431 lines
22 KiB
C
431 lines
22 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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* Copyright (c) 2012-, Open Perception, 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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*/
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#pragma once
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#include <pcl/pcl_base.h> // for IndicesConstPtr
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#include <pcl/point_cloud.h>
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#include <pcl/for_each_type.h>
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#include <pcl/common/concatenate.h>
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#include <pcl/common/copy_point.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 Generic search class. All search wrappers must inherit from this.
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*
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* Each search method must implement 2 different types of search:
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* - \b nearestKSearch - search for K-nearest neighbors.
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* - \b radiusSearch - search for all nearest neighbors in a sphere of a given radius
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*
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* The input to each search method can be given in 3 different ways:
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* - as a query point
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* - as a (cloud, index) pair
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* - as an index
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*
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* For the latter option, it is assumed that the user specified the input
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* via a \ref setInputCloud () method first.
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*
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* \note In case of an error, all methods are supposed to return 0 as the number of neighbors found.
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*
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* \note libpcl_search deals with three-dimensional search problems. For higher
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* level dimensional search, please refer to the libpcl_kdtree module.
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*
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* \author Radu B. Rusu
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* \ingroup search
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*/
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template<typename PointT>
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class Search
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{
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public:
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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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using Ptr = shared_ptr<pcl::search::Search<PointT> >;
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using ConstPtr = shared_ptr<const pcl::search::Search<PointT> >;
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using IndicesPtr = pcl::IndicesPtr;
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using IndicesConstPtr = pcl::IndicesConstPtr;
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/** Constructor. */
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Search (const std::string& name = "", bool sorted = false);
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/** Destructor. */
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virtual
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~Search ()
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{
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}
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/** \brief Returns the search method name
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*/
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virtual const std::string&
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getName () const;
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/** \brief sets whether the results should be sorted (ascending in the distance) or not
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* \param[in] sorted should be true if the results should be sorted by the distance in ascending order.
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* Otherwise the results may be returned in any order.
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*/
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virtual void
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setSortedResults (bool sorted);
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/** \brief Gets whether the results should be sorted (ascending in the distance) or not
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* Otherwise the results may be returned in any order.
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*/
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virtual bool
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getSortedResults ();
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/** \brief Pass the input dataset that the search will be performed on.
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* \param[in] cloud a const pointer to the PointCloud data
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* \param[in] indices the point indices subset that is to be used from the cloud
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*/
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virtual void
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setInputCloud (const PointCloudConstPtr& cloud,
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const IndicesConstPtr &indices = IndicesConstPtr ());
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/** \brief Get a pointer to the input point cloud dataset. */
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virtual PointCloudConstPtr
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getInputCloud () const
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{
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return (input_);
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}
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/** \brief Get a pointer to the vector of indices used. */
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virtual IndicesConstPtr
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getIndices () const
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{
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return (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] 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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virtual 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 = 0;
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/** \brief Search for k-nearest neighbors for the given query point.
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* This method accepts a different template parameter for the point type.
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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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template <typename PointTDiff> inline int
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nearestKSearchT (const PointTDiff &point, int k,
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Indices &k_indices, std::vector<float> &k_sqr_distances) const
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{
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PointT p;
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copyPoint (point, p);
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return (nearestKSearch (p, k, k_indices, k_sqr_distances));
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}
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/** \brief Search for k-nearest neighbors for the given query point.
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*
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* \attention This method does not do any bounds checking for the input index
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* (i.e., index >= cloud.size () || index < 0), and assumes valid (i.e., finite) data.
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*
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* \param[in] cloud the point cloud data
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* \param[in] index a \a valid index in \a cloud representing a \a valid (i.e., finite) 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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*
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* \return number of neighbors found
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*
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* \exception asserts in debug mode if the index is not between 0 and the maximum number of points
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*/
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virtual int
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nearestKSearch (const PointCloud &cloud, index_t index, int k,
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Indices &k_indices,
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std::vector<float> &k_sqr_distances) const;
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/** \brief Search for k-nearest neighbors for the given query point (zero-copy).
