298 lines
14 KiB
C
298 lines
14 KiB
C
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/*
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* Software License Agreement (BSD License)
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*
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* Point Cloud Library (PCL) - www.pointclouds.org
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* Copyright (c) 2010-2011, Willow Garage, Inc.
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*
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above
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* copyright notice, this list of conditions and the following
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* disclaimer in the documentation and/or other materials provided
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* with the distribution.
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* * Neither the name of the copyright holder(s) nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*
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* $Id$
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*/
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#pragma once
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#include <pcl/search/search.h>
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#include <pcl/octree/octree_search.h>
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namespace pcl
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{
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namespace search
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{
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/** \brief @b search::Octree is a wrapper class which implements nearest neighbor search operations based on the
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* pcl::octree::Octree structure.
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*
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* The octree pointcloud class needs to be initialized with its voxel
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* resolution. Its bounding box is automatically adjusted according to the
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* pointcloud dimension or it can be predefined. Note: The tree depth
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* equates to the resolution and the bounding box dimensions of the
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* octree.
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*
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* \note typename: PointT: type of point used in pointcloud
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* \note typename: LeafT: leaf node class (usuallt templated with integer indices values)
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* \note typename: OctreeT: octree implementation ()
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*
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* \author Julius Kammerl
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* \ingroup search
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*/
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template<typename PointT,
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typename LeafTWrap = pcl::octree::OctreeContainerPointIndices,
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typename BranchTWrap = pcl::octree::OctreeContainerEmpty,
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typename OctreeT = pcl::octree::OctreeBase<LeafTWrap, BranchTWrap > >
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class Octree: public Search<PointT>
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{
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public:
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// public typedefs
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using Ptr = shared_ptr<pcl::search::Octree<PointT,LeafTWrap,BranchTWrap,OctreeT> >;
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using ConstPtr = shared_ptr<const pcl::search::Octree<PointT,LeafTWrap,BranchTWrap,OctreeT> >;
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using PointCloud = pcl::PointCloud<PointT>;
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using PointCloudPtr = typename PointCloud::Ptr;
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using PointCloudConstPtr = typename PointCloud::ConstPtr;
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// Boost shared pointers
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using OctreePointCloudSearchPtr = typename pcl::octree::OctreePointCloudSearch<PointT, LeafTWrap, BranchTWrap>::Ptr;
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using OctreePointCloudSearchConstPtr = typename pcl::octree::OctreePointCloudSearch<PointT, LeafTWrap, BranchTWrap>::ConstPtr;
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OctreePointCloudSearchPtr tree_;
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using pcl::search::Search<PointT>::input_;
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using pcl::search::Search<PointT>::indices_;
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using pcl::search::Search<PointT>::sorted_results_;
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/** \brief Octree constructor.
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* \param[in] resolution octree resolution at lowest octree level
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*/
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Octree (const double resolution)
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: Search<PointT> ("Octree")
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, tree_ (new pcl::octree::OctreePointCloudSearch<PointT, LeafTWrap, BranchTWrap> (resolution))
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{
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}
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/** \brief Empty Destructor. */
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~Octree ()
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{
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}
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/** \brief Provide a pointer to the input dataset.
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* \param[in] cloud the const boost shared pointer to a PointCloud message
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*/
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inline void
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setInputCloud (const PointCloudConstPtr &cloud)
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{
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tree_->deleteTree ();
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tree_->setInputCloud (cloud);
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tree_->addPointsFromInputCloud ();
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input_ = cloud;
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}
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/** \brief Provide a pointer to the input dataset.
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* \param[in] cloud the const boost shared pointer to a PointCloud message
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* \param[in] indices the point indices subset that is to be used from \a cloud
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*/
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inline void
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setInputCloud (const PointCloudConstPtr &cloud, const IndicesConstPtr& indices) override
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{
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tree_->deleteTree ();
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tree_->setInputCloud (cloud, indices);
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tree_->addPointsFromInputCloud ();
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input_ = cloud;
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indices_ = indices;
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}
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/** \brief Search for the k-nearest neighbors for the given query point.
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* \param[in] cloud the point cloud data
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* \param[in] index the index in \a cloud representing the query point
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* \param[in] k the number of neighbors to search for
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* \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
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* \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
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* a priori!)
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* \return number of neighbors found
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*/
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inline int
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nearestKSearch (const PointCloud &cloud, index_t index, int k, Indices &k_indices,
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std::vector<float> &k_sqr_distances) const override
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{
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return (tree_->nearestKSearch (cloud, index, k, k_indices, k_sqr_distances));
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}
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/** \brief Search for the k-nearest neighbors for the given query point.
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* \param[in] point the given query point
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* \param[in] k the number of neighbors to search for
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* \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
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* \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
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* a priori!)
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* \return number of neighbors found
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*/
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inline int
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nearestKSearch (const PointT &point, int k, Indices &k_indices,
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std::vector<float> &k_sqr_distances) const override
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{
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return (tree_->nearestKSearch (point, k, k_indices, k_sqr_distances));
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}
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/** \brief Search for the k-nearest neighbors for the given query point (zero-copy).
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*
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* \param[in] index the index representing the query point in the
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* dataset given by \a setInputCloud if indices were given in
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* setInputCloud, index will be the position in the indices vector
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* \param[in] k the number of neighbors to search for
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* \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
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* \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
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* a priori!)
