271 lines
11 KiB
C++
271 lines
11 KiB
C++
/*
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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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*/
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#pragma once
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#include <pcl/memory.h>
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#include <pcl/pcl_base.h>
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#include <pcl/pcl_macros.h>
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#include <pcl/console/print.h> // for PCL_WARN
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#include <pcl/search/search.h> // for Search
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#include <functional>
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namespace pcl
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{
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using IndicesClusters = std::vector<pcl::PointIndices>;
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using IndicesClustersPtr = shared_ptr<std::vector<pcl::PointIndices> >;
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/** \brief @b ConditionalEuclideanClustering performs segmentation based on Euclidean distance and a user-defined clustering condition.
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* \details The condition that need to hold is currently passed using a function pointer.
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* For more information check the documentation of setConditionFunction() or the usage example below:
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* \code
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* bool
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* enforceIntensitySimilarity (const pcl::PointXYZI& point_a, const pcl::PointXYZI& point_b, float squared_distance)
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* {
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* if (std::abs (point_a.intensity - point_b.intensity) < 0.1f)
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* return (true);
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* else
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* return (false);
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* }
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* // ...
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* // Somewhere down to the main code
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* // ...
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* pcl::ConditionalEuclideanClustering<pcl::PointXYZI> cec (true);
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* cec.setInputCloud (cloud_in);
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* cec.setConditionFunction (&enforceIntensitySimilarity);
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* // Points within this distance from one another are going to need to validate the enforceIntensitySimilarity function to be part of the same cluster:
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* cec.setClusterTolerance (0.09f);
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* // Size constraints for the clusters:
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* cec.setMinClusterSize (5);
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* cec.setMaxClusterSize (30);
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* // The resulting clusters (an array of pointindices):
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* cec.segment (*clusters);
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* // The clusters that are too small or too large in size can also be extracted separately:
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* cec.getRemovedClusters (small_clusters, large_clusters);
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* \endcode
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* \author Frits Florentinus
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* \ingroup segmentation
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*/
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template<typename PointT>
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class ConditionalEuclideanClustering : public PCLBase<PointT>
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{
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protected:
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using SearcherPtr = typename pcl::search::Search<PointT>::Ptr;
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using PCLBase<PointT>::input_;
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using PCLBase<PointT>::indices_;
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using PCLBase<PointT>::initCompute;
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using PCLBase<PointT>::deinitCompute;
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public:
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/** \brief Constructor.
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* \param[in] extract_removed_clusters Set to true if you want to be able to extract the clusters that are too large or too small (default = false)
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*/
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ConditionalEuclideanClustering (bool extract_removed_clusters = false) :
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searcher_ (),
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condition_function_ (),
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cluster_tolerance_ (0.0f),
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min_cluster_size_ (1),
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max_cluster_size_ (std::numeric_limits<int>::max ()),
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extract_removed_clusters_ (extract_removed_clusters),
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small_clusters_ (new pcl::IndicesClusters),
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large_clusters_ (new pcl::IndicesClusters)
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{
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}
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/** \brief Provide a pointer to the search object.
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* \param[in] tree a pointer to the spatial search object.
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*/
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inline void
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setSearchMethod (const SearcherPtr &tree)
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{
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searcher_ = tree;
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}
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/** \brief Get a pointer to the search method used.
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*/
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inline const SearcherPtr&
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getSearchMethod () const
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{
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return searcher_;
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}
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/** \brief Set the condition that needs to hold for neighboring points to be considered part of the same cluster.
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* \details Any two points within a certain distance from one another will need to evaluate this condition in order to be made part of the same cluster.
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* The distance can be set using setClusterTolerance().
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* <br>
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* Note that for a point to be part of a cluster, the condition only needs to hold for at least 1 point pair.
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* To clarify, the following statement is false:
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* Any two points within a cluster always evaluate this condition function to true.
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* <br><br>
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* The input arguments of the condition function are:
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* <ul>
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* <li>PointT The first point of the point pair</li>
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* <li>PointT The second point of the point pair</li>
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* <li>float The squared distance between the points</li>
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* </ul>
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* The output argument is a boolean, returning true will merge the second point into the cluster of the first point.
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* \param[in] condition_function The condition function that needs to hold for clustering
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*/
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inline void
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setConditionFunction (bool (*condition_function) (const PointT&, const PointT&, float))
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{
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condition_function_ = condition_function;
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}
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/** \brief Set the condition that needs to hold for neighboring points to be considered part of the same cluster.
