--- /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.
+//
+// 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 "_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);
+
+ 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 =========================*/
+
projects / opencv / blobdiff