Point Cloud Library (PCL) 1.14.0
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extract_labeled_clusters.hpp
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36
37#ifndef PCL_SEGMENTATION_IMPL_EXTRACT_LABELED_CLUSTERS_H_
38#define PCL_SEGMENTATION_IMPL_EXTRACT_LABELED_CLUSTERS_H_
39
40#include <pcl/segmentation/extract_labeled_clusters.h>
41
42//////////////////////////////////////////////////////////////////////////////////////////////
43template <typename PointT>
44void
46 const PointCloud<PointT>& cloud,
47 const typename search::Search<PointT>::Ptr& tree,
48 float tolerance,
49 std::vector<std::vector<PointIndices>>& labeled_clusters,
50 unsigned int min_pts_per_cluster,
51 unsigned int max_pts_per_cluster)
52{
53 if (tree->getInputCloud()->size() != cloud.size()) {
54 PCL_ERROR("[pcl::extractLabeledEuclideanClusters] Tree built for a different point "
55 "cloud dataset (%lu) than the input cloud (%lu)!\n",
56 tree->getInputCloud()->size(),
57 cloud.size());
58 return;
59 }
60 // Create a bool vector of processed point indices, and initialize it to false
61 std::vector<bool> processed(cloud.size(), false);
62
63 Indices nn_indices;
64 std::vector<float> nn_distances;
65
66 // Process all points in the indices vector
67 for (index_t i = 0; i < static_cast<index_t>(cloud.size()); ++i) {
68 if (processed[i])
69 continue;
70
71 Indices seed_queue;
72 int sq_idx = 0;
73 seed_queue.push_back(i);
74
75 processed[i] = true;
76
77 while (sq_idx < static_cast<int>(seed_queue.size())) {
78 // Search for sq_idx
79 int ret = tree->radiusSearch(seed_queue[sq_idx],
80 tolerance,
81 nn_indices,
82 nn_distances,
83 std::numeric_limits<int>::max());
84 if (ret == -1)
85 PCL_ERROR("radiusSearch on tree came back with error -1");
86 if (!ret) {
87 sq_idx++;
88 continue;
89 }
90
91 for (std::size_t j = 1; j < nn_indices.size();
92 ++j) // nn_indices[0] should be sq_idx
93 {
94 if (processed[nn_indices[j]]) // Has this point been processed before ?
95 continue;
96 if (cloud[i].label == cloud[nn_indices[j]].label) {
97 // Perform a simple Euclidean clustering
98 seed_queue.push_back(nn_indices[j]);
99 processed[nn_indices[j]] = true;
100 }
101 }
102
103 sq_idx++;
104 }
105
106 // If this queue is satisfactory, add to the clusters
107 if (seed_queue.size() >= min_pts_per_cluster &&
108 seed_queue.size() <= max_pts_per_cluster) {
110 r.indices.resize(seed_queue.size());
111 for (std::size_t j = 0; j < seed_queue.size(); ++j)
112 r.indices[j] = seed_queue[j];
113 // After clustering, indices are out of order, so sort them
114 std::sort(r.indices.begin(), r.indices.end());
115
116 r.header = cloud.header;
117 labeled_clusters[cloud[i].label].push_back(
118 r); // We could avoid a copy by working directly in the vector
119 }
120 }
121}
122//////////////////////////////////////////////////////////////////////////////////////////////
123//////////////////////////////////////////////////////////////////////////////////////////////
124//////////////////////////////////////////////////////////////////////////////////////////////
125
126template <typename PointT>
127void
129 std::vector<std::vector<PointIndices>>& labeled_clusters)
130{
131 if (!initCompute() || (input_ && input_->empty()) ||
132 (indices_ && indices_->empty())) {
133 labeled_clusters.clear();
134 return;
135 }
136
137 // Initialize the spatial locator
138 if (!tree_) {
139 if (input_->isOrganized())
141 else
142 tree_.reset(new pcl::search::KdTree<PointT>(false));
143 }
144
145 // Send the input dataset to the spatial locator
146 tree_->setInputCloud(input_);
147 extractLabeledEuclideanClusters(*input_,
148 tree_,
149 static_cast<float>(cluster_tolerance_),
150 labeled_clusters,
151 min_pts_per_cluster_,
152 max_pts_per_cluster_);
153
154 // Sort the clusters based on their size (largest one first)
155 for (auto& labeled_cluster : labeled_clusters)
156 std::sort(labeled_cluster.rbegin(), labeled_cluster.rend(), comparePointClusters);
157
158 deinitCompute();
159}
160
161#define PCL_INSTANTIATE_LabeledEuclideanClusterExtraction(T) \
162 template class PCL_EXPORTS pcl::LabeledEuclideanClusterExtraction<T>;
163#define PCL_INSTANTIATE_extractLabeledEuclideanClusters(T) \
164 template void PCL_EXPORTS pcl::extractLabeledEuclideanClusters<T>( \
165 const pcl::PointCloud<T>&, \
166 const typename pcl::search::Search<T>::Ptr&, \
167 float, \
168 std::vector<std::vector<pcl::PointIndices>>&, \
169 unsigned int, \
170 unsigned int);
171
172#endif // PCL_EXTRACT_CLUSTERS_IMPL_H_
void extract(std::vector< std::vector< PointIndices > > &labeled_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:65
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 extractLabeledEuclideanClusters(const PointCloud< PointT > &cloud, const typename search::Search< PointT >::Ptr &tree, float tolerance, std::vector< std::vector< PointIndices > > &labeled_clusters, unsigned int min_pts_per_cluster=1, unsigned int max_pts_per_cluster=std::numeric_limits< unsigned int >::max())
Decompose a region of space into clusters based on the Euclidean distance between points.
detail::int_type_t< detail::index_type_size, detail::index_type_signed > index_t
Type used for an index in PCL.
Definition types.h:112
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
::pcl::PCLHeader header