvGetQuadsTransformStruct( CvStereoCamera* stereoCamera);
CVAPI(void) icvComputeStereoParamsForCameras(CvStereoCamera* stereoCamera);
CVAPI(void) icvGetCutPiece( double* areaLineCoef1,double* 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(double* direct,CvPoint2D64f point,double* normDirect);
CVAPI(double) icvGetVect(CvPoint2D64f basePoint,CvPoint2D64f point1,CvPoint2D64f point2);
CVAPI(void) icvProjectPointToDirect( CvPoint2D64f point,double* lineCoeff,
CvPoint2D64f* projectPoint);
CVAPI(void) icvGetDistanceFromPointToDirect( CvPoint2D64f point,double* 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 Tree *
\****************************************************************************************/
/* Contour tree header */
typedef struct CvContourTree
{
CV_SEQUENCE_FIELDS()
CvPoint p1; /* the first point of the binary tree root segment */
CvPoint p2; /* the last point of the binary tree root segment */
} CvContourTree;
/* Builds hierarhical representation of a contour */
CVAPI(CvContourTree*) cvCreateContourTree( const CvSeq* contour,
CvMemStorage* storage,
double threshold );
/* Reconstruct (completelly or partially) contour a from contour tree */
CVAPI(CvSeq*) cvContourFromContourTree( const CvContourTree* tree,
CvMemStorage* storage,
CvTermCriteria criteria );
/* Compares two contour trees */
enum { CV_CONTOUR_TREES_MATCH_I1 = 1 };
CVAPI(double) cvMatchContourTrees( const CvContourTree* tree1,
const CvContourTree* tree2,
int method, double threshold );
/****************************************************************************************\
* 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 );
/****************************************************************************************\
* Active Contours *
\****************************************************************************************/
#define CV_VALUE 1
#define CV_ARRAY 2
/* Updates active contour in order to minimize its cummulative
(internal and external) energy. */
CVAPI(void) cvSnakeImage( const IplImage* image, CvPoint* points,
int length, float* alpha,
float* beta, float* gamma,
int coeff_usage, CvSize win,
CvTermCriteria criteria, int calc_gradient CV_DEFAULT(1));
/****************************************************************************************\
* 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 Cv3dTracker2dTrackedObject
{
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 Cv3dTrackerTrackedObject
{
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 Cv3dTrackerCameraInfo
{
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 Cv3dTrackerCameraIntrinsics
{
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 ************************/
typedef struct CvMatrix3
{
float m[3][3];
} CvMatrix3;
/* 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 );
typedef struct CvConDensation
{
int MP;
int DP;
float* DynamMatr; /* Matrix of the linear Dynamics system */
float* State; /* Vector of State */
int SamplesNum; /* Number of the Samples */
float** flSamples; /* arr of the Sample Vectors */
float** flNewSamples; /* temporary array of the Sample Vectors */
float* flConfidence; /* Confidence for each Sample */
float* flCumulative; /* Cumulative confidence */
float* Temp; /* Temporary vector */
float* RandomSample; /* RandomVector to update sample set */
struct CvRandState* RandS; /* Array of structures to generate random vectors */
} CvConDensation;
/* Creates ConDensation filter state */
CVAPI(CvConDensation*) cvCreateConDensation( int dynam_params,
int measure_params,
int sample_count );
/* Releases ConDensation filter state */
CVAPI(void) cvReleaseConDensation( CvConDensation** condens );
/* Updates ConDensation filter by time (predict future state of the system) */
CVAPI(void) cvConDensUpdateByTime( CvConDensation* condens);
/* Initializes ConDensation filter samples */
CVAPI(void) cvConDensInitSampleSet( CvConDensation* condens, CvMat* lower_bound, CvMat* upper_bound );
CV_INLINE int iplWidth( const IplImage* img )
{
return !img ? 0 : !img->roi ? img->width : img->roi->width;
}
CV_INLINE int iplHeight( const IplImage* img )
{
return !img ? 0 : !img->roi ? img->height : img->roi->height;
}
#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 <iosfwd>
#include <limits>
class CV_EXPORTS CvImage
{
public:
CvImage() : image(0), refcount(0) {}
CvImage( CvSize _size, int _depth, int _channels )
{
image = cvCreateImage( _size, _depth, _channels );
refcount = image ? new int(1) : 0;
}
CvImage( IplImage* img ) : image(img)
{
refcount = image ? new int(1) : 0;
}
CvImage( const CvImage& img ) : image(img.image), refcount(img.refcount)
{
if( refcount ) ++(*refcount);
}
CvImage( const char* filename, const char* imgname=0, int color=-1 ) : image(0), refcount(0)
{ load( filename, imgname, color ); }
CvImage( CvFileStorage* fs, const char* mapname, const char* imgname ) : image(0), refcount(0)
{ read( fs, mapname, imgname ); }
CvImage( CvFileStorage* fs, const char* seqname, int idx ) : image(0), refcount(0)
{ read( fs, seqname, idx ); }
~CvImage()
{
if( refcount && !(--*refcount) )
{
cvReleaseImage( &image );
delete refcount;
}
}
CvImage clone() { return CvImage(image ? cvCloneImage(image) : 0); }
void create( CvSize _size, int _depth, int _channels )
{
if( !image || !refcount ||
image->width != _size.width || image->height != _size.height ||
image->depth != _depth || image->nChannels != _channels )
attach( cvCreateImage( _size, _depth, _channels ));
}
void release() { detach(); }
void clear() { detach(); }
void attach( IplImage* img, bool use_refcount=true )
{
if( refcount && --*refcount == 0 )
{
cvReleaseImage( &image );
delete refcount;
}
image = img;
refcount = use_refcount && image ? new int(1) : 0;
}
void detach()
{
if( refcount && --*refcount == 0 )
{
cvReleaseImage( &image );
delete refcount;
}
image = 0;
refcount = 0;
}
bool load( const char* filename, const char* imgname=0, int color=-1 );
bool read( CvFileStorage* fs, const char* mapname, const char* imgname );
bool read( CvFileStorage* fs, const char* seqname, int idx );
void save( const char* filename, const char* imgname, const int* params=0 );
void write( CvFileStorage* fs, const char* imgname );
void show( const char* window_name );
bool is_valid() { return image != 0; }
int width() const { return image ? image->width : 0; }
int height() const { return image ? image->height : 0; }
CvSize size() const { return image ? cvSize(image->width, image->height) : cvSize(0,0); }
CvSize roi_size() const
{
return !image ? cvSize(0,0) :
!image->roi ? cvSize(image->width,image->height) :
cvSize(image->roi->width, image->roi->height);
}
CvRect roi() const
{
return !image ? cvRect(0,0,0,0) :
!image->roi ? cvRect(0,0,image->width,image->height) :
cvRect(image->roi->xOffset,image->roi->yOffset,
image->roi->width,image->roi->height);
}
int coi() const { return !image || !image->roi ? 0 : image->roi->coi; }
void set_roi(CvRect _roi) { cvSetImageROI(image,_roi); }
void reset_roi() { cvResetImageROI(image); }
void set_coi(int _coi) { cvSetImageCOI(image,_coi); }
int depth() const { return image ? image->depth : 0; }
int channels() const { return image ? image->nChannels : 0; }
int pix_size() const { return image ? ((image->depth & 255)>>3)*image->nChannels : 0; }
uchar* data() { return image ? (uchar*)image->imageData : 0; }
const uchar* data() const { return image ? (const uchar*)image->imageData : 0; }
int step() const { return image ? image->widthStep : 0; }
int origin() const { return image ? image->origin : 0; }
uchar* roi_row(int y)
{
assert(0<=y);
assert(!image ?
1 : image->roi ?
y<image->roi->height : y<image->height);
return !image ? 0 :
!image->roi ?
(uchar*)(image->imageData + y*image->widthStep) :
(uchar*)(image->imageData + (y+image->roi->yOffset)*image->widthStep +
image->roi->xOffset*((image->depth & 255)>>3)*image->nChannels);
}
const uchar* roi_row(int y) const
{
assert(0<=y);
assert(!image ?
1 : image->roi ?
y<image->roi->height : y<image->height);
return !image ? 0 :
!image->roi ?
