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3504 lines
135 KiB
C++
3504 lines
135 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#ifndef __OPENCV_LEGACY_HPP__
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#define __OPENCV_LEGACY_HPP__
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#include "opencv2/imgproc/imgproc_c.h"
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#include "opencv2/calib3d/calib3d_c.h"
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#include "opencv2/ml.hpp"
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#ifdef __cplusplus
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#include "opencv2/features2d.hpp"
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extern "C" {
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#endif
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CVAPI(CvSeq*) cvSegmentImage( const CvArr* srcarr, CvArr* dstarr,
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double canny_threshold,
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double ffill_threshold,
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CvMemStorage* storage );
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/****************************************************************************************\
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* Eigen objects *
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\****************************************************************************************/
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typedef int (CV_CDECL * CvCallback)(int index, void* buffer, void* user_data);
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typedef union
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{
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CvCallback callback;
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void* data;
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}
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CvInput;
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#define CV_EIGOBJ_NO_CALLBACK 0
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#define CV_EIGOBJ_INPUT_CALLBACK 1
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#define CV_EIGOBJ_OUTPUT_CALLBACK 2
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#define CV_EIGOBJ_BOTH_CALLBACK 3
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/* Calculates covariation matrix of a set of arrays */
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CVAPI(void) cvCalcCovarMatrixEx( int nObjects, void* input, int ioFlags,
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int ioBufSize, uchar* buffer, void* userData,
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IplImage* avg, float* covarMatrix );
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/* Calculates eigen values and vectors of covariation matrix of a set of
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arrays */
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CVAPI(void) cvCalcEigenObjects( int nObjects, void* input, void* output,
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int ioFlags, int ioBufSize, void* userData,
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CvTermCriteria* calcLimit, IplImage* avg,
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float* eigVals );
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/* Calculates dot product (obj - avg) * eigObj (i.e. projects image to eigen vector) */
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CVAPI(double) cvCalcDecompCoeff( IplImage* obj, IplImage* eigObj, IplImage* avg );
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/* Projects image to eigen space (finds all decomposion coefficients */
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CVAPI(void) cvEigenDecomposite( IplImage* obj, int nEigObjs, void* eigInput,
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int ioFlags, void* userData, IplImage* avg,
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float* coeffs );
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/* Projects original objects used to calculate eigen space basis to that space */
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CVAPI(void) cvEigenProjection( void* eigInput, int nEigObjs, int ioFlags,
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void* userData, float* coeffs, IplImage* avg,
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IplImage* proj );
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/****************************************************************************************\
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* 1D/2D HMM *
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\****************************************************************************************/
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typedef struct CvImgObsInfo
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{
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int obs_x;
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int obs_y;
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int obs_size;
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float* obs;//consequtive observations
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int* state;/* arr of pairs superstate/state to which observation belong */
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int* mix; /* number of mixture to which observation belong */
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} CvImgObsInfo;/*struct for 1 image*/
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typedef CvImgObsInfo Cv1DObsInfo;
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typedef struct CvEHMMState
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{
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int num_mix; /*number of mixtures in this state*/
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float* mu; /*mean vectors corresponding to each mixture*/
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float* inv_var; /* square root of inversed variances corresp. to each mixture*/
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float* log_var_val; /* sum of 0.5 (LN2PI + ln(variance[i]) ) for i=1,n */
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float* weight; /*array of mixture weights. Summ of all weights in state is 1. */
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} CvEHMMState;
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typedef struct CvEHMM
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{
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int level; /* 0 - lowest(i.e its states are real states), ..... */
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int num_states; /* number of HMM states */
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float* transP;/*transition probab. matrices for states */
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float** obsProb; /* if level == 0 - array of brob matrices corresponding to hmm
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if level == 1 - martix of matrices */
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union
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{
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CvEHMMState* state; /* if level == 0 points to real states array,
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if not - points to embedded hmms */
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struct CvEHMM* ehmm; /* pointer to an embedded model or NULL, if it is a leaf */
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} u;
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} CvEHMM;
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/*CVAPI(int) icvCreate1DHMM( CvEHMM** this_hmm,
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int state_number, int* num_mix, int obs_size );
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CVAPI(int) icvRelease1DHMM( CvEHMM** phmm );
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CVAPI(int) icvUniform1DSegm( Cv1DObsInfo* obs_info, CvEHMM* hmm );
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CVAPI(int) icvInit1DMixSegm( Cv1DObsInfo** obs_info_array, int num_img, CvEHMM* hmm);
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CVAPI(int) icvEstimate1DHMMStateParams( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm);
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CVAPI(int) icvEstimate1DObsProb( CvImgObsInfo* obs_info, CvEHMM* hmm );
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CVAPI(int) icvEstimate1DTransProb( Cv1DObsInfo** obs_info_array,
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int num_seq,
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CvEHMM* hmm );
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CVAPI(float) icvViterbi( Cv1DObsInfo* obs_info, CvEHMM* hmm);
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CVAPI(int) icv1DMixSegmL2( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm );*/
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/*********************************** Embedded HMMs *************************************/
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/* Creates 2D HMM */
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CVAPI(CvEHMM*) cvCreate2DHMM( int* stateNumber, int* numMix, int obsSize );
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/* Releases HMM */
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CVAPI(void) cvRelease2DHMM( CvEHMM** hmm );
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#define CV_COUNT_OBS(roi, win, delta, numObs ) \
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{ \
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(numObs)->width =((roi)->width -(win)->width +(delta)->width)/(delta)->width; \
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(numObs)->height =((roi)->height -(win)->height +(delta)->height)/(delta)->height;\
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}
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/* Creates storage for observation vectors */
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CVAPI(CvImgObsInfo*) cvCreateObsInfo( CvSize numObs, int obsSize );
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/* Releases storage for observation vectors */
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CVAPI(void) cvReleaseObsInfo( CvImgObsInfo** obs_info );
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/* The function takes an image on input and and returns the sequnce of observations
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to be used with an embedded HMM; Each observation is top-left block of DCT
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coefficient matrix */
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CVAPI(void) cvImgToObs_DCT( const CvArr* arr, float* obs, CvSize dctSize,
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CvSize obsSize, CvSize delta );
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/* Uniformly segments all observation vectors extracted from image */
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CVAPI(void) cvUniformImgSegm( CvImgObsInfo* obs_info, CvEHMM* ehmm );
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/* Does mixture segmentation of the states of embedded HMM */
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CVAPI(void) cvInitMixSegm( CvImgObsInfo** obs_info_array,
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int num_img, CvEHMM* hmm );
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/* Function calculates means, variances, weights of every Gaussian mixture
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of every low-level state of embedded HMM */
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CVAPI(void) cvEstimateHMMStateParams( CvImgObsInfo** obs_info_array,
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int num_img, CvEHMM* hmm );
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/* Function computes transition probability matrices of embedded HMM
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given observations segmentation */
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CVAPI(void) cvEstimateTransProb( CvImgObsInfo** obs_info_array,
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int num_img, CvEHMM* hmm );
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/* Function computes probabilities of appearing observations at any state
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(i.e. computes P(obs|state) for every pair(obs,state)) */
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CVAPI(void) cvEstimateObsProb( CvImgObsInfo* obs_info,
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CvEHMM* hmm );
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/* Runs Viterbi algorithm for embedded HMM */
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CVAPI(float) cvEViterbi( CvImgObsInfo* obs_info, CvEHMM* hmm );
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/* Function clusters observation vectors from several images
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given observations segmentation.
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Euclidean distance used for clustering vectors.
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Centers of clusters are given means of every mixture */
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CVAPI(void) cvMixSegmL2( CvImgObsInfo** obs_info_array,
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int num_img, CvEHMM* hmm );
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/****************************************************************************************\
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* A few functions from old stereo gesture recognition demosions *
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\****************************************************************************************/
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/* Creates hand mask image given several points on the hand */
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CVAPI(void) cvCreateHandMask( CvSeq* hand_points,
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IplImage *img_mask, CvRect *roi);
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/* Finds hand region in range image data */
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CVAPI(void) cvFindHandRegion (CvPoint3D32f* points, int count,
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CvSeq* indexs,
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float* line, CvSize2D32f size, int flag,
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CvPoint3D32f* center,
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CvMemStorage* storage, CvSeq **numbers);
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/* Finds hand region in range image data (advanced version) */
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CVAPI(void) cvFindHandRegionA( CvPoint3D32f* points, int count,
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CvSeq* indexs,
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float* line, CvSize2D32f size, int jc,
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CvPoint3D32f* center,
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CvMemStorage* storage, CvSeq **numbers);
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/* Calculates the cooficients of the homography matrix */
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CVAPI(void) cvCalcImageHomography( float* line, CvPoint3D32f* center,
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float* intrinsic, float* homography );
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/****************************************************************************************\
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* More operations on sequences *
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\****************************************************************************************/
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/*****************************************************************************************/
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#define CV_CURRENT_INT( reader ) (*((int *)(reader).ptr))
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#define CV_PREV_INT( reader ) (*((int *)(reader).prev_elem))
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#define CV_GRAPH_WEIGHTED_VERTEX_FIELDS() CV_GRAPH_VERTEX_FIELDS()\
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float weight;
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#define CV_GRAPH_WEIGHTED_EDGE_FIELDS() CV_GRAPH_EDGE_FIELDS()
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typedef struct CvGraphWeightedVtx
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{
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CV_GRAPH_WEIGHTED_VERTEX_FIELDS()
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} CvGraphWeightedVtx;
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typedef struct CvGraphWeightedEdge
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{
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CV_GRAPH_WEIGHTED_EDGE_FIELDS()
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} CvGraphWeightedEdge;
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typedef enum CvGraphWeightType
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{
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CV_NOT_WEIGHTED,
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CV_WEIGHTED_VTX,
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CV_WEIGHTED_EDGE,
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CV_WEIGHTED_ALL
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} CvGraphWeightType;
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/* Calculates histogram of a contour */
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CVAPI(void) cvCalcPGH( const CvSeq* contour, CvHistogram* hist );
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#define CV_DOMINANT_IPAN 1
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/* Finds high-curvature points of the contour */
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CVAPI(CvSeq*) cvFindDominantPoints( CvSeq* contour, CvMemStorage* storage,
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int method CV_DEFAULT(CV_DOMINANT_IPAN),
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double parameter1 CV_DEFAULT(0),
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double parameter2 CV_DEFAULT(0),
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double parameter3 CV_DEFAULT(0),
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double parameter4 CV_DEFAULT(0));
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/*****************************************************************************************/
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/*******************************Stereo correspondence*************************************/
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typedef struct CvCliqueFinder
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{
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CvGraph* graph;
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int** adj_matr;
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int N; //graph size
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// stacks, counters etc/
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int k; //stack size
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int* current_comp;
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int** All;
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int* ne;
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int* ce;
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int* fixp; //node with minimal disconnections
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int* nod;
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int* s; //for selected candidate
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int status;
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int best_score;
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int weighted;
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int weighted_edges;
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float best_weight;
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float* edge_weights;
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float* vertex_weights;
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float* cur_weight;
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float* cand_weight;
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} CvCliqueFinder;
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#define CLIQUE_TIME_OFF 2
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#define CLIQUE_FOUND 1
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#define CLIQUE_END 0
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/*CVAPI(void) cvStartFindCliques( CvGraph* graph, CvCliqueFinder* finder, int reverse,
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int weighted CV_DEFAULT(0), int weighted_edges CV_DEFAULT(0));
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CVAPI(int) cvFindNextMaximalClique( CvCliqueFinder* finder, int* clock_rest CV_DEFAULT(0) );
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CVAPI(void) cvEndFindCliques( CvCliqueFinder* finder );
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CVAPI(void) cvBronKerbosch( CvGraph* graph );*/
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/*F///////////////////////////////////////////////////////////////////////////////////////
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//
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// Name: cvSubgraphWeight
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// Purpose: finds weight of subgraph in a graph
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// Context:
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// Parameters:
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// graph - input graph.
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// subgraph - sequence of pairwise different ints. These are indices of vertices of subgraph.
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// weight_type - describes the way we measure weight.
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// one of the following:
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// CV_NOT_WEIGHTED - weight of a clique is simply its size
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// CV_WEIGHTED_VTX - weight of a clique is the sum of weights of its vertices
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// CV_WEIGHTED_EDGE - the same but edges
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// CV_WEIGHTED_ALL - the same but both edges and vertices
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// weight_vtx - optional vector of floats, with size = graph->total.
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// If weight_type is either CV_WEIGHTED_VTX or CV_WEIGHTED_ALL
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// weights of vertices must be provided. If weight_vtx not zero
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// these weights considered to be here, otherwise function assumes
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// that vertices of graph are inherited from CvGraphWeightedVtx.
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// weight_edge - optional matrix of floats, of width and height = graph->total.
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// If weight_type is either CV_WEIGHTED_EDGE or CV_WEIGHTED_ALL
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// weights of edges ought to be supplied. If weight_edge is not zero
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// function finds them here, otherwise function expects
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// edges of graph to be inherited from CvGraphWeightedEdge.
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// If this parameter is not zero structure of the graph is determined from matrix
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// rather than from CvGraphEdge's. In particular, elements corresponding to
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// absent edges should be zero.
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// Returns:
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// weight of subgraph.
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// Notes:
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//F*/
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/*CVAPI(float) cvSubgraphWeight( CvGraph *graph, CvSeq *subgraph,
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CvGraphWeightType weight_type CV_DEFAULT(CV_NOT_WEIGHTED),
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CvVect32f weight_vtx CV_DEFAULT(0),
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CvMatr32f weight_edge CV_DEFAULT(0) );*/
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/*F///////////////////////////////////////////////////////////////////////////////////////
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//
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// Name: cvFindCliqueEx
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// Purpose: tries to find clique with maximum possible weight in a graph
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// Context:
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// Parameters:
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// graph - input graph.
