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 *  Copyright NumFOCUS
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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.
 *  You may obtain a copy of the License at
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 *         https://www.apache.org/licenses/LICENSE-2.0.txt
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 *  distributed under the License is distributed on an "AS IS" BASIS,
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//  Software Guide : BeginCommandLineArgs
//    INPUTS:  {BrainProtonDensitySlice.png}
//    OUTPUTS: {ConfidenceConnectedOutput1.png}
//    ARGUMENTS:    60 116
//  Software Guide : EndCommandLineArgs
//  Software Guide : BeginCommandLineArgs
//    INPUTS:  {BrainProtonDensitySlice.png}
//    OUTPUTS: {ConfidenceConnectedOutput2.png}
//    ARGUMENTS:    81 112
//  Software Guide : EndCommandLineArgs
//  Software Guide : BeginCommandLineArgs
//    INPUTS:  {BrainProtonDensitySlice.png}
//    OUTPUTS: {ConfidenceConnectedOutput3.png}
//    ARGUMENTS:    107 69
//  Software Guide : EndCommandLineArgs

// Software Guide : BeginLatex
//
// \index{itk::FloodFillIterator!In Region Growing}
// \index{itk::ConfidenceConnectedImageFilter}
// \index{itk::ConfidenceConnectedImageFilter!header}
//
// The following example illustrates the use of the
// \doxygen{ConfidenceConnectedImageFilter}. The criterion used by the
// ConfidenceConnectedImageFilter is based on simple statistics of the
// current region. First, the algorithm computes the mean and standard
// deviation of intensity values for all the pixels currently included in the
// region. A user-provided factor is used to multiply the standard deviation
// and define a range around the mean. Neighbor pixels whose intensity values
// fall inside the range are accepted and included in the region. When no
// more neighbor pixels are found that satisfy the criterion, the algorithm
// is considered to have finished its first iteration. At that point, the
// mean and standard deviation of the intensity levels are recomputed using
// all the pixels currently included in the region. This mean and standard
// deviation defines a new intensity range that is used to visit current
// region neighbors and evaluate whether their intensity falls inside the
// range.  This iterative process is repeated until no more pixels are added
// or the maximum number of iterations is reached. The following equation
// illustrates the inclusion criterion used by this filter,
//
// \begin{equation}
// I(\mathbf{X}) \in [ m - f \sigma , m + f \sigma ]
// \end{equation}
//
// where $m$ and $\sigma$ are the mean and standard deviation of the  region
// intensities, $f$ is a factor defined by the user, $I()$ is the image and
// $\mathbf{X}$ is the position of the particular neighbor pixel being
// considered for inclusion in the region.
//
// Let's look at the minimal code required to use this algorithm. First, the
// following header defining the \doxygen{ConfidenceConnectedImageFilter}
// class must be included.
//
// Software Guide : EndLatex


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


#include "itkCastImageFilter.h"


//  Software Guide : BeginLatex
//
//  Noise present in the image can reduce the capacity of this filter to grow
//  large regions. When faced with noisy images, it is usually convenient to
//  pre-process the image by using an edge-preserving smoothing filter. Any of
//  the filters discussed in Section~\ref{sec:EdgePreservingSmoothingFilters}
//  can be used to this end. In this particular example we use the
//  \doxygen{CurvatureFlowImageFilter}, hence we need to include its header
//  file.
//
//  Software Guide : EndLatex

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


#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"


int
main(int argc, char * argv[])
{
  if (argc < 5)
  {
    std::cerr << "Missing Parameters " << std::endl;
    std::cerr << "Usage: " << argv[0];
    std::cerr << " inputImage  outputImage seedX seedY " << std::endl;
    return EXIT_FAILURE;
  }


  //  Software Guide : BeginLatex
  //
  //  We now define the image type using a pixel type and a particular
  //  dimension. In this case the \code{float} type is used for the pixels due
  //  to the requirements of the smoothing filter.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using InternalPixelType = float;
  constexpr unsigned int Dimension = 2;
  using InternalImageType = itk::Image<InternalPixelType, Dimension>;
  // Software Guide : EndCodeSnippet

  using OutputPixelType = unsigned char;
  using OutputImageType = itk::Image<OutputPixelType, Dimension>;

  using CastingFilterType =
    itk::CastImageFilter<InternalImageType, OutputImageType>;
  auto caster = CastingFilterType::New();

  // Read the input image
  const auto input = itk::ReadImage<InternalImageType>(argv[1]);

  //  Software Guide : BeginLatex
  //
  //  The smoothing filter type is instantiated using the image type as
  //  a template parameter.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using CurvatureFlowImageFilterType =
    itk::CurvatureFlowImageFilter<InternalImageType, InternalImageType>;
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  Next the filter is created by invoking the \code{New()} method and
  //  assigning the result to a \doxygen{SmartPointer}.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  auto smoothing = CurvatureFlowImageFilterType::New();
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  We now declare the type of the region growing filter. In this case it is
  //  the \code{ConfidenceConnectedImageFilter}.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using ConnectedFilterType =
    itk::ConfidenceConnectedImageFilter<InternalImageType, InternalImageType>;
  // Software Guide : EndCodeSnippet

