Examples/Filtering/DanielssonDistanceMapImageFilter.cxx

/*=========================================================================
 *
 *  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:  {FivePointsDilated.png}
//    OUTPUTS: {DanielssonDistanceMapImageFilterOutput1.png}
//    OUTPUTS: {DanielssonDistanceMapImageFilterOutput2.png}
//    ARGUMENTS: {DanielssonDistanceMapImageFilterOutput3.mhd}
//  Software Guide : EndCommandLineArgs

// Software Guide : BeginLatex
//
// This example illustrates the use of the
// \doxygen{DanielssonDistanceMapImageFilter}.  This filter generates a
// distance map from the input image using the algorithm developed by
// Danielsson \cite{Danielsson1980}. As secondary outputs, a Voronoi
// partition of the input elements is produced, as well as a vector image
// with the components of the distance vector to the closest point. The input
// to the map is assumed to be a set of points on the input image. The label
// of each group of pixels is assigned by the
// \doxygen{ConnectedComponentImageFilter}.
//
// \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!Instantiation}
// \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!Header}
//
// The first step required to use this filter is to include its header file.
//
// Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
#include "itkDanielssonDistanceMapImageFilter.h"
// Software Guide : EndCodeSnippet

#include "itkImage.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkConnectedComponentImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"


int main( int argc, char * argv[] )
{
  if( argc < 5 )
    {
    std::cerr << "Usage: " << argv[0];
    std::cerr << " inputImageFile outputDistanceMapImageFile ";
    std::cerr << " outputVoronoiMapImageFile ";
    std::cerr << " outputVectorMapImageFile ";
    std::cerr << std::endl;
    return EXIT_FAILURE;
    }

  //  Software Guide : BeginLatex
  //
  //  Then we must decide what pixel types to use for the input and output
  //  images. Since the output will contain distances measured in pixels, the
  //  pixel type should be able to represent at least the width of the image,
  //  or said in $N$-dimensional terms, the maximum extension along all the dimensions.
  //  The input, output (distance map), and voronoi partition image types are
  //  now defined using their respective pixel type and dimension.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using InputPixelType = unsigned char;
  using OutputPixelType = unsigned short;
  using VoronoiPixelType = unsigned char;
  using InputImageType = itk::Image< InputPixelType,  2 >;
  using OutputImageType = itk::Image< OutputPixelType, 2 >;
  using VoronoiImageType = itk::Image< VoronoiPixelType, 2 >;
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  The filter type can be instantiated using the input and output image
  //  types defined above. A filter object is created with the \code{New()}
  //  method.
  //
  //  \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!instantiation}
  //  \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!New()}
  //  \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!Pointer}
  //
  //  Software Guide : EndLatex

  using LabelerType = itk::ConnectedComponentImageFilter<
               InputImageType, InputImageType >;
  LabelerType::Pointer labeler = LabelerType::New();

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

  using RescalerType = itk::RescaleIntensityImageFilter<
                   OutputImageType, OutputImageType >;
  RescalerType::Pointer scaler = RescalerType::New();

  using VoronoiRescalerType = itk::RescaleIntensityImageFilter<
                   VoronoiImageType, VoronoiImageType >;
  VoronoiRescalerType::Pointer voronoiScaler = VoronoiRescalerType::New();

  //
  // Reader and Writer types are instantiated.
  //
  using ReaderType = itk::ImageFileReader< InputImageType  >;
  using WriterType = itk::ImageFileWriter< OutputImageType >;
  using VoronoiWriterType = itk::ImageFileWriter< VoronoiImageType >;

  ReaderType::Pointer reader = ReaderType::New();
  WriterType::Pointer writer = WriterType::New();
  VoronoiWriterType::Pointer voronoiWriter = VoronoiWriterType::New();

  reader->SetFileName( argv[1] );
  writer->SetFileName( argv[2] );
  voronoiWriter->SetFileName( argv[3] );