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// storage - memory storage to be used by the result.
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// is_complementary - optional flag showing whether function should seek for clique
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// in complementary graph.
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// weight_type - describes our notion about weight.
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// one of the following:
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// CV_NOT_WEIGHTED - weight of a clique is simply its size
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// CV_WEIGHTED_VTX - weight of a clique is the sum of weights of its vertices
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// CV_WEIGHTED_EDGE - the same but edges
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// CV_WEIGHTED_ALL - the same but both edges and vertices
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// weight_vtx - optional vector of floats, with size = graph->total.
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// If weight_type is either CV_WEIGHTED_VTX or CV_WEIGHTED_ALL
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// weights of vertices must be provided. If weight_vtx not zero
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// these weights considered to be here, otherwise function assumes
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// that vertices of graph are inherited from CvGraphWeightedVtx.
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// weight_edge - optional matrix of floats, of width and height = graph->total.
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// If weight_type is either CV_WEIGHTED_EDGE or CV_WEIGHTED_ALL
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// weights of edges ought to be supplied. If weight_edge is not zero
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// function finds them here, otherwise function expects
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// edges of graph to be inherited from CvGraphWeightedEdge.
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// Note that in case of CV_WEIGHTED_EDGE or CV_WEIGHTED_ALL
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// nonzero is_complementary implies nonzero weight_edge.
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// start_clique - optional sequence of pairwise different ints. They are indices of
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// vertices that shall be present in the output clique.
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// subgraph_of_ban - optional sequence of (maybe equal) ints. They are indices of
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// vertices that shall not be present in the output clique.
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// clique_weight_ptr - optional output parameter. Weight of found clique stored here.
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// num_generations - optional number of generations in evolutionary part of algorithm,
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// zero forces to return first found clique.
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// quality - optional parameter determining degree of required quality/speed tradeoff.
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// Must be in the range from 0 to 9.
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// 0 is fast and dirty, 9 is slow but hopefully yields good clique.
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// Returns:
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// sequence of pairwise different ints.
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// These are indices of vertices that form found clique.
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// Notes:
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// in cases of CV_WEIGHTED_EDGE and CV_WEIGHTED_ALL weights should be nonnegative.
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// start_clique has a priority over subgraph_of_ban.
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//F*/
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/*CVAPI(CvSeq*) cvFindCliqueEx( CvGraph *graph, CvMemStorage *storage,
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int is_complementary CV_DEFAULT(0),
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CvGraphWeightType weight_type CV_DEFAULT(CV_NOT_WEIGHTED),
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CvVect32f weight_vtx CV_DEFAULT(0),
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CvMatr32f weight_edge CV_DEFAULT(0),
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CvSeq *start_clique CV_DEFAULT(0),
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CvSeq *subgraph_of_ban CV_DEFAULT(0),
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float *clique_weight_ptr CV_DEFAULT(0),
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int num_generations CV_DEFAULT(3),
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int quality CV_DEFAULT(2) );*/
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#define CV_UNDEF_SC_PARAM 12345 //default value of parameters
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#define CV_IDP_BIRCHFIELD_PARAM1 25
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#define CV_IDP_BIRCHFIELD_PARAM2 5
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#define CV_IDP_BIRCHFIELD_PARAM3 12
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#define CV_IDP_BIRCHFIELD_PARAM4 15
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#define CV_IDP_BIRCHFIELD_PARAM5 25
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#define CV_DISPARITY_BIRCHFIELD 0
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/*F///////////////////////////////////////////////////////////////////////////
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//
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// Name: cvFindStereoCorrespondence
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// Purpose: find stereo correspondence on stereo-pair
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// Context:
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// Parameters:
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// leftImage - left image of stereo-pair (format 8uC1).
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// rightImage - right image of stereo-pair (format 8uC1).
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// mode - mode of correspondence retrieval (now CV_DISPARITY_BIRCHFIELD only)
|
|
// dispImage - destination disparity image
|
|
// maxDisparity - maximal disparity
|
|
// param1, param2, param3, param4, param5 - parameters of algorithm
|
|
// Returns:
|
|
// Notes:
|
|
// Images must be rectified.
|
|
// All images must have format 8uC1.
|
|
//F*/
|
|
CVAPI(void)
|
|
cvFindStereoCorrespondence(
|
|
const CvArr* leftImage, const CvArr* rightImage,
|
|
int mode,
|
|
CvArr* dispImage,
|
|
int maxDisparity,
|
|
double param1 CV_DEFAULT(CV_UNDEF_SC_PARAM),
|
|
double param2 CV_DEFAULT(CV_UNDEF_SC_PARAM),
|
|
double param3 CV_DEFAULT(CV_UNDEF_SC_PARAM),
|
|
double param4 CV_DEFAULT(CV_UNDEF_SC_PARAM),
|
|
double param5 CV_DEFAULT(CV_UNDEF_SC_PARAM) );
|
|
|
|
/*****************************************************************************************/
|
|
/************ Epiline functions *******************/
|
|
|
|
|
|
|
|
typedef struct CvStereoLineCoeff
|
|
{
|
|
double Xcoef;
|
|
double XcoefA;
|
|
double XcoefB;
|
|
double XcoefAB;
|
|
|
|
double Ycoef;
|
|
double YcoefA;
|
|
double YcoefB;
|
|
double YcoefAB;
|
|
|
|
double Zcoef;
|
|
double ZcoefA;
|
|
double ZcoefB;
|
|
double ZcoefAB;
|
|
}CvStereoLineCoeff;
|
|
|
|
|
|
typedef struct CvCamera
|
|
{
|
|
float imgSize[2]; /* size of the camera view, used during calibration */
|
|
float matrix[9]; /* intinsic camera parameters: [ fx 0 cx; 0 fy cy; 0 0 1 ] */
|
|
float distortion[4]; /* distortion coefficients - two coefficients for radial distortion
|
|
and another two for tangential: [ k1 k2 p1 p2 ] */
|
|
float rotMatr[9];
|
|
float transVect[3]; /* rotation matrix and transition vector relatively
|
|
to some reference point in the space. */
|
|
} CvCamera;
|
|
|
|
typedef struct CvStereoCamera
|
|
{
|
|
CvCamera* camera[2]; /* two individual camera parameters */
|
|
float fundMatr[9]; /* fundamental matrix */
|
|
|
|
/* New part for stereo */
|
|
CvPoint3D32f epipole[2];
|
|
CvPoint2D32f quad[2][4]; /* coordinates of destination quadrangle after
|
|
epipolar geometry rectification */
|
|
double coeffs[2][3][3];/* coefficients for transformation */
|
|
CvPoint2D32f border[2][4];
|
|
CvSize warpSize;
|
|
CvStereoLineCoeff* lineCoeffs;
|
|
int needSwapCameras;/* flag set to 1 if need to swap cameras for good reconstruction */
|
|
float rotMatrix[9];
|
|
float transVector[3];
|
|
} CvStereoCamera;
|
|
|
|
|
|
typedef struct CvContourOrientation
|
|
{
|
|
float egvals[2];
|
|
float egvects[4];
|
|
|
|
float max, min; // minimum and maximum projections
|
|
int imax, imin;
|
|
} CvContourOrientation;
|
|
|
|
#define CV_CAMERA_TO_WARP 1
|
|
#define CV_WARP_TO_CAMERA 2
|
|
|
|
CVAPI(int) icvConvertWarpCoordinates(double coeffs[3][3],
|
|
CvPoint2D32f* cameraPoint,
|
|
CvPoint2D32f* warpPoint,
|
|
int direction);
|
|
|
|
CVAPI(int) icvGetSymPoint3D( CvPoint3D64f pointCorner,
|
|
CvPoint3D64f point1,
|
|
CvPoint3D64f point2,
|
|
CvPoint3D64f *pointSym2);
|
|
|
|
CVAPI(void) icvGetPieceLength3D(CvPoint3D64f point1,CvPoint3D64f point2,double* dist);
|
|
|
|
CVAPI(int) icvCompute3DPoint( double alpha,double betta,
|
|
CvStereoLineCoeff* coeffs,
|
|
CvPoint3D64f* point);
|
|
|
|
CVAPI(int) icvCreateConvertMatrVect( double* rotMatr1,
|
|
double* transVect1,
|
|
double* rotMatr2,
|
|
double* transVect2,
|
|
double* convRotMatr,
|
|
double* convTransVect);
|
|
|
|
CVAPI(int) icvConvertPointSystem(CvPoint3D64f M2,
|
|
CvPoint3D64f* M1,
|
|
double* rotMatr,
|
|
double* transVect
|
|
);
|
|
|
|
CVAPI(int) icvComputeCoeffForStereo( CvStereoCamera* stereoCamera);
|
|
|
|
CVAPI(int) icvGetCrossPieceVector(CvPoint2D32f p1_start,CvPoint2D32f p1_end,CvPoint2D32f v2_start,CvPoint2D32f v2_end,CvPoint2D32f *cross);
|
|
CVAPI(int) icvGetCrossLineDirect(CvPoint2D32f p1,CvPoint2D32f p2,float a,float b,float c,CvPoint2D32f* cross);
|
|
CVAPI(float) icvDefinePointPosition(CvPoint2D32f point1,CvPoint2D32f point2,CvPoint2D32f point);
|
|
CVAPI(int) icvStereoCalibration( int numImages,
|
|
int* nums,
|
|
CvSize imageSize,
|
|
CvPoint2D32f* imagePoints1,
|
|
CvPoint2D32f* imagePoints2,
|
|
CvPoint3D32f* objectPoints,
|
|
CvStereoCamera* stereoparams
|
|
);
|
|
|
|
|
|
CVAPI(int) icvComputeRestStereoParams(CvStereoCamera *stereoparams);
|
|
|
|
CVAPI(void) cvComputePerspectiveMap( const double coeffs[3][3], CvArr* rectMapX, CvArr* rectMapY );
|
|
|
|
CVAPI(int) icvComCoeffForLine( CvPoint2D64f point1,
|
|
CvPoint2D64f point2,
|
|
CvPoint2D64f point3,
|
|
CvPoint2D64f point4,
|
|
double* camMatr1,
|
|
double* rotMatr1,
|
|
double* transVect1,
|
|
double* camMatr2,
|
|
double* rotMatr2,
|
|
double* transVect2,
|
|
CvStereoLineCoeff* coeffs,
|
|
int* needSwapCameras);
|
|
|
|
CVAPI(int) icvGetDirectionForPoint( CvPoint2D64f point,
|
|
double* camMatr,
|
|
CvPoint3D64f* direct);
|
|
|
|
CVAPI(int) icvGetCrossLines(CvPoint3D64f point11,CvPoint3D64f point12,
|
|
CvPoint3D64f point21,CvPoint3D64f point22,
|
|
CvPoint3D64f* midPoint);
|
|
|
|
CVAPI(int) icvComputeStereoLineCoeffs( CvPoint3D64f pointA,
|
|
CvPoint3D64f pointB,
|
|
CvPoint3D64f pointCam1,
|
|
double gamma,
|
|