(const uchar*)(image->imageData + y*image->widthStep) :
(const uchar*)(image->imageData + (y+image->roi->yOffset)*image->widthStep +
image->roi->xOffset*((image->depth & 255)>>3)*image->nChannels);
}
operator const IplImage* () const { return image; }
operator IplImage* () { return image; }
CvImage& operator = (const CvImage& img)
{
if( img.refcount )
++*img.refcount;
if( refcount && !(--*refcount) )
cvReleaseImage( &image );
image=img.image;
refcount=img.refcount;
return *this;
}
protected:
IplImage* image;
int* refcount;
};
class CV_EXPORTS CvMatrix
{
public:
CvMatrix() : matrix(0) {}
CvMatrix( int _rows, int _cols, int _type )
{ matrix = cvCreateMat( _rows, _cols, _type ); }
CvMatrix( int _rows, int _cols, int _type, CvMat* hdr,
void* _data=0, int _step=CV_AUTOSTEP )
{ matrix = cvInitMatHeader( hdr, _rows, _cols, _type, _data, _step ); }
CvMatrix( int rows, int cols, int type, CvMemStorage* storage, bool alloc_data=true );
CvMatrix( int _rows, int _cols, int _type, void* _data, int _step=CV_AUTOSTEP )
{ matrix = cvCreateMatHeader( _rows, _cols, _type );
cvSetData( matrix, _data, _step ); }
CvMatrix( CvMat* m )
{ matrix = m; }
CvMatrix( const CvMatrix& m )
{
matrix = m.matrix;
addref();
}
CvMatrix( const char* filename, const char* matname=0, int color=-1 ) : matrix(0)
{ load( filename, matname, color ); }
CvMatrix( CvFileStorage* fs, const char* mapname, const char* matname ) : matrix(0)
{ read( fs, mapname, matname ); }
CvMatrix( CvFileStorage* fs, const char* seqname, int idx ) : matrix(0)
{ read( fs, seqname, idx ); }
~CvMatrix()
{
release();
}
CvMatrix clone() { return CvMatrix(matrix ? cvCloneMat(matrix) : 0); }
void set( CvMat* m, bool add_ref )
{
release();
matrix = m;
if( add_ref )
addref();
}
void create( int _rows, int _cols, int _type )
{
if( !matrix || !matrix->refcount ||
matrix->rows != _rows || matrix->cols != _cols ||
CV_MAT_TYPE(matrix->type) != _type )
set( cvCreateMat( _rows, _cols, _type ), false );
}
void addref() const
{
if( matrix )
{
if( matrix->hdr_refcount )
++matrix->hdr_refcount;
else if( matrix->refcount )
++*matrix->refcount;
}
}
void release()
{
if( matrix )
{
if( matrix->hdr_refcount )
{
if( --matrix->hdr_refcount == 0 )
cvReleaseMat( &matrix );
}
else if( matrix->refcount )
{
if( --*matrix->refcount == 0 )
cvFree( &matrix->refcount );
}
matrix = 0;
}
}
void clear()
{
release();
}
bool load( const char* filename, const char* matname=0, int color=-1 );
bool read( CvFileStorage* fs, const char* mapname, const char* matname );
bool read( CvFileStorage* fs, const char* seqname, int idx );
void save( const char* filename, const char* matname, const int* params=0 );
void write( CvFileStorage* fs, const char* matname );
void show( const char* window_name );
bool is_valid() { return matrix != 0; }
int rows() const { return matrix ? matrix->rows : 0; }
int cols() const { return matrix ? matrix->cols : 0; }
CvSize size() const
{
return !matrix ? cvSize(0,0) : cvSize(matrix->rows,matrix->cols);
}
int type() const { return matrix ? CV_MAT_TYPE(matrix->type) : 0; }
int depth() const { return matrix ? CV_MAT_DEPTH(matrix->type) : 0; }
int channels() const { return matrix ? CV_MAT_CN(matrix->type) : 0; }
int pix_size() const { return matrix ? CV_ELEM_SIZE(matrix->type) : 0; }
uchar* data() { return matrix ? matrix->data.ptr : 0; }
const uchar* data() const { return matrix ? matrix->data.ptr : 0; }
int step() const { return matrix ? matrix->step : 0; }
void set_data( void* _data, int _step=CV_AUTOSTEP )
{ cvSetData( matrix, _data, _step ); }
uchar* row(int i) { return !matrix ? 0 : matrix->data.ptr + i*matrix->step; }
const uchar* row(int i) const
{ return !matrix ? 0 : matrix->data.ptr + i*matrix->step; }
operator const CvMat* () const { return matrix; }
operator CvMat* () { return matrix; }
CvMatrix& operator = (const CvMatrix& _m)
{
_m.addref();
release();
matrix = _m.matrix;
return *this;
}
protected:
CvMat* matrix;
};
/****************************************************************************************\
* CamShiftTracker *
\****************************************************************************************/
class CV_EXPORTS CvCamShiftTracker
{
public:
CvCamShiftTracker();
virtual ~CvCamShiftTracker();
/**** Characteristics of the object that are calculated by track_object method *****/
float get_orientation() const // orientation of the object in degrees
{ return m_box.angle; }
float get_length() const // the larger linear size of the object
{ return m_box.size.height; }
float get_width() const // the smaller linear size of the object
{ return m_box.size.width; }
CvPoint2D32f get_center() const // center of the object
{ return m_box.center; }
CvRect get_window() const // bounding rectangle for the object
{ return m_comp.rect; }
/*********************** Tracking parameters ************************/
int get_threshold() const // thresholding value that applied to back project
{ return m_threshold; }
int get_hist_dims( int* dims = 0 ) const // returns number of histogram dimensions and sets
{ return m_hist ? cvGetDims( m_hist->bins, dims ) : 0; }
int get_min_ch_val( int channel ) const // get the minimum allowed value of the specified channel
{ return m_min_ch_val[channel]; }
int get_max_ch_val( int channel ) const // get the maximum allowed value of the specified channel
{ return m_max_ch_val[channel]; }
// set initial object rectangle (must be called before initial calculation of the histogram)
bool set_window( CvRect window)
{ m_comp.rect = window; return true; }
bool set_threshold( int threshold ) // threshold applied to the histogram bins
{ m_threshold = threshold; return true; }
bool set_hist_bin_range( int dim, int min_val, int max_val );
bool set_hist_dims( int c_dims, int* dims );// set the histogram parameters
bool set_min_ch_val( int channel, int val ) // set the minimum allowed value of the specified channel
{ m_min_ch_val[channel] = val; return true; }
bool set_max_ch_val( int channel, int val ) // set the maximum allowed value of the specified channel
{ m_max_ch_val[channel] = val; return true; }
/************************ The processing methods *********************************/
// update object position
virtual bool track_object( const IplImage* cur_frame );
// update object histogram
virtual bool update_histogram( const IplImage* cur_frame );
// reset histogram
virtual void reset_histogram();
/************************ Retrieving internal data *******************************/
// get back project image
virtual IplImage* get_back_project()
{ return m_back_project; }
float query( int* bin ) const
{ return m_hist ? (float)cvGetRealND(m_hist->bins, bin) : 0.f; }
protected:
// internal method for color conversion: fills m_color_planes group
virtual void color_transform( const IplImage* img );
CvHistogram* m_hist;
CvBox2D m_box;
CvConnectedComp m_comp;
float m_hist_ranges_data[CV_MAX_DIM][2];
float* m_hist_ranges[CV_MAX_DIM];
int m_min_ch_val[CV_MAX_DIM];
int m_max_ch_val[CV_MAX_DIM];
int m_threshold;
IplImage* m_color_planes[CV_MAX_DIM];
IplImage* m_back_project;
IplImage* m_temp;
IplImage* m_mask;
};
/****************************************************************************************\
* Expectation - Maximization *
\****************************************************************************************/
struct CV_EXPORTS_W_MAP CvEMParams
{
CvEMParams();
CvEMParams( int nclusters, int cov_mat_type=cv::EM::COV_MAT_DIAGONAL,
int start_step=cv::EM::START_AUTO_STEP,
CvTermCriteria term_crit=cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, FLT_EPSILON),
const CvMat* probs=0, const CvMat* weights=0, const CvMat* means=0, const CvMat** covs=0 );
CV_PROP_RW int nclusters;
CV_PROP_RW int cov_mat_type;
CV_PROP_RW int start_step;
const CvMat* probs;
const CvMat* weights;
const CvMat* means;
const CvMat** covs;
CV_PROP_RW CvTermCriteria term_crit;
};
class CV_EXPORTS_W CvEM : public CvStatModel
{
public:
// Type of covariation matrices
enum { COV_MAT_SPHERICAL=cv::EM::COV_MAT_SPHERICAL,
COV_MAT_DIAGONAL =cv::EM::COV_MAT_DIAGONAL,
COV_MAT_GENERIC =cv::EM::COV_MAT_GENERIC };
// The initial step
enum { START_E_STEP=cv::EM::START_E_STEP,
START_M_STEP=cv::EM::START_M_STEP,
START_AUTO_STEP=cv::EM::START_AUTO_STEP };
CV_WRAP CvEM();
CvEM( const CvMat* samples, const CvMat* sampleIdx=0,
CvEMParams params=CvEMParams(), CvMat* labels=0 );
virtual ~CvEM();
virtual bool train( const CvMat* samples, const CvMat* sampleIdx=0,
CvEMParams params=CvEMParams(), CvMat* labels=0 );
virtual float predict( const CvMat* sample, CV_OUT CvMat* probs ) const;
CV_WRAP CvEM( const cv::Mat& samples, const cv::Mat& sampleIdx=cv::Mat(),
CvEMParams params=CvEMParams() );
CV_WRAP virtual bool train( const cv::Mat& samples,
const cv::Mat& sampleIdx=cv::Mat(),
CvEMParams params=CvEMParams(),
CV_OUT cv::Mat* labels=0 );
CV_WRAP virtual float predict( const cv::Mat& sample, CV_OUT cv::Mat* probs=0 ) const;
CV_WRAP virtual double calcLikelihood( const cv::Mat &sample ) const;
CV_WRAP int getNClusters() const;
CV_WRAP cv::Mat getMeans() const;
CV_WRAP void getCovs(CV_OUT std::vector<cv::Mat>& covs) const;
CV_WRAP cv::Mat getWeights() const;
CV_WRAP cv::Mat getProbs() const;
CV_WRAP inline double getLikelihood() const { return emObj.isTrained() ? logLikelihood : DBL_MAX; }
CV_WRAP virtual void clear();
int get_nclusters() const;
const CvMat* get_means() const;
const CvMat** get_covs() const;
const CvMat* get_weights() const;
const CvMat* get_probs() const;
inline double get_log_likelihood() const { return getLikelihood(); }
virtual void read( CvFileStorage* fs, CvFileNode* node );
virtual void write( CvFileStorage* fs, const char* name ) const;
protected:
void set_mat_hdrs();
cv::EM emObj;
cv::Mat probs;
double logLikelihood;
CvMat meansHdr;
std::vector<CvMat> covsHdrs;
std::vector<CvMat*> covsPtrs;
CvMat weightsHdr;
CvMat probsHdr;
};
namespace cv
{
typedef CvEMParams EMParams;
typedef CvEM ExpectationMaximization;
/*!
