/*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
// For Open Source Computer Vision Library
//
// 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 "_cv.h"
/*F///////////////////////////////////////////////////////////////////////////////////////
// Name: cvCreateConDensation
// Purpose: Creating CvConDensation structure and allocating memory for it
// Context:
// Parameters:
// Kalman - double pointer to CvConDensation structure
// DP - dimension of the dynamical vector
// MP - dimension of the measurement vector
// SamplesNum - number of samples in sample set used in algorithm
// Returns:
// Notes:
//
//F*/
CV_IMPL CvConDensation* cvCreateConDensation( int DP, int MP, int SamplesNum )
{
int i;
CvConDensation *CD = 0;
CV_FUNCNAME( "cvCreateConDensation" );
__BEGIN__;
if( DP < 0 || MP < 0 || SamplesNum < 0 )
CV_ERROR( CV_StsOutOfRange, "" );
/* allocating memory for the structure */
CV_CALL( CD = (CvConDensation *) cvAlloc( sizeof( CvConDensation )));
/* setting structure params */
CD->SamplesNum = SamplesNum;
CD->DP = DP;
CD->MP = MP;
/* allocating memory for structure fields */
CV_CALL( CD->flSamples = (float **) cvAlloc( sizeof( float * ) * SamplesNum ));
CV_CALL( CD->flNewSamples = (float **) cvAlloc( sizeof( float * ) * SamplesNum ));
CV_CALL( CD->flSamples[0] = (float *) cvAlloc( sizeof( float ) * SamplesNum * DP ));
CV_CALL( CD->flNewSamples[0] = (float *) cvAlloc( sizeof( float ) * SamplesNum * DP ));
/* setting pointers in pointer's arrays */
for( i = 1; i < SamplesNum; i++ )
{
CD->flSamples[i] = CD->flSamples[i - 1] + DP;
CD->flNewSamples[i] = CD->flNewSamples[i - 1] + DP;
}
CV_CALL( CD->State = (float *) cvAlloc( sizeof( float ) * DP ));
CV_CALL( CD->DynamMatr = (float *) cvAlloc( sizeof( float ) * DP * DP ));
CV_CALL( CD->flConfidence = (float *) cvAlloc( sizeof( float ) * SamplesNum ));
CV_CALL( CD->flCumulative = (float *) cvAlloc( sizeof( float ) * SamplesNum ));
CV_CALL( CD->RandS = (CvRandState *) cvAlloc( sizeof( CvRandState ) * DP ));
CV_CALL( CD->Temp = (float *) cvAlloc( sizeof( float ) * DP ));
CV_CALL( CD->RandomSample = (float *) cvAlloc( sizeof( float ) * DP ));
/* Returning created structure */
__END__;
return CD;
}
/*F///////////////////////////////////////////////////////////////////////////////////////
// Name: cvReleaseConDensation
// Purpose: Releases CvConDensation structure and frees memory allocated for it
// Context:
// Parameters:
// Kalman - double pointer to CvConDensation structure
// DP - dimension of the dynamical vector
// MP - dimension of the measurement vector
// SamplesNum - number of samples in sample set used in algorithm
// Returns:
// Notes:
//
//F*/
CV_IMPL void
cvReleaseConDensation( CvConDensation ** ConDensation )
{
CV_FUNCNAME( "cvReleaseConDensation" );
__BEGIN__;
CvConDensation *CD = *ConDensation;
if( !ConDensation )
CV_ERROR( CV_StsNullPtr, "" );
if( !CD )
EXIT;
/* freeing the memory */
cvFree( &CD->State );
cvFree( &CD->DynamMatr);
cvFree( &CD->flConfidence );
cvFree( &CD->flCumulative );
cvFree( &CD->flSamples[0] );
cvFree( &CD->flNewSamples[0] );
cvFree( &CD->flSamples );
cvFree( &CD->flNewSamples );
cvFree( &CD->Temp );
cvFree( &CD->RandS );
cvFree( &CD->RandomSample );
/* release structure */
cvFree( ConDensation );
__END__;
}
/*F///////////////////////////////////////////////////////////////////////////////////////
// Name: cvConDensUpdateByTime
// Purpose: Performing Time Update routine for ConDensation algorithm
// Context:
// Parameters:
