/*=========================================================================
 *
 *  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.
 *
 *=========================================================================*/
#include "itkRegistrationParameterScalesFromJacobian.h"
#include "itkImageToImageMetricv4.h"

#include "itkAffineTransform.h"
#include "itkDisplacementFieldTransform.h"
#include "itkMath.h"

/**
 *  \class RegistrationParameterScalesFromJacobianTestMetric for test.
 *  Create a simple metric to use for testing here.
 */
template <typename TFixedImage, typename TMovingImage, typename TVirtualImage = TFixedImage>
class RegistrationParameterScalesFromJacobianTestMetric
  : public itk::ImageToImageMetricv4<TFixedImage, TMovingImage, TVirtualImage>
{
public:
  /** Standard class type aliases. */
  using Self = RegistrationParameterScalesFromJacobianTestMetric;
  using Superclass = itk::ImageToImageMetricv4<TFixedImage, TMovingImage, TVirtualImage>;
  using Pointer = itk::SmartPointer<Self>;
  using ConstPointer = itk::SmartPointer<const Self>;

  using typename Superclass::MeasureType;
  using typename Superclass::DerivativeType;
  using typename Superclass::ParametersType;
  using typename Superclass::ParametersValueType;

  itkOverrideGetNameOfClassMacro(RegistrationParameterScalesFromJacobianTestMetric);

  itkNewMacro(Self);

  // Pure virtual functions that all Metrics must provide
  [[nodiscard]] unsigned int
  GetNumberOfParameters() const override
  {
    return 5;
  }

  MeasureType
  GetValue() const override
  {
    return 1.0;
  }

  void
  GetValueAndDerivative(MeasureType & value, DerivativeType & derivative) const override
  {
    value = 1.0;
    derivative.Fill(0.0);
  }

  [[nodiscard]] unsigned int
  GetNumberOfLocalParameters() const override
  {
    return 0;
  }

  void
  UpdateTransformParameters(const DerivativeType &, ParametersValueType) override
  {}

  const ParametersType &
  GetParameters() const override
  {
    return m_Parameters;
  }

  void
  Initialize() override
  {}

  ParametersType m_Parameters;

  // Image related types
  using FixedImageType = TFixedImage;
  using MovingImageType = TMovingImage;
  using VirtualImageType = TVirtualImage;

  using FixedImageConstPointer = typename FixedImageType::ConstPointer;
  using MovingImageConstPointer = typename MovingImageType::ConstPointer;
  using VirtualImagePointer = typename VirtualImageType::Pointer;
  using VirtualRegionType = typename VirtualImageType::RegionType;

  /* Image dimension accessors */
  static constexpr itk::SizeValueType FixedImageDimension = FixedImageType::ImageDimension;
  static constexpr itk::SizeValueType MovingImageDimension = MovingImageType::ImageDimension;
  static constexpr itk::SizeValueType VirtualImageDimension = VirtualImageType::ImageDimension;

private:
  RegistrationParameterScalesFromJacobianTestMetric() = default;
  ~RegistrationParameterScalesFromJacobianTestMetric() override = default;
};

/**
 */
int
itkRegistrationParameterScalesFromJacobianTest(int, char *[])
{

  // Image begins
  constexpr itk::SizeValueType ImageDimension{ 2 };
  using PixelType = double;
  using FloatType = double;

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

  auto                            fixedImage = FixedImageType::New();
  auto                            movingImage = MovingImageType::New();
  const VirtualImageType::Pointer virtualImage = fixedImage;

  auto size = MovingImageType::SizeType::Filled(100);

  movingImage->SetRegions(size);
  fixedImage->SetRegions(size);
  // Image done

  // Transform begins
  using MovingTransformType = itk::AffineTransform<double, ImageDimension>;
  auto movingTransform = MovingTransformType::New();
  movingTransform->SetIdentity();

  using FixedTransformType = itk::TranslationTransform<double, ImageDimension>;
  auto fixedTransform = FixedTransformType::New();
  fixedTransform->SetIdentity();
  // Transform done

