/*
 * Copyright (C) 1999-2011 Insight Software Consortium
 * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
 *
 * This file is part of Orfeo Toolbox
 *
 *     https://www.orfeo-toolbox.org/
 *
 * 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
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * 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.
 */



//  Software Guide : BeginCommandLineArgs
//  INPUTS: {QB_Suburb.png}
//  OUTPUTS: {IsolatedConnectedImageFilterOutput1.png}
//  61 140 150 63 43
//  Software Guide : EndCommandLineArgs

// Software Guide : BeginLatex
//
// The following example illustrates the use of the
// \doxygen{itk}{IsolatedConnectedImageFilter}.  This filter is a close variant of
// the \doxygen{itk}{ConnectedThresholdImageFilter}.  In this filter two seeds and a
// lower threshold are provided by the user. The filter will grow a region
// connected to the first seed and \textbf{not connected} to the second one. In
// order to do this, the filter finds an intensity value that could be used as
// upper threshold for the first seed. A binary search is used to find the
// value that separates both seeds.
//
// This example closely follows the previous ones. Only the relevant pieces
// of code are highlighted here.
//
// Software Guide : EndLatex

//  Software Guide : BeginLatex
//
//  The header of the IsolatedConnectedImageFilter is included below.
//
//  \index{itk::Isolated\-Connected\-Image\-Filter!header}
//
//  Software Guide : EndLatex

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

#include "otbImage.h"
#include "itkUnaryFunctorImageFilter.h"
#include "itkCastImageFilter.h"
#include "itkCurvatureFlowImageFilter.h"
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"

int main(int argc, char *argv[])
{
  if (argc < 8)
    {
    std::cerr << "Missing Parameters " << std::endl;
    std::cerr << "Usage: " << argv[0];
    std::cerr << " inputImage  outputImage seedX1 seedY1";
    std::cerr << " lowerThreshold seedX2 seedY2" << std::endl;
    return EXIT_FAILURE;
    }

  //  Software Guide : BeginLatex
  //
  //  We define the image type using a pixel type and a particular
  //  dimension.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  typedef   float InternalPixelType;
  const unsigned int Dimension = 2;
  typedef otb::Image<InternalPixelType, Dimension> InternalImageType;
  // Software Guide : EndCodeSnippet

  typedef unsigned char                          OutputPixelType;
  typedef otb::Image<OutputPixelType, Dimension> OutputImageType;
  typedef itk::CastImageFilter<InternalImageType, OutputImageType>
  CastingFilterType;

  CastingFilterType::Pointer caster = CastingFilterType::New();

  // We instantiate reader and writer types
  //
  typedef  otb::ImageFileReader<InternalImageType> ReaderType;
  typedef  otb::ImageFileWriter<OutputImageType>   WriterType;

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

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

  typedef itk::CurvatureFlowImageFilter<InternalImageType, InternalImageType>
  CurvatureFlowImageFilterType;
  CurvatureFlowImageFilterType::Pointer smoothing =
    CurvatureFlowImageFilterType::New();

  //  Software Guide : BeginLatex
  //
  //  The IsolatedConnectedImageFilter is instantiated in the lines below.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  typedef itk::IsolatedConnectedImageFilter<InternalImageType,
      InternalImageType>
  ConnectedFilterType;
  // Software Guide : EndCodeSnippet

  //  Software Guide : BeginLatex
  //
  //  One filter of this class is constructed using the \code{New()} method.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  ConnectedFilterType::Pointer isolatedConnected = ConnectedFilterType::New();
  // Software Guide : EndCodeSnippet

  //  Software Guide : BeginLatex
  //
  //  Now it is time to connect the pipeline.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  smoothing->SetInput(reader->GetOutput());
  isolatedConnected->SetInput(smoothing->GetOutput());
  caster->SetInput(isolatedConnected->GetOutput());
  writer->SetInput(caster->GetOutput());
  // Software Guide : EndCodeSnippet

  smoothing->SetNumberOfIterations(5);
  smoothing->SetTimeStep(0.125);

  //  Software Guide : BeginLatex
  //
  //  The IsolatedConnectedImageFilter expects the user to specify a
  //  threshold and two seeds. In this example, we take all of them from the
  //  command line arguments.
  //
  //  \index{itk::Isolated\-Connected\-Image\-Filter!SetLower()}
  //  \index{itk::Isolated\-Connected\-Image\-Filter!SetSeed1()}
  //  \index{itk::Isolated\-Connected\-Image\-Filter!SetSeed2()}
  //
  //  Software Guide : EndLatex

