// --------------------------------- OpenCV license.txt ---------------------------
(* //    IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
  //
  //    By downloading, copying, installing or using the software you agree to this license.
  //    If you do not agree to this license, do not download, install,
  //    copy or use the software.
  //
  //
  //                             License Agreement
  //                  For Open Source Computer Vision Library
  //
  //   Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
  //   Copyright (C) 2009, Willow Garage Inc., all rights reserved.
  //   Third party copyrights are property of their respective owners.
  //
  //   Redistribution and use in source and binary forms, with or without modification,
  //   are permitted provided that the following conditions are met:
  //
  //     * Redistribution's of source code must retain the above copyright notice,
  //       this list of conditions and the following disclaimer.
  //
  //     * Redistribution's in binary form must reproduce the above copyright notice,
  //       this list of conditions and the following disclaimer in the documentation
  //       and/or other materials provided with the distribution.
  //
  //     * The name of the copyright holders may not be used to endorse or promote products
  //       derived from this software without specific prior written permission.
  //
  //   This software is provided by the copyright holders and contributors "as is" and
  //   any express or implied warranties, including, but not limited to, the implied
  //   warranties of merchantability and fitness for a particular purpose are disclaimed.
  //   In no event shall the Intel Corporation or contributors be liable for any direct,
  //   indirect, incidental, special, exemplary, or consequential damages
  //   (including, but not limited to, procurement of substitute goods or services;
  //   loss of use, data, or profits; or business interruption) however caused
  //   and on any theory of liability, whether in contract, strict liability,
  //   or tort (including negligence or otherwise) arising in any way out of
  //   the use of this software, even if advised of the possibility of such damage. *)

(* /  **************************************************************************************************
  //                                 Project Delphi-OpenCV
  //  **************************************************************************************************
  //  Contributor:
  //  laentir Valetov
  //  email:laex@bk.ru
  //  **************************************************************************************************
  //  You may retrieve the latest version of this file at the GitHub,
  //  located at git://github.com/Laex/Delphi-OpenCV.git
  //  **************************************************************************************************
  //  License:
  //  The contents of this file are subject to the Mozilla Public License Version 1.1 (the "License");
  //  you may not use this file except in compliance with the License. You may obtain a copy of the
  //  License at http://www.mozilla.org/MPL/
  //
  //  Software distributed under the License is distributed on an "AS IS" basis, WITHOUT WARRANTY OF
  //  ANY KIND, either express or implied. See the License for the specific language governing rights
  //  and limitations under the License.
  //
  //  Alternatively, the contents of this file may be used under the terms of the
  //  GNU Lesser General Public License (the  "LGPL License"), in which case the
  //  provisions of the LGPL License are applicable instead of those above.
  //  If you wish to allow use of your version of this file only under the terms
  //  of the LGPL License and not to allow others to use your version of this file
  //  under the MPL, indicate your decision by deleting  the provisions above and
  //  replace  them with the notice and other provisions required by the LGPL
  //  License.  If you do not delete the provisions above, a recipient may use
  //  your version of this file under either the MPL or the LGPL License.
  //
  //  For more information about the LGPL: http://www.gnu.org/copyleft/lesser.html
  //  **************************************************************************************************
  //  Warning: Using Delphi XE3 syntax!
  //  **************************************************************************************************
  //  The Initial Developer of the Original Code:
  //  OpenCV: open source computer vision library
  //  Homepage:    http://opencv.org
  //  Online docs: http://docs.opencv.org
  //  Q&A forum:   http://answers.opencv.org
  //  Dev zone:    http://code.opencv.org
  //  **************************************************************************************************
  //  Original file:
  //  opencv\modules\legacy\include\opencv2\legacy.hpp
  //  ************************************************************************************************* *)

{$IFDEF DEBUG}
{$A8,B-,C+,D+,E-,F-,G+,H+,I+,J-,K-,L+,M-,N+,O-,P+,Q+,R+,S-,T-,U-,V+,W+,X+,Y+,Z1}
{$ELSE}
{$A8,B-,C-,D-,E-,F-,G+,H+,I+,J-,K-,L-,M-,N+,O+,P+,Q-,R-,S-,T-,U-,V+,W-,X+,Y-,Z1}
{$ENDIF}
{$WARN SYMBOL_DEPRECATED OFF}
{$WARN SYMBOL_PLATFORM OFF}
{$WARN UNIT_PLATFORM OFF}
{$WARN UNSAFE_TYPE OFF}
{$WARN UNSAFE_CODE OFF}
{$WARN UNSAFE_CAST OFF}
{$POINTERMATH ON}
unit legacy;

interface

Uses Windows, Core.types_c, imgproc.types_c;

