Examples/Filtering/SmoothingRecursiveGaussianImageFilter2.cxx

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//  Software Guide : BeginLatex
//
//  Setting up a pipeline of $m$ filters in order to smooth an N-dimensional
//  image may be a lot of work to do for achieving a simple goal. In order to
//  avoid this inconvenience, a filter packaging this $m$ filters internally
//  is available. This filter is the
//  \doxygen{SmoothingRecursiveGaussianImageFilter}.
//
//  \index{itk::SmoothingRecursiveGaussianImageFilter}
//
//  Software Guide : EndLatex
 
 
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkRescaleIntensityImageFilter.h"
 
//  Software Guide : BeginLatex
//
//  In order to use this filter the following header file must be included.
//
//  \index{itk::SmoothingRecursiveGaussianImageFilter!header}
//
//  Software Guide : EndLatex
 
// Software Guide : BeginCodeSnippet
#include "itkSmoothingRecursiveGaussianImageFilter.h"
// Software Guide : EndCodeSnippet
 
 
int
main(int argc, char * argv[])
{
  if (argc < 4)
  {
    std::cerr << "Usage: " << std::endl;
    std::cerr << argv[0] << "  inputImageFile  outputImageFile  sigma "
              << std::endl;
    return EXIT_FAILURE;
  }
 
 
  //  Software Guide : BeginLatex
  //
  //  Appropriate pixel types must be selected to support input and output
  //  images.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using InputPixelType = float;
  using OutputPixelType = float;
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  With them, the input and output image types can be instantiated.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  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. If the second template parameter is omitted, the
  //  filter will assume that the output image has the same type as the input
  //  image.
  //
  //  \index{itk::SmoothingRecursiveGaussianImageFilter!Instantiation}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using FilterType =
    itk::SmoothingRecursiveGaussianImageFilter<InputImageType,
                                               OutputImageType>;
  // Software Guide : EndCodeSnippet
 
 
  ReaderType::Pointer reader = ReaderType::New();
  reader->SetFileName(argv[1]);
 
 
  //  Software Guide : BeginLatex
  //
  //  Now a single filter is enough for smoothing the image along all the
  //  dimensions.  The filter is created by invoking the \code{New()} method
  //  and assigning the result to a \doxygen{SmartPointer}.
  //
  //  \index{itk::SmoothingRecursiveGaussianImageFilter!New()}
  //  \index{itk::SmoothingRecursiveGaussianImageFilter!Pointer}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  FilterType::Pointer filter = FilterType::New();
  // Software Guide : EndCodeSnippet
 
  //  Software Guide : BeginLatex
  //
  //  As in the previous examples we should decide what type of normalization
  //  to use during the computation of the Gaussians. The method
  //  \code{SetNormalizeAcrossScale()} serves this purpose.
  //  \index{SmoothingRecursiveGaussianImageFilter!SetNormalizeAcrossScale()}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  filter->SetNormalizeAcrossScale(false);
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  The input image can be obtained from the output of another filter. Here,
  //  an image reader is used as source. The image is passed directly to the
  //  smoothing filter.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  filter->SetInput(reader->GetOutput());
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  It is now time for selecting the $\sigma$ of the Gaussian to use for
  //  smoothing the data. Note that $\sigma$ is considered to be in
  //  millimeters.  That is, at the moment of applying the smoothing process,
  //  the filter will take into account the spacing values defined in the
  //  image.
  //
  //  \index{itk::SmoothingRecursiveGaussianImageFilter!SetSigma()}
  //  \index{SetSigma()!itk::SmoothingRecursiveGaussianImageFilter}
  //
  //  Software Guide : EndLatex
 
  const double sigma = std::stod(argv[3]);
 
  // Software Guide : BeginCodeSnippet
  filter->SetSigma(sigma);
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  Finally the pipeline is executed by invoking the \code{Update()} method.
  //
  //  \index{itk::SmoothingRecursiveGaussianImageFilter!Update()}
  //
  //  Software Guide : EndLatex
 
 
  // Software Guide : BeginCodeSnippet
  filter->Update();
  // Software Guide : EndCodeSnippet
 
 
  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]{SmoothingRecursiveGaussianImageFilterOutput3}
  // \includegraphics[width=0.44\textwidth]{SmoothingRecursiveGaussianImageFilterOutput5}
  // \itkcaption[SmoothingRecursiveGaussianImageFilter output]{Effect of the
  // SmoothingRecursiveGaussianImageFilter on a slice from a MRI proton
  // density image of the brain.}
  // \label{fig:SmoothingRecursiveGaussianImageFilterInputOutput} \end{figure}
  //
  //  Figure \ref{fig:SmoothingRecursiveGaussianImageFilterInputOutput}
  //  illustrates the effect of this filter on a MRI proton density image of
  //  the brain using a $\sigma$ value of $3$ (left) and a value of $5$
  //  (right).  The figure shows how the attenuation of noise can be
  //  regulated by selecting an appropriate sigma.  This type of scale-tunable
  //  filter is suitable for performing scale-space analysis.
  //
  //  Software Guide : EndLatex
 
 
  return EXIT_SUCCESS;
}