  InternalImageType::IndexType indexSeed1;

  indexSeed1[0] = atoi(argv[3]);
  indexSeed1[1] = atoi(argv[4]);

  const InternalPixelType lowerThreshold = atof(argv[5]);

  InternalImageType::IndexType indexSeed2;

  indexSeed2[0] = atoi(argv[6]);
  indexSeed2[1] = atoi(argv[7]);

  // Software Guide : BeginCodeSnippet
  isolatedConnected->SetLower(lowerThreshold);
  isolatedConnected->SetSeed1(indexSeed1);
  isolatedConnected->SetSeed2(indexSeed2);
  // Software Guide : EndCodeSnippet

  //  Software Guide : BeginLatex
  //
  //  As in the \doxygen{itk}{ConnectedThresholdImageFilter} we must now specify
  //  the intensity value to be set on the output pixels and at least one
  //  seed point to define the initial region.
  //
  //  \index{itk::Isolated\-Connected\-Image\-Filter!SetReplaceValue()}
  //
  //  Software Guide : EndLatex

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

  //  Software Guide : BeginLatex
  //
  //  The invocation of the \code{Update()} method on the writer triggers the
  //  execution of the pipeline.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  try
    {
    writer->Update();
    }
  catch (itk::ExceptionObject& excep)
    {
    std::cerr << "Exception caught !" << std::endl;
    std::cerr << excep << std::endl;
    }
  // Software Guide : EndCodeSnippet

  //  Software Guide : BeginLatex
  //
  //  The intensity value allowing us to separate both regions can be
  //  recovered with the method \code{GetIsolatedValue()}
  //
  //  \index{itk::Isolated\-Connected\-Image\-Filter!GetIsolatedValue()}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  std::cout << "Isolated Value Found = ";
  std::cout << isolatedConnected->GetIsolatedValue()  << std::endl;
  // Software Guide : EndCodeSnippet

  //  Software Guide : BeginLatex
  //
  //  Let's now run this example using the image
  //  \code{QB\_Suburb.png} provided in the directory
  //  \code{Examples/Data}. We can easily segment
  //  structures by providing seed pairs in the appropriate locations and
  //  defining values for the lower threshold. It is important to keep in
  //  mind in this and the previous examples that the segmentation is being
  //  performed in the smoothed version of the image. The selection of
  //  threshold values should therefore be performed in the smoothed image
  //  since the distribution of intensities could be quite different from
  //  that of the input image.  As a reminder of this fact, Figure
  //  \ref{fig:IsolatedConnectedImageFilterOutput} presents, from left to
  //  right, the input image and the result of smoothing with the
  //  \doxygen{itk}{CurvatureFlowImageFilter} followed by segmentation results.
  //
  //  This filter is intended to be used in cases where adjacent
  //  structures are difficult to separate. Selecting one seed in one structure
  //  and the other seed in the adjacent structure creates the appropriate
  //  setup for computing the threshold that will separate both structures.
  //  Table~\ref{tab:IsolatedConnectedImageFilterOutput} presents the
  //  parameters used to obtain the images shown in
  //  Figure~\ref{fig:IsolatedConnectedImageFilterOutput}.
  //
  //  \begin{table}
  //  \begin{center}
  //  \begin{tabular}{|l|c|c|c|c|}
  //  \hline
  //  Adjacent Structures & Seed1 & Seed2 & Lower & Isolated value found       \\ \hline
  //  Gray matter vs White matter & $(61, 140)$ & $(63, 43)$ & $150$ & $183.31$  \\ \hline
  //  \end{tabular}
  //  \end{center}
  //  \itkcaption[IsolatedConnectedImageFilter example parameters]{Parameters
  //  used for separating white matter from gray matter in
  //  Figure~\ref{fig:IsolatedConnectedImageFilterOutput} using the
  //  IsolatedConnectedImageFilter.\label{tab:IsolatedConnectedImageFilterOutput}}
  //  \end{table}
  //
  // \begin{figure} \center
  // \includegraphics[width=0.32\textwidth]{QB_Suburb.eps}
  // \includegraphics[width=0.32\textwidth]{IsolatedConnectedImageFilterOutput0.eps}
  // \includegraphics[width=0.32\textwidth]{IsolatedConnectedImageFilterOutput1.eps}
  // \itkcaption[IsolatedConnected segmentation results]{Segmentation results of
  // the IsolatedConnectedImageFilter.}
  // \label{fig:IsolatedConnectedImageFilterOutput}
  // \end{figure}
  //
  //
  //  Software Guide : EndLatex

  return EXIT_SUCCESS;
}
