/*
 * Software License Agreement (BSD License)
 *
 *  Point Cloud Library (PCL) - www.pointclouds.org
 *  Copyright (c) 2014-, Open Perception, Inc.
 *  Copyright (C) 2008 Ben Gurion University of the Negev, Beer Sheva, Israel.
 *
 *  All rights reserved
 *
 *  Redistribution and use in source and binary forms, with or without
 *  modification, are permitted provided that the following conditions are met
 *
 *   * Redistributions of source code must retain the above copyright
 *     notice, this list of conditions and the following disclaimer.
 *   * Redistributions in binary form must reproduce the above
 *     copyright notice, this list of conditions and the following
 *     disclaimer in the documentation and/or other materials provided
 *     with the distribution.
 *   * Neither the name of the copyright holder(s) nor the names of its
 *     contributors may be used to endorse or promote products derived
 *     from this software without specific prior written permission.
 *
 *  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
 *  "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
 *  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
 *  FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
 *  COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
 *  INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
 *  BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 *  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
 *  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
 *  LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
 *  ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
 *  POSSIBILITY OF SUCH DAMAGE.
 *
 */

#pragma once

#include <pcl/common/common.h>
#include <pcl/registration/matching_candidate.h>
#include <pcl/registration/registration.h>

namespace pcl {
/** \brief Compute the mean point density of a given point cloud.
 * \param[in] cloud pointer to the input point cloud
 * \param[in] max_dist maximum distance of a point to be considered as a neighbor
 * \param[in] nr_threads number of threads to use (default = 1, only used if OpenMP flag
 * is set) \return the mean point density of a given point cloud
 */
template <typename PointT>
inline float
getMeanPointDensity(const typename pcl::PointCloud<PointT>::ConstPtr& cloud,
                    float max_dist,
                    int nr_threads = 1);

/** \brief Compute the mean point density of a given point cloud.
 * \param[in] cloud pointer to the input point cloud
 * \param[in] indices the vector of point indices to use from \a cloud
 * \param[in] max_dist maximum distance of a point to be considered as a neighbor
 * \param[in] nr_threads number of threads to use (default = 1, only used if OpenMP flag
 * is set) \return the mean point density of a given point cloud
 */
template <typename PointT>
inline float
getMeanPointDensity(const typename pcl::PointCloud<PointT>::ConstPtr& cloud,
                    const pcl::Indices& indices,
                    float max_dist,
                    int nr_threads = 1);

namespace registration {
/** \brief FPCSInitialAlignment computes corresponding four point congruent sets as
 * described in: "4-points congruent sets for robust pairwise surface registration",
 * Dror Aiger, Niloy Mitra, Daniel Cohen-Or. ACM Transactions on Graphics, vol. 27(3),
 * 2008 \author P.W.Theiler \ingroup registration
 */
template <typename PointSource,
          typename PointTarget,
          typename NormalT = pcl::Normal,
          typename Scalar = float>
class FPCSInitialAlignment : public Registration<PointSource, PointTarget, Scalar> {
public:
  /** \cond */
  using Ptr =
      shared_ptr<FPCSInitialAlignment<PointSource, PointTarget, NormalT, Scalar>>;
  using ConstPtr =
      shared_ptr<const FPCSInitialAlignment<PointSource, PointTarget, NormalT, Scalar>>;

  using KdTreeReciprocal = pcl::search::KdTree<PointSource>;
  using KdTreeReciprocalPtr = typename KdTreeReciprocal::Ptr;

  using PointCloudTarget = pcl::PointCloud<PointTarget>;
  using PointCloudSource = pcl::PointCloud<PointSource>;
  using PointCloudSourcePtr = typename PointCloudSource::Ptr;
  using PointCloudSourceIterator = typename PointCloudSource::iterator;

  using Normals = pcl::PointCloud<NormalT>;
  using NormalsConstPtr = typename Normals::ConstPtr;

  using MatchingCandidate = pcl::registration::MatchingCandidate;
  using MatchingCandidates = pcl::registration::MatchingCandidates;
  /** \endcond */

  /** \brief Constructor.
   * Resets the maximum number of iterations to 0 thus forcing an internal computation
   * if not set by the user. Sets the number of RANSAC iterations to 1000 and the
   * standard transformation estimation to TransformationEstimation3Point.
   */
  FPCSInitialAlignment();

  /** \brief Destructor. */
  ~FPCSInitialAlignment(){};

  /** \brief Provide a pointer to the vector of target indices.
   * \param[in] target_indices a pointer to the target indices
   */
  inline void
  setTargetIndices(const IndicesPtr& target_indices)
  {
    target_indices_ = target_indices;
  };

  /** \return a pointer to the vector of target indices. */
  inline IndicesPtr
  getTargetIndices() const
  {
    return (target_indices_);
  };

