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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 itkMiniPipelineSeparableImageFilter_hxx
#define itkMiniPipelineSeparableImageFilter_hxx
#include "itkMiniPipelineSeparableImageFilter.h"
#include "itkProgressAccumulator.h"
/*
*
* This code was contributed in the Insight Journal paper:
* "Efficient implementation of kernel filtering"
* by Beare R., Lehmann G
* https://hdl.handle.net/1926/555
* http://www.insight-journal.org/browse/publication/160
*
*/
namespace itk
{
template< typename TInputImage, typename TOutputImage, typename TFilter >
MiniPipelineSeparableImageFilter< TInputImage, TOutputImage, TFilter >
::MiniPipelineSeparableImageFilter()
{
// create the pipeline
for ( unsigned i = 0; i < ImageDimension; i++ )
{
m_Filters[i] = FilterType::New();
m_Filters[i]->ReleaseDataFlagOn();
if ( i > 0 )
{
m_Filters[i]->SetInput( m_Filters[i - 1]->GetOutput() );
}
}
m_Cast = CastType::New();
m_Cast->SetInput( m_Filters[ImageDimension - 1]->GetOutput() );
m_Cast->SetInPlace(true);
}
template< typename TInputImage, typename TOutputImage, typename TFilter >
void
MiniPipelineSeparableImageFilter< TInputImage, TOutputImage, TFilter >
::Modified() const
{
Superclass::Modified();
for ( unsigned i = 0; i < ImageDimension; i++ )
{
m_Filters[i]->Modified();
}
m_Cast->Modified();
}
template< typename TInputImage, typename TOutputImage, typename TFilter >
void
MiniPipelineSeparableImageFilter< TInputImage, TOutputImage, TFilter >
::SetNumberOfThreads(ThreadIdType nb)
{
Superclass::SetNumberOfThreads(nb);
for ( unsigned i = 0; i < ImageDimension; i++ )
{
m_Filters[i]->SetNumberOfThreads(nb);
}
m_Cast->SetNumberOfThreads(nb);
}
template< typename TInputImage, typename TOutputImage, typename TFilter >
void
MiniPipelineSeparableImageFilter< TInputImage, TOutputImage, TFilter >
::SetRadius(const RadiusType & radius)
{
Superclass::SetRadius(radius);
// set up the kernels
for ( unsigned i = 0; i < ImageDimension; i++ )
{
RadiusType rad;
rad.Fill(0);
rad[i] = radius[i];
m_Filters[i]->SetRadius(rad);
}
}
template< typename TInputImage, typename TOutputImage, typename TFilter >
void
MiniPipelineSeparableImageFilter< TInputImage, TOutputImage, TFilter >
::GenerateData()
{
this->AllocateOutputs();
// set up the pipeline
m_Filters[0]->SetInput( this->GetInput() );
// Create a process accumulator for tracking the progress of this minipipeline
ProgressAccumulator::Pointer progress = ProgressAccumulator::New();
progress->SetMiniPipelineFilter(this);
for ( unsigned i = 0; i < ImageDimension; i++ )
{
progress->RegisterInternalFilter(m_Filters[i], 1.0 / ImageDimension);
}
m_Cast->GraftOutput( this->GetOutput() );
m_Cast->Update();
this->GraftOutput( m_Cast->GetOutput() );
}
}
#endif
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