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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 itkZeroCrossingBasedEdgeDetectionImageFilter_hxx
#define itkZeroCrossingBasedEdgeDetectionImageFilter_hxx
#include "itkZeroCrossingBasedEdgeDetectionImageFilter.h"
#include "itkDiscreteGaussianImageFilter.h"
#include "itkLaplacianImageFilter.h"
#include "itkZeroCrossingImageFilter.h"
#include "itkProgressAccumulator.h"
namespace itk
{
template< typename TInputImage, typename TOutputImage >
void
ZeroCrossingBasedEdgeDetectionImageFilter< TInputImage, TOutputImage >
::GenerateData()
{
typename InputImageType::ConstPointer input = this->GetInput();
// Create the filters that are needed
typename DiscreteGaussianImageFilter< TInputImage, TOutputImage >::Pointer
gaussianFilter =
DiscreteGaussianImageFilter< TInputImage, TOutputImage >::New();
typename LaplacianImageFilter< TInputImage, TOutputImage >::Pointer laplacianFilter =
LaplacianImageFilter< TInputImage, TOutputImage >::New();
typename ZeroCrossingImageFilter< TInputImage, TOutputImage >::Pointer
zerocrossingFilter =
ZeroCrossingImageFilter< TInputImage, TOutputImage >::New();
// Create a process accumulator for tracking the progress of this minipipeline
ProgressAccumulator::Pointer progress = ProgressAccumulator::New();
progress->SetMiniPipelineFilter(this);
//Construct the mini-pipeline
// Apply the Gaussian filter
gaussianFilter->SetVariance(m_Variance);
gaussianFilter->SetMaximumError(m_MaximumError);
gaussianFilter->SetInput(input);
progress->RegisterInternalFilter(gaussianFilter, 1.0f / 3.0f);
// Calculate the laplacian of the smoothed image
laplacianFilter->SetInput( gaussianFilter->GetOutput() );
progress->RegisterInternalFilter(laplacianFilter, 1.0f / 3.0f);
// Find the zero-crossings of the laplacian
zerocrossingFilter->SetInput( laplacianFilter->GetOutput() );
zerocrossingFilter->SetBackgroundValue(m_BackgroundValue);
zerocrossingFilter->SetForegroundValue(m_ForegroundValue);
zerocrossingFilter->GraftOutput( this->GetOutput() );
progress->RegisterInternalFilter(zerocrossingFilter, 1.0f / 3.0f);
zerocrossingFilter->Update();
// Graft the output of the mini-pipeline back onto the filter's output.
// This action copies back the region ivars and meta-data
this->GraftOutput( zerocrossingFilter->GetOutput() );
}
template< typename TInputImage, typename TOutputImage >
void
ZeroCrossingBasedEdgeDetectionImageFilter< TInputImage, TOutputImage >
::PrintSelf(std::ostream & os, Indent indent) const
{
Superclass::PrintSelf(os, indent);
os << indent << "Variance: " << m_Variance << std::endl;
os << indent << "MaximumError: " << m_MaximumError << std::endl;
os << indent << "ForegroundValue: "
<< static_cast< typename NumericTraits< OutputImagePixelType >::PrintType >( m_ForegroundValue )
<< std::endl;
os << indent << "BackgroundValue: "
<< static_cast< typename NumericTraits< OutputImagePixelType >::PrintType >( m_BackgroundValue )
<< std::endl;
}
} //end of itk namespace
#endif
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