  /** \brief Provide a pointer to the normals of the source point cloud.
   * \param[in] source_normals pointer to the normals of the source pointer cloud.
   */
  inline void
  setSourceNormals(const NormalsConstPtr& source_normals)
  {
    source_normals_ = source_normals;
  };

  /** \return the normals of the source point cloud. */
  inline NormalsConstPtr
  getSourceNormals() const
  {
    return (source_normals_);
  };

  /** \brief Provide a pointer to the normals of the target point cloud.
   * \param[in] target_normals point to the normals of the target point cloud.
   */
  inline void
  setTargetNormals(const NormalsConstPtr& target_normals)
  {
    target_normals_ = target_normals;
  };

  /** \return the normals of the target point cloud. */
  inline NormalsConstPtr
  getTargetNormals() const
  {
    return (target_normals_);
  };

  /** \brief Set the number of used threads if OpenMP is activated.
   * \param[in] nr_threads the number of used threads
   */
  inline void
  setNumberOfThreads(int nr_threads)
  {
    nr_threads_ = nr_threads;
  };

  /** \return the number of threads used if OpenMP is activated. */
  inline int
  getNumberOfThreads() const
  {
    return (nr_threads_);
  };

  /** \brief Set the constant factor delta which weights the internally calculated
   * parameters. \param[in] delta the weight factor delta \param[in] normalize flag if
   * delta should be normalized according to point cloud density
   */
  inline void
  setDelta(float delta, bool normalize = false)
  {
    delta_ = delta;
    normalize_delta_ = normalize;
  };

  /** \return the constant factor delta which weights the internally calculated
   * parameters. */
  inline float
  getDelta() const
  {
    return (delta_);
  };

  /** \brief Set the approximate overlap between source and target.
   * \param[in] approx_overlap the estimated overlap
   */
  inline void
  setApproxOverlap(float approx_overlap)
  {
    approx_overlap_ = approx_overlap;
  };

  /** \return the approximated overlap between source and target. */
  inline float
  getApproxOverlap() const
  {
    return (approx_overlap_);
  };

  /** \brief Set the scoring threshold used for early finishing the method.
   * \param[in] score_threshold early terminating score criteria
   */
  inline void
  setScoreThreshold(float score_threshold)
  {
    score_threshold_ = score_threshold;
  };

  /** \return the scoring threshold used for early finishing the method. */
  inline float
  getScoreThreshold() const
  {
    return (score_threshold_);
  };

  /** \brief Set the number of source samples to use during alignment.
   * \param[in] nr_samples the number of source samples
   */
  inline void
  setNumberOfSamples(int nr_samples)
  {
    nr_samples_ = nr_samples;
  };

  /** \return the number of source samples to use during alignment. */
  inline int
  getNumberOfSamples() const
  {
    return (nr_samples_);
  };

  /** \brief Set the maximum normal difference between valid point correspondences in
   * degree. \param[in] max_norm_diff the maximum difference in degree
   */
  inline void
  setMaxNormalDifference(float max_norm_diff)
  {
    max_norm_diff_ = max_norm_diff;
  };

  /** \return the maximum normal difference between valid point correspondences in
   * degree. */
  inline float
  getMaxNormalDifference() const
  {
    return (max_norm_diff_);
  };

  /** \brief Set the maximum computation time in seconds.
   * \param[in] max_runtime the maximum runtime of the method in seconds
   */
  inline void
  setMaxComputationTime(int max_runtime)
  {
    max_runtime_ = max_runtime;
  };

  /** \return the maximum computation time in seconds. */
  inline int
  getMaxComputationTime() const
  {
    return (max_runtime_);
  };

  /** \return the fitness score of the best scored four-point match. */
  inline float
  getFitnessScore() const
  {
    return (fitness_score_);
  };

protected:
  using PCLBase<PointSource>::deinitCompute;
  using PCLBase<PointSource>::input_;
  using PCLBase<PointSource>::indices_;

  using Registration<PointSource, PointTarget, Scalar>::reg_name_;
  using Registration<PointSource, PointTarget, Scalar>::target_;
  using Registration<PointSource, PointTarget, Scalar>::tree_;
  using Registration<PointSource, PointTarget, Scalar>::correspondences_;
  using Registration<PointSource, PointTarget, Scalar>::target_cloud_updated_;
  using Registration<PointSource, PointTarget, Scalar>::final_transformation_;
  using Registration<PointSource, PointTarget, Scalar>::max_iterations_;
  using Registration<PointSource, PointTarget, Scalar>::ransac_iterations_;
  using Registration<PointSource, PointTarget, Scalar>::transformation_estimation_;
  using Registration<PointSource, PointTarget, Scalar>::converged_;