// 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                                    *
// ****************************************************************************************

const
  CV_VALUE = 1;
  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));
  }
procedure cvSnakeImage(const image: pIplImage; points: pCvPointArray; length: Integer; alpha: PSingle; beta: PSingle;
  gamma: PSingle; coeff_usage: Integer; win: TCvSize; criteria: TCvTermCriteria; calc_gradient: Integer = 1); cdecl;

/// ****************************************************************************************\
// *                                    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 String& name=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 String& name=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(String url, bool append=false);
// void savePosteriors2(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(String file_url);
// void saveAllBytePosteriors(String file_url);
// void setFloatPosteriorsFromTextfile_176(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
//
// 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 String &pca_filename, const String &train_path = String(), const String &images_list = 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 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 String &pca_filename,
// const String &train_path = String (), const String &images_list = 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),
// String pcaFilename = String(),
// String trainPath = String(), String trainImagesList = String(),
// float minScale = GET_MIN_SCALE(), float maxScale = GET_MAX_SCALE(),
// float stepScale = GET_STEP_SCALE() );
//
// int poseCount;
// Size patchSize;
// String pcaFilename;
// String trainPath;
// 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 String& filename );
//
// int nclasses;
// int patchSize;
// int signatureSize;
// int nstructs;
// int structSize;
// int nviews;
// int compressionMethod;
// PatchGenerator patchGenerator;
//
// 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 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 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 String& name=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                                       *
// \****************************************************************************************/
type

  pCvSubdiv2DEdge = ^TCvSubdiv2DEdge;

  TCvSubdiv2DEdge = size_t;
  //
  // #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;

Type
  TCvSubdiv2DPointLocation = Integer;

const
  { CvSubdiv2DPointLocation enum }
  CV_PTLOC_ERROR = -2;
  CV_PTLOC_OUTSIDE_RECT = -1;
  CV_PTLOC_INSIDE = 0;
  CV_PTLOC_VERTEX = 1;
  CV_PTLOC_ON_EDGE = 2;

Type
  TCvNextEdgeType = Integer;

const
  { CvNextEdgeType enum }
  CV_NEXT_AROUND_ORG = $00;
  CV_NEXT_AROUND_DST = $22;
  CV_PREV_AROUND_ORG = $11;
  CV_PREV_AROUND_DST = $33;
  CV_NEXT_AROUND_LEFT = $13;
  CV_NEXT_AROUND_RIGHT = $31;
  CV_PREV_AROUND_LEFT = $20;
  CV_PREV_AROUND_RIGHT = $02;

  /// * 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 );
procedure cvInitSubdivDelaunay2D(subdiv: pCvSubdiv2D; rect: TCvRect); cdecl;

/// * Creates new subdivision */
// CVAPI(CvSubdiv2D*)  cvCreateSubdiv2D( int subdiv_type, int header_size,
// int vtx_size, int quadedge_size,
// CvMemStorage* storage );
function cvCreateSubdiv2D(subdiv_type: Integer; header_size: Integer; vtx_size: Integer; quadedge_size: Integer;
  storage: pCvMemStorage): pCvSubdiv2D; cdecl;

/// ************************* 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);
function cvSubdivDelaunay2DInsert(subdiv: pCvSubdiv2D; pt: TCvPoint2D32f): pCvSubdiv2DPoint; cdecl;

/// * 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) );

function cvSubdiv2DLocate(subdiv: pCvSubdiv2D; pt: TCvPoint2D32f; edge: pCvSubdiv2DEdge; vertex: pCvSubdiv2DPoint = nil)
  : TCvSubdiv2DPointLocation; cdecl;

/// * Calculates Voronoi tesselation (i.e. coordinates of Voronoi points) */
// CVAPI(void)  cvCalcSubdivVoronoi2D( CvSubdiv2D* subdiv );
procedure cvCalcSubdivVoronoi2D(subdiv: pCvSubdiv2D); cdecl;

