0.引言

  因笔者课题涉及点云处理，需要通过PCL进行点云数据一系列处理分析，查阅现有网络资料，对常用PCL点云分割进行代码实现，本文记录分割实现过程。

1.CloudCompare界面设计配准(segment)按钮

  （1）设计.ui文件
  ①设计按钮
  

  ②编译.ui
  

  （2）修改mainwindow.h文件
  

//点云分割
void doActionPCLEuclidean_Seg(); // 欧式聚类分割  
void doActionPCLRegion_Seg(); // 基于区域生长的分割

  （3）修改mainwindow.cpp文件
  ①添加头文件
  

#include <pcl/ModelCoefficients.h>
#include <pcl/sample_consensus/method_types.h>//模型定义头文件  
#include <pcl/sample_consensus/model_types.h>//随机参数估计方法头文件  
#include <pcl/segmentation/sac_segmentation.h>//基于采样一致性分割的类的头文件  
#include <pcl/segmentation/extract_clusters.h>  
#include <pcl/segmentation/region_growing.h>//区域生成分割的头文件  
#include <pcl/filters/extract_indices.h>

  ②添加实现代码
  

//欧式聚类分割
void MainWindow::doActionPCLEuclidean_Seg()  
{
      
}  
//基于区域生长的分割  
void MainWindow::PCLRegion_Seg()  
{
      
}

  ③添加信号槽函数
  

connect(m_UI->actionEuclidean_Seg, &QAction::triggered, this, &MainWindow::doActionPCLEuclidean_Seg);//欧式聚类分割
connect(m_UI->actionRegion_Seg, &QAction::triggered, this, &MainWindow::doActionPCLRegion_Seg);//基于区域生长的分割

  （4）生成
  

2.欧式聚类分割（Euclidean_Seg）

  （1）实现代码

//欧式聚类分割
void MainWindow::doActionPCLEuclidean_Seg()  
{
      
    if (getSelectedEntities().size() != 1)  
    {
      
        ccLog::Print(QStringLiteral("只能选择一个点云实体"));  
    return;  
    }  
    ccHObject* entity = getSelectedEntities()[0];  
    ccPointCloud* ccCloud = ccHObjectCaster::ToPointCloud(entity);  
    // ---------------------------读取数据到PCL----------------------------------  
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);  
    cloud->resize(ccCloud->size());  
    pcl::PointCloud<pcl::PointNormal>::Ptr incloud(new pcl::PointCloud <pcl::PointNormal>());  
    for (int i = 0; i < cloud->size(); ++i)  
    {
      
        const CCVector3* point = ccCloud->getPoint(i);  
        cloud->points[i].x = point->x;  
        cloud->points[i].y = point->y;  
        cloud->points[i].z = point->z;  
    pcl::PointNormal pt;  
    pt.x = point->x;  
    pt.y = point->y;  
    pt.z = point->z;  
    incloud->points.push_back(pt);  
    }  
    pcl::VoxelGrid<pcl::PointXYZ> vg;  
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered(new pcl::PointCloud<pcl::PointXYZ>);  
    vg.setInputCloud(cloud);  
    vg.setLeafSize(0.05f, 0.05f, 0.05f);  
    vg.filter(*cloud_filtered);  
    pcl::SACSegmentation<pcl::PointXYZ> seg;//实例化一个分割对象  
    pcl::PointIndices::Ptr inliers(new pcl::PointIndices);//实例化一个索引  
    pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients);//实例化模型参数  
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_plane(new pcl::PointCloud<pcl::PointXYZ>());//提取到的平面保存至cloud_plane  
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_f(new pcl::PointCloud<pcl::PointXYZ>());//提取到的平面保存至cloud_plane  
    //pcl::PCDWriter writer;  
    seg.setOptimizeCoefficients(true);//参数优化  
    seg.setModelType(pcl::SACMODEL_PLANE);//模型类型：平面  
    seg.setMethodType(pcl::SAC_RANSAC);//参数估计方法  
    seg.setMaxIterations(100);//最大迭代次数  
    seg.setDistanceThreshold(0.02);//设置内点到模型的距离允许最大值  
    //创建一个文件夹来放点云  
    ccHObject* CloudGroup = new ccHObject(QString("SegmentGroup"));  
    int i = 0, nr_points = (int)cloud_filtered->points.size();//计数变量i，记下提取的平面的个数  
    dispToConsole("cloud_filtered->points.size()1:" + QString::number(cloud_filtered->points.size()), ccMainAppInterface::STD_CONSOLE_MESSAGE);  
    while (cloud_filtered->points.size() > 0.3 * nr_points)  
    {
      