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*
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* \attention This method does not do any bounds checking for the input index
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* (i.e., index >= cloud.size () || index < 0), and assumes valid (i.e., finite) data.
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*
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* \param[in] index a \a valid index representing a \a valid query point in the dataset given
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* by \a setInputCloud. If indices were given in setInputCloud, index will be the position in
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* the indices vector.
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*
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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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* \exception asserts in debug mode if the index is not between 0 and the maximum number of points
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*/
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virtual int
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nearestKSearch (index_t index, int k,
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Indices &k_indices,
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std::vector<float> &k_sqr_distances) const;
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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] indices a vector of point cloud indices to query for nearest neighbors
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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, k_indices[i] corresponds to the neighbors of the query point i
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* \param[out] k_sqr_distances the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i
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*/
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virtual void
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nearestKSearch (const PointCloud& cloud, const Indices& indices,
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int k, std::vector<Indices>& k_indices,
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std::vector< std::vector<float> >& k_sqr_distances) const;
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/** \brief Search for the k-nearest neighbors for the given query point. Use this method if the query points are of a different type than the points in the data set (e.g. PointXYZRGBA instead of PointXYZ).
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* \param[in] cloud the point cloud data
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* \param[in] indices a vector of point cloud indices to query for nearest neighbors
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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, k_indices[i] corresponds to the neighbors of the query point i
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* \param[out] k_sqr_distances the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i
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* \note This method copies the input point cloud of type PointTDiff to a temporary cloud of type PointT and performs the batch search on the new cloud. You should prefer the single-point search if you don't use a search algorithm that accelerates batch NN search.
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*/
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template <typename PointTDiff> void
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nearestKSearchT (const pcl::PointCloud<PointTDiff> &cloud, const Indices& indices, int k, std::vector<Indices> &k_indices,
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std::vector< std::vector<float> > &k_sqr_distances) const
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{
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// Copy all the data fields from the input cloud to the output one
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using FieldListInT = typename pcl::traits::fieldList<PointT>::type;
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using FieldListOutT = typename pcl::traits::fieldList<PointTDiff>::type;
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using FieldList = typename pcl::intersect<FieldListInT, FieldListOutT>::type;
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pcl::PointCloud<PointT> pc;
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if (indices.empty ())
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{
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pc.resize (cloud.size());
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for (std::size_t i = 0; i < cloud.size(); i++)
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{
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pcl::for_each_type <FieldList> (pcl::NdConcatenateFunctor <PointTDiff, PointT> (
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cloud[i], pc[i]));
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}
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nearestKSearch (pc,Indices(),k,k_indices,k_sqr_distances);
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}
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else
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{
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pc.resize (indices.size());
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for (std::size_t i = 0; i < indices.size(); i++)
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{
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pcl::for_each_type <FieldList> (pcl::NdConcatenateFunctor <PointTDiff, PointT> (
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cloud[indices[i]], pc[i]));
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}
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nearestKSearch (pc,Indices(),k,k_indices,k_sqr_distances);
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}
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}
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/** \brief Search for all the nearest neighbors of the query point in a given radius.
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* \param[in] point the given query point
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* \param[in] radius the radius of the sphere bounding all of p_q's neighbors
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* \param[out] k_indices the resultant indices of the neighboring points
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* \param[out] k_sqr_distances the resultant squared distances to the neighboring points
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* \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
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* 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
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* returned.
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* \return number of neighbors found in radius
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*/
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virtual int
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radiusSearch (const PointT& point, double radius, Indices& k_indices,
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std::vector<float>& k_sqr_distances, unsigned int max_nn = 0) const = 0;
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/** \brief Search for all the nearest neighbors of the query point in a given radius.
