Euclidean Cluster Extraction
欧式聚类流程如下：
http://pointclouds.org/documentation/tutorials/cluster_extraction.php#cluster-extraction
#include <pcl/kdtree/kdtree.h>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/segmentation/extract_clusters.h>
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);
// Creating the KdTree object for the search method of the extraction
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>);
tree->setInputCloud (cloud_filtered);
std::vector<pcl::PointIndices> cluster_indices;
pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;
ec.setClusterTolerance (0.02); //设置近邻搜索的搜索半径2cm
ec.setMinClusterSize (100); //设置一个聚类需要的最少点数目
ec.setMaxClusterSize (25000); //设置一个聚类需要的最大数目
ec.setSearchMethod (tree); //设置点云的搜索方法
ec.setInputCloud (cloud_filtered);
ec.extract (cluster_indices); //从点云中提取聚类并保存到cluster_indices中
//保存聚类后的点到磁盘
int j = 0;
for (std::vector<pcl::PointIndices>::const_iterator it = cluster_indices.begin (); it != cluster_indices.end (); ++it)
{
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster (new pcl::PointCloud<pcl::PointXYZ>);
for (std::vector<int>::const_iterator pit = it->indices.begin (); pit != it->indices.end (); ++pit)
cloud_cluster->points.push_back (cloud_filtered->points[*pit]); //*
cloud_cluster->width = cloud_cluster->points.size ();
cloud_cluster->height = 1;
cloud_cluster->is_dense = true;
std::cout << "PointCloud representing the Cluster: " << cloud_cluster->points.size () << " data points." << std::endl;
std::stringstream ss;
ss << "cloud_cluster_" << j << ".pcd";
writer.write<pcl::PointXYZ> (ss.str (), *cloud_cluster, false); //*
j++;
}