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

//    INPUTS: brainweb1e1a10f20.mha
//    INPUTS: brainweb1e1a10f20Rot10Tx15.mha
//    ARGUMENTS: ImageRegistration8Output.mhd
//    ARGUMENTS: ImageRegistration8DifferenceBefore.mhd
//    ARGUMENTS: ImageRegistration8DifferenceAfter.mhd
//    OUTPUTS: {ImageRegistration8Output.png}
//    OUTPUTS: {ImageRegistration8DifferenceBefore.png}
//    OUTPUTS: {ImageRegistration8DifferenceAfter.png}
//    OUTPUTS: {ImageRegistration8RegisteredSlice.png}

//
// This example illustrates the use of the \doxygen{VersorRigid3DTransform}
// 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:RigidRegistrationIn2D}.  The major difference is that this
// example is done in $3D$. The class \doxygen{CenteredTransformInitializer} is
// used to initialize the center and translation of the transform.  The case of
// rigid registration of 3D images is probably one of the most commonly found
// cases of image registration.
//
//
// \index{itk::Versor\-Rigid3D\-Transform}
// \index{itk::Centered\-Transform\-Initializer!In 3D}
//
//
#include "itkImageRegistrationMethod.h"
#include "itkMeanSquaresImageToImageMetric.h"

//
//  The following are the most relevant headers of this example.
//
//  \index{itk::Versor\-Rigid3D\-Transform!header}
//  \index{itk::Centered\-Transform\-Initializer!header}
//

#include "itkVersorRigid3DTransform.h"
#include "itkCenteredTransformInitializer.h"

//
//  The parameter space of the \code{VersorRigid3DTransform} is not a vector
//  space, because addition is not a closed operation in the space
//  of versor components. Hence, we need to use Versor composition operation to
//  update the first three components of the parameter array (rotation parameters),
//  and Vector addition for updating the last three components of the parameters
//  array (translation parameters)~\cite{Hamilton1866,Joly1905}.
//
//  In the previous version of ITK, a special optimizer, \doxygen{VersorRigid3DTransformOptimizer}
//  was needed for registration to deal with versor computations.
//  Fortunately in ITKv4, the \doxygen{RegularStepGradientDescentOptimizerv4}
//  can be used for both vector and versor transform optimizations because, in the new
//  registration framework, the task of updating parameters is delegated to the
//  moving transform itself. The \code{UpdateTransformParameters} method is implemented
//  in the \doxygen{Transform} class as a virtual function, and all the derived transform
//  classes can have their own implementations of this function. Due to this
//  fact, the updating function is re-implemented for versor transforms
//  so it can handle versor composition of the rotation parameters.
//
#include "itkVersorRigid3DTransformOptimizer.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkResampleImageFilter.h"
#include "itkCastImageFilter.h"
#include "itkSubtractImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkExtractImageFilter.h"

//  The following section of code implements a Command observer
//  that will 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::VersorRigid3DTransformOptimizer;
  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 "itkTestDriverIncludeRequiredFactories.h"
int
main(int argc, char * argv[])
{
  RegisterRequiredFactories();
  if (argc < 4)
  {
    std::cerr << "Missing Parameters " << std::endl;
    std::cerr << "Usage: " << argv[0];
    std::cerr << " fixedImageFile  movingImageFile ";
    std::cerr << " outputImagefile  [differenceBeforeRegistration] ";
    std::cerr << " [differenceAfterRegistration] ";
    std::cerr << " [sliceBeforeRegistration] ";
    std::cerr << " [sliceDifferenceBeforeRegistration] ";
    std::cerr << " [sliceDifferenceAfterRegistration] ";
    std::cerr << " [sliceAfterRegistration] " << std::endl;
    return EXIT_FAILURE;
  }
  constexpr unsigned int Dimension = 3;
  using PixelType = float;
  using FixedImageType = itk::Image<PixelType, Dimension>;
  using MovingImageType = itk::Image<PixelType, Dimension>;

