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 *
 *  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
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#ifndef itkBSplineScatteredDataPointSetToImageFilter_h
#define itkBSplineScatteredDataPointSetToImageFilter_h

#include "itkPointSetToImageFilter.h"

#include "itkBSplineKernelFunction.h"
#include "itkCoxDeBoorBSplineKernelFunction.h"
#include "itkVectorContainer.h"

#include "vnl/vnl_matrix.h"

namespace itk
{
/** \class BSplineScatteredDataPointSetToImageFilter
 * \brief Image filter which provides a B-spline output approximation.
 *
 * Given an n-D image with scattered data, this filter finds
 * a fast approximation to that irregularly spaced data using uniform
 * B-splines. The traditional method of inverting the observation
 * matrix to find a least-squares fit is made obsolete.  Therefore,
 * memory issues are not a concern and inverting large matrices is
 * not applicable. In addition, this allows fitting to be multi-threaded.
 * This class generalizes from Lee's original paper to encompass
 * n-D data in m-D parametric space and any *feasible* B-spline order as well
 * as the option of specifying a confidence value for each point.
 *
 * In addition to specifying the input point set, one must specify the number
 * of control points.  The specified number of control points must be
 * greater than m_SplineOrder.  If one wishes to use the multilevel component of
 * this algorithm, one must also specify the number of levels in the
 * hierarchy.  If this is desired, the number of control points becomes
 * the number of control points for the coarsest level.  The algorithm
 * then increases the number of control points at each level so that
 * the B-spline n-D grid is refined to twice the previous level.
 *
 * There are two parts to fitting scattered data: the parameterization
 * assignment problem and the fitting problem given a parameterization.
 * This filter only addresses the second problem in that the user must
 * provide a parametric value for each scattered datum. Different parametric
 * assignment schemes result in different B-spline object outputs.
 *
 * This filter is general in that it accepts n-D scattered data in m-D parametric
 * dimensions. Input to this filter is an m-D point set with a Vector data type
 * of n dimensions. This means that the parametric values are stored in the
 * points container of the point set whereas the scattered data are stored in
 * the points data container of the point set.
 *
 * Typical B-spline objects include curves, which have a parametric dimension of
 * 1 and a data dimension of 2 or 3 (depending on the space in which the curve
 * resides) and deformation fields which commonly have parametric and data
 * dimensions of 2 or 3 (again depending on the space of the field).
 * As an example, a curve through a set of 2D points has data dimension 2 and
 * parametric dimension 1. The univariate curve could be represented as: <x(u),y(u)>
 * Another example is a 3D deformation of 3D points, which has parametric
 * dimension 3 and data dimension 3 and can be represented as:
 * <dx(u,v,w), dy(u,v,w), dz(u,v,w)>. However, as mentioned before, the code is
 * general such that, if the user wanted, she could model a time varying 3-D
 * displacement field which resides in 4-D space as
 * <dx(u, v, w, t), dy(u, v, w, t), dz(u, v, w, t)>.
 *
 * The output is an image defining the sampled B-spline parametric domain where
 * each pixel houses the sampled B-spline object value. For a curve fit to 3-D
 * points, the output is a 1-D image where each voxel contains a vector with
 * the approximated (x,y,z) location. The continuous, finite, rectilinear domain
 * (as well as the sampling rate) is specified via the combination of the SetSpacing()
 * and SetSize() functions.  For a 2-D deformation on 2-D points, the output is a 2-D image
 * where each voxel contains the approximated (dx, dy) vector.
 *
 * The parameterization must be specified using SetPoint, where the actual
 * coordinates of the point are set via SetPointData. For example, to compute a
 * spline through the (ordered) 2D points (5,6) and (7,8), you should use:
 *
   \code
   using DataType = itk::Vector< float, 2 >;
   PointSetType::PointType param0;
   param0[0] = 0.0;
   DataType p0;
   p0[0] =  10.0; p0[1]= 10.0;
   pointSet->SetPoint(0, param0);
   pointSet->SetPointData( 0, p0 );