/// * 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);
// }
function cvSubdiv2DRotateEdge(edge: TCvSubdiv2DEdge; rotate: Integer): TCvSubdiv2DEdge; // inline;

// 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);
// }
function cvSubdiv2DGetEdge(edge: TCvSubdiv2DEdge; _type: TCvNextEdgeType): TCvSubdiv2DEdge; // inline;

// CV_INLINE  CvSubdiv2DPoint*  cvSubdiv2DEdgeOrg( CvSubdiv2DEdge edge )
// {
// CvQuadEdge2D* e = (CvQuadEdge2D*)(edge & ~3);
// return (CvSubdiv2DPoint*)e->pt[edge & 3];
// }
function cvSubdiv2DEdgeOrg(edge: TCvSubdiv2DEdge): pCvSubdiv2DPoint; // inline;

// CV_INLINE  CvSubdiv2DPoint*  cvSubdiv2DEdgeDst( CvSubdiv2DEdge edge )
// {
// CvQuadEdge2D* e = (CvQuadEdge2D*)(edge & ~3);
// return (CvSubdiv2DPoint*)e->pt[(edge + 2) & 3];
// }
function cvSubdiv2DEdgeDst(edge: TCvSubdiv2DEdge): pCvSubdiv2DPoint; // inline;

/// ****************************************************************************************\
// *                           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

Type
  pCvStereoGCState = ^TCvStereoGCState;

  TCvStereoGCState = packed record
    Ithreshold: Integer;
    interactionRadius: Integer;
    K, lambda, lambda1, lambda2: Single;
    occlusionCost: Integer;
    minDisparity: Integer;
    numberOfDisparities: Integer;
    maxIters: Integer;
    left: pCvMat;
    right: pCvMat;
    dispLeft: pCvMat;
    dispRight: pCvMat;
    ptrLeft: pCvMat;
    ptrRight: pCvMat;
    vtxBuf: pCvMat;
    edgeBuf: pCvMat;
  end;

  // CVAPI(CvStereoGCState*) cvCreateStereoGCState( int numberOfDisparities, int maxIters );
function cvCreateStereoGCState(numberOfDisparities: Integer; maxIters: Integer): pCvStereoGCState; cdecl;

// CVAPI(void) cvReleaseStereoGCState( CvStereoGCState** state );
procedure cvReleaseStereoGCState(Var state: pCvStereoGCState); cdecl;

{
  CVAPI(void) cvFindStereoCorrespondenceGC(
  const CvArr* left,
  const CvArr* right,
  CvArr* disparityLeft,
  CvArr* disparityRight,
  CvStereoGCState* state,
  int useDisparityGuess CV_DEFAULT(0) );
}

procedure cvFindStereoCorrespondenceGC(const left: pIplImage; const right: pIplImage; disparityLeft: pCvMat;
  disparityRight: pCvMat; state: pCvStereoGCState; useDisparityGuess: Integer = 0); cdecl;

/// * 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
// */
//
const
  CV_BG_MODEL_FGD = 0;
  CV_BG_MODEL_MOG = 1; // * "Mixture of Gaussians".	*/
  CV_BG_MODEL_FGD_SIMPLE = 2;

Type

  ppCvBGStatModel = ^pCvBGStatModel;
  pCvBGStatModel = ^TCvBGStatModel;

  // typedef void (CV_CDECL * CvReleaseBGStatModel)( struct CvBGStatModel** bg_model );
  TCvReleaseBGStatModel = procedure(Var bg_model: pCvBGStatModel); cdecl;
  // typedef int (CV_CDECL * CvUpdateBGStatModel)( IplImage* curr_frame, struct CvBGStatModel* bg_model, double learningRate );
  TCvUpdateBGStatModel = function(curr_frame: pIplImage; bg_model: pCvBGStatModel; learningRate: double)
    : Integer; cdecl;

  TCvBGStatModel = packed record
    _type: Integer; // *type of BG model
    release: TCvReleaseBGStatModel;
    update: TCvUpdateBGStatModel;
    background: pIplImage; // *8UC3 reference background image
    foreground: pIplImage; // *8UC1 foreground image
    layers: pIplImage; // *8UC3 reference background image, can be null
    layer_count: Integer; // * can be zero
    storage: pCvMemStorage; // *storage for foreground_regions
    foreground_regions: pCvSeq; // *foreground object contours
  end;

