/*=========================================================================
 *
 *  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 "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkFastMarchingImageFilter.h"
#include "itkLevelSetContainer.h"
#include "itkLevelSetEquationChanAndVeseExternalTerm.h"
#include "itkLevelSetEquationTermContainer.h"
#include "itkLevelSetEquationContainer.h"
#include "itkAtanRegularizedHeavisideStepFunction.h"
#include "itkLevelSetEvolution.h"
#include "itkLevelSetEvolutionNumberOfIterationsStoppingCriterion.h"
#include "itkTestingMacros.h"

int
itkSingleLevelSetDenseImage2DTest(int argc, char * argv[])
{
  if (argc < 6)
  {
    std::cerr << "Missing parameters." << std::endl;
    std::cerr << "Usage:" << std::endl;
    std::cerr << itkNameOfTestExecutableMacro(argv)
              << " inputFilename seedPosition0 seedPosition1 initialDistance outputFilename" << std::endl;
    return EXIT_FAILURE;
  }

  constexpr unsigned int Dimension = 2;

  using InputPixelType = unsigned short;
  using InputImageType = itk::Image<InputPixelType, Dimension>;
  using ReaderType = itk::ImageFileReader<InputImageType>;

  using PixelType = float;
  using ImageType = itk::Image<PixelType, Dimension>;
  using LevelSetType = itk::LevelSetDenseImage<ImageType>;
  using LevelSetOutputRealType = LevelSetType::OutputRealType;
  using IteratorType = itk::ImageRegionIteratorWithIndex<ImageType>;

  using IdentifierType = itk::IdentifierType;
  using LevelSetContainerType = itk::LevelSetContainer<IdentifierType, LevelSetType>;
  using ChanAndVeseInternalTermType =
    itk::LevelSetEquationChanAndVeseInternalTerm<InputImageType, LevelSetContainerType>;
  using ChanAndVeseExternalTermType =
    itk::LevelSetEquationChanAndVeseExternalTerm<InputImageType, LevelSetContainerType>;
  using TermContainerType = itk::LevelSetEquationTermContainer<InputImageType, LevelSetContainerType>;

  using EquationContainerType = itk::LevelSetEquationContainer<TermContainerType>;

  using LevelSetEvolutionType = itk::LevelSetEvolution<EquationContainerType, LevelSetType>;

  using HeavisideFunctionBaseType =
    itk::AtanRegularizedHeavisideStepFunction<LevelSetOutputRealType, LevelSetOutputRealType>;

  using FastMarchingFilterType = itk::FastMarchingImageFilter<ImageType, ImageType>;
  using NodeContainer = FastMarchingFilterType::NodeContainer;
  using NodeType = FastMarchingFilterType::NodeType;

  // Read the image to be segmented
  auto reader = ReaderType::New();
  reader->SetFileName(argv[1]);
  reader->Update();
  InputImageType::Pointer input = reader->GetOutput();

  auto fastMarching = FastMarchingFilterType::New();

  auto seeds = NodeContainer::New();

  ImageType::IndexType seedPosition;
  seedPosition[0] = std::stoi(argv[2]);
  seedPosition[1] = std::stoi(argv[3]);

  const double initialDistance = std::stod(argv[4]);
  const double seedValue = -initialDistance;

  NodeType node;
  node.SetValue(seedValue);
  node.SetIndex(seedPosition);

  //  The list of nodes is initialized and then every node is inserted using
  //  the \code{InsertElement()}.
  //
  seeds->Initialize();
  seeds->InsertElement(0, node);

  //  The set of seed nodes is passed now to the
  //  FastMarchingImageFilter with the method
  //  \code{SetTrialPoints()}.
  //
  fastMarching->SetTrialPoints(seeds);

  //  Since the FastMarchingImageFilter is used here just as a
  //  Distance Map generator. It does not require a speed image as input.
  //  Instead the constant value $1.0$ is passed using the
  //  \code{SetSpeedConstant()} method.
  //
  fastMarching->SetSpeedConstant(1.0);

  //  The FastMarchingImageFilter requires the user to specify the
  //  size of the image to be produced as output. This is done using the
  //  \code{SetOutputSize()}. Note that the size is obtained here from the
  //  output image of the smoothing filter. The size of this image is valid
  //  only after the \code{Update()} methods of this filter has been called
  //  directly or indirectly.
  //
  fastMarching->SetOutputSize(input->GetBufferedRegion().GetSize());
  fastMarching->Update();

