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/*=========================================================================
*
* 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 itkGaussianSpatialFunction_hxx
#define itkGaussianSpatialFunction_hxx
#include <cmath>
#include "itkMath.h"
#include "itkGaussianSpatialFunction.h"
namespace itk
{
template< typename TOutput, unsigned int VImageDimension, typename TInput >
GaussianSpatialFunction< TOutput, VImageDimension, TInput >
::GaussianSpatialFunction() :
m_Scale( 1.0 ),
m_Normalized( false )
{
m_Mean = ArrayType::Filled(10.0);
m_Sigma = ArrayType::Filled(5.0);
}
template< typename TOutput, unsigned int VImageDimension, typename TInput >
GaussianSpatialFunction< TOutput, VImageDimension, TInput >
::~GaussianSpatialFunction()
{}
template< typename TOutput, unsigned int VImageDimension, typename TInput >
typename GaussianSpatialFunction< TOutput, VImageDimension, TInput >::OutputType
GaussianSpatialFunction< TOutput, VImageDimension, TInput >
::Evaluate(const TInput & position) const
{
// We have to compute the Gaussian in several stages, because of the
// n-dimensional generalization
// Normalizing the Gaussian is important for statistical applications
// but is generally not desirable for creating images because of the
// very small numbers involved (would need to use doubles)
double prefixDenom = 1.0;
if ( m_Normalized )
{
const double squareRootOfTwoPi = std::sqrt(2.0 * itk::Math::pi);
for ( unsigned int i = 0; i < VImageDimension; ++i )
{
prefixDenom *= m_Sigma[i] * squareRootOfTwoPi;
}
}
double suffixExp = 0;
for ( unsigned int i = 0; i < VImageDimension; ++i )
{
suffixExp += ( position[i] - m_Mean[i] ) * ( position[i] - m_Mean[i] )
/ ( 2 * m_Sigma[i] * m_Sigma[i] );
}
const double value = m_Scale * ( 1 / prefixDenom ) * std::exp(-1 * suffixExp);
return static_cast< TOutput >( value );
}
template< typename TOutput, unsigned int VImageDimension, typename TInput >
void
GaussianSpatialFunction< TOutput, VImageDimension, TInput >
::PrintSelf(std::ostream & os, Indent indent) const
{
Superclass::PrintSelf(os, indent);
os << indent << "Sigma: " << m_Sigma << std::endl;
os << indent << "Mean: " << m_Mean << std::endl;
os << indent << "Scale: " << m_Scale << std::endl;
os << indent << "Normalized?: " << m_Normalized << std::endl;
}
} // end namespace itk
#endif
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