mls.h

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#ifndef PCL_MLS_H_
#define PCL_MLS_H_

// PCL includes
#include <pcl/pcl_base.h>
#include <pcl/search/pcl_search.h>
#include <pcl/common/common.h>

#include <pcl/surface/boost.h>
#include <pcl/surface/eigen.h>
#include <pcl/surface/processing.h>
#include <map>

namespace pcl
{
  template <typename PointInT, typename PointOutT>
  class MovingLeastSquares: public CloudSurfaceProcessing<PointInT, PointOutT>
  {
    public:
      typedef boost::shared_ptr<MovingLeastSquares<PointInT, PointOutT> > Ptr;
      typedef boost::shared_ptr<const MovingLeastSquares<PointInT, PointOutT> > ConstPtr;

      using PCLBase<PointInT>::input_;
      using PCLBase<PointInT>::indices_;
      using PCLBase<PointInT>::fake_indices_;
      using PCLBase<PointInT>::initCompute;
      using PCLBase<PointInT>::deinitCompute;

      typedef typename pcl::search::Search<PointInT> KdTree;
      typedef typename pcl::search::Search<PointInT>::Ptr KdTreePtr;
      typedef pcl::PointCloud<pcl::Normal> NormalCloud;
      typedef pcl::PointCloud<pcl::Normal>::Ptr NormalCloudPtr;

      typedef pcl::PointCloud<PointOutT> PointCloudOut;
      typedef typename PointCloudOut::Ptr PointCloudOutPtr;
      typedef typename PointCloudOut::ConstPtr PointCloudOutConstPtr;

      typedef pcl::PointCloud<PointInT> PointCloudIn;
      typedef typename PointCloudIn::Ptr PointCloudInPtr;
      typedef typename PointCloudIn::ConstPtr PointCloudInConstPtr;

      typedef boost::function<int (int, double, std::vector<int> &, std::vector<float> &)> SearchMethod;

      enum UpsamplingMethod {NONE, DISTINCT_CLOUD, SAMPLE_LOCAL_PLANE, RANDOM_UNIFORM_DENSITY, VOXEL_GRID_DILATION};

      MovingLeastSquares () : CloudSurfaceProcessing<PointInT, PointOutT> (),
                              normals_ (),
                              distinct_cloud_ (),
                              search_method_ (),
                              tree_ (),
                              order_ (2),
                              polynomial_fit_ (true),
                              search_radius_ (0.0),
                              sqr_gauss_param_ (0.0),
                              compute_normals_ (false),
                              upsample_method_ (NONE),
                              upsampling_radius_ (0.0),
                              upsampling_step_ (0.0),
                              desired_num_points_in_radius_ (0),
                              mls_results_ (),
                              voxel_size_ (1.0),
                              dilation_iteration_num_ (0),
                              nr_coeff_ (),
                              corresponding_input_indices_ (),
                              rng_alg_ (),
                              rng_uniform_distribution_ ()
                              {};
      
      virtual ~MovingLeastSquares () {}


      inline void
      setComputeNormals (bool compute_normals) { compute_normals_ = compute_normals; }

      inline void
      setSearchMethod (const KdTreePtr &tree)
      {
        tree_ = tree;
        // Declare the search locator definition
        int (KdTree::*radiusSearch)(int index, double radius, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances, unsigned int max_nn) const = &KdTree::radiusSearch;
        search_method_ = boost::bind (radiusSearch, boost::ref (tree_), _1, _2, _3, _4, 0);
      }

      inline KdTreePtr 
      getSearchMethod () { return (tree_); }

      inline void 
      setPolynomialOrder (int order) { order_ = order; }

      inline int 
      getPolynomialOrder () { return (order_); }

      inline void 
      setPolynomialFit (bool polynomial_fit) { polynomial_fit_ = polynomial_fit; }

      inline bool 
      getPolynomialFit () { return (polynomial_fit_); }

      inline void 
      setSearchRadius (double radius) { search_radius_ = radius; sqr_gauss_param_ = search_radius_ * search_radius_; }

      inline double 
      getSearchRadius () { return (search_radius_); }

      inline void 
      setSqrGaussParam (double sqr_gauss_param) { sqr_gauss_param_ = sqr_gauss_param; }

