itkSimpleMultiResolutionImageRegistrationUI.h

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/*=========================================================================
 *
 *  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.
 *
 *=========================================================================*/
#ifndef itkSimpleMultiResolutionImageRegistrationUI_h
#define itkSimpleMultiResolutionImageRegistrationUI_h
 
#include "itkMultiResolutionImageRegistrationMethod.h"
#include "itkCommand.h"
#include "itkArray.h"
#include "itkGradientDescentOptimizer.h"
 
// The following classes are examples of simple user interface
// that controls a MultiResolutionImageRegistrationMethod process
 
template <typename TRegistrator>
class SimpleMultiResolutionImageRegistrationUI
{
public:
  SimpleMultiResolutionImageRegistrationUI(TRegistrator * ptr)
    : m_Tag(0)
  {
 
    if (!ptr)
    {
      return;
    }
    m_Registrator = ptr;
    typename itk::SimpleMemberCommand<SimpleMultiResolutionImageRegistrationUI>::Pointer iterationCommand =
      itk::SimpleMemberCommand<SimpleMultiResolutionImageRegistrationUI>::New();
 
    iterationCommand->SetCallbackFunction(this, &SimpleMultiResolutionImageRegistrationUI::StartNewLevel);
 
    m_Tag = m_Registrator->AddObserver(itk::IterationEvent(), iterationCommand);
  }
 
  virtual ~SimpleMultiResolutionImageRegistrationUI()
  {
    if (m_Registrator)
    {
      m_Registrator->RemoveObserver(m_Tag);
    }
  }
 
  virtual void
  StartNewLevel()
  {
    std::cout << "--- Starting level " << m_Registrator->GetCurrentLevel() << std::endl;
  }
 
protected:
  typename TRegistrator::Pointer m_Registrator{};
  unsigned long                  m_Tag{};
};
 
 
// This UI supports registration methods with gradient descent
// type optimizers.
// This UI allows the number of iterations and learning rate
// to be changes at each resolution level.
template <typename TRegistration>
class ITK_TEMPLATE_EXPORT SimpleMultiResolutionImageRegistrationUI2
  : public SimpleMultiResolutionImageRegistrationUI<TRegistration>
{
public:
  using Superclass = SimpleMultiResolutionImageRegistrationUI<
    itk::MultiResolutionImageRegistrationMethod<itk::Image<float, 3>, itk::Image<float, 3>>>;
 
  SimpleMultiResolutionImageRegistrationUI2(TRegistration * ptr)
    : Superclass(ptr)
  {}
  ~SimpleMultiResolutionImageRegistrationUI2() override = default;
 
  void
  SetNumberOfIterations(itk::Array<unsigned int> & iter)
  {
    m_NumberOfIterations = iter;
  }
 
  void
  SetLearningRates(itk::Array<double> & rates)
  {
    m_LearningRates = rates;
  }
 
  void
  StartNewLevel() override
  {
 
    // call the superclass's implementation
    this->Superclass::StartNewLevel();
 
    if (!this->m_Registrator)
    {
      return;
    }
 
    // Try to cast the optimizer to a gradient descent type,
    // return if casting didn't work.
    itk::GradientDescentOptimizer::Pointer optimizer =
      dynamic_cast<itk::GradientDescentOptimizer *>(this->m_Registrator->GetModifiableOptimizer());
    if (!optimizer)
    {
      return;
    }
 
    unsigned int level = this->m_Registrator->GetCurrentLevel();
    if (m_NumberOfIterations.Size() >= level + 1)
    {
      optimizer->SetNumberOfIterations(m_NumberOfIterations[level]);
    }
 
    if (m_LearningRates.Size() >= level + 1)
    {
      optimizer->SetLearningRate(m_LearningRates[level]);
    }
 
    std::cout << " No. Iterations: " << optimizer->GetNumberOfIterations()
              << " Learning rate: " << optimizer->GetLearningRate() << std::endl;
  }
 
private:
  itk::Array<unsigned int> m_NumberOfIterations{};
  itk::Array<double>       m_LearningRates{};
};
 
 
#endif