  //  Software Guide : BeginLatex
  //
  //  The input to the filter is taken from a reader and its output is passed
  //  to a \doxygen{RescaleIntensityImageFilter} and then to a writer. The
  //  scaler and writer are both templated over the image type, so we
  //  instantiate a separate pipeline for the voronoi partition map starting
  //  at the scaler.
  //
  //  \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!SetInput()}
  //  \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!GetOutput()}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  labeler->SetInput(reader->GetOutput() );
  filter->SetInput( labeler->GetOutput() );
  scaler->SetInput( filter->GetOutput() );
  writer->SetInput( scaler->GetOutput() );
  // Software Guide : EndCodeSnippet

  //  Software Guide : BeginLatex
  //
  //  The Voronoi map is obtained with the \code{GetVoronoiMap()} method. In
  //  the lines below we connect this output to the intensity rescaler.
  //
  //  \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!GetVoronoiMap()}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  voronoiScaler->SetInput( filter->GetVoronoiMap() );
  voronoiWriter->SetInput( voronoiScaler->GetOutput() );
  // Software Guide : EndCodeSnippet


  scaler->SetOutputMaximum( 65535L );
  scaler->SetOutputMinimum(     0L );
  voronoiScaler->SetOutputMaximum( 255 );
  voronoiScaler->SetOutputMinimum( 0 );

  //  Software Guide : BeginLatex
  //
  // \begin{figure}
  // \center
  // \includegraphics[width=0.32\textwidth]{FivePointsDilated}
  // \includegraphics[width=0.32\textwidth]{DanielssonDistanceMapImageFilterOutput1}
  // \includegraphics[width=0.32\textwidth]{DanielssonDistanceMapImageFilterOutput2}
  // \itkcaption[DanielssonDistanceMapImageFilter
  // output]{DanielssonDistanceMapImageFilter output. Set of pixels, distance
  // map and Voronoi partition.}
  // \label{fig:DanielssonDistanceMapImageFilterInputOutput}
  // \end{figure}
  //
  //  Figure \ref{fig:DanielssonDistanceMapImageFilterInputOutput} illustrates
  //  the effect of this filter on a binary image with a set of points. The
  //  input image is shown at the left, and the distance map at the center and
  //  the Voronoi partition at the right. This filter computes distance maps in
  //  N-dimensions and is therefore capable of producing $N$-dimensional Voronoi
  //  partitions.
  //
  //  \index{Voronoi partitions}
  //  \index{Voronoi partitions!itk::Danielsson\-Distance\-Map\-Image\-Filter}
  //
  //  Software Guide : EndLatex

  writer->Update();
  voronoiWriter->Update();

  //  Software Guide : BeginLatex
  //
  //  The distance filter also produces an image of \doxygen{Offset} pixels
  //  representing the vectorial distance to the closest object in the scene.
  //  The type of this output image is defined by the VectorImageType
  //  trait of the filter type.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using OffsetImageType = FilterType::VectorImageType;
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  We can use this type for instantiating an \doxygen{ImageFileWriter} type
  //  and creating an object of this class in the following lines.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using WriterOffsetType = itk::ImageFileWriter< OffsetImageType >;
  WriterOffsetType::Pointer offsetWriter = WriterOffsetType::New();
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  The output of the distance filter can be connected as input to the
  //  writer.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  offsetWriter->SetInput(  filter->GetVectorDistanceMap()  );
  // Software Guide : EndCodeSnippet


  offsetWriter->SetFileName( argv[4]  );


  //  Software Guide : BeginLatex
  //
  //  Execution of the writer is triggered by the invocation of the
  //  \code{Update()} method. Since this method can potentially throw
  //  exceptions it must be placed in a \code{try/catch} block.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  try
    {
    offsetWriter->Update();
    }
  catch( itk::ExceptionObject & exp )
    {
    std::cerr << "Exception caught !" << std::endl;
    std::cerr <<     exp    << std::endl;
    }
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  Note that only the \doxygen{MetaImageIO} class supports reading and
  //  writing images of pixel type \doxygen{Offset}.
  //
  //  Software Guide : EndLatex

  return EXIT_SUCCESS;
}