DanielssonDistanceMapImageFilter.cxx

Example usage:

./DanielssonDistanceMapImageFilter Input/FivePoints.png Output/DanielssonDistanceMapImageFilterOutput1.png Output/DanielssonDistanceMapImageFilterOutput2.png

Example source code (DanielssonDistanceMapImageFilter.cxx):

// This example illustrates the use of the
// \doxygen{itk}{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. Each
// point/pixel is considered to be a separate entity even if they share the
// same gray level value.
//
// \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.

#include "itkConnectedComponentImageFilter.h"
#include "itkDanielssonDistanceMapImageFilter.h"

#include "otbImage.h"
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
#include "itkUnaryFunctorImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"

int main(int argc, char* argv[])
{
  if (argc < 4)
  {
    std::cerr << "Usage: " << argv[0];
    std::cerr << " inputImageFile outputDistanceMapImageFile ";
    std::cerr << " outputVoronoiMapImageFilter ";
    std::cerr << std::endl;
    return EXIT_FAILURE;
  }

  //  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 $N-D$ terms, the maximum extension along all the dimensions.
  //  The input and output image types are now defined using their respective
  //  pixel type and dimension.

  using InputPixelType  = unsigned char;
  using OutputPixelType = unsigned short;
  using InputImageType  = otb::Image<InputPixelType, 2>;
  using OutputImageType = otb::Image<OutputPixelType, 2>;

  //  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}

  using ConnectedType                        = itk::ConnectedComponentImageFilter<InputImageType, InputImageType>;
  ConnectedType::Pointer connectedComponents = ConnectedType::New();

  using FilterType           = itk::DanielssonDistanceMapImageFilter<InputImageType, OutputImageType, OutputImageType>;
  FilterType::Pointer filter = FilterType::New();

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

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

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

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

  //  The input to the filter is taken from a reader and its output is passed
  //  to a \doxygen{itk}{RescaleIntensityImageFilter} and then to a writer.
  //
  //  \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!SetInput()}
  //  \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!GetOutput()}

  connectedComponents->SetInput(reader->GetOutput());
  filter->SetInput(connectedComponents->GetOutput());
  scaler->SetInput(filter->GetOutput());
  writer->SetInput(scaler->GetOutput());

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

  //  The type of input image has to be specified. In this case, a binary
  //  image is selected.
  //
  //  \index{itk::Danielsson\-Distance\-MapImage\-Filter!InputIsBinaryOn()}

  filter->InputIsBinaryOff();

  // \begin{figure}
  // \center
  // \includegraphics[width=0.32\textwidth]{FivePoints.eps}
  // \includegraphics[width=0.32\textwidth]{DanielssonDistanceMapImageFilterOutput1.eps}
  // \includegraphics[width=0.32\textwidth]{DanielssonDistanceMapImageFilterOutput2.eps}
  // \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 left, the distance map at the center and the
  //  Voronoi partition at right. This filter computes distance maps in
  //  N-dimensions and is therefore capable of producing $N-D$ Voronoi
  //  partitions.
  //
  //  \index{Voronoi partitions}
  //  \index{Voronoi partitions!itk::Danielsson\-Distance\-Map\-Image\-Filter}

  writer->Update();
  const char* voronoiMapFileName = argv[3];

  //  The Voronoi map is obtained with the \code{GetVoronoiMap()} method. In
  //  the lines below we connect this output to the intensity rescaler and
  //  save the result in a file.
  //
  //  \index{itk::Danielsson\-Distance\-Map\-Image\-Filter!GetVoronoiMap()}

  scaler->SetInput(filter->GetVoronoiMap());
  writer->SetFileName(voronoiMapFileName);
  writer->Update();

  //  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.


  return EXIT_SUCCESS;
}