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# -*- coding: utf-8 -*-
from libc.stddef cimport size_t
from libcpp.vector cimport vector
from libcpp.string cimport string
from libcpp cimport bool
# main
cimport pcl_defs as cpp
# boost
from boost_shared_ptr cimport shared_ptr
# Base Interface
# Search.h
# namespace pcl
# namespace search
# template<typename PointT>
# class Search
cdef extern from "pcl/Search/Search.h" namespace "pcl::search":
Search[T]:
Search()
# Search (const string& name = "", bool sorted = false)
# public:
# typedef pcl::PointCloud<PointT> PointCloud;
# typedef typename PointCloud::Ptr PointCloudPtr;
# typedef typename PointCloud::ConstPtr PointCloudConstPtr;
# typedef boost::shared_ptr<pcl::search::Search<PointT> > Ptr;
# typedef boost::shared_ptr<const pcl::search::Search<PointT> > ConstPtr;
# typedef boost::shared_ptr<std::vector<int> > IndicesPtr;
# typedef boost::shared_ptr<const std::vector<int> > IndicesConstPtr;
# /** \brief returns the search method name
string getName ()
# /** \brief sets whether the results should be sorted (ascending in the distance) or not
# * \param[in] sorted should be true if the results should be sorted by the distance in ascending order.
# * Otherwise the results may be returned in any order.
void setSortedResults (bool sorted)
# /** \brief Pass the input dataset that the search will be performed on.
# * \param[in] cloud a const pointer to the PointCloud data
# * \param[in] indices the point indices subset that is to be used from the cloud
# virtual void setInputCloud (const PointCloudConstPtr& cloud, const IndicesConstPtr &indices = IndicesConstPtr ())
# /** \brief Get a pointer to the input point cloud dataset. */
# virtual PointCloudConstPtr getInputCloud () const
# /** \brief Get a pointer to the vector of indices used. */
# virtual IndicesConstPtr getIndices () const
# /** \brief Search for the k-nearest neighbors for the given query point.
# * \param[in] point the given query point
# * \param[in] k the number of neighbors to search for
# * \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
# * \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
# * a priori!)
# * \return number of neighbors found
# virtual int nearestKSearch (const PointT &point, int k, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances) const = 0;
# /** \brief Search for k-nearest neighbors for the given query point.
# * This method accepts a different template parameter for the point type.
# * \param[in] point the given query point
# * \param[in] k the number of neighbors to search for
# * \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
# * \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
# * a priori!)
# * \return number of neighbors found
# template <typename PointTDiff>
# inline int nearestKSearchT (const PointTDiff &point, int k, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances) const
# /** \brief Search for k-nearest neighbors for the given query point.
# * \attention This method does not do any bounds checking for the input index
# * (i.e., index >= cloud.points.size () || index < 0), and assumes valid (i.e., finite) data.
# * \param[in] cloud the point cloud data
# * \param[in] index a \a valid index in \a cloud representing a \a valid (i.e., finite) query point
# * \param[in] k the number of neighbors to search for
# * \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
# * \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
# * a priori!)
# * \return number of neighbors found
# * \exception asserts in debug mode if the index is not between 0 and the maximum number of points
# virtual int nearestKSearch (const PointCloud &cloud, int index, int k, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances) const
#
# /** \brief Search for k-nearest neighbors for the given query point (zero-copy).
# * \attention This method does not do any bounds checking for the input index
# * (i.e., index >= cloud.points.size () || index < 0), and assumes valid (i.e., finite) data.
# * \param[in] index a \a valid index representing a \a valid query point in the dataset given
# * by \a setInputCloud. If indices were given in setInputCloud, index will be the position in
# * the indices vector.
# * \param[in] k the number of neighbors to search for
# * \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
# * \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
# * a priori!)
# * \return number of neighbors found
# * \exception asserts in debug mode if the index is not between 0 and the maximum number of points
# virtual int nearestKSearch (int index, int k, vector[int] &k_indices, vector[float] &k_sqr_distances) const
#
# /** \brief Search for the k-nearest neighbors for the given query point.
# * \param[in] cloud the point cloud data
# * \param[in] indices a vector of point cloud indices to query for nearest neighbors
# * \param[in] k the number of neighbors to search for
# * \param[out] k_indices the resultant indices of the neighboring points, k_indices[i] corresponds to the neighbors of the query point i
# * \param[out] k_sqr_distances the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i
# virtual void nearestKSearch (const PointCloud& cloud, vector[int]& indices, int k, vector[vector[int] ]& k_indices, std::vector< std::vector<float> >& k_sqr_distances) const
#
# /** \brief Search for the k-nearest neighbors for the given query point.
# * Use this method if the query points are of a different type than the points in the data set (e.g. PointXYZRGBA instead of PointXYZ).