      inline double 
      getSqrGaussParam () const { return (sqr_gauss_param_); }

      inline void
      setUpsamplingMethod (UpsamplingMethod method) { upsample_method_ = method; }

      inline void
      setDistinctCloud (PointCloudInConstPtr distinct_cloud) { distinct_cloud_ = distinct_cloud; }

      inline PointCloudInConstPtr
      getDistinctCloud () { return distinct_cloud_; }


      inline void
      setUpsamplingRadius (double radius) { upsampling_radius_ = radius; }

      inline double
      getUpsamplingRadius () { return upsampling_radius_; }

      inline void
      setUpsamplingStepSize (double step_size) { upsampling_step_ = step_size; }


      inline double
      getUpsamplingStepSize () { return upsampling_step_; }

      inline void
      setPointDensity (int desired_num_points_in_radius) { desired_num_points_in_radius_ = desired_num_points_in_radius; }


      inline int
      getPointDensity () { return desired_num_points_in_radius_; }

      inline void
      setDilationVoxelSize (float voxel_size) { voxel_size_ = voxel_size; }


      inline float
      getDilationVoxelSize () { return voxel_size_; }

      inline void
      setDilationIterations (int iterations) { dilation_iteration_num_ = iterations; }

      inline int
      getDilationIterations () { return dilation_iteration_num_; }

      void 
      process (PointCloudOut &output);


      inline PointIndicesPtr
      getCorrespondingIndices () { return (corresponding_input_indices_); }

    protected:
      NormalCloudPtr normals_;

      PointCloudInConstPtr distinct_cloud_;

      SearchMethod search_method_;

      KdTreePtr tree_;

      int order_;

      bool polynomial_fit_;

      double search_radius_;

      double sqr_gauss_param_;

      bool compute_normals_;

      UpsamplingMethod upsample_method_;

      double upsampling_radius_;

      double upsampling_step_;

      int desired_num_points_in_radius_;

      
      struct MLSResult
      {
        MLSResult () : mean (), plane_normal (), u_axis (), v_axis (), c_vec (), num_neighbors (), curvature (), valid (false) {}

        MLSResult (const Eigen::Vector3d &a_mean,
                   const Eigen::Vector3d &a_plane_normal,
                   const Eigen::Vector3d &a_u,
                   const Eigen::Vector3d &a_v,
                   const Eigen::VectorXd a_c_vec,
                   const int a_num_neighbors,
                   const float &a_curvature);

        Eigen::Vector3d mean, plane_normal, u_axis, v_axis;
        Eigen::VectorXd c_vec;
        int num_neighbors;
        float curvature;
        bool valid;
      };

      std::vector<MLSResult> mls_results_;

      
      class MLSVoxelGrid
      {
        public:
          struct Leaf { Leaf () : valid (true) {} bool valid; };

          MLSVoxelGrid (PointCloudInConstPtr& cloud,
                        IndicesPtr &indices,
                        float voxel_size);

          void
          dilate ();

          inline void
          getIndexIn1D (const Eigen::Vector3i &index, uint64_t &index_1d) const
          {
            index_1d = index[0] * data_size_ * data_size_ +
                       index[1] * data_size_ + index[2];
          }

          inline void
          getIndexIn3D (uint64_t index_1d, Eigen::Vector3i& index_3d) const
          {
            index_3d[0] = static_cast<Eigen::Vector3i::Scalar> (index_1d / (data_size_ * data_size_));
            index_1d -= index_3d[0] * data_size_ * data_size_;
            index_3d[1] = static_cast<Eigen::Vector3i::Scalar> (index_1d / data_size_);
            index_1d -= index_3d[1] * data_size_;
            index_3d[2] = static_cast<Eigen::Vector3i::Scalar> (index_1d);
          }

          inline void
          getCellIndex (const Eigen::Vector3f &p, Eigen::Vector3i& index) const
          {
            for (int i = 0; i < 3; ++i)
              index[i] = static_cast<Eigen::Vector3i::Scalar> ((p[i] - bounding_min_(i)) / voxel_size_);
          }

