Point Cloud Library (PCL) 1.15.0
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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 // After clustering, indices are out of order, so sort them
110 std::sort (r.indices.begin (), r.indices.end ());
111
112 r.header = cloud.header;
113 clusters.push_back (r); // We could avoid a copy by working directly in the vector
114 }
115 else
116 {
117 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",
118 seed_queue.size (), min_pts_per_cluster, max_pts_per_cluster);
119 }
120 }
121}
122
123//////////////////////////////////////////////////////////////////////////////////////////////
124template <typename PointT> void
126 const Indices &indices,
127 const typename search::Search<PointT>::Ptr &tree,
128 float tolerance, std::vector<PointIndices> &clusters,
129 unsigned int min_pts_per_cluster,
130 unsigned int max_pts_per_cluster)
131{
132 // \note If the tree was created over <cloud, indices>, we guarantee a 1-1 mapping between what the tree returns
133 //and indices[i]
134 if (tree->getInputCloud()->size() != cloud.size()) {
135 PCL_ERROR("[pcl::extractEuclideanClusters] Tree built for a different point cloud "
136 "dataset (%zu) than the input cloud (%zu)!\n",
137 static_cast<std::size_t>(tree->getInputCloud()->size()),
138 static_cast<std::size_t>(cloud.size()));
139 return;
140 }
141 if (tree->getIndices()->size() != indices.size()) {
142 PCL_ERROR("[pcl::extractEuclideanClusters] Tree built for a different set of "
143 "indices (%zu) than the input set (%zu)!\n",
144 static_cast<std::size_t>(tree->getIndices()->size()),
145 indices.size());
146 return;
147 }
148 // Check if the tree is sorted -- if it is we don't need to check the first element
149 int nn_start_idx = tree->getSortedResults () ? 1 : 0;
150
151 // Create a bool vector of processed point indices, and initialize it to false
152 std::vector<bool> processed (cloud.size (), false);
153
154 Indices nn_indices;
155 std::vector<float> nn_distances;
156 // Process all points in the indices vector
157 for (const auto &index : indices)
158 {
159 if (processed[index])
160 continue;
161
162 Indices seed_queue;
163 int sq_idx = 0;
164 seed_queue.push_back (index);
165
166 processed[index] = true;
167
168 while (sq_idx < static_cast<int> (seed_queue.size ()))
169 {
170 // Search for sq_idx
171 int ret = tree->radiusSearch (cloud[seed_queue[sq_idx]], tolerance, nn_indices, nn_distances);
172 if( ret == -1)
173 {
174 PCL_ERROR("[pcl::extractEuclideanClusters] Received error code -1 from radiusSearch\n");
175 return;
176 }
177 if (!ret)
178 {
179 sq_idx++;
180 continue;
181 }
182
183 for (std::size_t j = nn_start_idx; j < nn_indices.size (); ++j) // can't assume sorted (default isn't!)
184 {
185 if (nn_indices[j] == UNAVAILABLE || processed[nn_indices[j]]) // Has this point been processed before ?
186 continue;
187
188 // Perform a simple Euclidean clustering
189 seed_queue.push_back (nn_indices[j]);
190 processed[nn_indices[j]] = true;
191 }
192
193 sq_idx++;
194 }
195
196 // If this queue is satisfactory, add to the clusters
197 if (seed_queue.size () >= min_pts_per_cluster && seed_queue.size () <= max_pts_per_cluster)
198 {
200 r.indices.resize (seed_queue.size ());
201 for (std::size_t j = 0; j < seed_queue.size (); ++j)
202 // This is the only place where indices come into play
203 r.indices[j] = seed_queue[j];
204
205 // After clustering, indices are out of order, so sort them
206 std::sort (r.indices.begin (), r.indices.end ());
207
208 r.header = cloud.header;
209 clusters.push_back (r); // We could avoid a copy by working directly in the vector
210 }
211 else
212 {
213 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",
214 seed_queue.size (), min_pts_per_cluster, max_pts_per_cluster);
215 }
216 }
217}
218
219//////////////////////////////////////////////////////////////////////////////////////////////
220//////////////////////////////////////////////////////////////////////////////////////////////
221//////////////////////////////////////////////////////////////////////////////////////////////
222
223template <typename PointT> void
224pcl::EuclideanClusterExtraction<PointT>::extract (std::vector<PointIndices> &clusters)
225{
226 if (!initCompute () ||
227 (input_ && input_->points.empty ()) ||
228 (indices_ && indices_->empty ()))
229 {
230 clusters.clear ();
231 return;
232 }
233
234 // Initialize the spatial locator
235 if (!tree_)
236 {
237 if (input_->isOrganized ())
238 tree_.reset (new pcl::search::OrganizedNeighbor<PointT> ());
239 else
240 tree_.reset (new pcl::search::KdTree<PointT> (false));
241 }
242
243 // Send the input dataset to the spatial locator
244 tree_->setInputCloud (input_, indices_);
245 extractEuclideanClusters (*input_, *indices_, tree_, static_cast<float> (cluster_tolerance_), clusters, min_pts_per_cluster_, max_pts_per_cluster_);
246
247 //tree_->setInputCloud (input_);
248 //extractEuclideanClusters (*input_, tree_, cluster_tolerance_, clusters, min_pts_per_cluster_, max_pts_per_cluster_);
249
250 // Sort the clusters based on their size (largest one first)
251 std::sort (clusters.rbegin (), clusters.rend (), comparePointClusters);
252
253 deinitCompute ();
254}
255
256#define PCL_INSTANTIATE_EuclideanClusterExtraction(T) template class PCL_EXPORTS pcl::EuclideanClusterExtraction<T>;
257#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);
258#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);
259
260#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.
pcl::PCLHeader header
The point cloud header.
std::size_t size() const
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 neighbor search in organized projectable point clo...
Definition organized.h:66
virtual bool getSortedResults()
Gets whether the results should be sorted (ascending in the distance) or not Otherwise the results ma...
Definition search.hpp:68
virtual IndicesConstPtr getIndices() const
Get a pointer to the vector of indices used.
Definition search.h:131
shared_ptr< pcl::search::Search< PointT > > Ptr
Definition search.h:81
virtual PointCloudConstPtr getInputCloud() const
Get a pointer to the input point cloud dataset.
Definition search.h:124
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.
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