  // Define the Heaviside function
  auto heaviside = HeavisideFunctionBaseType::New();
  heaviside->SetEpsilon(1.0);

  // Map of levelset bases
  auto level_set = LevelSetType::New();
  level_set->SetImage(fastMarching->GetOutput());

  // Insert the levelsets in a levelset container
  auto lscontainer = LevelSetContainerType::New();
  lscontainer->SetHeaviside(heaviside);

  bool levelSetNotYetAdded = lscontainer->AddLevelSet(0, level_set, false);
  if (!levelSetNotYetAdded)
  {
    return EXIT_FAILURE;
  }
  std::cout << "Level set container created" << std::endl;

  // **************** CREATE ALL TERMS ****************

  // -----------------------------
  // *** 1st Level Set phi ***

  // Create ChanAndVese internal term for phi_{1}
  auto cvInternalTerm0 = ChanAndVeseInternalTermType::New();
  cvInternalTerm0->SetInput(input);
  cvInternalTerm0->SetCoefficient(1.0);
  std::cout << "LevelSet 1: CV internal term created" << std::endl;

  // Create ChanAndVese external term for phi_{1}
  auto cvExternalTerm0 = ChanAndVeseExternalTermType::New();
  cvExternalTerm0->SetInput(input);
  cvExternalTerm0->SetCoefficient(1.0);
  std::cout << "LevelSet 1: CV external term created" << std::endl;

  // **************** CREATE ALL EQUATIONS ****************

  // Create Term Container
  auto termContainer0 = TermContainerType::New();
  termContainer0->SetInput(input);
  termContainer0->SetCurrentLevelSetId(0);
  termContainer0->SetLevelSetContainer(lscontainer);

  termContainer0->AddTerm(0, cvInternalTerm0);
  termContainer0->AddTerm(1, cvExternalTerm0);
  std::cout << "Term container 0 created" << std::endl;

  auto equationContainer = EquationContainerType::New();
  equationContainer->SetLevelSetContainer(lscontainer);
  equationContainer->AddEquation(0, termContainer0);

  using StoppingCriterionType = itk::LevelSetEvolutionNumberOfIterationsStoppingCriterion<LevelSetContainerType>;
  auto criterion = StoppingCriterionType::New();
  criterion->SetNumberOfIterations(50);

  auto evolution = LevelSetEvolutionType::New();
  evolution->SetEquationContainer(equationContainer);
  evolution->SetStoppingCriterion(criterion);
  evolution->SetLevelSetContainer(lscontainer);

  try
  {
    evolution->Update();
  }
  catch (const itk::ExceptionObject & err)
  {
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
  }

  auto outputImage = ImageType::New();
  outputImage->SetRegions(input->GetLargestPossibleRegion());
  outputImage->CopyInformation(input);
  outputImage->Allocate();
  outputImage->FillBuffer(0);

  IteratorType oIt(outputImage, outputImage->GetLargestPossibleRegion());
  oIt.GoToBegin();

  ImageType::IndexType idx;

  while (!oIt.IsAtEnd())
  {
    idx = oIt.GetIndex();
    oIt.Set(level_set->Evaluate(idx));
    ++oIt;
  }

  using OutputWriterType = itk::ImageFileWriter<ImageType>;
  auto writer = OutputWriterType::New();
  writer->SetFileName(argv[5]);
  writer->SetInput(outputImage);

  try
  {
    writer->Update();
  }
  catch (const itk::ExceptionObject & err)
  {
    std::cout << err << std::endl;
  }

  PixelType mean = cvInternalTerm0->GetMean();
  if ((mean < 24900) || (mean > 24910))
  {
    std::cerr << "( ( mean < 24900 ) || ( mean > 24910 ) )" << std::endl;
    std::cerr << "mean = " << mean << std::endl;
    return EXIT_FAILURE;
  }

  mean = cvExternalTerm0->GetMean();
  if ((mean < 1350) || (mean > 1360))
  {
    std::cerr << "( ( mean < 1350 ) || ( mean > 1360 ) )" << std::endl;
    std::cerr << "mean = " << mean << std::endl;
    return EXIT_FAILURE;
  }

  return EXIT_SUCCESS;
}