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/*=========================================================================
*
* Copyright NumFOCUS
*
* 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
*
* https://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 itkGaussianDerivativeSpatialFunction_hxx
#define itkGaussianDerivativeSpatialFunction_hxx
#include <cmath>
#include "itkMath.h"
namespace itk
{
template <typename TOutput, unsigned int VImageDimension, typename TInput>
auto
GaussianDerivativeSpatialFunction<TOutput, VImageDimension, TInput>::Evaluate(const TInput & position) const
-> OutputType
{
// 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;
if (m_Normalized)
{
prefixDenom = m_Sigma[m_Direction] * m_Sigma[m_Direction];
for (unsigned int i = 0; i < VImageDimension; ++i)
{
prefixDenom *= m_Sigma[i];
}
prefixDenom *= 2 * std::pow(2 * itk::Math::pi, VImageDimension / 2.0);
}
else
{
prefixDenom = 1.0;
}
double suffixExp = 0;
for (unsigned int i = 0; i < VImageDimension; ++i)
{
suffixExp += (position[m_Direction] - m_Mean[m_Direction]) * (position[m_Direction] - m_Mean[m_Direction]) /
(2 * m_Sigma[m_Direction] * m_Sigma[m_Direction]);
}
double value =
-2 * (position[m_Direction] - m_Mean[m_Direction]) * m_Scale * (1 / prefixDenom) * std::exp(-1 * suffixExp);
return static_cast<TOutput>(value);
}
/** Evaluate the function at a given position and return a vector */
template <typename TOutput, unsigned int VImageDimension, typename TInput>
auto
GaussianDerivativeSpatialFunction<TOutput, VImageDimension, TInput>::EvaluateVector(const TInput & position) const
-> VectorType
{
VectorType gradient;
for (unsigned int i = 0; i < VImageDimension; ++i)
{
m_Direction = i;
gradient[i] = this->Evaluate(position);
}
return gradient;
}
template <typename TOutput, unsigned int VImageDimension, typename TInput>
void
GaussianDerivativeSpatialFunction<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 ? "On" : "Off") << std::endl;
os << indent << "Direction: " << m_Direction << std::endl;
}
} // end namespace itk
#endif
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