【PCL】欧式聚类提取（Segmentation）

#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/search/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()
{
	// 读取点云数据
	pcl::PCDReader reader;
	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>), cloud_f(new pcl::PointCloud<pcl::PointXYZ>);
	reader.read("data/table_scene_lms400.pcd", *cloud);
	std::cout << "PointCloud before filtering has: " << cloud->size() << " data points." << std::endl;
	// 创建滤波对象: 体素1cm的下采样
	pcl::VoxelGrid<pcl::PointXYZ> vg;
	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered(new pcl::PointCloud<pcl::PointXYZ>);
	vg.setInputCloud(cloud);
	vg.setLeafSize(0.01f, 0.01f, 0.01f);
	vg.filter(*cloud_filtered);
	std::cout << "PointCloud after filtering has: " << cloud_filtered->size() << " data points." << std::endl; //*

	// 为平面模型创建分割对象，并设置所有参数
	pcl::SACSegmentation<pcl::PointXYZ> seg;
	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);
	seg.setModelType(pcl::SACMODEL_PLANE);
	seg.setMethodType(pcl::SAC_RANSAC);
	seg.setMaxIterations(100);
	seg.setDistanceThreshold(0.02);

	int nr_points = (int)cloud_filtered->size();
	while (cloud_filtered->size() > 0.3 * nr_points)
	{
	    // 从剩下的点云中分割出最大的平面成分
		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;
		    }
	
		// 从输入点云中提取平面片段
		pcl::ExtractIndices<pcl::PointXYZ> extract;
	    extract.setInputCloud(cloud_filtered);
	    extract.setIndices(inliers);
	    extract.setNegative(false);
	
		// 求出与平面相关的点
	    extract.filter(*cloud_plane);
	    std::cout << "PointCloud representing the planar component: " << cloud_plane->size() << " data points." << std::endl;
	
		// 移除平面，提取其余部分
		extract.setNegative(true);
	    extract.filter(*cloud_f);
	    * cloud_filtered = *cloud_f;
	}

// 为提取的搜索方法创建KdTree对象
	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); // 2cm
	ec.setMinClusterSize(100);	//最小聚类尺寸
	ec.setMaxClusterSize(25000);	//最大聚类尺寸
	ec.setSearchMethod(tree);	//tree搜索方法
	ec.setInputCloud(cloud_filtered);	//将滤波后的点云作为输入
	ec.extract(cluster_indices);	//导出聚类片段数据

	int j = 0;
	for (const auto& cluster : cluster_indices)
	{
	    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster(new pcl::PointCloud<pcl::PointXYZ>);
	    for (const auto& idx : cluster.indices) {
		      cloud_cluster->push_back((*cloud_filtered)[idx]);
		
		} //*
	    cloud_cluster->width = cloud_cluster->size();
	    cloud_cluster->height = 1;
	    cloud_cluster->is_dense = true;
	
		std::cout << "PointCloud representing the Cluster: " << cloud_cluster->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;
}

#include "euclidean_cluster_core.h"

int main(int argc, char **argv)
{
    ros::init(argc, argv, "euclidean_cluster");

    ros::NodeHandle nh;	//nh对象

    EuClusterCore core(nh);     //欧式聚类处理
    
    return 0;
}

EuClusterCore::EuClusterCore(ros::NodeHandle &nh)
{
    seg_distance_ = {15, 30, 45, 60};	//分割距离
    cluster_distance_ = {0.5, 1.0, 1.5, 2.0, 2.5};	//cluster距离
    sub_point_cloud_ = nh.subscribe("/filtered_points_no_ground", 5, &EuClusterCore::point_cb, this);	//订阅
    pub_bounding_boxs_ = nh.advertise<jsk_recognition_msgs::BoundingBoxArray>("/detected_bounding_boxs", 5);	//发布

    ros::spin();
}

  void voxel_grid_filer(pcl::PointCloud<pcl::PointXYZ>::Ptr in, pcl::PointCloud<pcl::PointXYZ>::Ptr out, 
  						double leaf_size);

  void cluster_by_distance(pcl::PointCloud<pcl::PointXYZ>::Ptr in_pc, std::vector<Detected_Obj> &obj_list);

  void cluster_segment(pcl::PointCloud<pcl::PointXYZ>::Ptr in_pc,
                       double in_max_cluster_distance, std::vector<Detected_Obj> & obj_list);

  void point_cb(const sensor_msgs::PointCloud2ConstPtr &in_cloud_ptr);

  void publish_cloud(const ros::Publisher &in_publisher,
                     const pcl::PointCloud<pcl::PointXYZ>::Ptr in_cloud_to_publish_ptr,
                     const std_msgs::Header &in_header);