Loading...
Searching...
No Matches
extract_clusters.hpp
/*
* Software License Agreement (BSD License)
*
* Copyright (c) 2009, Willow Garage, Inc.
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the copyright holder(s) nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*
* $Id$
*
*/
#ifndef PCL_SEGMENTATION_IMPL_EXTRACT_CLUSTERS_H_
#define PCL_SEGMENTATION_IMPL_EXTRACT_CLUSTERS_H_
#include <pcl/segmentation/extract_clusters.h>
#include <pcl/search/organized.h> // for OrganizedNeighbor
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::extractEuclideanClusters (const PointCloud<PointT> &cloud,
const typename search::Search<PointT>::Ptr &tree,
float tolerance, std::vector<PointIndices> &clusters,
unsigned int min_pts_per_cluster,
unsigned int max_pts_per_cluster)
{
if (tree->getInputCloud ()->size () != cloud.size ())
{
PCL_ERROR("[pcl::extractEuclideanClusters] Tree built for a different point cloud "
"dataset (%zu) than the input cloud (%zu)!\n",
static_cast<std::size_t>(tree->getInputCloud()->size()),
static_cast<std::size_t>(cloud.size()));
return;
}
// Check if the tree is sorted -- if it is we don't need to check the first element
int nn_start_idx = tree->getSortedResults () ? 1 : 0;
// Create a bool vector of processed point indices, and initialize it to false
std::vector<bool> processed (cloud.size (), false);
Indices nn_indices;
std::vector<float> nn_distances;
// Process all points in the indices vector
for (int i = 0; i < static_cast<int> (cloud.size ()); ++i)
{
if (processed[i])
continue;
Indices seed_queue;
int sq_idx = 0;
seed_queue.push_back (i);
processed[i] = true;
while (sq_idx < static_cast<int> (seed_queue.size ()))
{
// Search for sq_idx
if (!tree->radiusSearch (seed_queue[sq_idx], tolerance, nn_indices, nn_distances))
{
sq_idx++;
continue;
}
for (std::size_t j = nn_start_idx; j < nn_indices.size (); ++j) // can't assume sorted (default isn't!)
{
if (nn_indices[j] == UNAVAILABLE || processed[nn_indices[j]]) // Has this point been processed before ?
continue;
// Perform a simple Euclidean clustering
seed_queue.push_back (nn_indices[j]);
processed[nn_indices[j]] = true;
}
sq_idx++;
}
// If this queue is satisfactory, add to the clusters
if (seed_queue.size () >= min_pts_per_cluster && seed_queue.size () <= max_pts_per_cluster)
{
pcl::PointIndices r;
r.indices.resize (seed_queue.size ());
for (std::size_t j = 0; j < seed_queue.size (); ++j)
r.indices[j] = seed_queue[j];
// After clustering, indices are out of order, so sort them
std::sort (r.indices.begin (), r.indices.end ());
r.header = cloud.header;
clusters.push_back (r); // We could avoid a copy by working directly in the vector
}
else
{
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",
seed_queue.size (), min_pts_per_cluster, max_pts_per_cluster);
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::extractEuclideanClusters (const PointCloud<PointT> &cloud,
const Indices &indices,
const typename search::Search<PointT>::Ptr &tree,
float tolerance, std::vector<PointIndices> &clusters,
unsigned int min_pts_per_cluster,
unsigned int max_pts_per_cluster)
{
// \note If the tree was created over <cloud, indices>, we guarantee a 1-1 mapping between what the tree returns
//and indices[i]
if (tree->getInputCloud()->size() != cloud.size()) {
PCL_ERROR("[pcl::extractEuclideanClusters] Tree built for a different point cloud "
"dataset (%zu) than the input cloud (%zu)!\n",
static_cast<std::size_t>(tree->getInputCloud()->size()),
static_cast<std::size_t>(cloud.size()));
return;
}
if (tree->getIndices()->size() != indices.size()) {
PCL_ERROR("[pcl::extractEuclideanClusters] Tree built for a different set of "
"indices (%zu) than the input set (%zu)!\n",
static_cast<std::size_t>(tree->getIndices()->size()),
indices.size());
return;
}
// Check if the tree is sorted -- if it is we don't need to check the first element
int nn_start_idx = tree->getSortedResults () ? 1 : 0;
// Create a bool vector of processed point indices, and initialize it to false
std::vector<bool> processed (cloud.size (), false);
Indices nn_indices;
std::vector<float> nn_distances;
// Process all points in the indices vector
for (const auto &index : indices)
{
if (processed[index])
continue;
Indices seed_queue;
int sq_idx = 0;
seed_queue.push_back (index);
processed[index] = true;
while (sq_idx < static_cast<int> (seed_queue.size ()))
{
// Search for sq_idx
int ret = tree->radiusSearch (cloud[seed_queue[sq_idx]], tolerance, nn_indices, nn_distances);
if( ret == -1)
{
PCL_ERROR("[pcl::extractEuclideanClusters] Received error code -1 from radiusSearch\n");
return;
}
if (!ret)
{
sq_idx++;
continue;
}
for (std::size_t j = nn_start_idx; j < nn_indices.size (); ++j) // can't assume sorted (default isn't!)
