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/*========================================================================= |
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Program: Insight Segmentation & Registration Toolkit |
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Module: $RCSfile: itkGaussianSpatialFunction.txx.html,v $ |
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Language: C++ |
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Date: $Date: 2006/01/17 19:15:36 $ |
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Version: $Revision: 1.4 $ |
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Copyright (c) Insight Software Consortium. All rights reserved. |
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See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details. |
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This software is distributed WITHOUT ANY WARRANTY; without even |
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the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR |
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PURPOSE. See the above copyright notices for more information. |
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=========================================================================*/ |
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#ifndef __itkGaussianSpatialFunction_txx |
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#define __itkGaussianSpatialFunction_txx |
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#include <math.h> |
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#include "itkGaussianSpatialFunction.h" |
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namespace itk |
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{ |
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template <typename TOutput, unsigned int VImageDimension, typename TInput> |
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GaussianSpatialFunction<TOutput, VImageDimension, TInput> |
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::GaussianSpatialFunction() |
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{ |
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m_Mean = ArrayType::Filled(10.0); |
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m_Sigma = ArrayType::Filled(5.0); |
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m_Scale = 1.0; |
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m_Normalized = false; |
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} |
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template <typename TOutput, unsigned int VImageDimension, typename TInput> |
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GaussianSpatialFunction<TOutput, VImageDimension, TInput> |
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::~GaussianSpatialFunction() |
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{ |
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} |
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template <typename TOutput, unsigned int VImageDimension, typename TInput> |
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typename GaussianSpatialFunction<TOutput, VImageDimension, TInput>::OutputType |
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GaussianSpatialFunction<TOutput, VImageDimension, TInput> |
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::Evaluate(const TInput& position) const |
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{ |
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// We have to compute the gaussian in several stages, because of the |
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// n-dimensional generalization |
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// Normalizing the Gaussian is important for statistical applications |
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// but is generally not desirable for creating images because of the |
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// very small numbers involved (would need to use doubles) |
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double prefixDenom; |
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if (m_Normalized) |
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{ |
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prefixDenom = m_Sigma[0]; |
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for(unsigned int i = 1; i < VImageDimension; i++) |
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{ |
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prefixDenom *= m_Sigma[i]; |
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} |
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prefixDenom *= 2 * 3.1415927; |
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} |
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else |
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{ |
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prefixDenom = 1.0; |
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} |
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double suffixExp = 0; |
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for(unsigned int i = 0; i < VImageDimension; i++) |
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{ |
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suffixExp += (position[i] - m_Mean[i])*(position[i] - m_Mean[i]) / (2 * m_Sigma[i] * m_Sigma[i]); |
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} |
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double value = m_Scale * (1 / prefixDenom) * exp(-1 * suffixExp); |
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return (TOutput) value; |
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} |
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template <typename TOutput, unsigned int VImageDimension, typename TInput> |
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void |
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GaussianSpatialFunction<TOutput, VImageDimension, TInput> |
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::PrintSelf(std::ostream& os, Indent indent) const |
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{ |
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Superclass::PrintSelf(os,indent); |
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unsigned int i; |
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os << indent << "Sigma: ["; |
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for (i=0; i+1 < VImageDimension ; i++) |
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{ |
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os << m_Sigma[i] << ", "; |
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} |
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os << "]" << std::endl; |
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os << indent << "Mean: ["; |
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for (i=0; i+1 < VImageDimension ; i++) |
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{ |
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os << m_Mean[i] << ", "; |
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} |
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os << "]" << std::endl; |
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os << indent << "Scale: " << m_Scale << std::endl; |
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os << indent << "Normalized?: " << m_Normalized << std::endl; |
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} |
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} // end namespace itk |
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#endif |
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