The Patch Generator class
*/
class CV_EXPORTS PatchGenerator
{
public:
PatchGenerator();
PatchGenerator(double _backgroundMin, double _backgroundMax,
double _noiseRange, bool _randomBlur=true,
double _lambdaMin=0.6, double _lambdaMax=1.5,
double _thetaMin=-CV_PI, double _thetaMax=CV_PI,
double _phiMin=-CV_PI, double _phiMax=CV_PI );
void operator()(const Mat& image, Point2f pt, Mat& patch, Size patchSize, RNG& rng) const;
void operator()(const Mat& image, const Mat& transform, Mat& patch,
Size patchSize, RNG& rng) const;
void warpWholeImage(const Mat& image, Mat& matT, Mat& buf,
CV_OUT Mat& warped, int border, RNG& rng) const;
void generateRandomTransform(Point2f srcCenter, Point2f dstCenter,
CV_OUT Mat& transform, RNG& rng,
bool inverse=false) const;
void setAffineParam(double lambda, double theta, double phi);
double backgroundMin, backgroundMax;
double noiseRange;
bool randomBlur;
double lambdaMin, lambdaMax;
double thetaMin, thetaMax;
double phiMin, phiMax;
};
class CV_EXPORTS LDetector
{
public:
LDetector();
LDetector(int _radius, int _threshold, int _nOctaves,
int _nViews, double _baseFeatureSize, double _clusteringDistance);
void operator()(const Mat& image,
CV_OUT std::vector<KeyPoint>& keypoints,
int maxCount=0, bool scaleCoords=true) const;
void operator()(const std::vector<Mat>& pyr,
CV_OUT std::vector<KeyPoint>& keypoints,
int maxCount=0, bool scaleCoords=true) const;
void getMostStable2D(const Mat& image, CV_OUT std::vector<KeyPoint>& keypoints,
int maxCount, const PatchGenerator& patchGenerator) const;
void setVerbose(bool verbose);
void read(const FileNode& node);
void write(FileStorage& fs, const std::string& name=std::string()) const;
int radius;
int threshold;
int nOctaves;
int nViews;
bool verbose;
double baseFeatureSize;
double clusteringDistance;
};
typedef LDetector YAPE;
class CV_EXPORTS FernClassifier
{
public:
FernClassifier();
FernClassifier(const FileNode& node);
FernClassifier(const std::vector<std::vector<Point2f> >& points,
const std::vector<Mat>& refimgs,
const std::vector<std::vector<int> >& labels=std::vector<std::vector<int> >(),
int _nclasses=0, int _patchSize=PATCH_SIZE,
int _signatureSize=DEFAULT_SIGNATURE_SIZE,
int _nstructs=DEFAULT_STRUCTS,
int _structSize=DEFAULT_STRUCT_SIZE,
int _nviews=DEFAULT_VIEWS,
int _compressionMethod=COMPRESSION_NONE,
const PatchGenerator& patchGenerator=PatchGenerator());
virtual ~FernClassifier();
virtual void read(const FileNode& n);
virtual void write(FileStorage& fs, const std::string& name=std::string()) const;
virtual void trainFromSingleView(const Mat& image,
const std::vector<KeyPoint>& keypoints,
int _patchSize=PATCH_SIZE,
int _signatureSize=DEFAULT_SIGNATURE_SIZE,
int _nstructs=DEFAULT_STRUCTS,
int _structSize=DEFAULT_STRUCT_SIZE,
int _nviews=DEFAULT_VIEWS,
int _compressionMethod=COMPRESSION_NONE,
const PatchGenerator& patchGenerator=PatchGenerator());
virtual void train(const std::vector<std::vector<Point2f> >& points,
const std::vector<Mat>& refimgs,
const std::vector<std::vector<int> >& labels=std::vector<std::vector<int> >(),
int _nclasses=0, int _patchSize=PATCH_SIZE,
int _signatureSize=DEFAULT_SIGNATURE_SIZE,
int _nstructs=DEFAULT_STRUCTS,
int _structSize=DEFAULT_STRUCT_SIZE,
int _nviews=DEFAULT_VIEWS,
int _compressionMethod=COMPRESSION_NONE,
const PatchGenerator& patchGenerator=PatchGenerator());
virtual int operator()(const Mat& img, Point2f kpt, std::vector<float>& signature) const;
virtual int operator()(const Mat& patch, std::vector<float>& signature) const;
virtual void clear();
virtual bool empty() const;
void setVerbose(bool verbose);
int getClassCount() const;
int getStructCount() const;
int getStructSize() const;
int getSignatureSize() const;
int getCompressionMethod() const;
Size getPatchSize() const;
struct Feature
{
uchar x1, y1, x2, y2;
Feature() : x1(0), y1(0), x2(0), y2(0) {}
Feature(int _x1, int _y1, int _x2, int _y2)
: x1((uchar)_x1), y1((uchar)_y1), x2((uchar)_x2), y2((uchar)_y2)
{}
template<typename _Tp> bool operator ()(const Mat_<_Tp>& patch) const
{ return patch(y1,x1) > patch(y2, x2); }
};
enum
{
PATCH_SIZE = 31,
DEFAULT_STRUCTS = 50,
DEFAULT_STRUCT_SIZE = 9,
DEFAULT_VIEWS = 5000,
DEFAULT_SIGNATURE_SIZE = 176,
COMPRESSION_NONE = 0,
COMPRESSION_RANDOM_PROJ = 1,
COMPRESSION_PCA = 2,
DEFAULT_COMPRESSION_METHOD = COMPRESSION_NONE
};
protected:
virtual void prepare(int _nclasses, int _patchSize, int _signatureSize,
int _nstructs, int _structSize,
int _nviews, int _compressionMethod);
virtual void finalize(RNG& rng);
virtual int getLeaf(int fidx, const Mat& patch) const;
bool verbose;
int nstructs;
int structSize;
int nclasses;
int signatureSize;
int compressionMethod;
int leavesPerStruct;
Size patchSize;
std::vector<Feature> features;
std::vector<int> classCounters;
std::vector<float> posteriors;
};
/****************************************************************************************\
* Calonder Classifier *
\****************************************************************************************/
struct RTreeNode;
struct CV_EXPORTS BaseKeypoint
{
int x;
int y;
IplImage* image;
BaseKeypoint()
: x(0), y(0), image(NULL)
{}
BaseKeypoint(int _x, int _y, IplImage* _image)
: x(_x), y(_y), image(_image)
{}
};
class CV_EXPORTS RandomizedTree
{
public:
friend class RTreeClassifier;
static const uchar PATCH_SIZE = 32;
static const int DEFAULT_DEPTH = 9;
static const int DEFAULT_VIEWS = 5000;
static const size_t DEFAULT_REDUCED_NUM_DIM = 176;
static float GET_LOWER_QUANT_PERC() { return .03f; }
static float GET_UPPER_QUANT_PERC() { return .92f; }
RandomizedTree();
~RandomizedTree();
void train(std::vector<BaseKeypoint> const& base_set, RNG &rng,
int depth, int views, size_t reduced_num_dim, int num_quant_bits);
void train(std::vector<BaseKeypoint> const& base_set, RNG &rng,
PatchGenerator &make_patch, int depth, int views, size_t reduced_num_dim,
int num_quant_bits);
// following two funcs are EXPERIMENTAL (do not use unless you know exactly what you do)
static void quantizeVector(float *vec, int dim, int N, float bnds[2], int clamp_mode=0);
static void quantizeVector(float *src, int dim, int N, float bnds[2], uchar *dst);
// patch_data must be a 32x32 array (no row padding)
float* getPosterior(uchar* patch_data);
const float* getPosterior(uchar* patch_data) const;
uchar* getPosterior2(uchar* patch_data);
const uchar* getPosterior2(uchar* patch_data) const;
void read(const char* file_name, int num_quant_bits);
void read(std::istream &is, int num_quant_bits);
void write(const char* file_name) const;
void write(std::ostream &os) const;
int classes() { return classes_; }
int depth() { return depth_; }
