/*=========================================================================
 *
 *  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 : BeginCommandLineArgs
//    INPUTS:  {BrainT1SliceBorder20.png}
//    INPUTS:  {BrainProtonDensitySliceShifted13x17y.png}
//    OUTPUTS: {MultiResImageRegistration1Output.png}
//    ARGUMENTS: 128
//    OUTPUTS: {MultiResImageRegistration1CheckerboardBefore.png}
//    OUTPUTS: {MultiResImageRegistration1CheckerboardAfter.png}
//  Software Guide : EndCommandLineArgs

// Software Guide : BeginLatex
//
// \index{itk::ImageRegistrationMethodv4!Multi-Resolution}
// \index{itk::ImageRegistrationMethodv4!Multi-Modality}
//
// This example illustrates the use of the
// \doxygen{ImageRegistrationMethodv4} to solve a simple
// multi-modality registration problem by a multi-resolution approach.
// Since ITKv4 registration method is designed based on a multi-resolution
// structure, a separate set of classes are no longer required to run
// the registration process of this example.
//
// This a great advantage over the previous versions of ITK, as
// in ITKv3 we had to use a different filter
// (\doxygen{MultiResolutionImageRegistrationMethod})
// to run a multi-resolution process. Also, we had to use image pyramids
// filters
// (\doxygen{MultiResolutionPyramidImageFilter}) for creating the sequence of
// downsampled images. Hence, you can see how ITKv4 framework is
// more user-friendly in more complex situations.
//
// To begin the example, we include the headers of the registration
// components we will use.
//
// Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
#include "itkImageRegistrationMethodv4.h"
#include "itkTranslationTransform.h"
#include "itkMattesMutualInformationImageToImageMetricv4.h"
#include "itkRegularStepGradientDescentOptimizerv4.h"
// Software Guide : EndCodeSnippet


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

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


// Software Guide : BeginLatex
//
// The ImageRegistrationMethodv4 solves a registration
// problem in a coarse-to-fine manner as illustrated in Figure
// \ref{fig:MultiResRegistrationConcept}. The registration is first performed
// at the coarsest level using the images at the first level of the fixed and
// moving image pyramids. The transform parameters determined by the
// registration are then used to initialize the registration at the next finer
// level using images from the second level of the pyramids. This process is
// repeated as we work up to the finest level of image resolution.
//
// \begin{figure}
// \center
// \includegraphics[width=\textwidth]{MultiResRegistrationConcept}
// \itkcaption[Conceptual representation of Multi-Resolution
// registration]{Conceptual representation of the multi-resolution
// registration process.} \label{fig:MultiResRegistrationConcept} \end{figure}
//
// Software Guide : EndLatex


// Software Guide : BeginLatex
//
// In a typical registration scenario, a user will tweak component settings
// or even swap out components between multi-resolution levels. For example,
// when optimizing at a coarse resolution, it may be possible to take more
// aggressive step sizes and have a more relaxed convergence criterion.
//
// Tweaking the components between resolution levels can be done using ITK's
// implementation of the \emph{Command/Observer} design pattern. Before
// beginning registration at each resolution level,
// where ImageRegistrationMethodv4 invokes a
// \code{MultiResolutionIterationEvent()}. The registration components can
// be changed by implementing a \doxygen{Command} which responds to the
// event. A brief description of the interaction between events and commands
// was previously presented in Section \ref{sec:MonitoringImageRegistration}.
//
// We will illustrate this mechanism by changing the parameters of the
// optimizer between each resolution level by way of a simple interface
// command. First, we include the header file of the Command class.
//
// Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
#include "itkCommand.h"
// Software Guide : EndCodeSnippet

// Software Guide : BeginLatex
//
// Our new interface command class is called
// \code{RegistrationInterfaceCommand}. It derives from
// Command and is templated over the
// multi-resolution registration type.
//
// Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
template <typename TRegistration>
class RegistrationInterfaceCommand : public itk::Command
{
  // Software Guide : EndCodeSnippet