# * \param[in] cloud the point cloud data
# * \param[in] indices a vector of point cloud indices to query for nearest neighbors
# * \param[in] k the number of neighbors to search for
# * \param[out] k_indices the resultant indices of the neighboring points, k_indices[i] corresponds to the neighbors of the query point i
# * \param[out] k_sqr_distances the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i
# * \note This method copies the input point cloud of type PointTDiff to a temporary cloud of type PointT and performs the batch search on the new cloud. You should prefer the single-point search if you don't use a search algorithm that accelerates batch NN search.
# template <typename PointTDiff>
# void nearestKSearchT (const pcl::PointCloud<PointTDiff> &cloud, const std::vector<int>& indices, int k, std::vector< std::vector<int> > &k_indices, std::vector< std::vector<float> > &k_sqr_distances) const
#
# /** \brief Search for all the nearest neighbors of the query point in a given radius.
# * \param[in] point the given query point
# * \param[in] radius the radius of the sphere bounding all of p_q's neighbors
# * \param[out] k_indices the resultant indices of the neighboring points
# * \param[out] k_sqr_distances the resultant squared distances to the neighboring points
# * \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
# * 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
# * returned.
# * \return number of neighbors found in radius
# virtual int radiusSearch (const PointT& point, double radius, std::vector<int>& k_indices, std::vector<float>& k_sqr_distances, unsigned int max_nn = 0) const = 0;
#
# /** \brief Search for all the nearest neighbors of the query point in a given radius.
# * \param[in] point the given query point
# * \param[in] radius the radius of the sphere bounding all of p_q's neighbors
# * \param[out] k_indices the resultant indices of the neighboring points
# * \param[out] k_sqr_distances the resultant squared distances to the neighboring points
# * \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
# * 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
# * returned.
# * \return number of neighbors found in radius
# template <typename PointTDiff>
# inline int radiusSearchT (const PointTDiff &point, double radius, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const
#
# /** \brief Search for all the nearest neighbors of the query point in a given radius.
# * \attention This method does not do any bounds checking for the input index
# * (i.e., index >= cloud.points.size () || index < 0), and assumes valid (i.e., finite) data.
# * \param[in] cloud the point cloud data
# * \param[in] index a \a valid index in \a cloud representing a \a valid (i.e., finite) query point
# * \param[in] radius the radius of the sphere bounding all of p_q's neighbors
# * \param[out] k_indices the resultant indices of the neighboring points
# * \param[out] k_sqr_distances the resultant squared distances to the neighboring points
# * \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
# * 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
# * returned.
# * \return number of neighbors found in radius
# * \exception asserts in debug mode if the index is not between 0 and the maximum number of points
# virtual int radiusSearch (const PointCloud &cloud, int index, double radius, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const
#
# /** \brief Search for all the nearest neighbors of the query point in a given radius (zero-copy).
# * \attention This method does not do any bounds checking for the input index
# * (i.e., index >= cloud.points.size () || index < 0), and assumes valid (i.e., finite) data.
# * \param[in] index a \a valid index representing a \a valid query point in the dataset given
# * by \a setInputCloud. If indices were given in setInputCloud, index will be the position in
# * the indices vector.
# * \param[in] radius the radius of the sphere bounding all of p_q's neighbors
# * \param[out] k_indices the resultant indices of the neighboring points
# * \param[out] k_sqr_distances the resultant squared distances to the neighboring points
# * \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
# * 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
# * returned.
# * \return number of neighbors found in radius
# * \exception asserts in debug mode if the index is not between 0 and the maximum number of points
# virtual int radiusSearch (int index, double radius, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const
#
# /** \brief Search for all the nearest neighbors of the query point in a given radius.
# * \param[in] cloud the point cloud data
# * \param[in] indices the indices in \a cloud. If indices is empty, neighbors will be searched for all points.
# * \param[in] radius the radius of the sphere bounding all of p_q's neighbors
# * \param[out] k_indices the resultant indices of the neighboring points, k_indices[i] corresponds to the neighbors of the query point i
# * \param[out] k_sqr_distances the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i
# * \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
# * 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
# * returned.
# virtual void radiusSearch (const PointCloud& cloud,
# const std::vector<int>& indices,
# double radius,
# std::vector< std::vector<int> >& k_indices,
# std::vector< std::vector<float> > &k_sqr_distances,
# unsigned int max_nn = 0) const
#
# /** \brief Search for all the nearest neighbors of the query points in a given radius.
# * \param[in] cloud the point cloud data
# * \param[in] indices a vector of point cloud indices to query for nearest neighbors
# * \param[in] radius the radius of the sphere bounding all of p_q's neighbors
# * \param[out] k_indices the resultant indices of the neighboring points, k_indices[i] corresponds to the neighbors of the query point i
# * \param[out] k_sqr_distances the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i
# * \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
# * 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
# * returned.
# * \note This method copies the input point cloud of type PointTDiff to a temporary cloud of type PointT and performs the batch search on the new cloud. You should prefer the single-point search if you don't use a search algorithm that accelerates batch NN search.