   PointSetType::PointType param1;
   param1[0] = 1.0;
   DataType p1;
   p1[0] =  80.0; p1[1]= 50.0;
   pointSet->SetPoint(1, param1);
   pointSet->SetPointData( 1, p1 );
   \endcode
 *
 * \author Nicholas J. Tustison
 *
 * This code was contributed in the Insight Journal paper:
 * "N-D C^k B-Spline Scattered Data Approximation"
 * by Nicholas J. Tustison, James C. Gee
 * https://www.insight-journal.org/browse/publication/57
 *
 *
 * \par REFERENCE
 * S. Lee, G. Wolberg, and S. Y. Shin, "Scattered Data Interpolation
 * with Multilevel B-Splines", IEEE Transactions on Visualization and
 * Computer Graphics, 3(3):228-244, 1997.
 *
 * \par REFERENCE
 * N.J. Tustison and J.C. Gee, "Generalized n-D C^k Scattered Data Approximation
 * with Confidence Values", Proceedings of the MIAR conference, August 2006.
 *
 * \ingroup ITKImageGrid
 *
 * \sphinx
 * \sphinxexample{Filtering/ImageGrid/FitSplineIntoPointSet,Fit Spline Into Point Set}
 * \endsphinx
 */

template <typename TInputPointSet, typename TOutputImage>
class ITK_TEMPLATE_EXPORT BSplineScatteredDataPointSetToImageFilter
  : public PointSetToImageFilter<TInputPointSet, TOutputImage>
{
public:
  ITK_DISALLOW_COPY_AND_MOVE(BSplineScatteredDataPointSetToImageFilter);

  /** Standard class type aliases. */
  using Self = BSplineScatteredDataPointSetToImageFilter;
  using Superclass = PointSetToImageFilter<TInputPointSet, TOutputImage>;
  using Pointer = SmartPointer<Self>;
  using ConstPointer = SmartPointer<const Self>;

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

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

  /** Extract dimension from the output image. */
  static constexpr unsigned int ImageDimension = TOutputImage::ImageDimension;

  using ImageType = TOutputImage;
  using PointSetType = TInputPointSet;

  /** Image type alias support */
  using PixelType = typename ImageType::PixelType;
  using RegionType = typename ImageType::RegionType;
  using OutputImageRegionType = RegionType;
  using SizeType = typename ImageType::SizeType;
  using IndexType = typename ImageType::IndexType;

  /** PointSet type alias support */
  using PointType = typename PointSetType::PointType;
  using PointSetPointer = typename PointSetType::Pointer;
  using PointDataType = typename PointSetType::PixelType;
  using PointDataContainerType = typename PointSetType::PointDataContainer;
  using PointDataContainerPointer = typename PointDataContainerType::Pointer;

  /** Other type alias. */
  using RealType = float;
  using WeightsContainerType = VectorContainer<unsigned int, RealType>;

  /** Image types. */
  using PointDataImageType = Image<PointDataType, Self::ImageDimension>;
  using RealImageType = Image<RealType, Self::ImageDimension>;
  using RealImagePointer = typename RealImageType::Pointer;
  using PointDataImagePointer = typename PointDataImageType::Pointer;
  using ArrayType = FixedArray<unsigned int, Self::ImageDimension>;
  using RealArrayType = FixedArray<RealType, Self::ImageDimension>;

  /** Interpolation kernel type (default spline order = 3). */
  using KernelType = CoxDeBoorBSplineKernelFunction<3>;
  using KernelOrder0Type = BSplineKernelFunction<0>;
  using KernelOrder1Type = BSplineKernelFunction<1>;
  using KernelOrder2Type = BSplineKernelFunction<2>;
  using KernelOrder3Type = BSplineKernelFunction<3>;


