232 lines
8.2 KiB
C++
232 lines
8.2 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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* $id $
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*/
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#ifndef PCL_SEGMENTATION_IMPL_SEEDED_HUE_SEGMENTATION_H_
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#define PCL_SEGMENTATION_IMPL_SEEDED_HUE_SEGMENTATION_H_
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#include <pcl/segmentation/seeded_hue_segmentation.h>
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#include <pcl/console/print.h> // for PCL_ERROR
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#include <pcl/search/organized.h> // for OrganizedNeighbor
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#include <pcl/search/kdtree.h> // for KdTree
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//////////////////////////////////////////////////////////////////////////////////////////////
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void
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pcl::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)
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{
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if (tree->getInputCloud ()->size () != cloud.size ())
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{
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PCL_ERROR("[pcl::seededHueSegmentation] Tree built for a different point cloud "
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"dataset (%zu) than the input cloud (%zu)!\n",
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static_cast<std::size_t>(tree->getInputCloud()->size()),
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static_cast<std::size_t>(cloud.size()));
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return;
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}
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// Create a bool vector of processed point indices, and initialize it to false
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std::vector<bool> processed (cloud.size (), false);
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Indices nn_indices;
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std::vector<float> nn_distances;
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// Process all points in the indices vector
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for (const auto &i : indices_in.indices)
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{
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if (processed[i])
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continue;
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processed[i] = true;
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Indices seed_queue;
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int sq_idx = 0;
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seed_queue.push_back (i);
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PointXYZRGB p;
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p = cloud[i];
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PointXYZHSV h;
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PointXYZRGBtoXYZHSV(p, h);
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while (sq_idx < static_cast<int> (seed_queue.size ()))
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{
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int ret = tree->radiusSearch (seed_queue[sq_idx], tolerance, nn_indices, nn_distances, std::numeric_limits<int>::max());
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if(ret == -1)
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PCL_ERROR("[pcl::seededHueSegmentation] radiusSearch returned error code -1\n");
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// Search for sq_idx
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if (!ret)
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{
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sq_idx++;
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continue;
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}
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for (std::size_t j = 1; j < nn_indices.size (); ++j) // nn_indices[0] should be sq_idx
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{
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if (processed[nn_indices[j]]) // Has this point been processed before ?
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continue;
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PointXYZRGB p_l;
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p_l = cloud[nn_indices[j]];
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PointXYZHSV h_l;
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PointXYZRGBtoXYZHSV(p_l, h_l);
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if (std::fabs(h_l.h - h.h) < delta_hue)
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{
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seed_queue.push_back (nn_indices[j]);
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processed[nn_indices[j]] = true;
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}
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}
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sq_idx++;
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}
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// Copy the seed queue into the output indices
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for (const auto &l : seed_queue)
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indices_out.indices.push_back(l);
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}
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// This is purely esthetical, can be removed for speed purposes
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std::sort (indices_out.indices.begin (), indices_out.indices.end ());
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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void
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pcl::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)
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{
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if (tree->getInputCloud ()->size () != cloud.size ())
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{
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PCL_ERROR("[pcl::seededHueSegmentation] Tree built for a different point cloud "
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"dataset (%zu) than the input cloud (%zu)!\n",
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static_cast<std::size_t>(tree->getInputCloud()->size()),
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static_cast<std::size_t>(cloud.size()));
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return;
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}
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// Create a bool vector of processed point indices, and initialize it to false
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std::vector<bool> processed (cloud.size (), false);
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Indices nn_indices;
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std::vector<float> nn_distances;
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// Process all points in the indices vector
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for (const auto &i : indices_in.indices)
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{
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if (processed[i])
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continue;
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processed[i] = true;
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Indices seed_queue;
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int sq_idx = 0;
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seed_queue.push_back (i);
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PointXYZRGB p;
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p = cloud[i];
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PointXYZHSV h;
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PointXYZRGBtoXYZHSV(p, h);
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while (sq_idx < static_cast<int> (seed_queue.size ()))
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{
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int ret = tree->radiusSearch (seed_queue[sq_idx], tolerance, nn_indices, nn_distances, std::numeric_limits<int>::max());
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if(ret == -1)
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PCL_ERROR("[pcl::seededHueSegmentation] radiusSearch returned error code -1\n");
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// Search for sq_idx
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if (!ret)
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{
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sq_idx++;
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continue;
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}
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for (std::size_t j = 1; j < nn_indices.size (); ++j) // nn_indices[0] should be sq_idx
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{
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if (processed[nn_indices[j]]) // Has this point been processed before ?
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continue;
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PointXYZRGB p_l;
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p_l = cloud[nn_indices[j]];
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PointXYZHSV h_l;
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PointXYZRGBtoXYZHSV(p_l, h_l);
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if (std::fabs(h_l.h - h.h) < delta_hue)
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{
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seed_queue.push_back (nn_indices[j]);
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processed[nn_indices[j]] = true;
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}
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}
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sq_idx++;
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}
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// Copy the seed queue into the output indices
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for (const auto &l : seed_queue)
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indices_out.indices.push_back(l);
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}
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// This is purely esthetical, can be removed for speed purposes
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std::sort (indices_out.indices.begin (), indices_out.indices.end ());
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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//////////////////////////////////////////////////////////////////////////////////////////////
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void
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pcl::SeededHueSegmentation::segment (PointIndices &indices_in, PointIndices &indices_out)
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{
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if (!initCompute () ||
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(input_ && input_->points.empty ()) ||
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(indices_ && indices_->empty ()))
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{
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indices_out.indices.clear ();
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return;
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}
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// Initialize the spatial locator
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if (!tree_)
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{
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if (input_->isOrganized ())
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tree_.reset (new pcl::search::OrganizedNeighbor<PointXYZRGB> ());
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else
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tree_.reset (new pcl::search::KdTree<PointXYZRGB> (false));
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}
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// Send the input dataset to the spatial locator
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tree_->setInputCloud (input_);
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seededHueSegmentation (*input_, tree_, static_cast<float> (cluster_tolerance_), indices_in, indices_out, delta_hue_);
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deinitCompute ();
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}
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#endif // PCL_EXTRACT_CLUSTERS_IMPL_H_
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