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| 1 | +/*********************************************************************** |
| 2 | + * Software License Agreement (BSD License) |
| 3 | + * |
| 4 | + * Copyright 2011-16 Jose Luis Blanco ([email protected]). |
| 5 | + * All rights reserved. |
| 6 | + * |
| 7 | + * Redistribution and use in source and binary forms, with or without |
| 8 | + * modification, are permitted provided that the following conditions |
| 9 | + * are met: |
| 10 | + * |
| 11 | + * 1. Redistributions of source code must retain the above copyright |
| 12 | + * notice, this list of conditions and the following disclaimer. |
| 13 | + * 2. Redistributions in binary form must reproduce the above copyright |
| 14 | + * notice, this list of conditions and the following disclaimer in the |
| 15 | + * documentation and/or other materials provided with the distribution. |
| 16 | + * |
| 17 | + * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR |
| 18 | + * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES |
| 19 | + * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. |
| 20 | + * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, |
| 21 | + * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT |
| 22 | + * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, |
| 23 | + * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY |
| 24 | + * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT |
| 25 | + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF |
| 26 | + * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| 27 | + *************************************************************************/ |
| 28 | + |
| 29 | +#pragma once |
| 30 | + |
| 31 | +#include "nanoflann.hpp" |
| 32 | + |
| 33 | +#include <vector> |
| 34 | + |
| 35 | +// ===== This example shows how to use nanoflann with these types of containers: ======= |
| 36 | +//typedef std::vector<std::vector<double> > my_vector_of_vectors_t; |
| 37 | +//typedef std::vector<Eigen::VectorXd> my_vector_of_vectors_t; // This requires #include <Eigen/Dense> |
| 38 | +// ===================================================================================== |
| 39 | + |
| 40 | + |
| 41 | +/** A simple vector-of-vectors adaptor for nanoflann, without duplicating the storage. |
| 42 | + * The i'th vector represents a point in the state space. |
| 43 | + * |
| 44 | + * \tparam DIM If set to >0, it specifies a compile-time fixed dimensionality for the points in the data set, allowing more compiler optimizations. |
| 45 | + * \tparam num_t The type of the point coordinates (typically, double or float). |
| 46 | + * \tparam Distance The distance metric to use: nanoflann::metric_L1, nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc. |
| 47 | + * \tparam IndexType The type for indices in the KD-tree index (typically, size_t of int) |
| 48 | + */ |
| 49 | +template <class VectorOfVectorsType, typename num_t = double, int DIM = -1, class Distance = nanoflann::metric_L2, typename IndexType = size_t> |
| 50 | +struct KDTreeVectorOfVectorsAdaptor |
| 51 | +{ |
| 52 | + typedef KDTreeVectorOfVectorsAdaptor<VectorOfVectorsType, num_t, DIM,Distance> self_t; |
| 53 | + typedef typename Distance::template traits<num_t, self_t>::distance_t metric_t; |
| 54 | + typedef nanoflann::KDTreeSingleIndexAdaptor< metric_t, self_t, DIM, IndexType> index_t; |
| 55 | + |
| 56 | + index_t* index; //! The kd-tree index for the user to call its methods as usual with any other FLANN index. |
| 57 | + |
| 58 | + /// Constructor: takes a const ref to the vector of vectors object with the data points |
| 59 | + KDTreeVectorOfVectorsAdaptor(const size_t /* dimensionality */, const VectorOfVectorsType &mat, const int leaf_max_size = 10) : m_data(mat) |
| 60 | + { |
| 61 | + assert(mat.size() != 0 && mat[0].size() != 0); |
| 62 | + const size_t dims = mat[0].size(); |
| 63 | + if (DIM>0 && static_cast<int>(dims) != DIM) |
| 64 | + throw std::runtime_error("Data set dimensionality does not match the 'DIM' template argument"); |
| 65 | + index = new index_t( static_cast<int>(dims), *this /* adaptor */, nanoflann::KDTreeSingleIndexAdaptorParams(leaf_max_size ) ); |
| 66 | + index->buildIndex(); |
| 67 | + } |
| 68 | + |
| 69 | + ~KDTreeVectorOfVectorsAdaptor() { |
| 70 | + delete index; |
| 71 | + } |
| 72 | + |
| 73 | + const VectorOfVectorsType &m_data; |
| 74 | + |
| 75 | + /** Query for the \a num_closest closest points to a given point (entered as query_point[0:dim-1]). |
| 76 | + * Note that this is a short-cut method for index->findNeighbors(). |
| 77 | + * The user can also call index->... methods as desired. |
| 78 | + * \note nChecks_IGNORED is ignored but kept for compatibility with the original FLANN interface. |
| 79 | + */ |
| 80 | + //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 |
| 81 | + inline void query(const num_t *query_point, const size_t num_closest, IndexType *out_indices, num_t *out_distances_sq) const |
| 82 | + { |
| 83 | + nanoflann::KNNResultSet<num_t, IndexType> resultSet(num_closest); |
| 84 | + resultSet.init(out_indices, out_distances_sq); |
| 85 | + index->findNeighbors(resultSet, query_point, nanoflann::SearchParams()); |
| 86 | + } |
| 87 | + |
| 88 | + /** @name Interface expected by KDTreeSingleIndexAdaptor |
| 89 | + * @{ */ |
| 90 | + |
| 91 | + const self_t & derived() const { |
| 92 | + return *this; |
| 93 | + } |
| 94 | + self_t & derived() { |
| 95 | + return *this; |
| 96 | + } |
| 97 | + |
| 98 | + // Must return the number of data points |
| 99 | + inline size_t kdtree_get_point_count() const { |
| 100 | + return m_data.size(); |
| 101 | + } |
| 102 | + |
| 103 | + // Returns the dim'th component of the idx'th point in the class: |
| 104 | + inline num_t kdtree_get_pt(const size_t idx, const size_t dim) const { |
| 105 | + return m_data[idx][dim]; |
| 106 | + } |
| 107 | + |
| 108 | + // Optional bounding-box computation: return false to default to a standard bbox computation loop. |
| 109 | + // Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again. |
| 110 | + // Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds) |
| 111 | + template <class BBOX> |
| 112 | + bool kdtree_get_bbox(BBOX & /*bb*/) const { |
| 113 | + return false; |
| 114 | + } |
| 115 | + |
| 116 | + /** @} */ |
| 117 | +}; // end of KDTreeVectorOfVectorsAdaptor |
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