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#ifndef PCL_SEARCH_SEARCH_H_
#define PCL_SEARCH_SEARCH_H_
#include <pcl/point_cloud.h>
#include <pcl/for_each_type.h>
#include <pcl/common/concatenate.h>
#include <pcl/common/copy_point.h>
namespace pcl
{
namespace search
{
/** \brief Generic search class. All search wrappers must inherit from this.
*
* Each search method must implement 2 different types of search:
* - \b nearestKSearch - search for K-nearest neighbors.
* - \b radiusSearch - search for all nearest neighbors in a sphere of a given radius
*
* The input to each search method can be given in 3 different ways:
* - as a query point
* - as a (cloud, index) pair
* - as an index
*
* For the latter option, it is assumed that the user specified the input
* via a \ref setInputCloud () method first.
*
* \note In case of an error, all methods are supposed to return 0 as the number of neighbors found.
*
* \note libpcl_search deals with three-dimensional search problems. For higher
* level dimensional search, please refer to the libpcl_kdtree module.
*
* \author Radu B. Rusu
* \ingroup search
*/
template<typename PointT>
class Search
{
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;
/** Constructor. */
Search (const std::string& name = "", bool sorted = false);
/** Destructor. */
virtual
~Search ()
{
}
/** \brief Returns the search method name
*/
virtual const std::string&
getName () const;
/** \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.
*/
virtual void
setSortedResults (bool sorted);
/** \brief Gets whether the results should be sorted (ascending in the distance) or not
* Otherwise the results may be returned in any order.
*/
virtual bool
getSortedResults ();
/** \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
{
return (input_);
}
/** \brief Get a pointer to the vector of indices used. */
virtual IndicesConstPtr
getIndices () const
{
return (indices_);
}
/** \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
{
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.
*
* \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,
std::vector<int> &k_indices,
std::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, 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 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
{
// Copy all the data fields from the input cloud to the output one
typedef typename pcl::traits::fieldList<PointT>::type FieldListInT;
typedef typename pcl::traits::fieldList<PointTDiff>::type FieldListOutT;
typedef typename pcl::intersect<FieldListInT, FieldListOutT>::type FieldList;
pcl::PointCloud<PointT> pc;
if (indices.empty ())
{
pc.resize (cloud.size());
for (size_t i = 0; i < cloud.size(); i++)
{
pcl::for_each_type <FieldList> (pcl::NdConcatenateFunctor <PointTDiff, PointT> (
cloud[i], pc[i]));
}
nearestKSearch (pc,std::vector<int>(),k,k_indices,k_sqr_distances);
}
else
{
pc.resize (indices.size());
for (size_t i = 0; i < indices.size(); i++)
{
pcl::for_each_type <FieldList> (pcl::NdConcatenateFunctor <PointTDiff, PointT> (
cloud[indices[i]], pc[i]));
}
nearestKSearch (pc,std::vector<int>(),k,k_indices,k_sqr_distances);
}
}
/** \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
{
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.
*
* \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
{
// Copy all the data fields from the input cloud to the output one
typedef typename pcl::traits::fieldList<PointT>::type FieldListInT;
typedef typename pcl::traits::fieldList<PointTDiff>::type FieldListOutT;
typedef typename pcl::intersect<FieldListInT, FieldListOutT>::type FieldList;
pcl::PointCloud<PointT> pc;
if (indices.empty ())
{
pc.resize (cloud.size ());
for (size_t i = 0; i < cloud.size (); ++i)
pcl::for_each_type <FieldList> (pcl::NdConcatenateFunctor <PointTDiff, PointT> (cloud[i], pc[i]));
radiusSearch (pc, std::vector<int> (), radius, k_indices, k_sqr_distances, max_nn);
}
else
{
pc.resize (indices.size ());
for (size_t i = 0; i < indices.size (); ++i)
pcl::for_each_type <FieldList> (pcl::NdConcatenateFunctor <PointTDiff, PointT> (cloud[indices[i]], pc[i]));
radiusSearch (pc, std::vector<int>(), radius, k_indices, k_sqr_distances, max_nn);
}
}
protected:
void
sortResults (std::vector<int>& indices, std::vector<float>& distances) const;
PointCloudConstPtr input_;
IndicesConstPtr indices_;
bool sorted_results_;
std::string name_;
private:
struct Compare
{
Compare (const std::vector<float>& distances)
: distances_ (distances)
{
}
bool
operator () (int first, int second) const
{
return (distances_ [first] < distances_[second]);
}
const std::vector<float>& distances_;
};
}; // class Search
} // namespace search
} // namespace pcl
#ifdef PCL_NO_PRECOMPILE
#include <pcl/search/impl/search.hpp>
#endif
#endif //#ifndef _PCL_SEARCH_SEARCH_H_