/*=========================================================================
 *
 *  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.
 *
 *=========================================================================*/

//
// This example illustrates how to combine the MutualInformation metric with an
// Evolutionary algorithm for optimization.  Evolutionary algorithms are
// naturally well-suited for optimizing the Mutual Information metric given its
// random and noisy behavior.
//
// The structure of the example is almost identical to the one illustrated in
// ImageRegistration4. Therefore we focus here on the setup that is
// specifically required for the evolutionary optimizer.
//
//
// \index{itk::ImageRegistrationMethod!Multi-Modality}
// \index{itk::OnePlusOneEvolutionaryOptimizer!Multi-Modality}
//


#include "itkImageRegistrationMethod.h"
#include "itkTranslationTransform.h"
#include "itkMattesMutualInformationImageToImageMetric.h"
#include "itkOnePlusOneEvolutionaryOptimizer.h"
#include "itkNormalVariateGenerator.h"


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

#include "itkResampleImageFilter.h"
#include "itkCastImageFilter.h"


//  The following section of code implements a Command observer
//  used to monitor the evolution of the registration process.
//
#include "itkCommand.h"
class CommandIterationUpdate : public itk::Command
{
public:
  using Self = CommandIterationUpdate;
  using Superclass = itk::Command;
  using Pointer = itk::SmartPointer<Self>;
  itkNewMacro(Self);

protected:
  CommandIterationUpdate() { m_LastMetricValue = 0.0; };

public:
  using OptimizerType = itk::OnePlusOneEvolutionaryOptimizer;
  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 = static_cast<OptimizerPointer>(object);
    if (!itk::IterationEvent().CheckEvent(&event))
    {
      return;
    }
    double currentValue = optimizer->GetValue();
    // Only print out when the Metric value changes
    if (std::fabs(m_LastMetricValue - currentValue) > 1e-7)
    {
      std::cout << optimizer->GetCurrentIteration() << "   ";
      std::cout << currentValue << "   ";
      std::cout << optimizer->GetCurrentPosition() << std::endl;
      m_LastMetricValue = currentValue;
    }
  }

private:
  double m_LastMetricValue;
};


#include "itkTestDriverIncludeRequiredIOFactories.h"
int
main(int argc, char * argv[])
{
  RegisterRequiredFactories();
  if (argc < 3)
  {
    std::cerr << "Missing Parameters " << std::endl;
    std::cerr << "Usage: " << argv[0];
    std::cerr << " fixedImageFile  movingImageFile ";
    std::cerr << "outputImagefile ";
    std::cerr << "[useExplicitPDFderivatives ] " << std::endl;
    return EXIT_FAILURE;
  }

  constexpr unsigned int Dimension = 2;
  using PixelType = unsigned short;

  using FixedImageType = itk::Image<PixelType, Dimension>;
  using MovingImageType = itk::Image<PixelType, Dimension>;

  using TransformType = itk::TranslationTransform<double, Dimension>;
  using OptimizerType = itk::OnePlusOneEvolutionaryOptimizer;
  using InterpolatorType = itk::LinearInterpolateImageFunction<MovingImageType, double>;
  using RegistrationType = itk::ImageRegistrationMethod<FixedImageType, MovingImageType>;

  //
  //  In this example the image types and all registration components,
  //  except the metric, are declared as in Section
  //  \ref{sec:IntroductionImageRegistration}.
  //  The Mattes mutual information metric type is
  //  instantiated using the image types.
  //

  using MetricType = itk::MattesMutualInformationImageToImageMetric<FixedImageType, MovingImageType>;

  TransformType::Pointer    transform = TransformType::New();
  OptimizerType::Pointer    optimizer = OptimizerType::New();
  InterpolatorType::Pointer interpolator = InterpolatorType::New();
  RegistrationType::Pointer registration = RegistrationType::New();

  registration->SetOptimizer(optimizer);
  registration->SetTransform(transform);
  registration->SetInterpolator(interpolator);

  MetricType::Pointer metric = MetricType::New();
  registration->SetMetric(metric);

  metric->SetNumberOfHistogramBins(20);
  metric->SetNumberOfSpatialSamples(10000);

  if (argc > 4)
  {
    // Define whether to calculate the metric derivative by explicitly
    // computing the derivatives of the joint PDF with respect to the Transform
    // parameters, or doing it by progressively accumulating contributions from
    // each bin in the joint PDF.
    metric->SetUseExplicitPDFDerivatives(std::stoi(argv[4]));
  }

  using FixedImageReaderType = itk::ImageFileReader<FixedImageType>;
  using MovingImageReaderType = itk::ImageFileReader<MovingImageType>;