//void setKeepFloatPosteriors(bool b) { keep_float_posteriors_ = b; }
void discardFloatPosteriors() { freePosteriors(1); }
inline void applyQuantization(int num_quant_bits) { makePosteriors2(num_quant_bits); }
// debug
void savePosteriors(std::string url, bool append=false);
void savePosteriors2(std::string url, bool append=false);
private:
int classes_;
int depth_;
int num_leaves_;
std::vector<RTreeNode> nodes_;
float **posteriors_; // 16-bytes aligned posteriors
uchar **posteriors2_; // 16-bytes aligned posteriors
std::vector<int> leaf_counts_;
void createNodes(int num_nodes, RNG &rng);
void allocPosteriorsAligned(int num_leaves, int num_classes);
void freePosteriors(int which); // which: 1=posteriors_, 2=posteriors2_, 3=both
void init(int classes, int depth, RNG &rng);
void addExample(int class_id, uchar* patch_data);
void finalize(size_t reduced_num_dim, int num_quant_bits);
int getIndex(uchar* patch_data) const;
inline float* getPosteriorByIndex(int index);
inline const float* getPosteriorByIndex(int index) const;
inline uchar* getPosteriorByIndex2(int index);
inline const uchar* getPosteriorByIndex2(int index) const;
//void makeRandomMeasMatrix(float *cs_phi, PHI_DISTR_TYPE dt, size_t reduced_num_dim);
void convertPosteriorsToChar();
void makePosteriors2(int num_quant_bits);
void compressLeaves(size_t reduced_num_dim);
void estimateQuantPercForPosteriors(float perc[2]);
};
inline uchar* getData(IplImage* image)
{
return reinterpret_cast<uchar*>(image->imageData);
}
inline float* RandomizedTree::getPosteriorByIndex(int index)
{
return const_cast<float*>(const_cast<const RandomizedTree*>(this)->getPosteriorByIndex(index));
}
inline const float* RandomizedTree::getPosteriorByIndex(int index) const
{
return posteriors_[index];
}
inline uchar* RandomizedTree::getPosteriorByIndex2(int index)
{
return const_cast<uchar*>(const_cast<const RandomizedTree*>(this)->getPosteriorByIndex2(index));
}
inline const uchar* RandomizedTree::getPosteriorByIndex2(int index) const
{
return posteriors2_[index];
}
struct CV_EXPORTS RTreeNode
{
short offset1, offset2;
RTreeNode() {}
RTreeNode(uchar x1, uchar y1, uchar x2, uchar y2)
: offset1(y1*RandomizedTree::PATCH_SIZE + x1),
offset2(y2*RandomizedTree::PATCH_SIZE + x2)
{}
//! Left child on 0, right child on 1
inline bool operator() (uchar* patch_data) const
{
return patch_data[offset1] > patch_data[offset2];
}
};
class CV_EXPORTS RTreeClassifier
{
public:
static const int DEFAULT_TREES = 48;
static const size_t DEFAULT_NUM_QUANT_BITS = 4;
RTreeClassifier();
void train(std::vector<BaseKeypoint> const& base_set,
RNG &rng,
int num_trees = RTreeClassifier::DEFAULT_TREES,
int depth = RandomizedTree::DEFAULT_DEPTH,
int views = RandomizedTree::DEFAULT_VIEWS,
size_t reduced_num_dim = RandomizedTree::DEFAULT_REDUCED_NUM_DIM,
int num_quant_bits = DEFAULT_NUM_QUANT_BITS);
void train(std::vector<BaseKeypoint> const& base_set,
RNG &rng,
PatchGenerator &make_patch,
int num_trees = RTreeClassifier::DEFAULT_TREES,
int depth = RandomizedTree::DEFAULT_DEPTH,
int views = RandomizedTree::DEFAULT_VIEWS,
size_t reduced_num_dim = RandomizedTree::DEFAULT_REDUCED_NUM_DIM,
int num_quant_bits = DEFAULT_NUM_QUANT_BITS);
// sig must point to a memory block of at least classes()*sizeof(float|uchar) bytes
void getSignature(IplImage *patch, uchar *sig) const;
void getSignature(IplImage *patch, float *sig) const;
void getSparseSignature(IplImage *patch, float *sig, float thresh) const;
// TODO: deprecated in favor of getSignature overload, remove
void getFloatSignature(IplImage *patch, float *sig) const { getSignature(patch, sig); }
static int countNonZeroElements(float *vec, int n, double tol=1e-10);
static inline void safeSignatureAlloc(uchar **sig, int num_sig=1, int sig_len=176);
static inline uchar* safeSignatureAlloc(int num_sig=1, int sig_len=176);
inline int classes() const { return classes_; }
inline int original_num_classes() const { return original_num_classes_; }
void setQuantization(int num_quant_bits);
void discardFloatPosteriors();
void read(const char* file_name);
void read(std::istream &is);
void write(const char* file_name) const;
void write(std::ostream &os) const;
// experimental and debug
void saveAllFloatPosteriors(std::string file_url);
void saveAllBytePosteriors(std::string file_url);
void setFloatPosteriorsFromTextfile_176(std::string url);
float countZeroElements();
std::vector<RandomizedTree> trees_;
private:
int classes_;
int num_quant_bits_;
mutable uchar **posteriors_;
mutable unsigned short *ptemp_;
int original_num_classes_;
bool keep_floats_;
};
/****************************************************************************************\
* One-Way Descriptor *
\****************************************************************************************/
// CvAffinePose: defines a parameterized affine transformation of an image patch.
// An image patch is rotated on angle phi (in degrees), then scaled lambda1 times
// along horizontal and lambda2 times along vertical direction, and then rotated again
// on angle (theta - phi).
class CV_EXPORTS CvAffinePose
{
public:
float phi;
float theta;
float lambda1;
float lambda2;
};
class CV_EXPORTS OneWayDescriptor
{
public:
OneWayDescriptor();
~OneWayDescriptor();
// allocates memory for given descriptor parameters
void Allocate(int pose_count, CvSize size, int nChannels);
// GenerateSamples: generates affine transformed patches with averaging them over small transformation variations.
// If external poses and transforms were specified, uses them instead of generating random ones
// - pose_count: the number of poses to be generated
// - frontal: the input patch (can be a roi in a larger image)
// - norm: if nonzero, normalizes the output patch so that the sum of pixel intensities is 1
void GenerateSamples(int pose_count, IplImage* frontal, int norm = 0);
// GenerateSamplesFast: generates affine transformed patches with averaging them over small transformation variations.
// Uses precalculated transformed pca components.
// - frontal: the input patch (can be a roi in a larger image)
// - pca_hr_avg: pca average vector
// - pca_hr_eigenvectors: pca eigenvectors
// - pca_descriptors: an array of precomputed descriptors of pca components containing their affine transformations
// pca_descriptors[0] corresponds to the average, pca_descriptors[1]-pca_descriptors[pca_dim] correspond to eigenvectors
void GenerateSamplesFast(IplImage* frontal, CvMat* pca_hr_avg,
CvMat* pca_hr_eigenvectors, OneWayDescriptor* pca_descriptors);
// sets the poses and corresponding transforms
void SetTransforms(CvAffinePose* poses, CvMat** transforms);
// Initialize: builds a descriptor.