  // Metric begins
  using MetricType = RegistrationParameterScalesFromJacobianTestMetric<FixedImageType, MovingImageType>;
  auto metric = MetricType::New();

  metric->SetVirtualDomainFromImage(virtualImage);
  metric->SetFixedImage(fixedImage);
  metric->SetMovingImage(movingImage);

  metric->SetFixedTransform(fixedTransform);
  metric->SetMovingTransform(movingTransform);
  // Metric done

  // Scales for the affine transform from transform jacobians
  using RegistrationParameterScalesFromJacobianType = itk::RegistrationParameterScalesFromJacobian<MetricType>;
  const RegistrationParameterScalesFromJacobianType::Pointer jacobianScaleEstimator =
    RegistrationParameterScalesFromJacobianType::New();

  jacobianScaleEstimator->SetMetric(metric);
  jacobianScaleEstimator->SetTransformForward(true); // by default
  jacobianScaleEstimator->Print(std::cout);
  std::cout << std::endl;

  RegistrationParameterScalesFromJacobianType::ScalesType jacobianScales(movingTransform->GetNumberOfParameters());
  jacobianScaleEstimator->EstimateScales(jacobianScales);
  std::cout << "Jacobian scales for the affine transform = " << jacobianScales << std::endl;

  // Check the correctness
  RegistrationParameterScalesFromJacobianType::ScalesType theoreticalJacobianScales(
    movingTransform->GetNumberOfParameters());
  VirtualImageType::PointType upperPoint;
  virtualImage->TransformIndexToPhysicalPoint(virtualImage->GetLargestPossibleRegion().GetUpperIndex(), upperPoint);

  itk::SizeValueType param = 0;
  for (itk::SizeValueType row = 0; row < ImageDimension; ++row)
  {
    for (itk::SizeValueType col = 0; col < ImageDimension; ++col)
    {
      // uses the corners for affine transform
      // = (0 + 0 + n*n + n*n)/4 = n*n/2
      theoreticalJacobianScales[param++] = upperPoint[col] * upperPoint[col] / 2.0;
    }
  }
  for (itk::SizeValueType row = 0; row < ImageDimension; ++row)
  {
    theoreticalJacobianScales[param++] = 1;
  }

  bool jacobianPass = true;
  for (itk::SizeValueType p = 0; p < jacobianScales.GetSize(); ++p)
  {
    if (itk::Math::Absolute((jacobianScales[p] - theoreticalJacobianScales[p]) / theoreticalJacobianScales[p]) > 0.01)
    {
      jacobianPass = false;
      break;
    }
  }
  if (!jacobianPass)
  {
    std::cout << "Failed: the jacobian scales for the affine transform are not correct." << std::endl;
  }
  else
  {
    std::cout << "Passed: the jacobian scales for the affine transform are correct." << std::endl;
  }

  bool nonUniformForJacobian = false;
  for (itk::SizeValueType p = 1; p < jacobianScales.GetSize(); ++p)
  {
    if (itk::Math::NotExactlyEquals(jacobianScales[p], jacobianScales[0]))
    {
      nonUniformForJacobian = true;
      break;
    }
  }
  if (!nonUniformForJacobian)
  {
    std::cout << "Error: the jacobian scales for an affine transform are equal for all parameters." << std::endl;
  }

  // Testing the step scale for the affine transform
  MovingTransformType::ParametersType movingStep(movingTransform->GetNumberOfParameters());
  movingStep = movingTransform->GetParameters();
  const FloatType stepScale = jacobianScaleEstimator->EstimateStepScale(movingStep);
  std::cout << "The step scale of Jacobian for the affine transform = " << stepScale << std::endl;
  const FloatType learningRate = 1.0 / stepScale;
  std::cout << "The learning rate of Jacobian for the affine transform = " << learningRate << std::endl;