  //
  //  The Transform class is instantiated using the code below. The only
  //  template parameter to this class is the representation type of the
  //  space coordinates.
  //
  //  \index{itk::Versor\-Rigid3D\-Transform!Instantiation}
  //

  using TransformType = itk::VersorRigid3DTransform<double>;
  using OptimizerType = itk::VersorRigid3DTransformOptimizer;
  using MetricType = itk::MeanSquaresImageToImageMetric<FixedImageType, MovingImageType>;
  using InterpolatorType = itk::LinearInterpolateImageFunction<MovingImageType, double>;
  using RegistrationType = itk::ImageRegistrationMethod<FixedImageType, MovingImageType>;

  MetricType::Pointer       metric = MetricType::New();
  OptimizerType::Pointer    optimizer = OptimizerType::New();
  InterpolatorType::Pointer interpolator = InterpolatorType::New();
  RegistrationType::Pointer registration = RegistrationType::New();

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

  //
  //  The initial transform object is constructed below. This transform will be initialized,
  //  and its initial parameters will be used when
  //  the registration process starts.
  //
  //  \index{itk::Versor\-Rigid3D\-Transform!New()}
  //  \index{itk::Versor\-Rigid3D\-Transform!Pointer}
  //  \index{itk::Registration\-Method!SetTransform()}
  //
  TransformType::Pointer transform = TransformType::New();
  registration->SetTransform(transform);
  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 input images are taken from readers. It is not necessary here to
  //  explicitly call \code{Update()} on the readers since the
  //  \doxygen{CenteredTransformInitializer} will do it as part of its
  //  computations. The following code instantiates the type of the
  //  initializer. This class is templated over the fixed and moving image type
  //  as well as the transform type. An initializer is then constructed by
  //  calling the \code{New()} method and assigning the result to a smart
  //  pointer.
  //
  // \index{itk::Centered\-Transform\-Initializer!Instantiation}
  // \index{itk::Centered\-Transform\-Initializer!New()}
  // \index{itk::Centered\-Transform\-Initializer!SmartPointer}
  //
  using TransformInitializerType = itk::CenteredTransformInitializer<TransformType, FixedImageType, MovingImageType>;
  TransformInitializerType::Pointer initializer = TransformInitializerType::New();

  //
  //  The initializer is now connected to the transform and to the fixed and
  //  moving images.
  //

  initializer->SetTransform(transform);
  initializer->SetFixedImage(fixedImageReader->GetOutput());
  initializer->SetMovingImage(movingImageReader->GetOutput());
  //
  //  The use of the geometrical centers is selected by calling
  //  \code{GeometryOn()} while the use of center of mass is selected by
  //  calling \code{MomentsOn()}.  Below we select the center of mass mode.
  //
  //  \index{Centered\-Transform\-Initializer!MomentsOn()}
  //  \index{Centered\-Transform\-Initializer!GeometryOn()}
  //
  initializer->MomentsOn();

  //
  //  Finally, the computation of the center and translation is triggered by
  //  the \code{InitializeTransform()} method. The resulting values will be
  //  passed directly to the transform.
  //

  initializer->InitializeTransform();

  //
  //  The rotation part of the transform is initialized using a
  //  \doxygen{Versor} which is simply a unit quaternion.  The
  //  \code{VersorType} can be obtained from the transform traits. The versor
  //  itself defines the type of the vector used to indicate the rotation axis.
  //  This trait can be extracted as \code{VectorType}. The following lines
  //  create a versor object and initialize its parameters by passing a
  //  rotation axis and an angle.
  //
  using VersorType = TransformType::VersorType;
  using VectorType = VersorType::VectorType;
  VersorType rotation;
  VectorType axis;
  axis[0] = 0.0;
  axis[1] = 0.0;
  axis[2] = 1.0;
  constexpr double angle = 0;
  rotation.Set(axis, angle);
  transform->SetRotation(rotation);

