/*=========================================================================
 *
 *  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 itkGrayscaleMorphologicalClosingImageFilter_hxx
#define itkGrayscaleMorphologicalClosingImageFilter_hxx

#include "itkNumericTraits.h"
#include "itkProgressAccumulator.h"
#include <string>
#include "itkCropImageFilter.h"
#include "itkConstantPadImageFilter.h"

namespace itk
{
template <typename TInputImage, typename TOutputImage, typename TKernel>
GrayscaleMorphologicalClosingImageFilter<TInputImage, TOutputImage, TKernel>::GrayscaleMorphologicalClosingImageFilter()
{
  m_BasicErodeFilter = BasicErodeFilterType::New();
  m_BasicDilateFilter = BasicDilateFilterType::New();
  m_HistogramErodeFilter = HistogramErodeFilterType::New();
  m_HistogramDilateFilter = HistogramDilateFilterType::New();
  m_VanHerkGilWermanDilateFilter = VanHerkGilWermanDilateFilterType::New();
  m_VanHerkGilWermanErodeFilter = VanHerkGilWermanErodeFilterType::New();
  m_AnchorFilter = AnchorFilterType::New();
  m_Algorithm = AlgorithmEnum::HISTO;
  m_SafeBorder = true;
}

template <typename TInputImage, typename TOutputImage, typename TKernel>
void
GrayscaleMorphologicalClosingImageFilter<TInputImage, TOutputImage, TKernel>::SetKernel(const KernelType & kernel)
{
  const auto * flatKernel = dynamic_cast<const FlatKernelType *>(&kernel);

  if (flatKernel != nullptr && flatKernel->GetDecomposable())
  {
    m_AnchorFilter->SetKernel(*flatKernel);
    m_Algorithm = AlgorithmEnum::ANCHOR;
  }
  else if (m_HistogramErodeFilter->GetUseVectorBasedAlgorithm())
  {
    // histogram based filter is as least as good as the basic one, so always
    // use it
    m_Algorithm = AlgorithmEnum::HISTO;
    m_HistogramErodeFilter->SetKernel(kernel);
    m_HistogramDilateFilter->SetKernel(kernel);
  }
  else
  {
    // basic filter can be better than the histogram based one
    // apply a poor heuristic to find the best one. What is very important is to
    // select the histogram for large kernels

    // we need to set the kernel on the histogram filter to compare basic and
    // histogram algorithm
    m_HistogramErodeFilter->SetKernel(kernel);

    if (this->GetKernel().Size() < m_HistogramErodeFilter->GetPixelsPerTranslation() * 4.0)
    {
      m_BasicErodeFilter->SetKernel(kernel);
      m_BasicDilateFilter->SetKernel(kernel);
      m_Algorithm = AlgorithmEnum::BASIC;
    }
    else
    {
      m_HistogramDilateFilter->SetKernel(kernel);
      m_Algorithm = AlgorithmEnum::HISTO;
    }
  }

  Superclass::SetKernel(kernel);
}

template <typename TInputImage, typename TOutputImage, typename TKernel>
void
GrayscaleMorphologicalClosingImageFilter<TInputImage, TOutputImage, TKernel>::SetAlgorithm(AlgorithmEnum algo)
{
  const auto * flatKernel = dynamic_cast<const FlatKernelType *>(&this->GetKernel());

  if (m_Algorithm != algo)
  {
    if (algo == AlgorithmEnum::BASIC)
    {
      m_BasicErodeFilter->SetKernel(this->GetKernel());
      m_BasicDilateFilter->SetKernel(this->GetKernel());
    }
    else if (algo == AlgorithmEnum::HISTO)
    {
      m_HistogramErodeFilter->SetKernel(this->GetKernel());
      m_HistogramDilateFilter->SetKernel(this->GetKernel());
    }
    else if (flatKernel != nullptr && flatKernel->GetDecomposable() && algo == AlgorithmEnum::ANCHOR)
    {
      m_AnchorFilter->SetKernel(*flatKernel);
    }
    else if (flatKernel != nullptr && flatKernel->GetDecomposable() && algo == AlgorithmEnum::VHGW)
    {
      m_VanHerkGilWermanDilateFilter->SetKernel(*flatKernel);
      m_VanHerkGilWermanErodeFilter->SetKernel(*flatKernel);
    }
    else
    {
      itkExceptionMacro("Invalid algorithm");
    }

    m_Algorithm = algo;
    this->Modified();
  }
}

template <typename TInputImage, typename TOutputImage, typename TKernel>
void
GrayscaleMorphologicalClosingImageFilter<TInputImage, TOutputImage, TKernel>::GenerateData()
{
  // Create a process accumulator for tracking the progress of this minipipeline
  auto progress = ProgressAccumulator::New();

  progress->SetMiniPipelineFilter(this);

