174 lines
7.4 KiB
C
174 lines
7.4 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/pcl_base.h>
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#include <pcl/point_types_conversion.h>
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#include <pcl/search/search.h> // for Search
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namespace pcl
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{
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//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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/** \brief Decompose a region of space into clusters based on the Euclidean distance between points
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* \param[in] cloud the point cloud message
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* \param[in] tree the spatial locator (e.g., kd-tree) used for nearest neighbors searching
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* \note the tree has to be created as a spatial locator on \a cloud
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* \param[in] tolerance the spatial cluster tolerance as a measure in L2 Euclidean space
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* \param[in] indices_in the cluster containing the seed point indices (as a vector of PointIndices)
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* \param[out] indices_out
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* \param[in] delta_hue
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* \todo look how to make this templated!
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* \ingroup segmentation
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*/
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void
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seededHueSegmentation (const PointCloud<PointXYZRGB> &cloud,
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const search::Search<PointXYZRGB>::Ptr &tree,
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float tolerance,
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PointIndices &indices_in,
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PointIndices &indices_out,
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float delta_hue = 0.0);
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/** \brief Decompose a region of space into clusters based on the Euclidean distance between points
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* \param[in] cloud the point cloud message
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* \param[in] tree the spatial locator (e.g., kd-tree) used for nearest neighbors searching
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* \note the tree has to be created as a spatial locator on \a cloud
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* \param[in] tolerance the spatial cluster tolerance as a measure in L2 Euclidean space
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* \param[in] indices_in the cluster containing the seed point indices (as a vector of PointIndices)
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* \param[out] indices_out
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* \param[in] delta_hue
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* \todo look how to make this templated!
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* \ingroup segmentation
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*/
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void
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seededHueSegmentation (const PointCloud<PointXYZRGB> &cloud,
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const search::Search<PointXYZRGBL>::Ptr &tree,
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float tolerance,
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PointIndices &indices_in,
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PointIndices &indices_out,
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float delta_hue = 0.0);
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//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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/** \brief SeededHueSegmentation
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* \author Koen Buys
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* \ingroup segmentation
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*/
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class SeededHueSegmentation: public PCLBase<PointXYZRGB>
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{
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using BasePCLBase = PCLBase<PointXYZRGB>;
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public:
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using PointCloud = pcl::PointCloud<PointXYZRGB>;
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using PointCloudPtr = PointCloud::Ptr;
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using PointCloudConstPtr = PointCloud::ConstPtr;
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using KdTree = pcl::search::Search<PointXYZRGB>;
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using KdTreePtr = pcl::search::Search<PointXYZRGB>::Ptr;
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using PointIndicesPtr = PointIndices::Ptr;
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using PointIndicesConstPtr = PointIndices::ConstPtr;
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//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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/** \brief Empty constructor. */
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SeededHueSegmentation () : cluster_tolerance_ (0), delta_hue_ (0.0)
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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 KdTreePtr &tree) { tree_ = tree; }
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/** \brief Get a pointer to the search method used. */
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inline KdTreePtr
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getSearchMethod () const { return (tree_); }
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/** \brief Set the spatial cluster tolerance as a measure in the L2 Euclidean space
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* \param[in] tolerance the spatial cluster tolerance as a measure in the L2 Euclidean space
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*/
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inline void
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setClusterTolerance (double tolerance) { cluster_tolerance_ = tolerance; }
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/** \brief Get the spatial cluster tolerance as a measure in the L2 Euclidean space. */
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inline double
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getClusterTolerance () const { return (cluster_tolerance_); }
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/** \brief Set the tollerance on the hue
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* \param[in] delta_hue the new delta hue
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*/
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inline void
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setDeltaHue (float delta_hue) { delta_hue_ = delta_hue; }
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/** \brief Get the tolerance on the hue */
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inline float
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getDeltaHue () const { return (delta_hue_); }
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/** \brief Cluster extraction in a PointCloud given by <setInputCloud (), setIndices ()>
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* \param[in] indices_in
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* \param[out] indices_out
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*/
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void
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segment (PointIndices &indices_in, PointIndices &indices_out);
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protected:
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// Members derived from the base class
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using BasePCLBase::input_;
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using BasePCLBase::indices_;
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using BasePCLBase::initCompute;
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using BasePCLBase::deinitCompute;
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/** \brief A pointer to the spatial search object. */
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KdTreePtr tree_;
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/** \brief The spatial cluster tolerance as a measure in the L2 Euclidean space. */
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double cluster_tolerance_;
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/** \brief The allowed difference on the hue*/
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float delta_hue_;
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/** \brief Class getName method. */
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virtual std::string getClassName () const { return ("seededHueSegmentation"); }
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};
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}
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#ifdef PCL_NO_PRECOMPILE
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#include <pcl/segmentation/impl/seeded_hue_segmentation.hpp>
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#endif
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