  //
  //  We now pass the parameters of the current transform as the initial
  //  parameters to be used when the registration process starts.
  //
  registration->SetInitialTransformParameters(transform->GetParameters());
  using OptimizerScalesType = OptimizerType::ScalesType;
  OptimizerScalesType optimizerScales(transform->GetNumberOfParameters());
  const double        translationScale = 1.0 / 1000.0;
  optimizerScales[0] = 1.0;
  optimizerScales[1] = 1.0;
  optimizerScales[2] = 1.0;
  optimizerScales[3] = translationScale;
  optimizerScales[4] = translationScale;
  optimizerScales[5] = translationScale;
  optimizer->SetScales(optimizerScales);
  optimizer->SetMaximumStepLength(0.2000);
  optimizer->SetMinimumStepLength(0.0001);
  optimizer->SetNumberOfIterations(200);

  // 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::cerr << "ExceptionObject caught !" << std::endl;
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
  }
  OptimizerType::ParametersType finalParameters = registration->GetLastTransformParameters();

  const double       versorX = finalParameters[0];
  const double       versorY = finalParameters[1];
  const double       versorZ = finalParameters[2];
  const double       finalTranslationX = finalParameters[3];
  const double       finalTranslationY = finalParameters[4];
  const double       finalTranslationZ = finalParameters[5];
  const unsigned int numberOfIterations = optimizer->GetCurrentIteration();
  const double       bestValue = optimizer->GetValue();

  // Print out results
  //
  std::cout << std::endl << std::endl;
  std::cout << "Result = " << std::endl;
  std::cout << " versor X      = " << versorX << std::endl;
  std::cout << " versor Y      = " << versorY << std::endl;
  std::cout << " versor Z      = " << versorZ << std::endl;
  std::cout << " Translation X = " << finalTranslationX << std::endl;
  std::cout << " Translation Y = " << finalTranslationY << std::endl;
  std::cout << " Translation Z = " << finalTranslationZ << std::endl;
  std::cout << " Iterations    = " << numberOfIterations << std::endl;
  std::cout << " Metric value  = " << bestValue << std::endl;

  //
  //  Let's execute this example over some of the images available in the ftp
  //  site
  //
  //  \url{ftp://public.kitware.com/pub/itk/Data/BrainWeb}
  //
  //  Note that the images in the ftp site are compressed in \code{.tgz} files.
  //  You should download these files an uncompress them in your local system.
  //  After decompressing and extracting the files you could take a pair of
  //  volumes, for example the pair:
  //
  //  \begin{itemize}
  //  \item \code{brainweb1e1a10f20.mha}
  //  \item \code{brainweb1e1a10f20Rot10Tx15.mha}
  //  \end{itemize}
  //
  //  The second image is the result of intentionally rotating the first image
  //  by $10$ degrees around the origin and shifting it $15mm$ in $X$.  The
  //  registration takes $24$ iterations and produces:
  //
  //  \begin{center}
  //  \begin{verbatim}
  //  [-6.03744e-05, 5.91487e-06, -0.0871932, 2.64659, -17.4637, -0.00232496]
  //  \end{verbatim}
  //  \end{center}
  //
  //  That are interpreted as
  //
  //  \begin{itemize}
  //  \item Versor        = $(-6.03744e-05, 5.91487e-06, -0.0871932)$
  //  \item Translation   = $(2.64659,  -17.4637,  -0.00232496)$ millimeters
  //  \end{itemize}
  //
  //  This Versor is equivalent to a rotation of $9.98$ degrees around the $Z$
  //  axis.
  //
  //  Note that the reported translation is not the translation of $(15.0,0.0,0.0)$
  //  that we may be naively expecting. The reason is that the
  //  \code{VersorRigid3DTransform} is applying the rotation around the center
  //  found by the \code{CenteredTransformInitializer} and then adding the
  //  translation vector shown above.
  //
  //  It is more illustrative in this case to take a look at the actual
  //  rotation matrix and offset resulting form the $6$ parameters.
  //
  transform->SetParameters(finalParameters);
  TransformType::MatrixType matrix = transform->GetMatrix();
  TransformType::OffsetType offset = transform->GetOffset();
  std::cout << "Matrix = " << std::endl << matrix << std::endl;
  std::cout << "Offset = " << std::endl << offset << std::endl;