  // #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*/

  /// / Releases memory used by BGStatModel
  // CVAPI(void) cvReleaseBGStatModel( CvBGStatModel** bg_model );
procedure cvReleaseBGStatModel(Var bg_model: pCvBGStatModel); cdecl;

// Updates statistical model and returns number of found foreground regions
// CVAPI(int) cvUpdateBGStatModel( IplImage* current_frame, CvBGStatModel*  bg_model,
// double learningRate CV_DEFAULT(-1));
function cvUpdateBGStatModel(current_frame: pIplImage; bg_model: pCvBGStatModel; learningRate: double = -1)
  : Integer; cdecl;

/// / 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
//

const
  // Default parameters of foreground detection algorithm:
  CV_BGFG_FGD_LC = 128;
  CV_BGFG_FGD_N1C = 15;
  CV_BGFG_FGD_N2C = 25;

  CV_BGFG_FGD_LCC = 64;
  CV_BGFG_FGD_N1CC = 25;
  CV_BGFG_FGD_N2CC = 40;
  // Background reference image update parameter: */
  CV_BGFG_FGD_ALPHA_1 = 0.1;

  /// * stat model update parameter
  // * 0.002f ~ 1K frame(~45sec), 0.005 ~ 18sec (if 25fps and absolutely static BG)
  // */
  CV_BGFG_FGD_ALPHA_2 = 0.005;

  // * start value for alpha parameter (to fast initiate statistic model) */
  CV_BGFG_FGD_ALPHA_3 = 0.1;
  CV_BGFG_FGD_DELTA = 2;
  CV_BGFG_FGD_T = 0.9;
  CV_BGFG_FGD_MINAREA = 15;
  CV_BGFG_FGD_BG_UPDATE_TRESH = 0.5;

  /// * 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.
  // */
Type
  pCvFGDStatModelParams = ^TCvFGDStatModelParams;

  TCvFGDStatModelParams = packed record
    Lc: Integer; // Quantized levels per 'color' component. Power of two, typically 32, 64 or 128.
    N1c: Integer; // Number of color vectors used to model normal background color variation at a given pixel.
    N2c: Integer; // 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.
    Lcc: Integer;
    // Quantized levels per 'color co-occurrence' component.  Power of two, typically 16, 32 or 64.
    N1cc: Integer;
    // Number of color co-occurrence vectors used to model normal background color variation at a given pixel.
    N2cc: Integer;
    // 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.
    is_obj_without_holes: Integer; // If TRUE we ignore holes within foreground blobs. Defaults to TRUE.
    perform_morphing: Integer; // Number of erode-dilate-erode foreground-blob cleanup iterations.
    // These erase one-pixel junk blobs and merge almost-touching blobs. Default value is 1.
    alpha1: Single; // How quickly we forget old background pixel values seen.  Typically set to 0.1
    alpha2: Single; // "Controls speed of feature learning". Depends on T. Typical value circa 0.005.
    alpha3: Single; // Alternate to alpha2, used (e.g.) for quicker initial convergence. Typical value 0.1.
    delta: Single; // Affects color and color co-occurrence quantization, typically set to 2.
    T: Single;
    // "A percentage value which determines when new features can be recognized as new background." (Typically 0.9).
    minArea: Single; // Discard foreground blobs whose bounding box is smaller than this threshold.
  end;
  //
  // 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));
function cvCreateFGDStatModel(first_frame: pIplImage; parameters: pCvFGDStatModelParams = nil): pCvBGStatModel;
cdecl

// 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": */
const
  CV_BGFG_MOG_MAX_NGAUSSIANS = 500;

  // * default parameters of gaussian background detection algorithm */
  CV_BGFG_MOG_BACKGROUND_THRESHOLD = 0.7; // * threshold sum of weights for background test */
  CV_BGFG_MOG_STD_THRESHOLD = 2.5; // * lambda=2.5 is 99% */
  CV_BGFG_MOG_WINDOW_SIZE = 200; // * Learning rate; alpha = 1/CV_GBG_WINDOW_SIZE */
  CV_BGFG_MOG_NGAUSSIANS = 5; // * = K = number of Gaussians in mixture */
  CV_BGFG_MOG_WEIGHT_INIT = 0.05;
  CV_BGFG_MOG_SIGMA_INIT = 30;
  CV_BGFG_MOG_MINAREA = 15;

