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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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 *         http://www.apache.org/licenses/LICENSE-2.0.txt
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 *  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.
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//
//  This example illustrates how to do registration with a 2D Rigid Transform
//  and with MutualInformation metric.
//


#include "itkImageRegistrationMethod.h"

#include "itkCenteredRigid2DTransform.h"
#include "itkCenteredTransformInitializer.h"

#include "itkMattesMutualInformationImageToImageMetric.h"

#include "itkRegularStepGradientDescentOptimizer.h"
#include "itkMersenneTwisterRandomVariateGenerator.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() = default;

public:
  using OptimizerType = itk::RegularStepGradientDescentOptimizer;
  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;
    }
    std::cout << optimizer->GetCurrentIteration() << "   ";
    std::cout << optimizer->GetValue() << "   ";
    std::cout << optimizer->GetCurrentPosition() << std::endl;
  }
};


#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::cerr << "[useCachingBSplineWeights ] " << std::endl;
    return EXIT_FAILURE;
  }

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

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

  // The CenteredRigid2DTransform applies a rigid transform in 2D space.
  using TransformType = itk::CenteredRigid2DTransform<double>;
  using OptimizerType = itk::RegularStepGradientDescentOptimizer;

  using InterpolatorType = itk::LinearInterpolateImageFunction<MovingImageType, double>;
  using RegistrationType = itk::ImageRegistrationMethod<FixedImageType, MovingImageType>;


  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);

  // For consistent results when regression testing.
  metric->ReinitializeSeed(121212);


  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]));
  }

  if (argc > 5)
  {
    // Define whether to cache the BSpline weights and indexes corresponding to
    // each one of the samples used to compute the metric. Enabling caching will
    // make the algorithm run faster but it will have a cost on the amount of memory
    // that needs to be allocated. This option is only relevant when using the
    // BSplineTransform.
    metric->SetUseCachingOfBSplineWeights(std::stoi(argv[5]));
  }


  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());

  // The \doxygen{CenteredRigid2DTransform} is initialized by 5 parameters,
  // indicating the angle of rotation, the center coordinates and the
  // translation to be applied after rotation. The initialization is done
  // by the \doxygen{CenteredTransformInitializer}.
  // The transform can operate in two modes, one assumes that the
  // anatomical objects to be registered are centered in their respective
  // images. Hence the best initial guess for the registration is the one
  // that superimposes those two centers.
  // This second approach assumes that the moments of the anatomical
  // objects are similar for both images and hence the best initial guess
  // for registration is to superimpose both mass centers. The center of
  // mass is computed from the moments obtained from the gray level values.
  // Here we adopt the first approach. The \code{GeometryOn()} method
  // toggles between the approaches.
  using TransformInitializerType = itk::CenteredTransformInitializer<TransformType, FixedImageType, MovingImageType>;
  TransformInitializerType::Pointer initializer = TransformInitializerType::New();
  initializer->SetTransform(transform);

  initializer->SetFixedImage(fixedImageReader->GetOutput());
  initializer->SetMovingImage(movingImageReader->GetOutput());
  initializer->GeometryOn();
  initializer->InitializeTransform();

  transform->SetAngle(0.0);


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

  // The optimizer scales the metrics (the gradient in this case) by the
  // scales during each iteration. Hence a large value of the center scale
  // will prevent movement along the center during optimization. Here we
  // assume that the fixed and moving images are likely to be related by
  // a translation.
  using OptimizerScalesType = OptimizerType::ScalesType;
  OptimizerScalesType optimizerScales(transform->GetNumberOfParameters());

  const double     translationScale = 1.0 / 128.0;
  constexpr double centerScale = 1000.0; // prevents it from moving
                                         // during the optimization
  optimizerScales[0] = 1.0;
  optimizerScales[1] = centerScale;
  optimizerScales[2] = centerScale;
  optimizerScales[3] = translationScale;
  optimizerScales[4] = translationScale;

  optimizer->SetScales(optimizerScales);

  optimizer->SetMaximumStepLength(0.5);
  optimizer->SetMinimumStepLength(0.0001);
  optimizer->SetNumberOfIterations(400);

  // 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 << "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;
  }

  using ParametersType = RegistrationType::ParametersType;

  ParametersType finalParameters = registration->GetLastTransformParameters();

  const double finalAngle = finalParameters[0];
  const double finalRotationCenterX = finalParameters[1];
  const double finalRotationCenterY = finalParameters[2];
  const double finalTranslationX = finalParameters[3];
  const double finalTranslationY = finalParameters[4];

  unsigned int numberOfIterations = optimizer->GetCurrentIteration();

  double bestValue = optimizer->GetValue();

  // Print out results
  //

  const double finalAngleInDegrees = finalAngle * 180 / itk::Math::pi;

  std::cout << "Result = " << std::endl;
  std::cout << " Angle (radians) " << finalAngle << std::endl;
  std::cout << " Angle (degrees) " << finalAngleInDegrees << std::endl;
  std::cout << " Center X      = " << finalRotationCenterX << std::endl;
  std::cout << " Center Y      = " << finalRotationCenterY << std::endl;
  std::cout << " Translation X = " << finalTranslationX << std::endl;
  std::cout << " Translation Y = " << finalTranslationY << std::endl;
  std::cout << " Iterations    = " << numberOfIterations << std::endl;
  std::cout << " Metric value  = " << bestValue << std::endl;


  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 OutputImageType = itk::Image<PixelType, Dimension>;

  using WriterType = itk::ImageFileWriter<OutputImageType>;

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

  writer->SetFileName(argv[3]);

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

  return EXIT_SUCCESS;
}

//
//  Let's execute this example over some of the images provided in
//  \code{Examples/Data}, for example:
//
//  \begin{itemize}
//  \item \code{BrainProtonDensitySlice.png}
//  \item \code{BrainProtonDensitySliceBorder20.png}
//  \end{itemize}
//
//  The second image is the result of intentionally shifting the first
//  image by $20mm$ in $X$ and $20mm$ in
//  $Y$. Both images have unit-spacing and are shown in Figure
//  \ref{fig:FixedMovingImageRegistration1}. The example
//  yielded the following results.
//
//  \begin{verbatim}
//  Translation X = 20
//  Translation Y = 20
//  \end{verbatim}
//  These values match the true misalignment introduced in the moving image.