/*=========================================================================
 *
 *  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 itkLandweberDeconvolutionImageFilter_hxx
#define itkLandweberDeconvolutionImageFilter_hxx


namespace itk
{

template <typename TInputImage, typename TKernelImage, typename TOutputImage, typename TInternalPrecision>
LandweberDeconvolutionImageFilter<TInputImage, TKernelImage, TOutputImage, TInternalPrecision>::
  LandweberDeconvolutionImageFilter()
{
  m_Alpha = 0.1;
  m_TransformedInput = nullptr;
}

template <typename TInputImage, typename TKernelImage, typename TOutputImage, typename TInternalPrecision>
LandweberDeconvolutionImageFilter<TInputImage, TKernelImage, TOutputImage, TInternalPrecision>::
  ~LandweberDeconvolutionImageFilter()
{
  m_TransformedInput = nullptr;
}

template <typename TInputImage, typename TKernelImage, typename TOutputImage, typename TInternalPrecision>
void
LandweberDeconvolutionImageFilter<TInputImage, TKernelImage, TOutputImage, TInternalPrecision>::Initialize(
  ProgressAccumulator * progress,
  float                 progressWeight,
  float                 iterationProgressWeight)
{
  this->Superclass::Initialize(progress, 0.5f * progressWeight, iterationProgressWeight);

  this->PrepareInput(this->GetInput(), m_TransformedInput, progress, 0.5f * progressWeight);

  // Set up minipipeline to compute estimate at each iteration
  m_LandweberFilter = LandweberFilterType::New();

  LandweberFunctor functor;
  functor.m_Alpha = m_Alpha;
  m_LandweberFilter->SetFunctor(functor);
  m_LandweberFilter->SetNumberOfWorkUnits(this->GetNumberOfWorkUnits());
  // Transform of current estimate will be set as input 1 in Iteration()
  m_LandweberFilter->SetInput2(this->m_TransferFunction);
  m_LandweberFilter->SetInput3(m_TransformedInput);
  m_LandweberFilter->ReleaseDataFlagOn();
  progress->RegisterInternalFilter(m_LandweberFilter, 0.3f * iterationProgressWeight);

  m_IFFTFilter = IFFTFilterType::New();
  m_IFFTFilter->SetNumberOfWorkUnits(this->GetNumberOfWorkUnits());
  m_IFFTFilter->SetActualXDimensionIsOdd(this->GetXDimensionIsOdd());
  m_IFFTFilter->SetInput(m_LandweberFilter->GetOutput());
  m_IFFTFilter->ReleaseDataFlagOn();
  progress->RegisterInternalFilter(m_IFFTFilter, 0.7f * iterationProgressWeight);
}

template <typename TInputImage, typename TKernelImage, typename TOutputImage, typename TInternalPrecision>
void
LandweberDeconvolutionImageFilter<TInputImage, TKernelImage, TOutputImage, TInternalPrecision>::Iteration(
  ProgressAccumulator * progress,
  float                 iterationProgressWeight)
{
  // Set up minipipeline to compute the new estimate
  InternalComplexImagePointerType transformedEstimate;
  this->TransformPaddedInput(this->m_CurrentEstimate, transformedEstimate, progress, 0.1f * iterationProgressWeight);

  // Set the inputs
  m_LandweberFilter->SetInput1(transformedEstimate);

  // Trigger the update
  m_IFFTFilter->UpdateLargestPossibleRegion();

  // Store the current estimate
  this->m_CurrentEstimate = m_IFFTFilter->GetOutput();
  this->m_CurrentEstimate->DisconnectPipeline();
}

template <typename TInputImage, typename TKernelImage, typename TOutputImage, typename TInternalPrecision>
void
LandweberDeconvolutionImageFilter<TInputImage, TKernelImage, TOutputImage, TInternalPrecision>::Finish(
  ProgressAccumulator * progress,
  float                 progressWeight)
{
  this->Superclass::Finish(progress, progressWeight);

  m_LandweberFilter = nullptr;
  m_IFFTFilter = nullptr;
}

template <typename TInputImage, typename TKernelImage, typename TOutputImage, typename TInternalPrecision>
void
LandweberDeconvolutionImageFilter<TInputImage, TKernelImage, TOutputImage, TInternalPrecision>::PrintSelf(
  std::ostream & os,
  Indent         indent) const
{
  this->Superclass::PrintSelf(os, indent);

  os << indent << "Alpha: " << m_Alpha << std::endl;
}
} // end namespace itk

#endif