          inline void
          getPosition (const uint64_t &index_1d, Eigen::Vector3f &point) const
          {
            Eigen::Vector3i index_3d;
            getIndexIn3D (index_1d, index_3d);
            for (int i = 0; i < 3; ++i)
              point[i] = static_cast<Eigen::Vector3f::Scalar> (index_3d[i]) * voxel_size_ + bounding_min_[i];
          }

          typedef std::map<uint64_t, Leaf> HashMap;
          HashMap voxel_grid_;
          Eigen::Vector4f bounding_min_, bounding_max_;
          uint64_t data_size_;
          float voxel_size_;
      };


      float voxel_size_;

      int dilation_iteration_num_; 

      int nr_coeff_;

      PointIndicesPtr corresponding_input_indices_;

      inline int
      searchForNeighbors (int index, std::vector<int> &indices, std::vector<float> &sqr_distances) const
      {
        return (search_method_ (index, search_radius_, indices, sqr_distances));
      }

      void
      computeMLSPointNormal (int index,
                             const std::vector<int> &nn_indices,
                             std::vector<float> &nn_sqr_dists,
                             PointCloudOut &projected_points,
                             NormalCloud &projected_points_normals,
                             PointIndices &corresponding_input_indices,
                             MLSResult &mls_result) const;

      void
      projectPointToMLSSurface (float &u_disp, float &v_disp,
                                Eigen::Vector3d &u_axis, Eigen::Vector3d &v_axis,
                                Eigen::Vector3d &n_axis,
                                Eigen::Vector3d &mean,
                                float &curvature,
                                Eigen::VectorXd &c_vec,
                                int num_neighbors,
                                PointOutT &result_point,
                                pcl::Normal &result_normal) const;

      void
      copyMissingFields (const PointInT &point_in,
                         PointOutT &point_out) const;

      virtual void performProcessing (PointCloudOut &output);

      void performUpsampling (PointCloudOut &output);

    private:
      boost::mt19937 rng_alg_;

      boost::shared_ptr<boost::variate_generator<boost::mt19937&, 
                                                 boost::uniform_real<float> > 
                       > rng_uniform_distribution_;

      std::string getClassName () const { return ("MovingLeastSquares"); }
      
    public:
        EIGEN_MAKE_ALIGNED_OPERATOR_NEW
  };

#ifdef _OPENMP

  template <typename PointInT, typename PointOutT>
  class MovingLeastSquaresOMP: public MovingLeastSquares<PointInT, PointOutT>
  {
    public:
      typedef boost::shared_ptr<MovingLeastSquares<PointInT, PointOutT> > Ptr;
      typedef boost::shared_ptr<const MovingLeastSquares<PointInT, PointOutT> > ConstPtr;

      using PCLBase<PointInT>::input_;
      using PCLBase<PointInT>::indices_;
      using MovingLeastSquares<PointInT, PointOutT>::normals_;
      using MovingLeastSquares<PointInT, PointOutT>::corresponding_input_indices_;
      using MovingLeastSquares<PointInT, PointOutT>::nr_coeff_;
      using MovingLeastSquares<PointInT, PointOutT>::order_;
      using MovingLeastSquares<PointInT, PointOutT>::compute_normals_;

      typedef pcl::PointCloud<pcl::Normal> NormalCloud;
      typedef pcl::PointCloud<pcl::Normal>::Ptr NormalCloudPtr;

      typedef pcl::PointCloud<PointOutT> PointCloudOut;
      typedef typename PointCloudOut::Ptr PointCloudOutPtr;
      typedef typename PointCloudOut::ConstPtr PointCloudOutConstPtr;

      MovingLeastSquaresOMP (unsigned int threads = 0) : threads_ (threads)
      {

      }

      inline void
      setNumberOfThreads (unsigned int threads = 0)
      {
        threads_ = threads;
      }

    protected:
      virtual void performProcessing (PointCloudOut &output);

      unsigned int threads_;
  };
#endif
}

#ifdef PCL_NO_PRECOMPILE
#include <pcl/surface/impl/mls.hpp>
#endif

#endif  /* #ifndef PCL_MLS_H_ */