Examples/Filtering/BinomialBlurImageFilter.cxx

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 *  Licensed under the Apache License, Version 2.0 (the "License");
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//  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
  using InputPixelType = float;
  using OutputPixelType = float;

  using InputImageType = itk::Image< InputPixelType,  2 >;
  using OutputImageType = itk::Image< OutputPixelType, 2 >;
  // Software Guide : EndCodeSnippet


  using ReaderType = itk::ImageFileReader< InputImageType >;


  //  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
  using FilterType = itk::BinomialBlurImageFilter<
                 InputImageType, OutputImageType >;
  FilterType::Pointer filter = FilterType::New();
  // Software Guide : EndCodeSnippet


  ReaderType::Pointer reader = ReaderType::New();
  reader->SetFileName( argv[1] );
  const unsigned int repetitions = std::stoi( 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
  //
  using WritePixelType = unsigned char;
  using WriteImageType = itk::Image< WritePixelType, 2 >;
  using RescaleFilterType = itk::RescaleIntensityImageFilter<
               OutputImageType, WriteImageType >;

  RescaleFilterType::Pointer rescaler = RescaleFilterType::New();
  rescaler->SetOutputMinimum(   0 );
  rescaler->SetOutputMaximum( 255 );

  using WriterType = itk::ImageFileWriter< WriteImageType >;
  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;
}