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moved some old stuff to the legacy module; merge "compat_c.h" headers and moved to the legacy as well. moved implementation of many non-critical/obsolete inline functions and methods to .cpp to improve Opencv build time

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Vadim Pisarevsky
2010-11-26 17:58:20 +00:00
parent fbdb4f4ab5
commit 54ef4c08c2
32 changed files with 1661 additions and 1289 deletions
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modules/legacy/src/blobtrackpostprockalman.cpp Normal file
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
/*======================= KALMAN FILTER =========================*/
/* State vector is (x,y,w,h,dx,dy,dw,dh). */
/* Measurement is (x,y,w,h). */
/* Dynamic matrix A: */
const float A8[] = { 1, 0, 0, 0, 1, 0, 0, 0,
0, 1, 0, 0, 0, 1, 0, 0,
0, 0, 1, 0, 0, 0, 1, 0,
0, 0, 0, 1, 0, 0, 0, 1,
0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 1};
/* Measurement matrix H: */
const float H8[] = { 1, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0};
/* Matrices for zero size velocity: */
/* Dinamic matrix A: */
const float A6[] = { 1, 0, 0, 0, 1, 0,
0, 1, 0, 0, 0, 1,
0, 0, 1, 0, 0, 0,
0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 1};
/* Measurement matrix H: */
const float H6[] = { 1, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0,
0, 0, 0, 1, 0, 0};
#define STATE_NUM 6
#define A A6
#define H H6
class CvBlobTrackPostProcKalman:public CvBlobTrackPostProcOne
{
private:
CvBlob m_Blob;
CvKalman* m_pKalman;
int m_Frame;
float m_ModelNoise;
float m_DataNoisePos;
float m_DataNoiseSize;
public:
CvBlobTrackPostProcKalman();
~CvBlobTrackPostProcKalman();
CvBlob* Process(CvBlob* pBlob);
void Release();
virtual void ParamUpdate();
}; /* class CvBlobTrackPostProcKalman */
CvBlobTrackPostProcKalman::CvBlobTrackPostProcKalman()
{
m_ModelNoise = 1e-6f;
m_DataNoisePos = 1e-6f;
m_DataNoiseSize = 1e-1f;
#if STATE_NUM>6
m_DataNoiseSize *= (float)pow(20.,2.);
#else
m_DataNoiseSize /= (float)pow(20.,2.);
#endif
AddParam("ModelNoise",&m_ModelNoise);
AddParam("DataNoisePos",&m_DataNoisePos);
AddParam("DataNoiseSize",&m_DataNoiseSize);
m_Frame = 0;
m_pKalman = cvCreateKalman(STATE_NUM,4);
memcpy( m_pKalman->transition_matrix->data.fl, A, sizeof(A));
memcpy( m_pKalman->measurement_matrix->data.fl, H, sizeof(H));
cvSetIdentity( m_pKalman->process_noise_cov, cvRealScalar(m_ModelNoise) );
cvSetIdentity( m_pKalman->measurement_noise_cov, cvRealScalar(m_DataNoisePos) );
CV_MAT_ELEM(*m_pKalman->measurement_noise_cov, float, 2,2) = m_DataNoiseSize;
CV_MAT_ELEM(*m_pKalman->measurement_noise_cov, float, 3,3) = m_DataNoiseSize;
cvSetIdentity( m_pKalman->error_cov_post, cvRealScalar(1));
cvZero(m_pKalman->state_post);
cvZero(m_pKalman->state_pre);