  FixedImageReaderType::Pointer  fixedImageReader = FixedImageReaderType::New();
  MovingImageReaderType::Pointer movingImageReader = MovingImageReaderType::New();

  fixedImageReader->SetFileName(argv[1]);
  movingImageReader->SetFileName(argv[2]);

  registration->SetFixedImage(fixedImageReader->GetOutput());
  registration->SetMovingImage(movingImageReader->GetOutput());

  fixedImageReader->Update();

  registration->SetFixedImageRegion(fixedImageReader->GetOutput()->GetBufferedRegion());


  using ParametersType = RegistrationType::ParametersType;
  ParametersType initialParameters(transform->GetNumberOfParameters());

  initialParameters[0] = 0.0; // Initial offset in mm along X
  initialParameters[1] = 0.0; // Initial offset in mm along Y

  registration->SetInitialTransformParameters(initialParameters);


  //
  //  Evolutionary algorithms are based on testing random variations
  //  of parameters. In order to support the computation of random values,
  //  ITK provides a family of random number generators. In this example, we
  //  use the \doxygen{NormalVariateGenerator} which generates values with a
  //  normal distribution.
  //
  //  \index{itk::NormalVariateGenerator!New()}
  //  \index{itk::NormalVariateGenerator!Pointer}
  //

  using GeneratorType = itk::Statistics::NormalVariateGenerator;

  GeneratorType::Pointer generator = GeneratorType::New();


  //
  //  The random number generator must be initialized with a seed.
  //
  //  \index{itk::NormalVariateGenerator!Initialize()}
  //

  generator->Initialize(12345);


  //
  //  Another significant difference in the metric is that it
  //  computes the negative mutual information and hence we
  //  need to minimize the cost function in this case. In this
  //  example we will use the same optimization parameters as in
  //  Section \ref{sec:IntroductionImageRegistration}.
  //

  optimizer->MaximizeOff();

  optimizer->SetNormalVariateGenerator(generator);
  optimizer->Initialize(10);
  optimizer->SetEpsilon(1.0);
  optimizer->SetMaximumIteration(4000);


  // Create the Command observer and register it with the optimizer.
  //
  CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
  optimizer->AddObserver(itk::IterationEvent(), observer);


  try
  {
    registration->Update();
    std::cout << "Registration completed!" << std::endl;
    std::cout << "Optimizer stop condition: " << registration->GetOptimizer()->GetStopConditionDescription()
              << std::endl;
  }
  catch (const itk::ExceptionObject & err)
  {
    std::cout << "ExceptionObject caught !" << std::endl;
    std::cout << err << std::endl;
    return EXIT_FAILURE;
  }

  ParametersType finalParameters = registration->GetLastTransformParameters();

  double TranslationAlongX = finalParameters[0];
  double TranslationAlongY = finalParameters[1];

  unsigned int numberOfIterations = optimizer->GetCurrentIteration();

  double bestValue = optimizer->GetValue();


  // Print out results
  //
  std::cout << "Result = " << std::endl;
  std::cout << " Translation X = " << TranslationAlongX << std::endl;
  std::cout << " Translation Y = " << TranslationAlongY << std::endl;
  std::cout << " Iterations    = " << numberOfIterations << std::endl;
  std::cout << " Metric value  = " << bestValue << std::endl;


  //
  //  This example is executed using the same multi-modality images as
  //  in the previous one.  The registration converges after $24$ iterations and produces
  //  the following results:
  //
  //  \begin{verbatim}
  //  Translation X = 13.1719
  //  Translation Y = 16.9006
  //  \end{verbatim}
  //  These values are a very close match to
  //  the true misalignment introduced in the moving image.
  //


  using ResampleFilterType = itk::ResampleImageFilter<MovingImageType, FixedImageType>;

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

  finalTransform->SetParameters(finalParameters);
  finalTransform->SetFixedParameters(transform->GetFixedParameters());

  ResampleFilterType::Pointer resample = ResampleFilterType::New();

  resample->SetTransform(finalTransform);
  resample->SetInput(movingImageReader->GetOutput());

  FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();

  resample->SetSize(fixedImage->GetLargestPossibleRegion().GetSize());
  resample->SetOutputOrigin(fixedImage->GetOrigin());
  resample->SetOutputSpacing(fixedImage->GetSpacing());
  resample->SetOutputDirection(fixedImage->GetDirection());
  resample->SetDefaultPixelValue(100);


  using OutputPixelType = unsigned char;
  using OutputImageType = itk::Image<OutputPixelType, Dimension>;
  using CastFilterType = itk::CastImageFilter<FixedImageType, OutputImageType>;
  using WriterType = itk::ImageFileWriter<OutputImageType>;

  WriterType::Pointer     writer = WriterType::New();
  CastFilterType::Pointer caster = CastFilterType::New();

  writer->SetFileName(argv[3]);

  caster->SetInput(resample->GetOutput());
  writer->SetInput(caster->GetOutput());
  writer->Update();

  return EXIT_SUCCESS;
}