CvStereoLineCoeff* coeffs);
|
|
|
|
/*CVAPI(int) icvComputeFundMatrEpipoles ( double* camMatr1,
|
|
double* rotMatr1,
|
|
double* transVect1,
|
|
double* camMatr2,
|
|
double* rotMatr2,
|
|
double* transVect2,
|
|
CvPoint2D64f* epipole1,
|
|
CvPoint2D64f* epipole2,
|
|
double* fundMatr);*/
|
|
|
|
CVAPI(int) icvGetAngleLine( CvPoint2D64f startPoint, CvSize imageSize,CvPoint2D64f *point1,CvPoint2D64f *point2);
|
|
|
|
CVAPI(void) icvGetCoefForPiece( CvPoint2D64f p_start,CvPoint2D64f p_end,
|
|
double *a,double *b,double *c,
|
|
int* result);
|
|
|
|
/*CVAPI(void) icvGetCommonArea( CvSize imageSize,
|
|
CvPoint2D64f epipole1,CvPoint2D64f epipole2,
|
|
double* fundMatr,
|
|
double* coeff11,double* coeff12,
|
|
double* coeff21,double* coeff22,
|
|
int* result);*/
|
|
|
|
CVAPI(void) icvComputeeInfiniteProject1(double* rotMatr,
|
|
double* camMatr1,
|
|
double* camMatr2,
|
|
CvPoint2D32f point1,
|
|
CvPoint2D32f *point2);
|
|
|
|
CVAPI(void) icvComputeeInfiniteProject2(double* rotMatr,
|
|
double* camMatr1,
|
|
double* camMatr2,
|
|
CvPoint2D32f* point1,
|
|
CvPoint2D32f point2);
|
|
|
|
CVAPI(void) icvGetCrossDirectDirect( double* direct1,double* direct2,
|
|
CvPoint2D64f *cross,int* result);
|
|
|
|
CVAPI(void) icvGetCrossPieceDirect( CvPoint2D64f p_start,CvPoint2D64f p_end,
|
|
double a,double b,double c,
|
|
CvPoint2D64f *cross,int* result);
|
|
|
|
CVAPI(void) icvGetCrossPiecePiece( CvPoint2D64f p1_start,CvPoint2D64f p1_end,
|
|
CvPoint2D64f p2_start,CvPoint2D64f p2_end,
|
|
CvPoint2D64f* cross,
|
|
int* result);
|
|
|
|
CVAPI(void) icvGetPieceLength(CvPoint2D64f point1,CvPoint2D64f point2,double* dist);
|
|
|
|
CVAPI(void) icvGetCrossRectDirect( CvSize imageSize,
|
|
double a,double b,double c,
|
|
CvPoint2D64f *start,CvPoint2D64f *end,
|
|
int* result);
|
|
|
|
CVAPI(void) icvProjectPointToImage( CvPoint3D64f point,
|
|
double* camMatr,double* rotMatr,double* transVect,
|
|
CvPoint2D64f* projPoint);
|
|
|
|
CVAPI(void) icvGetQuadsTransform( CvSize imageSize,
|
|
double* camMatr1,
|
|
double* rotMatr1,
|
|
double* transVect1,
|
|
double* camMatr2,
|
|
double* rotMatr2,
|
|
double* transVect2,
|
|
CvSize* warpSize,
|
|
double quad1[4][2],
|
|
double quad2[4][2],
|
|
double* fundMatr,
|
|
CvPoint3D64f* epipole1,
|
|
CvPoint3D64f* epipole2
|
|
);
|
|
|
|
CVAPI(void) icvGetQuadsTransformStruct( CvStereoCamera* stereoCamera);
|
|
|
|
CVAPI(void) icvComputeStereoParamsForCameras(CvStereoCamera* stereoCamera);
|
|
|
|
CVAPI(void) icvGetCutPiece( double* areaLineCoef1,double* areaLineCoef2,
|
|
CvPoint2D64f epipole,
|
|
CvSize imageSize,
|
|
CvPoint2D64f* point11,CvPoint2D64f* point12,
|
|
CvPoint2D64f* point21,CvPoint2D64f* point22,
|
|
int* result);
|
|
|
|
CVAPI(void) icvGetMiddleAnglePoint( CvPoint2D64f basePoint,
|
|
CvPoint2D64f point1,CvPoint2D64f point2,
|
|
CvPoint2D64f* midPoint);
|
|
|
|
CVAPI(void) icvGetNormalDirect(double* direct,CvPoint2D64f point,double* normDirect);
|
|
|
|
CVAPI(double) icvGetVect(CvPoint2D64f basePoint,CvPoint2D64f point1,CvPoint2D64f point2);
|
|
|
|
CVAPI(void) icvProjectPointToDirect( CvPoint2D64f point,double* lineCoeff,
|
|
CvPoint2D64f* projectPoint);
|
|
|
|
CVAPI(void) icvGetDistanceFromPointToDirect( CvPoint2D64f point,double* lineCoef,double*dist);
|
|
|
|
CVAPI(IplImage*) icvCreateIsometricImage( IplImage* src, IplImage* dst,
|
|
int desired_depth, int desired_num_channels );
|
|
|
|
CVAPI(void) cvDeInterlace( const CvArr* frame, CvArr* fieldEven, CvArr* fieldOdd );
|
|
|
|
/*CVAPI(int) icvSelectBestRt( int numImages,
|
|
int* numPoints,
|
|
CvSize imageSize,
|
|
CvPoint2D32f* imagePoints1,
|
|
CvPoint2D32f* imagePoints2,
|
|
CvPoint3D32f* objectPoints,
|
|
|
|
CvMatr32f cameraMatrix1,
|
|
CvVect32f distortion1,
|
|
CvMatr32f rotMatrs1,
|
|
CvVect32f transVects1,
|
|
|
|
CvMatr32f cameraMatrix2,
|
|
CvVect32f distortion2,
|
|
CvMatr32f rotMatrs2,
|
|
CvVect32f transVects2,
|
|
|
|
CvMatr32f bestRotMatr,
|
|
CvVect32f bestTransVect
|
|
);*/
|
|
|
|
|
|
/****************************************************************************************\
|
|
* Contour Tree *
|
|
\****************************************************************************************/
|
|
|
|
/* Contour tree header */
|
|
typedef struct CvContourTree
|
|
{
|
|
CV_SEQUENCE_FIELDS()
|
|
CvPoint p1; /* the first point of the binary tree root segment */
|
|
CvPoint p2; /* the last point of the binary tree root segment */
|
|
} CvContourTree;
|
|
|
|
/* Builds hierarhical representation of a contour */
|
|
CVAPI(CvContourTree*) cvCreateContourTree( const CvSeq* contour,
|
|
CvMemStorage* storage,
|
|
double threshold );
|
|
|
|
/* Reconstruct (completelly or partially) contour a from contour tree */
|
|
CVAPI(CvSeq*) cvContourFromContourTree( const CvContourTree* tree,
|
|
CvMemStorage* storage,
|
|
CvTermCriteria criteria );
|
|
|
|
/* Compares two contour trees */
|
|
enum { CV_CONTOUR_TREES_MATCH_I1 = 1 };
|
|
|
|
CVAPI(double) cvMatchContourTrees( const CvContourTree* tree1,
|
|
const CvContourTree* tree2,
|
|
int method, double threshold );
|
|
|
|
/****************************************************************************************\
|
|
* Contour Morphing *
|
|
\****************************************************************************************/
|
|
|
|
/* finds correspondence between two contours */
|
|
CvSeq* cvCalcContoursCorrespondence( const CvSeq* contour1,
|
|
const CvSeq* contour2,
|
|
CvMemStorage* storage);
|
|
|
|
/* morphs contours using the pre-calculated correspondence:
|
|
alpha=0 ~ contour1, alpha=1 ~ contour2 */
|
|
CvSeq* cvMorphContours( const CvSeq* contour1, const CvSeq* contour2,
|
|
CvSeq* corr, double alpha,
|
|
CvMemStorage* storage );
|
|
|
|
|
|
/****************************************************************************************\
|
|
* Active Contours *
|
|
\****************************************************************************************/
|
|
|
|
#define CV_VALUE 1
|
|
#define CV_ARRAY 2
|
|
/* Updates active contour in order to minimize its cummulative
|
|
(internal and external) energy. */
|
|
CVAPI(void) cvSnakeImage( const IplImage* image, CvPoint* points,
|
|
int length, float* alpha,
|
|
float* beta, float* gamma,
|
|
int coeff_usage, CvSize win,
|
|
CvTermCriteria criteria, int calc_gradient CV_DEFAULT(1));
|
|
|
|
/****************************************************************************************\
|
|
* Texture Descriptors *
|
|
\****************************************************************************************/
|
|
|
|
#define CV_GLCM_OPTIMIZATION_NONE -2
|
|
#define CV_GLCM_OPTIMIZATION_LUT -1
|
|
#define CV_GLCM_OPTIMIZATION_HISTOGRAM 0
|
|
|
|
#define CV_GLCMDESC_OPTIMIZATION_ALLOWDOUBLENEST 10
|
|
#define CV_GLCMDESC_OPTIMIZATION_ALLOWTRIPLENEST 11
|
|
#define CV_GLCMDESC_OPTIMIZATION_HISTOGRAM 4
|
|
|
|
#define CV_GLCMDESC_ENTROPY 0
|
|
#define CV_GLCMDESC_ENERGY 1
|
|
#define CV_GLCMDESC_HOMOGENITY 2
|
|
#define CV_GLCMDESC_CONTRAST 3
|
|
#define CV_GLCMDESC_CLUSTERTENDENCY 4
|
|
#define CV_GLCMDESC_CLUSTERSHADE 5
|
|
#define CV_GLCMDESC_CORRELATION 6
|
|
#define CV_GLCMDESC_CORRELATIONINFO1 7
|
|
#define CV_GLCMDESC_CORRELATIONINFO2 8
|
|
#define CV_GLCMDESC_MAXIMUMPROBABILITY 9
|
|
|
|
#define CV_GLCM_ALL 0
|
|
#define CV_GLCM_GLCM 1
|
|
#define CV_GLCM_DESC 2
|
|
|
|
typedef struct CvGLCM CvGLCM;
|
|
|
|
CVAPI(CvGLCM*) cvCreateGLCM( const IplImage* srcImage,
|
|
int stepMagnitude,
|
|
const int* stepDirections CV_DEFAULT(0),
|
|
int numStepDirections CV_DEFAULT(0),
|
|
int optimizationType CV_DEFAULT(CV_GLCM_OPTIMIZATION_NONE));
|
|
|
|
CVAPI(void) cvReleaseGLCM( CvGLCM** GLCM, int flag CV_DEFAULT(CV_GLCM_ALL));
|
|
|
|
CVAPI(void) cvCreateGLCMDescriptors( CvGLCM* destGLCM,
|
|
int descriptorOptimizationType
|
|
CV_DEFAULT(CV_GLCMDESC_OPTIMIZATION_ALLOWDOUBLENEST));
|
|
|
|
CVAPI(double) cvGetGLCMDescriptor( CvGLCM* GLCM, int step, int descriptor );
|
|
|
|
CVAPI(void) cvGetGLCMDescriptorStatistics( CvGLCM* GLCM, int descriptor,
|
|
double* average, double* standardDeviation );
|
|
|
|
CVAPI(IplImage*) cvCreateGLCMImage( CvGLCM* GLCM, int step );
|
|
|
|
/****************************************************************************************\
|
|
* Face eyes&mouth tracking *
|
|
\****************************************************************************************/
|
|
|
|
|
|
typedef struct CvFaceTracker CvFaceTracker;
|
|
|
|
#define CV_NUM_FACE_ELEMENTS 3
|
|
enum CV_FACE_ELEMENTS
|
|
{
|
|
CV_FACE_MOUTH = 0,
|
|
CV_FACE_LEFT_EYE = 1,
|
|
CV_FACE_RIGHT_EYE = 2
|
|
};
|
|
|
|
CVAPI(CvFaceTracker*) cvInitFaceTracker(CvFaceTracker* pFaceTracking, const IplImage* imgGray,
|
|
CvRect* pRects, int nRects);
|
|
CVAPI(int) cvTrackFace( CvFaceTracker* pFaceTracker, IplImage* imgGray,
|
|
CvRect* pRects, int nRects,
|
|
CvPoint* ptRotate, double* dbAngleRotate);
|
|
CVAPI(void) cvReleaseFaceTracker(CvFaceTracker** ppFaceTracker);
|
|
|
|
|
|
typedef struct CvFace
|
|
{
|
|
CvRect MouthRect;
|
|
CvRect LeftEyeRect;
|
|
CvRect RightEyeRect;
|
|
} CvFaceData;
|
|
|
|
CvSeq * cvFindFace(IplImage * Image,CvMemStorage* storage);
|
|
CvSeq * cvPostBoostingFindFace(IplImage * Image,CvMemStorage* storage);
|
|
|
|
|
|
/****************************************************************************************\
|
|
* 3D Tracker *
|
|
\****************************************************************************************/
|
|
|
|
typedef unsigned char CvBool;
|
|
|
|
typedef struct Cv3dTracker2dTrackedObject
|
|
{
|
|
int id;
|
|
CvPoint2D32f p; // pgruebele: So we do not loose precision, this needs to be float
|
|
} Cv3dTracker2dTrackedObject;
|
|
|
|
CV_INLINE Cv3dTracker2dTrackedObject cv3dTracker2dTrackedObject(int id, CvPoint2D32f p)
|
|
{
|
|
Cv3dTracker2dTrackedObject r;
|
|
r.id = id;
|
|
r.p = p;
|
|
return r;
|
|
}
|
|
|
|
typedef struct Cv3dTrackerTrackedObject
|
|
{
|
|
int id;
|
|
CvPoint3D32f p; // location of the tracked object
|
|
} Cv3dTrackerTrackedObject;
|
|
|
|
CV_INLINE Cv3dTrackerTrackedObject cv3dTrackerTrackedObject(int id, CvPoint3D32f p)
|
|
{
|
|
Cv3dTrackerTrackedObject r;
|
|
r.id = id;
|
|
r.p = p;
|
|
return r;
|
|
}
|
|
|
|
typedef struct Cv3dTrackerCameraInfo
|
|
{
|
|
CvBool valid;
|
|
float mat[4][4]; /* maps camera coordinates to world coordinates */
|
|
CvPoint2D32f principal_point; /* copied from intrinsics so this structure */
|
|
/* has all the info we need */
|
|
} Cv3dTrackerCameraInfo;
|
|
|
|
typedef struct Cv3dTrackerCameraIntrinsics
|
|
{
|
|
CvPoint2D32f principal_point;
|
|
float focal_length[2];
|
|
float distortion[4];
|
|
} Cv3dTrackerCameraIntrinsics;
|
|
|
|
CVAPI(CvBool) cv3dTrackerCalibrateCameras(int num_cameras,
|
|
const Cv3dTrackerCameraIntrinsics camera_intrinsics[], /* size is num_cameras */
|
|
CvSize etalon_size,
|
|
float square_size,
|
|
IplImage *samples[], /* size is num_cameras */
|
|
Cv3dTrackerCameraInfo camera_info[]); /* size is num_cameras */
|
|
|
|
CVAPI(int) cv3dTrackerLocateObjects(int num_cameras, int num_objects,
|
|
const Cv3dTrackerCameraInfo camera_info[], /* size is num_cameras */
|
|
const Cv3dTracker2dTrackedObject tracking_info[], /* size is num_objects*num_cameras */
|
|
Cv3dTrackerTrackedObject tracked_objects[]); /* size is num_objects */
|
|
/****************************************************************************************
|
|
tracking_info is a rectangular array; one row per camera, num_objects elements per row.
|
|
The id field of any unused slots must be -1. Ids need not be ordered or consecutive. On
|
|
completion, the return value is the number of objects located; i.e., the number of objects
|
|
visible by more than one camera. The id field of any unused slots in tracked objects is
|
|
set to -1.
