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asmorkalovasmorkalov
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Merge pull request #4005 from vrabaud:nanoflann
Bump nanoflann from 1.3.2 to 1.7.1
2 parents a7631fc + af714b9 commit de21064

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‎modules/alphamat/src/3rdparty/KDTreeVectorOfVectorsAdaptor.h‎

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@@ -29,89 +29,106 @@
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#pragma once
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#include "nanoflann.hpp"
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#include <vector>
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// ===== This example shows how to use nanoflann with these types of containers: =======
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//typedef std::vector<std::vector<double> > my_vector_of_vectors_t;
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//typedef std::vector<Eigen::VectorXd> my_vector_of_vectors_t; // This requires #include <Eigen/Dense>
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// =====================================================================================
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/** A simple vector-of-vectors adaptor for nanoflann, without duplicating the storage.
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* The i'th vector represents a point in the state space.
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*
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* \tparam DIM If set to >0, it specifies a compile-time fixed dimensionality for the points in the data set, allowing more compiler optimizations.
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* \tparam num_t The type of the point coordinates (typically, double or float).
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* \tparam Distance The distance metric to use: nanoflann::metric_L1, nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc.
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* \tparam IndexType The type for indices in the KD-tree index (typically, size_t of int)
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*/
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template <class VectorOfVectorsType, typename num_t = double, int DIM = -1, class Distance = nanoflann::metric_L2, typename IndexType = size_t>
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// ===== This example shows how to use nanoflann with these types of containers:
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// using my_vector_of_vectors_t = std::vector<std::vector<double> > ;
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//
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// The next one requires #include <Eigen/Dense>
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// using my_vector_of_vectors_t = std::vector<Eigen::VectorXd> ;
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// =============================================================================
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/** A simple vector-of-vectors adaptor for nanoflann, without duplicating the
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* storage. The i'th vector represents a point in the state space.
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*
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* \tparam DIM If set to >0, it specifies a compile-time fixed dimensionality
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* for the points in the data set, allowing more compiler optimizations.
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* \tparam num_t The type of the point coordinates (typ. double or float).
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* \tparam Distance The distance metric to use: nanoflann::metric_L1,
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* nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc.
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* \tparam IndexType The type for indices in the KD-tree index
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* (typically, size_t of int)
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*/
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template <
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class VectorOfVectorsType, typename num_t = double, int DIM = -1,
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class Distance = nanoflann::metric_L2, typename IndexType = size_t>
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struct KDTreeVectorOfVectorsAdaptor
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{
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typedef KDTreeVectorOfVectorsAdaptor<VectorOfVectorsType, num_t, DIM,Distance> self_t;
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typedef typename Distance::template traits<num_t, self_t>::distance_t metric_t;
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typedef nanoflann::KDTreeSingleIndexAdaptor< metric_t, self_t, DIM, IndexType> index_t;
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index_t* index; //! The kd-tree index for the user to call its methods as usual with any other FLANN index.
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/// Constructor: takes a const ref to the vector of vectors object with the data points
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KDTreeVectorOfVectorsAdaptor(const size_t /* dimensionality */, const VectorOfVectorsType &mat, const int leaf_max_size = 10) : m_data(mat)
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{
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assert(mat.size() != 0 && mat[0].size() != 0);
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const size_t dims = mat[0].size();
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if (DIM>0 && static_cast<int>(dims) != DIM)
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throw std::runtime_error("Data set dimensionality does not match the 'DIM' template argument");
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index = new index_t( static_cast<int>(dims), *this /* adaptor */, nanoflann::KDTreeSingleIndexAdaptorParams(leaf_max_size ) );
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index->buildIndex();
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}
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~KDTreeVectorOfVectorsAdaptor() {
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delete index;
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}
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const VectorOfVectorsType &m_data;
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/** Query for the \a num_closest closest points to a given point (entered as query_point[0:dim-1]).
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* Note that this is a short-cut method for index->findNeighbors().
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* The user can also call index->... methods as desired.
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* \note nChecks_IGNORED is ignored but kept for compatibility with the original FLANN interface.
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*/
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//inline void query(const num_t *query_point, const size_t num_closest, IndexType *out_indices, num_t *out_distances_sq, const int nChecks_IGNORED = 10) const
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inline void query(const num_t *query_point, const size_t num_closest, IndexType *out_indices, num_t *out_distances_sq) const
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{
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nanoflann::KNNResultSet<num_t, IndexType> resultSet(num_closest);
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resultSet.init(out_indices, out_distances_sq);
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index->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
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}