  // Software Guide : BeginLatex
  //
  // We then define \code{Self}, \code{Superclass}, \code{Pointer},
  // \code{New()} and a constructor in a similar fashion to the
  // \code{CommandIterationUpdate} class in Section
  // \ref{sec:MonitoringImageRegistration}.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
public:
  using Self = RegistrationInterfaceCommand;
  using Superclass = itk::Command;
  using Pointer = itk::SmartPointer<Self>;
  itkNewMacro(Self);

protected:
  RegistrationInterfaceCommand() = default;
  // Software Guide : EndCodeSnippet

  // Software Guide : BeginLatex
  //
  // For convenience, we declare types useful for converting pointers
  // in the \code{Execute()} method.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
public:
  using RegistrationType = TRegistration;
  using RegistrationPointer = RegistrationType *;
  using OptimizerType = itk::RegularStepGradientDescentOptimizerv4<double>;
  using OptimizerPointer = OptimizerType *;
  // Software Guide : EndCodeSnippet

  // Software Guide : BeginLatex
  //
  // Two arguments are passed to the \code{Execute()} method: the first
  // is the pointer to the object which invoked the event and the
  // second is the event that was invoked.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  void
  Execute(itk::Object * object, const itk::EventObject & event) override
  {
    // Software Guide : EndCodeSnippet

    // Software Guide : BeginLatex
    //
    // First we verify that the event invoked is of the right type,
    // \code{itk::MultiResolutionIterationEvent()}.
    // If not, we return without any further action.
    //
    // Software Guide : EndLatex

    // Software Guide : BeginCodeSnippet
    if (!(itk::MultiResolutionIterationEvent().CheckEvent(&event)))
    {
      return;
    }
    // Software Guide : EndCodeSnippet

    // Software Guide : BeginLatex
    //
    // We then convert the input object pointer to a RegistrationPointer.
    // Note that no error checking is done here to verify the
    // \code{dynamic\_cast} was successful since we know the actual object
    // is a registration method. Then we ask for the optimizer object
    // from the registration method.
    //
    // Software Guide : EndLatex

    // Software Guide : BeginCodeSnippet
    auto registration = static_cast<RegistrationPointer>(object);
    auto optimizer =
      static_cast<OptimizerPointer>(registration->GetModifiableOptimizer());
    // Software Guide : EndCodeSnippet

    unsigned int currentLevel = registration->GetCurrentLevel();
    typename RegistrationType::ShrinkFactorsPerDimensionContainerType
      shrinkFactors =
        registration->GetShrinkFactorsPerDimension(currentLevel);
    typename RegistrationType::SmoothingSigmasArrayType smoothingSigmas =
      registration->GetSmoothingSigmasPerLevel();

    std::cout << "-------------------------------------" << std::endl;
    std::cout << " Current level = " << currentLevel << std::endl;
    std::cout << "    shrink factor = " << shrinkFactors << std::endl;
    std::cout << "    smoothing sigma = ";
    std::cout << smoothingSigmas[currentLevel] << std::endl;
    std::cout << std::endl;

    // Software Guide : BeginLatex
    //
    // If this is the first resolution level we set the learning rate
    // (representing the first step size) and the minimum step length
    // (representing the convergence criterion) to large values.  At each
    // subsequent resolution level, we will reduce the minimum step length by
    // a factor of 5 in order to allow the optimizer to focus on progressively
    // smaller regions. The learning rate is set up to the current step
    // length. In this way, when the optimizer is reinitialized at the
    // beginning of the registration process for the next level, the step
    // length will simply start with the last value used for the previous
    // level. This will guarantee the continuity of the path taken by the
    // optimizer through the parameter space.
    //
    // Software Guide : EndLatex