  //  Software Guide : BeginLatex
  //
  //  Then, we construct one filter of this class using the \code{New()}
  //  method.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  auto confidenceConnected = ConnectedFilterType::New();
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  Now it is time to create a simple, linear pipeline. A file reader is
  //  added at the beginning of the pipeline and a cast filter is
  //  added at the end. The cast filter is required here to convert
  //  \code{float} pixel types to integer types since only a few image file
  //  formats support \code{float} types.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  smoothing->SetInput(input);
  confidenceConnected->SetInput(smoothing->GetOutput());
  caster->SetInput(confidenceConnected->GetOutput());
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  \code{CurvatureFlowImageFilter} requires two parameters.  The
  //  following are typical values for $2D$ images. However they may have to
  //  be adjusted depending on the amount of noise present in the input
  //  image.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  smoothing->SetNumberOfIterations(5);
  smoothing->SetTimeStep(0.125);
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  \code{ConfidenceConnectedImageFilter} also requires two parameters.
  //  First, the factor $f$ defines how large the range of
  //  intensities will be. Small values of the multiplier will restrict the
  //  inclusion of pixels to those having very similar intensities to those
  //  in the current region. Larger values of the multiplier will relax the
  //  accepting condition and will result in more generous growth of the
  //  region. Values that are too large will cause the region to grow into
  //  neighboring regions which may belong to separate anatomical
  //  structures. This is not desirable behavior.
  //
  //  \index{itk::ConfidenceConnectedImageFilter!SetMultiplier()}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  confidenceConnected->SetMultiplier(2.5);
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  The number of iterations is specified based on the homogeneity of the
  //  intensities of the anatomical structure to be segmented. Highly
  //  homogeneous regions may only require a couple of iterations. Regions
  //  with ramp effects, like MRI images with inhomogeneous fields, may
  //  require more iterations. In practice, it seems to be more important to
  //  carefully select the multiplier factor than the number of iterations.
  //  However, keep in mind that there is no guarantee that this
  //  algorithm will converge on a stable region. It is possible that by
  //  letting the algorithm run for more iterations the region will end up
  //  engulfing the entire image.
  //
  //  \index{itk::ConfidenceConnectedImageFilter!SetNumberOfIterations()}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  confidenceConnected->SetNumberOfIterations(5);
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  The output of this filter is a binary image with zero-value pixels
  //  everywhere except on the extracted region. The intensity value to be
  //  set inside the region is selected with the method
  //  \code{SetReplaceValue()}.
  //
  //  \index{itk::ConfidenceConnectedImageFilter!SetReplaceValue()}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  confidenceConnected->SetReplaceValue(255);
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  The initialization of the algorithm requires the user to provide a seed
  //  point. It is convenient to select this point to be placed in a
  //  \emph{typical} region of the anatomical structure to be segmented. A
  //  small neighborhood around the seed point will be used to compute the
  //  initial mean and standard deviation for the inclusion criterion. The
  //  seed is passed in the form of an \doxygen{Index} to the \code{SetSeed()}
  //  method.
  //
  //  \index{itk::ConfidenceConnectedImageFilter!SetSeed()}
  //  \index{itk::ConfidenceConnectedImageFilter!SetInitialNeighborhoodRadius()}
  //
  //  Software Guide : EndLatex

  InternalImageType::IndexType index;

  index[0] = std::stoi(argv[3]);
  index[1] = std::stoi(argv[4]);


  // Software Guide : BeginCodeSnippet
  confidenceConnected->SetSeed(index);
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  The size of the initial neighborhood around the seed is defined with the
  //  method \code{SetInitialNeighborhoodRadius()}. The neighborhood will be
  //  defined as an $N$-dimensional rectangular region with $2r+1$ pixels on
  //  the side, where $r$ is the value passed as initial neighborhood radius.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  confidenceConnected->SetInitialNeighborhoodRadius(2);
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  The invocation of \code{WriteImage} triggers the
  //  execution of the pipeline.  It is recommended to place write calls in a
  //  \code{try/catch} block in case errors occur and exceptions are thrown.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  try
  {
    itk::WriteImage(caster->GetOutput(), argv[2]);
  }
  catch (const itk::ExceptionObject & excep)
  {
    std::cerr << "Exception caught !" << std::endl;
    std::cerr << excep << std::endl;
  }
  // Software Guide : EndCodeSnippet


  // clang-format off
  //  Software Guide : BeginLatex
  //
  //  Let's now run this example using as input the image
  //  \code{BrainProtonDensitySlice.png} provided in the directory
  //  \code{Examples/Data}. We can easily segment the major anatomical
  //  structures by providing seeds in the appropriate locations. For example
  //
  //  \begin{center}
  //  \begin{tabular}{|l|c|c|}
  //  \hline
  //  Structure & Seed Index & Output Image \\ \hline
  //  White matter & $(60,116)$ & Second from left in Figure \ref{fig:ConfidenceConnectedOutput} \\ \hline
  //  Ventricle    & $(81,112)$ & Third  from left in Figure \ref{fig:ConfidenceConnectedOutput} \\ \hline
  //  Gray matter  & $(107,69)$ & Fourth from left in Figure \ref{fig:ConfidenceConnectedOutput} \\ \hline
  //  \end{tabular}
  //  \end{center}
  //
  // \begin{figure} \center
  // \includegraphics[width=0.24\textwidth]{BrainProtonDensitySlice}
  // \includegraphics[width=0.24\textwidth]{ConfidenceConnectedOutput1}
  // \includegraphics[width=0.24\textwidth]{ConfidenceConnectedOutput2}
  // \includegraphics[width=0.24\textwidth]{ConfidenceConnectedOutput3}
  // \itkcaption[ConfidenceConnected segmentation results]{Segmentation results
  // for the ConfidenceConnected filter for various seed points.}
  // \label{fig:ConfidenceConnectedOutput}
  // \end{figure}
  //
  //  Note that the gray matter is not being completely segmented.  This
  //  illustrates the vulnerability of the region growing methods when the
  //  anatomical structures to be segmented do not have a homogeneous
  //  statistical distribution over the image space. You may want to
  //  experiment with different numbers of iterations to verify how the
  //  accepted region will extend.
  //
  //  Software Guide : EndLatex
  //  clang-format on


  return EXIT_SUCCESS;
}
