Point Cloud Library (PCL)
1.7.0
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00001 /* 00002 * Software License Agreement (BSD License) 00003 * 00004 * Point Cloud Library (PCL) - www.pointclouds.org 00005 * Copyright (c) 2012-, Open Perception, Inc. 00006 * 00007 * All rights reserved. 00008 * 00009 * Redistribution and use in source and binary forms, with or without 00010 * modification, are permitted provided that the following conditions 00011 * are met: 00012 * 00013 * * Redistributions of source code must retain the above copyright 00014 * notice, this list of conditions and the following disclaimer. 00015 * * Redistributions in binary form must reproduce the above 00016 * copyright notice, this list of conditions and the following 00017 * disclaimer in the documentation and/or other materials provided 00018 * with the distribution. 00019 * * Neither the name of the copyright holder(s) nor the names of its 00020 * contributors may be used to endorse or promote products derived 00021 * from this software without specific prior written permission. 00022 * 00023 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 00024 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 00025 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS 00026 * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE 00027 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, 00028 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, 00029 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 00030 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 00031 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT 00032 * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN 00033 * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 00034 * POSSIBILITY OF SUCH DAMAGE. 00035 * 00036 */ 00037 00038 #ifndef PCL_SEARCH_KDTREE_IMPL_HPP_ 00039 #define PCL_SEARCH_KDTREE_IMPL_HPP_ 00040 00041 #include <pcl/search/kdtree.h> 00042 #include <pcl/search/impl/search.hpp> 00043 00044 /////////////////////////////////////////////////////////////////////////////////////////// 00045 template <typename PointT> 00046 pcl::search::KdTree<PointT>::KdTree (bool sorted) 00047 : pcl::search::Search<PointT> ("KdTree", sorted) 00048 , tree_ (new pcl::KdTreeFLANN<PointT> (sorted)) 00049 { 00050 } 00051 00052 /////////////////////////////////////////////////////////////////////////////////////////// 00053 template <typename PointT> void 00054 pcl::search::KdTree<PointT>::setPointRepresentation ( 00055 const PointRepresentationConstPtr &point_representation) 00056 { 00057 tree_->setPointRepresentation (point_representation); 00058 } 00059 00060 /////////////////////////////////////////////////////////////////////////////////////////// 00061 template <typename PointT> void 00062 pcl::search::KdTree<PointT>::setSortedResults (bool sorted_results) 00063 { 00064 sorted_results_ = sorted_results; 00065 tree_->setSortedResults (sorted_results); 00066 } 00067 00068 /////////////////////////////////////////////////////////////////////////////////////////// 00069 template <typename PointT> void 00070 pcl::search::KdTree<PointT>::setEpsilon (float eps) 00071 { 00072 tree_->setEpsilon (eps); 00073 } 00074 00075 /////////////////////////////////////////////////////////////////////////////////////////// 00076 template <typename PointT> void 00077 pcl::search::KdTree<PointT>::setInputCloud ( 00078 const PointCloudConstPtr& cloud, 00079 const IndicesConstPtr& indices) 00080 { 00081 tree_->setInputCloud (cloud, indices); 00082 input_ = cloud; 00083 indices_ = indices; 00084 } 00085 00086 /////////////////////////////////////////////////////////////////////////////////////////// 00087 template <typename PointT> int 00088 pcl::search::KdTree<PointT>::nearestKSearch ( 00089 const PointT &point, int k, std::vector<int> &k_indices, 00090 std::vector<float> &k_sqr_distances) const 00091 { 00092 return (tree_->nearestKSearch (point, k, k_indices, k_sqr_distances)); 00093 } 00094 00095 /////////////////////////////////////////////////////////////////////////////////////////// 00096 template <typename PointT> int 00097 pcl::search::KdTree<PointT>::radiusSearch ( 00098 const PointT& point, double radius, 00099 std::vector<int> &k_indices, std::vector<float> &k_sqr_distances, 00100 unsigned int max_nn) const 00101 { 00102 return (tree_->radiusSearch (point, radius, k_indices, k_sqr_distances, max_nn)); 00103 } 00104 00105 #define PCL_INSTANTIATE_KdTree(T) template class PCL_EXPORTS pcl::search::KdTree<T>; 00106 00107 #endif //#ifndef _PCL_SEARCH_KDTREE_IMPL_HPP_ 00108 00109