    // Software Guide : BeginCodeSnippet
    if (registration->GetCurrentLevel() == 0)
    {
      optimizer->SetLearningRate(16.00);
      optimizer->SetMinimumStepLength(2.5);
    }
    else
    {
      optimizer->SetLearningRate(optimizer->GetCurrentStepLength());
      optimizer->SetMinimumStepLength(optimizer->GetMinimumStepLength() *
                                      0.2);
    }
    // Software Guide : EndCodeSnippet
  }

  // Software Guide : BeginLatex
  //
  // Another version of the \code{Execute()} method accepting a \code{const}
  // input object is also required since this method is defined as pure
  // virtual in the base class.  This version simply returns without taking
  // any action.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  void
  Execute(const itk::Object *, const itk::EventObject &) override
  {
    return;
  }
};
// Software Guide : EndCodeSnippet

//  The following section of code implements an observer
//  that will monitor the evolution of the registration process.
//
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::RegularStepGradientDescentOptimizerv4<double>;
  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::cout << m_CumulativeIterationIndex++ << std::endl;
  }

private:
  unsigned int m_CumulativeIterationIndex{ 0 };
};


int
main(int argc, const char * argv[])
{
  if (argc < 4)
  {
    std::cerr << "Missing Parameters " << std::endl;
    std::cerr << "Usage: " << argv[0];
    std::cerr << " fixedImageFile  movingImageFile ";
    std::cerr << " outputImagefile [backgroundGrayLevel]";
    std::cerr << " [checkerBoardBefore] [checkerBoardAfter]";
    std::cerr << " [numberOfBins] " << std::endl;
    return EXIT_FAILURE;
  }

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

  const std::string fixedImageFile = argv[1];
  const std::string movingImageFile = argv[2];
  const std::string outImagefile = argv[3];
  const PixelType backgroundGrayLevel = (argc > 4) ? std::stoi(argv[4]) : 100;
  const std::string checkerBoardBefore = (argc > 5) ? argv[5] : "";
  const std::string checkerBoardAfter = (argc > 6) ? argv[6] : "";
  const int         numberOfBins = (argc > 7) ? std::stoi(argv[7]) : 0;

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

  //  Software Guide : BeginLatex
  //
  //  The fixed and moving image types are defined as in previous
  //  examples. The downsampled images for different resolution levels
  //  are created internally by the registration method based on the
  //  values provided for \emph{ShrinkFactor} and \emph{SmoothingSigma}
  //  vectors.
  //
  //  The types for the registration components are then derived using
  //  the fixed and moving image type, as in previous examples.
  //
  //  Software Guide : EndLatex

  using TransformType = itk::TranslationTransform<double, Dimension>;

  using OptimizerType = itk::RegularStepGradientDescentOptimizerv4<double>;

  using MetricType =
    itk::MattesMutualInformationImageToImageMetricv4<FixedImageType,
                                                     MovingImageType>;
  using RegistrationType = itk::
    ImageRegistrationMethodv4<FixedImageType, MovingImageType, TransformType>;

  //  All the components are instantiated using their \code{New()} method
  //  and connected to the registration object as in previous example.
  //
  auto transform = TransformType::New();
  auto optimizer = OptimizerType::New();
  auto metric = MetricType::New();
  auto registration = RegistrationType::New();

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

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

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

  fixedImageReader->SetFileName(fixedImageFile);
  movingImageReader->SetFileName(movingImageFile);

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


  using ParametersType = OptimizerType::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

  transform->SetParameters(initialParameters);

  registration->SetInitialTransform(transform);
  registration->InPlaceOn();

  metric->SetNumberOfHistogramBins(24);

  if (argc > 7)
  {
    // optionally, override the values with numbers taken from the command
    // line arguments.
    metric->SetNumberOfHistogramBins(numberOfBins);
  }

  //  Software Guide : BeginLatex
  //
  //  To set the optimizer parameters, note that \emph{LearningRate}
  //  and \emph{MinimumStepLength} are set in the observer at the beginning
  //  of each resolution level. The other optimizer parameters are set
  //  as follows.
  //
  //  Software Guide : EndLatex

