PCL European point cloud clustering

 

#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>
#include <pcl/visualization/pcl_visualizer.h>
int
main(int argc, char** argv)
{
    // Read in the cloud data
    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; //*

    // 下采样滤波
    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->points.size() << " data points." << std::endl; //*

    //创建分割对象和参数
    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 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
        seg.setInputCloud(cloud_filtered);
        seg.segment(*inliers, *coefficients);
        //提取点
        pcl::ExtractIndices<pcl::PointXYZ> extract;
        extract.setInputCloud(cloud_filtered);
        extract.setIndices(inliers);
        extract.setNegative(false);
        // 提取的平面上的点保存到cloud_plane
        extract.filter(*cloud_plane);
        std::cout <<cloud_plane->points.size() <<:: endl STD;
         // the Remove The Planar inliers, The REST Extract 
        extract.setNegative ( to true ); 
        extract.filter ( * cloud_f); 
        cloud_filtered = cloud_f; 
    } 
    // Create a tree object Kd as the extraction method used in cloud point , 
    PCL :: :: Search KdTree <PCL PointXYZ ::> :: the Ptr Tree ( new new PCL :: :: Search KdTree <PCL :: PointXYZ> ); 
    Tree -> setInputCloud (cloud_filtered); 
    STD :: Vector <PCL: : PointIndices> cluster_indices; 
    PCL EuclideanClusterExtraction :: <PCL :: PointXYZ> EC; // cluster 
    ec.setClusterTolerance (0.02 ); // set the search radius neighbor searching 2cm 
    ec.setMinClusterSize ( 100 );     // get a minimum number of points required for a cluster 100 
    ec.setMaxClusterSize ( 25000 );   // set the maximum number of points required for a cluster as mesh 25000 
    ec.setSearchMethod (Tree);      // set point cloud search mechanism 
    ec.setInputCloud (cloud_filtered); // set the original point cloud 
    ec.extract (cluster_indices);   // extracted from the point cloud clustering 

    // visualization part 
    PCL :: :: PCLVisualizer Visualization Viewer ( " segmention " ); 
    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";
        
        int v2(0);
        pcl::visualization::PointCloudColorHandlerRandom<pcl::PointXYZ> cloud_in_color_h(cloud);//赋予显示点云的颜色,随机
        viewer.addPointCloud(cloud_cluster, cloud_in_color_h, std::to_string(j));
        j++;
    
    Wait until the visualization window is closed.//
    }
    the while (! viewer.wasStopped ()) 
    { 
        viewer.spinOnce ( 100 ); // refresh the screen display time, 100 to allow the maximum time to re-render a number of ms. 
        :: :: this_thread SLEEP Boost (Boost :: :: posix_time microseconds ( 100000 )); 
    } 

    return ( 0 ); 
}