|
|
****************************************************************************************/
|
|
|
|
|
|
/****************************************************************************************\
|
|
* Skeletons and Linear-Contour Models *
|
|
\****************************************************************************************/
|
|
|
|
typedef enum CvLeeParameters
|
|
{
|
|
CV_LEE_INT = 0,
|
|
CV_LEE_FLOAT = 1,
|
|
CV_LEE_DOUBLE = 2,
|
|
CV_LEE_AUTO = -1,
|
|
CV_LEE_ERODE = 0,
|
|
CV_LEE_ZOOM = 1,
|
|
CV_LEE_NON = 2
|
|
} CvLeeParameters;
|
|
|
|
#define CV_NEXT_VORONOISITE2D( SITE ) ((SITE)->edge[0]->site[((SITE)->edge[0]->site[0] == (SITE))])
|
|
#define CV_PREV_VORONOISITE2D( SITE ) ((SITE)->edge[1]->site[((SITE)->edge[1]->site[0] == (SITE))])
|
|
#define CV_FIRST_VORONOIEDGE2D( SITE ) ((SITE)->edge[0])
|
|
#define CV_LAST_VORONOIEDGE2D( SITE ) ((SITE)->edge[1])
|
|
#define CV_NEXT_VORONOIEDGE2D( EDGE, SITE ) ((EDGE)->next[(EDGE)->site[0] != (SITE)])
|
|
#define CV_PREV_VORONOIEDGE2D( EDGE, SITE ) ((EDGE)->next[2 + ((EDGE)->site[0] != (SITE))])
|
|
#define CV_VORONOIEDGE2D_BEGINNODE( EDGE, SITE ) ((EDGE)->node[((EDGE)->site[0] != (SITE))])
|
|
#define CV_VORONOIEDGE2D_ENDNODE( EDGE, SITE ) ((EDGE)->node[((EDGE)->site[0] == (SITE))])
|
|
#define CV_TWIN_VORONOISITE2D( SITE, EDGE ) ( (EDGE)->site[((EDGE)->site[0] == (SITE))])
|
|
|
|
#define CV_VORONOISITE2D_FIELDS() \
|
|
struct CvVoronoiNode2D *node[2]; \
|
|
struct CvVoronoiEdge2D *edge[2];
|
|
|
|
typedef struct CvVoronoiSite2D
|
|
{
|
|
CV_VORONOISITE2D_FIELDS()
|
|
struct CvVoronoiSite2D *next[2];
|
|
} CvVoronoiSite2D;
|
|
|
|
#define CV_VORONOIEDGE2D_FIELDS() \
|
|
struct CvVoronoiNode2D *node[2]; \
|
|
struct CvVoronoiSite2D *site[2]; \
|
|
struct CvVoronoiEdge2D *next[4];
|
|
|
|
typedef struct CvVoronoiEdge2D
|
|
{
|
|
CV_VORONOIEDGE2D_FIELDS()
|
|
} CvVoronoiEdge2D;
|
|
|
|
#define CV_VORONOINODE2D_FIELDS() \
|
|
CV_SET_ELEM_FIELDS(CvVoronoiNode2D) \
|
|
CvPoint2D32f pt; \
|
|
float radius;
|
|
|
|
typedef struct CvVoronoiNode2D
|
|
{
|
|
CV_VORONOINODE2D_FIELDS()
|
|
} CvVoronoiNode2D;
|
|
|
|
#define CV_VORONOIDIAGRAM2D_FIELDS() \
|
|
CV_GRAPH_FIELDS() \
|
|
CvSet *sites;
|
|
|
|
typedef struct CvVoronoiDiagram2D
|
|
{
|
|
CV_VORONOIDIAGRAM2D_FIELDS()
|
|
} CvVoronoiDiagram2D;
|
|
|
|
/* Computes Voronoi Diagram for given polygons with holes */
|
|
CVAPI(int) cvVoronoiDiagramFromContour(CvSeq* ContourSeq,
|
|
CvVoronoiDiagram2D** VoronoiDiagram,
|
|
CvMemStorage* VoronoiStorage,
|
|
CvLeeParameters contour_type CV_DEFAULT(CV_LEE_INT),
|
|
int contour_orientation CV_DEFAULT(-1),
|
|
int attempt_number CV_DEFAULT(10));
|
|
|
|
/* Computes Voronoi Diagram for domains in given image */
|
|
CVAPI(int) cvVoronoiDiagramFromImage(IplImage* pImage,
|
|
CvSeq** ContourSeq,
|
|
CvVoronoiDiagram2D** VoronoiDiagram,
|
|
CvMemStorage* VoronoiStorage,
|
|
CvLeeParameters regularization_method CV_DEFAULT(CV_LEE_NON),
|
|
float approx_precision CV_DEFAULT(CV_LEE_AUTO));
|
|
|
|
/* Deallocates the storage */
|
|
CVAPI(void) cvReleaseVoronoiStorage(CvVoronoiDiagram2D* VoronoiDiagram,
|
|
CvMemStorage** pVoronoiStorage);
|
|
|
|
/*********************** Linear-Contour Model ****************************/
|
|
|
|
struct CvLCMEdge;
|
|
struct CvLCMNode;
|
|
|
|
typedef struct CvLCMEdge
|
|
{
|
|
CV_GRAPH_EDGE_FIELDS()
|
|
CvSeq* chain;
|
|
float width;
|
|
int index1;
|
|
int index2;
|
|
} CvLCMEdge;
|
|
|
|
typedef struct CvLCMNode
|
|
{
|
|
CV_GRAPH_VERTEX_FIELDS()
|
|
CvContour* contour;
|
|
} CvLCMNode;
|
|
|
|
|
|
/* Computes hybrid model from Voronoi Diagram */
|
|
CVAPI(CvGraph*) cvLinearContorModelFromVoronoiDiagram(CvVoronoiDiagram2D* VoronoiDiagram,
|
|
float maxWidth);
|
|
|
|
/* Releases hybrid model storage */
|
|
CVAPI(int) cvReleaseLinearContorModelStorage(CvGraph** Graph);
|
|
|
|
|
|
/* two stereo-related functions */
|
|
|
|
CVAPI(void) cvInitPerspectiveTransform( CvSize size, const CvPoint2D32f vertex[4], double matrix[3][3],
|
|
CvArr* rectMap );
|
|
|
|
/*CVAPI(void) cvInitStereoRectification( CvStereoCamera* params,
|
|
CvArr* rectMap1, CvArr* rectMap2,
|
|
int do_undistortion );*/
|
|
|
|
/*************************** View Morphing Functions ************************/
|
|
|
|
typedef struct CvMatrix3
|
|
{
|
|
float m[3][3];
|
|
} CvMatrix3;
|
|
|
|
/* The order of the function corresponds to the order they should appear in
|
|
the view morphing pipeline */
|
|
|
|
/* Finds ending points of scanlines on left and right images of stereo-pair */
|
|
CVAPI(void) cvMakeScanlines( const CvMatrix3* matrix, CvSize img_size,
|
|
int* scanlines1, int* scanlines2,
|
|
int* lengths1, int* lengths2,
|
|
int* line_count );
|
|
|
|
/* Grab pixel values from scanlines and stores them sequentially
|
|
(some sort of perspective image transform) */
|
|
CVAPI(void) cvPreWarpImage( int line_count,
|
|
IplImage* img,
|
|
uchar* dst,
|
|
int* dst_nums,
|
|
int* scanlines);
|
|
|
|
/* Approximate each grabbed scanline by a sequence of runs
|
|
(lossy run-length compression) */
|
|
CVAPI(void) cvFindRuns( int line_count,
|
|
uchar* prewarp1,
|
|
uchar* prewarp2,
|
|
int* line_lengths1,
|
|
int* line_lengths2,
|
|
int* runs1,
|
|
int* runs2,
|
|
int* num_runs1,
|
|
int* num_runs2);
|
|
|
|
/* Compares two sets of compressed scanlines */
|
|
CVAPI(void) cvDynamicCorrespondMulti( int line_count,
|
|
int* first,
|
|
int* first_runs,
|
|
int* second,
|
|
int* second_runs,
|
|
int* first_corr,
|
|
int* second_corr);
|
|
|
|
/* Finds scanline ending coordinates for some intermediate "virtual" camera position */
|
|
CVAPI(void) cvMakeAlphaScanlines( int* scanlines1,
|
|
int* scanlines2,
|
|
int* scanlinesA,
|
|
int* lengths,
|
|
int line_count,
|
|
float alpha);
|
|
|
|
/* Blends data of the left and right image scanlines to get
|
|
pixel values of "virtual" image scanlines */
|
|
CVAPI(void) cvMorphEpilinesMulti( int line_count,
|
|
uchar* first_pix,
|
|
int* first_num,
|
|
uchar* second_pix,
|
|
int* second_num,
|
|
uchar* dst_pix,
|
|
int* dst_num,
|
|
float alpha,
|
|
int* first,
|
|
int* first_runs,
|
|
int* second,
|
|
int* second_runs,
|
|
int* first_corr,
|
|
int* second_corr);
|
|
|
|
/* Does reverse warping of the morphing result to make
|
|
it fill the destination image rectangle */
|
|
CVAPI(void) cvPostWarpImage( int line_count,
|
|
uchar* src,
|
|
int* src_nums,
|
|
IplImage* img,
|
|
int* scanlines);
|
|
|
|
/* Deletes Moire (missed pixels that appear due to discretization) */
|
|
CVAPI(void) cvDeleteMoire( IplImage* img );
|
|
|
|
|
|
typedef struct CvConDensation
|
|
{
|
|
int MP;
|
|
int DP;
|
|
float* DynamMatr; /* Matrix of the linear Dynamics system */
|
|
float* State; /* Vector of State */
|
|
int SamplesNum; /* Number of the Samples */
|
|
float** flSamples; /* arr of the Sample Vectors */
|
|
float** flNewSamples; /* temporary array of the Sample Vectors */
|
|
float* flConfidence; /* Confidence for each Sample */
|
|
float* flCumulative; /* Cumulative confidence */
|
|
float* Temp; /* Temporary vector */
|
|
float* RandomSample; /* RandomVector to update sample set */
|
|
struct CvRandState* RandS; /* Array of structures to generate random vectors */
|
|
} CvConDensation;
|
|
|
|
/* Creates ConDensation filter state */
|
|
CVAPI(CvConDensation*) cvCreateConDensation( int dynam_params,
|
|
int measure_params,
|
|
int sample_count );
|
|
|
|
/* Releases ConDensation filter state */
|
|
CVAPI(void) cvReleaseConDensation( CvConDensation** condens );
|
|
|
|
/* Updates ConDensation filter by time (predict future state of the system) */
|
|
CVAPI(void) cvConDensUpdateByTime( CvConDensation* condens);
|
|
|
|
/* Initializes ConDensation filter samples */
|
|
CVAPI(void) cvConDensInitSampleSet( CvConDensation* condens, CvMat* lower_bound, CvMat* upper_bound );
|
|
|
|
CV_INLINE int iplWidth( const IplImage* img )
|
|
{
|
|
return !img ? 0 : !img->roi ? img->width : img->roi->width;
|
|
}
|
|
|
|
CV_INLINE int iplHeight( const IplImage* img )
|
|
{
|
|
return !img ? 0 : !img->roi ? img->height : img->roi->height;
|
|
}
|
|
|
|
#ifdef __cplusplus
|
|
}
|
|
#endif
|
|
|
|
#ifdef __cplusplus
|
|
|
|
/****************************************************************************************\
|
|
* Calibration engine *
|
|
\****************************************************************************************/
|
|
|
|
typedef enum CvCalibEtalonType
|
|
{
|
|
CV_CALIB_ETALON_USER = -1,
|
|
CV_CALIB_ETALON_CHESSBOARD = 0,
|
|
CV_CALIB_ETALON_CHECKERBOARD = CV_CALIB_ETALON_CHESSBOARD
|
|
}
|
|
CvCalibEtalonType;
|
|
|
|
class CV_EXPORTS CvCalibFilter
|
|
{
|
|
public:
|
|
/* Constructor & destructor */
|
|
CvCalibFilter();
|
|
virtual ~CvCalibFilter();
|
|
|
|
/* Sets etalon type - one for all cameras.
|
|
etalonParams is used in case of pre-defined etalons (such as chessboard).
|
|
Number of elements in etalonParams is determined by etalonType.
|
|
E.g., if etalon type is CV_ETALON_TYPE_CHESSBOARD then:
|
|
etalonParams[0] is number of squares per one side of etalon
|
|
etalonParams[1] is number of squares per another side of etalon
|
|
etalonParams[2] is linear size of squares in the board in arbitrary units.
|
|
pointCount & points are used in case of
|
|
CV_CALIB_ETALON_USER (user-defined) etalon. */
|
|
virtual bool
|
|
SetEtalon( CvCalibEtalonType etalonType, double* etalonParams,
|
|
int pointCount = 0, CvPoint2D32f* points = 0 );
|
|
|
|
/* Retrieves etalon parameters/or and points */
|
|
virtual CvCalibEtalonType
|
|
GetEtalon( int* paramCount = 0, const double** etalonParams = 0,
|
|
int* pointCount = 0, const CvPoint2D32f** etalonPoints = 0 ) const;
|
|
|
|
/* Sets number of cameras calibrated simultaneously. It is equal to 1 initially */
|
|
virtual void SetCameraCount( int cameraCount );
|
|
|
|
/* Retrieves number of cameras */
|
|
int GetCameraCount() const { return cameraCount; }
|
|
|
|
/* Starts cameras calibration */
|
|
virtual bool SetFrames( int totalFrames );
|
|
|
|
/* Stops cameras calibration */
|
|
virtual void Stop( bool calibrate = false );
|
|
|
|
/* Retrieves number of cameras */
|
|
bool IsCalibrated() const { return isCalibrated; }
|
|
|
|
/* Feeds another serie of snapshots (one per each camera) to filter.
