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# -*- coding: utf-8 -*-
from libcpp.vector cimport vector
# main
cimport pcl_defs as cpp
from libcpp.memory cimport shared_ptr
# flann.h
###
# io.h
# namespace pcl
# {
# /** \brief Get a set of approximate indices for a given point cloud into a reference point cloud.
# * The coordinates of the two point clouds can differ. The method uses an internal KdTree for
# * finding the closest neighbors from \a cloud_in in \a cloud_ref.
# *
# * \param[in] cloud_in the input point cloud dataset
# * \param[in] cloud_ref the reference point cloud dataset
# * \param[out] indices the resultant set of nearest neighbor indices of \a cloud_in in \a cloud_ref
# * \ingroup kdtree
# */
# template <typename PointT> void
# getApproximateIndices (const typename pcl::PointCloud<PointT>::Ptr &cloud_in,
# const typename pcl::PointCloud<PointT>::Ptr &cloud_ref,
# std::vector<int> &indices);
#
# /** \brief Get a set of approximate indices for a given point cloud into a reference point cloud.
# * The coordinates of the two point clouds can differ. The method uses an internal KdTree for
# * finding the closest neighbors from \a cloud_in in \a cloud_ref.
# *
# * \param[in] cloud_in the input point cloud dataset
# * \param[in] cloud_ref the reference point cloud dataset
# * \param[out] indices the resultant set of nearest neighbor indices of \a cloud_in in \a cloud_ref
# * \ingroup kdtree
# */
# template <typename Point1T, typename Point2T> void
# getApproximateIndices (const typename pcl::PointCloud<Point1T>::Ptr &cloud_in,
# const typename pcl::PointCloud<Point2T>::Ptr &cloud_ref,
# std::vector<int> &indices);
# }
###
# kdtree.h
# namespace pcl
# template <typename PointT>
# class KdTree
cdef extern from "pcl/kdtree/kdtree.h" namespace "pcl::search":
cdef cppclass KdTree[T]:
KdTree()
# KdTree (bool sorted)
void setInputCloud (shared_ptr[cpp.PointCloud[T]])
# public:
# typedef boost::shared_ptr <std::vector<int> > IndicesPtr;
# typedef boost::shared_ptr <const std::vector<int> > IndicesConstPtr;
# typedef pcl::PointCloud<PointT> PointCloud;
# typedef boost::shared_ptr<PointCloud> PointCloudPtr;
# typedef boost::shared_ptr<const PointCloud> PointCloudConstPtr;
# typedef pcl::PointRepresentation<PointT> PointRepresentation;
# //typedef boost::shared_ptr<PointRepresentation> PointRepresentationPtr;
# typedef boost::shared_ptr<const PointRepresentation> PointRepresentationConstPtr;
# // Boost shared pointers
# typedef boost::shared_ptr<KdTree<PointT> > Ptr;
# typedef boost::shared_ptr<const KdTree<PointT> > ConstPtr;
# /** \brief Empty constructor for KdTree. Sets some internal values to their defaults.
# * \param[in] sorted set to true if the application that the tree will be used for requires sorted nearest neighbor indices (default). False otherwise.
# */
# KdTree (bool sorted = true)
# /** \brief Provide a pointer to the input dataset.
# * \param[in] cloud the const boost shared pointer to a PointCloud message
# * \param[in] indices the point indices subset that is to be used from \a cloud - if NULL the whole cloud is used
# virtual void setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr &indices = IndicesConstPtr ())
# /** \brief Get a pointer to the vector of indices used. */
# inline IndicesConstPtr getIndices () const
# /** \brief Get a pointer to the input point cloud dataset. */
# inline PointCloudConstPtr getInputCloud () const
# /** \brief Provide a pointer to the point representation to use to convert points into k-D vectors.
# * \param[in] point_representation the const boost shared pointer to a PointRepresentation
# inline void setPointRepresentation (const PointRepresentationConstPtr &point_representation)
# /** \brief Get a pointer to the point representation used when converting points into k-D vectors. */
# inline PointRepresentationConstPtr getPointRepresentation () const
# /** \brief Search for k-nearest neighbors for the given query point.
# * \param[in] p_q 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 &p_q, 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.
