+++ /dev/null
-/*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.
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-// 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
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-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
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-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
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-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#include "_cvaux.h"
-
-/*======================= 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);
-}
-
-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);
-}
-
-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 =========================*/
-
projects / opencv / blobdiff