/*=========================================================================
 *
 *  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
 *
 *         http://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.
 *
 *=========================================================================*/

// This one header will include all SimpleITK filters and external
// objects.
#include <SimpleITK.h>
#include <iostream>
#include <stdlib.h>
#include <iomanip>

namespace sitk = itk::simple;


class IterationUpdate : public sitk::Command
{
public:
  IterationUpdate(const sitk::ImageRegistrationMethod & m)
    : m_Method(m)
  {}

  void
  Execute() override
  {
    // use sitk's output operator for std::vector etc..
    using sitk::operator<<;

    // stash the stream state
    std::ios state(NULL);
    state.copyfmt(std::cout);
    std::cout << std::fixed << std::setfill(' ') << std::setprecision(5);
    if (m_Method.GetOptimizerIteration() == 0)
    {
      std::cout << "\tLevel: " << std::setw(3) << m_Method.GetCurrentLevel() << std::endl;
      std::cout << "\tScales: " << m_Method.GetOptimizerScales() << std::endl;
    }
    std::cout << '#' << m_Method.GetOptimizerIteration() << std::endl;
    std::cout << "\tMetric Value: " << m_Method.GetMetricValue() << std::endl;
    std::cout << "\tLearning Rate: " << m_Method.GetOptimizerLearningRate() << std::endl;
    if (m_Method.GetOptimizerConvergenceValue() != std::numeric_limits<double>::max())
    {
      std::cout << "\tConvergence Value: " << std::scientific << m_Method.GetOptimizerConvergenceValue() << std::endl;
    }
    std::cout.copyfmt(state);
  }

private:
  const sitk::ImageRegistrationMethod & m_Method;
};

class MultiResolutionIterationUpdate : public sitk::Command
{
public:
  MultiResolutionIterationUpdate(const sitk::ImageRegistrationMethod & m)
    : m_Method(m)
  {}

  void
  Execute() override
  {
    // use sitk's output operator for std::vector etc..
    using sitk::operator<<;

    // stash the stream state
    std::ios state(NULL);
    state.copyfmt(std::cout);
    std::cout << std::fixed << std::setfill(' ') << std::setprecision(5);
    std::cout << "\tStop Condition: " << m_Method.GetOptimizerStopConditionDescription() << std::endl;
    std::cout << "============= Resolution Change =============" << std::endl;
    std::cout.copyfmt(state);
  }

private:
  const sitk::ImageRegistrationMethod & m_Method;
};


int
main(int argc, char * argv[])
{

  if (argc < 4)
  {
    std::cerr << "Usage: " << argv[0] << " <fixedImageFilter> <movingImageFile> <outputTransformFile>" << std::endl;
    return 1;
  }

  sitk::Image fixed = sitk::ReadImage(argv[1], sitk::sitkFloat32);

  sitk::Image moving = sitk::ReadImage(argv[2], sitk::sitkFloat32);

  sitk::Transform initialTx =
    sitk::CenteredTransformInitializer(fixed, moving, sitk::AffineTransform(fixed.GetDimension()));

  sitk::ImageRegistrationMethod R;

  {
    std::vector<unsigned int> shrinkFactors = { 3, 2, 1 };

    std::vector<double> smoothingSigmas = { 2.0, 1.0, 1.0 };

    R.SetShrinkFactorsPerLevel(shrinkFactors);
    R.SetSmoothingSigmasPerLevel(smoothingSigmas);
  }

  R.SetMetricAsJointHistogramMutualInformation(20);
  R.MetricUseFixedImageGradientFilterOff();
  R.MetricUseFixedImageGradientFilterOff();

  {
    double                                                  learningRate = 1.0;
    unsigned int                                            numberOfIterations = 100;
    double                                                  convergenceMinimumValue = 1e-6;
    unsigned int                                            convergenceWindowSize = 10;
    sitk::ImageRegistrationMethod::EstimateLearningRateType estimateLearningRate = R.EachIteration;
    R.SetOptimizerAsGradientDescent(
      learningRate, numberOfIterations, convergenceMinimumValue, convergenceWindowSize, estimateLearningRate);
  }
  R.SetOptimizerScalesFromPhysicalShift();

  R.SetInitialTransform(initialTx);

  R.SetInterpolator(sitk::sitkLinear);

  IterationUpdate cmd(R);
  R.AddCommand(sitk::sitkIterationEvent, cmd);

  MultiResolutionIterationUpdate cmd2(R);
  R.AddCommand(sitk::sitkMultiResolutionIterationEvent, cmd2);

  sitk::Transform outTx1 = R.Execute(fixed, moving);

  std::cout << "-------" << std::endl;
  std::cout << outTx1.ToString() << std::endl;
  std::cout << "Optimizer stop condition: " << R.GetOptimizerStopConditionDescription() << std::endl;
  std::cout << " Iteration: " << R.GetOptimizerIteration() << std::endl;
  std::cout << " Metric value: " << R.GetMetricValue() << std::endl;


  sitk::Image displacementField = sitk::Image(fixed.GetSize(), sitk::sitkVectorFloat64);
  displacementField.CopyInformation(fixed);
  sitk::DisplacementFieldTransform displacementTx(displacementField);
  const double                     varianceForUpdateField = 0.0;
  const double                     varianceForTotalField = 1.5;
  displacementTx.SetSmoothingGaussianOnUpdate(varianceForUpdateField, varianceForTotalField);


  R.SetMovingInitialTransform(outTx1);
  R.SetInitialTransform(displacementTx, true);

  R.SetMetricAsANTSNeighborhoodCorrelation(4);
  R.MetricUseFixedImageGradientFilterOff();
  R.MetricUseFixedImageGradientFilterOff();

  {
    std::vector<unsigned int> shrinkFactors = { 3, 2, 1 };

    std::vector<double> smoothingSigmas = { 2.0, 1.0, 1.0 };

    R.SetShrinkFactorsPerLevel(shrinkFactors);
    R.SetSmoothingSigmasPerLevel(smoothingSigmas);
  }

  R.SetOptimizerScalesFromPhysicalShift();

  {
    double                                                  learningRate = 1.0;
    unsigned int                                            numberOfIterations = 300;
    double                                                  convergenceMinimumValue = 1e-6;
    unsigned int                                            convergenceWindowSize = 10;
    sitk::ImageRegistrationMethod::EstimateLearningRateType estimateLearningRate = R.EachIteration;
    R.SetOptimizerAsGradientDescent(
      learningRate, numberOfIterations, convergenceMinimumValue, convergenceWindowSize, estimateLearningRate);
  }
  R.Execute(fixed, moving);


  std::cout << "-------" << std::endl;
  std::cout << displacementTx.ToString() << std::endl;
  std::cout << "Optimizer stop condition: " << R.GetOptimizerStopConditionDescription() << std::endl;
  std::cout << " Iteration: " << R.GetOptimizerIteration() << std::endl;
  std::cout << " Metric value: " << R.GetMetricValue() << std::endl;


  sitk::CompositeTransform outTx({ outTx1, displacementTx });
  sitk::WriteTransform(outTx, argv[3]);

  return 0;
}
