Point Cloud Library (PCL) 1.12.0
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extract_clusters.hpp
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37
38#ifndef PCL_SEGMENTATION_IMPL_EXTRACT_CLUSTERS_H_
39#define PCL_SEGMENTATION_IMPL_EXTRACT_CLUSTERS_H_
40
41#include <pcl/segmentation/extract_clusters.h>
42#include <pcl/search/organized.h> // for OrganizedNeighbor
43
44//////////////////////////////////////////////////////////////////////////////////////////////
45template <typename PointT> void
47 const typename search::Search<PointT>::Ptr &tree,
48 float tolerance, std::vector<PointIndices> &clusters,
49 unsigned int min_pts_per_cluster,
50 unsigned int max_pts_per_cluster)
51{
52 if (tree->getInputCloud ()->size () != cloud.size ())
53 {
54 PCL_ERROR("[pcl::extractEuclideanClusters] Tree built for a different point cloud "
55 "dataset (%zu) than the input cloud (%zu)!\n",
56 static_cast<std::size_t>(tree->getInputCloud()->size()),
57 static_cast<std::size_t>(cloud.size()));
58 return;
59 }
60 // Check if the tree is sorted -- if it is we don't need to check the first element
61 int nn_start_idx = tree->getSortedResults () ? 1 : 0;
62 // Create a bool vector of processed point indices, and initialize it to false
63 std::vector<bool> processed (cloud.size (), false);
64
65 Indices nn_indices;
66 std::vector<float> nn_distances;
67 // Process all points in the indices vector
68 for (int i = 0; i < static_cast<int> (cloud.size ()); ++i)
69 {
70 if (processed[i])
71 continue;
72
73 Indices seed_queue;
74 int sq_idx = 0;
75 seed_queue.push_back (i);
76
77 processed[i] = true;
78
79 while (sq_idx < static_cast<int> (seed_queue.size ()))
80 {
81 // Search for sq_idx
82 if (!tree->radiusSearch (seed_queue[sq_idx], tolerance, nn_indices, nn_distances))
83 {
84 sq_idx++;
85 continue;
86 }
87
88 for (std::size_t j = nn_start_idx; j < nn_indices.size (); ++j) // can't assume sorted (default isn't!)
89 {
90 if (nn_indices[j] == UNAVAILABLE || processed[nn_indices[j]]) // Has this point been processed before ?
91 continue;
92
93 // Perform a simple Euclidean clustering
94 seed_queue.push_back (nn_indices[j]);
95 processed[nn_indices[j]] = true;
96 }
97
98 sq_idx++;
99 }
100
101 // If this queue is satisfactory, add to the clusters
102 if (seed_queue.size () >= min_pts_per_cluster && seed_queue.size () <= max_pts_per_cluster)
103 {
105 r.indices.resize (seed_queue.size ());
106 for (std::size_t j = 0; j < seed_queue.size (); ++j)
107 r.indices[j] = seed_queue[j];
108
109 // These two lines should not be needed: (can anyone confirm?) -FF
110 std::sort (r.indices.begin (), r.indices.end ());
111 r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()), r.indices.end ());
112
113 r.header = cloud.header;
114 clusters.push_back (r); // We could avoid a copy by working directly in the vector
115 }
116 else
117 {
118 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",
119 seed_queue.size (), min_pts_per_cluster, max_pts_per_cluster);
120 }
121 }
122}
123
124//////////////////////////////////////////////////////////////////////////////////////////////
125/** @todo: fix the return value, make sure the exit is not needed anymore*/
126template <typename PointT> void
128 const Indices &indices,
129 const typename search::Search<PointT>::Ptr &tree,
130 float tolerance, std::vector<PointIndices> &clusters,
131 unsigned int min_pts_per_cluster,
132 unsigned int max_pts_per_cluster)
133{
134 // \note If the tree was created over <cloud, indices>, we guarantee a 1-1 mapping between what the tree returns
135 //and indices[i]
136 if (tree->getInputCloud()->size() != cloud.size()) {
137 PCL_ERROR("[pcl::extractEuclideanClusters] Tree built for a different point cloud "
138 "dataset (%zu) than the input cloud (%zu)!\n",
139 static_cast<std::size_t>(tree->getInputCloud()->size()),
140 static_cast<std::size_t>(cloud.size()));
141 return;
142 }
143 if (tree->getIndices()->size() != indices.size()) {
144 PCL_ERROR("[pcl::extractEuclideanClusters] Tree built for a different set of "
145 "indices (%zu) than the input set (%zu)!\n",
146 static_cast<std::size_t>(tree->getIndices()->size()),
147 indices.size());
148 return;
149 }
150 // Check if the tree is sorted -- if it is we don't need to check the first element
151 int nn_start_idx = tree->getSortedResults () ? 1 : 0;
152
153 // Create a bool vector of processed point indices, and initialize it to false
154 std::vector<bool> processed (cloud.size (), false);
155
156 Indices nn_indices;
157 std::vector<float> nn_distances;
158 // Process all points in the indices vector
159 for (const auto &index : indices)
160 {
161 if (processed[index])
162 continue;
163
164 Indices seed_queue;
165 int sq_idx = 0;
166 seed_queue.push_back (index);
167
168 processed[index] = true;
169
170 while (sq_idx < static_cast<int> (seed_queue.size ()))
171 {
172 // Search for sq_idx
173 int ret = tree->radiusSearch (cloud[seed_queue[sq_idx]], tolerance, nn_indices, nn_distances);
174 if( ret == -1)
175 {
176 PCL_ERROR("[pcl::extractEuclideanClusters] Received error code -1 from radiusSearch\n");
177 exit(0);
178 }
179 if (!ret)
180 {
181 sq_idx++;
182 continue;
183 }
184
185 for (std::size_t j = nn_start_idx; j < nn_indices.size (); ++j) // can't assume sorted (default isn't!)