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* \param[in] point the given query point
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* \param[in] radius the radius of the sphere bounding all of p_q's neighbors
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* \param[out] k_indices the resultant indices of the neighboring points
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* \param[out] k_sqr_distances the resultant squared distances to the neighboring points
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* \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
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* 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
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* returned.
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* \return number of neighbors found in radius
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*/
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template <typename PointTDiff> inline int
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radiusSearchT (const PointTDiff &point, double radius, Indices &k_indices,
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std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const
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{
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PointT p;
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copyPoint (point, p);
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return (radiusSearch (p, radius, k_indices, k_sqr_distances, max_nn));
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}
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/** \brief Search for all the nearest neighbors of the query point in a given radius.
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*
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* \attention This method does not do any bounds checking for the input index
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* (i.e., index >= cloud.size () || index < 0), and assumes valid (i.e., finite) data.
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*
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* \param[in] cloud the point cloud data
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* \param[in] index a \a valid index in \a cloud representing a \a valid (i.e., finite) query point
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* \param[in] radius the radius of the sphere bounding all of p_q's neighbors
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* \param[out] k_indices the resultant indices of the neighboring points
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* \param[out] k_sqr_distances the resultant squared distances to the neighboring points
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* \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
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* 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
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* returned.
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* \return number of neighbors found in radius
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*
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* \exception asserts in debug mode if the index is not between 0 and the maximum number of points
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*/
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virtual int
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radiusSearch (const PointCloud &cloud, index_t index, double radius,
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Indices &k_indices, std::vector<float> &k_sqr_distances,
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unsigned int max_nn = 0) const;
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/** \brief Search for all the nearest neighbors of the query point in a given radius (zero-copy).
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*
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* \attention This method does not do any bounds checking for the input index
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* (i.e., index >= cloud.size () || index < 0), and assumes valid (i.e., finite) data.
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*
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* \param[in] index a \a valid index representing a \a valid query point in the dataset given
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* by \a setInputCloud. If indices were given in setInputCloud, index will be the position in
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* the indices vector.
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*
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* \param[in] radius the radius of the sphere bounding all of p_q's neighbors
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* \param[out] k_indices the resultant indices of the neighboring points
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* \param[out] k_sqr_distances the resultant squared distances to the neighboring points
|
||
|
|
* \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
|
||
|
|
* 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
|
||
|
|
* returned.
|
||
|
|
* \return number of neighbors found in radius
|
||
|
|
*
|
||
|
|
* \exception asserts in debug mode if the index is not between 0 and the maximum number of points
|
||
|
|
*/
|
||
|
|
virtual int
|
||
|
|
radiusSearch (index_t index, double radius, Indices &k_indices,
|
||
|
|
std::vector<float> &k_sqr_distances, unsigned int max_nn = 0) const;
|
||
|
|
|
||
|
|
/** \brief Search for all the nearest neighbors of the query point in a given radius.
|
||
|
|
* \param[in] cloud the point cloud data
|
||
|
|
* \param[in] indices the indices in \a cloud. If indices is empty, neighbors will be searched for all points.
|
||
|
|
* \param[in] radius the radius of the sphere bounding all of p_q's neighbors
|
||
|
|
* \param[out] k_indices the resultant indices of the neighboring points, k_indices[i] corresponds to the neighbors of the query point i
|
||
|
|
* \param[out] k_sqr_distances the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i
|
||
|
|
* \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
|
||
|
|
* 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
|
||
|
|
* returned.
|
||
|
|
*/
|
||
|
|
virtual void
|
||
|
|
radiusSearch (const PointCloud& cloud,
|
||
|
|
const Indices& indices,
|
||
|
|
double radius,
|
||
|
|
std::vector<Indices>& k_indices,
|
||
|
|
std::vector< std::vector<float> > &k_sqr_distances,
|
||
|
|
unsigned int max_nn = 0) const;
|
||
|
|
|
||
|
|
/** \brief Search for all the nearest neighbors of the query points in a given radius.