  CV_BGFG_MOG_NCOLORS = 3;

type
  pCvGaussBGStatModelParams = ^TCvGaussBGStatModelParams;

  TCvGaussBGStatModelParams = packed record
    win_size: Integer; // * = 1/alpha
    n_gauss: Integer;
    bg_threshold, std_threshold, minArea: double;
    weight_init, variance_init: double;
  end;

  pCvGaussBGValues = ^TCvGaussBGValues;

  TCvGaussBGValues = packed record
    match_sum: Integer;
    weight: double;
    variance: array [0 .. CV_BGFG_MOG_NCOLORS - 1] of double;
    mean: array [0 .. CV_BGFG_MOG_NCOLORS - 1] of double;
  end;

  pCvGaussBGPoint = ^TCvGaussBGPoint;

  TCvGaussBGPoint = packed record
    g_values: pCvGaussBGValues;
  end;

  pCvGaussBGModel = ^TCvGaussBGModel;

  TCvGaussBGModel = packed record
    // CV_BG_STAT_MODEL_FIELDS();
    _type: Integer; // type of BG model
    release: TCvReleaseBGStatModel;
    update: TCvUpdateBGStatModel;
    background: pIplImage; // 8UC3 reference background image
    foreground: pIplImage; // 8UC1 foreground image
    layers: pIplImage; // 8UC3 reference background image, can be null
    layer_count: Integer; // can be zero
    storage: pCvMemStorage; // storage for foreground_regions
    foreground_regions: pCvSeq; // foreground object contours
    params: TCvGaussBGStatModelParams;
    g_point: pCvGaussBGPoint;
    countFrames: Integer;
    mog: Pointer;
  end;

  // * Creates Gaussian mixture background model */
  // CVAPI(CvBGStatModel*) cvCreateGaussianBGModel( IplImage* first_frame, CvGaussBGStatModelParams* parameters CV_DEFAULT(NULL));
function cvCreateGaussianBGModel(first_frame: pIplImage; parameters: pCvGaussBGStatModelParams = nil)
  : pCvBGStatModel; cdecl;

type
  pCvBGCodeBookElem = ^TCvBGCodeBookElem;

  TCvBGCodeBookElem = packed record
    next: pCvBGCodeBookElem;
    tLastUpdate: Integer;
    stale: Integer;
    boxMin: array [0 .. 2] of byte;
    boxMax: array [0 .. 2] of byte;
    learnMin: array [0 .. 2] of byte;
    learnMax: array [0 .. 2] of byte;
  end;

  pCvBGCodeBookModel = ^TCvBGCodeBookModel;

  TCvBGCodeBookModel = packed record
    size: TCvSize;
    T: Integer;
    cbBounds: array [0 .. 2] of byte;
    modMin: array [0 .. 2] of byte;
    modMax: array [0 .. 2] of byte;
    cbmap: pCvBGCodeBookElem;
    storage: pCvMemStorage;
    freeList: pCvBGCodeBookElem;
  end;

  // CVAPI(CvBGCodeBookModel*) cvCreateBGCodeBookModel( void );
function cvCreateBGCodeBookModel: pCvBGCodeBookModel; cdecl;
// CVAPI(void) cvReleaseBGCodeBookModel( CvBGCodeBookModel** model );
procedure cvReleaseBGCodeBookModel(model: pCvBGCodeBookModel); cdecl;
// CVAPI(void) cvBGCodeBookUpdate( CvBGCodeBookModel* model, const CvArr* image, CvRect roi CV_DEFAULT(cvRect(0,0,0,0)),const CvArr* mask CV_DEFAULT(0) );
procedure cvBGCodeBookUpdate(model: pCvBGCodeBookModel; const image: pIplImage;
  roi: TCvRect { =CV_DEFAULT(cvRect(0,0,0,0)) }; const mask: pCvArr { =0 } ); cdecl;
// CVAPI(int) cvBGCodeBookDiff( const CvBGCodeBookModel* model, const CvArr* image, CvArr* fgmask, CvRect roi CV_DEFAULT(cvRect(0,0,0,0)) );
function cvBGCodeBookDiff(const model: pCvBGCodeBookModel; const image: pCvArr; fgmask: pCvArr;
  roi: TCvRect { = cvRect(0,0,0,0) } ): Integer; cdecl;
// CVAPI(void) cvBGCodeBookClearStale( CvBGCodeBookModel* model, int staleThresh, CvRect roi CV_DEFAULT(cvRect(0,0,0,0)), const CvArr* mask CV_DEFAULT(0) );
procedure cvBGCodeBookClearStale(model: pCvBGCodeBookModel; staleThresh: Integer; roi: TCvRect { =cvRect(0,0,0,0) };
  const mask: pCvArr  = nil  ); cdecl;
// 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)));
function cvSegmentFGMask(fgmask: pCvArr; poly1Hull0: Integer { =1 }; perimScale: Single { = 4 };
  storage: pCvMemStorage { =nil }; offset: TCvPoint { =cvPoint(0,0) } ): pCvSeq; cdecl;