|
|
Etalon points on these images are found automatically.
|
|
If the function can't locate points, it returns false */
|
|
virtual bool FindEtalon( IplImage** imgs );
|
|
|
|
/* The same but takes matrices */
|
|
virtual bool FindEtalon( CvMat** imgs );
|
|
|
|
/* Lower-level function for feeding filter with already found etalon points.
|
|
Array of point arrays for each camera is passed. */
|
|
virtual bool Push( const CvPoint2D32f** points = 0 );
|
|
|
|
/* Returns total number of accepted frames and, optionally,
|
|
total number of frames to collect */
|
|
virtual int GetFrameCount( int* framesTotal = 0 ) const;
|
|
|
|
/* Retrieves camera parameters for specified camera.
|
|
If camera is not calibrated the function returns 0 */
|
|
virtual const CvCamera* GetCameraParams( int idx = 0 ) const;
|
|
|
|
virtual const CvStereoCamera* GetStereoParams() const;
|
|
|
|
/* Sets camera parameters for all cameras */
|
|
virtual bool SetCameraParams( CvCamera* params );
|
|
|
|
/* Saves all camera parameters to file */
|
|
virtual bool SaveCameraParams( const char* filename );
|
|
|
|
/* Loads all camera parameters from file */
|
|
virtual bool LoadCameraParams( const char* filename );
|
|
|
|
/* Undistorts images using camera parameters. Some of src pointers can be NULL. */
|
|
virtual bool Undistort( IplImage** src, IplImage** dst );
|
|
|
|
/* Undistorts images using camera parameters. Some of src pointers can be NULL. */
|
|
virtual bool Undistort( CvMat** src, CvMat** dst );
|
|
|
|
/* Returns array of etalon points detected/partally detected
|
|
on the latest frame for idx-th camera */
|
|
virtual bool GetLatestPoints( int idx, CvPoint2D32f** pts,
|
|
int* count, bool* found );
|
|
|
|
/* Draw the latest detected/partially detected etalon */
|
|
virtual void DrawPoints( IplImage** dst );
|
|
|
|
/* Draw the latest detected/partially detected etalon */
|
|
virtual void DrawPoints( CvMat** dst );
|
|
|
|
virtual bool Rectify( IplImage** srcarr, IplImage** dstarr );
|
|
virtual bool Rectify( CvMat** srcarr, CvMat** dstarr );
|
|
|
|
protected:
|
|
|
|
enum { MAX_CAMERAS = 3 };
|
|
|
|
/* etalon data */
|
|
CvCalibEtalonType etalonType;
|
|
int etalonParamCount;
|
|
double* etalonParams;
|
|
int etalonPointCount;
|
|
CvPoint2D32f* etalonPoints;
|
|
CvSize imgSize;
|
|
CvMat* grayImg;
|
|
CvMat* tempImg;
|
|
CvMemStorage* storage;
|
|
|
|
/* camera data */
|
|
int cameraCount;
|
|
CvCamera cameraParams[MAX_CAMERAS];
|
|
CvStereoCamera stereo;
|
|
CvPoint2D32f* points[MAX_CAMERAS];
|
|
CvMat* undistMap[MAX_CAMERAS][2];
|
|
CvMat* undistImg;
|
|
int latestCounts[MAX_CAMERAS];
|
|
CvPoint2D32f* latestPoints[MAX_CAMERAS];
|
|
CvMat* rectMap[MAX_CAMERAS][2];
|
|
|
|
/* Added by Valery */
|
|
//CvStereoCamera stereoParams;
|
|
|
|
int maxPoints;
|
|
int framesTotal;
|
|
int framesAccepted;
|
|
bool isCalibrated;
|
|
};
|
|
|
|
#include <iosfwd>
|
|
#include <limits>
|
|
|
|
class CV_EXPORTS CvImage
|
|
{
|
|
public:
|
|
CvImage() : image(0), refcount(0) {}
|
|
CvImage( CvSize _size, int _depth, int _channels )
|
|
{
|
|
image = cvCreateImage( _size, _depth, _channels );
|
|
refcount = image ? new int(1) : 0;
|
|
}
|
|
|
|
CvImage( IplImage* img ) : image(img)
|
|
{
|
|
refcount = image ? new int(1) : 0;
|
|
}
|
|
|
|
CvImage( const CvImage& img ) : image(img.image), refcount(img.refcount)
|
|
{
|
|
if( refcount ) ++(*refcount);
|
|
}
|
|
|
|
CvImage( const char* filename, const char* imgname=0, int color=-1 ) : image(0), refcount(0)
|
|
{ load( filename, imgname, color ); }
|
|
|
|
CvImage( CvFileStorage* fs, const char* mapname, const char* imgname ) : image(0), refcount(0)
|
|
{ read( fs, mapname, imgname ); }
|
|
|
|
CvImage( CvFileStorage* fs, const char* seqname, int idx ) : image(0), refcount(0)
|
|
{ read( fs, seqname, idx ); }
|
|
|
|
~CvImage()
|
|
{
|
|
if( refcount && !(--*refcount) )
|
|
{
|
|
cvReleaseImage( &image );
|
|
delete refcount;
|
|
}
|
|
}
|
|
|
|
CvImage clone() { return CvImage(image ? cvCloneImage(image) : 0); }
|
|
|
|
void create( CvSize _size, int _depth, int _channels )
|
|
{
|
|
if( !image || !refcount ||
|
|
image->width != _size.width || image->height != _size.height ||
|
|
image->depth != _depth || image->nChannels != _channels )
|
|
attach( cvCreateImage( _size, _depth, _channels ));
|
|
}
|
|
|
|
void release() { detach(); }
|
|
void clear() { detach(); }
|
|
|
|
void attach( IplImage* img, bool use_refcount=true )
|
|
{
|
|
if( refcount && --*refcount == 0 )
|
|
{
|
|
cvReleaseImage( &image );
|
|
delete refcount;
|
|
}
|
|
image = img;
|
|
refcount = use_refcount && image ? new int(1) : 0;
|
|
}
|
|
|
|
void detach()
|
|
{
|
|
if( refcount && --*refcount == 0 )
|
|
{
|
|
cvReleaseImage( &image );
|
|
delete refcount;
|
|
}
|
|
image = 0;
|
|
refcount = 0;
|
|
}
|
|
|
|
bool load( const char* filename, const char* imgname=0, int color=-1 );
|
|
bool read( CvFileStorage* fs, const char* mapname, const char* imgname );
|
|
bool read( CvFileStorage* fs, const char* seqname, int idx );
|
|
void save( const char* filename, const char* imgname, const int* params=0 );
|
|
void write( CvFileStorage* fs, const char* imgname );
|
|
|
|
void show( const char* window_name );
|
|
bool is_valid() { return image != 0; }
|
|
|
|
int width() const { return image ? image->width : 0; }
|
|
int height() const { return image ? image->height : 0; }
|
|
|
|
CvSize size() const { return image ? cvSize(image->width, image->height) : cvSize(0,0); }
|
|
|
|
CvSize roi_size() const
|
|
{
|
|
return !image ? cvSize(0,0) :
|
|
!image->roi ? cvSize(image->width,image->height) :
|
|
cvSize(image->roi->width, image->roi->height);
|
|
}
|
|
|
|
CvRect roi() const
|
|
{
|
|
return !image ? cvRect(0,0,0,0) :
|
|
!image->roi ? cvRect(0,0,image->width,image->height) :
|
|
cvRect(image->roi->xOffset,image->roi->yOffset,
|
|
image->roi->width,image->roi->height);
|
|
}
|
|
|
|
int coi() const { return !image || !image->roi ? 0 : image->roi->coi; }
|
|
|
|
void set_roi(CvRect _roi) { cvSetImageROI(image,_roi); }
|
|
void reset_roi() { cvResetImageROI(image); }
|
|
void set_coi(int _coi) { cvSetImageCOI(image,_coi); }
|
|
int depth() const { return image ? image->depth : 0; }
|
|
int channels() const { return image ? image->nChannels : 0; }
|
|
int pix_size() const { return image ? ((image->depth & 255)>>3)*image->nChannels : 0; }
|
|
|
|
uchar* data() { return image ? (uchar*)image->imageData : 0; }
|
|
const uchar* data() const { return image ? (const uchar*)image->imageData : 0; }
|
|
int step() const { return image ? image->widthStep : 0; }
|
|
int origin() const { return image ? image->origin : 0; }
|
|
|
|
uchar* roi_row(int y)
|
|
{
|
|
assert(0<=y);
|
|
assert(!image ?
|
|
1 : image->roi ?
|
|
y<image->roi->height : y<image->height);
|
|
|
|
return !image ? 0 :
|
|
!image->roi ?
|
|
(uchar*)(image->imageData + y*image->widthStep) :
|
|
(uchar*)(image->imageData + (y+image->roi->yOffset)*image->widthStep +
|
|
image->roi->xOffset*((image->depth & 255)>>3)*image->nChannels);
|
|
}
|
|
|
|
const uchar* roi_row(int y) const
|
|
{
|
|
assert(0<=y);
|
|
assert(!image ?
|
|
1 : image->roi ?
|
|
y<image->roi->height : y<image->height);
|
|
|
|
return !image ? 0 :
|
|
!image->roi ?
|
|
(const uchar*)(image->imageData + y*image->widthStep) :
|
|
(const uchar*)(image->imageData + (y+image->roi->yOffset)*image->widthStep +
|
|
image->roi->xOffset*((image->depth & 255)>>3)*image->nChannels);
|
|
}
|
|
|
|
operator const IplImage* () const { return image; }
|
|
operator IplImage* () { return image; }
|
|
|
|
CvImage& operator = (const CvImage& img)
|
|
{
|
|
if( img.refcount )
|
|
++*img.refcount;
|
|
if( refcount && !(--*refcount) )
|
|
cvReleaseImage( &image );
|
|
image=img.image;
|
|
refcount=img.refcount;
|
|
return *this;
|
|
}
|
|
|
|
protected:
|
|
IplImage* image;
|
|
int* refcount;
|
|
};
|
|
|
|
|
|
class CV_EXPORTS CvMatrix
|
|
{
|
|
public:
|
|
CvMatrix() : matrix(0) {}
|
|
CvMatrix( int _rows, int _cols, int _type )
|
|
{ matrix = cvCreateMat( _rows, _cols, _type ); }
|
|
|
|
CvMatrix( int _rows, int _cols, int _type, CvMat* hdr,
|
|
void* _data=0, int _step=CV_AUTOSTEP )
|
|
{ matrix = cvInitMatHeader( hdr, _rows, _cols, _type, _data, _step ); }
|
|
|
|
CvMatrix( int rows, int cols, int type, CvMemStorage* storage, bool alloc_data=true );
|
|
|
|
CvMatrix( int _rows, int _cols, int _type, void* _data, int _step=CV_AUTOSTEP )
|
|
{ matrix = cvCreateMatHeader( _rows, _cols, _type );
|
|
cvSetData( matrix, _data, _step ); }
|
|
|
|
CvMatrix( CvMat* m )
|
|
{ matrix = m; }
|
|
|
|
CvMatrix( const CvMatrix& m )
|
|
{
|
|
matrix = m.matrix;
|
|
addref();
|
|
}
|
|
|
|
CvMatrix( const char* filename, const char* matname=0, int color=-1 ) : matrix(0)
|
|
{ load( filename, matname, color ); }
|
|
|
|
CvMatrix( CvFileStorage* fs, const char* mapname, const char* matname ) : matrix(0)
|
|
{ read( fs, mapname, matname ); }
|
|
|
|
CvMatrix( CvFileStorage* fs, const char* seqname, int idx ) : matrix(0)
|
|
{ read( fs, seqname, idx ); }
|
|
|
|
~CvMatrix()
|
|
{
|
|
release();
|
|
}
|
|
|
|
CvMatrix clone() { return CvMatrix(matrix ? cvCloneMat(matrix) : 0); }
|
|
|
|
void set( CvMat* m, bool add_ref )
|
|
{
|
|
release();
|
|
matrix = m;
|
|
if( add_ref )
|
|
addref();
|
|
}
|
|
|
|
void create( int _rows, int _cols, int _type )
