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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 itkShapeDetectionLevelSetImageFilter_h
#define itkShapeDetectionLevelSetImageFilter_h

#include "itkSegmentationLevelSetImageFilter.h"
#include "itkShapeDetectionLevelSetFunction.h"

namespace itk
{
/** \class ShapeDetectionLevelSetImageFilter
 * \brief Segments structures in images based on a user supplied edge potential map.
 *
 * \par IMPORTANT
 * The SegmentationLevelSetImageFilter class and the
 * ShapeDetectionLevelSetFunction class contain additional information necessary
 * to gain full understanding of how to use this filter.
 *
 * \par OVERVIEW
 * This class is a level set method segmentation filter. An initial contour
 * is propagated outwards (or inwards) until it ''sticks'' to the shape boundaries.
 * This is done by using a level set speed function based on a user supplied
 * edge potential map. This approach for segmentation follows that of
 * Malladi et al (1995).
 *
 * \par INPUTS
 * This filter requires two inputs.  The first input is a initial level set.
 * The initial level set is a real image which contains the initial contour/surface
 * as the zero level set. For example, a signed distance function from the initial
 * contour/surface is typically used. Note that for this algorithm the initial contour
 * has to be wholly within (or wholly outside) the structure to be segmented.
 *
 * \par
 * The second input is the feature image.  For this filter, this is the edge
 * potential map. General characteristics of an edge potential map is that
 * it has values close to zero in regions near the edges and values close
 * to one inside the shape itself. Typically, the edge potential map is compute
 * from the image gradient, for example:
 *
 * \f[ g(I) = 1 / ( 1 + | (\nabla * G)(I)| ) \f]
 * \f[ g(I) = \exp^{-|(\nabla * G)(I)|} \f]
 *
 * where \f$ I \f$ is image intensity and
 * \f$ (\nabla * G) \f$ is the derivative of Gaussian operator.
 *
 * \par
 * See SegmentationLevelSetImageFilter and SparseFieldLevelSetImageFilter
 * for more information on Inputs.
 *
 * \par PARAMETERS
 * The PropagationScaling parameter can be used to switch from propagation outwards
 * (POSITIVE scaling parameter) versus propagating inwards (NEGATIVE scaling
 * parameter).
 *
 * The smoothness of the resulting contour/surface can be adjusted using a combination
 * of PropagationScaling and CurvatureScaling parameters. The larger the CurvatureScaling
 * parameter, the smoother the resulting contour. The CurvatureScaling parameter should
 * be non-negative for proper operation of this algorithm.
 * To follow the implementation in Malladi et al paper,
 * set the PropagationScaling to \f$\pm 1.0\f$ and CurvatureScaling to \f$ \epsilon \f$.
 *
 * Note that there is no advection term for this filter. Setting the
 * advection scaling will have no effect.
 *
 * \par OUTPUTS
 * The filter outputs a single, scalar, real-valued image.
 * Negative values in the output image represent the inside of the segmented region
 * and positive values in the image represent the outside of the segmented region.  The
 * zero crossings of the image correspond to the position of the propagating
 * front.
 *
 * \par
 * See SparseFieldLevelSetImageFilter and
 * SegmentationLevelSetImageFilter for more information.
 *
 * \par REFERENCES
 * \par
 *    "Shape Modeling with Front Propagation: A Level Set Approach",
 *    R. Malladi, J. A. Sethian and B. C. Vermuri.
 *    IEEE Trans. on Pattern Analysis and Machine Intelligence,
 *    Vol 17, No. 2, pp 158-174, February 1995
 *
 * \sa SegmentationLevelSetImageFilter
 * \sa ShapeDetectionLevelSetFunction
 * \sa SparseFieldLevelSetImageFilter
 *
 * \ingroup LevelSetSegmentation
 * \ingroup ITKLevelSets
 */
template <typename TInputImage, typename TFeatureImage, typename TOutputPixelType = float>
class ITK_TEMPLATE_EXPORT ShapeDetectionLevelSetImageFilter
  : public SegmentationLevelSetImageFilter<TInputImage, TFeatureImage, TOutputPixelType>
{
public:
  ITK_DISALLOW_COPY_AND_MOVE(ShapeDetectionLevelSetImageFilter);

  /** Standard class type aliases */
  using Self = ShapeDetectionLevelSetImageFilter;
  using Superclass = SegmentationLevelSetImageFilter<TInputImage, TFeatureImage, TOutputPixelType>;
  using Pointer = SmartPointer<Self>;
  using ConstPointer = SmartPointer<const Self>;

  /** Inherited type alias from the superclass. */
  using typename Superclass::ValueType;
  using typename Superclass::OutputImageType;
  using typename Superclass::FeatureImageType;

  /** Type of the segmentation function */
  using ShapeDetectionFunctionType = ShapeDetectionLevelSetFunction<OutputImageType, FeatureImageType>;
  using ShapeDetectionFunctionPointer = typename ShapeDetectionFunctionType::Pointer;

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

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

protected:
  ~ShapeDetectionLevelSetImageFilter() override = default;
  ShapeDetectionLevelSetImageFilter();

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

  /** Overridden from Superclass to handle the case when PropagationScaling is zero
   * and CurvatureScaling is non-zero.*/
  void
  GenerateData() override;

private:
  ShapeDetectionFunctionPointer m_ShapeDetectionFunction{};
};
} // end namespace itk

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

#endif