  /** \brief Rigid transformation computation method.
   * \param output the transformed input point cloud dataset using the rigid
   * transformation found \param guess The computed transforamtion
   */
  void
  computeTransformation(PointCloudSource& output,
                        const Eigen::Matrix4f& guess) override;

  /** \brief Internal computation initialization. */
  virtual bool
  initCompute();

  /** \brief Select an approximately coplanar set of four points from the source cloud.
   * \param[out] base_indices selected source cloud indices, further used as base (B)
   * \param[out] ratio the two diagonal intersection ratios (r1,r2) of the base points
   * \return
   * * < 0 no coplanar four point sets with large enough sampling distance was found
   * * = 0 a set of four congruent points was selected
   */
  int
  selectBase(pcl::Indices& base_indices, float (&ratio)[2]);

  /** \brief Select randomly a triplet of points with large point-to-point distances.
   * The minimum point sampling distance is calculated based on the estimated point
   * cloud overlap during initialization.
   *
   * \param[out] base_indices indices of base B
   * \return
   * * < 0 no triangle with large enough base lines could be selected
   * * = 0 base triangle succesully selected
   */
  int
  selectBaseTriangle(pcl::Indices& base_indices);

  /** \brief Setup the base (four coplanar points) by ordering the points and computing
   * intersection ratios and segment to segment distances of base diagonal.
   *
   * \param[in,out] base_indices indices of base B (will be reordered)
   * \param[out] ratio diagonal intersection ratios of base points
   */
  void
  setupBase(pcl::Indices& base_indices, float (&ratio)[2]);

  /** \brief Calculate intersection ratios and segment to segment distances of base
   * diagonals. \param[in] base_indices indices of base B \param[out] ratio diagonal
   * intersection ratios of base points \return quality value of diagonal intersection
   */
  float
  segmentToSegmentDist(const pcl::Indices& base_indices, float (&ratio)[2]);

  /** \brief Search for corresponding point pairs given the distance between two base
   * points.
   *
   * \param[in] idx1 first index of current base segment (in source cloud)
   * \param[in] idx2 second index of current base segment (in source cloud)
   * \param[out] pairs resulting point pairs with point-to-point distance close to
   * ref_dist \return
   * * < 0 no corresponding point pair was found
   * * = 0 at least one point pair candidate was found
   */
  virtual int
  bruteForceCorrespondences(int idx1, int idx2, pcl::Correspondences& pairs);

  /** \brief Determine base matches by combining the point pair candidate and search for
   * coinciding intersection points using the diagonal segment ratios of base B. The
   * coincidation threshold is calculated during initialization (coincidation_limit_).
   *
   * \param[in] base_indices indices of base B
   * \param[out] matches vector of candidate matches w.r.t the base B
   * \param[in] pairs_a point pairs corresponding to points of 1st diagonal of base B
   * \param[in] pairs_b point pairs corresponding to points of 2nd diagonal of base B
   * \param[in] ratio diagonal intersection ratios of base points
   * \return
   * * < 0 no base match could be found
   * * = 0 at least one base match was found
   */
  virtual int
  determineBaseMatches(const pcl::Indices& base_indices,
                       std::vector<pcl::Indices>& matches,
                       const pcl::Correspondences& pairs_a,
                       const pcl::Correspondences& pairs_b,
                       const float (&ratio)[2]);

  /** \brief Check if outer rectangle distance of matched points fit with the base
   * rectangle.
   *
   * \param[in] match_indices indices of match M
   * \param[in] ds edge lengths of base B
   * \return
   * * < 0 at least one edge of the match M has no corresponding one in the base B
   * * = 0 edges of match M fits to the ones of base B
   */
  int
  checkBaseMatch(const pcl::Indices& match_indices, const float (&ds)[4]);

  /** \brief Method to handle current candidate matches. Here we validate and evaluate
   * the matches w.r.t the base and store the best fitting match (together with its
   * score and estimated transformation). \note For forwards compatibility the results
   * are stored in 'vectors of size 1'.
   *
   * \param[in] base_indices indices of base B
   * \param[in,out] matches vector of candidate matches w.r.t the base B. The candidate
   * matches are reordered during this step. \param[out] candidates vector which
   * contains the candidates matches M
   */
  virtual void
  handleMatches(const pcl::Indices& base_indices,
                std::vector<pcl::Indices>& matches,
                MatchingCandidates& candidates);

  /** \brief Sets the correspondences between the base B and the match M by using the
   * distance of each point to the centroid of the rectangle.
   *
   * \param[in] base_indices indices of base B
   * \param[in] match_indices indices of match M
   * \param[out] correspondences resulting correspondences
   */
  virtual void
  linkMatchWithBase(const pcl::Indices& base_indices,
                    pcl::Indices& match_indices,
                    pcl::Correspondences& correspondences);

