Point Cloud Library (PCL)
1.3.1
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IN NO EVENT SHALL THE 00025 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, 00026 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, 00027 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; 00028 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 00029 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT 00030 * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN 00031 * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 00032 * POSSIBILITY OF SUCH DAMAGE. 00033 * 00034 * 00035 */ 00036 00037 #ifndef PCL_SEGMENTATION_IMPL_EXTRACT_LABELED_CLUSTERS_H_ 00038 #define PCL_SEGMENTATION_IMPL_EXTRACT_LABELED_CLUSTERS_H_ 00039 00040 #include "pcl/segmentation/extract_labeled_clusters.h" 00041 00043 template <typename PointT> void 00044 pcl::extractLabeledEuclideanClusters (const PointCloud<PointT> &cloud, 00045 const boost::shared_ptr<search::Search<PointT> > &tree, 00046 float tolerance, 00047 std::vector<std::vector<PointIndices> > &labeled_clusters, 00048 unsigned int min_pts_per_cluster, 00049 unsigned int max_pts_per_cluster, 00050 unsigned int max_label) 00051 { 00052 if (tree->getInputCloud ()->points.size () != cloud.points.size ()) 00053 { 00054 PCL_ERROR ("[pcl::extractLabeledEuclideanClusters] Tree built for a different point cloud dataset (%lu) than the input cloud (%lu)!\n", (unsigned long)tree->getInputCloud ()->points.size (), (unsigned long)cloud.points.size ()); 00055 return; 00056 } 00057 // Create a bool vector of processed point indices, and initialize it to false 00058 std::vector<bool> processed (cloud.points.size (), false); 00059 00060 std::vector<int> nn_indices; 00061 std::vector<float> nn_distances; 00062 00063 // Process all points in the indices vector 00064 for (size_t i = 0; i < cloud.points.size (); ++i) 00065 { 00066 if (processed[i]) 00067 continue; 00068 00069 std::vector<int> seed_queue; 00070 int sq_idx = 0; 00071 seed_queue.push_back (i); 00072 00073 processed[i] = true; 00074 00075 while (sq_idx < (int)seed_queue.size ()) 00076 { 00077 // Search for sq_idx 00078 if (!tree->radiusSearch (seed_queue[sq_idx], tolerance, nn_indices, nn_distances)) 00079 { 00080 sq_idx++; 00081 continue; 00082 } 00083 00084 for (size_t j = 1; j < nn_indices.size (); ++j) // nn_indices[0] should be sq_idx 00085 { 00086 if (processed[nn_indices[j]]) // Has this point been processed before ? 00087 continue; 00088 if (cloud.points[i].label == cloud.points[nn_indices[j]].label) 00089 { 00090 // Perform a simple Euclidean clustering 00091 seed_queue.push_back (nn_indices[j]); 00092 processed[nn_indices[j]] = true; 00093 } 00094 } 00095 00096 sq_idx++; 00097 } 00098 00099 // If this queue is satisfactory, add to the clusters 00100 if (seed_queue.size () >= min_pts_per_cluster && seed_queue.size () <= max_pts_per_cluster) 00101 { 00102 pcl::PointIndices r; 00103 r.indices.resize (seed_queue.size ()); 00104 for (size_t j = 0; j < seed_queue.size (); ++j) 00105 r.indices[j] = seed_queue[j]; 00106 00107 std::sort (r.indices.begin (), r.indices.end ()); 00108 r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()), r.indices.end ()); 00109 00110 r.header = cloud.header; 00111 labeled_clusters[cloud.points[i].label].push_back (r); // We could avoid a copy by working directly in the vector 00112 } 00113 } 00114 } 00118 00119 template <typename PointT> void 00120 pcl::LabeledEuclideanClusterExtraction<PointT>::extract (std::vector<std::vector<PointIndices> > &labeled_clusters) 00121 { 00122 if (!initCompute () || 00123 (input_ != 0 && input_->points.empty ()) || 00124 (indices_ != 0 && indices_->empty ())) 00125 { 00126 labeled_clusters.clear (); 00127 return; 00128 } 00129 00130 // Initialize the spatial locator 00131 if (!tree_) 00132 { 00133 if (input_->isOrganized ()) 00134 tree_.reset (new pcl::search::OrganizedNeighbor<PointT> ()); 00135 else 00136 tree_.reset (new pcl::search::KdTree<PointT> (false)); 00137 } 00138 00139 // Send the input dataset to the spatial locator 00140 tree_->setInputCloud (input_); 00141 extractLabeledEuclideanClusters (*input_, tree_, cluster_tolerance_, labeled_clusters, min_pts_per_cluster_, max_pts_per_cluster_, max_label_); 00142 00143 // Sort the clusters based on their size (largest one first) 00144 for(unsigned int i = 0; i < labeled_clusters.size(); i++) 00145 std::sort (labeled_clusters[i].rbegin (), labeled_clusters[i].rend (), comparePointClusters); 00146 00147 deinitCompute (); 00148 } 00149 00150 #define PCL_INSTANTIATE_LabeledEuclideanClusterExtraction(T) template class PCL_EXPORTS pcl::LabeledEuclideanClusterExtraction<T>; 00151 #define PCL_INSTANTIATE_extractLabeledEuclideanClusters(T) template void PCL_EXPORTS pcl::extractLabeledEuclideanClusters<T>(const pcl::PointCloud<T> &, const boost::shared_ptr<pcl::search::Search<T> > &, float , std::vector<std::vector<pcl::PointIndices> > &, unsigned int, unsigned int, unsigned int); 00152 00153 #endif // PCL_EXTRACT_CLUSTERS_IMPL_H_