SetModuleName("Kalman");
}
CvBlobTrackPostProcKalman::~CvBlobTrackPostProcKalman()
{
cvReleaseKalman(&m_pKalman);
}
void CvBlobTrackPostProcKalman::ParamUpdate()
{
cvSetIdentity( m_pKalman->process_noise_cov, cvRealScalar(m_ModelNoise) );
cvSetIdentity( m_pKalman->measurement_noise_cov, cvRealScalar(m_DataNoisePos) );
CV_MAT_ELEM(*m_pKalman->measurement_noise_cov, float, 2,2) = m_DataNoiseSize;
CV_MAT_ELEM(*m_pKalman->measurement_noise_cov, float, 3,3) = m_DataNoiseSize;
}
CvBlob* CvBlobTrackPostProcKalman::Process(CvBlob* pBlob)
{
CvBlob* pBlobRes = &m_Blob;
float Z[4];
CvMat Zmat = cvMat(4,1,CV_32F,Z);
m_Blob = pBlob[0];
if(m_Frame < 2)
{ /* First call: */
m_pKalman->state_post->data.fl[0+4] = CV_BLOB_X(pBlob)-m_pKalman->state_post->data.fl[0];
m_pKalman->state_post->data.fl[1+4] = CV_BLOB_Y(pBlob)-m_pKalman->state_post->data.fl[1];
if(m_pKalman->DP>6)
{
m_pKalman->state_post->data.fl[2+4] = CV_BLOB_WX(pBlob)-m_pKalman->state_post->data.fl[2];
m_pKalman->state_post->data.fl[3+4] = CV_BLOB_WY(pBlob)-m_pKalman->state_post->data.fl[3];
}
m_pKalman->state_post->data.fl[0] = CV_BLOB_X(pBlob);
m_pKalman->state_post->data.fl[1] = CV_BLOB_Y(pBlob);
m_pKalman->state_post->data.fl[2] = CV_BLOB_WX(pBlob);
m_pKalman->state_post->data.fl[3] = CV_BLOB_WY(pBlob);
}
else
{ /* Nonfirst call: */
cvKalmanPredict(m_pKalman,0);
Z[0] = CV_BLOB_X(pBlob);
Z[1] = CV_BLOB_Y(pBlob);
Z[2] = CV_BLOB_WX(pBlob);
Z[3] = CV_BLOB_WY(pBlob);
cvKalmanCorrect(m_pKalman,&Zmat);
cvMatMulAdd(m_pKalman->measurement_matrix, m_pKalman->state_post, NULL, &Zmat);
CV_BLOB_X(pBlobRes) = Z[0];
CV_BLOB_Y(pBlobRes) = Z[1];
// CV_BLOB_WX(pBlobRes) = Z[2];
// CV_BLOB_WY(pBlobRes) = Z[3];
}
m_Frame++;
return pBlobRes;
}
void CvBlobTrackPostProcKalman::Release()
{
delete this;
}
CvBlobTrackPostProcOne* cvCreateModuleBlobTrackPostProcKalmanOne()
{
return (CvBlobTrackPostProcOne*) new CvBlobTrackPostProcKalman;
}
CvBlobTrackPostProc* cvCreateModuleBlobTrackPostProcKalman()
{
return cvCreateBlobTrackPostProcList(cvCreateModuleBlobTrackPostProcKalmanOne);
}
/*======================= KALMAN FILTER =========================*/
/*======================= KALMAN PREDICTOR =========================*/
class CvBlobTrackPredictKalman:public CvBlobTrackPredictor
{
private:
CvBlob m_BlobPredict;
CvKalman* m_pKalman;
int m_Frame;
float m_ModelNoise;
float m_DataNoisePos;
float m_DataNoiseSize;
public:
CvBlobTrackPredictKalman();
~CvBlobTrackPredictKalman();
CvBlob* Predict();
void Update(CvBlob* pBlob);
virtual void ParamUpdate();
void Release()
{
delete this;
}
}; /* class CvBlobTrackPredictKalman */
void CvBlobTrackPredictKalman::ParamUpdate()
{
cvSetIdentity( m_pKalman->process_noise_cov, cvRealScalar(m_ModelNoise) );
cvSetIdentity( m_pKalman->measurement_noise_cov, cvRealScalar(m_DataNoisePos) );
