Program Listing for File keypoint.h¶
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#ifndef PCL_KEYPOINT_H_
#define PCL_KEYPOINT_H_
// PCL includes
#include <pcl/pcl_base.h>
#include <boost/function.hpp>
#include <boost/bind.hpp>
#include <pcl/search/pcl_search.h>
#include <pcl/pcl_config.h>
namespace pcl
{
/** \brief @b Keypoint represents the base class for key points.
* \author Bastian Steder
* \ingroup keypoints
*/
template <typename PointInT, typename PointOutT>
class Keypoint : public PCLBase<PointInT>
{
public:
typedef boost::shared_ptr<Keypoint<PointInT, PointOutT> > Ptr;
typedef boost::shared_ptr<const Keypoint<PointInT, PointOutT> > ConstPtr;
using PCLBase<PointInT>::indices_;
using PCLBase<PointInT>::input_;
typedef PCLBase<PointInT> BaseClass;
typedef typename pcl::search::Search<PointInT> KdTree;
typedef typename pcl::search::Search<PointInT>::Ptr KdTreePtr;
typedef pcl::PointCloud<PointInT> PointCloudIn;
typedef typename PointCloudIn::Ptr PointCloudInPtr;
typedef typename PointCloudIn::ConstPtr PointCloudInConstPtr;
typedef pcl::PointCloud<PointOutT> PointCloudOut;
typedef boost::function<int (int, double, std::vector<int> &, std::vector<float> &)> SearchMethod;
typedef boost::function<int (const PointCloudIn &cloud, int index, double, std::vector<int> &, std::vector<float> &)> SearchMethodSurface;
public:
/** \brief Empty constructor. */
Keypoint () :
BaseClass (),
name_ (),
search_method_ (),
search_method_surface_ (),
surface_ (),
tree_ (),
search_parameter_ (0),
search_radius_ (0),
k_ (0)
{};
/** \brief Empty destructor */
virtual ~Keypoint () {}
/** \brief Provide a pointer to the input dataset that we need to estimate features at every point for.
* \param cloud the const boost shared pointer to a PointCloud message
*/
virtual void
setSearchSurface (const PointCloudInConstPtr &cloud) { surface_ = cloud; }
/** \brief Get a pointer to the surface point cloud dataset. */
inline PointCloudInConstPtr
getSearchSurface () { return (surface_); }
/** \brief Provide a pointer to the search object.
* \param tree a pointer to the spatial search object.
*/
inline void
setSearchMethod (const KdTreePtr &tree) { tree_ = tree; }
/** \brief Get a pointer to the search method used. */
inline KdTreePtr
getSearchMethod () { return (tree_); }
/** \brief Get the internal search parameter. */
inline double
getSearchParameter () { return (search_parameter_); }
/** \brief Set the number of k nearest neighbors to use for the feature estimation.
* \param k the number of k-nearest neighbors
*/
inline void
setKSearch (int k) { k_ = k; }
/** \brief get the number of k nearest neighbors used for the feature estimation. */
inline int
getKSearch () { return (k_); }
/** \brief Set the sphere radius that is to be used for determining the nearest neighbors used for the
* key point detection
* \param radius the sphere radius used as the maximum distance to consider a point a neighbor
*/
inline void
setRadiusSearch (double radius) { search_radius_ = radius; }
/** \brief Get the sphere radius used for determining the neighbors. */
inline double
getRadiusSearch () { return (search_radius_); }
/** \brief \return the keypoints indices in the input cloud.
* \note not all the daughter classes populate the keypoints indices so check emptiness before use.
*/
pcl::PointIndicesConstPtr
getKeypointsIndices () { return (keypoints_indices_); }
/** \brief Base method for key point detection for all points given in <setInputCloud (), setIndices ()> using
* the surface in setSearchSurface () and the spatial locator in setSearchMethod ()
* \param output the resultant point cloud model dataset containing the estimated features
*/
inline void
compute (PointCloudOut &output);
/** \brief Search for k-nearest neighbors using the spatial locator from \a setSearchmethod, and the given surface
* from \a setSearchSurface.
* \param index the index of the query point
* \param parameter the search parameter (either k or radius)
* \param indices the resultant vector of indices representing the k-nearest neighbors
* \param distances the resultant vector of distances representing the distances from the query point to the
* k-nearest neighbors
*/
inline int
searchForNeighbors (int index, double parameter, std::vector<int> &indices, std::vector<float> &distances) const
{
if (surface_ == input_) // if the two surfaces are the same
return (search_method_ (index, parameter, indices, distances));
else
return (search_method_surface_ (*input_, index, parameter, indices, distances));
}
protected:
using PCLBase<PointInT>::deinitCompute;
virtual bool
initCompute ();
/** \brief The key point detection method's name. */
std::string name_;
/** \brief The search method template for indices. */
SearchMethod search_method_;
/** \brief The search method template for points. */
SearchMethodSurface search_method_surface_;
/** \brief An input point cloud describing the surface that is to be used for nearest neighbors estimation. */
PointCloudInConstPtr surface_;
/** \brief A pointer to the spatial search object. */
KdTreePtr tree_;
/** \brief The actual search parameter (casted from either \a search_radius_ or \a k_). */
double search_parameter_;
/** \brief The nearest neighbors search radius for each point. */
double search_radius_;
/** \brief The number of K nearest neighbors to use for each point. */
int k_;
/** \brief Indices of the keypoints in the input cloud. */
pcl::PointIndicesPtr keypoints_indices_;
/** \brief Get a string representation of the name of this class. */
inline const std::string&
getClassName () const { return (name_); }
/** \brief Abstract key point detection method. */
virtual void
detectKeypoints (PointCloudOut &output) = 0;
};
}
#include <pcl/keypoints/impl/keypoint.hpp>
#endif //#ifndef PCL_KEYPOINT_H_