/*=========================================================================
 *
 *  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
 *
 *         http://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.
 *
 *=========================================================================*/

// Software Guide : BeginLatex
//
// This example illustrates how to perform Iterative Closest Point (ICP)
// registration in ITK. The main class featured in this section is the
// \doxygen{EuclideanDistancePointMetric}.
//
// Software Guide : EndLatex

// Software Guide : BeginLatex
//
// The first step is to include the relevant headers.
//
// Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
#include "itkTranslationTransform.h"
#include "itkEuclideanDistancePointMetric.h"
#include "itkLevenbergMarquardtOptimizer.h"
#include "itkPointSetToPointSetRegistrationMethod.h"
// Software Guide : EndCodeSnippet

#include <iostream>
#include <fstream>

class CommandIterationUpdate : public itk::Command
{
public:
  using Self = CommandIterationUpdate;
  using Superclass = itk::Command;
  using Pointer = itk::SmartPointer<Self>;
  itkNewMacro(Self);

protected:
  CommandIterationUpdate() = default;

public:
  using OptimizerType = itk::LevenbergMarquardtOptimizer;
  using OptimizerPointer = const OptimizerType *;

  void
  Execute(itk::Object * caller, const itk::EventObject & event) override
  {
    Execute((const itk::Object *)caller, event);
  }

  void
  Execute(const itk::Object * object, const itk::EventObject & event) override
  {
    auto optimizer = dynamic_cast<OptimizerPointer>(object);
    if (optimizer == nullptr)
    {
      itkExceptionMacro("Could not cast optimizer.");
    }

    if (!itk::IterationEvent().CheckEvent(&event))
    {
      return;
    }

    std::cout << "Value = " << optimizer->GetCachedValue() << std::endl;
    std::cout << "Position = " << optimizer->GetCachedCurrentPosition();
    std::cout << std::endl << std::endl;
  }
};


int
main(int argc, char * argv[])
{

  if (argc < 3)
  {
    std::cerr << "Arguments Missing. " << std::endl;
    std::cerr
      << "Usage:  IterativeClosestPoint1   fixedPointsFile  movingPointsFile "
      << std::endl;
    return EXIT_FAILURE;
  }

  // Software Guide : BeginLatex
  //
  // Next, define the necessary types for the fixed and moving pointsets and
  // point containers.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  constexpr unsigned int Dimension = 2;

  using PointSetType = itk::PointSet<float, Dimension>;

  PointSetType::Pointer fixedPointSet = PointSetType::New();
  PointSetType::Pointer movingPointSet = PointSetType::New();

  using PointType = PointSetType::PointType;

  using PointsContainer = PointSetType::PointsContainer;

  PointsContainer::Pointer fixedPointContainer = PointsContainer::New();
  PointsContainer::Pointer movingPointContainer = PointsContainer::New();

  PointType fixedPoint;
  PointType movingPoint;
  // Software Guide : EndCodeSnippet

  // Read the file containing coordinates of fixed points.
  std::ifstream fixedFile;
  fixedFile.open(argv[1]);
  if (fixedFile.fail())
  {
    std::cerr << "Error opening points file with name : " << std::endl;
    std::cerr << argv[1] << std::endl;
    return EXIT_FAILURE;
  }

  unsigned int pointId = 0;
  fixedFile >> fixedPoint;
  while (!fixedFile.eof())
  {
    fixedPointContainer->InsertElement(pointId, fixedPoint);
    fixedFile >> fixedPoint;
    pointId++;
  }
  fixedPointSet->SetPoints(fixedPointContainer);
  std::cout << "Number of fixed Points = "
            << fixedPointSet->GetNumberOfPoints() << std::endl;

  // Read the file containing coordinates of moving points.
  std::ifstream movingFile;
  movingFile.open(argv[2]);
  if (movingFile.fail())
  {
    std::cerr << "Error opening points file with name : " << std::endl;
    std::cerr << argv[2] << std::endl;
    return EXIT_FAILURE;
  }

  pointId = 0;
  movingFile >> movingPoint;
  while (!movingFile.eof())
  {
    movingPointContainer->InsertElement(pointId, movingPoint);
    movingFile >> movingPoint;
    pointId++;
  }
  movingPointSet->SetPoints(movingPointContainer);
  std::cout << "Number of moving Points = "
            << movingPointSet->GetNumberOfPoints() << std::endl;

  // Software Guide : BeginLatex
  //
  // After the points are read in from files, set up the metric type.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using MetricType =
    itk::EuclideanDistancePointMetric<PointSetType, PointSetType>;

  MetricType::Pointer metric = MetricType::New();
  // Software Guide : EndCodeSnippet

  // Software Guide : BeginLatex
  //
  // Now, setup the transform, optimizers, and registration method using the
  // point set types defined earlier.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using TransformType = itk::TranslationTransform<double, Dimension>;

  TransformType::Pointer transform = TransformType::New();


  // Optimizer Type
  using OptimizerType = itk::LevenbergMarquardtOptimizer;

  OptimizerType::Pointer optimizer = OptimizerType::New();
  optimizer->SetUseCostFunctionGradient(false);

  // Registration Method
  using RegistrationType =
    itk::PointSetToPointSetRegistrationMethod<PointSetType, PointSetType>;

  RegistrationType::Pointer registration = RegistrationType::New();

  // Scale the translation components of the Transform in the Optimizer
  OptimizerType::ScalesType scales(transform->GetNumberOfParameters());
  scales.Fill(0.01);
  // Software Guide : EndCodeSnippet

  // Software Guide : BeginLatex
  //
  // Next we setup the convergence criteria, and other properties required
  // by the optimizer.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  unsigned long numberOfIterations = 100;
  double        gradientTolerance = 1e-5; // convergence criterion
  double        valueTolerance = 1e-5;    // convergence criterion
  double        epsilonFunction = 1e-6;   // convergence criterion


  optimizer->SetScales(scales);
  optimizer->SetNumberOfIterations(numberOfIterations);
  optimizer->SetValueTolerance(valueTolerance);
  optimizer->SetGradientTolerance(gradientTolerance);
  optimizer->SetEpsilonFunction(epsilonFunction);
  // Software Guide : EndCodeSnippet

  // Software Guide : BeginLatex
  //
  // In this case we start from an identity transform, but in reality the user
  // will usually be able to provide a better guess than this.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  transform->SetIdentity();
  // Software Guide : EndCodeSnippet

  registration->SetInitialTransformParameters(transform->GetParameters());

  // Software Guide : BeginLatex
  //
  // Finally, connect all the components required for the registration, and an
  // observer.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  registration->SetMetric(metric);
  registration->SetOptimizer(optimizer);
  registration->SetTransform(transform);
  registration->SetFixedPointSet(fixedPointSet);
  registration->SetMovingPointSet(movingPointSet);

  // Connect an observer
  CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
  optimizer->AddObserver(itk::IterationEvent(), observer);
  // Software Guide : EndCodeSnippet

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

  std::cout << "Solution = " << transform->GetParameters() << std::endl;
  return EXIT_SUCCESS;
}