  FloatType                   theoreticalStepScale = 0.0;
  FloatType                   count = 0.0;
  VirtualImageType::PointType lowerPoint;
  virtualImage->TransformIndexToPhysicalPoint(virtualImage->GetLargestPossibleRegion().GetIndex(), lowerPoint);

  for (FloatType x = lowerPoint[0]; x <= upperPoint[0]; x += upperPoint[0] - lowerPoint[0])
  {
    for (FloatType y = lowerPoint[1]; y <= upperPoint[1]; y += upperPoint[1] - lowerPoint[1])
    {
      theoreticalStepScale += std::sqrt(x * x + y * y);
      count++;
    }
  }
  theoreticalStepScale /= count;

  bool stepScalePass = false;
  if (itk::Math::Absolute((stepScale - theoreticalStepScale) / theoreticalStepScale) < 0.01)
  {
    stepScalePass = true;
  }
  if (!stepScalePass)
  {
    std::cout << "Failed: the step scale for the affine transform is not correct." << std::endl;
  }
  else
  {
    std::cout << "Passed: the step scale for the affine transform is correct." << std::endl;
  }

  // Testing local scales for a transform with local support, ex. DisplacementFieldTransform
  using DisplacementTransformType = itk::DisplacementFieldTransform<double, ImageDimension>;
  using FieldType = DisplacementTransformType::DisplacementFieldType;
  using VectorType = itk::Vector<double, ImageDimension>;

  constexpr VectorType zero{};

  auto field = FieldType::New();
  field->SetRegions(virtualImage->GetLargestPossibleRegion());
  field->SetSpacing(virtualImage->GetSpacing());
  field->SetOrigin(virtualImage->GetOrigin());
  field->SetDirection(virtualImage->GetDirection());
  field->Allocate();
  field->FillBuffer(zero);

  auto displacementTransform = DisplacementTransformType::New();
  displacementTransform->SetDisplacementField(field);

  metric->SetMovingTransform(displacementTransform);
  jacobianScaleEstimator->SetTransformForward(true);
  RegistrationParameterScalesFromJacobianType::ScalesType localScales;
  jacobianScaleEstimator->EstimateScales(localScales);
  std::cout << "Shift scales for the displacement field transform = " << localScales << std::endl;

  // Check the correctness
  RegistrationParameterScalesFromJacobianType::ScalesType theoreticalLocalScales(
    displacementTransform->GetNumberOfLocalParameters());
  theoreticalLocalScales.Fill(1.0);

  bool displacementPass = true;
  for (itk::SizeValueType p = 0; p < theoreticalLocalScales.GetSize(); ++p)
  {
    if (itk::Math::Absolute((localScales[p] - theoreticalLocalScales[p]) / theoreticalLocalScales[p]) > 0.01)
    {
      displacementPass = false;
      break;
    }
  }
  if (!displacementPass)
  {
    std::cout << "Failed: the shift scales for the displacement field transform are not correct." << std::endl;
  }
  else
  {
    std::cout << "Passed: the shift scales for the displacement field transform are correct." << std::endl;
  }
  // Testing scales with local support done

  // Testing the step scale for the displacement field transform
  DisplacementTransformType::ParametersType displacementStep(displacementTransform->GetNumberOfParameters(), 1.0);
  const FloatType localStepScale = jacobianScaleEstimator->EstimateStepScale(displacementStep);
  std::cout << "The step scale of Jacobian for the displacement field transform = " << localStepScale << std::endl;
  const FloatType localLearningRate = 1.0 / localStepScale;
  std::cout << "The learning rate of Jacobian for the displacement field transform = " << localLearningRate
            << std::endl;

  bool            localStepScalePass = false;
  const FloatType theoreticalLocalStepScale = std::sqrt(2.0);
  if (itk::Math::Absolute((localStepScale - theoreticalLocalStepScale) / theoreticalLocalStepScale) < 0.01)
  {
    localStepScalePass = true;
  }
  if (!localStepScalePass)
  {
    std::cout << "Failed: the step scale for the displacement field transform is not correct." << std::endl;
  }
  else
  {
    std::cout << "Passed: the step scale for the displacement field transform is correct." << std::endl;
  }
  // Testing the step scale with local support done

  // Check the correctness of all cases above
  std::cout << std::endl;
  if (jacobianPass && nonUniformForJacobian && stepScalePass && displacementPass && localStepScalePass)
  {
    std::cout << "Test passed" << std::endl;
    return EXIT_SUCCESS;
  }

  std::cout << "Test failed" << std::endl;
  return EXIT_FAILURE;
}