|
|
{
|
|
if( !matrix || !matrix->refcount ||
|
|
matrix->rows != _rows || matrix->cols != _cols ||
|
|
CV_MAT_TYPE(matrix->type) != _type )
|
|
set( cvCreateMat( _rows, _cols, _type ), false );
|
|
}
|
|
|
|
void addref() const
|
|
{
|
|
if( matrix )
|
|
{
|
|
if( matrix->hdr_refcount )
|
|
++matrix->hdr_refcount;
|
|
else if( matrix->refcount )
|
|
++*matrix->refcount;
|
|
}
|
|
}
|
|
|
|
void release()
|
|
{
|
|
if( matrix )
|
|
{
|
|
if( matrix->hdr_refcount )
|
|
{
|
|
if( --matrix->hdr_refcount == 0 )
|
|
cvReleaseMat( &matrix );
|
|
}
|
|
else if( matrix->refcount )
|
|
{
|
|
if( --*matrix->refcount == 0 )
|
|
cvFree( &matrix->refcount );
|
|
}
|
|
matrix = 0;
|
|
}
|
|
}
|
|
|
|
void clear()
|
|
{
|
|
release();
|
|
}
|
|
|
|
bool load( const char* filename, const char* matname=0, int color=-1 );
|
|
bool read( CvFileStorage* fs, const char* mapname, const char* matname );
|
|
bool read( CvFileStorage* fs, const char* seqname, int idx );
|
|
void save( const char* filename, const char* matname, const int* params=0 );
|
|
void write( CvFileStorage* fs, const char* matname );
|
|
|
|
void show( const char* window_name );
|
|
|
|
bool is_valid() { return matrix != 0; }
|
|
|
|
int rows() const { return matrix ? matrix->rows : 0; }
|
|
int cols() const { return matrix ? matrix->cols : 0; }
|
|
|
|
CvSize size() const
|
|
{
|
|
return !matrix ? cvSize(0,0) : cvSize(matrix->rows,matrix->cols);
|
|
}
|
|
|
|
int type() const { return matrix ? CV_MAT_TYPE(matrix->type) : 0; }
|
|
int depth() const { return matrix ? CV_MAT_DEPTH(matrix->type) : 0; }
|
|
int channels() const { return matrix ? CV_MAT_CN(matrix->type) : 0; }
|
|
int pix_size() const { return matrix ? CV_ELEM_SIZE(matrix->type) : 0; }
|
|
|
|
uchar* data() { return matrix ? matrix->data.ptr : 0; }
|
|
const uchar* data() const { return matrix ? matrix->data.ptr : 0; }
|
|
int step() const { return matrix ? matrix->step : 0; }
|
|
|
|
void set_data( void* _data, int _step=CV_AUTOSTEP )
|
|
{ cvSetData( matrix, _data, _step ); }
|
|
|
|
uchar* row(int i) { return !matrix ? 0 : matrix->data.ptr + i*matrix->step; }
|
|
const uchar* row(int i) const
|
|
{ return !matrix ? 0 : matrix->data.ptr + i*matrix->step; }
|
|
|
|
operator const CvMat* () const { return matrix; }
|
|
operator CvMat* () { return matrix; }
|
|
|
|
CvMatrix& operator = (const CvMatrix& _m)
|
|
{
|
|
_m.addref();
|
|
release();
|
|
matrix = _m.matrix;
|
|
return *this;
|
|
}
|
|
|
|
protected:
|
|
CvMat* matrix;
|
|
};
|
|
|
|
/****************************************************************************************\
|
|
* CamShiftTracker *
|
|
\****************************************************************************************/
|
|
|
|
class CV_EXPORTS CvCamShiftTracker
|
|
{
|
|
public:
|
|
|
|
CvCamShiftTracker();
|
|
virtual ~CvCamShiftTracker();
|
|
|
|
/**** Characteristics of the object that are calculated by track_object method *****/
|
|
float get_orientation() const // orientation of the object in degrees
|
|
{ return m_box.angle; }
|
|
float get_length() const // the larger linear size of the object
|
|
{ return m_box.size.height; }
|
|
float get_width() const // the smaller linear size of the object
|
|
{ return m_box.size.width; }
|
|
CvPoint2D32f get_center() const // center of the object
|
|
{ return m_box.center; }
|
|
CvRect get_window() const // bounding rectangle for the object
|
|
{ return m_comp.rect; }
|
|
|
|
/*********************** Tracking parameters ************************/
|
|
int get_threshold() const // thresholding value that applied to back project
|
|
{ return m_threshold; }
|
|
|
|
int get_hist_dims( int* dims = 0 ) const // returns number of histogram dimensions and sets
|
|
{ return m_hist ? cvGetDims( m_hist->bins, dims ) : 0; }
|
|
|
|
int get_min_ch_val( int channel ) const // get the minimum allowed value of the specified channel
|
|
{ return m_min_ch_val[channel]; }
|
|
|
|
int get_max_ch_val( int channel ) const // get the maximum allowed value of the specified channel
|
|
{ return m_max_ch_val[channel]; }
|
|
|
|
// set initial object rectangle (must be called before initial calculation of the histogram)
|
|
bool set_window( CvRect window)
|
|
{ m_comp.rect = window; return true; }
|
|
|
|
bool set_threshold( int threshold ) // threshold applied to the histogram bins
|
|
{ m_threshold = threshold; return true; }
|
|
|
|
bool set_hist_bin_range( int dim, int min_val, int max_val );
|
|
|
|
bool set_hist_dims( int c_dims, int* dims );// set the histogram parameters
|
|
|
|
bool set_min_ch_val( int channel, int val ) // set the minimum allowed value of the specified channel
|
|
{ m_min_ch_val[channel] = val; return true; }
|
|
bool set_max_ch_val( int channel, int val ) // set the maximum allowed value of the specified channel
|
|
{ m_max_ch_val[channel] = val; return true; }
|
|
|
|
/************************ The processing methods *********************************/
|
|
// update object position
|
|
virtual bool track_object( const IplImage* cur_frame );
|
|
|
|
// update object histogram
|
|
virtual bool update_histogram( const IplImage* cur_frame );
|
|
|
|
// reset histogram
|
|
virtual void reset_histogram();
|
|
|
|
/************************ Retrieving internal data *******************************/
|
|
// get back project image
|
|
virtual IplImage* get_back_project()
|
|
{ return m_back_project; }
|
|
|
|
float query( int* bin ) const
|
|
{ return m_hist ? (float)cvGetRealND(m_hist->bins, bin) : 0.f; }
|
|
|
|
protected:
|
|
|
|
// internal method for color conversion: fills m_color_planes group
|
|
virtual void color_transform( const IplImage* img );
|
|
|
|
CvHistogram* m_hist;
|
|
|
|
CvBox2D m_box;
|
|
CvConnectedComp m_comp;
|
|
|
|
float m_hist_ranges_data[CV_MAX_DIM][2];
|
|
float* m_hist_ranges[CV_MAX_DIM];
|
|
|
|
int m_min_ch_val[CV_MAX_DIM];
|
|
int m_max_ch_val[CV_MAX_DIM];
|
|
int m_threshold;
|
|
|
|
IplImage* m_color_planes[CV_MAX_DIM];
|
|
IplImage* m_back_project;
|
|
IplImage* m_temp;
|
|
IplImage* m_mask;
|
|
};
|
|
|
|
/****************************************************************************************\
|
|
* Expectation - Maximization *
|
|
\****************************************************************************************/
|
|
struct CV_EXPORTS_W_MAP CvEMParams
|
|
{
|
|
CvEMParams();
|
|
CvEMParams( int nclusters, int cov_mat_type=cv::EM::COV_MAT_DIAGONAL,
|
|
int start_step=cv::EM::START_AUTO_STEP,
|
|
CvTermCriteria term_crit=cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, FLT_EPSILON),
|
|
const CvMat* probs=0, const CvMat* weights=0, const CvMat* means=0, const CvMat** covs=0 );
|
|
|
|
CV_PROP_RW int nclusters;
|
|
CV_PROP_RW int cov_mat_type;
|
|
CV_PROP_RW int start_step;
|
|
const CvMat* probs;
|
|
const CvMat* weights;
|
|
const CvMat* means;
|
|
const CvMat** covs;
|
|
CV_PROP_RW CvTermCriteria term_crit;
|
|
};
|
|
|
|
|
|
class CV_EXPORTS_W CvEM : public CvStatModel
|
|
{
|
|
public:
|
|
// Type of covariation matrices
|
|
enum { COV_MAT_SPHERICAL=cv::EM::COV_MAT_SPHERICAL,
|
|
COV_MAT_DIAGONAL =cv::EM::COV_MAT_DIAGONAL,
|
|
COV_MAT_GENERIC =cv::EM::COV_MAT_GENERIC };
|
|
|
|
// The initial step
|
|
enum { START_E_STEP=cv::EM::START_E_STEP,
|
|
START_M_STEP=cv::EM::START_M_STEP,
|
|
START_AUTO_STEP=cv::EM::START_AUTO_STEP };
|
|
|
|
CV_WRAP CvEM();
|
|
CvEM( const CvMat* samples, const CvMat* sampleIdx=0,
|
|
CvEMParams params=CvEMParams(), CvMat* labels=0 );
|
|
|
|
virtual ~CvEM();
|
|
|
|
virtual bool train( const CvMat* samples, const CvMat* sampleIdx=0,
|
|
CvEMParams params=CvEMParams(), CvMat* labels=0 );
|
|
|
|
virtual float predict( const CvMat* sample, CV_OUT CvMat* probs ) const;
|
|
|
|
CV_WRAP CvEM( const cv::Mat& samples, const cv::Mat& sampleIdx=cv::Mat(),
|
|
CvEMParams params=CvEMParams() );
|
|
|
|
CV_WRAP virtual bool train( const cv::Mat& samples,
|
|
const cv::Mat& sampleIdx=cv::Mat(),
|
|
CvEMParams params=CvEMParams(),
|
|
CV_OUT cv::Mat* labels=0 );
|
|
|
|
CV_WRAP virtual float predict( const cv::Mat& sample, CV_OUT cv::Mat* probs=0 ) const;
|
|
CV_WRAP virtual double calcLikelihood( const cv::Mat &sample ) const;
|
|
|
|
CV_WRAP int getNClusters() const;
|
|
CV_WRAP cv::Mat getMeans() const;
|
|
CV_WRAP void getCovs(CV_OUT std::vector<cv::Mat>& covs) const;
|
|
CV_WRAP cv::Mat getWeights() const;
|
|
CV_WRAP cv::Mat getProbs() const;
|
|
|
|
CV_WRAP inline double getLikelihood() const { return emObj.isTrained() ? logLikelihood : DBL_MAX; }
|
|
|
|
CV_WRAP virtual void clear();
|
|
|
|
int get_nclusters() const;
|
|
const CvMat* get_means() const;
|
|
const CvMat** get_covs() const;
|
|
const CvMat* get_weights() const;
|
|
const CvMat* get_probs() const;
|
|
|
|
inline double get_log_likelihood() const { return getLikelihood(); }
|
|
|
|
virtual void read( CvFileStorage* fs, CvFileNode* node );
|
|
virtual void write( CvFileStorage* fs, const char* name ) const;
|
|
|
|
protected:
|
|
void set_mat_hdrs();
|
|
|
|
cv::EM emObj;
|
|
cv::Mat probs;
|
|
double logLikelihood;
|
|
|
|
CvMat meansHdr;
|
|
std::vector<CvMat> covsHdrs;
|
|
std::vector<CvMat*> covsPtrs;
|
|
CvMat weightsHdr;
|
|
CvMat probsHdr;
|
|
};
|
|
|
|
namespace cv
|
|
{
|
|
|
|
typedef CvEMParams EMParams;
|
|
typedef CvEM ExpectationMaximization;
|
|
|
|
/*!
