kdtree.h
Go to the documentation of this file.
00001 /*
00002  * Software License Agreement (BSD License)
00003  *
00004  *  Point Cloud Library (PCL) - www.pointclouds.org
00005  *  Copyright (c) 2010-2011, Willow Garage, 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 Willow Garage, Inc. 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  * $Id: kdtree.h 4826 2012-02-28 21:33:11Z bouffa $
00037  */
00038 
00039 #ifndef PCL_SEARCH_KDTREE_H_
00040 #define PCL_SEARCH_KDTREE_H_
00041 
00042 #include <pcl/search/search.h>
00043 #include <pcl/kdtree/kdtree.h>
00044 #include <pcl/kdtree/kdtree_flann.h>
00045 
00046 namespace pcl
00047 {
00048   namespace search
00049   {
00058     template<typename PointT>
00059     class KdTree: public Search<PointT>
00060     {
00061       public:
00062         typedef typename Search<PointT>::PointCloud PointCloud;
00063         typedef typename Search<PointT>::PointCloudConstPtr PointCloudConstPtr;
00064 
00065         typedef boost::shared_ptr<std::vector<int> > IndicesPtr;
00066         typedef boost::shared_ptr<const std::vector<int> > IndicesConstPtr;
00067 
00068         using pcl::search::Search<PointT>::indices_;
00069         using pcl::search::Search<PointT>::input_;
00070         using pcl::search::Search<PointT>::getIndices;
00071         using pcl::search::Search<PointT>::getInputCloud;
00072         using pcl::search::Search<PointT>::nearestKSearch;
00073         using pcl::search::Search<PointT>::radiusSearch;
00074         using pcl::search::Search<PointT>::sorted_results_;
00075 
00076         typedef boost::shared_ptr<KdTree<PointT> > Ptr;
00077         typedef boost::shared_ptr<const KdTree<PointT> > ConstPtr;
00078 
00079         typedef boost::shared_ptr<pcl::KdTreeFLANN<PointT> > KdTreeFLANNPtr;
00080         typedef boost::shared_ptr<const pcl::KdTreeFLANN<PointT> > KdTreeFLANNConstPtr;
00081 
00089         KdTree (bool sorted = true) 
00090           : Search<PointT> ("KdTree", sorted)
00091           , tree_ (new pcl::KdTreeFLANN<PointT> (sorted))
00092         {
00093         }
00094 
00096         virtual
00097         ~KdTree ()
00098         {
00099         }
00100 
00104         virtual void 
00105         setSortedResults (bool sorted_results)
00106         {
00107           sorted_results_ = sorted_results;
00108           tree_->setSortedResults (sorted_results);
00109         }
00110         
00114         inline void
00115         setEpsilon (float eps)
00116         {
00117           tree_->setEpsilon (eps);
00118         }
00119 
00121         inline float
00122         getEpsilon () const
00123         {
00124           return (tree_->getEpsilon ());
00125         }
00126 
00131         inline void
00132         setInputCloud (const PointCloudConstPtr& cloud, const IndicesConstPtr& indices = IndicesConstPtr ())
00133         {
00134           tree_->setInputCloud (cloud, indices);
00135           input_ = cloud;
00136           indices_ = indices;
00137         }
00138 
00147         inline int
00148         nearestKSearch (const PointT &point, int k, std::vector<int> &k_indices, std::vector<float> &k_sqr_distances) const
00149         {
00150           return (tree_->nearestKSearch (point, k, k_indices, k_sqr_distances));
00151         }
00152 
00163         inline int
00164         radiusSearch (const PointT& point, double radius, 
00165                       std::vector<int> &k_indices, std::vector<float> &k_sqr_distances,
00166                       unsigned int max_nn = 0) const
00167         {
00168           return (tree_->radiusSearch (point, radius, k_indices, k_sqr_distances, max_nn));
00169         }
00170 
00171       protected:
00173         KdTreeFLANNPtr tree_;
00174     };
00175   }
00176 }
00177 
00178 #define PCL_INSTANTIATE_KdTree(T) template class PCL_EXPORTS pcl::search::KdTree<T>;
00179 
00180 #endif    // PCL_SEARCH_KDTREE_H_
00181 


pcl
Author(s): Open Perception
autogenerated on Mon Oct 6 2014 03:15:31