Examples/Iterators/NeighborhoodIterators5.cxx

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 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
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#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkConstNeighborhoodIterator.h"
#include "itkImageRegionIterator.h"
#include "itkNeighborhoodAlgorithm.h"
#include "itkGaussianOperator.h"
#include "itkNeighborhoodInnerProduct.h"
 
// Software Guide : BeginLatex
//
// This example introduces slice-based neighborhood processing.  A slice, in
// this context, is a 1D path through an ND neighborhood. Slices are defined
// for generic arrays by the \code{std::slice} class as a start index, a step
// size, and an end index.  Slices simplify the implementation of certain
// neighborhood calculations.  They also provide a mechanism for taking inner
// products with subregions of neighborhoods.
//
// Suppose, for example, that we want to take partial derivatives in the $y$
// direction of a neighborhood, but offset those derivatives by one pixel
// position along the positive $x$ direction.  For a $3\times3$, 2D
// neighborhood iterator, we can construct an \code{std::slice}, \code{(start
// = 2, stride = 3, end = 8)}, that represents the neighborhood offsets $(1,
// -1)$, $(1, 0)$, $(1, 1)$ (see Figure~\ref{fig:NeighborhoodIteratorFig2}).
// If we pass this slice as an extra argument to the
// \doxygen{NeighborhoodInnerProduct} function, then the inner product is
// taken only along that slice.  This ``sliced'' inner product with a 1D
// \doxygen{DerivativeOperator} gives the desired derivative.
//
// The previous separable Gaussian filtering example can be rewritten using
// slices and slice-based inner products.  In general, slice-based processing
// is most useful when doing many different calculations on the same
// neighborhood, where defining multiple iterators as in
// Section~\ref{sec:NeighborhoodExample4} becomes impractical or inefficient.
// Good examples of slice-based neighborhood processing can be found in any of
// the ND anisotropic diffusion function objects, such as
// \doxygen{CurvatureNDAnisotropicDiffusionFunction}.
//
// Software Guide : EndLatex
 
int
main(int argc, char ** argv)
{
  if (argc < 4)
  {
    std::cerr << "Missing parameters. " << std::endl;
    std::cerr << "Usage: " << std::endl;
    std::cerr << argv[0] << " inputImageFile outputImageFile sigma"
              << std::endl;
    return EXIT_FAILURE;
  }
 
  using PixelType = float;
  using ImageType = itk::Image<PixelType, 2>;
  using ReaderType = itk::ImageFileReader<ImageType>;
 
  using NeighborhoodIteratorType = itk::ConstNeighborhoodIterator<ImageType>;
  using IteratorType = itk::ImageRegionIterator<ImageType>;
 
  auto reader = ReaderType::New();
  reader->SetFileName(argv[1]);
  try
  {
    reader->Update();
  }
  catch (const itk::ExceptionObject & err)
  {
    std::cerr << "ExceptionObject caught !" << std::endl;
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
  }
 
  auto output = ImageType::New();
  output->SetRegions(reader->GetOutput()->GetRequestedRegion());
  output->Allocate();
 
  itk::NeighborhoodInnerProduct<ImageType> innerProduct;
 
  using FaceCalculatorType =
    itk::NeighborhoodAlgorithm::ImageBoundaryFacesCalculator<ImageType>;
 
  FaceCalculatorType                         faceCalculator;
  FaceCalculatorType::FaceListType           faceList;
  FaceCalculatorType::FaceListType::iterator fit;
 
  IteratorType             out;
  NeighborhoodIteratorType it;
 
  // Software Guide: BeginLatex
  //
  // The first difference between this example and the previous example is
  // that the Gaussian operator is only initialized once.  Its direction is
  // not important because it is only a 1D array of coefficients.
  //
  // Software Guide: EndLatex
 
  // Software Guide : BeginCodeSnippet
  itk::GaussianOperator<PixelType, 2> gaussianOperator;
  gaussianOperator.SetDirection(0);
  gaussianOperator.SetVariance(std::stod(argv[3]) * std::stod(argv[3]));
  gaussianOperator.CreateDirectional();
  // Software Guide : EndCodeSnippet
 
  // Software Guide : BeginLatex
  //
  // Next we need to define a radius for the iterator.  The radius in all
  // directions matches that of the single extent of the Gaussian operator,
  // defining a square neighborhood.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  NeighborhoodIteratorType::RadiusType radius;
  radius.Fill(gaussianOperator.GetRadius()[0]);
  // Software Guide EndCodeSnippet
 
  // Software Guide : BeginLatex
  //
  // The inner product and face calculator are defined for the main processing
  // loop as before, but now the iterator is reinitialized each iteration with
  // the square \code{radius} instead of the radius of the operator.  The
  // inner product is taken using a slice along the axial direction
  // corresponding to the current iteration.  Note the use of
  // \code{GetSlice()} to return the proper slice from the iterator itself.
  // \code{GetSlice()} can only be used to return the slice along the complete
  // extent of the axial direction of a neighborhood.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  ImageType::Pointer input = reader->GetOutput();
  faceList = faceCalculator(input, output->GetRequestedRegion(), radius);
 
  for (unsigned int i = 0; i < ImageType::ImageDimension; ++i)
  {
    for (fit = faceList.begin(); fit != faceList.end(); ++fit)
    {
      it = NeighborhoodIteratorType(radius, input, *fit);
      out = IteratorType(output, *fit);
      for (it.GoToBegin(), out.GoToBegin(); !it.IsAtEnd(); ++it, ++out)
      {
        out.Set(innerProduct(it.GetSlice(i), it, gaussianOperator));
      }
    }
 
    // Swap the input and output buffers
    if (i != ImageType::ImageDimension - 1)
    {
      ImageType::Pointer tmp = input;
      input = output;
      output = tmp;
    }
  }
  // Software Guide : EndCodeSnippet
 
 
  // Software Guide : BeginLatex
  //
  // This technique produces exactly the same results as the previous example.
  // A little experimentation, however, will reveal that it is less efficient
  // since the neighborhood iterator is keeping track of extra, unused pixel
  // locations for each iteration, while the previous example only references
  // those pixels that it needs.  In cases, however, where an algorithm takes
  // multiple derivatives or convolution products over the same neighborhood,
  // slice-based processing can increase efficiency and simplify the
  // implementation.
  //
  // Software Guide : EndLatex
 
  using WritePixelType = unsigned char;
  using WriteImageType = itk::Image<WritePixelType, 2>;
  using WriterType = itk::ImageFileWriter<WriteImageType>;
 
  using RescaleFilterType =
    itk::RescaleIntensityImageFilter<ImageType, WriteImageType>;
 
  auto rescaler = RescaleFilterType::New();
 
  rescaler->SetOutputMinimum(0);
  rescaler->SetOutputMaximum(255);
  rescaler->SetInput(output);
 
  auto writer = WriterType::New();
  writer->SetFileName(argv[2]);
  writer->SetInput(rescaler->GetOutput());
  try
  {
    writer->Update();
  }
  catch (const itk::ExceptionObject & err)
  {
    std::cerr << "ExceptionObject caught !" << std::endl;
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
  }
 
  return EXIT_SUCCESS;
}