  /** \brief Validate the matching by computing the transformation between the source
   * and target based on the four matched points and by comparing the mean square error
   * (MSE) to a threshold. The MSE limit was calculated during initialization
   * (max_mse_).
   *
   * \param[in] base_indices indices of base B
   * \param[in] match_indices indices of match M
   * \param[in] correspondences corresondences between source and target
   * \param[out] transformation resulting transformation matrix
   * \return
   * * < 0 MSE bigger than max_mse_
   * * = 0 MSE smaller than max_mse_
   */
  virtual int
  validateMatch(const pcl::Indices& base_indices,
                const pcl::Indices& match_indices,
                const pcl::Correspondences& correspondences,
                Eigen::Matrix4f& transformation);

  /** \brief Validate the transformation by calculating the number of inliers after
   * transforming the source cloud. The resulting fitness score is later used as the
   * decision criteria of the best fitting match.
   *
   * \param[out] transformation updated orientation matrix using all inliers
   * \param[out] fitness_score current best fitness_score
   * \note fitness score is only updated if the score of the current transformation
   * exceeds the input one. \return
   * * < 0 if previous result is better than the current one (score remains)
   * * = 0 current result is better than the previous one (score updated)
   */
  virtual int
  validateTransformation(Eigen::Matrix4f& transformation, float& fitness_score);

  /** \brief Final computation of best match out of vector of best matches. To avoid
   * cross thread dependencies during parallel running, a best match for each try was
   * calculated. \note For forwards compatibility the candidates are stored in vectors
   * of 'vectors of size 1'. \param[in] candidates vector of candidate matches
   */
  virtual void
  finalCompute(const std::vector<MatchingCandidates>& candidates);

  /** \brief Normals of source point cloud. */
  NormalsConstPtr source_normals_;

  /** \brief Normals of target point cloud. */
  NormalsConstPtr target_normals_;

  /** \brief Number of threads for parallelization (standard = 1).
   * \note Only used if run compiled with OpenMP.
   */
  int nr_threads_;

  /** \brief Estimated overlap between source and target (standard = 0.5). */
  float approx_overlap_;

  /** \brief Delta value of 4pcs algorithm (standard = 1.0).
   * It can be used as:
   * * absolute value (normalization = false), value should represent the point accuracy
   * to ensure finding neighbors between source <-> target
   * * relative value (normalization = true), to adjust the internally calculated point
   * accuracy (= point density)
   */
  float delta_;

  /** \brief Score threshold to stop calculation with success.
   * If not set by the user it depends on the size of the approximated overlap
   */
  float score_threshold_;

  /** \brief The number of points to uniformly sample the source point cloud. (standard
   * = 0 => full cloud). */
  int nr_samples_;

  /** \brief Maximum normal difference of corresponding point pairs in degrees (standard
   * = 90). */
  float max_norm_diff_;

  /** \brief Maximum allowed computation time in seconds (standard = 0 => ~unlimited).
   */
  int max_runtime_;

  /** \brief Resulting fitness score of the best match. */
  float fitness_score_;

  /** \brief Estimated diamter of the target point cloud. */
  float diameter_;

  /** \brief Estimated squared metric overlap between source and target.
   * \note Internally calculated using the estimated overlap and the extent of the
   * source cloud. It is used to derive the minimum sampling distance of the base points
   * as well as to calculated the number of tries to reliably find a correct match.
   */
  float max_base_diameter_sqr_;

  /** \brief Use normals flag. */
  bool use_normals_;

  /** \brief Normalize delta flag. */
  bool normalize_delta_;

  /** \brief A pointer to the vector of source point indices to use after sampling. */
  pcl::IndicesPtr source_indices_;

  /** \brief A pointer to the vector of target point indices to use after sampling. */
  pcl::IndicesPtr target_indices_;

  /** \brief Maximal difference between corresponding point pairs in source and target.
   * \note Internally calculated using an estimation of the point density.
   */
  float max_pair_diff_;

  /** \brief Maximal difference between the length of the base edges and valid match
   * edges. \note Internally calculated using an estimation of the point density.
   */
  float max_edge_diff_;

  /** \brief Maximal distance between coinciding intersection points to find valid
   * matches. \note Internally calculated using an estimation of the point density.
   */
  float coincidation_limit_;

  /** \brief Maximal mean squared errors of a transformation calculated from a candidate
   * match. \note Internally calculated using an estimation of the point density.
   */
  float max_mse_;

  /** \brief Maximal squared point distance between source and target points to count as
   * inlier. \note Internally calculated using an estimation of the point density.
   */
  float max_inlier_dist_sqr_;

  /** \brief Definition of a small error. */
  const float small_error_;
};
}; // namespace registration
}; // namespace pcl

#include <pcl/registration/impl/ia_fpcs.hpp>