# * \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.
# * 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 (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, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances) const
# * \brief Search for all the nearest neighbors of the query point in a given radius.
# * \param[in] p_q 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 &p_q, 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.
# * \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.
# * \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 (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 Set the search epsilon precision (error bound) for nearest neighbors searches.
# * \param[in] eps precision (error bound) for nearest neighbors searches
# virtual inline void setEpsilon (float eps)
# * \brief Get the search epsilon precision (error bound) for nearest neighbors searches. */
# inline float getEpsilon () const
# * \brief Minimum allowed number of k nearest neighbors points that a viable result must contain.
# * \param[in] min_pts the minimum number of neighbors in a viable neighborhood
# inline void setMinPts (int min_pts)
# * \brief Get the minimum allowed number of k nearest neighbors points that a viable result must contain. */
# inline int getMinPts () const
ctypedef KdTree[cpp.PointXYZ] KdTree_t
ctypedef KdTree[cpp.PointXYZI] KdTree_PointXYZI_t
ctypedef KdTree[cpp.PointXYZRGB] KdTree_PointXYZRGB_t
ctypedef KdTree[cpp.PointXYZRGBA] KdTree_PointXYZRGBA_t
ctypedef shared_ptr[KdTree[cpp.PointXYZ]] KdTreePtr_t
ctypedef shared_ptr[KdTree[cpp.PointXYZI]] KdTree_PointXYZI_Ptr_t
ctypedef shared_ptr[KdTree[cpp.PointXYZRGB]] KdTree_PointXYZRGB_Ptr_t
ctypedef shared_ptr[KdTree[cpp.PointXYZRGBA]] KdTree_PointXYZRGBA_Ptr_t
###
# kdtree_flann.h
# NG
# cdef cppclass KdTreeFLANN[T](KdTree[T]):
# namespace pcl
# template <typename PointT, typename Dist = flann::L2_Simple<float> >
# class KdTreeFLANN : public pcl::KdTree<PointT>
cdef extern from "pcl/kdtree/kdtree_flann.h" namespace "pcl":
cdef cppclass KdTreeFLANN[T]:
KdTreeFLANN()
# KdTreeFLANN (bool sorted)
# KdTreeFLANN (const KdTreeFLANN<PointT> &k) :
# inline KdTreeFLANN<PointT>& operator = (const KdTreeFLANN<PointT>& k)
void setInputCloud (shared_ptr[cpp.PointCloud[T]])
# /** \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] point a given \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
# */
# int nearestKSearch (cpp.PointCloud[T], int, vector[int], vector[float])
# inline define
int nearestKSearch (cpp.PointCloud[T], int, int, vector[int], vector[float])
# int nearestKSearch (const PointT &point, int k, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances) 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] point a given \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
# */
# int radiusSearch (cpp.PointCloud[T], double, vector[int], vector[float])
# int radiusSearch (cpp.PointCloud[T], double, vector[int], vector[float], unsigned int)
# inline define
int radiusSearch (cpp.PointCloud[T], int, double, vector[int], vector[float])