|
|
The Patch Generator class
|
|
*/
|
|
class CV_EXPORTS PatchGenerator
|
|
{
|
|
public:
|
|
PatchGenerator();
|
|
PatchGenerator(double _backgroundMin, double _backgroundMax,
|
|
double _noiseRange, bool _randomBlur=true,
|
|
double _lambdaMin=0.6, double _lambdaMax=1.5,
|
|
double _thetaMin=-CV_PI, double _thetaMax=CV_PI,
|
|
double _phiMin=-CV_PI, double _phiMax=CV_PI );
|
|
void operator()(const Mat& image, Point2f pt, Mat& patch, Size patchSize, RNG& rng) const;
|
|
void operator()(const Mat& image, const Mat& transform, Mat& patch,
|
|
Size patchSize, RNG& rng) const;
|
|
void warpWholeImage(const Mat& image, Mat& matT, Mat& buf,
|
|
CV_OUT Mat& warped, int border, RNG& rng) const;
|
|
void generateRandomTransform(Point2f srcCenter, Point2f dstCenter,
|
|
CV_OUT Mat& transform, RNG& rng,
|
|
bool inverse=false) const;
|
|
void setAffineParam(double lambda, double theta, double phi);
|
|
|
|
double backgroundMin, backgroundMax;
|
|
double noiseRange;
|
|
bool randomBlur;
|
|
double lambdaMin, lambdaMax;
|
|
double thetaMin, thetaMax;
|
|
double phiMin, phiMax;
|
|
};
|
|
|
|
|
|
class CV_EXPORTS LDetector
|
|
{
|
|
public:
|
|
LDetector();
|
|
LDetector(int _radius, int _threshold, int _nOctaves,
|
|
int _nViews, double _baseFeatureSize, double _clusteringDistance);
|
|
void operator()(const Mat& image,
|
|
CV_OUT std::vector<KeyPoint>& keypoints,
|
|
int maxCount=0, bool scaleCoords=true) const;
|
|
void operator()(const std::vector<Mat>& pyr,
|
|
CV_OUT std::vector<KeyPoint>& keypoints,
|
|
int maxCount=0, bool scaleCoords=true) const;
|
|
void getMostStable2D(const Mat& image, CV_OUT std::vector<KeyPoint>& keypoints,
|
|
int maxCount, const PatchGenerator& patchGenerator) const;
|
|
void setVerbose(bool verbose);
|
|
|
|
void read(const FileNode& node);
|
|
void write(FileStorage& fs, const 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 *
|
|
\****************************************************************************************/
|
|
|
|
typedef size_t CvSubdiv2DEdge;
|
|
|
|
#define CV_QUADEDGE2D_FIELDS() \
|
|
int flags; \
|
|
struct CvSubdiv2DPoint* pt[4]; \
|
|
CvSubdiv2DEdge next[4];
|
|
|
|
#define CV_SUBDIV2D_POINT_FIELDS()\
|
|
int flags; \
|
|
CvSubdiv2DEdge first; \
|
|
CvPoint2D32f pt; \
|
|
int id;
|
|
|
|
#define CV_SUBDIV2D_VIRTUAL_POINT_FLAG (1 << 30)
|
|
|
|
typedef struct CvQuadEdge2D
|
|
{
|
|
CV_QUADEDGE2D_FIELDS()
|
|
}
|
|
CvQuadEdge2D;
|
|
|
|
typedef struct CvSubdiv2DPoint
|
|
{
|
|
CV_SUBDIV2D_POINT_FIELDS()
|
|
}
|
|
CvSubdiv2DPoint;
|
|
|
|
#define CV_SUBDIV2D_FIELDS() \
|
|
CV_GRAPH_FIELDS() \
|
|
int quad_edges; \
|
|
int is_geometry_valid; \
|
|
CvSubdiv2DEdge recent_edge; \
|
|
CvPoint2D32f topleft; \
|
|
CvPoint2D32f bottomright;
|
|
|
|
typedef struct CvSubdiv2D
|
|
{
|
|
CV_SUBDIV2D_FIELDS()
|
|
}
|
|
CvSubdiv2D;
|
|
|
|
typedef enum CvSubdiv2DPointLocation
|
|
{
|
|
CV_PTLOC_ERROR = -2,
|
|
CV_PTLOC_OUTSIDE_RECT = -1,
|
|
CV_PTLOC_INSIDE = 0,
|
|
CV_PTLOC_VERTEX = 1,
|
|
CV_PTLOC_ON_EDGE = 2
|
|
}
|
|
CvSubdiv2DPointLocation;
|
|
|
|
typedef enum CvNextEdgeType
|
|
{
|
|
CV_NEXT_AROUND_ORG = 0x00,
|
|
CV_NEXT_AROUND_DST = 0x22,
|
|
CV_PREV_AROUND_ORG = 0x11,
|
|
CV_PREV_AROUND_DST = 0x33,
|
|
CV_NEXT_AROUND_LEFT = 0x13,
|
|
CV_NEXT_AROUND_RIGHT = 0x31,
|
|
CV_PREV_AROUND_LEFT = 0x20,
|
|
CV_PREV_AROUND_RIGHT = 0x02
|
|
}
|
|
CvNextEdgeType;
|
|
|
|
/* get the next edge with the same origin point (counterwise) */
|
|
#define CV_SUBDIV2D_NEXT_EDGE( edge ) (((CvQuadEdge2D*)((edge) & ~3))->next[(edge)&3])
|
|
|
|
|
|
/* Initializes Delaunay triangulation */
|
|
CVAPI(void) cvInitSubdivDelaunay2D( CvSubdiv2D* subdiv, CvRect rect );
|
|
|
|
/* Creates new subdivision */
|
|
CVAPI(CvSubdiv2D*) cvCreateSubdiv2D( int subdiv_type, int header_size,
|
|
int vtx_size, int quadedge_size,
|
|
CvMemStorage* storage );
|
|
|
|
/************************* high-level subdivision functions ***************************/
|
|
|
|
/* Simplified Delaunay diagram creation */
|
|
CV_INLINE CvSubdiv2D* cvCreateSubdivDelaunay2D( CvRect rect, CvMemStorage* storage )
|
|
{
|
|
CvSubdiv2D* subdiv = cvCreateSubdiv2D( CV_SEQ_KIND_SUBDIV2D, sizeof(*subdiv),
|
|
sizeof(CvSubdiv2DPoint), sizeof(CvQuadEdge2D), storage );
|
|
|
|
cvInitSubdivDelaunay2D( subdiv, rect );
|
|
return subdiv;
|
|
}
|
|
|
|
|
|
/* Inserts new point to the Delaunay triangulation */
|
|
CVAPI(CvSubdiv2DPoint*) cvSubdivDelaunay2DInsert( CvSubdiv2D* subdiv, CvPoint2D32f pt);
|
|
|
|
/* Locates a point within the Delaunay triangulation (finds the edge
|
|
the point is left to or belongs to, or the triangulation point the given
|
|
point coinsides with */
|
|
CVAPI(CvSubdiv2DPointLocation) cvSubdiv2DLocate(
|
|
CvSubdiv2D* subdiv, CvPoint2D32f pt,
|
|
CvSubdiv2DEdge* edge,
|
|
CvSubdiv2DPoint** vertex CV_DEFAULT(NULL) );
|
|
|
|
/* Calculates Voronoi tesselation (i.e. coordinates of Voronoi points) */
|
|
CVAPI(void) cvCalcSubdivVoronoi2D( CvSubdiv2D* subdiv );
|
|
|
|
|
|
/* Removes all Voronoi points from the tesselation */
|
|
CVAPI(void) cvClearSubdivVoronoi2D( CvSubdiv2D* subdiv );
|
|
|
|
|
|
/* Finds the nearest to the given point vertex in subdivision. */
|
|
CVAPI(CvSubdiv2DPoint*) cvFindNearestPoint2D( CvSubdiv2D* subdiv, CvPoint2D32f pt );
|
|
|
|
|
|
/************ Basic quad-edge navigation and operations ************/
|
|
|
|
CV_INLINE CvSubdiv2DEdge cvSubdiv2DNextEdge( CvSubdiv2DEdge edge )
|
|
{
|
|
return CV_SUBDIV2D_NEXT_EDGE(edge);
|
|
}
|
|
|
|
|
|
CV_INLINE CvSubdiv2DEdge cvSubdiv2DRotateEdge( CvSubdiv2DEdge edge, int rotate )
|
|
{
|
|
return (edge & ~3) + ((edge + rotate) & 3);
|
|
}
|
|
|
|
CV_INLINE CvSubdiv2DEdge cvSubdiv2DSymEdge( CvSubdiv2DEdge edge )
|
|
{
|
|
return edge ^ 2;
|
|
}
|
|
|
|
CV_INLINE CvSubdiv2DEdge cvSubdiv2DGetEdge( CvSubdiv2DEdge edge, CvNextEdgeType type )
|
|
{
|
|
CvQuadEdge2D* e = (CvQuadEdge2D*)(edge & ~3);
|
|
edge = e->next[(edge + (int)type) & 3];
|
|
return (edge & ~3) + ((edge + ((int)type >> 4)) & 3);
|
|
}
|
|
|
|
|
|
CV_INLINE CvSubdiv2DPoint* cvSubdiv2DEdgeOrg( CvSubdiv2DEdge edge )
|
|
{
|
|
CvQuadEdge2D* e = (CvQuadEdge2D*)(edge & ~3);
|
|
return (CvSubdiv2DPoint*)e->pt[edge & 3];
|
|
}
|
|
|
|
|
|
CV_INLINE CvSubdiv2DPoint* cvSubdiv2DEdgeDst( CvSubdiv2DEdge edge )
|
|
{
|
|
CvQuadEdge2D* e = (CvQuadEdge2D*)(edge & ~3);
|
|
return (CvSubdiv2DPoint*)e->pt[(edge + 2) & 3];
|
|
}
|
|
|
|
/****************************************************************************************\
|
|
* Additional operations on Subdivisions *
|
|
\****************************************************************************************/
|
|
|
|
// paints voronoi diagram: just demo function
|
|
CVAPI(void) icvDrawMosaic( CvSubdiv2D* subdiv, IplImage* src, IplImage* dst );
|
|
|
|
// checks planar subdivision for correctness. It is not an absolute check,
|
|
// but it verifies some relations between quad-edges
|
|
CVAPI(int) icvSubdiv2DCheck( CvSubdiv2D* subdiv );
|
|
|
|
// returns squared distance between two 2D points with floating-point coordinates.
|
|
CV_INLINE double icvSqDist2D32f( CvPoint2D32f pt1, CvPoint2D32f pt2 )
|
|
{
|
|
double dx = pt1.x - pt2.x;
|
|
double dy = pt1.y - pt2.y;
|
|
|
|
return dx*dx + dy*dy;
|
|
}
|
|
|
|
|
|
|
|
|
|
CV_INLINE double cvTriangleArea( CvPoint2D32f a, CvPoint2D32f b, CvPoint2D32f c )
|
|
{
|
|
return ((double)b.x - a.x) * ((double)c.y - a.y) - ((double)b.y - a.y) * ((double)c.x - a.x);
|
|
}
|
|
|
|
|
|
/* Constructs kd-tree from set of feature descriptors */
|
|
CVAPI(struct CvFeatureTree*) cvCreateKDTree(CvMat* desc);
|
|
|
|
/* Constructs spill-tree from set of feature descriptors */
|
|
CVAPI(struct CvFeatureTree*) cvCreateSpillTree( const CvMat* raw_data,
|
|
const int naive CV_DEFAULT(50),
|
|
const double rho CV_DEFAULT(.7),
|
|
const double tau CV_DEFAULT(.1) );
|
|
|
|
/* Release feature tree */
|
|
CVAPI(void) cvReleaseFeatureTree(struct CvFeatureTree* tr);
|
|
|
|
/* Searches feature tree for k nearest neighbors of given reference points,
|
|
searching (in case of kd-tree/bbf) at most emax leaves. */
|
|
CVAPI(void) cvFindFeatures(struct CvFeatureTree* tr, const CvMat* query_points,
|
|
CvMat* indices, CvMat* dist, int k, int emax CV_DEFAULT(20));
|
|
|
|
/* Search feature tree for all points that are inlier to given rect region.
|
|
Only implemented for kd trees */
|
|
CVAPI(int) cvFindFeaturesBoxed(struct CvFeatureTree* tr,
|
|
CvMat* bounds_min, CvMat* bounds_max,
|
|
CvMat* out_indices);
|
|
|
|
|
|
/* Construct a Locality Sensitive Hash (LSH) table, for indexing d-dimensional vectors of
|
|
given type. Vectors will be hashed L times with k-dimensional p-stable (p=2) functions. */
|
|
CVAPI(struct CvLSH*) cvCreateLSH(struct CvLSHOperations* ops, int d,
|
|
int L CV_DEFAULT(10), int k CV_DEFAULT(10),
|
|
int type CV_DEFAULT(CV_64FC1), double r CV_DEFAULT(4),
|
|
int64 seed CV_DEFAULT(-1));
|
|
|
|
/* Construct in-memory LSH table, with n bins. */
|
|
CVAPI(struct CvLSH*) cvCreateMemoryLSH(int d, int n, int L CV_DEFAULT(10), int k CV_DEFAULT(10),
|
|
int type CV_DEFAULT(CV_64FC1), double r CV_DEFAULT(4),
|
|
int64 seed CV_DEFAULT(-1));
|
|
|
|
/* Free the given LSH structure. */
|
|
CVAPI(void) cvReleaseLSH(struct CvLSH** lsh);
|
|
|
|
/* Return the number of vectors in the LSH. */
|
|
CVAPI(unsigned int) LSHSize(struct CvLSH* lsh);
|
|
|
|
/* Add vectors to the LSH structure, optionally returning indices. */
|
|
CVAPI(void) cvLSHAdd(struct CvLSH* lsh, const CvMat* data, CvMat* indices CV_DEFAULT(0));
|
|
|
|
/* Remove vectors from LSH, as addressed by given indices. */
|
|
CVAPI(void) cvLSHRemove(struct CvLSH* lsh, const CvMat* indices);
|
|
|
|
/* Query the LSH n times for at most k nearest points; data is n x d,
|
|
indices and dist are n x k. At most emax stored points will be accessed. */
|
|
CVAPI(void) cvLSHQuery(struct CvLSH* lsh, const CvMat* query_points,
|
|
CvMat* indices, CvMat* dist, int k, int emax);
|
|
|
|
/* Kolmogorov-Zabin stereo-correspondence algorithm (a.k.a. KZ1) */
|
|
#define CV_STEREO_GC_OCCLUDED SHRT_MAX
|
|
|
|
typedef struct CvStereoGCState
|
|
{
|
|
int Ithreshold;
|
|
int interactionRadius;
|
|
float K, lambda, lambda1, lambda2;
|
|
int occlusionCost;
|
|
int minDisparity;
|
|
int numberOfDisparities;
|
|
int maxIters;
|
|
|
|
CvMat* left;
|
|
CvMat* right;
|
|
CvMat* dispLeft;
|
|
CvMat* dispRight;
|
|
CvMat* ptrLeft;
|
|
CvMat* ptrRight;
|
|
CvMat* vtxBuf;
|
|
CvMat* edgeBuf;
|
|
} CvStereoGCState;
|
|
|
|
CVAPI(CvStereoGCState*) cvCreateStereoGCState( int numberOfDisparities, int maxIters );
|
|
CVAPI(void) cvReleaseStereoGCState( CvStereoGCState** state );
|
|
|
|
CVAPI(void) cvFindStereoCorrespondenceGC( const CvArr* left, const CvArr* right,
|
|
CvArr* disparityLeft, CvArr* disparityRight,
|
|
CvStereoGCState* state,
|
|
int useDisparityGuess CV_DEFAULT(0) );
|
|
|
|
/* Calculates optical flow for 2 images using classical Lucas & Kanade algorithm */
|
|
CVAPI(void) cvCalcOpticalFlowLK( const CvArr* prev, const CvArr* curr,
|
|
CvSize win_size, CvArr* velx, CvArr* vely );
|
|
|
|
/* Calculates optical flow for 2 images using block matching algorithm */
|
|
CVAPI(void) cvCalcOpticalFlowBM( const CvArr* prev, const CvArr* curr,
|
|
CvSize block_size, CvSize shift_size,
|
|
CvSize max_range, int use_previous,
|
|
CvArr* velx, CvArr* vely );
|
|
|
|
/* Calculates Optical flow for 2 images using Horn & Schunck algorithm */
|
|
CVAPI(void) cvCalcOpticalFlowHS( const CvArr* prev, const CvArr* curr,
|
|
int use_previous, CvArr* velx, CvArr* vely,
|
|
double lambda, CvTermCriteria criteria );
|
|
|
|
|
|
/****************************************************************************************\
|
|
* Background/foreground segmentation *
|
|
\****************************************************************************************/
|
|
|
|
/* We discriminate between foreground and background pixels
|
|
* by building and maintaining a model of the background.