  //
  //  The output of this print statements is
  //
  //  \begin{center}
  //  \begin{verbatim}
  //  Matrix =
  //      0.984795 0.173722 2.23132e-05
  //      -0.173722 0.984795 0.000119257
  //      -1.25621e-06 -0.00012132 1
  //
  //  Offset =
  //      [-15.0105, -0.00672343, 0.0110854]
  //  \end{verbatim}
  //  \end{center}
  //
  //  From the rotation matrix it is possible to deduce that the rotation is
  //  happening in the X,Y plane and that the angle is on the order of
  //  $\arcsin{(0.173722)}$ which is very close to 10 degrees, as we expected.
  //

  //
  // \begin{figure}
  // \center
  // \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceBorder20}
  // \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceR10X13Y17}
  // \itkcaption[CenteredTransformInitializer input images]{Fixed and moving image
  // provided as input to the registration method using
  // CenteredTransformInitializer.}
  // \label{fig:FixedMovingImageRegistration8}
  // \end{figure}
  //
  //
  // \begin{figure}
  // \center
  // \includegraphics[width=0.32\textwidth]{ImageRegistration8Output}
  // \includegraphics[width=0.32\textwidth]{ImageRegistration8DifferenceBefore}
  // \includegraphics[width=0.32\textwidth]{ImageRegistration8DifferenceAfter}
  // \itkcaption[CenteredTransformInitializer output images]{Resampled moving
  // image (left). Differences between fixed and moving images, before (center)
  // and after (right) registration with the
  // CenteredTransformInitializer.}
  // \label{fig:ImageRegistration8Outputs}
  // \end{figure}
  //
  // Figure \ref{fig:ImageRegistration8Outputs} shows the output of the
  // registration. The center image in this figure shows the differences
  // between the fixed image and the resampled moving image before the
  // registration. The image on the right side presents the difference between
  // the fixed image and the resampled moving image after the registration has
  // been performed. Note that these images are individual slices extracted
  // from the actual volumes. For details, look at the source code of this
  // example, where the ExtractImageFilter is used to extract a slice from the
  // the center of each one of the volumes. One of the main purposes of this
  // example is to illustrate that the toolkit can perform registration on
  // images of any dimension. The only limitations are, as usual, the amount of
  // memory available for the images and the amount of computation time that it
  // will take to complete the optimization process.
  //
  // \begin{figure}
  // \center
  // \includegraphics[height=0.32\textwidth]{ImageRegistration8TraceMetric}
  // \includegraphics[height=0.32\textwidth]{ImageRegistration8TraceAngle}
  // \includegraphics[height=0.32\textwidth]{ImageRegistration8TraceTranslations}
  // \itkcaption[CenteredTransformInitializer output plots]{Plots of the metric,
  // rotation angle, center of rotation and translations during the
  // registration using CenteredTransformInitializer.}
  // \label{fig:ImageRegistration8Plots}
  // \end{figure}
  //
  //  Figure \ref{fig:ImageRegistration8Plots} shows the plots of the main
  //  output parameters of the registration process. The metric values at every
  //  iteration. The Z component of the versor is plotted as an indication of
  //  how the rotation progress. The X,Y translation components of the
  //  registration are plotted at every iteration too.
  //
  //  Shell and Gnuplot scripts for generating the diagrams in
  //  Figure~\ref{fig:ImageRegistration8Plots} are available in the \code{ITKSoftwareGuide}
  //  Git repository under the directory
  //
  //  \code{ITKSoftwareGuide/SoftwareGuide/Art}.
  //
  //  You are strongly encouraged to run the example code, since only in this
  //  way you can gain a first hand experience with the behavior of the
  //  registration process. Once again, this is a simple reflection of the
  //  philosophy that we put forward in this book:
  //
  //  \emph{If you can not replicate it, then it does not exist!}.
  //
  //  We have seen enough published papers with pretty pictures, presenting
  //  results that in practice are impossible to replicate. That is vanity, not
  //  science.
  //

