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

// Software Guide : BeginLatex
//
// This example illustrates the use of the \doxygen{BSplineTransform}
// class for performing registration of two $3D$ images. The example code is
// for the most part identical to the code presented in
// Section~\ref{sec:BSplinesMultiGridImageRegistration}. The major difference
// is that in this example we set the image dimension to 3 and replace the
// \doxygen{LBFGSOptimizerv4} optimizer with the \doxygen{LBFGSBOptimizerv4}.
// We made the modification because we found that LBFGS does not behave well
// when the starting position is at or close to optimal; instead we used
// LBFGSB in unconstrained mode.
//
//
// \index{itk::BSplineTransform}
// \index{itk::BSplineTransform!DeformableRegistration}
// \index{itk::LBFGSBOptimizerv4}
//
//
// Software Guide : EndLatex

#include "itkImageRegistrationMethodv4.h"
#include "itkMeanSquaresImageToImageMetricv4.h"

//  Software Guide : BeginLatex
//
//  The following are the most relevant headers to this example.
//
//  \index{itk::BSplineTransform!header}
//  \index{itk::LBFGSBOptimizerv4!header}
//
//  Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
#include "itkBSplineTransform.h"
#include "itkLBFGSBOptimizerv4.h"
// Software Guide : EndCodeSnippet

//  Software Guide : BeginLatex
//
//  The parameter space of the \code{BSplineTransform} is composed by
//  the set of all the deformations associated with the nodes of the BSpline
//  grid.  This large number of parameters enables it to represent a wide
//  variety of deformations, at the cost of requiring a
//  significant amount of computation time.
//
//  \index{itk::BSplineTransform!header}
//
//  Software Guide : EndLatex

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

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

#include "itkIdentityTransform.h"

#include "itkBSplineTransformInitializer.h"
#include "itkTransformToDisplacementFieldFilter.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::LBFGSBOptimizerv4;
  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->GetCurrentMetricValue() << "   ";
    std::cout << optimizer->GetInfinityNormOfProjectedGradient() << std::endl;
  }
};


int
main(int argc, char * argv[])
{
  if (argc < 4)
  {
    std::cerr << "Missing Parameters " << std::endl;
    std::cerr << "Usage: " << argv[0];
    std::cerr << " fixedImageFile  movingImageFile outputImagefile  ";
    std::cerr << " [differenceOutputfile] [differenceBeforeRegistration] ";
    std::cerr << " [deformationField] ";
    return EXIT_FAILURE;
  }

  constexpr unsigned int ImageDimension = 3;
  using PixelType = float;

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


  //  Software Guide : BeginLatex
  //
  //  We instantiate now the type of the \code{BSplineTransform} using
  //  as template parameters the type for coordinates representation, the
  //  dimension of the space, and the order of the BSpline.
  //
  //  \index{BSplineTransform!New}
  //  \index{BSplineTransform!Instantiation}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  const unsigned int     SpaceDimension = ImageDimension;
  constexpr unsigned int SplineOrder = 3;
  using CoordinateRepType = double;

  using TransformType =
    itk::BSplineTransform<CoordinateRepType, SpaceDimension, SplineOrder>;
  // Software Guide : EndCodeSnippet


  using OptimizerType = itk::LBFGSBOptimizerv4;


  using MetricType =
    itk::MeanSquaresImageToImageMetricv4<FixedImageType, MovingImageType>;

  using RegistrationType =
    itk::ImageRegistrationMethodv4<FixedImageType, MovingImageType>;

  auto metric = MetricType::New();
  auto optimizer = OptimizerType::New();
  auto registration = RegistrationType::New();


  registration->SetMetric(metric);
  registration->SetOptimizer(optimizer);


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

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

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

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

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

  fixedImageReader->Update();

  //  Software Guide : BeginLatex
  //
  //  The transform object is constructed, initialized like previous examples
  //  and passed to the registration method.
  //
  //  \index{itk::ImageRegistrationMethodv4!SetInitialTransform()}
  //  \index{itk::ImageRegistrationMethodv4!InPlaceOn()}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  auto outputBSplineTransform = TransformType::New();
  // Software Guide : EndCodeSnippet

  // Initialize the transform
  using InitializerType =
    itk::BSplineTransformInitializer<TransformType, FixedImageType>;

  auto transformInitializer = InitializerType::New();

  unsigned int numberOfGridNodesInOneDimension = 8;

  TransformType::MeshSizeType meshSize;
  meshSize.Fill(numberOfGridNodesInOneDimension - SplineOrder);

  transformInitializer->SetTransform(outputBSplineTransform);
  transformInitializer->SetImage(fixedImage);
  transformInitializer->SetTransformDomainMeshSize(meshSize);
  transformInitializer->InitializeTransform();

  // Set transform to identity
  using ParametersType = TransformType::ParametersType;
  const unsigned int numberOfParameters =
    outputBSplineTransform->GetNumberOfParameters();
  ParametersType parameters(numberOfParameters);
  parameters.Fill(0.0);
  outputBSplineTransform->SetParameters(parameters);

  // Software Guide : BeginCodeSnippet
  registration->SetInitialTransform(outputBSplineTransform);
  registration->InPlaceOn();
  // Software Guide : EndCodeSnippet

  //  A single level registration process is run using
  //  the shrink factor 1 and smoothing sigma 0.
  //
  constexpr unsigned int numberOfLevels = 1;

  RegistrationType::ShrinkFactorsArrayType shrinkFactorsPerLevel;
  shrinkFactorsPerLevel.SetSize(numberOfLevels);
  shrinkFactorsPerLevel[0] = 1;

