libinsighttoolkit4-dev 4.5.0-3 / usr / include / ITK-4.5 / itkManifoldParzenWindowsPointSetFunction.hxx

/*=========================================================================
 *
 *  Copyright Insight Software Consortium
 *
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
 *  You may obtain a copy of the License at
 *
 *         http://www.apache.org/licenses/LICENSE-2.0.txt
 *
 *  Unless required by applicable law or agreed to in writing, software
 *  distributed under the License is distributed on an "AS IS" BASIS,
 *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  See the License for the specific language governing permissions and
 *  limitations under the License.
 *
 *=========================================================================*/
#ifndef __itkManifoldParzenWindowsPointSetFunction_hxx
#define __itkManifoldParzenWindowsPointSetFunction_hxx

#include "itkManifoldParzenWindowsPointSetFunction.h"

#include "vnl/vnl_math.h"

namespace itk
{

template <typename TPointSet, typename TOutput, typename TCoordRep>
ManifoldParzenWindowsPointSetFunction<TPointSet, TOutput, TCoordRep>
::ManifoldParzenWindowsPointSetFunction() :
  m_PointsLocator( NULL ),
  m_CovarianceKNeighborhood( 5 ),
  m_EvaluationKNeighborhood( 50 ),
  m_RegularizationSigma( 1.0 ),
  m_KernelSigma( 1.0 ),
  m_Normalize( true ),
  m_UseAnisotropicCovariances( true )
{
}

template <typename TPointSet, typename TOutput, typename TCoordRep>
ManifoldParzenWindowsPointSetFunction<TPointSet, TOutput, TCoordRep>
::~ManifoldParzenWindowsPointSetFunction()
{
}

template <typename TPointSet, typename TOutput, typename TCoordRep>
void
ManifoldParzenWindowsPointSetFunction<TPointSet, TOutput, TCoordRep>
::SetInputPointSet( const InputPointSetType * ptr )
{
  this->m_PointSet = ptr;

  /**
   * Generate KdTree and create set of gaussians from input point set
   */
  std::vector<typename GaussianType::Pointer> inputGaussians;
  inputGaussians.resize( this->GetInputPointSet()->GetNumberOfPoints() );
  this->m_Gaussians.resize( this->GetInputPointSet()->GetNumberOfPoints() );

  const PointsContainer * points = this->GetInputPointSet()->GetPoints();

  IdentifierType count = NumericTraits< IdentifierType >::Zero;

  typename PointSetType::PointsContainerConstIterator It = points->Begin();

  while( It != points->End() )
    {
    CovarianceMatrixType covariance( PointDimension, PointDimension );

    covariance.SetIdentity();
    covariance *= this->m_KernelSigma;

    inputGaussians[count] = GaussianType::New();
    inputGaussians[count]->SetMeasurementVectorSize( PointDimension );
    inputGaussians[count]->SetMean( It.Value() );
    inputGaussians[count]->SetCovariance( covariance );

    count++;
    ++It;
    }

  this->m_PointsLocator = PointsLocatorType::New();
  this->m_PointsLocator->SetPoints( const_cast<PointsContainer *>( points ) );
  this->m_PointsLocator->Initialize();

  /**
   * Calculate covariance matrices
   */

  It = points->Begin();
  while( It != points->End() )
    {
    PointType point = It.Value();
    unsigned long index = It.Index();

    this->m_Gaussians[index] = GaussianType::New();
    this->m_Gaussians[index]->SetMeasurementVectorSize( PointDimension );
    this->m_Gaussians[index]->SetMean( inputGaussians[index]->GetMean() );

    if( this->m_CovarianceKNeighborhood > 0
      && this->m_UseAnisotropicCovariances )
      {
      CovarianceMatrixType Cout( PointDimension, PointDimension );
      Cout.Fill( 0 );

      typename PointsLocatorType::NeighborsIdentifierType neighbors;
      this->m_PointsLocator->Search( point, this->m_CovarianceKNeighborhood, neighbors );

      RealType denominator = 0.0;
      for( unsigned int j = 0; j < this->m_CovarianceKNeighborhood; j++ )
        {
        if( neighbors[j] != index
          && neighbors[j] < this->GetInputPointSet()->GetNumberOfPoints() )
          {
          PointType neighbor =
            this->GetInputPointSet()->GetPoint( neighbors[j] );