  using ResampleFilterType = itk::ResampleImageFilter<MovingImageType, FixedImageType>;
  TransformType::Pointer finalTransform = TransformType::New();
  finalTransform->SetCenter(transform->GetCenter());
  finalTransform->SetParameters(finalParameters);
  finalTransform->SetFixedParameters(transform->GetFixedParameters());
  ResampleFilterType::Pointer resampler = ResampleFilterType::New();
  resampler->SetTransform(finalTransform);
  resampler->SetInput(movingImageReader->GetOutput());
  FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
  resampler->SetSize(fixedImage->GetLargestPossibleRegion().GetSize());
  resampler->SetOutputOrigin(fixedImage->GetOrigin());
  resampler->SetOutputSpacing(fixedImage->GetSpacing());
  resampler->SetOutputDirection(fixedImage->GetDirection());
  resampler->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(resampler->GetOutput());
  writer->SetInput(caster->GetOutput());
  writer->Update();

  using DifferenceFilterType = itk::SubtractImageFilter<FixedImageType, FixedImageType, FixedImageType>;
  DifferenceFilterType::Pointer difference = DifferenceFilterType::New();

  using RescalerType = itk::RescaleIntensityImageFilter<FixedImageType, OutputImageType>;
  RescalerType::Pointer intensityRescaler = RescalerType::New();
  intensityRescaler->SetInput(difference->GetOutput());
  intensityRescaler->SetOutputMinimum(0);
  intensityRescaler->SetOutputMaximum(255);
  difference->SetInput1(fixedImageReader->GetOutput());
  difference->SetInput2(resampler->GetOutput());
  resampler->SetDefaultPixelValue(1);
  WriterType::Pointer writer2 = WriterType::New();
  writer2->SetInput(intensityRescaler->GetOutput());

  // Compute the difference image between the
  // fixed and resampled moving image.
  if (argc > 5)
  {
    writer2->SetFileName(argv[5]);
    writer2->Update();
  }

  using IdentityTransformType = itk::IdentityTransform<double, Dimension>;
  IdentityTransformType::Pointer identity = IdentityTransformType::New();
  // Compute the difference image between the
  // fixed and moving image before registration.
  if (argc > 4)
  {
    resampler->SetTransform(identity);
    writer2->SetFileName(argv[4]);
    writer2->Update();
  }
  //
  //  Here we extract slices from the input volume, and the difference volumes
  //  produced before and after the registration.  These slices are presented as
  //  figures in the Software Guide.
  //
  //
  using OutputSliceType = itk::Image<OutputPixelType, 2>;
  using ExtractFilterType = itk::ExtractImageFilter<OutputImageType, OutputSliceType>;
  ExtractFilterType::Pointer extractor = ExtractFilterType::New();
  extractor->SetDirectionCollapseToSubmatrix();
  extractor->InPlaceOn();

  FixedImageType::RegionType inputRegion = fixedImage->GetLargestPossibleRegion();
  FixedImageType::SizeType   size = inputRegion.GetSize();
  FixedImageType::IndexType  start = inputRegion.GetIndex();
  // Select one slice as output
  size[2] = 0;
  start[2] = 90;
  FixedImageType::RegionType desiredRegion;
  desiredRegion.SetSize(size);
  desiredRegion.SetIndex(start);
  extractor->SetExtractionRegion(desiredRegion);
  using SliceWriterType = itk::ImageFileWriter<OutputSliceType>;
  SliceWriterType::Pointer sliceWriter = SliceWriterType::New();
  sliceWriter->SetInput(extractor->GetOutput());
  if (argc > 6)
  {
    extractor->SetInput(caster->GetOutput());
    resampler->SetTransform(identity);
    sliceWriter->SetFileName(argv[6]);
    sliceWriter->Update();
  }
  if (argc > 7)
  {
    extractor->SetInput(intensityRescaler->GetOutput());
    resampler->SetTransform(identity);
    sliceWriter->SetFileName(argv[7]);
    sliceWriter->Update();
  }
  if (argc > 8)
  {
    resampler->SetTransform(finalTransform);
    sliceWriter->SetFileName(argv[8]);
    sliceWriter->Update();
  }
  if (argc > 9)
  {
    extractor->SetInput(caster->GetOutput());
    resampler->SetTransform(finalTransform);
    sliceWriter->SetFileName(argv[9]);
    sliceWriter->Update();
  }
  return EXIT_SUCCESS;
}