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* This is an overloaded function provided for convenience. See the documentation for setConditionFunction(). */
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inline void
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setConditionFunction (std::function<bool (const PointT&, const PointT&, float)> condition_function)
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{
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condition_function_ = condition_function;
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}
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/** \brief Set the spatial tolerance for new cluster candidates.
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* \details Any two points within this distance from one another will need to evaluate a certain condition in order to be made part of the same cluster.
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* The condition can be set using setConditionFunction().
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* \param[in] cluster_tolerance The distance to scan for cluster candidates (default = 0.0)
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*/
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inline void
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setClusterTolerance (float cluster_tolerance)
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{
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cluster_tolerance_ = cluster_tolerance;
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}
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/** \brief Get the spatial tolerance for new cluster candidates.*/
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inline float
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getClusterTolerance ()
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{
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return (cluster_tolerance_);
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}
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/** \brief Set the minimum number of points that a cluster needs to contain in order to be considered valid.
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* \param[in] min_cluster_size The minimum cluster size (default = 1)
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*/
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inline void
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setMinClusterSize (int min_cluster_size)
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{
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min_cluster_size_ = min_cluster_size;
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}
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/** \brief Get the minimum number of points that a cluster needs to contain in order to be considered valid.*/
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inline int
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getMinClusterSize ()
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{
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return (min_cluster_size_);
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}
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/** \brief Set the maximum number of points that a cluster needs to contain in order to be considered valid.
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* \param[in] max_cluster_size The maximum cluster size (default = unlimited)
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*/
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inline void
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setMaxClusterSize (int max_cluster_size)
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{
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max_cluster_size_ = max_cluster_size;
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}
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/** \brief Get the maximum number of points that a cluster needs to contain in order to be considered valid.*/
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inline int
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getMaxClusterSize ()
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{
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return (max_cluster_size_);
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}
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/** \brief Segment the input into separate clusters.
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* \details The input can be set using setInputCloud() and setIndices().
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* <br>
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* The size constraints for the resulting clusters can be set using setMinClusterSize() and setMaxClusterSize().
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* <br>
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* The region growing parameters can be set using setConditionFunction() and setClusterTolerance().
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* <br>
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* \param[out] clusters The resultant set of indices, indexing the points of the input cloud that correspond to the clusters
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*/
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void
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segment (IndicesClusters &clusters);
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/** \brief Get the clusters that are invalidated due to size constraints.
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* \note The constructor of this class needs to be initialized with true, and the segment method needs to have been called prior to using this method.
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* \param[out] small_clusters The resultant clusters that contain less than min_cluster_size points
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* \param[out] large_clusters The resultant clusters that contain more than max_cluster_size points
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*/
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inline void
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getRemovedClusters (IndicesClustersPtr &small_clusters, IndicesClustersPtr &large_clusters)
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{
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if (!extract_removed_clusters_)
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{
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PCL_WARN("[pcl::ConditionalEuclideanClustering::getRemovedClusters] You need to set extract_removed_clusters to true (in this class' constructor) if you want to use this functionality.\n");
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return;
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}
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small_clusters = small_clusters_;
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large_clusters = large_clusters_;
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}
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private:
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/** \brief A pointer to the spatial search object */
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SearcherPtr searcher_;
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/** \brief The condition function that needs to hold for clustering */
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std::function<bool (const PointT&, const PointT&, float)> condition_function_;
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/** \brief The distance to scan for cluster candidates (default = 0.0) */
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float cluster_tolerance_;
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/** \brief The minimum cluster size (default = 1) */
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int min_cluster_size_;
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/** \brief The maximum cluster size (default = unlimited) */
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int max_cluster_size_;
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/** \brief Set to true if you want to be able to extract the clusters that are too large or too small (default = false) */
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bool extract_removed_clusters_;
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/** \brief The resultant clusters that contain less than min_cluster_size points */
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pcl::IndicesClustersPtr small_clusters_;
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/** \brief The resultant clusters that contain more than max_cluster_size points */
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pcl::IndicesClustersPtr large_clusters_;
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public:
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PCL_MAKE_ALIGNED_OPERATOR_NEW
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};
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}
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#ifdef PCL_NO_PRECOMPILE
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#include <pcl/segmentation/impl/conditional_euclidean_clustering.hpp>
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#endif
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