  RegistrationType::SmoothingSigmasArrayType smoothingSigmasPerLevel;
  smoothingSigmasPerLevel.SetSize(numberOfLevels);
  smoothingSigmasPerLevel[0] = 0;

  registration->SetNumberOfLevels(numberOfLevels);
  registration->SetSmoothingSigmasPerLevel(smoothingSigmasPerLevel);
  registration->SetShrinkFactorsPerLevel(shrinkFactorsPerLevel);

  //  Software Guide : BeginLatex
  //
  //  Next we set the parameters of the LBFGSB Optimizer. Note that
  //  this optimizer does not support scales estimator and sets all
  //  the parameters scales to one.
  //  Also, we should set the boundary condition for each variable, where
  //  \code{boundSelect[i]} can be set as: \code{UNBOUNDED},
  //  \code{LOWERBOUNDED}, \code{BOTHBOUNDED}, \code{UPPERBOUNDED}.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  const unsigned int numParameters =
    outputBSplineTransform->GetNumberOfParameters();
  OptimizerType::BoundSelectionType boundSelect(numParameters);
  OptimizerType::BoundValueType     upperBound(numParameters);
  OptimizerType::BoundValueType     lowerBound(numParameters);

  boundSelect.Fill(OptimizerType::UNBOUNDED);
  upperBound.Fill(0.0);
  lowerBound.Fill(0.0);

  optimizer->SetBoundSelection(boundSelect);
  optimizer->SetUpperBound(upperBound);
  optimizer->SetLowerBound(lowerBound);

  optimizer->SetCostFunctionConvergenceFactor(1e+12);
  optimizer->SetGradientConvergenceTolerance(1.0e-35);
  optimizer->SetNumberOfIterations(500);
  optimizer->SetMaximumNumberOfFunctionEvaluations(500);
  optimizer->SetMaximumNumberOfCorrections(5);
  // Software Guide : EndCodeSnippet

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

  std::cout << "Starting Registration " << std::endl;

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

  OptimizerType::ParametersType finalParameters =
    outputBSplineTransform->GetParameters();

  std::cout << "Last Transform Parameters" << std::endl;
  std::cout << finalParameters << std::endl;

  // Finally we use the last transform in order to resample the image.
  //
  using ResampleFilterType =
    itk::ResampleImageFilter<MovingImageType, FixedImageType>;

  auto resample = ResampleFilterType::New();

  resample->SetTransform(outputBSplineTransform);
  resample->SetInput(movingImageReader->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, ImageDimension>;

  using CastFilterType =
    itk::CastImageFilter<FixedImageType, OutputImageType>;

  using WriterType = itk::ImageFileWriter<OutputImageType>;


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


  writer->SetFileName(argv[3]);


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


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

  using DifferenceFilterType =
    itk::SquaredDifferenceImageFilter<FixedImageType,
                                      FixedImageType,
                                      OutputImageType>;

  auto difference = DifferenceFilterType::New();

  auto writer2 = WriterType::New();
  writer2->SetInput(difference->GetOutput());


  // Compute the difference image between the
  // fixed and resampled moving image.
  if (argc >= 5)
  {
    difference->SetInput1(fixedImageReader->GetOutput());
    difference->SetInput2(resample->GetOutput());
    writer2->SetFileName(argv[4]);
    try
    {
      writer2->Update();
    }
    catch (const itk::ExceptionObject & err)
    {
      std::cerr << "ExceptionObject caught !" << std::endl;
      std::cerr << err << std::endl;
      return EXIT_FAILURE;
    }
  }


  // Compute the difference image between the
  // fixed and moving image before registration.
  if (argc >= 6)
  {
    writer2->SetFileName(argv[5]);
    difference->SetInput1(fixedImageReader->GetOutput());
    difference->SetInput2(movingImageReader->GetOutput());
    try
    {
      writer2->Update();
    }
    catch (const itk::ExceptionObject & err)
    {
      std::cerr << "ExceptionObject caught !" << std::endl;
      std::cerr << err << std::endl;
      return EXIT_FAILURE;
    }
  }

  // Generate the explicit deformation field resulting from
  // the registration.
  using VectorPixelType = itk::Vector<float, ImageDimension>;
  using DisplacementFieldImageType =
    itk::Image<VectorPixelType, ImageDimension>;

  using DisplacementFieldGeneratorType =
    itk::TransformToDisplacementFieldFilter<DisplacementFieldImageType,
                                            CoordinateRepType>;

  /** Create an setup displacement field generator. */
  auto dispfieldGenerator = DisplacementFieldGeneratorType::New();
  dispfieldGenerator->UseReferenceImageOn();
  dispfieldGenerator->SetReferenceImage(fixedImage);
  dispfieldGenerator->SetTransform(outputBSplineTransform);
  try
  {
    dispfieldGenerator->Update();
  }
  catch (const itk::ExceptionObject & err)
  {
    std::cerr << "Exception detected while generating deformation field";
    std::cerr << " : " << err << std::endl;
    return EXIT_FAILURE;
  }

  using FieldWriterType = itk::ImageFileWriter<DisplacementFieldImageType>;
  auto fieldWriter = FieldWriterType::New();

  fieldWriter->SetInput(dispfieldGenerator->GetOutput());

  if (argc >= 7)
  {
    fieldWriter->SetFileName(argv[6]);
    try
    {
      fieldWriter->Update();
    }
    catch (const itk::ExceptionObject & excp)
    {
      std::cerr << "Exception thrown " << std::endl;
      std::cerr << excp << std::endl;
      return EXIT_FAILURE;
    }
  }

  return EXIT_SUCCESS;
}
