Examples/Filtering/GradientMagnitudeImageFilter.cxx

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 *  Copyright NumFOCUS
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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: {GradientMagnitudeImageFilterOutput.png}
//  Software Guide : EndCommandLineArgs
//
//  Software Guide : BeginLatex
//
//  The magnitude of the image gradient is extensively used in image analysis,
//  mainly to help in the determination of object contours and the
//  separation of homogeneous regions. The
//  \doxygen{GradientMagnitudeImageFilter} computes the magnitude of the
//  image gradient at each pixel location using a simple finite differences
//  approach. For example, in the case of $2D$ the computation is equivalent
//  to convolving the image with masks of type
//
//  \begin{center}
//  \begin{picture}(200,50)
//  \put( 5.0,32.0){\framebox(30.0,15.0){-1}}
//  \put(35.0,32.0){\framebox(30.0,15.0){0}}
//  \put(65.0,32.0){\framebox(30.0,15.0){1}}
//  \put(105.0,17.0){\framebox(20.0,15.0){1}}
//  \put(105.0,32.0){\framebox(20.0,15.0){0}}
//  \put(105.0,47.0){\framebox(20.0,15.0){-1}}
//  \end{picture}
//  \end{center}
//
//  then adding the sum of their squares and computing the square root of the
//  sum.
//
//  This filter will work on images of any dimension thanks to the internal
//  use of \doxygen{NeighborhoodIterator} and \doxygen{NeighborhoodOperator}.
//
//  \index{itk::GradientMagnitudeImageFilter}
//
//  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::GradientMagnitudeImageFilter!header}
//
//  Software Guide : EndLatex
 
// Software Guide : BeginCodeSnippet
#include "itkGradientMagnitudeImageFilter.h"
// Software Guide : EndCodeSnippet
 
int
main(int argc, char * argv[])
{
  if (argc < 3)
  {
    std::cerr << "Usage: " << std::endl;
    std::cerr << argv[0] << "  inputImageFile  outputImageFile " << std::endl;
    return EXIT_FAILURE;
  }
 
 
  //  Software Guide : BeginLatex
  //
  //  Types should be chosen for the pixels of the input and output images.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using InputPixelType = float;
  using OutputPixelType = float;
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  The input and output image types can be defined using the pixel types.
  //
  //  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 type of the gradient magnitude filter is defined by the
  //  input image and the output image types.
  //
  //  \index{itk::GradientMagnitudeImageFilter!instantiation}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using FilterType =
    itk::GradientMagnitudeImageFilter<InputImageType, OutputImageType>;
  // Software Guide : EndCodeSnippet
 
 
  auto reader = ReaderType::New();
  reader->SetFileName(argv[1]);
 
 
  //  Software Guide : BeginLatex
  //
  //  A filter object is created by invoking the \code{New()} method and
  //  assigning the result to a \doxygen{SmartPointer}.
  //
  //  \index{itk::GradientMagnitudeImageFilter!New()}
  //  \index{itk::GradientMagnitudeImageFilter!Pointer}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  auto filter = FilterType::New();
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  The input image can be obtained from the output of another filter. Here,
  //  the source is an image reader.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  filter->SetInput(reader->GetOutput());
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  Finally, the filter is executed by invoking the \code{Update()} method.
  //
  //  \index{itk::GradientMagnitudeImageFilter!Update()}
  //
  //  Software Guide : EndLatex
 
 
  // Software Guide : BeginCodeSnippet
  filter->Update();
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  If the output of this filter has been connected to other filters in a
  //  pipeline, updating any of the downstream filters will also trigger an
  //  update of this filter. For example, the gradient magnitude filter may be
  //  connected to an image writer.
  //
  //  Software Guide : EndLatex
 
  using WritePixelType = unsigned char;
  using WriteImageType = itk::Image<WritePixelType, 2>;
  using RescaleFilterType =
    itk::RescaleIntensityImageFilter<OutputImageType, WriteImageType>;
 
  auto rescaler = RescaleFilterType::New();
 
  rescaler->SetOutputMinimum(0);
  rescaler->SetOutputMaximum(255);
 
  using WriterType = itk::ImageFileWriter<WriteImageType>;
  auto writer = WriterType::New();
  writer->SetFileName(argv[2]);
 
  // Software Guide : BeginCodeSnippet
  rescaler->SetInput(filter->GetOutput());
  writer->SetInput(rescaler->GetOutput());
  writer->Update();
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  // \begin{figure}
  // \center
  // \includegraphics[width=0.44\textwidth]{BrainProtonDensitySlice}
  // \includegraphics[width=0.44\textwidth]{GradientMagnitudeImageFilterOutput}
  // \itkcaption[GradientMagnitudeImageFilter output]{Effect of the
  // GradientMagnitudeImageFilter on a slice from a MRI proton density image
  // of the brain.}
  // \label{fig:GradientMagnitudeImageFilterInputOutput}
  // \end{figure}
  //
  //  Figure \ref{fig:GradientMagnitudeImageFilterInputOutput} illustrates the
  //  effect of the gradient magnitude filter on a MRI proton density image of
  //  the brain. The figure shows the sensitivity of this filter to noisy
  //  data.
  //
  //  Attention should be paid to the image type chosen to represent the
  //  output image since the dynamic range of the gradient magnitude image is
  //  usually smaller than the dynamic range of the input image. As always,
  //  there are exceptions to this rule, for example, synthetic images that
  //  contain high contrast objects.
  //
  //  This filter does not apply any smoothing to the image before computing
  //  the gradients. The results can therefore be very sensitive to noise and
  //  may not be the best choice for scale-space analysis.
  //
  //  Software Guide : EndLatex
 
 
  return EXIT_SUCCESS;
}