/usr/include/InsightToolkit/Common/itkCovarianceImageFunction.txx is in libinsighttoolkit3-dev 3.20.1-1.
This file is owned by root:root, with mode 0o644.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 | /*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkCovarianceImageFunction.txx
Language: C++
Date: $Date$
Version: $Revision$
Copyright (c) Insight Software Consortium. All rights reserved.
See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#ifndef __itkCovarianceImageFunction_txx
#define __itkCovarianceImageFunction_txx
#include "itkCovarianceImageFunction.h"
#include "itkMatrix.h"
#include "itkNumericTraits.h"
#include "itkConstNeighborhoodIterator.h"
namespace itk
{
/**
* Constructor
*/
template <class TInputImage, class TCoordRep>
CovarianceImageFunction<TInputImage,TCoordRep>
::CovarianceImageFunction()
{
m_NeighborhoodRadius = 1;
}
/**
*
*/
template <class TInputImage, class TCoordRep>
void
CovarianceImageFunction<TInputImage,TCoordRep>
::PrintSelf(std::ostream& os, Indent indent) const
{
this->Superclass::PrintSelf(os,indent);
os << indent << "NeighborhoodRadius: " << m_NeighborhoodRadius << std::endl;
}
/**
*
*/
template <class TInputImage, class TCoordRep>
typename CovarianceImageFunction<TInputImage,TCoordRep>
::RealType
CovarianceImageFunction<TInputImage,TCoordRep>
::EvaluateAtIndex(const IndexType& index) const
{
typedef typename TInputImage::PixelType PixelType;
typedef typename PixelType::ValueType PixelComponentType;
typedef typename NumericTraits< PixelComponentType >::RealType PixelComponentRealType;
const unsigned int VectorDimension =
::itk::GetVectorDimension< PixelType >::VectorDimension;
RealType covariance = RealType( VectorDimension, VectorDimension );
if( !this->GetInputImage() )
{
itkExceptionMacro( << "No image connected to CovarianceImageFunction");
covariance.fill( NumericTraits< PixelComponentRealType >::max() );
return covariance;
}
if ( !this->IsInsideBuffer( index ) )
{
covariance.fill( NumericTraits< PixelComponentRealType >::max() );
return covariance;
}
covariance.fill( NumericTraits< PixelComponentRealType >::Zero );
typedef vnl_vector< PixelComponentRealType > MeanVectorType;
MeanVectorType mean = MeanVectorType( VectorDimension );
mean.fill( NumericTraits< PixelComponentRealType >::Zero );
// Create an N-d neighborhood kernel, using a zeroflux boundary condition
typename InputImageType::SizeType kernelSize;
kernelSize.Fill( m_NeighborhoodRadius );
ConstNeighborhoodIterator<InputImageType>
it(kernelSize, this->GetInputImage(), this->GetInputImage()->GetBufferedRegion());
// Set the iterator at the desired location
it.SetLocation(index);
// Walk the neighborhood
const unsigned int size = it.Size();
for (unsigned int i = 0; i < size; ++i)
{
const PixelType pixel = it.GetPixel(i);
for(unsigned int dimx=0; dimx<VectorDimension; dimx++)
{
mean[ dimx ] += pixel[ dimx ];
for(unsigned int dimy=0; dimy<VectorDimension; dimy++)
{
covariance[dimx][dimy] +=
static_cast<PixelComponentRealType>( pixel[dimx] ) *
static_cast<PixelComponentRealType>( pixel[dimy] );
}
}
}
const PixelComponentRealType rsize =
static_cast< PixelComponentRealType >( size );
mean /= rsize;
for(unsigned int dimx=0; dimx<VectorDimension; dimx++)
{
for(unsigned int dimy=0; dimy<VectorDimension; dimy++)
{
covariance[dimx][dimy] /= rsize;
covariance[dimx][dimy] -= mean[dimx] * mean[dimy];
}
}
return ( covariance );
}
} // end namespace itk
#endif
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