{
if (nn_indices[j] == UNAVAILABLE || processed[nn_indices[j]]) // Has this point been processed before ?
continue;
// Perform a simple Euclidean clustering
seed_queue.push_back (nn_indices[j]);
processed[nn_indices[j]] = true;
}
sq_idx++;
}
// If this queue is satisfactory, add to the clusters
if (seed_queue.size () >= min_pts_per_cluster && seed_queue.size () <= max_pts_per_cluster)
{
pcl::PointIndices r;
r.indices.resize (seed_queue.size ());
for (std::size_t j = 0; j < seed_queue.size (); ++j)
// This is the only place where indices come into play
r.indices[j] = seed_queue[j];
// After clustering, indices are out of order, so sort them
std::sort (r.indices.begin (), r.indices.end ());
r.header = cloud.header;
clusters.push_back (r); // We could avoid a copy by working directly in the vector
}
else
{
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",
seed_queue.size (), min_pts_per_cluster, max_pts_per_cluster);
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::EuclideanClusterExtraction<PointT>::extract (std::vector<PointIndices> &clusters)
{
if (!initCompute () ||
(input_ && input_->points.empty ()) ||
(indices_ && indices_->empty ()))
{
clusters.clear ();
return;
}
// Initialize the spatial locator
if (!tree_)
{
if (input_->isOrganized ())
tree_.reset (new pcl::search::OrganizedNeighbor<PointT> ());
else
tree_.reset (new pcl::search::KdTree<PointT> (false));
}
// Send the input dataset to the spatial locator
tree_->setInputCloud (input_, indices_);
extractEuclideanClusters (*input_, *indices_, tree_, static_cast<float> (cluster_tolerance_), clusters, min_pts_per_cluster_, max_pts_per_cluster_);
//tree_->setInputCloud (input_);
//extractEuclideanClusters (*input_, tree_, cluster_tolerance_, clusters, min_pts_per_cluster_, max_pts_per_cluster_);
// Sort the clusters based on their size (largest one first)
std::sort (clusters.rbegin (), clusters.rend (), comparePointClusters);
deinitCompute ();
}
#define PCL_INSTANTIATE_EuclideanClusterExtraction(T) template class PCL_EXPORTS pcl::EuclideanClusterExtraction<T>;
#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);
#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);
#endif // PCL_EXTRACT_CLUSTERS_IMPL_H_
pcl::EuclideanClusterExtraction::extract
void extract(std::vector< PointIndices > &clusters)
Cluster extraction in a PointCloud given by <setInputCloud (), setIndices ()>
Definition extract_clusters.hpp:224
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition point_cloud.h:173
pcl::PointCloud::header
pcl::PCLHeader header
The point cloud header.
Definition point_cloud.h:392
pcl::PointCloud::size
std::size_t size() const
Definition point_cloud.h:443
pcl::search::KdTree
search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...
Definition kdtree.h:62
pcl::search::OrganizedNeighbor
OrganizedNeighbor is a class for optimized nearest neighbor search in organized projectable point clo...
Definition organized.h:65
pcl::search::Search::getSortedResults
virtual bool getSortedResults()
Gets whether the results should be sorted (ascending in the distance) or not Otherwise the results ma...
Definition search.hpp:68
pcl::search::Search::getIndices
virtual IndicesConstPtr getIndices() const
Get a pointer to the vector of indices used.
Definition search.h:131
pcl::search::Search::Ptr
shared_ptr< pcl::search::Search< PointT > > Ptr
Definition search.h:81
pcl::search::Search::getInputCloud
virtual PointCloudConstPtr getInputCloud() const
Get a pointer to the input point cloud dataset.
Definition search.h:124
pcl::search::Search::radiusSearch
virtual int radiusSearch(const PointT &point, double radius, Indices &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const =0
Search for all the nearest neighbors of the query point in a given radius.
pcl::extractEuclideanClusters
void extractEuclideanClusters(const PointCloud< PointT > &cloud, const typename search::Search< PointT >::Ptr &tree, float tolerance, std::vector< PointIndices > &clusters, unsigned int min_pts_per_cluster=1, unsigned int max_pts_per_cluster=(std::numeric_limits< int >::max)())
Decompose a region of space into clusters based on the Euclidean distance between points.
Definition extract_clusters.hpp:46
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
pcl::PointIndices
Definition PointIndices.h:12
pcl::PointIndices::header
::pcl::PCLHeader header
Definition PointIndices.h:18
pcl::PointIndices::indices
Indices indices
Definition PointIndices.h:20