在PCL中实现欧氏聚类提取

1、数据集

table_scene_lms400.pcd

2、代码

#include <pcl/ModelCoefficients.h> #include <pcl/point_types.h> #include <pcl/io/pcd_io.h> #include <pcl/filters/extract_indices.h> #include <pcl/filters/voxel_grid.h> #include <pcl/features/normal_3d.h> #include <pcl/kdtree/kdtree.h> #include <pcl/sample_consensus/method_types.h> #include <pcl/sample_consensus/model_types.h> #include <pcl/segmentation/sac_segmentation.h> #include <pcl/segmentation/extract_clusters.h> int main (int argc, char** argv) { //读入点云数据table_scene_lms400.pcd pcl::PCDReader reader; pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>), cloud_f (new pcl::PointCloud<pcl::PointXYZ>); reader.read ("table_scene_lms400.pcd", *cloud); std::cout << "PointCloud before filtering has: " << cloud->points.size () << " data points." << std::endl; //* /*从输入的.PCD文件载入数据后，我们创建了一个VoxelGrid滤波器对数据进行下采样，我们在这里进行下采样的原 因是来加速处理过程，越少的点意味着分割循环中处理起来越快。*/ // Create the filtering object: downsample the dataset using a leaf size of 1cm pcl::VoxelGrid<pcl::PointXYZ> vg; //体素栅格下采样对象 pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>); vg.setInputCloud (cloud); vg.setLeafSize (0.01f, 0.01f, 0.01f); //设置采样的体素大小 vg.filter (*cloud_filtered); //执行采样保存数据 std::cout << "PointCloud after filtering has: " << cloud_filtered->points.size () << " data points." << std::endl; //* // Create the segmentation object for the planar model and set all the parameters pcl::SACSegmentation<pcl::PointXYZ> seg;//创建分割对象 pcl::PointIndices::Ptr inliers (new pcl::PointIndices); pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_plane (new pcl::PointCloud<pcl::PointXYZ> ()); pcl::PCDWriter writer; seg.setOptimizeCoefficients (true); //设置对估计的模型参数进行优化处理 seg.setModelType (pcl::SACMODEL_PLANE);//设置分割模型类别 seg.setMethodType (pcl::SAC_RANSAC);//设置用哪个随机参数估计方法 seg.setMaxIterations (100); //设置最大迭代次数 seg.setDistanceThreshold (0.02); //设置判断是否为模型内点的距离阈值 int i=0, nr_points = (int) cloud_filtered->points.size (); while (cloud_filtered->points.size () > 0.3 * nr_points) { // Segment the largest planar component from the remaining cloud /*为了处理点云中包含多个模型，我们在一个循环中执行该过程，并在每次模型被提取后，我们保存剩余的点，进行迭代。模型内点通过分割过程获取，如下*/ seg.setInputCloud (cloud_filtered); seg.segment (*inliers, *coefficients); if (inliers->indices.size () == 0) { std::cout << "Could not estimate a planar model for the given dataset." << std::endl; break; } //移去平面局内点，提取剩余点云 pcl::ExtractIndices<pcl::PointXYZ> extract; //创建点云提取对象 extract.setInputCloud (cloud_filtered); //设置输入点云 extract.setIndices (inliers); //设置分割后的内点为需要提取的点集 extract.setNegative (false); //设置提取内点而非外点 // Get the points associated with the planar surface extract.filter (*cloud_plane); //提取输出存储到cloud_plane std::cout << "PointCloud representing the planar component: " << cloud_plane->points.size () << " data points." << std::endl; // Remove the planar inliers, extract the rest extract.setNegative (true); extract.filter (*cloud_f); *cloud_filtered = *cloud_f; } // 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); //创建点云索引向量，用于存储实际的点云信息 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为2cm ec.setMinClusterSize (100);//设置一个聚类需要的最少点数目为100 ec.setMaxClusterSize (25000);//设置一个聚类需要的最大点数目为25000 ec.setSearchMethod (tree);//设置点云的搜索机制 ec.setInputCloud (cloud_filtered); ec.extract (cluster_indices);//从点云中提取聚类，并将点云索引保存在cluster_indices中 /*为了从点云索引向量中分割出每个聚类，必须迭代访问点云索引，每次创建一个新的点云数据集，并且将所有当前聚类的点写入到点云数据集中。*/ //迭代访问点云索引cluster_indices，直到分割出所有聚类 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>); //创建新的点云数据集cloud_cluster，将所有当前聚类写入到点云数据集中 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++; } return (0); }