/*=========================================================================
 *
 *  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 "itkImageRegistrationMethod.h"
#include "itkMattesMutualInformationImageToImageMetric.h"
#include "itkGradientDescentOptimizer.h"

#include "itkTextOutput.h"
#include "itkCommandIterationUpdate.h"
#include "vnl/vnl_sample.h"
namespace
{

double
F(itk::Vector<double, 3> & v);
}


/**
 *  This program test one instantiation of the itk::ImageRegistrationMethod class
 *
 *  This file tests the combination of:
 *   - MattesMutualInformation
 *   - AffineTransform
 *   - GradientDescentOptimizer
 *   - BSplineInterpolateImageFunction
 *
 *  The test image pattern consists of a 3D gaussian in the middle
 *  with some directional pattern on the outside.
 *
 *  The image value is shifted left by 24000.0 such that all the image value
 *  has a negative value. Without the fix of initial ImageTrueMax value in
 *  itkMattesMutualInformationImageToImageMetric.hxx, this test will fail
 *  because the image values all will be in the leftmost histogram bin.
 *
 *  One image is scaled and shifted relative to the other.
 *
 * Notes:
 * =======
 * This example performs an affine registration
 * between a moving (source) and fixed (target) image using mutual information.
 * It uses a simple steepest descent optimizer to find the
 * best affine transform to register the moving image onto the fixed
 * image.
 *
 * The mutual information value and its derivatives are estimated
 * using spatial sampling.
 *
 * The registration uses a simple stochastic gradient ascent scheme. Steps
 * are repeatedly taken that are proportional to the approximate
 * deriviative of the mutual information with respect to the affine
 * transform parameters. The stepsize is governed by the LearningRate
 * parameter.
 *
 * Since the parameters of the linear part is different in magnitude
 * to the parameters in the offset part, scaling is required
 * to improve convergence. The scaling can set via the optimizer.
 *
 * In the optimizer's scale transform set the scaling for
 * all the translation parameters to TranslationScale^{-2}.
 * Set the scale for all other parameters to 1.0.
 *
 * Note: the optimization performance can be improved by
 * setting the image origin to center of mass of the image.
 *
 */
int
itkImageRegistrationMethodTest_17(int, char *[])
{

  itk::OutputWindow::SetInstance(itk::TextOutput::New().GetPointer());

  bool pass = true;

  constexpr unsigned int dimension = 3;
  unsigned int           j;

  using PixelType = float;

  // Fixed Image Type
  using FixedImageType = itk::Image<PixelType, dimension>;

  // Moving Image Type
  using MovingImageType = itk::Image<PixelType, dimension>;

  // Transform Type
  using TransformType = itk::AffineTransform<double, dimension>;

  // Optimizer Type
  using OptimizerType = itk::GradientDescentOptimizer;

  // Metric Type
  using MetricType = itk::MattesMutualInformationImageToImageMetric<FixedImageType, MovingImageType>;

  // Interpolation technique
  using InterpolatorType = itk::BSplineInterpolateImageFunction<MovingImageType, double>;

  // Registration Method
  using RegistrationType = itk::ImageRegistrationMethod<FixedImageType, MovingImageType>;


  auto metric = MetricType::New();
  auto transform = TransformType::New();
  auto optimizer = OptimizerType::New();
  auto fixedImage = FixedImageType::New();
  auto movingImage = MovingImageType::New();
  auto interpolator = InterpolatorType::New();
  auto registration = RegistrationType::New();

  /*********************************************************
   * Set up the two input images.
   * One image scaled and shifted with respect to the other.
   **********************************************************/
  double displacement[dimension] = { 3, 1, 1 };
  double scale[dimension] = { 0.90, 1.0, 1.0 };

  FixedImageType::SizeType   size = { { 100, 100, 40 } };
  FixedImageType::IndexType  index = { { 0, 0, 0 } };
  FixedImageType::RegionType region;
  region.SetSize(size);
  region.SetIndex(index);

  fixedImage->SetRegions(region);
  fixedImage->Allocate();

  movingImage->SetRegions(region);
  movingImage->Allocate();


  using MovingImageIterator = itk::ImageRegionIterator<MovingImageType>;
  using FixedImageIterator = itk::ImageRegionIterator<FixedImageType>;

  itk::Point<double, dimension> center;
  for (j = 0; j < dimension; ++j)
  {
    center[j] = 0.5 * static_cast<double>(region.GetSize()[j]);
  }

  itk::Point<double, dimension>  p;
  itk::Vector<double, dimension> d;

  MovingImageIterator mIter(movingImage, region);
  FixedImageIterator  fIter(fixedImage, region);

  while (!mIter.IsAtEnd())
  {
    for (j = 0; j < dimension; ++j)
    {
      p[j] = mIter.GetIndex()[j];
    }

    d = p - center;

    fIter.Set((PixelType)F(d));

    for (j = 0; j < dimension; ++j)
    {
      d[j] = d[j] * scale[j] + displacement[j];
    }

    mIter.Set((PixelType)F(d));