  // Allocate the output
  this->AllocateOutputs();

  // Delegate to a dilate filter.
  if (m_Algorithm == AlgorithmEnum::BASIC)
  {
    // Use itk::BasicErodeImageFilter
    if (m_SafeBorder)
    {
      using PadType = ConstantPadImageFilter<InputImageType, InputImageType>;
      auto pad = PadType::New();
      pad->SetPadLowerBound(this->GetKernel().GetRadius());
      pad->SetPadUpperBound(this->GetKernel().GetRadius());
      pad->SetConstant(NumericTraits<typename InputImageType::PixelType>::NonpositiveMin());
      pad->SetInput(this->GetInput());
      progress->RegisterInternalFilter(pad, 0.1f);

      m_BasicDilateFilter->SetInput(pad->GetOutput());
      progress->RegisterInternalFilter(m_BasicDilateFilter, 0.4f);

      m_BasicErodeFilter->SetInput(m_BasicDilateFilter->GetOutput());
      progress->RegisterInternalFilter(m_BasicErodeFilter, 0.4f);

      using CropType = CropImageFilter<TOutputImage, TOutputImage>;
      auto crop = CropType::New();
      crop->SetInput(m_BasicErodeFilter->GetOutput());
      crop->SetUpperBoundaryCropSize(this->GetKernel().GetRadius());
      crop->SetLowerBoundaryCropSize(this->GetKernel().GetRadius());
      progress->RegisterInternalFilter(crop, 0.1f);

      crop->GraftOutput(this->GetOutput());
      crop->Update();
      this->GraftOutput(crop->GetOutput());
    }
    else
    {
      m_BasicDilateFilter->SetInput(this->GetInput());
      progress->RegisterInternalFilter(m_BasicDilateFilter, 0.5f);

      m_BasicErodeFilter->SetInput(m_BasicDilateFilter->GetOutput());
      progress->RegisterInternalFilter(m_BasicErodeFilter, 0.5f);

      m_BasicErodeFilter->GraftOutput(this->GetOutput());
      m_BasicErodeFilter->Update();
      this->GraftOutput(m_BasicErodeFilter->GetOutput());
    }
  }
  else if (m_Algorithm == AlgorithmEnum::HISTO)
  {
    // Use itk::MovingHistogramErodeImageFilter
    if (m_SafeBorder)
    {
      using PadType = ConstantPadImageFilter<InputImageType, InputImageType>;
      auto pad = PadType::New();
      pad->SetPadLowerBound(this->GetKernel().GetRadius());
      pad->SetPadUpperBound(this->GetKernel().GetRadius());
      pad->SetConstant(NumericTraits<typename InputImageType::PixelType>::NonpositiveMin());
      pad->SetInput(this->GetInput());
      progress->RegisterInternalFilter(pad, 0.1f);

      m_HistogramDilateFilter->SetInput(pad->GetOutput());
      progress->RegisterInternalFilter(m_HistogramDilateFilter, 0.4f);

      m_HistogramErodeFilter->SetInput(m_HistogramDilateFilter->GetOutput());
      progress->RegisterInternalFilter(m_HistogramErodeFilter, 0.4f);

      using CropType = CropImageFilter<TOutputImage, TOutputImage>;
      auto crop = CropType::New();
      crop->SetInput(m_HistogramErodeFilter->GetOutput());
      crop->SetUpperBoundaryCropSize(this->GetKernel().GetRadius());
      crop->SetLowerBoundaryCropSize(this->GetKernel().GetRadius());
      progress->RegisterInternalFilter(crop, 0.1f);

      crop->GraftOutput(this->GetOutput());
      crop->Update();
      this->GraftOutput(crop->GetOutput());
    }
    else
    {
      m_HistogramDilateFilter->SetInput(this->GetInput());
      progress->RegisterInternalFilter(m_HistogramDilateFilter, 0.5f);

      m_HistogramErodeFilter->SetInput(m_HistogramDilateFilter->GetOutput());
      progress->RegisterInternalFilter(m_HistogramErodeFilter, 0.5f);

      m_HistogramErodeFilter->GraftOutput(this->GetOutput());
      m_HistogramErodeFilter->Update();
      this->GraftOutput(m_HistogramErodeFilter->GetOutput());
    }
  }
  else if (m_Algorithm == AlgorithmEnum::VHGW)
  {
    // Use itk::VanHerkGilWermanErodeImageFilter
    if (m_SafeBorder)
    {
      using PadType = ConstantPadImageFilter<InputImageType, InputImageType>;
      auto pad = PadType::New();
      pad->SetPadLowerBound(this->GetKernel().GetRadius());
      pad->SetPadUpperBound(this->GetKernel().GetRadius());
      pad->SetConstant(NumericTraits<typename InputImageType::PixelType>::NonpositiveMin());
      pad->SetInput(this->GetInput());
      progress->RegisterInternalFilter(pad, 0.1f);