  //  Software Guide : BeginCodeSnippet
  optimizer->SetNumberOfIterations(200);
  optimizer->SetRelaxationFactor(0.5);
  // Software Guide : EndCodeSnippet

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


  //  Software Guide : BeginLatex
  //
  //  We set the number of multi-resolution levels to three and set
  //  the corresponding shrink factor and smoothing sigma values for each
  //  resolution level. Using smoothing in the subsampled images in
  //  low-resolution levels can avoid large fluctuations in the
  //  metric function, which prevents the optimizer from becoming trapped in
  //  local minima. In this simple example we have no smoothing, and we have
  //  used small shrinkings for the first two resolution levels.
  //
  //  \index{itk::Image\-Registration\-Methodv4!SetNumberOfLevels()}
  //  \index{itk::Image\-Registration\-Methodv4!SetShrinkFactorsPerLevel()}
  //  \index{itk::Image\-Registration\-Methodv4!SetSmoothingSigmasPerLevel()}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  constexpr unsigned int numberOfLevels = 3;

  RegistrationType::ShrinkFactorsArrayType shrinkFactorsPerLevel;
  shrinkFactorsPerLevel.SetSize(3);
  shrinkFactorsPerLevel[0] = 3;
  shrinkFactorsPerLevel[1] = 2;
  shrinkFactorsPerLevel[2] = 1;

  RegistrationType::SmoothingSigmasArrayType smoothingSigmasPerLevel;
  smoothingSigmasPerLevel.SetSize(3);
  smoothingSigmasPerLevel[0] = 0;
  smoothingSigmasPerLevel[1] = 0;
  smoothingSigmasPerLevel[2] = 0;

  registration->SetNumberOfLevels(numberOfLevels);
  registration->SetShrinkFactorsPerLevel(shrinkFactorsPerLevel);
  registration->SetSmoothingSigmasPerLevel(smoothingSigmasPerLevel);
  // Software Guide : EndCodeSnippet

  //  Software Guide : BeginLatex
  //
  //  Once all the registration components are in place we can create
  //  an instance of our interface command and connect it to the
  //  registration object using the \code{AddObserver()} method.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using CommandType = RegistrationInterfaceCommand<RegistrationType>;
  auto command = CommandType::New();

  registration->AddObserver(itk::MultiResolutionIterationEvent(), command);
  // Software Guide : EndCodeSnippet

  //  Software Guide : BeginLatex
  //
  //  Then we trigger the registration process by calling \code{Update()}.
  //
  //  Software Guide : EndLatex

  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;
  }

  ParametersType finalParameters = transform->GetParameters();

  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;


  //  Software Guide : BeginLatex
  //
  //  Let's execute this example using the following images
  //
  //  \begin{itemize}
  //  \item BrainT1SliceBorder20.png
  //  \item BrainProtonDensitySliceShifted13x17y.png
  //  \end{itemize}
  //
  //  The output produced by the execution of the method is
  //
  //  \begin{verbatim}
  //  0   -0.316956   [11.4200, 11.2063]
  //  1   -0.562048   [18.2938, 25.6545]
  //  2   -0.407696   [11.3643, 21.6569]
  //  3   -0.5702     [13.7244, 18.4274]
  //  4   -0.803252   [11.1634, 15.3547]
  //
  //  0   -0.697586   [12.8778, 16.3846]
  //  1   -0.901984   [13.1794, 18.3617]
  //  2   -0.827423   [13.0545, 17.3695]
  //  3   -0.92754    [12.8528, 16.3901]
  //  4   -0.902671   [12.9426, 16.8819]
  //  5   -0.941212   [13.1402, 17.3413]
  //
  //  0   -0.922239   [13.0364, 17.1138]
  //  1   -0.930203   [12.9463, 16.8806]
  //  2   -0.930959   [13.0191, 16.9822]
  //
  //
  //  Result =
  //   Translation X = 13.0192
  //   Translation Y = 16.9823
  //   Iterations    = 4
  //   Metric value  = -0.929237
  //  \end{verbatim}
  //
  //  These values are a close match to the true misalignment of $(13,17)$
  //  introduced in the moving image.
  //
  //  Software Guide : EndLatex

