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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkZeroCrossingBasedEdgeDetectionImageFilter.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 __itkZeroCrossingBasedEdgeDetectionImageFilter_txx
#define __itkZeroCrossingBasedEdgeDetectionImageFilter_txx
#include "itkZeroCrossingBasedEdgeDetectionImageFilter.h"
#include "itkDiscreteGaussianImageFilter.h"
#include "itkLaplacianImageFilter.h"
#include "itkZeroCrossingImageFilter.h"
#include "itkProgressAccumulator.h"
namespace itk
{
template< class TInputImage, class 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< class TInputImage, class 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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