// - pose_count: the number of poses to build. If poses were set externally, uses them rather than generating random ones
// - frontal: input patch. Can be a roi in a larger image
// - feature_name: the feature name to be associated with the descriptor
// - norm: if 1, the affine transformed patches are normalized so that their sum is 1
void Initialize(int pose_count, IplImage* frontal, const char* feature_name = 0, int norm = 0);
// InitializeFast: builds a descriptor using precomputed descriptors of pca components
// - pose_count: the number of poses to build
// - frontal: input patch. Can be a roi in a larger image
// - feature_name: the feature name to be associated with the descriptor
// - pca_hr_avg: average vector for PCA
// - pca_hr_eigenvectors: PCA eigenvectors (one vector per row)
// - pca_descriptors: precomputed descriptors of PCA components, the first descriptor for the average vector
// followed by the descriptors for eigenvectors
void InitializeFast(int pose_count, IplImage* frontal, const char* feature_name,
CvMat* pca_hr_avg, CvMat* pca_hr_eigenvectors, OneWayDescriptor* pca_descriptors);
// ProjectPCASample: unwarps an image patch into a vector and projects it into PCA space
// - patch: input image patch
// - avg: PCA average vector
// - eigenvectors: PCA eigenvectors, one per row
// - pca_coeffs: output PCA coefficients
void ProjectPCASample(IplImage* patch, CvMat* avg, CvMat* eigenvectors, CvMat* pca_coeffs) const;
// InitializePCACoeffs: projects all warped patches into PCA space
// - avg: PCA average vector
// - eigenvectors: PCA eigenvectors, one per row
void InitializePCACoeffs(CvMat* avg, CvMat* eigenvectors);
// EstimatePose: finds the closest match between an input patch and a set of patches with different poses
// - patch: input image patch
// - pose_idx: the output index of the closest pose
// - distance: the distance to the closest pose (L2 distance)
void EstimatePose(IplImage* patch, int& pose_idx, float& distance) const;
// EstimatePosePCA: finds the closest match between an input patch and a set of patches with different poses.
// The distance between patches is computed in PCA space
// - patch: input image patch
// - pose_idx: the output index of the closest pose
// - distance: distance to the closest pose (L2 distance in PCA space)
// - avg: PCA average vector. If 0, matching without PCA is used
// - eigenvectors: PCA eigenvectors, one per row
void EstimatePosePCA(CvArr* patch, int& pose_idx, float& distance, CvMat* avg, CvMat* eigenvalues) const;
// GetPatchSize: returns the size of each image patch after warping (2 times smaller than the input patch)
CvSize GetPatchSize() const
{
return m_patch_size;
}
// GetInputPatchSize: returns the required size of the patch that the descriptor is built from
// (2 time larger than the patch after warping)
CvSize GetInputPatchSize() const
{
return cvSize(m_patch_size.width*2, m_patch_size.height*2);
}
// GetPatch: returns a patch corresponding to specified pose index
// - index: pose index
// - return value: the patch corresponding to specified pose index
IplImage* GetPatch(int index);
// GetPose: returns a pose corresponding to specified pose index
// - index: pose index
// - return value: the pose corresponding to specified pose index
CvAffinePose GetPose(int index) const;
// Save: saves all patches with different poses to a specified path
void Save(const char* path);
// ReadByName: reads a descriptor from a file storage
// - fs: file storage
// - parent: parent node
// - name: node name
// - return value: 1 if succeeded, 0 otherwise
int ReadByName(CvFileStorage* fs, CvFileNode* parent, const char* name);
// ReadByName: reads a descriptor from a file node
// - parent: parent node
// - name: node name
// - return value: 1 if succeeded, 0 otherwise
int ReadByName(const FileNode &parent, const char* name);
// Write: writes a descriptor into a file storage
// - fs: file storage
// - name: node name
void Write(CvFileStorage* fs, const char* name);
// GetFeatureName: returns a name corresponding to a feature
const char* GetFeatureName() const;
// GetCenter: returns the center of the feature
CvPoint GetCenter() const;
void SetPCADimHigh(int pca_dim_high) {m_pca_dim_high = pca_dim_high;};
void SetPCADimLow(int pca_dim_low) {m_pca_dim_low = pca_dim_low;};
int GetPCADimLow() const;
int GetPCADimHigh() const;
CvMat** GetPCACoeffs() const {return m_pca_coeffs;}
protected:
int m_pose_count; // the number of poses
CvSize m_patch_size; // size of each image
IplImage** m_samples; // an array of length m_pose_count containing the patch in different poses
IplImage* m_input_patch;
IplImage* m_train_patch;
CvMat** m_pca_coeffs; // an array of length m_pose_count containing pca decomposition of the patch in different poses
CvAffinePose* m_affine_poses; // an array of poses
CvMat** m_transforms; // an array of affine transforms corresponding to poses
std::string m_feature_name; // the name of the feature associated with the descriptor
CvPoint m_center; // the coordinates of the feature (the center of the input image ROI)
int m_pca_dim_high; // the number of descriptor pca components to use for generating affine poses
int m_pca_dim_low; // the number of pca components to use for comparison
};
// OneWayDescriptorBase: encapsulates functionality for training/loading a set of one way descriptors
// and finding the nearest closest descriptor to an input feature
class CV_EXPORTS OneWayDescriptorBase
{
public:
// creates an instance of OneWayDescriptor from a set of training files
// - patch_size: size of the input (large) patch
// - pose_count: the number of poses to generate for each descriptor
// - train_path: path to training files
// - pca_config: the name of the file that contains PCA for small patches (2 times smaller
// than patch_size each dimension
// - pca_hr_config: the name of the file that contains PCA for large patches (of patch_size size)
// - pca_desc_config: the name of the file that contains descriptors of PCA components
OneWayDescriptorBase(CvSize patch_size, int pose_count, const char* train_path = 0, const char* pca_config = 0,
const char* pca_hr_config = 0, const char* pca_desc_config = 0, int pyr_levels = 1,
int pca_dim_high = 100, int pca_dim_low = 100);
OneWayDescriptorBase(CvSize patch_size, int pose_count, const std::string &pca_filename, const std::string &train_path = std::string(), const std::string &images_list = std::string(),
float _scale_min = 0.7f, float _scale_max=1.5f, float _scale_step=1.2f, int pyr_levels = 1,
int pca_dim_high = 100, int pca_dim_low = 100);
virtual ~OneWayDescriptorBase();
void clear ();
// Allocate: allocates memory for a given number of descriptors
void Allocate(int train_feature_count);
// AllocatePCADescriptors: allocates memory for pca descriptors
void AllocatePCADescriptors();
// returns patch size
CvSize GetPatchSize() const {return m_patch_size;};
// returns the number of poses for each descriptor
int GetPoseCount() const {return m_pose_count;};
// returns the number of pyramid levels
int GetPyrLevels() const {return m_pyr_levels;};
// returns the number of descriptors
int GetDescriptorCount() const {return m_train_feature_count;};
// CreateDescriptorsFromImage: creates descriptors for each of the input features
// - src: input image
// - features: input features
// - pyr_levels: the number of pyramid levels
void CreateDescriptorsFromImage(IplImage* src, const std::vector<KeyPoint>& features);
// CreatePCADescriptors: generates descriptors for PCA components, needed for fast generation of feature descriptors
void CreatePCADescriptors();
// returns a feature descriptor by feature index
const OneWayDescriptor* GetDescriptor(int desc_idx) const {return &m_descriptors[desc_idx];};
// FindDescriptor: finds the closest descriptor
// - patch: input image patch
// - desc_idx: output index of the closest descriptor to the input patch
// - pose_idx: output index of the closest pose of the closest descriptor to the input patch
// - distance: distance from the input patch to the closest feature pose
// - _scales: scales of the input patch for each descriptor
// - scale_ranges: input scales variation (float[2])
void FindDescriptor(IplImage* patch, int& desc_idx, int& pose_idx, float& distance, float* _scale = 0, float* scale_ranges = 0) const;
// - patch: input image patch
// - n: number of the closest indexes
// - desc_idxs: output indexes of the closest descriptor to the input patch (n)
// - pose_idx: output indexes of the closest pose of the closest descriptor to the input patch (n)
// - distances: distance from the input patch to the closest feature pose (n)
// - _scales: scales of the input patch
// - scale_ranges: input scales variation (float[2])
void FindDescriptor(IplImage* patch, int n, std::vector<int>& desc_idxs, std::vector<int>& pose_idxs,
std::vector<float>& distances, std::vector<float>& _scales, float* scale_ranges = 0) const;
