265 lines
11 KiB
C++
265 lines
11 KiB
C++
/*
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* Software License Agreement (BSD License)
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*
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* Copyright (c) 2009, Willow Garage, Inc.
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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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*/
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#ifndef PCL_SEGMENTATION_IMPL_EXTRACT_CLUSTERS_H_
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#define PCL_SEGMENTATION_IMPL_EXTRACT_CLUSTERS_H_
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#include <pcl/segmentation/extract_clusters.h>
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#include <pcl/search/organized.h> // for OrganizedNeighbor
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointT> void
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pcl::extractEuclideanClusters (const PointCloud<PointT> &cloud,
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const typename search::Search<PointT>::Ptr &tree,
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float tolerance, std::vector<PointIndices> &clusters,
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unsigned int min_pts_per_cluster,
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unsigned int max_pts_per_cluster)
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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::extractEuclideanClusters] 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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// Check if the tree is sorted -- if it is we don't need to check the first element
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int nn_start_idx = tree->getSortedResults () ? 1 : 0;
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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 (int i = 0; i < static_cast<int> (cloud.size ()); ++i)
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{
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if (processed[i])
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continue;
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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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processed[i] = true;
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while (sq_idx < static_cast<int> (seed_queue.size ()))
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{
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// Search for sq_idx
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if (!tree->radiusSearch (seed_queue[sq_idx], tolerance, nn_indices, nn_distances))
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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 = nn_start_idx; j < nn_indices.size (); ++j) // can't assume sorted (default isn't!)
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{
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if (nn_indices[j] == UNAVAILABLE || processed[nn_indices[j]]) // Has this point been processed before ?
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continue;
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// Perform a simple Euclidean clustering
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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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sq_idx++;
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}
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// If this queue is satisfactory, add to the clusters
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if (seed_queue.size () >= min_pts_per_cluster && seed_queue.size () <= max_pts_per_cluster)
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{
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pcl::PointIndices r;
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r.indices.resize (seed_queue.size ());
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for (std::size_t j = 0; j < seed_queue.size (); ++j)
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r.indices[j] = seed_queue[j];
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// These two lines should not be needed: (can anyone confirm?) -FF
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std::sort (r.indices.begin (), r.indices.end ());
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r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()), r.indices.end ());
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r.header = cloud.header;
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clusters.push_back (r); // We could avoid a copy by working directly in the vector
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}
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else
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{
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PCL_DEBUG("[pcl::extractEuclideanClusters] This cluster has %zu points, which is not between %u and %u points, so it is not a final cluster\n",
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seed_queue.size (), min_pts_per_cluster, max_pts_per_cluster);
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}
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}
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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/** @todo: fix the return value, make sure the exit is not needed anymore*/
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template <typename PointT> void
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pcl::extractEuclideanClusters (const PointCloud<PointT> &cloud,
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const Indices &indices,
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const typename search::Search<PointT>::Ptr &tree,
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float tolerance, std::vector<PointIndices> &clusters,
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unsigned int min_pts_per_cluster,
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unsigned int max_pts_per_cluster)
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{
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// \note If the tree was created over <cloud, indices>, we guarantee a 1-1 mapping between what the tree returns
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//and indices[i]
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if (tree->getInputCloud()->size() != cloud.size()) {
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PCL_ERROR("[pcl::extractEuclideanClusters] 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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if (tree->getIndices()->size() != indices.size()) {
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PCL_ERROR("[pcl::extractEuclideanClusters] Tree built for a different set of "
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"indices (%zu) than the input set (%zu)!\n",
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static_cast<std::size_t>(tree->getIndices()->size()),
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indices.size());
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return;
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}
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// Check if the tree is sorted -- if it is we don't need to check the first element
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int nn_start_idx = tree->getSortedResults () ? 1 : 0;
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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 &index : indices)
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{
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if (processed[index])
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continue;
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Indices seed_queue;
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int sq_idx = 0;
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seed_queue.push_back (index);
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processed[index] = true;
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while (sq_idx < static_cast<int> (seed_queue.size ()))
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{
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// Search for sq_idx
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int ret = tree->radiusSearch (cloud[seed_queue[sq_idx]], tolerance, nn_indices, nn_distances);
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if( ret == -1)
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{
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PCL_ERROR("[pcl::extractEuclideanClusters] Received error code -1 from radiusSearch\n");
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exit(0);
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}
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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 = nn_start_idx; j < nn_indices.size (); ++j) // can't assume sorted (default isn't!)
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{
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if (nn_indices[j] == UNAVAILABLE || processed[nn_indices[j]]) // Has this point been processed before ?
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continue;
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// Perform a simple Euclidean clustering
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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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sq_idx++;
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}
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// If this queue is satisfactory, add to the clusters
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if (seed_queue.size () >= min_pts_per_cluster && seed_queue.size () <= max_pts_per_cluster)
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{
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pcl::PointIndices r;
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r.indices.resize (seed_queue.size ());
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for (std::size_t j = 0; j < seed_queue.size (); ++j)
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// This is the only place where indices come into play
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r.indices[j] = seed_queue[j];
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// These two lines should not be needed: (can anyone confirm?) -FF
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//r.indices.assign(seed_queue.begin(), seed_queue.end());
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std::sort (r.indices.begin (), r.indices.end ());
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r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()), r.indices.end ());
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r.header = cloud.header;
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clusters.push_back (r); // We could avoid a copy by working directly in the vector
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}
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else
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{
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PCL_DEBUG("[pcl::extractEuclideanClusters] This cluster has %zu points, which is not between %u and %u points, so it is not a final cluster\n",
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seed_queue.size (), min_pts_per_cluster, max_pts_per_cluster);
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}
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}
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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//////////////////////////////////////////////////////////////////////////////////////////////
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//////////////////////////////////////////////////////////////////////////////////////////////
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template <typename PointT> void
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pcl::EuclideanClusterExtraction<PointT>::extract (std::vector<PointIndices> &clusters)
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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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clusters.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<PointT> ());
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else
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tree_.reset (new pcl::search::KdTree<PointT> (false));
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}
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// Send the input dataset to the spatial locator
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tree_->setInputCloud (input_, indices_);
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extractEuclideanClusters (*input_, *indices_, tree_, static_cast<float> (cluster_tolerance_), clusters, min_pts_per_cluster_, max_pts_per_cluster_);
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//tree_->setInputCloud (input_);
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//extractEuclideanClusters (*input_, tree_, cluster_tolerance_, clusters, min_pts_per_cluster_, max_pts_per_cluster_);
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// Sort the clusters based on their size (largest one first)
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std::sort (clusters.rbegin (), clusters.rend (), comparePointClusters);
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deinitCompute ();
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}
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#define PCL_INSTANTIATE_EuclideanClusterExtraction(T) template class PCL_EXPORTS pcl::EuclideanClusterExtraction<T>;
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#define PCL_INSTANTIATE_extractEuclideanClusters(T) template void PCL_EXPORTS pcl::extractEuclideanClusters<T>(const pcl::PointCloud<T> &, const typename pcl::search::Search<T>::Ptr &, float , std::vector<pcl::PointIndices> &, unsigned int, unsigned int);
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#define PCL_INSTANTIATE_extractEuclideanClusters_indices(T) template void PCL_EXPORTS pcl::extractEuclideanClusters<T>(const pcl::PointCloud<T> &, const pcl::Indices &, const typename pcl::search::Search<T>::Ptr &, float , std::vector<pcl::PointIndices> &, unsigned int, unsigned int);
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#endif // PCL_EXTRACT_CLUSTERS_IMPL_H_
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