      m_VanHerkGilWermanDilateFilter->SetInput(pad->GetOutput());
      progress->RegisterInternalFilter(m_VanHerkGilWermanDilateFilter, 0.4f);

      m_VanHerkGilWermanErodeFilter->SetInput(m_VanHerkGilWermanDilateFilter->GetOutput());
      progress->RegisterInternalFilter(m_VanHerkGilWermanErodeFilter, 0.4f);

      using CropType = CropImageFilter<TOutputImage, TOutputImage>;
      auto crop = CropType::New();
      crop->SetInput(m_VanHerkGilWermanErodeFilter->GetOutput());
      crop->SetUpperBoundaryCropSize(this->GetKernel().GetRadius());
      crop->SetLowerBoundaryCropSize(this->GetKernel().GetRadius());
      progress->RegisterInternalFilter(crop, 0.1f);

      crop->GraftOutput(this->GetOutput());
      crop->Update();
      this->GraftOutput(crop->GetOutput());
    }
    else
    {
      m_VanHerkGilWermanDilateFilter->SetInput(this->GetInput());
      progress->RegisterInternalFilter(m_VanHerkGilWermanDilateFilter, 0.5f);

      m_VanHerkGilWermanErodeFilter->SetInput(m_VanHerkGilWermanDilateFilter->GetOutput());
      progress->RegisterInternalFilter(m_VanHerkGilWermanErodeFilter, 0.5f);

      m_VanHerkGilWermanErodeFilter->GraftOutput(this->GetOutput());
      m_VanHerkGilWermanErodeFilter->Update();
      this->GraftOutput(m_VanHerkGilWermanErodeFilter->GetOutput());
    }
  }
  else if (m_Algorithm == AlgorithmEnum::ANCHOR)
  {
    // Use itk::AnchorErodeImageFilter
    if (m_SafeBorder)
    {
      using PadType = ConstantPadImageFilter<InputImageType, InputImageType>;
      auto pad = PadType::New();
      pad->SetPadLowerBound(this->GetKernel().GetRadius());
      pad->SetPadUpperBound(this->GetKernel().GetRadius());
      pad->SetConstant(NumericTraits<typename InputImageType::PixelType>::NonpositiveMin());
      pad->SetInput(this->GetInput());
      progress->RegisterInternalFilter(pad, 0.1f);

      m_AnchorFilter->SetInput(pad->GetOutput());
      progress->RegisterInternalFilter(m_AnchorFilter, 0.8f);

      using CropType = CropImageFilter<TInputImage, TOutputImage>;
      auto crop = CropType::New();
      crop->SetInput(m_AnchorFilter->GetOutput());
      crop->SetUpperBoundaryCropSize(this->GetKernel().GetRadius());
      crop->SetLowerBoundaryCropSize(this->GetKernel().GetRadius());
      progress->RegisterInternalFilter(crop, 0.1f);

      crop->GraftOutput(this->GetOutput());
      crop->Update();
      this->GraftOutput(crop->GetOutput());
    }
    else
    {
      m_AnchorFilter->SetInput(this->GetInput());
      progress->RegisterInternalFilter(m_AnchorFilter, 0.9f);

      using CastType = CastImageFilter<TInputImage, TOutputImage>;
      auto cast = CastType::New();
      cast->SetInput(m_AnchorFilter->GetOutput());
      progress->RegisterInternalFilter(cast, 0.1f);

      cast->GraftOutput(this->GetOutput());
      cast->Update();
      this->GraftOutput(cast->GetOutput());
    }
  }
}

template <typename TInputImage, typename TOutputImage, typename TKernel>
void
GrayscaleMorphologicalClosingImageFilter<TInputImage, TOutputImage, TKernel>::Modified() const
{
  Superclass::Modified();
  m_BasicErodeFilter->Modified();
  m_BasicDilateFilter->Modified();
  m_HistogramErodeFilter->Modified();
  m_HistogramDilateFilter->Modified();
  m_VanHerkGilWermanDilateFilter->Modified();
  m_VanHerkGilWermanErodeFilter->Modified();
  m_AnchorFilter->Modified();
}

template <typename TInputImage, typename TOutputImage, typename TKernel>
void
GrayscaleMorphologicalClosingImageFilter<TInputImage, TOutputImage, TKernel>::PrintSelf(std::ostream & os,
                                                                                        Indent         indent) const
{
  Superclass::PrintSelf(os, indent);

  os << indent << "Algorithm: " << m_Algorithm << std::endl;
  os << indent << "SafeBorder: " << m_SafeBorder << std::endl;
}
} // end namespace itk
#endif
