/usr/include/OTB-6.4/otbLocalHistogramImageFunction.txx is in libotb-dev 6.4.0+dfsg-1.
This file is owned by root:root, with mode 0o644.
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* Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
*
* This file is part of Orfeo Toolbox
*
* https://www.orfeo-toolbox.org/
*
* 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
*
* 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 otbLocalHistogramImageFunction_txx
#define otbLocalHistogramImageFunction_txx
#include "otbLocalHistogramImageFunction.h"
#include "itkConstNeighborhoodIterator.h"
#include "itkExtractImageFilter.h"
#include "otbMath.h"
namespace otb
{
/**
* Constructor
*/
template <class TInputImage, class TCoordRep>
LocalHistogramImageFunction<TInputImage, TCoordRep>
::LocalHistogramImageFunction() :
m_NeighborhoodRadius(1), m_NumberOfHistogramBins(128), m_HistogramMin(0), m_HistogramMax(1), m_GaussianSmoothing(true)
{
}
template <class TInputImage, class TCoordRep>
void
LocalHistogramImageFunction<TInputImage, TCoordRep>
::PrintSelf(std::ostream& os, itk::Indent indent) const
{
this->Superclass::PrintSelf(os, indent);
os << indent << " Neighborhood radius value : " << this->GetNeighborhoodRadius() << std::endl;
os << indent << " Number Of Histogram Bins : " << this->GetNumberOfHistogramBins() << std::endl;
os << indent << " Histogram Minimum : " << this->GetHistogramMin() << std::endl;
os << indent << " Histogram Maximum : " << this->GetHistogramMax() << std::endl;
}
template <class TInputImage, class TCoordRep>
typename LocalHistogramImageFunction<TInputImage, TCoordRep>::OutputType
LocalHistogramImageFunction<TInputImage, TCoordRep>
::EvaluateAtIndex(const IndexType& index) const
{
typename HistogramType::Pointer histogram = HistogramType::New();
typename HistogramType::SizeType size(this->GetInputImage()->GetNumberOfComponentsPerPixel());
size.Fill(this->GetNumberOfHistogramBins());
typename HistogramType::MeasurementVectorType lowerBound(this->GetInputImage()->GetNumberOfComponentsPerPixel());
typename HistogramType::MeasurementVectorType upperBound(this->GetInputImage()->GetNumberOfComponentsPerPixel());
lowerBound.Fill( static_cast<typename HistogramType::MeasurementType>(this->GetHistogramMin()) );
upperBound.Fill( static_cast<typename HistogramType::MeasurementType>(this->GetHistogramMax()) );
histogram->SetMeasurementVectorSize(this->GetInputImage()->GetNumberOfComponentsPerPixel());
histogram->Initialize(size, lowerBound, upperBound );
histogram->SetToZero();
// Check for input image
if( !this->GetInputImage() )
{
return histogram;
}
// Check for out of buffer
if ( !this->IsInsideBuffer( index ) )
{
return histogram;
}
// Create an N-d neighborhood kernel, using a zeroflux boundary condition
typename InputImageType::SizeType kernelSize;
kernelSize.Fill( m_NeighborhoodRadius );
itk::ConstNeighborhoodIterator<InputImageType>
it(kernelSize, this->GetInputImage(), this->GetInputImage()->GetBufferedRegion());
// Set the iterator at the desired location
it.SetLocation(index);
// Define a gaussian kernel around the center location
double squaredRadius = m_NeighborhoodRadius * m_NeighborhoodRadius;
double squaredSigma = 0.25 * squaredRadius;
// Offset to be used in the loops
typename InputImageType::OffsetType offset;
// Fill the histogram
for(int i = -(int)m_NeighborhoodRadius; i< (int)m_NeighborhoodRadius; ++i)
{
for(int j = -(int)m_NeighborhoodRadius; j< (int)m_NeighborhoodRadius; ++j)
{
// Check if the current pixel lies within a disc of radius m_NeighborhoodRadius
double currentSquaredRadius = i*i+j*j;
if(currentSquaredRadius < squaredRadius)
{
// If so, compute the gaussian weighting (this could be
// computed once for all for the sake of optimisation) if necessary
double gWeight = 1.;
if(m_GaussianSmoothing)
{
gWeight = (1/vcl_sqrt(otb::CONST_2PI*squaredSigma)) * vcl_exp(- currentSquaredRadius/(2*squaredSigma));
}
// Compute pixel location
offset[0]=i;
offset[1]=j;
// Get the current value
typename HistogramType::MeasurementVectorType sample(this->GetInputImage()->GetNumberOfComponentsPerPixel());
sample[0] = it.GetPixel(offset);
// Populate histogram
histogram->IncreaseFrequencyOfMeasurement(sample, gWeight);
}
}
}
return histogram;
}
} // namespace otb
#endif
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