/*=========================================================================
 *
 *  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 itkIterativeDeconvolutionImageFilter_h
#define itkIterativeDeconvolutionImageFilter_h

#include "itkFFTConvolutionImageFilter.h"
#include "itkProgressAccumulator.h"

namespace itk
{
/**
 * \class IterativeDeconvolutionImageFilter
 * \brief Abstract base class for filters that implement iterative
 * deconvolution algorithms.
 *
 * This filter implements methods common to iterative deconvolution
 * algorithms. The input blurred image is set with the usual
 * SetInput() method and the blurring kernel is set with the
 * SetKernelImage() method.
 *
 * The number of iterations can be set with the method
 * SetNumberOfIterations(). This class emits an IterationEvent at the
 * start of each iteration. Iteration can be terminated early by
 * calling SetStopIteration( bool ) with the argument set to true. To
 * resume iterating, you must call SetStopIteration( bool ) with the
 * argument set to false before calling Update() a second time.
 *
 * This code was adapted from the Insight Journal contribution:
 *
 * "Deconvolution: infrastructure and reference algorithms"
 * by Gaetan Lehmann
 * https://www.insight-journal.org/browse/publication/753
 *
 * \ingroup ITKDeconvolution
 */
template <typename TInputImage,
          typename TKernelImage = TInputImage,
          typename TOutputImage = TInputImage,
          typename TInternalPrecision = double>
class ITK_TEMPLATE_EXPORT IterativeDeconvolutionImageFilter
  : public FFTConvolutionImageFilter<TInputImage, TKernelImage, TOutputImage, TInternalPrecision>
{
public:
  ITK_DISALLOW_COPY_AND_MOVE(IterativeDeconvolutionImageFilter);

  /** Standard type alias. */
  using Self = IterativeDeconvolutionImageFilter;
  using Superclass = FFTConvolutionImageFilter<TInputImage, TKernelImage, TOutputImage, TInternalPrecision>;
  using Pointer = SmartPointer<Self>;
  using ConstPointer = SmartPointer<const Self>;

  /** Other useful type alias. */
  using InputImageType = TInputImage;
  using KernelImageType = TKernelImage;
  using OutputImageType = TOutputImage;

  /** Internal types used by the FFT filters. */
  using typename Superclass::InternalImageType;
  using typename Superclass::InternalImagePointerType;
  using typename Superclass::InternalComplexType;
  using typename Superclass::InternalComplexImageType;
  using typename Superclass::InternalComplexImagePointerType;

  /** \see LightObject::GetNameOfClass() */
  itkOverrideGetNameOfClassMacro(IterativeDeconvolutionImageFilter);

  /** Set/get the number of iterations. */
  itkSetMacro(NumberOfIterations, unsigned int);
  itkGetMacro(NumberOfIterations, unsigned int);

  /** Set/get the current estimate. */
  itkGetModifiableObjectMacro(CurrentEstimate, InternalImageType);

  /** Stop iteration. When this method is called, the iteration will
   * halt after the current iteration has completed. */
  void
  SetStopIteration(bool stop)
  {
    // Don't call Modified() to avoid triggering a new update.
    m_StopIteration = stop;
  }
  itkGetConstMacro(StopIteration, bool);

  /** Get the current iteration. */
  itkGetConstMacro(Iteration, unsigned int);

protected:
  IterativeDeconvolutionImageFilter();
  ~IterativeDeconvolutionImageFilter() override;

  /** Runs before iterating . */
  virtual void
  Initialize(ProgressAccumulator * progress, float progressWeight, float iterationProgressWeight);

  /** Performs an iteration. */
  virtual void
  Iteration(ProgressAccumulator * itkNotUsed(progress), float itkNotUsed(iterationProgressWeight)) = 0;

  /** Runs after iterating. */
  virtual void
  Finish(ProgressAccumulator * progress, float progressWeight);

  /** This filter needs the entire image kernel, which in general is
   * going to be a different size then the output requested region. As
   * such, this filter needs to provide an implementation for
   * GenerateInputRequestedRegion() in order to inform the pipeline
   * execution model.
   *
   * \sa ProcessObject::GenerateInputRequestedRegion()  */
  void
  GenerateInputRequestedRegion() override;

  /** Generate the output image data. Uses a minipipeline, so
   * ThreadedGenerateData is not overridden. */
  void
  GenerateData() override;

  /** Discrete Fourier transform of the padded kernel. */
  InternalComplexImagePointerType m_TransferFunction{};

  /** Intermediate results. Protected for easy access by subclasses. */
  InternalImagePointerType m_CurrentEstimate{};

  using typename Superclass::FFTFilterType;
  using typename Superclass::IFFTFilterType;

  void
  PrintSelf(std::ostream & os, Indent indent) const override;

private:
  /** Number of iterations to run. */
  unsigned int m_NumberOfIterations{};

  /** The current iteration. */
  unsigned int m_Iteration{};

  /** Flag indicating whether iteration should be stopped. */
  bool m_StopIteration{};

  /** Modified times for the input and kernel. */
  ModifiedTimeType m_InputMTime{};
  ModifiedTimeType m_KernelMTime{};
};
} // end namespace itk

#ifndef ITK_MANUAL_INSTANTIATION
#  include "itkIterativeDeconvolutionImageFilter.hxx"
#endif

#endif
