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#ifndef PCL_KDTREE_KDTREE_H_
#define PCL_KDTREE_KDTREE_H_
#include <limits.h>
#include <pcl/pcl_macros.h>
#include <pcl/point_cloud.h>
#include <pcl/point_representation.h>
#include <pcl/common/io.h>
#include <pcl/common/copy_point.h>
namespace pcl
{
/** \brief KdTree represents the base spatial locator class for kd-tree implementations.
* \author Radu B Rusu, Bastian Steder, Michael Dixon
* \ingroup kdtree
*/
template <typename PointT>
class KdTree
{
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) : input_(), indices_(),
epsilon_(0.0f), min_pts_(1), sorted_(sorted),
point_representation_ (new DefaultPointRepresentation<PointT>)
{
};
/** \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 ())
{
input_ = cloud;
indices_ = indices;
}
/** \brief Get a pointer to the vector of indices used. */
inline IndicesConstPtr
getIndices () const
{
return (indices_);
}
/** \brief Get a pointer to the input point cloud dataset. */
inline PointCloudConstPtr
getInputCloud () const
{
return (input_);
}
/** \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)
{
point_representation_ = point_representation;
if (!input_) return;
setInputCloud (input_, indices_); // Makes sense in derived classes to reinitialize the tree
}
/** \brief Get a pointer to the point representation used when converting points into k-D vectors. */
inline PointRepresentationConstPtr
getPointRepresentation () const
{
return (point_representation_);
}
/** \brief Destructor for KdTree. Deletes all allocated data arrays and destroys the kd-tree structures. */
virtual ~KdTree () {};
/** \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
{
assert (index >= 0 && index < static_cast<int> (cloud.points.size ()) && "Out-of-bounds error in nearestKSearch!");
return (nearestKSearch (cloud.points[index], k, k_indices, k_sqr_distances));
}
/** \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
{
PointT p;
copyPoint (point, p);
return (nearestKSearch (p, k, k_indices, k_sqr_distances));
}
/** \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
{
if (indices_ == NULL)
{
assert (index >= 0 && index < static_cast<int> (input_->points.size ()) && "Out-of-bounds error in nearestKSearch!");
return (nearestKSearch (input_->points[index], k, k_indices, k_sqr_distances));
}
else
{
assert (index >= 0 && index < static_cast<int> (indices_->size ()) && "Out-of-bounds error in nearestKSearch!");
return (nearestKSearch (input_->points[(*indices_)[index]], k, k_indices, k_sqr_distances));
}
}
/** \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
{
assert (index >= 0 && index < static_cast<int> (cloud.points.size ()) && "Out-of-bounds error in radiusSearch!");
return (radiusSearch(cloud.points[index], radius, k_indices, k_sqr_distances, max_nn));
}
/** \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
{
PointT p;
copyPoint (point, p);
return (radiusSearch (p, radius, k_indices, k_sqr_distances, max_nn));
}
/** \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
{
if (indices_ == NULL)
{
assert (index >= 0 && index < static_cast<int> (input_->points.size ()) && "Out-of-bounds error in radiusSearch!");
return (radiusSearch (input_->points[index], radius, k_indices, k_sqr_distances, max_nn));
}
else
{
assert (index >= 0 && index < static_cast<int> (indices_->size ()) && "Out-of-bounds error in radiusSearch!");
return (radiusSearch (input_->points[(*indices_)[index]], radius, k_indices, k_sqr_distances, max_nn));
}
}
/** \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)
{
epsilon_ = eps;
}
/** \brief Get the search epsilon precision (error bound) for nearest neighbors searches. */
inline float
getEpsilon () const
{
return (epsilon_);
}
/** \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)
{
min_pts_ = min_pts;
}
/** \brief Get the minimum allowed number of k nearest neighbors points that a viable result must contain. */
inline int
getMinPts () const
{
return (min_pts_);
}
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 Minimum allowed number of k nearest neighbors points that a viable result must contain. */
int min_pts_;
/** \brief Return the radius search neighbours sorted **/
bool sorted_;
/** \brief For converting different point structures into k-dimensional vectors for nearest-neighbor search. */
PointRepresentationConstPtr point_representation_;
/** \brief Class getName method. */
virtual std::string
getName () const = 0;
};
}
#endif //#ifndef _PCL_KDTREE_KDTREE_H_