68 std::vector<std::vector<PointIndices>>& labeled_clusters,
69 unsigned int min_pts_per_cluster,
70 unsigned int max_pts_per_cluster)
72 if (tree->getInputCloud()->size() != cloud.
size()) {
73 PCL_ERROR(
"[pcl::extractLabeledEuclideanClusters] Tree built for a different point "
74 "cloud dataset (%lu) than the input cloud (%lu)!\n",
75 tree->getInputCloud()->size(),
80 std::vector<bool> processed(cloud.
size(),
false);
83 std::vector<float> nn_distances;
86 for (
index_t i = 0; i < static_cast<index_t>(cloud.
size()); ++i) {
92 seed_queue.push_back(i);
96 while (sq_idx <
static_cast<int>(seed_queue.size())) {
98 int ret = tree->radiusSearch(seed_queue[sq_idx],
102 std::numeric_limits<int>::max());
104 PCL_ERROR(
"radiusSearch on tree came back with error -1");
110 for (std::size_t j = 1; j < nn_indices.size();
113 if (processed[nn_indices[j]])
115 if (cloud[i].label == cloud[nn_indices[j]].label) {
117 seed_queue.push_back(nn_indices[j]);
118 processed[nn_indices[j]] =
true;
126 if (seed_queue.size() >= min_pts_per_cluster &&
127 seed_queue.size() <= max_pts_per_cluster) {
129 r.
indices.resize(seed_queue.size());
130 for (std::size_t j = 0; j < seed_queue.size(); ++j)
137 labeled_clusters[cloud[i].label].push_back(
149 std::vector<std::vector<PointIndices>>& labeled_clusters)
151 if (!initCompute() || (input_ && input_->empty()) ||
152 (indices_ && indices_->empty())) {
153 labeled_clusters.clear();
159 if (input_->isOrganized())
166 tree_->setInputCloud(input_);
167 extractLabeledEuclideanClusters(*input_,
169 static_cast<float>(cluster_tolerance_),
171 min_pts_per_cluster_,
172 max_pts_per_cluster_);
175 for (
auto& labeled_cluster : labeled_clusters)
176 std::sort(labeled_cluster.rbegin(), labeled_cluster.rend(), comparePointClusters);
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, unsigned int max_pts_per_cluster, unsigned int max_label)
Decompose a region of space into clusters based on the Euclidean distance between points.