Examples/Iterators/NeighborhoodIterators6.cxx

/*=========================================================================
 *
 *  Copyright NumFOCUS
 *
 *  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
 *
 *         https://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.
 *
 *=========================================================================*/
 
#include "itkImage.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkNeighborhoodIterator.h"
#include "itkFastMarchingImageFilter.h"
#include "itkRandomImageSource.h"
#include "itkAddImageFilter.h"
 
// Software Guide : BeginLatex
//
// Some image processing routines do not need to visit every pixel in an
// image. Flood-fill and connected-component algorithms, for example, only
// visit pixels that are locally connected to one another.  Algorithms
// such as these can be efficiently written using the random access
// capabilities of the neighborhood iterator.
//
// The following example finds local minima.  Given a seed point, we can
// search the neighborhood of that point and pick the smallest value $m$.
// While $m$ is not at the center of our current neighborhood, we move in the
// direction of $m$ and repeat the analysis.  Eventually we discover a local
// minimum and stop.  This algorithm is made trivially simple in ND using an
// ITK neighborhood iterator.
//
// To illustrate the process, we create an image that descends everywhere to a
// single minimum:  a positive distance transform to a point.  The details of
// creating the distance transform are not relevant to the discussion of
// neighborhood iterators, but can be found in the source code of this
// example. Some noise has been added to the distance transform image for
// additional interest.
//
// 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] << " outputImageFile startX startY" << std::endl;
    return EXIT_FAILURE;
  }
 
  using PixelType = float;
  using ImageType = itk::Image<PixelType, 2>;
  using NeighborhoodIteratorType = itk::NeighborhoodIterator<ImageType>;
 
  using FastMarchingFilterType =
    itk::FastMarchingImageFilter<ImageType, ImageType>;
 
  auto fastMarching = FastMarchingFilterType::New();
 
  using NodeContainer = FastMarchingFilterType::NodeContainer;
  using NodeType = FastMarchingFilterType::NodeType;
 
  auto seeds = NodeContainer::New();
 
  ImageType::IndexType seedPosition;
 
  seedPosition[0] = 128;
  seedPosition[1] = 128;
  constexpr double initialDistance = 1.0;
 
  NodeType node;
 
  const double seedValue = -initialDistance;
 
  ImageType::SizeType size = { { 256, 256 } };
 
  node.SetValue(seedValue);
  node.SetIndex(seedPosition);
  seeds->Initialize();
  seeds->InsertElement(0, node);
 
  fastMarching->SetTrialPoints(seeds);
  fastMarching->SetSpeedConstant(1.0);
 
  auto adder = itk::AddImageFilter<ImageType, ImageType, ImageType>::New();
  auto noise = itk::RandomImageSource<ImageType>::New();
 
  noise->SetSize(size.m_InternalArray);
  noise->SetMin(-.7);
  noise->SetMax(.8);
  adder->SetInput1(noise->GetOutput());
  adder->SetInput2(fastMarching->GetOutput());
 
  try
  {
    fastMarching->SetOutputSize(size);
    fastMarching->Update();
 
    adder->Update();
  }
  catch (const itk::ExceptionObject & excep)
  {
    std::cerr << "Exception caught !" << std::endl;
    std::cerr << excep << std::endl;
    return EXIT_FAILURE;
  }
 
  ImageType::Pointer input = adder->GetOutput();
 
  // Software Guide : BeginLatex
  //
  // The variable \code{input} is the pointer to the distance transform image.
  // The local minimum algorithm is initialized with a seed point read from
  // the command line.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  ImageType::IndexType index;
  index[0] = std::stoi(argv[2]);
  index[1] = std::stoi(argv[3]);
  // Software Guide : EndCodeSnippet
 
  // Software Guide : BeginLatex
  //
  // Next we create the neighborhood iterator and position it at the seed
  // point.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  NeighborhoodIteratorType::RadiusType radius;
  radius.Fill(1);
  NeighborhoodIteratorType it(radius, input, input->GetRequestedRegion());
 
  it.SetLocation(index);
  // Software Guide : EndCodeSnippet
 
  // Software Guide : BeginLatex
  //
  // Searching for the local minimum involves finding the minimum in the
  // current neighborhood, then shifting the neighborhood in the direction of
  // that minimum.  The \code{for} loop below records the \doxygen{Offset} of
  // the minimum neighborhood pixel.  The neighborhood iterator is then moved
  // using that offset.  When a local minimum is detected, \code{flag} will
  // remain false and the \code{while} loop will exit.  Note that this code is
  // valid for an image of any dimensionality.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  bool flag = true;
  while (flag == true)
  {
    NeighborhoodIteratorType::OffsetType nextMove;
    nextMove.Fill(0);
 
    flag = false;
 
    PixelType min = it.GetCenterPixel();
    for (unsigned int i = 0; i < it.Size(); ++i)
    {
      if (it.GetPixel(i) < min)
      {
        min = it.GetPixel(i);
        nextMove = it.GetOffset(i);
        flag = true;
      }
    }
    it.SetCenterPixel(255.0);
    it += nextMove;
  }
  // Software Guide : EndCodeSnippet
 
 
  // Software Guide : BeginLatex
  //
  // Figure~\ref{fig:NeighborhoodExample6} shows the results of the algorithm
  // for several seed points.  The white line is the path of the iterator from
  // the seed point to the minimum in the center of the image.  The effect of
  // the additive noise is visible as the small perturbations in the paths.
  //
  // \begin{figure} \centering
  // \includegraphics[width=0.3\textwidth]{NeighborhoodIterators6a}
  // \includegraphics[width=0.3\textwidth]{NeighborhoodIterators6b}
  // \includegraphics[width=0.3\textwidth]{NeighborhoodIterators6c}
  // \itkcaption[Finding local minima]{Paths traversed by the neighborhood
  // iterator from different seed points to the local minimum.
  // The true minimum is at the center
  // of the image.  The path of the iterator is shown in white. The effect of
  // noise in the image is seen as small perturbations in each path. }
  // \protect\label{fig:NeighborhoodExample6} \end{figure}
  //
  // 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(input);
 
  auto writer = WriterType::New();
  writer->SetFileName(argv[1]);
  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;
}