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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.
*
*=========================================================================*/
// Software Guide : BeginCommandLineArgs
// INPUTS: {BrainProtonDensitySlice.png}
// OUTPUTS: {BinomialBlurImageFilterOutput.png}
// ARGUMENTS: 5
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// The \doxygen{BinomialBlurImageFilter} computes a nearest neighbor average
// along each dimension. The process is repeated a number of times, as
// specified by the user. In principle, after a large number of iterations
// the result will approach the convolution with a Gaussian.
//
// \index{itk::Binomial\-Blur\-Image\-Filter}
//
// Software Guide : EndLatex
#include "itkImage.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkRescaleIntensityImageFilter.h"
// Software Guide : BeginLatex
//
// The first step required to use this filter is to include its header file.
//
// \index{itk::BinomialBlurImageFilter!header}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include "itkBinomialBlurImageFilter.h"
// Software Guide : EndCodeSnippet
int main( int argc, char * argv[] )
{
if( argc < 4 )
{
std::cerr << "Usage: " << std::endl;
std::cerr << argv[0] << " inputImageFile outputImageFile numberOfRepetitions" << std::endl;
return EXIT_FAILURE;
}
// Software Guide : BeginLatex
//
// Types should be chosen for the pixels of the input and output images.
// Image types can be instantiated using the pixel type and dimension.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef float InputPixelType;
typedef float OutputPixelType;
typedef itk::Image< InputPixelType, 2 > InputImageType;
typedef itk::Image< OutputPixelType, 2 > OutputImageType;
// Software Guide : EndCodeSnippet
typedef itk::ImageFileReader< InputImageType > ReaderType;
// Software Guide : BeginLatex
//
// The filter type is now instantiated using both the input image and the
// output image types. Then a filter object is created.
//
// \index{itk::BinomialBlurImageFilter!instantiation}
// \index{itk::BinomialBlurImageFilter!New()}
// \index{itk::BinomialBlurImageFilter!Pointer}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef itk::BinomialBlurImageFilter<
InputImageType, OutputImageType > FilterType;
FilterType::Pointer filter = FilterType::New();
// Software Guide : EndCodeSnippet
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName( argv[1] );
const unsigned int repetitions = atoi( argv[3] );
// Software Guide : BeginLatex
//
// The input image can be obtained from the output of another filter. Here,
// an image reader is used as the source. The number of repetitions is set with
// the \code{SetRepetitions()} method. Computation time will
// increase linearly with the number of repetitions selected. Finally, the
// filter can be executed by calling the \code{Update()} method.
//
// \index{itk::BinomialBlurImageFilter!Update()}
// \index{itk::BinomialBlurImageFilter!SetInput()}
// \index{itk::BinomialBlurImageFilter!SetRepetitions()}
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
filter->SetInput( reader->GetOutput() );
filter->SetRepetitions( repetitions );
filter->Update();
// Software Guide : EndCodeSnippet
// This section connects the filter output to a writer
//
typedef unsigned char WritePixelType;
typedef itk::Image< WritePixelType, 2 > WriteImageType;
typedef itk::RescaleIntensityImageFilter<
OutputImageType, WriteImageType > RescaleFilterType;
RescaleFilterType::Pointer rescaler = RescaleFilterType::New();
rescaler->SetOutputMinimum( 0 );
rescaler->SetOutputMaximum( 255 );
typedef itk::ImageFileWriter< WriteImageType > WriterType;
WriterType::Pointer writer = WriterType::New();
writer->SetFileName( argv[2] );
rescaler->SetInput( filter->GetOutput() );
writer->SetInput( rescaler->GetOutput() );
writer->Update();
// Software Guide : BeginLatex
//
// \begin{figure}
// \center
// \includegraphics[width=0.44\textwidth]{BrainProtonDensitySlice}
// \includegraphics[width=0.44\textwidth]{BinomialBlurImageFilterOutput}
// \itkcaption[BinomialBlurImageFilter output.]{Effect of the
// BinomialBlurImageFilter on a slice from a MRI proton density image of the
// brain.}
// \label{fig:BinomialBlurImageFilterInputOutput}
// \end{figure}
//
// Figure \ref{fig:BinomialBlurImageFilterInputOutput} illustrates the
// effect of this filter on a MRI proton density image of the brain.
//
// Note that the standard deviation $\sigma$ of the equivalent Gaussian is
// fixed. In the spatial spectrum, the effect of every iteration of this
// filter is like a multiplication with a sinus cardinal function.
//
// Software Guide : EndLatex
return EXIT_SUCCESS;
}
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