// FindDescriptor: finds the closest descriptor
// - src: input image
// - pt: center of the feature
// - desc_idx: output index of the closest descriptor to the input patch
// - pose_idx: output index of the closest pose of the closest descriptor to the input patch
// - distance: distance from the input patch to the closest feature pose
void FindDescriptor(IplImage* src, cv::Point2f pt, int& desc_idx, int& pose_idx, float& distance) const;
// InitializePoses: generates random poses
void InitializePoses();
// InitializeTransformsFromPoses: generates 2x3 affine matrices from poses (initializes m_transforms)
void InitializeTransformsFromPoses();
// InitializePoseTransforms: subsequently calls InitializePoses and InitializeTransformsFromPoses
void InitializePoseTransforms();
// InitializeDescriptor: initializes a descriptor
// - desc_idx: descriptor index
// - train_image: image patch (ROI is supported)
// - feature_label: feature textual label
void InitializeDescriptor(int desc_idx, IplImage* train_image, const char* feature_label);
void InitializeDescriptor(int desc_idx, IplImage* train_image, const KeyPoint& keypoint, const char* feature_label);
// InitializeDescriptors: load features from an image and create descriptors for each of them
void InitializeDescriptors(IplImage* train_image, const std::vector<KeyPoint>& features,
const char* feature_label = "", int desc_start_idx = 0);
// Write: writes this object to a file storage
// - fs: output filestorage
void Write (FileStorage &fs) const;
// Read: reads OneWayDescriptorBase object from a file node
// - fn: input file node
void Read (const FileNode &fn);
// LoadPCADescriptors: loads PCA descriptors from a file
// - filename: input filename
int LoadPCADescriptors(const char* filename);
// LoadPCADescriptors: loads PCA descriptors from a file node
// - fn: input file node
int LoadPCADescriptors(const FileNode &fn);
// SavePCADescriptors: saves PCA descriptors to a file
// - filename: output filename
void SavePCADescriptors(const char* filename);
// SavePCADescriptors: saves PCA descriptors to a file storage
// - fs: output file storage
void SavePCADescriptors(CvFileStorage* fs) const;
// GeneratePCA: calculate and save PCA components and descriptors
// - img_path: path to training PCA images directory
// - images_list: filename with filenames of training PCA images
void GeneratePCA(const char* img_path, const char* images_list, int pose_count=500);
// SetPCAHigh: sets the high resolution pca matrices (copied to internal structures)
void SetPCAHigh(CvMat* avg, CvMat* eigenvectors);
// SetPCALow: sets the low resolution pca matrices (copied to internal structures)
void SetPCALow(CvMat* avg, CvMat* eigenvectors);
int GetLowPCA(CvMat** avg, CvMat** eigenvectors)
{
*avg = m_pca_avg;
*eigenvectors = m_pca_eigenvectors;
return m_pca_dim_low;
};
int GetPCADimLow() const {return m_pca_dim_low;};
int GetPCADimHigh() const {return m_pca_dim_high;};
void ConvertDescriptorsArrayToTree(); // Converting pca_descriptors array to KD tree
// GetPCAFilename: get default PCA filename
static std::string GetPCAFilename () { return "pca.yml"; }
virtual bool empty() const { return m_train_feature_count <= 0 ? true : false; }
protected:
CvSize m_patch_size; // patch size
int m_pose_count; // the number of poses for each descriptor
int m_train_feature_count; // the number of the training features
OneWayDescriptor* m_descriptors; // array of train feature descriptors
CvMat* m_pca_avg; // PCA average Vector for small patches
CvMat* m_pca_eigenvectors; // PCA eigenvectors for small patches
CvMat* m_pca_hr_avg; // PCA average Vector for large patches
CvMat* m_pca_hr_eigenvectors; // PCA eigenvectors for large patches
OneWayDescriptor* m_pca_descriptors; // an array of PCA descriptors
cv::flann::Index* m_pca_descriptors_tree;
CvMat* m_pca_descriptors_matrix;
CvAffinePose* m_poses; // array of poses
CvMat** m_transforms; // array of affine transformations corresponding to poses
int m_pca_dim_high;
int m_pca_dim_low;
int m_pyr_levels;
float scale_min;
float scale_max;
float scale_step;
// SavePCAall: saves PCA components and descriptors to a file storage
// - fs: output file storage
void SavePCAall (FileStorage &fs) const;
// LoadPCAall: loads PCA components and descriptors from a file node
// - fn: input file node
void LoadPCAall (const FileNode &fn);
};
class CV_EXPORTS OneWayDescriptorObject : public OneWayDescriptorBase
{
public:
// creates an instance of OneWayDescriptorObject from a set of training files
// - patch_size: size of the input (large) patch
// - pose_count: the number of poses to generate for each descriptor
// - train_path: path to training files
// - pca_config: the name of the file that contains PCA for small patches (2 times smaller
// than patch_size each dimension
// - pca_hr_config: the name of the file that contains PCA for large patches (of patch_size size)
// - pca_desc_config: the name of the file that contains descriptors of PCA components
OneWayDescriptorObject(CvSize patch_size, int pose_count, const char* train_path, const char* pca_config,
const char* pca_hr_config = 0, const char* pca_desc_config = 0, int pyr_levels = 1);
OneWayDescriptorObject(CvSize patch_size, int pose_count, const std::string &pca_filename,
const std::string &train_path = std::string (), const std::string &images_list = std::string (),
float _scale_min = 0.7f, float _scale_max=1.5f, float _scale_step=1.2f, int pyr_levels = 1);
virtual ~OneWayDescriptorObject();
// Allocate: allocates memory for a given number of features
// - train_feature_count: the total number of features
// - object_feature_count: the number of features extracted from the object
void Allocate(int train_feature_count, int object_feature_count);
void SetLabeledFeatures(const std::vector<KeyPoint>& features) {m_train_features = features;};
std::vector<KeyPoint>& GetLabeledFeatures() {return m_train_features;};
const std::vector<KeyPoint>& GetLabeledFeatures() const {return m_train_features;};
std::vector<KeyPoint> _GetLabeledFeatures() const;
// IsDescriptorObject: returns 1 if descriptor with specified index is positive, otherwise 0
int IsDescriptorObject(int desc_idx) const;
// MatchPointToPart: returns the part number of a feature if it matches one of the object parts, otherwise -1
int MatchPointToPart(CvPoint pt) const;
// GetDescriptorPart: returns the part number of the feature corresponding to a specified descriptor
// - desc_idx: descriptor index
int GetDescriptorPart(int desc_idx) const;
void InitializeObjectDescriptors(IplImage* train_image, const std::vector<KeyPoint>& features,
const char* feature_label, int desc_start_idx = 0, float scale = 1.0f,
int is_background = 0);
// GetObjectFeatureCount: returns the number of object features
int GetObjectFeatureCount() const {return m_object_feature_count;};
protected:
int* m_part_id; // contains part id for each of object descriptors
std::vector<KeyPoint> m_train_features; // train features
int m_object_feature_count; // the number of the positive features
};
/*
* OneWayDescriptorMatcher
*/
class OneWayDescriptorMatcher;
typedef OneWayDescriptorMatcher OneWayDescriptorMatch;
class CV_EXPORTS OneWayDescriptorMatcher : public GenericDescriptorMatcher
{
public:
class CV_EXPORTS Params
{
public:
static const int POSE_COUNT = 500;
static const int PATCH_WIDTH = 24;
static const int PATCH_HEIGHT = 24;
static float GET_MIN_SCALE() { return 0.7f; }
static float GET_MAX_SCALE() { return 1.5f; }
static float GET_STEP_SCALE() { return 1.2f; }
Params( int poseCount = POSE_COUNT,
Size patchSize = Size(PATCH_WIDTH, PATCH_HEIGHT),
std::string pcaFilename = std::string(),
std::string trainPath = std::string(), std::string trainImagesList = std::string(),
float minScale = GET_MIN_SCALE(), float maxScale = GET_MAX_SCALE(),
float stepScale = GET_STEP_SCALE() );
int poseCount;
Size patchSize;
std::string pcaFilename;
std::string trainPath;
std::string trainImagesList;
float minScale, maxScale, stepScale;
};
OneWayDescriptorMatcher( const Params& params=Params() );
virtual ~OneWayDescriptorMatcher();
void initialize( const Params& params, const Ptr<OneWayDescriptorBase>& base=Ptr<OneWayDescriptorBase>() );
// Clears keypoints storing in collection and OneWayDescriptorBase
virtual void clear();
virtual void train();
virtual bool isMaskSupported();
virtual void read( const FileNode &fn );
virtual void write( FileStorage& fs ) const;
virtual bool empty() const;
virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;
protected:
// Matches a set of keypoints from a single image of the training set. A rectangle with a center in a keypoint
// and size (patch_width/2*scale, patch_height/2*scale) is cropped from the source image for each
// keypoint. scale is iterated from DescriptorOneWayParams::min_scale to DescriptorOneWayParams::max_scale.
// The minimum distance to each training patch with all its affine poses is found over all scales.