CV_MAT_ELEM(*m_pKalman->measurement_noise_cov, float, 2,2) = m_DataNoiseSize;
CV_MAT_ELEM(*m_pKalman->measurement_noise_cov, float, 3,3) = m_DataNoiseSize;
}
CvBlobTrackPredictKalman::CvBlobTrackPredictKalman()
{
m_ModelNoise = 1e-6f;
m_DataNoisePos = 1e-6f;
m_DataNoiseSize = 1e-1f;
#if STATE_NUM>6
m_DataNoiseSize *= (float)pow(20.,2.);
#else
m_DataNoiseSize /= (float)pow(20.,2.);
#endif
AddParam("ModelNoise",&m_ModelNoise);
AddParam("DataNoisePos",&m_DataNoisePos);
AddParam("DataNoiseSize",&m_DataNoiseSize);
m_Frame = 0;
m_pKalman = cvCreateKalman(STATE_NUM,4);
memcpy( m_pKalman->transition_matrix->data.fl, A, sizeof(A));
memcpy( m_pKalman->measurement_matrix->data.fl, H, sizeof(H));
cvSetIdentity( m_pKalman->process_noise_cov, cvRealScalar(m_ModelNoise) );
cvSetIdentity( m_pKalman->measurement_noise_cov, cvRealScalar(m_DataNoisePos) );
CV_MAT_ELEM(*m_pKalman->measurement_noise_cov, float, 2,2) = m_DataNoiseSize;
CV_MAT_ELEM(*m_pKalman->measurement_noise_cov, float, 3,3) = m_DataNoiseSize;
cvSetIdentity( m_pKalman->error_cov_post, cvRealScalar(1));
cvZero(m_pKalman->state_post);
cvZero(m_pKalman->state_pre);
SetModuleName("Kalman");
}
CvBlobTrackPredictKalman::~CvBlobTrackPredictKalman()
{
cvReleaseKalman(&m_pKalman);
}
CvBlob* CvBlobTrackPredictKalman::Predict()
{
if(m_Frame >= 2)
{
cvKalmanPredict(m_pKalman,0);
m_BlobPredict.x = m_pKalman->state_pre->data.fl[0];
m_BlobPredict.y = m_pKalman->state_pre->data.fl[1];
m_BlobPredict.w = m_pKalman->state_pre->data.fl[2];
m_BlobPredict.h = m_pKalman->state_pre->data.fl[3];
}
return &m_BlobPredict;
}
void CvBlobTrackPredictKalman::Update(CvBlob* pBlob)
{
float Z[4];
CvMat Zmat = cvMat(4,1,CV_32F,Z);
m_BlobPredict = pBlob[0];
if(m_Frame < 2)
{ /* First call: */
m_pKalman->state_post->data.fl[0+4] = CV_BLOB_X(pBlob)-m_pKalman->state_post->data.fl[0];
m_pKalman->state_post->data.fl[1+4] = CV_BLOB_Y(pBlob)-m_pKalman->state_post->data.fl[1];
if(m_pKalman->DP>6)
{
m_pKalman->state_post->data.fl[2+4] = CV_BLOB_WX(pBlob)-m_pKalman->state_post->data.fl[2];
m_pKalman->state_post->data.fl[3+4] = CV_BLOB_WY(pBlob)-m_pKalman->state_post->data.fl[3];
}
m_pKalman->state_post->data.fl[0] = CV_BLOB_X(pBlob);
m_pKalman->state_post->data.fl[1] = CV_BLOB_Y(pBlob);
m_pKalman->state_post->data.fl[2] = CV_BLOB_WX(pBlob);
m_pKalman->state_post->data.fl[3] = CV_BLOB_WY(pBlob);
}
else
{ /* Nonfirst call: */
Z[0] = CV_BLOB_X(pBlob);
Z[1] = CV_BLOB_Y(pBlob);
Z[2] = CV_BLOB_WX(pBlob);
Z[3] = CV_BLOB_WY(pBlob);
cvKalmanCorrect(m_pKalman,&Zmat);
}
cvKalmanPredict(m_pKalman,0);
m_Frame++;
} /* Update. */
CvBlobTrackPredictor* cvCreateModuleBlobTrackPredictKalman()
{
return (CvBlobTrackPredictor*) new CvBlobTrackPredictKalman;
}
/*======================= KALMAN PREDICTOR =========================*/