          RealType kernelValue = inputGaussians[index]->Evaluate( neighbor );

          denominator += kernelValue;
          if( kernelValue > 0.0 )
            {
            for( unsigned int m = 0; m < PointDimension; m++ )
              {
              for( unsigned int n = m; n < PointDimension; n++ )
                {
                RealType covariance = kernelValue * ( neighbor[m] - point[m] ) *
                  ( neighbor[n] - point[n] );
                Cout(m, n) += covariance;
                Cout(n, m) += covariance;
                }
              }
            }
          }
        }

      if( this->m_Normalize && denominator > 0.0 )
        {
        Cout /= denominator;
        }
      else
        {
        Cout /= static_cast<RealType>( this->m_CovarianceKNeighborhood );
        }
      for( unsigned int m = 0; m < PointDimension; m++ )
        {
        Cout(m, m) += vnl_math_sqr( this->m_RegularizationSigma );
        }

      this->m_Gaussians[index]->SetCovariance( Cout );
      }
    else
      {
      typename GaussianType::CovarianceMatrixType covariance
        ( PointDimension, PointDimension );
      covariance.SetIdentity();
      covariance *= vnl_math_sqr( this->m_RegularizationSigma );
      this->m_Gaussians[index]->SetCovariance( covariance );
      }
    ++It;
    }
}

template <typename TPointSet, typename TOutput, typename TCoordRep>
TOutput
ManifoldParzenWindowsPointSetFunction<TPointSet, TOutput, TCoordRep>
::Evaluate( const InputPointType &point ) const
{
  if( this->GetInputPointSet() == NULL )
    {
    itkExceptionMacro( "The input point set has not been specified." );
    }

  unsigned int numberOfNeighbors = vnl_math_min(
    this->m_EvaluationKNeighborhood,
    static_cast<unsigned int>( this->m_Gaussians.size() ) );

  OutputType sum = NumericTraits< OutputType>::Zero;

  if( numberOfNeighbors == this->m_Gaussians.size() )
    {
    for( unsigned int j = 0; j < this->m_Gaussians.size(); j++ )
      {
      sum += static_cast<OutputType>(
        this->m_Gaussians[j]->Evaluate( point ) );
      }
    }
  else
    {
    typename PointsLocatorType::NeighborsIdentifierType neighbors;
    this->m_PointsLocator->Search( point, numberOfNeighbors, neighbors );

    for( unsigned int j = 0; j < numberOfNeighbors; j++ )
      {
      sum += static_cast<OutputType>(
        this->m_Gaussians[neighbors[j]]->Evaluate( point ) );
      }
    }
  return static_cast<OutputType>(
    sum / static_cast<OutputType>( this->m_Gaussians.size() ) );
}

template <typename TPointSet, typename TOutput, typename TCoordRep>
typename ManifoldParzenWindowsPointSetFunction<TPointSet, TOutput, TCoordRep>::GaussianConstPointer
ManifoldParzenWindowsPointSetFunction<TPointSet, TOutput, TCoordRep>
::GetGaussian( PointIdentifier i ) const
{
  if( i < this->m_Gaussians.size() )
    {
    return this->m_Gaussians[i].GetPointer();
    }
  else
    {
    return NULL;
    }
}

/**
 * Standard "PrintSelf" method
 */
template <typename TPointSet, typename TOutput, typename TCoordRep>
void
ManifoldParzenWindowsPointSetFunction<TPointSet, TOutput, TCoordRep>
::PrintSelf(
  std::ostream& os,
  Indent indent) const
{
  os << indent << "Covariance neighborhood: "
               << this->m_CovarianceKNeighborhood << std::endl;
  os << indent << "Evaluation neighborhood: "
               << this->m_EvaluationKNeighborhood << std::endl;
  os << indent << "Regularization sigma: "
               << this->m_RegularizationSigma << std::endl;
  os << indent << "Kernel sigma: "
               << this->m_KernelSigma << std::endl;
  os << indent << "Normalize: "
               << this->m_Normalize << std::endl;
  os << indent << "Use anisotropic covariances: "
               << this->m_UseAnisotropicCovariances << std::endl;
}

}  //end namespace itk

#endif