# int radiusSearch (const PointT &point, double radius, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const;
# using KdTree<PointT>::input_;
# using KdTree<PointT>::indices_;
# using KdTree<PointT>::epsilon_;
# using KdTree<PointT>::sorted_;
# using KdTree<PointT>::point_representation_;
# using KdTree<PointT>::nearestKSearch;
# using KdTree<PointT>::radiusSearch;
# typedef typename KdTree<PointT>::PointCloud PointCloud;
# typedef typename KdTree<PointT>::PointCloudConstPtr PointCloudConstPtr;
# typedef boost::shared_ptr<std::vector<int> > IndicesPtr;
# typedef boost::shared_ptr<const std::vector<int> > IndicesConstPtr;
# typedef flann::Index<Dist> FLANNIndex;
# // Boost shared pointers
# typedef boost::shared_ptr<KdTreeFLANN<PointT> > Ptr;
# typedef boost::shared_ptr<const KdTreeFLANN<PointT> > ConstPtr;
# /** \brief Set the search epsilon precision (error bound) for nearest neighbors searches.
# * \param[in] eps precision (error bound) for nearest neighbors searches
# */
# inline void setEpsilon (float eps)
# inline void setSortedResults (bool sorted)
# inline Ptr makeShared ()
###
# template <>
# class KdTreeFLANN <Eigen::MatrixXf>
# public:
# typedef pcl::PointCloud<Eigen::MatrixXf> PointCloud;
# typedef PointCloud::ConstPtr PointCloudConstPtr;
# typedef boost::shared_ptr<std::vector<int> > IndicesPtr;
# typedef boost::shared_ptr<const std::vector<int> > IndicesConstPtr;
# typedef flann::Index<flann::L2_Simple<float> > FLANNIndex;
# typedef pcl::PointRepresentation<Eigen::MatrixXf> PointRepresentation;
# typedef boost::shared_ptr<const PointRepresentation> PointRepresentationConstPtr;
# // Boost shared pointers
# typedef boost::shared_ptr<KdTreeFLANN<Eigen::MatrixXf> > Ptr;
# typedef boost::shared_ptr<const KdTreeFLANN<Eigen::MatrixXf> > ConstPtr;
#
# KdTreeFLANN (bool sorted = true) :
# KdTreeFLANN (const KdTreeFLANN<Eigen::MatrixXf> &k) :
# inline KdTreeFLANN& operator = (const KdTreeFLANN<Eigen::MatrixXf>& k)
# inline void setEpsilon (float eps)
# inline Ptr makeShared ()
# void setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr &indices = IndicesConstPtr ())
# inline IndicesConstPtr getIndices () const
# inline PointCloudConstPtr getInputCloud () const
# template <typename T> int nearestKSearch (const T &point, int k, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances) const
# inline int nearestKSearch (const PointCloud &cloud, int index, int k, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances) const
# inline int nearestKSearch (int index, int k, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances) const
# template <typename T> int radiusSearch (const T &point, double radius, std::vector<int> &k_indices, std::vector<float> &k_sqr_dists, unsigned int max_nn = 0) const
# inline 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
# inline int radiusSearch (int index, double radius, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const
# /** \brief Get the search epsilon precision (error bound) for nearest neighbors searches. */
# inline float getEpsilon () const
# protected:
# /** \brief The input point cloud dataset containing the points we need to use. */
# PointCloudConstPtr input_;
# /** \brief A pointer to the vector of point indices to use. */
# IndicesConstPtr indices_;
# /** \brief Epsilon precision (error bound) for nearest neighbors searches. */
# float epsilon_;
# /** \brief Return the radius search neighbours sorted **/
# bool sorted_;
###
ctypedef KdTreeFLANN[cpp.PointXYZ] KdTreeFLANN_t
ctypedef KdTreeFLANN[cpp.PointXYZI] KdTreeFLANN_PointXYZI_t
ctypedef KdTreeFLANN[cpp.PointXYZRGB] KdTreeFLANN_PointXYZRGB_t
ctypedef KdTreeFLANN[cpp.PointXYZRGBA] KdTreeFLANN_PointXYZRGBA_t
ctypedef shared_ptr[KdTreeFLANN[cpp.PointXYZ]] KdTreeFLANNPtr_t
ctypedef shared_ptr[KdTreeFLANN[cpp.PointXYZI]] KdTreeFLANN_PointXYZI_Ptr_t
ctypedef shared_ptr[KdTreeFLANN[cpp.PointXYZRGB]] KdTreeFLANN_PointXYZRGB_Ptr_t
ctypedef shared_ptr[KdTreeFLANN[cpp.PointXYZRGBA]] KdTreeFLANN_PointXYZRGBA_Ptr_t
ctypedef shared_ptr[const KdTreeFLANN[cpp.PointXYZ]] KdTreeFLANNConstPtr_t
ctypedef shared_ptr[const KdTreeFLANN[cpp.PointXYZI]] KdTreeFLANN_PointXYZI_ConstPtr_t
ctypedef shared_ptr[const KdTreeFLANN[cpp.PointXYZRGB]] KdTreeFLANN_PointXYZRGB_ConstPtr_t
ctypedef shared_ptr[const KdTreeFLANN[cpp.PointXYZRGBA]] KdTreeFLANN_PointXYZRGBA_ConstPtr_t
###
|