implementation

Uses uLibName;

function cvCreateStereoGCState; external legacy_Dll;
procedure cvFindStereoCorrespondenceGC; external legacy_Dll;
procedure cvReleaseStereoGCState; external legacy_Dll;
procedure cvSnakeImage; external legacy_Dll;
function cvCreateSubdiv2D; external legacy_Dll;
procedure cvInitSubdivDelaunay2D; external legacy_Dll;

function cvSubdiv2DEdgeOrg(edge: TCvSubdiv2DEdge): pCvSubdiv2DPoint; inline;
Var
  e: pCvQuadEdge2D;
begin
  // CvQuadEdge2D* e = (CvQuadEdge2D*)(edge & ~3);
  e := pCvQuadEdge2D(edge and (not 3));
  // return (CvSubdiv2DPoint*)e->pt[edge & 3];
  Result := pCvSubdiv2DPoint(e^.pt[edge and 3]);
end;

function cvSubdiv2DEdgeDst(edge: TCvSubdiv2DEdge): pCvSubdiv2DPoint;
Var
  e: pCvQuadEdge2D;
begin
  // CvQuadEdge2D* e = (CvQuadEdge2D*)(edge & ~3);
  e := pCvQuadEdge2D(edge and (not 3));
  // return (CvSubdiv2DPoint*)e->pt[(edge + 2) & 3];
  Result := pCvSubdiv2DPoint(e^.pt[(edge + 2) and 3]);
end;

function cvSubdiv2DLocate; external legacy_Dll;

function cvSubdiv2DGetEdge(edge: TCvSubdiv2DEdge; _type: TCvNextEdgeType): TCvSubdiv2DEdge;
Var
  e: pCvQuadEdge2D;
begin
  // CvQuadEdge2D* e = (CvQuadEdge2D*)(edge & ~3);
  e := pCvQuadEdge2D(edge and (not 3));
  // edge = e->next[(edge + (int)type) & 3];
  edge := e^.next[(edge + _type) and 3];
  // return  (edge & ~3) + ((edge + ((int)type >> 4)) & 3);
  Result := (edge and (not 3)) + ((edge + (_type shr 4)) and 3);
end;

function cvSubdiv2DRotateEdge(edge: TCvSubdiv2DEdge; rotate: Integer): TCvSubdiv2DEdge;
begin
  // return  (edge & ~3) + ((edge + rotate) & 3);
  Result := (edge and (not 3)) + ((edge + rotate) and 3);
end;

procedure cvCalcSubdivVoronoi2D; external legacy_Dll;
function cvSubdivDelaunay2DInsert; external legacy_Dll;
function cvCreateGaussianBGModel; external legacy_Dll;
function cvUpdateBGStatModel; external legacy_Dll;
procedure cvReleaseBGStatModel; external legacy_Dll;
function cvCreateFGDStatModel; external legacy_Dll;
function cvCreateBGCodeBookModel; external legacy_Dll;
procedure cvReleaseBGCodeBookModel; external legacy_Dll;
procedure cvBGCodeBookUpdate; external legacy_Dll;
function cvBGCodeBookDiff; external legacy_Dll;
procedure cvBGCodeBookClearStale; external legacy_Dll;
function cvSegmentFGMask; external legacy_Dll;

end.