    ++fIter;
    ++mIter;
  }

  // set the image origin to be center of the image
  double transCenter[dimension];
  for (j = 0; j < dimension; ++j)
  {
    transCenter[j] = -0.5 * static_cast<double>(size[j]);
  }

  movingImage->SetOrigin(transCenter);
  fixedImage->SetOrigin(transCenter);


  /******************************************************************
   * Set up the optimizer.
   ******************************************************************/

  // set the translation scale
  using ScalesType = OptimizerType::ScalesType;
  ScalesType parametersScales(transform->GetNumberOfParameters());

  parametersScales.Fill(1.0);

  for (j = 9; j < 12; ++j)
  {
    parametersScales[j] = 0.0001;
  }

  optimizer->SetScales(parametersScales);
  optimizer->MaximizeOff();

  /******************************************************************
   * Set up the optimizer observer
   ******************************************************************/
  using CommandIterationType = itk::CommandIterationUpdate<OptimizerType>;
  auto iterationCommand = CommandIterationType::New();

  iterationCommand->SetOptimizer(optimizer);

  /******************************************************************
   * Set up the metric.
   ******************************************************************/
  // Build with debug config to see the ImageTrueMax
  // metric->SetDebug( true );
  metric->SetNumberOfSpatialSamples(
    static_cast<unsigned long>(0.01 * fixedImage->GetBufferedRegion().GetNumberOfPixels()));

  metric->SetNumberOfHistogramBins(50);

  for (unsigned int jj = 0; jj < dimension; ++jj)
  {
    size[jj] -= 4;
    index[jj] += 2;
  }
  region.SetSize(size);
  region.SetIndex(index);
  metric->SetFixedImageRegion(region);
  metric->ReinitializeSeed(121212);

  /******************************************************************
   * Set up the registrator.
   ******************************************************************/

  // connect up the components
  registration->SetMetric(metric);
  registration->SetOptimizer(optimizer);
  registration->SetTransform(transform);
  registration->SetFixedImage(fixedImage);
  registration->SetMovingImage(movingImage);
  registration->SetInterpolator(interpolator);

  // set initial parameters to identity
  RegistrationType::ParametersType initialParameters(transform->GetNumberOfParameters());

  initialParameters.Fill(0.0);
  initialParameters[0] = 1.0;
  initialParameters[4] = 1.0;
  initialParameters[8] = 1.0;


  /***********************************************************
   * Run the registration
   ************************************************************/
  constexpr unsigned int numberOfLoops = 2;
  unsigned int           iter[numberOfLoops] = { 50, 0 };
  double                 rates[numberOfLoops] = { 1e-3, 5e-4 };


  for (j = 0; j < numberOfLoops; ++j)
  {

    try
    {
      optimizer->SetNumberOfIterations(iter[j]);
      optimizer->SetLearningRate(rates[j]);
      registration->SetInitialTransformParameters(initialParameters);
      registration->Update();

      initialParameters = registration->GetLastTransformParameters();
    }
    catch (const itk::ExceptionObject & e)
    {
      std::cout << "Registration failed" << std::endl;
      std::cout << "Reason " << e.GetDescription() << std::endl;
      return EXIT_FAILURE;
    }
  }


  /***********************************************************
   * Check the results
   ************************************************************/
  RegistrationType::ParametersType solution = registration->GetLastTransformParameters();

  std::cout << "Solution is: " << solution << std::endl;


  RegistrationType::ParametersType trueParameters(transform->GetNumberOfParameters());
  trueParameters.Fill(0.0);
  trueParameters[0] = 1 / scale[0];
  trueParameters[4] = 1 / scale[1];
  trueParameters[8] = 1 / scale[2];
  trueParameters[9] = -displacement[0] / scale[0];
  trueParameters[10] = -displacement[1] / scale[1];
  trueParameters[11] = -displacement[2] / scale[2];

  std::cout << "True solution is: " << trueParameters << std::endl;

  for (j = 0; j < 9; ++j)
  {
    if (itk::Math::abs(solution[j] - trueParameters[j]) > 0.025)
    {
      pass = false;
    }
  }
  for (j = 9; j < 12; ++j)
  {
    if (itk::Math::abs(solution[j] - trueParameters[j]) > 1.0)
    {
      pass = false;
    }
  }

  if (!pass)
  {
    std::cout << "Test failed." << std::endl;
    return EXIT_FAILURE;
  }


  std::cout << "Test passed." << std::endl;
  return EXIT_SUCCESS;
}
namespace
{


/**
 * This function defines the test image pattern.
 * The pattern is a 3D gaussian in the middle
 * and some directional pattern on the outside.
 */
double
F(itk::Vector<double, 3> & v)
{
  double           x = v[0];
  double           y = v[1];
  double           z = v[2];
  constexpr double s = 50;
  double           value = 200.0 * std::exp(-(x * x + y * y + z * z) / (s * s));
  x -= 8;
  y += 3;
  z += 0;
  double r = std::sqrt(x * x + y * y + z * z);
  if (r > 35)
  {
    value = 2 * (itk::Math::abs(x) + 0.8 * itk::Math::abs(y) + 0.5 * itk::Math::abs(z));
  }
  if (r < 4)
  {
    value = 400;
  }

  return (value - 24000.0);
}
} // namespace