186 {
187 if (nn_indices[j] == UNAVAILABLE || processed[nn_indices[j]]) // Has this point been processed before ?
188 continue;
189
190 // Perform a simple Euclidean clustering
191 seed_queue.push_back (nn_indices[j]);
192 processed[nn_indices[j]] = true;
193 }
194
195 sq_idx++;
196 }
197
198 // If this queue is satisfactory, add to the clusters
199 if (seed_queue.size () >= min_pts_per_cluster && seed_queue.size () <= max_pts_per_cluster)
200 {
202 r.indices.resize (seed_queue.size ());
203 for (std::size_t j = 0; j < seed_queue.size (); ++j)
204 // This is the only place where indices come into play
205 r.indices[j] = seed_queue[j];
206
207 // These two lines should not be needed: (can anyone confirm?) -FF
208 //r.indices.assign(seed_queue.begin(), seed_queue.end());
209 std::sort (r.indices.begin (), r.indices.end ());
210 r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()), r.indices.end ());
211
212 r.header = cloud.header;
213 clusters.push_back (r); // We could avoid a copy by working directly in the vector
214 }
215 else
216 {
217 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",
218 seed_queue.size (), min_pts_per_cluster, max_pts_per_cluster);
219 }
220 }
221}
222
223//////////////////////////////////////////////////////////////////////////////////////////////
224//////////////////////////////////////////////////////////////////////////////////////////////
225//////////////////////////////////////////////////////////////////////////////////////////////
226
227template <typename PointT> void
228pcl::EuclideanClusterExtraction<PointT>::extract (std::vector<PointIndices> &clusters)
229{
230 if (!initCompute () ||
231 (input_ && input_->points.empty ()) ||
232 (indices_ && indices_->empty ()))
233 {
234 clusters.clear ();
235 return;
236 }
237
238 // Initialize the spatial locator
239 if (!tree_)
240 {
241 if (input_->isOrganized ())
242 tree_.reset (new pcl::search::OrganizedNeighbor<PointT> ());
243 else
244 tree_.reset (new pcl::search::KdTree<PointT> (false));
245 }
246
247 // Send the input dataset to the spatial locator
248 tree_->setInputCloud (input_, indices_);
249 extractEuclideanClusters (*input_, *indices_, tree_, static_cast<float> (cluster_tolerance_), clusters, min_pts_per_cluster_, max_pts_per_cluster_);
250
251 //tree_->setInputCloud (input_);
252 //extractEuclideanClusters (*input_, tree_, cluster_tolerance_, clusters, min_pts_per_cluster_, max_pts_per_cluster_);
253
254 // Sort the clusters based on their size (largest one first)
255 std::sort (clusters.rbegin (), clusters.rend (), comparePointClusters);
256
257 deinitCompute ();
258}
259
260#define PCL_INSTANTIATE_EuclideanClusterExtraction(T) template class PCL_EXPORTS pcl::EuclideanClusterExtraction<T>;
261#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);
262#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);
263
264#endif // PCL_EXTRACT_CLUSTERS_IMPL_H_
void extract(std::vector< PointIndices > &clusters)
Cluster extraction in a PointCloud given by <setInputCloud (), setIndices ()>
PointCloud represents the base class in PCL for storing collections of 3D points.
std::size_t size() const
pcl::PCLHeader header
The point cloud header.
search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...
Definition kdtree.h:62
OrganizedNeighbor is a class for optimized nearest neigbhor search in organized point clouds.
Definition organized.h:61
shared_ptr< pcl::search::Search< PointT > > Ptr
Definition search.h:81
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.
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
::pcl::PCLHeader header