|
|
* Any pixel which does not fit this model is then deemed
|
|
* to be foreground.
|
|
*
|
|
* At present we support two core background models,
|
|
* one of which has two variations:
|
|
*
|
|
* o CV_BG_MODEL_FGD: latest and greatest algorithm, described in
|
|
*
|
|
* Foreground Object Detection from Videos Containing Complex Background.
|
|
* Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian.
|
|
* ACM MM2003 9p
|
|
*
|
|
* o CV_BG_MODEL_FGD_SIMPLE:
|
|
* A code comment describes this as a simplified version of the above,
|
|
* but the code is in fact currently identical
|
|
*
|
|
* o CV_BG_MODEL_MOG: "Mixture of Gaussians", older algorithm, described in
|
|
*
|
|
* Moving target classification and tracking from real-time video.
|
|
* A Lipton, H Fujijoshi, R Patil
|
|
* Proceedings IEEE Workshop on Application of Computer Vision pp 8-14 1998
|
|
*
|
|
* Learning patterns of activity using real-time tracking
|
|
* C Stauffer and W Grimson August 2000
|
|
* IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8):747-757
|
|
*/
|
|
|
|
|
|
#define CV_BG_MODEL_FGD 0
|
|
#define CV_BG_MODEL_MOG 1 /* "Mixture of Gaussians". */
|
|
#define CV_BG_MODEL_FGD_SIMPLE 2
|
|
|
|
struct CvBGStatModel;
|
|
|
|
typedef void (CV_CDECL * CvReleaseBGStatModel)( struct CvBGStatModel** bg_model );
|
|
typedef int (CV_CDECL * CvUpdateBGStatModel)( IplImage* curr_frame, struct CvBGStatModel* bg_model,
|
|
double learningRate );
|
|
|
|
#define CV_BG_STAT_MODEL_FIELDS() \
|
|
int type; /*type of BG model*/ \
|
|
CvReleaseBGStatModel release; \
|
|
CvUpdateBGStatModel update; \
|
|
IplImage* background; /*8UC3 reference background image*/ \
|
|
IplImage* foreground; /*8UC1 foreground image*/ \
|
|
IplImage** layers; /*8UC3 reference background image, can be null */ \
|
|
int layer_count; /* can be zero */ \
|
|
CvMemStorage* storage; /*storage for foreground_regions*/ \
|
|
CvSeq* foreground_regions /*foreground object contours*/
|
|
|
|
typedef struct CvBGStatModel
|
|
{
|
|
CV_BG_STAT_MODEL_FIELDS();
|
|
} CvBGStatModel;
|
|
|
|
//
|
|
|
|
// Releases memory used by BGStatModel
|
|
CVAPI(void) cvReleaseBGStatModel( CvBGStatModel** bg_model );
|
|
|
|
// Updates statistical model and returns number of found foreground regions
|
|
CVAPI(int) cvUpdateBGStatModel( IplImage* current_frame, CvBGStatModel* bg_model,
|
|
double learningRate CV_DEFAULT(-1));
|
|
|
|
// Performs FG post-processing using segmentation
|
|
// (all pixels of a region will be classified as foreground if majority of pixels of the region are FG).
|
|
// parameters:
|
|
// segments - pointer to result of segmentation (for example MeanShiftSegmentation)
|
|
// bg_model - pointer to CvBGStatModel structure
|
|
CVAPI(void) cvRefineForegroundMaskBySegm( CvSeq* segments, CvBGStatModel* bg_model );
|
|
|
|
/* Common use change detection function */
|
|
CVAPI(int) cvChangeDetection( IplImage* prev_frame,
|
|
IplImage* curr_frame,
|
|
IplImage* change_mask );
|
|
|
|
/*
|
|
Interface of ACM MM2003 algorithm
|
|
*/
|
|
|
|
/* Default parameters of foreground detection algorithm: */
|
|
#define CV_BGFG_FGD_LC 128
|
|
#define CV_BGFG_FGD_N1C 15
|
|
#define CV_BGFG_FGD_N2C 25
|
|
|
|
#define CV_BGFG_FGD_LCC 64
|
|
#define CV_BGFG_FGD_N1CC 25
|
|
#define CV_BGFG_FGD_N2CC 40
|
|
|
|
/* Background reference image update parameter: */
|
|
#define CV_BGFG_FGD_ALPHA_1 0.1f
|
|
|
|
/* stat model update parameter
|
|
* 0.002f ~ 1K frame(~45sec), 0.005 ~ 18sec (if 25fps and absolutely static BG)
|
|
*/
|
|
#define CV_BGFG_FGD_ALPHA_2 0.005f
|
|
|
|
/* start value for alpha parameter (to fast initiate statistic model) */
|
|
#define CV_BGFG_FGD_ALPHA_3 0.1f
|
|
|
|
#define CV_BGFG_FGD_DELTA 2
|
|
|
|
#define CV_BGFG_FGD_T 0.9f
|
|
|
|
#define CV_BGFG_FGD_MINAREA 15.f
|
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#define CV_BGFG_FGD_BG_UPDATE_TRESH 0.5f
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/* See the above-referenced Li/Huang/Gu/Tian paper
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* for a full description of these background-model
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* tuning parameters.
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*
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* Nomenclature: 'c' == "color", a three-component red/green/blue vector.
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* We use histograms of these to model the range of
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* colors we've seen at a given background pixel.
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*
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* 'cc' == "color co-occurrence", a six-component vector giving
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* RGB color for both this frame and preceding frame.
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* We use histograms of these to model the range of
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* color CHANGES we've seen at a given background pixel.
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*/
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typedef struct CvFGDStatModelParams
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{
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int Lc; /* Quantized levels per 'color' component. Power of two, typically 32, 64 or 128. */
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int N1c; /* Number of color vectors used to model normal background color variation at a given pixel. */
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int N2c; /* Number of color vectors retained at given pixel. Must be > N1c, typically ~ 5/3 of N1c. */
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/* Used to allow the first N1c vectors to adapt over time to changing background. */
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int Lcc; /* Quantized levels per 'color co-occurrence' component. Power of two, typically 16, 32 or 64. */
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int N1cc; /* Number of color co-occurrence vectors used to model normal background color variation at a given pixel. */
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int N2cc; /* Number of color co-occurrence vectors retained at given pixel. Must be > N1cc, typically ~ 5/3 of N1cc. */
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/* Used to allow the first N1cc vectors to adapt over time to changing background. */
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int is_obj_without_holes;/* If TRUE we ignore holes within foreground blobs. Defaults to TRUE. */
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int perform_morphing; /* Number of erode-dilate-erode foreground-blob cleanup iterations. */
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/* These erase one-pixel junk blobs and merge almost-touching blobs. Default value is 1. */
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float alpha1; /* How quickly we forget old background pixel values seen. Typically set to 0.1 */
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float alpha2; /* "Controls speed of feature learning". Depends on T. Typical value circa 0.005. */
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float alpha3; /* Alternate to alpha2, used (e.g.) for quicker initial convergence. Typical value 0.1. */
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float delta; /* Affects color and color co-occurrence quantization, typically set to 2. */
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float T; /* "A percentage value which determines when new features can be recognized as new background." (Typically 0.9).*/
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float minArea; /* Discard foreground blobs whose bounding box is smaller than this threshold. */
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} CvFGDStatModelParams;
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typedef struct CvBGPixelCStatTable
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{
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float Pv, Pvb;
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uchar v[3];
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} CvBGPixelCStatTable;
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typedef struct CvBGPixelCCStatTable
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{
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float Pv, Pvb;
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uchar v[6];
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} CvBGPixelCCStatTable;
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typedef struct CvBGPixelStat
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{
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float Pbc;
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float Pbcc;
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CvBGPixelCStatTable* ctable;
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CvBGPixelCCStatTable* cctable;
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uchar is_trained_st_model;
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uchar is_trained_dyn_model;
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} CvBGPixelStat;
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typedef struct CvFGDStatModel
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{
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CV_BG_STAT_MODEL_FIELDS();
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CvBGPixelStat* pixel_stat;
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IplImage* Ftd;
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IplImage* Fbd;
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IplImage* prev_frame;
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CvFGDStatModelParams params;
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} CvFGDStatModel;
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/* Creates FGD model */
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CVAPI(CvBGStatModel*) cvCreateFGDStatModel( IplImage* first_frame,
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CvFGDStatModelParams* parameters CV_DEFAULT(NULL));
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/*
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Interface of Gaussian mixture algorithm
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"An improved adaptive background mixture model for real-time tracking with shadow detection"
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P. KadewTraKuPong and R. Bowden,
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Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001."
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http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
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*/
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/* Note: "MOG" == "Mixture Of Gaussians": */
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#define CV_BGFG_MOG_MAX_NGAUSSIANS 500
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/* default parameters of gaussian background detection algorithm */
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#define CV_BGFG_MOG_BACKGROUND_THRESHOLD 0.7 /* threshold sum of weights for background test */
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#define CV_BGFG_MOG_STD_THRESHOLD 2.5 /* lambda=2.5 is 99% */
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#define CV_BGFG_MOG_WINDOW_SIZE 200 /* Learning rate; alpha = 1/CV_GBG_WINDOW_SIZE */
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#define CV_BGFG_MOG_NGAUSSIANS 5 /* = K = number of Gaussians in mixture */
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#define CV_BGFG_MOG_WEIGHT_INIT 0.05
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#define CV_BGFG_MOG_SIGMA_INIT 30
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#define CV_BGFG_MOG_MINAREA 15.f
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#define CV_BGFG_MOG_NCOLORS 3
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typedef struct CvGaussBGStatModelParams
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|
{
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int win_size; /* = 1/alpha */
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|
int n_gauss;
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double bg_threshold, std_threshold, minArea;
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|
double weight_init, variance_init;
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}CvGaussBGStatModelParams;
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typedef struct CvGaussBGValues
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|
{
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int match_sum;
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double weight;
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double variance[CV_BGFG_MOG_NCOLORS];
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|
double mean[CV_BGFG_MOG_NCOLORS];
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} CvGaussBGValues;
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typedef struct CvGaussBGPoint
|
|
{
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|
CvGaussBGValues* g_values;
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|
} CvGaussBGPoint;
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|
|
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typedef struct CvGaussBGModel
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|
{
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|
CV_BG_STAT_MODEL_FIELDS();
|
|
CvGaussBGStatModelParams params;
|
|
CvGaussBGPoint* g_point;
|
|
int countFrames;
|
|
void* mog;
|
|
} CvGaussBGModel;
|
|
|
|
|
|
/* Creates Gaussian mixture background model */
|
|
CVAPI(CvBGStatModel*) cvCreateGaussianBGModel( IplImage* first_frame,
|
|
CvGaussBGStatModelParams* parameters CV_DEFAULT(NULL));
|
|
|
|
|
|
typedef struct CvBGCodeBookElem
|
|
{
|
|
struct CvBGCodeBookElem* next;
|
|
int tLastUpdate;
|
|
int stale;
|
|
uchar boxMin[3];
|
|
uchar boxMax[3];
|
|
uchar learnMin[3];
|
|
uchar learnMax[3];
|
|
} CvBGCodeBookElem;
|
|
|
|
typedef struct CvBGCodeBookModel
|
|
{
|
|
CvSize size;
|
|
int t;
|
|
uchar cbBounds[3];
|
|
uchar modMin[3];
|
|
uchar modMax[3];
|
|
CvBGCodeBookElem** cbmap;
|
|
CvMemStorage* storage;
|
|
CvBGCodeBookElem* freeList;
|
|
} CvBGCodeBookModel;
|
|
|
|
CVAPI(CvBGCodeBookModel*) cvCreateBGCodeBookModel( void );
|
|
CVAPI(void) cvReleaseBGCodeBookModel( CvBGCodeBookModel** model );
|
|
|
|
CVAPI(void) cvBGCodeBookUpdate( CvBGCodeBookModel* model, const CvArr* image,
|
|
CvRect roi CV_DEFAULT(cvRect(0,0,0,0)),
|
|
const CvArr* mask CV_DEFAULT(0) );
|
|
|
|
CVAPI(int) cvBGCodeBookDiff( const CvBGCodeBookModel* model, const CvArr* image,
|
|
CvArr* fgmask, CvRect roi CV_DEFAULT(cvRect(0,0,0,0)) );
|
|
|
|
CVAPI(void) cvBGCodeBookClearStale( CvBGCodeBookModel* model, int staleThresh,
|
|
CvRect roi CV_DEFAULT(cvRect(0,0,0,0)),
|
|
const CvArr* mask CV_DEFAULT(0) );
|
|
|
|
CVAPI(CvSeq*) cvSegmentFGMask( CvArr *fgmask, int poly1Hull0 CV_DEFAULT(1),
|
|
float perimScale CV_DEFAULT(4.f),
|
|
CvMemStorage* storage CV_DEFAULT(0),
|
|
CvPoint offset CV_DEFAULT(cvPoint(0,0)));
|
|
|
|
#ifdef __cplusplus
|
|
}
|
|
#endif
|
|
|
|
#endif
|
|
|
|
/* End of file. */
|