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

  auto resample = ResampleFilterType::New();

  resample->SetTransform(transform);
  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(backgroundGrayLevel);


  using OutputPixelType = unsigned char;

  using OutputImageType = itk::Image<OutputPixelType, Dimension>;

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

  using WriterType = itk::ImageFileWriter<OutputImageType>;


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


  writer->SetFileName(outImagefile);


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

  //
  // Generate checkerboards before and after registration
  //
  using CheckerBoardFilterType = itk::CheckerBoardImageFilter<FixedImageType>;

  auto checker = CheckerBoardFilterType::New();

  checker->SetInput1(fixedImage);
  checker->SetInput2(resample->GetOutput());

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

  resample->SetDefaultPixelValue(0);

  // Before registration
  auto identityTransform = TransformType::New();
  identityTransform->SetIdentity();
  resample->SetTransform(identityTransform);

  for (int q = 0; q < argc; ++q)
  {
    std::cout << q << " " << argv[q] << std::endl;
  }
  if (checkerBoardBefore != std::string(""))
  {
    writer->SetFileName(checkerBoardBefore);
    writer->Update();
  }


  // After registration
  resample->SetTransform(transform);
  if (checkerBoardAfter != std::string(""))
  {
    writer->SetFileName(checkerBoardAfter);
    writer->Update();
  }

  //  Software Guide : BeginLatex
  //
  // \begin{figure}
  // \center
  // \includegraphics[width=0.32\textwidth]{MultiResImageRegistration1Output}
  // \includegraphics[width=0.32\textwidth]{MultiResImageRegistration1CheckerboardBefore}
  // \includegraphics[width=0.32\textwidth]{MultiResImageRegistration1CheckerboardAfter}
  // \itkcaption[Multi-Resolution registration input images]{Mapped moving
  // image (left) and composition of fixed and moving images before (center)
  // and after (right) registration.}
  // \label{fig:MultiResImageRegistration1Output}
  // \end{figure}
  //
  //  The result of resampling the moving image is presented in the left image
  //  of Figure \ref{fig:MultiResImageRegistration1Output}. The center and
  //  right images of the figure depict a checkerboard composite of the fixed
  //  and moving images before and after registration.
  //
  //  Software Guide : EndLatex

  //  Software Guide : BeginLatex
  //
  // \begin{figure}
  // \center
  // \includegraphics[height=0.44\textwidth]{MultiResImageRegistration1TraceTranslations}
  // \includegraphics[height=0.44\textwidth]{MultiResImageRegistration1TraceMetric}
  // \itkcaption[Multi-Resolution registration output images]{Sequence of
  // translations and metric values at each iteration of the optimizer.}
  // \label{fig:MultiResImageRegistration1Trace}
  // \end{figure}
  //
  //  Figure \ref{fig:MultiResImageRegistration1Trace} (left) shows
  //  the sequence of translations followed by the optimizer as it searched
  //  the parameter space. The right side of the same figure shows the
  //  sequence of metric values computed as the optimizer searched the
  //  parameter space.  From the trace, we can see that with the more
  //  aggressive optimization parameters we get quite close to the optimal
  //  value within 5 iterations with the remaining iterations just doing fine
  //  adjustments. It is interesting to compare these results with those
  //  of the single resolution example in Section
  //  \ref{sec:MultiModalityRegistrationMattes}, where 46 iterations were
  //  required as more conservative optimization parameters had to be used.
  //
  //  Software Guide : EndLatex


  return EXIT_SUCCESS;
}