  /** Set the spline order assuming it is the same in all parametric dimensions.
   * The spline order determines the continuity between B-spline elements and
   * the degree of polynomial used to construct the B-spline elements. Default
   * = 3. */
  void
  SetSplineOrder(unsigned int);

  /** Set the spline order for each parametric dimension separately. The spline
   * order determines the continuity between B-spline elements and the degree of
   * polynomial used to construct the B-spline elements. Default = 3. */
  void
  SetSplineOrder(const ArrayType &);

  /** Get the spline order for all parametric dimensions. The spline order
   * determines the continuity between B-spline elements and the degree of
   * polynomial used to construct the B-spline elements. Default = 3. */
  itkGetConstReferenceMacro(SplineOrder, ArrayType);

  /** Set/Get the number of control points for each parametric dimension at the
   * initial fitting level. The B-spline mesh size is equal to the number
   * of control points minus the spline order. Default = 4 in each dimension.
   */
  itkSetMacro(NumberOfControlPoints, ArrayType);
  itkGetConstReferenceMacro(NumberOfControlPoints, ArrayType);

  /** Get the number of current control points for each parametric dimension at
   * the current fitting level. The B-spline mesh size is equal to the number
   * of control points minus the spline order. Default = 4 in each dimension.
   */
  itkGetConstReferenceMacro(CurrentNumberOfControlPoints, ArrayType);

  /** Set the number of fitting levels assuming the number of fitting levels is
   * the same for each parametric dimension. Starting with the mesh size
   * implied by setting the number of control points, the mesh size is doubled
   * at each fitting level. Default = 1 in all parametric dimensions. */
  void
  SetNumberOfLevels(unsigned int);

  /** Set the number of fitting levels in each parametric dimension separately.
   * Starting with the mesh size implied by setting the number of control
   * points, the mesh size is doubled at each fitting level. Default = 1 in all
   * parametric dimensions. */
  void
  SetNumberOfLevels(const ArrayType &);

  /** Get the number of fitting levels for all parametric dimensions. Starting
   * with the mesh size implied by setting the number of control points, the
   * mesh size is doubled at each fitting level. Default = 1 in all parametric
   * dimensions. */
  itkGetConstReferenceMacro(NumberOfLevels, ArrayType);

  /** Set/Get the epsilon used for B-splines. The B-spline parametric domain in
   * 1-D is defined on the half-closed interval [a,b). Extension to n-D is
   * defined similarly. This presents some difficulty for defining the
   * the image domain to be co-extensive with the parametric domain. We use
   * the B-spline epsilon to push the edge of the image boundary inside the
   * B-spline parametric domain. */
  itkSetMacro(BSplineEpsilon, RealType);
  itkGetConstMacro(BSplineEpsilon, RealType);

  /** Set/Get the array to define the periodicity of the dimensions in the
   * parametric space is to be.
   * This array of 0/1 values defines whether a particular dimension of the
   * parametric space is to be considered periodic or not. For example, if you
   * are using interpolating along a 1D closed curve, the array type will have
   * size 1, and you should set the first element of this array to the value
   * "1". In the case that you were interpolating in a planar surface with
   * cylindrical topology, the array type will have two components, and you
   * should set to "1" the component that goes around the cylinder, and set to
   * "0" the component that goes from the top of the cylinder to the bottom.
   * This will indicate the periodicity of that parameter to the filter.
   * Internally, in order to make periodic the domain of the parameter, the
   * filter will reuse some of the points at the beginning of the domain as if
   * they were also located at the end of the domain. The number of points to
   * be reused will depend on the spline order. As a user, you don't need to
   * replicate the points, the filter will do this for you. */
  itkSetMacro(CloseDimension, ArrayType);
  itkGetConstReferenceMacro(CloseDimension, ArrayType);

  /** A weighted fitting is possible where each input point is assigned a
   * relative weighting. */
  void
  SetPointWeights(WeightsContainerType * weights);