|
||
|
|
* \param[in] cloud the point cloud data
|
||
|
|
* \param[in] indices a vector of point cloud indices to query for nearest neighbors
|
||
|
|
* \param[in] radius the radius of the sphere bounding all of p_q's neighbors
|
||
|
|
* \param[out] k_indices the resultant indices of the neighboring points, k_indices[i] corresponds to the neighbors of the query point i
|
||
|
|
* \param[out] k_sqr_distances the resultant squared distances to the neighboring points, k_sqr_distances[i] corresponds to the neighbors of the query point i
|
||
|
|
* \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
|
||
|
|
* 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
|
||
|
|
* returned.
|
||
|
|
* \note This method copies the input point cloud of type PointTDiff to a temporary cloud of type PointT and performs the batch search on the new cloud. You should prefer the single-point search if you don't use a search algorithm that accelerates batch NN search.
|
||
|
|
*/
|
||
|
|
template <typename PointTDiff> void
|
||
|
|
radiusSearchT (const pcl::PointCloud<PointTDiff> &cloud,
|
||
|
|
const Indices& indices,
|
||
|
|
double radius,
|
||
|
|
std::vector<Indices> &k_indices,
|
||
|
|
std::vector< std::vector<float> > &k_sqr_distances,
|
||
|
|
unsigned int max_nn = 0) const
|
||
|
|
{
|
||
|
|
// Copy all the data fields from the input cloud to the output one
|
||
|
|
using FieldListInT = typename pcl::traits::fieldList<PointT>::type;
|
||
|
|
using FieldListOutT = typename pcl::traits::fieldList<PointTDiff>::type;
|
||
|
|
using FieldList = typename pcl::intersect<FieldListInT, FieldListOutT>::type;
|
||
|
|
|
||
|
|
pcl::PointCloud<PointT> pc;
|
||
|
|
if (indices.empty ())
|
||
|
|
{
|
||
|
|
pc.resize (cloud.size ());
|
||
|
|
for (std::size_t i = 0; i < cloud.size (); ++i)
|
||
|
|
pcl::for_each_type <FieldList> (pcl::NdConcatenateFunctor <PointTDiff, PointT> (cloud[i], pc[i]));
|
||
|
|
radiusSearch (pc, Indices (), radius, k_indices, k_sqr_distances, max_nn);
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
pc.resize (indices.size ());
|
||
|
|
for (std::size_t i = 0; i < indices.size (); ++i)
|
||
|
|
pcl::for_each_type <FieldList> (pcl::NdConcatenateFunctor <PointTDiff, PointT> (cloud[indices[i]], pc[i]));
|
||
|
|
radiusSearch (pc, Indices(), radius, k_indices, k_sqr_distances, max_nn);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
protected:
|
||
|
|
void
|
||
|
|
sortResults (Indices& indices, std::vector<float>& distances) const;
|
||
|
|
|
||
|
|
PointCloudConstPtr input_;
|
||
|
|
IndicesConstPtr indices_;
|
||
|
|
bool sorted_results_;
|
||
|
|
std::string name_;
|
||
|
|
|
||
|
|
private:
|
||
|
|
struct Compare
|
||
|
|
{
|
||
|
|
Compare (const std::vector<float>& distances)
|
||
|
|
: distances_ (distances)
|
||
|
|
{
|
||
|
|
}
|
||
|
|
|
||
|
|
bool
|
||
|
|
operator () (index_t first, index_t second) const
|
||
|
|
{
|
||
|
|
return (distances_ [first] < distances_[second]);
|
||
|
|
}
|
||
|
|
|
||
|
|
const std::vector<float>& distances_;
|
||
|
|
};
|
||
|
|
}; // class Search
|
||
|
|
} // namespace search
|
||
|
|
} // namespace pcl
|
||
|
|
|
||
|
|
#ifdef PCL_NO_PRECOMPILE
|
||
|
|
#include <pcl/search/impl/search.hpp>
|
||
|
|
#endif
|