# template <typename PointTDiff> void
# radiusSearchT (const pcl::PointCloud<PointTDiff> &cloud,
# const std::vector<int>& indices,
# double radius,
# std::vector< std::vector<int> > &k_indices,
# std::vector< std::vector<float> > &k_sqr_distances,
# unsigned int max_nn = 0) const
###
# template<typename PointT> void
# Search<PointT>::sortResults (std::vector<int>& indices, std::vector<float>& distances) const
# cdef extern from "pcl/Search/Search.h" namespace "pcl::search":
# cdef Search[T]::sortResults (std::vector<int>& indices, std::vector<float>& distances) const
###
# pcl_search target out
cdef extern from "pcl/Search/brute_force.h" namespace "pcl::search":
cdef cppclass BruteForce[PointT](Search[PointT]):
BruteForce()
# BruteForce (bool sorted_results = false)
# ctypedef typename Search<PointT>::PointCloud PointCloud;
# ctypedef typename Search<PointT>::PointCloudConstPtr PointCloudConstPtr;
# ctypedef shared_ptr[vector[int]] IndicesPtr;
# ctypedef shared_ptr[vector[int]] IndicesConstPtr;
# using Search<PointT>::input_;
# using Search<PointT>::indices_;
# using Search<PointT>::sorted_results_;
#
# cdef struct Entry
# Entry(int , float)
# Entry ()
# unsigned index;
# float distance;
# replace by some metric functor
# float getDistSqr (const PointT& point1, const PointT& point2) const;
float getDistSqr (const PointT& point1, const PointT& point2)
# int nearestKSearch (const PointT &point, int k, std::vector<int> &k_indices, std::vector<float> &k_distances) const;
int nearestKSearch (const PointT &point, int k, vector[int] &k_indices, vector[float] &k_distances)
# int radiusSearch (const PointT& point, double radius, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const;
int radiusSearch (const PointT& point, double radius, vector[int] &k_indices, vector[float] &k_sqr_distances, unsigned int max_nn)
# int denseKSearch (const PointT &point, int k, std::vector<int> &k_indices, std::vector<float> &k_distances) const;
int denseKSearch (const PointT &point, int k, vector[int] &k_indices, vector[float] &k_distances)
# int sparseKSearch (const PointT &point, int k, std::vector<int> &k_indices, std::vector<float> &k_distances) const;
int sparseKSearch (const PointT &point, int k, vector[int] &k_indices, vector[float] &k_distances)
# int denseRadiusSearch (const PointT& point, double radius, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const;
int denseRadiusSearch (const PointT& point, double radius, vector[int] &k_indices, vector[float] &k_sqr_distances, unsigned int max_nn)
# int sparseRadiusSearch (const PointT& point, double radius, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const;
int sparseRadiusSearch (const PointT& point, double radius, vector[int] &k_indices, vector[float] &k_sqr_distances, unsigned int max_nn)
# ctypedef BruteForce
###
# pcl_search target out
cdef extern from "pcl/Search/flann_search.h" namespace "pcl":
cdef cppclass FlannSearch[T](Search[PointT]):
VoxelGrid()
void setLeafSize (float, float, float)
void setInputCloud (shared_ptr[cpp.PointCloud[T]])
void filter(cpp.PointCloud[T] c)