  /** Set/Get whether or not the sampled output B-spline object is constructed.
   * The result of the fitting process is an n-D grid of control points which
   * describe the continuous B-spline object. */
  itkSetMacro(GenerateOutputImage, bool);
  itkGetConstReferenceMacro(GenerateOutputImage, bool);
  itkBooleanMacro(GenerateOutputImage);

  /** Get the control point lattice produced by the fitting process. */
  PointDataImagePointer
  GetPhiLattice()
  {
    return static_cast<PointDataImageType *>(this->ProcessObject::GetOutput(1));
  }

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

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

  void
  ThreadedGenerateData(const RegionType &, ThreadIdType) override;

  void
  DynamicThreadedGenerateData(const RegionType &) override
  {
    itkExceptionMacro("This class requires threadId so it must use classic multi-threading model");
  }

  void
  BeforeThreadedGenerateData() override;

  void
  AfterThreadedGenerateData() override;

  unsigned int
  SplitRequestedRegion(unsigned int, unsigned int, RegionType &) override;

  void
  GenerateData() override;

private:
  /** Function used to propagate the fitting solution at one fitting level
   * to the next level with the mesh resolution doubled. */
  void
  RefineControlPointLattice();

  /** This function is not used as it requires an evaluation of all
   * (SplineOrder+1)^ImageDimensions B-spline weights for each evaluation. */
  void
  GenerateOutputImage();

  /** Function used to generate the sampled B-spline object quickly. */
  void
  ThreadedGenerateDataForFitting(const RegionType &, ThreadIdType);

  /** Function used to generate the sampled B-spline object quickly. */
  void
  ThreadedGenerateDataForReconstruction(const RegionType &, ThreadIdType);

  /** Update the residuals for multi-level fitting. */
  void
  ThreadedGenerateDataForUpdatingResidualValues(const RegionType &, ThreadIdType);

  /** Sub-function used by GenerateOutputImageFast() to generate the sampled
   * B-spline object quickly. */
  void
  CollapsePhiLattice(PointDataImageType *, PointDataImageType *, const RealType, const unsigned int);

  /** Set the grid parametric domain parameters such as the origin, size,
   * spacing, and direction. */
  void
  SetPhiLatticeParametricDomainParameters();

  /** Convert number to index given a size of image. Used to index
   * the local control point neighborhoods. */
  IndexType
  NumberToIndex(const unsigned int, const SizeType);

  bool         m_DoMultilevel{ false };
  bool         m_GenerateOutputImage{ true };
  bool         m_UsePointWeights{ false };
  unsigned int m_MaximumNumberOfLevels{ 1 };
  unsigned int m_CurrentLevel{ 0 };
  ArrayType    m_NumberOfControlPoints{};
  ArrayType    m_CurrentNumberOfControlPoints{};
  ArrayType    m_CloseDimension{};
  ArrayType    m_SplineOrder{};
  ArrayType    m_NumberOfLevels{};

  typename WeightsContainerType::Pointer m_PointWeights{};

  typename PointDataImageType::Pointer m_PhiLattice{};
  typename PointDataImageType::Pointer m_PsiLattice{};

  vnl_matrix<RealType> m_RefinedLatticeCoefficients[ImageDimension]{};

  PointDataContainerPointer m_ResidualPointSetValues{};

  typename KernelType::Pointer m_Kernel[ImageDimension]{};

  typename KernelOrder0Type::Pointer m_KernelOrder0{};
  typename KernelOrder1Type::Pointer m_KernelOrder1{};
  typename KernelOrder2Type::Pointer m_KernelOrder2{};
  typename KernelOrder3Type::Pointer m_KernelOrder3{};

  std::vector<RealImagePointer>      m_OmegaLatticePerThread{};
  std::vector<PointDataImagePointer> m_DeltaLatticePerThread{};

  RealType m_BSplineEpsilon{ static_cast<RealType>(1e-3) };
  bool     m_IsFittingComplete{ false };
  bool     m_DoUpdateResidualValues{ false };
};
} // end namespace itk

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

#endif