// Kalman - pointer to CvConDensation structure
// Returns:
// Notes:
//
//F*/
CV_IMPL void
cvConDensUpdateByTime( CvConDensation * ConDens )
{
int i, j;
float Sum = 0;
CV_FUNCNAME( "cvConDensUpdateByTime" );
__BEGIN__;
if( !ConDens )
CV_ERROR( CV_StsNullPtr, "" );
/* Sets Temp to Zero */
icvSetZero_32f( ConDens->Temp, ConDens->DP, 1 );
/* Calculating the Mean */
for( i = 0; i < ConDens->SamplesNum; i++ )
{
icvScaleVector_32f( ConDens->flSamples[i], ConDens->State, ConDens->DP,
ConDens->flConfidence[i] );
icvAddVector_32f( ConDens->Temp, ConDens->State, ConDens->Temp, ConDens->DP );
Sum += ConDens->flConfidence[i];
ConDens->flCumulative[i] = Sum;
}
/* Taking the new vector from transformation of mean by dynamics matrix */
icvScaleVector_32f( ConDens->Temp, ConDens->Temp, ConDens->DP, 1.f / Sum );
icvTransformVector_32f( ConDens->DynamMatr, ConDens->Temp, ConDens->State, ConDens->DP,
ConDens->DP );
Sum = Sum / ConDens->SamplesNum;
/* Updating the set of random samples */
for( i = 0; i < ConDens->SamplesNum; i++ )
{
j = 0;
while( (ConDens->flCumulative[j] <= (float) i * Sum)&&(j<ConDens->SamplesNum-1))
{
j++;
}
icvCopyVector_32f( ConDens->flSamples[j], ConDens->DP, ConDens->flNewSamples[i] );
}
/* Adding the random-generated vector to every vector in sample set */
for( i = 0; i < ConDens->SamplesNum; i++ )
{
for( j = 0; j < ConDens->DP; j++ )
{
cvbRand( ConDens->RandS + j, ConDens->RandomSample + j, 1 );
}
icvTransformVector_32f( ConDens->DynamMatr, ConDens->flNewSamples[i],
ConDens->flSamples[i], ConDens->DP, ConDens->DP );
icvAddVector_32f( ConDens->flSamples[i], ConDens->RandomSample, ConDens->flSamples[i],
ConDens->DP );
}
__END__;
}
/*F///////////////////////////////////////////////////////////////////////////////////////
// Name: cvConDensInitSamplSet
// Purpose: Performing Time Update routine for ConDensation algorithm
// Context:
// Parameters:
// conDens - pointer to CvConDensation structure
// lowerBound - vector of lower bounds used to random update of sample set
// lowerBound - vector of upper bounds used to random update of sample set
// Returns:
// Notes:
//
//F*/
CV_IMPL void
cvConDensInitSampleSet( CvConDensation * conDens, CvMat * lowerBound, CvMat * upperBound )
{
int i, j;
float *LBound;
float *UBound;
float Prob = 1.f / conDens->SamplesNum;
CV_FUNCNAME( "cvConDensInitSampleSet" );
__BEGIN__;
if( !conDens || !lowerBound || !upperBound )
CV_ERROR( CV_StsNullPtr, "" );
if( CV_MAT_TYPE(lowerBound->type) != CV_32FC1 ||
!CV_ARE_TYPES_EQ(lowerBound,upperBound) )
CV_ERROR( CV_StsBadArg, "source has not appropriate format" );
if( (lowerBound->cols != 1) || (upperBound->cols != 1) )
CV_ERROR( CV_StsBadArg, "source has not appropriate size" );
if( (lowerBound->rows != conDens->DP) || (upperBound->rows != conDens->DP) )
CV_ERROR( CV_StsBadArg, "source has not appropriate size" );
LBound = lowerBound->data.fl;
UBound = upperBound->data.fl;
/* Initializing the structures to create initial Sample set */
for( i = 0; i < conDens->DP; i++ )
{
cvRandInit( &(conDens->RandS[i]),
LBound[i],
UBound[i],
i );
}
/* Generating the samples */
for( j = 0; j < conDens->SamplesNum; j++ )
{
for( i = 0; i < conDens->DP; i++ )
{
cvbRand( conDens->RandS + i, conDens->flSamples[j] + i, 1 );
}
conDens->flConfidence[j] = Prob;
}
/* Reinitializes the structures to update samples randomly */
for( i = 0; i < conDens->DP; i++ )
{
cvRandInit( &(conDens->RandS[i]),
(LBound[i] - UBound[i]) / 5,
(UBound[i] - LBound[i]) / 5,
i);
}
__END__;
}