// The class ID of a match is returned for each keypoint. The distance is calculated over PCA components
// loaded with DescriptorOneWay::Initialize, kd tree is used for finding minimum distances.
virtual void knnMatchImpl( const Mat& queryImage, std::vector<KeyPoint>& queryKeypoints,
std::vector<std::vector<DMatch> >& matches, int k,
const std::vector<Mat>& masks, bool compactResult );
virtual void radiusMatchImpl( const Mat& queryImage, std::vector<KeyPoint>& queryKeypoints,
std::vector<std::vector<DMatch> >& matches, float maxDistance,
const std::vector<Mat>& masks, bool compactResult );
Ptr<OneWayDescriptorBase> base;
Params params;
int prevTrainCount;
};
/*
* FernDescriptorMatcher
*/
class FernDescriptorMatcher;
typedef FernDescriptorMatcher FernDescriptorMatch;
class CV_EXPORTS FernDescriptorMatcher : public GenericDescriptorMatcher
{
public:
class CV_EXPORTS Params
{
public:
Params( int nclasses=0,
int patchSize=FernClassifier::PATCH_SIZE,
int signatureSize=FernClassifier::DEFAULT_SIGNATURE_SIZE,
int nstructs=FernClassifier::DEFAULT_STRUCTS,
int structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
int nviews=FernClassifier::DEFAULT_VIEWS,
int compressionMethod=FernClassifier::COMPRESSION_NONE,
const PatchGenerator& patchGenerator=PatchGenerator() );
Params( const std::string& filename );
int nclasses;
int patchSize;
int signatureSize;
int nstructs;
int structSize;
int nviews;
int compressionMethod;
PatchGenerator patchGenerator;
std::string filename;
};
FernDescriptorMatcher( const Params& params=Params() );
virtual ~FernDescriptorMatcher();
virtual void clear();
virtual void train();
virtual bool isMaskSupported();
virtual void read( const FileNode &fn );
virtual void write( FileStorage& fs ) const;
virtual bool empty() const;
virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;
protected:
virtual void knnMatchImpl( const Mat& queryImage, std::vector<KeyPoint>& queryKeypoints,
std::vector<std::vector<DMatch> >& matches, int k,
const std::vector<Mat>& masks, bool compactResult );
virtual void radiusMatchImpl( const Mat& queryImage, std::vector<KeyPoint>& queryKeypoints,
std::vector<std::vector<DMatch> >& matches, float maxDistance,
const std::vector<Mat>& masks, bool compactResult );
void trainFernClassifier();
void calcBestProbAndMatchIdx( const Mat& image, const Point2f& pt,
float& bestProb, int& bestMatchIdx, std::vector<float>& signature );
Ptr<FernClassifier> classifier;
Params params;
int prevTrainCount;
};
/*
* CalonderDescriptorExtractor
*/
template<typename T>
class CV_EXPORTS CalonderDescriptorExtractor : public DescriptorExtractor
{
public:
CalonderDescriptorExtractor( const std::string& classifierFile );
virtual void read( const FileNode &fn );
virtual void write( FileStorage &fs ) const;
virtual int descriptorSize() const { return classifier_.classes(); }
virtual int descriptorType() const { return DataType<T>::type; }
virtual bool empty() const;
protected:
virtual void computeImpl( const Mat& image, std::vector<KeyPoint>& keypoints, Mat& descriptors ) const;
RTreeClassifier classifier_;
static const int BORDER_SIZE = 16;
};
template<typename T>
CalonderDescriptorExtractor<T>::CalonderDescriptorExtractor(const std::string& classifier_file)
{
classifier_.read( classifier_file.c_str() );
}
template<typename T>
void CalonderDescriptorExtractor<T>::computeImpl( const Mat& image,
std::vector<KeyPoint>& keypoints,
Mat& descriptors) const
{
// Cannot compute descriptors for keypoints on the image border.
KeyPointsFilter::runByImageBorder(keypoints, image.size(), BORDER_SIZE);
/// @todo Check 16-byte aligned
descriptors.create((int)keypoints.size(), classifier_.classes(), cv::DataType<T>::type);
int patchSize = RandomizedTree::PATCH_SIZE;
int offset = patchSize / 2;
for (size_t i = 0; i < keypoints.size(); ++i)
{
cv::Point2f pt = keypoints[i].pt;
IplImage ipl = image( Rect((int)(pt.x - offset), (int)(pt.y - offset), patchSize, patchSize) );
classifier_.getSignature( &ipl, descriptors.ptr<T>((int)i));
}
}
template<typename T>
void CalonderDescriptorExtractor<T>::read( const FileNode& )
{}
template<typename T>
void CalonderDescriptorExtractor<T>::write( FileStorage& ) const
{}
template<typename T>
bool CalonderDescriptorExtractor<T>::empty() const
{
return classifier_.trees_.empty();
}
////////////////////// Brute Force Matcher //////////////////////////
template<class Distance>
class CV_EXPORTS BruteForceMatcher : public BFMatcher
{
public:
BruteForceMatcher( Distance d = Distance() ) : BFMatcher(Distance::normType, false) {(void)d;}
virtual ~BruteForceMatcher() {}
};
/****************************************************************************************\
* Planar Object Detection *
\****************************************************************************************/
class CV_EXPORTS PlanarObjectDetector
{
public:
PlanarObjectDetector();
PlanarObjectDetector(const FileNode& node);
PlanarObjectDetector(const std::vector<Mat>& pyr, int _npoints=300,
int _patchSize=FernClassifier::PATCH_SIZE,
int _nstructs=FernClassifier::DEFAULT_STRUCTS,
int _structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
int _nviews=FernClassifier::DEFAULT_VIEWS,
const LDetector& detector=LDetector(),
const PatchGenerator& patchGenerator=PatchGenerator());
virtual ~PlanarObjectDetector();
virtual void train(const std::vector<Mat>& pyr, int _npoints=300,
int _patchSize=FernClassifier::PATCH_SIZE,
int _nstructs=FernClassifier::DEFAULT_STRUCTS,
int _structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
int _nviews=FernClassifier::DEFAULT_VIEWS,
const LDetector& detector=LDetector(),
const PatchGenerator& patchGenerator=PatchGenerator());
virtual void train(const std::vector<Mat>& pyr, const std::vector<KeyPoint>& keypoints,
int _patchSize=FernClassifier::PATCH_SIZE,
int _nstructs=FernClassifier::DEFAULT_STRUCTS,
int _structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
int _nviews=FernClassifier::DEFAULT_VIEWS,
const LDetector& detector=LDetector(),
const PatchGenerator& patchGenerator=PatchGenerator());
Rect getModelROI() const;
std::vector<KeyPoint> getModelPoints() const;
const LDetector& getDetector() const;
const FernClassifier& getClassifier() const;
void setVerbose(bool verbose);
void read(const FileNode& node);
void write(FileStorage& fs, const std::string& name=std::string()) const;
bool operator()(const Mat& image, CV_OUT Mat& H, CV_OUT std::vector<Point2f>& corners) const;
bool operator()(const std::vector<Mat>& pyr, const std::vector<KeyPoint>& keypoints,
CV_OUT Mat& H, CV_OUT std::vector<Point2f>& corners,
CV_OUT std::vector<int>* pairs=0) const;
protected:
bool verbose;
Rect modelROI;
std::vector<KeyPoint> modelPoints;
LDetector ldetector;
FernClassifier fernClassifier;
};
}
// 2009-01-12, Xavier Delacour <xavier.delacour@gmail.com>
struct lsh_hash {
int h1, h2;
};
struct CvLSHOperations
{
virtual ~CvLSHOperations() {}
virtual int vector_add(const void* data) = 0;
virtual void vector_remove(int i) = 0;
virtual const void* vector_lookup(int i) = 0;
virtual void vector_reserve(int n) = 0;
virtual unsigned int vector_count() = 0;
virtual void hash_insert(lsh_hash h, int l, int i) = 0;
virtual void hash_remove(lsh_hash h, int l, int i) = 0;
virtual int hash_lookup(lsh_hash h, int l, int* ret_i, int ret_i_max) = 0;
};
#endif
#ifdef __cplusplus
extern "C" {
#endif
/* Splits color or grayscale image into multiple connected components
of nearly the same color/brightness using modification of Burt algorithm.