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/** @name Interface expected by KDTreeSingleIndexAdaptor
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* @{ */
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const self_t & derived() const {
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return *this;
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}
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self_t & derived() {
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return *this;
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}
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// Must return the number of data points
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inline size_t kdtree_get_point_count() const {
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return m_data.size();
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}
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// Returns the dim'th component of the idx'th point in the class:
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inline num_t kdtree_get_pt(const size_t idx, const size_t dim) const {
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return m_data[idx][dim];
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}
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// Optional bounding-box computation: return false to default to a standard bbox computation loop.
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// Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again.
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// Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds)
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template <class BBOX>
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bool kdtree_get_bbox(BBOX & /*bb*/) const {
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return false;
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}
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/** @} */
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using self_t = KDTreeVectorOfVectorsAdaptor<
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VectorOfVectorsType, num_t, DIM, Distance, IndexType>;
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using metric_t =
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typename Distance::template traits<num_t, self_t>::distance_t;
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using index_t =
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nanoflann::KDTreeSingleIndexAdaptor<metric_t, self_t, DIM, IndexType>;
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/** The kd-tree index for the user to call its methods as usual with any
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* other FLANN index */
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index_t* index = nullptr;
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/// Constructor: takes a const ref to the vector of vectors object with the
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/// data points
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KDTreeVectorOfVectorsAdaptor(
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const size_t /* dimensionality */, const VectorOfVectorsType& mat,
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const int leaf_max_size = 10, const unsigned int n_thread_build = 1)
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: m_data(mat)
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{
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assert(mat.size() != 0 && mat[0].size() != 0);
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const size_t dims = mat[0].size();
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if (DIM > 0 && static_cast<int>(dims) != DIM)
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throw std::runtime_error(
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"Data set dimensionality does not match the 'DIM' template "
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"argument");
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index = new index_t(
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static_cast<int>(dims), *this /* adaptor */,
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nanoflann::KDTreeSingleIndexAdaptorParams(
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leaf_max_size, nanoflann::KDTreeSingleIndexAdaptorFlags::None,
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n_thread_build));
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}
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~KDTreeVectorOfVectorsAdaptor() { delete index; }
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const VectorOfVectorsType& m_data;
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/** Query for the \a num_closest closest points to a given point
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* (entered as query_point[0:dim-1]).
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* Note that this is a short-cut method for index->findNeighbors().
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* The user can also call index->... methods as desired.
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*/
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inline void query(
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const num_t* query_point, const size_t num_closest,
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IndexType* out_indices, num_t* out_distances_sq) const
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{
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nanoflann::KNNResultSet<num_t, IndexType> resultSet(num_closest);
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resultSet.init(out_indices, out_distances_sq);
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index->findNeighbors(resultSet, query_point);
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}
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/** @name Interface expected by KDTreeSingleIndexAdaptor
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* @{ */
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const self_t& derived() const { return *this; }
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self_t& derived() { return *this; }
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// Must return the number of data points
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inline size_t kdtree_get_point_count() const { return m_data.size(); }
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// Returns the dim'th component of the idx'th point in the class:
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inline num_t kdtree_get_pt(const size_t idx, const size_t dim) const
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{
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return m_data[idx][dim];
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}
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// Optional bounding-box computation: return false to default to a standard
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// bbox computation loop.
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// Return true if the BBOX was already computed by the class and returned
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// in "bb" so it can be avoided to redo it again. Look at bb.size() to
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// find out the expected dimensionality (e.g. 2 or 3 for point clouds)
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template <class BBOX>
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bool kdtree_get_bbox(BBOX& /*bb*/) const
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{
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return false;
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}
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/** @} */
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}; // end of KDTreeVectorOfVectorsAdaptor

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