5.x merge 4.x

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

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