# # ctypedef typename Search<PointT>::PointCloud PointCloud;
# # ctypedef typename Search<PointT>::PointCloudConstPtr PointCloudConstPtr;
# ctypedef sharedptr[vector[int]] IndicesPtr;
# ctypedef sharedptr[vector[int]] IndicesConstPtr;
# # ctypedef flann::NNIndex[FlannDistance] Index;
# ctypedef sharedptr[flann::NNIndex[FlannDistance]] IndexPtr;
# ctypedef sharedptr[flann::Matrix[float]] MatrixPtr;
# ctypedef sharedptr[flann::Matrix[float]] MatrixConstPtr;
# # ctypedef pcl::PointRepresentation<PointT> PointRepresentation;
# //typedef boost::shared_ptr<PointRepresentation> PointRepresentationPtr;
# ctypedef sharedptr[PointRepresentation] PointRepresentationConstPtr;
# # using Search<PointT>::input_;
# # using Search<PointT>::indices_;
# # using Search<PointT>::sorted_results_;
# public:
# ctypedef sharedptr[FlannSearch[PointT]] Ptr;
# ctypedef sharedptr[FlannSearch[PointT]] ConstPtr;
# # cdef cppclass FlannIndexCreator
# # virtual IndexPtr createIndex (MatrixConstPtr data)=0;
# # class KdTreeIndexCreator: public FlannIndexCreator
# cdef cppclass KdTreeIndexCreator:
# # KdTreeIndexCreator (unsigned int max_leaf_size=15)
# KdTreeIndexCreator (unsigned int)
# # virtual IndexPtr createIndex (MatrixConstPtr data);
# cdef FlannSearch (bool sorted = true, FlannIndexCreator* creator = new KdTreeIndexCreator());
# cdef void setEpsilon (double eps)
# cdef double getEpsilon ()
# cdef void setInputCloud (const PointCloudConstPtr& cloud, const IndicesConstPtr& indices = IndicesConstPtr ());
# cdef int nearestKSearch (const PointT &point, int k, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances) const;
# cdef void nearestKSearch (const PointCloud& cloud, const std::vector<int>& indices, int k,
# std::vector< std::vector<int> >& k_indices, std::vector< std::vector<float> >& k_sqr_distances) const;
# cdef int radiusSearch (const PointT& point, double radius,
# std::vector<int> &k_indices, std::vector<float> &k_sqr_distances,
# unsigned int max_nn = 0) const;
# cdef void radiusSearch (const PointCloud& cloud, const std::vector<int>& indices, double radius, std::vector< std::vector<int> >& k_indices,
# vector[vector[float]] k_sqr_distances, unsigned int max_nn=0) const;
# cdef void setPointRepresentation (const PointRepresentationConstPtr &point_representation)
# cdef PointRepresentationConstPtr getPointRepresentation ()
###
# Conflict pcl_kdtree ?
# cdef extern from "pcl/Search/kdtree.h" namespace "pcl::search":
# cdef cppclass KdTree[PointT](Search[PointT]):
# # KdTree()
# KdTree (bool)
# # public:
# # ctypedef typename Search<PointT>::PointCloud PointCloud;
# # ctypedef typename Search<PointT>::PointCloudConstPtr PointCloudConstPtr;
#
# # ctypedef boost::shared_ptr<std::vector<int> > IndicesPtr;
# # ctypedef boost::shared_ptr<const std::vector<int> > IndicesConstPtr;
# # using pcl::search::Search<PointT>::indices_;
# # using pcl::search::Search<PointT>::input_;
# # using pcl::search::Search<PointT>::getIndices;
# # using pcl::search::Search<PointT>::getInputCloud;
# # using pcl::search::Search<PointT>::nearestKSearch;
# # using pcl::search::Search<PointT>::radiusSearch;
# # using pcl::search::Search<PointT>::sorted_results_;
# # typedef boost::shared_ptr<KdTree<PointT> > Ptr;
# # typedef boost::shared_ptr<const KdTree<PointT> > ConstPtr;
# # typedef boost::shared_ptr<pcl::KdTreeFLANN<PointT> > KdTreeFLANNPtr;
# # typedef boost::shared_ptr<const pcl::KdTreeFLANN<PointT> > KdTreeFLANNConstPtr;
#
# void setSortedResults (bool sorted_results)
#
# void setEpsilon (float eps)
#
# float getEpsilon ()
#
# # void setInputCloud (const PointCloudConstPtr& cloud, const IndicesConstPtr& indices = IndicesConstPtr ())
#
# # int nearestKSearch (const PointT &point, int k, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances) const
# int nearestKSearch (const PointT &point, int k, vector[int] &k_indices, vector[float] &k_sqr_distances)
#
# int radiusSearch (const PointT& point, double radius, vector[int] &k_indices, vector[float] &k_sqr_distances, unsigned int max_nn)