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* \return number of neighbors found
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*/
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inline int
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nearestKSearch (index_t index, int k, Indices &k_indices, std::vector<float> &k_sqr_distances) const override
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{
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return (tree_->nearestKSearch (index, k, k_indices, k_sqr_distances));
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}
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/** \brief search for all neighbors of query point that are within a given radius.
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* \param cloud the point cloud data
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* \param index the index in \a cloud representing the query point
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* \param radius the radius of the sphere bounding all of p_q's neighbors
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* \param k_indices the resultant indices of the neighboring points
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* \param k_sqr_distances the resultant squared distances to the neighboring points
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* \param max_nn if given, bounds the maximum returned neighbors to this value
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* \return number of neighbors found in radius
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*/
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inline int
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radiusSearch (const PointCloud &cloud,
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index_t index,
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double radius,
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Indices &k_indices,
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std::vector<float> &k_sqr_distances,
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unsigned int max_nn = 0) const override
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{
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tree_->radiusSearch (cloud, index, radius, k_indices, k_sqr_distances, max_nn);
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if (sorted_results_)
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this->sortResults (k_indices, k_sqr_distances);
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return (static_cast<int> (k_indices.size ()));
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}
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/** \brief search for all neighbors of query point that are within a given radius.
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* \param p_q the given query point
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* \param radius the radius of the sphere bounding all of p_q's neighbors
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* \param k_indices the resultant indices of the neighboring points
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* \param k_sqr_distances the resultant squared distances to the neighboring points
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* \param max_nn if given, bounds the maximum returned neighbors to this value
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* \return number of neighbors found in radius
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*/
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inline int
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radiusSearch (const PointT &p_q,
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double radius,
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Indices &k_indices,
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std::vector<float> &k_sqr_distances,
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unsigned int max_nn = 0) const override
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{
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tree_->radiusSearch (p_q, radius, k_indices, k_sqr_distances, max_nn);
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if (sorted_results_)
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this->sortResults (k_indices, k_sqr_distances);
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return (static_cast<int> (k_indices.size ()));
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}
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/** \brief search for all neighbors of query point that are within a given radius.
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* \param index index representing the query point in the dataset given by \a setInputCloud.
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* If indices were given in setInputCloud, index will be the position in the indices vector
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* \param radius radius of the sphere bounding all of p_q's neighbors
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* \param k_indices the resultant indices of the neighboring points
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* \param k_sqr_distances the resultant squared distances to the neighboring points
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* \param max_nn if given, bounds the maximum returned neighbors to this value
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* \return number of neighbors found in radius
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*/
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inline int
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radiusSearch (index_t index, double radius, Indices &k_indices,
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std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const override
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{
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tree_->radiusSearch (index, radius, k_indices, k_sqr_distances, max_nn);
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if (sorted_results_)
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this->sortResults (k_indices, k_sqr_distances);
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return (static_cast<int> (k_indices.size ()));
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}
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/** \brief Search for approximate nearest neighbor at the query point.
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* \param[in] cloud the point cloud data
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* \param[in] query_index the index in \a cloud representing the query point
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* \param[out] result_index the resultant index of the neighbor point
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* \param[out] sqr_distance the resultant squared distance to the neighboring point
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* \return number of neighbors found
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*/
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inline void
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approxNearestSearch (const PointCloudConstPtr &cloud, index_t query_index, index_t &result_index,
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float &sqr_distance)
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{
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return (tree_->approxNearestSearch ((*cloud)[query_index], result_index, sqr_distance));
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}
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/** \brief Search for approximate nearest neighbor at the query point.
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* \param[in] p_q the given query point
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* \param[out] result_index the resultant index of the neighbor point
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* \param[out] sqr_distance the resultant squared distance to the neighboring point
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*/
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inline void
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approxNearestSearch (const PointT &p_q, index_t &result_index, float &sqr_distance)
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{
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return (tree_->approxNearestSearch (p_q, result_index, sqr_distance));
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}
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/** \brief Search for approximate nearest neighbor at the query point.
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* \param query_index index representing the query point in the dataset given by \a setInputCloud.
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* If indices were given in setInputCloud, index will be the position in the indices vector.
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* \param result_index the resultant index of the neighbor point
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* \param sqr_distance the resultant squared distance to the neighboring point
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* \return number of neighbors found
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*/
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inline void
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approxNearestSearch (index_t query_index, index_t &result_index, float &sqr_distance)
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{
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return (tree_->approxNearestSearch (query_index, result_index, sqr_distance));
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}
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/** \brief Search for points within rectangular search area
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* \param[in] min_pt lower corner of search area
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* \param[in] max_pt upper corner of search area
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* \param[out] k_indices the resultant point indices
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* \return number of points found within search area
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*/
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inline uindex_t
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boxSearch(const Eigen::Vector3f &min_pt, const Eigen::Vector3f &max_pt, Indices &k_indices) const
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{
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return (tree_->boxSearch(min_pt, max_pt, k_indices));
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}
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};
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}
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}
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#ifdef PCL_NO_PRECOMPILE
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#include <pcl/octree/impl/octree_search.hpp>
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#else
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#define PCL_INSTANTIATE_Octree(T) template class PCL_EXPORTS pcl::search::Octree<T>;
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#endif
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