Examples/Iterators/NeighborhoodIterators3.cxx

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 *  Copyright NumFOCUS
 *
 *  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"
 
// Software Guide : BeginLatex
//
// This example illustrates a technique for improving the efficiency of
// neighborhood calculations by eliminating unnecessary bounds checking. As
// described in Section~\ref{sec:NeighborhoodIterators}, the neighborhood
// iterator automatically enables or disables bounds checking based on the
// iteration region in which it is initialized. By splitting our image into
// boundary and non-boundary regions, and then processing each region using a
// different neighborhood iterator, the algorithm will only perform
// bounds-checking on those pixels for which it is actually required.  This
// trick can provide a significant speedup for simple algorithms such as our
// Sobel edge detection, where iteration speed is a critical.
//
// Splitting the image into the necessary regions is an easy task when you use
// the \doxygen{NeighborhoodAlgorithm::ImageBoundaryFacesCalculator}.  The
// face calculator is so named because it returns a list of the ``faces'' of
// the ND dataset.  Faces are those regions whose pixels all lie within a
// distance $d$ from the boundary, where $d$ is the radius of the neighborhood
// stencil used for the numerical calculations. In other words, faces are
// those regions where a neighborhood iterator of radius $d$ will always
// overlap the boundary of the image. The face calculator also returns the
// single \emph{inner} region, in which out-of-bounds values are never
// required and bounds checking is not necessary.
//
// The face calculator object is defined in \code{itkNeighborhoodAlgorithm.h}.
// We include this file in addition to those from the previous two examples.
//
// Software Guide : EndLatex
 
#include "itkSobelOperator.h"
#include "itkNeighborhoodInnerProduct.h"
 
// Software Guide : BeginCodeSnippet
#include "itkNeighborhoodAlgorithm.h"
// Software Guide : EndCodeSnippet
 
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 direction"
              << 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::SobelOperator<PixelType, 2> sobelOperator;
  sobelOperator.SetDirection(std::stoi(argv[3]));
  sobelOperator.CreateDirectional();
 
  itk::NeighborhoodInnerProduct<ImageType> innerProduct;
 
  // Software Guide : BeginLatex
  //
  // First we load the input image and create the output image and inner
  // product function as in the previous examples.  The image iterators will
  // be created in a later step.  Next we create a face calculator object.  An
  // empty list is created to hold the regions that will later on be returned
  // by the face calculator.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using FaceCalculatorType =
    itk::NeighborhoodAlgorithm::ImageBoundaryFacesCalculator<ImageType>;
 
  FaceCalculatorType               faceCalculator;
  FaceCalculatorType::FaceListType faceList;
  // Software Guide : EndCodeSnippet
 
  // Software Guide : BeginLatex
  //
  // The face calculator function is invoked by passing it an image pointer,
  // an image region, and a neighborhood radius.  The image pointer is the
  // same image used to initialize the neighborhood iterator, and the image
  // region is the region that the algorithm is going to process.  The radius
  // is the radius of the iterator.
  //
  // Notice that in this case the image region is given as the region of the
  // \emph{output} image and the image pointer is given as that of the
  // \emph{input} image.  This is important if the input and output images
  // differ in size, i.e. the input image is larger than the output image. ITK
  // image filters, for example, operate on data from the input image but only
  // generate results in the \code{RequestedRegion} of the output image, which
  // may be smaller than the full extent of the input.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  faceList = faceCalculator(reader->GetOutput(),
                            output->GetRequestedRegion(),
                            sobelOperator.GetRadius());
  // Software Guide : EndCodeSnippet
 
  // Software Guide : BeginLatex
  //
  // The face calculator has returned a list of $2N+1$ regions. The first
  // element in the list is always the inner region, which may or may not be
  // important depending on the application.  For our purposes it does not
  // matter because all regions are processed the same way.  We use an
  // iterator to traverse the list of faces.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  FaceCalculatorType::FaceListType::iterator fit;
  // Software Guide : EndCodeSnippet
 
  // Software Guide : BeginLatex
  //
  // We now rewrite the main loop of the previous example so that each region
  // in the list is processed by a separate iterator.  The iterators \code{it}
  // and \code{out} are reinitialized over each region in turn.  Bounds
  // checking is automatically enabled for those regions that require it, and
  // disabled for the region that does not.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  IteratorType             out;
  NeighborhoodIteratorType it;
 
  for (fit = faceList.begin(); fit != faceList.end(); ++fit)
  {
    it = NeighborhoodIteratorType(
      sobelOperator.GetRadius(), reader->GetOutput(), *fit);
    out = IteratorType(output, *fit);
 
    for (it.GoToBegin(), out.GoToBegin(); !it.IsAtEnd(); ++it, ++out)
    {
      out.Set(innerProduct(it, sobelOperator));
    }
  }
  // Software Guide : EndCodeSnippet
 
 
  // Software Guide : BeginLatex
  //
  // The output is written as before.  Results for this example are the same
  // as the previous example.  You may not notice the speedup except on larger
  // images.  When moving to 3D and higher dimensions, the effects are greater
  // because the volume to surface area ratio is usually larger.  In other
  // words, as the number of interior pixels increases relative to the number
  // of face pixels, there is a corresponding increase in efficiency from
  // disabling bounds checking on interior pixels.
  //
  // 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;
}