#
#
###
# Conflict pcl_Octree ?
# cdef extern from "pcl/Search/Octree.h" namespace "pcl::search":
# cdef cppclass Octree[PointT](Search[PointT]):
# # Octree (const double resolution)
# Octree (double)
#
# # public:
# # ctypedef boost::shared_ptr<std::vector<int> > IndicesPtr;
# # ctypedef boost::shared_ptr<const std::vector<int> > IndicesConstPtr;
# # ctypedef pcl::PointCloud<PointT> PointCloud;
# # ctypedef boost::shared_ptr<PointCloud> PointCloudPtr;
# # ctypedef boost::shared_ptr<const PointCloud> PointCloudConstPtr;
# # ctypedef boost::shared_ptr<pcl::octree::OctreePointCloudSearch<PointT, LeafTWrap, BranchTWrap> > Ptr;
# # ctypedef boost::shared_ptr<const pcl::octree::OctreePointCloudSearch<PointT, LeafTWrap, BranchTWrap> > ConstPtr;
# # Ptr tree_;
# # using pcl::search::Search<PointT>::input_;
# # using pcl::search::Search<PointT>::indices_;
# # using pcl::search::Search<PointT>::sorted_results_;
#
# # void setInputCloud (const PointCloudConstPtr &cloud)
# void setInputCloud (const shared_ptr[cpp.PointCloud[PointT]] &cloud)
#
# # void setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr& indices)
# # void setInputCloud (const shared_ptr[cpp.PointCloud[PointT]] &cloud, const IndicesConstPtr& indices)
#
# int nearestKSearch (const cpp.PointCloud[PointT] &cloud, int index, int k, vector[int] &k_indices, vector[float] &k_sqr_distances)
#
# # int nearestKSearch (const PointT &point, int k, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances) const
# int nearestKSearch (const PointT &point, int k, vector[int] &k_indices, vector[float] &k_sqr_distances)
#
# # int nearestKSearch (int index, int k, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances) const
# int nearestKSearch (int index, int k, vector[int] &k_indices, vector[float] &k_sqr_distances)
#
# # int radiusSearch ( const PointCloud &cloud, int index, double radius, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const
# int radiusSearch ( const cpp.PointCloud[PointT] &cloud, int index, double radius, vector[int] &k_indices, vector[float] &k_sqr_distances, unsigned int max_nn)
#
# # int radiusSearch (const PointT &p_q, double radius, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const
# int radiusSearch (const PointT &p_q, double radius, vector[int] &k_indices, vector[float] &k_sqr_distances, unsigned int max_nn)
#
# # cdef int radiusSearch (int index, double radius, vector[int] &k_indices, vector[float] &k_sqr_distances, unsigned int max_nn = 0) const
# int radiusSearch (int index, double radius, vector[int] &k_indices, vector[float] &k_sqr_distances, unsigned int max_nn)
#
# # cdef void approxNearestSearch ( const PointCloudConstPtr &cloud, int query_index, int &result_index, float &sqr_distance)
# void approxNearestSearch ( const shared_ptr[cpp.PointCloud[PointT]] &cloud, int query_index, int &result_index, float &sqr_distance)
#
# # cdef void approxNearestSearch ( const PointT &p_q, int &result_index, float &sqr_distance)
#
# # cdef void approxNearestSearch (int query_index, int &result_index, float &sqr_distance)
#
#
####
cdef extern from "pcl/Search/organized.h" namespace "pcl::search":
cdef cppclass OrganizedNeighbor[PointT](Search[PointT]):
OrganizedNeighbor()
# OrganizedNeighbor (bool sorted_results = false, float eps = 1e-4f, unsigned pyramid_level = 5)
# public:
# ctypedef pcl::PointCloud<PointT> PointCloud;
# ctypedef boost::shared_ptr<PointCloud> PointCloudPtr;
# ctypedef boost::shared_ptr<const PointCloud> PointCloudConstPtr;
# ctypedef boost::shared_ptr<const std::vector<int> > IndicesConstPtr;
# ctypedef boost::shared_ptr<pcl::search::OrganizedNeighbor<PointT> > Ptr;
# ctypedef boost::shared_ptr<const pcl::search::OrganizedNeighbor<PointT> > ConstPtr;
# using pcl::search::Search<PointT>::indices_;
# using pcl::search::Search<PointT>::sorted_results_;
# using pcl::search::Search<PointT>::input_;
# bool isValid () const
bool isValid ()
# void computeCameraMatrix (Eigen::Matrix3f& camera_matrix) const;
# void computeCameraMatrix (eigen3.Matrix3f& camera_matrix)
# void setInputCloud (const PointCloudConstPtr& cloud, const IndicesConstPtr &indices = IndicesConstPtr ())
# int radiusSearch (const PointT &p_q, double radius, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const;
int radiusSearch (const PointT &p_q, double radius, vector[int] &k_indices, vector[float] &k_sqr_distances, unsigned int max_nn)
void estimateProjectionMatrix ()
int nearestKSearch ( const PointT &p_q, int k, vector[int] &k_indices, vector[float] &k_sqr_distances)
# bool projectPoint (const PointT& p, pcl::PointXY& q) const;
###
# pcl_search.h
# include header
###
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