/usr/include/ITK-4.5/itkGradientNDAnisotropicDiffusionFunction.hxx is in libinsighttoolkit4-dev 4.5.0-3.
This file is owned by root:root, with mode 0o644.
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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 __itkGradientNDAnisotropicDiffusionFunction_hxx
#define __itkGradientNDAnisotropicDiffusionFunction_hxx
#include "itkNumericTraits.h"
#include "itkGradientNDAnisotropicDiffusionFunction.h"
namespace itk
{
template< typename TImage >
double GradientNDAnisotropicDiffusionFunction< TImage >
::m_MIN_NORM = 1.0e-10;
template< typename TImage >
GradientNDAnisotropicDiffusionFunction< TImage >
::GradientNDAnisotropicDiffusionFunction()
{
unsigned int i, j;
RadiusType r;
for ( i = 0; i < ImageDimension; ++i )
{
r[i] = 1;
}
this->SetRadius(r);
// Dummy neighborhood used to set up the slices.
Neighborhood< PixelType, ImageDimension > it;
it.SetRadius(r);
// Slice the neighborhood
m_Center = it.Size() / 2;
for ( i = 0; i < ImageDimension; ++i )
{
m_Stride[i] = it.GetStride(i);
}
for ( i = 0; i < ImageDimension; ++i )
{
x_slice[i] = std::slice(m_Center - m_Stride[i], 3, m_Stride[i]);
}
for ( i = 0; i < ImageDimension; ++i )
{
for ( j = 0; j < ImageDimension; ++j )
{
// For taking derivatives in the i direction that are offset one
// pixel in the j direction.
xa_slice[i][j] =
std::slice( ( m_Center + m_Stride[j] ) - m_Stride[i], 3, m_Stride[i] );
xd_slice[i][j] =
std::slice( ( m_Center - m_Stride[j] ) - m_Stride[i], 3, m_Stride[i] );
}
}
// Allocate the derivative operator.
dx_op.SetDirection(0); // Not relevant, will be applied in a slice-based
// fashion.
dx_op.SetOrder(1);
dx_op.CreateDirectional();
}
template< typename TImage >
typename GradientNDAnisotropicDiffusionFunction< TImage >::PixelType
GradientNDAnisotropicDiffusionFunction< TImage >
::ComputeUpdate(const NeighborhoodType & it, void *,
const FloatOffsetType &)
{
unsigned int i, j;
double accum;
double accum_d;
double Cx;
double Cxd;
// PixelType is scalar in this context
PixelRealType delta;
PixelRealType dx_forward;
PixelRealType dx_backward;
PixelRealType dx[ImageDimension];
PixelRealType dx_aug;
PixelRealType dx_dim;
delta = NumericTraits< PixelRealType >::Zero;
// Calculate the centralized derivatives for each dimension.
for ( i = 0; i < ImageDimension; i++ )
{
dx[i] = ( it.GetPixel(m_Center + m_Stride[i]) - it.GetPixel(m_Center - m_Stride[i]) ) / 2.0f;
dx[i] *= this->m_ScaleCoefficients[i];
}
for ( i = 0; i < ImageDimension; i++ )
{
// ``Half'' directional derivatives
dx_forward = it.GetPixel(m_Center + m_Stride[i])
- it.GetPixel(m_Center);
dx_forward *= this->m_ScaleCoefficients[i];
dx_backward = it.GetPixel(m_Center)
- it.GetPixel(m_Center - m_Stride[i]);
dx_backward *= this->m_ScaleCoefficients[i];
// Calculate the conductance terms. Conductance varies with each
// dimension because the gradient magnitude approximation is different
// along each dimension.
accum = 0.0;
accum_d = 0.0;
for ( j = 0; j < ImageDimension; j++ )
{
if ( j != i )
{
dx_aug = ( it.GetPixel(m_Center + m_Stride[i] + m_Stride[j])
- it.GetPixel(m_Center + m_Stride[i] - m_Stride[j]) ) / 2.0f;
dx_aug *= this->m_ScaleCoefficients[j];
dx_dim = ( it.GetPixel(m_Center - m_Stride[i] + m_Stride[j])
- it.GetPixel(m_Center - m_Stride[i] - m_Stride[j]) ) / 2.0f;
dx_dim *= this->m_ScaleCoefficients[j];
accum += 0.25f * vnl_math_sqr(dx[j] + dx_aug);
accum_d += 0.25f * vnl_math_sqr(dx[j] + dx_dim);
}
}
if ( m_K == 0.0 )
{
Cx = 0.0;
Cxd = 0.0;
}
else
{
Cx = vcl_exp( ( vnl_math_sqr(dx_forward) + accum ) / m_K );
Cxd = vcl_exp( ( vnl_math_sqr(dx_backward) + accum_d ) / m_K );
}
// Conductance modified first order derivatives.
dx_forward = dx_forward * Cx;
dx_backward = dx_backward * Cxd;
// Conductance modified second order derivative.
delta += dx_forward - dx_backward;
}
return static_cast< PixelType >( delta );
}
} // end namespace itk
#endif
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