comp with contain a pointer to sequence (CvSeq)
of connected components (CvConnectedComp) */
CVAPI(void) cvPyrSegmentation( IplImage* src, IplImage* dst,
CvMemStorage* storage, CvSeq** comp,
int level, double threshold1,
double threshold2 );
/****************************************************************************************\
* Planar subdivisions *
\****************************************************************************************/
typedef size_t CvSubdiv2DEdge;
#define CV_QUADEDGE2D_FIELDS() \
int flags; \
struct CvSubdiv2DPoint* pt[4]; \
CvSubdiv2DEdge next[4];
#define CV_SUBDIV2D_POINT_FIELDS()\
int flags; \
CvSubdiv2DEdge first; \
CvPoint2D32f pt; \
int id;
#define CV_SUBDIV2D_VIRTUAL_POINT_FLAG (1 << 30)
typedef struct CvQuadEdge2D
{
CV_QUADEDGE2D_FIELDS()
}
CvQuadEdge2D;
typedef struct CvSubdiv2DPoint
{
CV_SUBDIV2D_POINT_FIELDS()
}
CvSubdiv2DPoint;
#define CV_SUBDIV2D_FIELDS() \
CV_GRAPH_FIELDS() \
int quad_edges; \
int is_geometry_valid; \
CvSubdiv2DEdge recent_edge; \
CvPoint2D32f topleft; \
CvPoint2D32f bottomright;
typedef struct CvSubdiv2D
{
CV_SUBDIV2D_FIELDS()
}
CvSubdiv2D;
typedef enum CvSubdiv2DPointLocation
{
CV_PTLOC_ERROR = -2,
CV_PTLOC_OUTSIDE_RECT = -1,
CV_PTLOC_INSIDE = 0,
CV_PTLOC_VERTEX = 1,
CV_PTLOC_ON_EDGE = 2
}
CvSubdiv2DPointLocation;
typedef enum CvNextEdgeType
{
CV_NEXT_AROUND_ORG = 0x00,
CV_NEXT_AROUND_DST = 0x22,
CV_PREV_AROUND_ORG = 0x11,
CV_PREV_AROUND_DST = 0x33,
CV_NEXT_AROUND_LEFT = 0x13,
CV_NEXT_AROUND_RIGHT = 0x31,
CV_PREV_AROUND_LEFT = 0x20,
CV_PREV_AROUND_RIGHT = 0x02
}
CvNextEdgeType;
/* get the next edge with the same origin point (counterwise) */
#define CV_SUBDIV2D_NEXT_EDGE( edge ) (((CvQuadEdge2D*)((edge) & ~3))->next[(edge)&3])
/* Initializes Delaunay triangulation */
CVAPI(void) cvInitSubdivDelaunay2D( CvSubdiv2D* subdiv, CvRect rect );
/* Creates new subdivision */
CVAPI(CvSubdiv2D*) cvCreateSubdiv2D( int subdiv_type, int header_size,
int vtx_size, int quadedge_size,
CvMemStorage* storage );
/************************* high-level subdivision functions ***************************/
/* Simplified Delaunay diagram creation */
CV_INLINE CvSubdiv2D* cvCreateSubdivDelaunay2D( CvRect rect, CvMemStorage* storage )
{
CvSubdiv2D* subdiv = cvCreateSubdiv2D( CV_SEQ_KIND_SUBDIV2D, sizeof(*subdiv),
sizeof(CvSubdiv2DPoint), sizeof(CvQuadEdge2D), storage );
cvInitSubdivDelaunay2D( subdiv, rect );
return subdiv;
}
/* Inserts new point to the Delaunay triangulation */
CVAPI(CvSubdiv2DPoint*) cvSubdivDelaunay2DInsert( CvSubdiv2D* subdiv, CvPoint2D32f pt);
/* Locates a point within the Delaunay triangulation (finds the edge
the point is left to or belongs to, or the triangulation point the given
point coinsides with */
CVAPI(CvSubdiv2DPointLocation) cvSubdiv2DLocate(
CvSubdiv2D* subdiv, CvPoint2D32f pt,
CvSubdiv2DEdge* edge,
CvSubdiv2DPoint** vertex CV_DEFAULT(NULL) );
/* Calculates Voronoi tesselation (i.e. coordinates of Voronoi points) */
CVAPI(void) cvCalcSubdivVoronoi2D( CvSubdiv2D* subdiv );
/* Removes all Voronoi points from the tesselation */
CVAPI(void) cvClearSubdivVoronoi2D( CvSubdiv2D* subdiv );
/* Finds the nearest to the given point vertex in subdivision. */
CVAPI(CvSubdiv2DPoint*) cvFindNearestPoint2D( CvSubdiv2D* subdiv, CvPoint2D32f pt );
/************ Basic quad-edge navigation and operations ************/
CV_INLINE CvSubdiv2DEdge cvSubdiv2DNextEdge( CvSubdiv2DEdge edge )
{
return CV_SUBDIV2D_NEXT_EDGE(edge);
}
CV_INLINE CvSubdiv2DEdge cvSubdiv2DRotateEdge( CvSubdiv2DEdge edge, int rotate )
{
return (edge & ~3) + ((edge + rotate) & 3);
}
CV_INLINE CvSubdiv2DEdge cvSubdiv2DSymEdge( CvSubdiv2DEdge edge )
{
return edge ^ 2;
}
CV_INLINE CvSubdiv2DEdge cvSubdiv2DGetEdge( CvSubdiv2DEdge edge, CvNextEdgeType type )
{
CvQuadEdge2D* e = (CvQuadEdge2D*)(edge & ~3);
edge = e->next[(edge + (int)type) & 3];
return (edge & ~3) + ((edge + ((int)type >> 4)) & 3);
}
CV_INLINE CvSubdiv2DPoint* cvSubdiv2DEdgeOrg( CvSubdiv2DEdge edge )
{
CvQuadEdge2D* e = (CvQuadEdge2D*)(edge & ~3);
return (CvSubdiv2DPoint*)e->pt[edge & 3];
}
CV_INLINE CvSubdiv2DPoint* cvSubdiv2DEdgeDst( CvSubdiv2DEdge edge )
{
CvQuadEdge2D* e = (CvQuadEdge2D*)(edge & ~3);
return (CvSubdiv2DPoint*)e->pt[(edge + 2) & 3];
}
/****************************************************************************************\
* 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;
}
CV_INLINE double cvTriangleArea( CvPoint2D32f a, CvPoint2D32f b, CvPoint2D32f c )
{
return ((double)b.x - a.x) * ((double)c.y - a.y) - ((double)b.y - a.y) * ((double)c.x - a.x);
}
/* Constructs kd-tree from set of feature descriptors */
CVAPI(struct CvFeatureTree*) cvCreateKDTree(CvMat* desc);
/* Constructs spill-tree from set of feature descriptors */
CVAPI(struct CvFeatureTree*) cvCreateSpillTree( const CvMat* raw_data,
const int naive CV_DEFAULT(50),
const double rho CV_DEFAULT(.7),
const double tau CV_DEFAULT(.1) );
/* Release feature tree */
CVAPI(void) cvReleaseFeatureTree(struct CvFeatureTree* tr);
/* Searches feature tree for k nearest neighbors of given reference points,
searching (in case of kd-tree/bbf) at most emax leaves. */
CVAPI(void) cvFindFeatures(struct CvFeatureTree* tr, const CvMat* query_points,
CvMat* indices, CvMat* dist, int k, int emax CV_DEFAULT(20));
/* Search feature tree for all points that are inlier to given rect region.
Only implemented for kd trees */
CVAPI(int) cvFindFeaturesBoxed(struct CvFeatureTree* tr,
CvMat* bounds_min, CvMat* bounds_max,
CvMat* out_indices);
/* Construct a Locality Sensitive Hash (LSH) table, for indexing d-dimensional vectors of
given type. Vectors will be hashed L times with k-dimensional p-stable (p=2) functions. */
CVAPI(struct CvLSH*) cvCreateLSH(struct CvLSHOperations* ops, int d,
int L CV_DEFAULT(10), int k CV_DEFAULT(10),
int type CV_DEFAULT(CV_64FC1), double r CV_DEFAULT(4),
int64 seed CV_DEFAULT(-1));
/* Construct in-memory LSH table, with n bins. */
CVAPI(struct CvLSH*) cvCreateMemoryLSH(int d, int n, int L CV_DEFAULT(10), int k CV_DEFAULT(10),
int type CV_DEFAULT(CV_64FC1), double r CV_DEFAULT(4),
int64 seed CV_DEFAULT(-1));
/* Free the given LSH structure. */
CVAPI(void) cvReleaseLSH(struct CvLSH** lsh);
/* Return the number of vectors in the LSH. */
CVAPI(unsigned int) LSHSize(struct CvLSH* lsh);
/* Add vectors to the LSH structure, optionally returning indices. */
CVAPI(void) cvLSHAdd(struct CvLSH* lsh, const CvMat* data, CvMat* indices CV_DEFAULT(0));
/* Remove vectors from LSH, as addressed by given indices. */
CVAPI(void) cvLSHRemove(struct CvLSH* lsh, const CvMat* indices);
/* Query the LSH n times for at most k nearest points; data is n x d,
indices and dist are n x k. At most emax stored points will be accessed. */
CVAPI(void) cvLSHQuery(struct CvLSH* lsh, const CvMat* query_points,
CvMat* indices, CvMat* dist, int k, int emax);
/* Kolmogorov-Zabin stereo-correspondence algorithm (a.k.a. KZ1) */
#define CV_STEREO_GC_OCCLUDED SHRT_MAX
typedef struct CvStereoGCState
{
int Ithreshold;
int interactionRadius;
float K, lambda, lambda1, lambda2;
int occlusionCost;
int minDisparity;
int numberOfDisparities;
int maxIters;
CvMat* left;
CvMat* right;
CvMat* dispLeft;
CvMat* dispRight;
CvMat* ptrLeft;
CvMat* ptrRight;
CvMat* vtxBuf;
CvMat* edgeBuf;
} CvStereoGCState;
CVAPI(CvStereoGCState*) cvCreateStereoGCState( int numberOfDisparities, int maxIters );
CVAPI(void) cvReleaseStereoGCState( CvStereoGCState** state );
CVAPI(void) cvFindStereoCorrespondenceGC( const CvArr* left, const CvArr* right,
CvArr* disparityLeft, CvArr* disparityRight,
CvStereoGCState* state,
int useDisparityGuess CV_DEFAULT(0) );
/* 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 );
/****************************************************************************************\
* 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;
void* mog;
} 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( void );
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
#endif
/* End of file. */
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