/*=========================================================================
 *
 *  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 itkThresholdSegmentationLevelSetFunction_h
#define itkThresholdSegmentationLevelSetFunction_h

#include "itkSegmentationLevelSetFunction.h"
#include "itkNumericTraits.h"
namespace itk
{
/** \class ThresholdSegmentationLevelSetFunction
 *
 * \brief This function is used in ThresholdSegmentationLevelSetImageFilter to
 * segment structures in images based on intensity values.
 *
 * \par  SegmentationLevelSetFunction is a subclass of the generic LevelSetFunction.
 * It is useful for segmentations based on intensity values in an image.  It works
 * by constructing a speed term (feature image) with positive values inside an
 * intensity window (between a low and high threshold) and negative values
 * outside that intensity window.  The evolving level set front will lock onto
 * regions that are at the edges of the intensity window.
 *
 *  You may optionally add a Laplacian calculation on the image to the
 *  threshold-based speed term by setting the EdgeWeight parameter to a
 *  non-zero value.  The Laplacian term will cause the evolving surface to
 *  be more strongly attracted to image edges.   Several parameters control a
 *  preprocessing FeatureImage smoothing stage applied only to the Laplacian
 *  calculation.
 *
 *  \par
 *  Image \f$ f \f$ is thresholded pixel by pixel using upper threshold
 *  \f$ U \f$ and lower threshold \f$ L \f$ according to the following formula.
 *
 * \par
 *  \f$  f(x) = \left\{ \begin{array}{ll} g(x) - L & \mbox{if g(x) < (U-L)/2 + L} \\ U - g(x) & \mbox{otherwise}
 * \end{array} \right. \f$
 *
 * \sa SegmentationLevelSetImageFunction
 *  \sa ThresholdSegmentationLevelSetImageFilter
 * \ingroup ITKLevelSets
 */
template <typename TImageType, typename TFeatureImageType = TImageType>
class ITK_TEMPLATE_EXPORT ThresholdSegmentationLevelSetFunction
  : public SegmentationLevelSetFunction<TImageType, TFeatureImageType>
{
public:
  ITK_DISALLOW_COPY_AND_MOVE(ThresholdSegmentationLevelSetFunction);

  /** Standard class type aliases. */
  using Self = ThresholdSegmentationLevelSetFunction;
  using Superclass = SegmentationLevelSetFunction<TImageType, TFeatureImageType>;
  using Pointer = SmartPointer<Self>;
  using ConstPointer = SmartPointer<const Self>;
  using FeatureImageType = TFeatureImageType;

  /** Method for creation through the object factory. */
  itkNewMacro(Self);

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

  /** Extract some parameters from the superclass. */
  using typename Superclass::ImageType;
  using typename Superclass::ScalarValueType;
  using typename Superclass::FeatureScalarType;
  using typename Superclass::RadiusType;

  /** Extract some parameters from the superclass. */
  static constexpr unsigned int ImageDimension = Superclass::ImageDimension;

  /** Set/Get threshold values */
  /** @ITKStartGrouping */
  void
  SetUpperThreshold(FeatureScalarType f)
  {
    m_UpperThreshold = f;
  }
  FeatureScalarType
  GetUpperThreshold() const
  {
    return m_UpperThreshold;
  }
  void
  SetLowerThreshold(FeatureScalarType f)
  {
    m_LowerThreshold = f;
  }
  FeatureScalarType
  GetLowerThreshold() const
  {
    return m_LowerThreshold;
  }
  /** @ITKEndGrouping */
  void
  CalculateSpeedImage() override;

  void
  Initialize(const RadiusType & r) override
  {
    Superclass::Initialize(r);

    this->SetAdvectionWeight(ScalarValueType{});
    this->SetPropagationWeight(-1.0 * NumericTraits<ScalarValueType>::OneValue());
    this->SetCurvatureWeight(NumericTraits<ScalarValueType>::OneValue());
  }

  /** Set/Get the weight applied to the edge (Laplacian) attractor in the speed
   *  term function. Zero will turn this term off. */
  void
  SetEdgeWeight(const ScalarValueType p)
  {
    m_EdgeWeight = p;
  }

  ScalarValueType
  GetEdgeWeight() const
  {
    return m_EdgeWeight;
  }

  /** Anisotropic diffusion is applied to the FeatureImage before calculating
   * the Laplacian (edge) term. This method sets/gets the smoothing
   * conductance. */
  void
  SetSmoothingConductance(const ScalarValueType p)
  {
    m_SmoothingConductance = p;
  }

  ScalarValueType
  GetSmoothingConductance() const
  {
    return m_SmoothingConductance;
  }

  /** Anisotropic diffusion is applied to the FeatureImage before calculating
   * the Laplacian (edge) term. This method sets/gets the number of diffusion
   * iterations. */
  void
  SetSmoothingIterations(const int p)
  {
    m_SmoothingIterations = p;
  }

  [[nodiscard]] int
  GetSmoothingIterations() const
  {
    return m_SmoothingIterations;
  }

  /** Anisotropic diffusion is applied to the FeatureImage before calculating
   * the Laplacian (edge) term. This method sets/gets the diffusion time
   * step. */
  void
  SetSmoothingTimeStep(const ScalarValueType i)
  {
    m_SmoothingTimeStep = i;
  }

  ScalarValueType
  GetSmoothingTimeStep() const
  {
    return m_SmoothingTimeStep;
  }

protected:
  ThresholdSegmentationLevelSetFunction()
    : m_UpperThreshold(NumericTraits<FeatureScalarType>::max())
    , m_LowerThreshold(NumericTraits<FeatureScalarType>::NonpositiveMin())
  {
    this->SetAdvectionWeight(0.0);
    this->SetPropagationWeight(1.0);
    this->SetCurvatureWeight(1.0);
    this->SetSmoothingIterations(5);
    this->SetSmoothingConductance(0.8);
    this->SetSmoothingTimeStep(0.1);
    this->SetEdgeWeight(0.0);
  }

  ~ThresholdSegmentationLevelSetFunction() override = default;

  void
  PrintSelf(std::ostream & os, Indent indent) const override
  {
    Superclass::PrintSelf(os, indent);
    os << indent << "UpperThreshold: " << m_UpperThreshold << std::endl;
    os << indent << "LowerThreshold: " << m_LowerThreshold << std::endl;
    os << indent << "EdgeWeight: " << m_EdgeWeight << std::endl;
    os << indent << "SmoothingTimeStep: " << m_SmoothingTimeStep << std::endl;
    os << indent << "SmoothingIterations: " << m_SmoothingIterations << std::endl;
    os << indent << "SmoothingConductance: " << m_SmoothingConductance << std::endl;
  }

  FeatureScalarType m_UpperThreshold{};
  FeatureScalarType m_LowerThreshold{};
  ScalarValueType   m_EdgeWeight{};
  ScalarValueType   m_SmoothingConductance{};
  int               m_SmoothingIterations{};
  ScalarValueType   m_SmoothingTimeStep{};
};
} // end namespace itk

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

#endif