    // Segment the largest planar component from the remaining cloud  
    seg.setInputCloud(cloud_filtered);//设置要分割的点云  
    seg.segment(*inliers, *coefficients);//输出平面点的索引和参数  
    // Extract the planar inliers from the input cloud  
    pcl::ExtractIndices<pcl::PointXYZ> extract;//实例化一个提取对象  
    extract.setInputCloud(cloud_filtered);//设置要提取的点云  
    extract.setIndices(inliers);//根据分割得到的平面的索引提取平面  
    extract.setNegative(false);//提取内点  
      // Write the planar inliers to disk  
    extract.filter(*cloud_plane);//保存提取到的平面  
    //std::cout << "PointCloud representing the planar component: " << cloud_plane->points.size() << " data points." << std::endl;  
    //存写指针的参数  
    cloud_plane->width = cloud_plane->points.size();  
    cloud_plane->height = 1;  
    cloud_plane->resize(cloud_plane->width);  
    cloud_plane->is_dense = false;  
    ccPointCloud* newPointCloud = new ccPointCloud(QString::number(i + 1) + ".1segment");  
    for (int i = 0; i < cloud_plane->size(); ++i)  
    {
      
        double x = cloud_plane->points[i].x;  
        double y = cloud_plane->points[i].y;  
        double z = cloud_plane->points[i].z;  
        newPointCloud->addPoint(CCVector3(x, y, z));  
    }  
    newPointCloud->setRGBColor(ccColor::Rgba(255, 100, 100, 255));  
    newPointCloud->setRGBColor(ccColor::Rgba(rand() % 205 + 50, rand() % 155 + 100, rand() % 105 + 150, 255));  
    newPointCloud->showColors(true);  
    CloudGroup->addChild(newPointCloud);  
    //CloudGroup->getLastChild()->setEnabled(false);  
    addToDB(newPointCloud);  
    //计数变量加1  
    i++;  
    // Remove the planar inliers, extract the rest  
    extract.setNegative(true);//提取外点（除第一个平面之外的点）  
    extract.filter(*cloud_f);//保存除平面之外的剩余点  
    cloud_filtered = cloud_f;//将剩余点作为下一次分割、提取的平面的输入点云  
    }  
    // 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);//将无法提取平面的点云作为cloud_filtered  
    std::vector<pcl::PointIndices> cluster_indices;//保存每一种聚类，每一种聚类下还有具体的聚类的点  
    pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;//实例化一个欧式聚类提取对象  
    ec.setClusterTolerance(0.02); // 近邻搜索的搜索半径为2cm，重要参数  
    ec.setMinClusterSize(100);//设置一个聚类需要的最少点数目为100  
    ec.setMaxClusterSize(25000);//一个聚类最大点数目为25000  
    ec.setSearchMethod(tree);//设置点云的搜索机制  
    ec.setInputCloud(cloud_filtered);//设置输入点云  
    ec.extract(cluster_indices);//将聚类结果保存至cluster_indices中  
    //迭代访问点云索引cluster_indices，直到分割出所有聚类,一个循环提取出一类点云  
    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，直到分割出所有聚类  
        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;  
        ccPointCloud* newPointCloud = new ccPointCloud(QString::number(i + 1) + ".2segment");  
        for (int i = 0; i < cloud_cluster->size(); ++i)  
        {
      
            double x = cloud_cluster->points[i].x;  
            double y = cloud_cluster->points[i].y;  
            double z = cloud_cluster->points[i].z;  
            newPointCloud->addPoint(CCVector3(x, y, z));  
        }  
        newPointCloud->setRGBColor(ccColor::Rgba(255, 255, 255, 255));  
        newPointCloud->showColors(true);  
    CloudGroup->addChild(newPointCloud);  
    CloudGroup->getLastChild()->setEnabled(false);  
    addToDB(newPointCloud);  
    j++;  
    }  
    m_ccRoot->addElement(CloudGroup);  
}

  （2）分割结果
  ①分割前
  

  ②分割后
  

3.基于区域生长的分割（Region_Seg）

  （1）实现代码

//基于区域生长的分割
void MainWindow::doActionPCLRegion_Seg()  
{
      
    if (getSelectedEntities().size() != 1)  
    {
      
        ccLog::Print(QStringLiteral("只能选择一个点云实体"));  
    return;  
    }  
    ccHObject* entity = getSelectedEntities()[0];  
    ccPointCloud* ccCloud = ccHObjectCaster::ToPointCloud(entity);  
    // ---------------------------读取数据到PCL----------------------------------  
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);  
    cloud->resize(ccCloud->size());  
    pcl::PointCloud<pcl::PointNormal>::Ptr incloud(new pcl::PointCloud <pcl::PointNormal>());  
    for (int i = 0; i < cloud->size(); ++i)  
    {
      
        const CCVector3* point = ccCloud->getPoint(i);  
        cloud->points[i].x = point->x;  
        cloud->points[i].y = point->y;  
        cloud->points[i].z = point->z;  
    pcl::PointNormal pt;  
    pt.x = point->x;  
    pt.y = point->y;  
    pt.z = point->z;  
    incloud->points.push_back(pt);  
    }  
    int KN_normal = 50; //设置默认输入参数  
    bool Bool_Cuting = false;//设置默认输入参数  
    float far_cuting = 10, near_cuting = 0, SmoothnessThreshold = 30.0, CurvatureThreshold = 0.05;//设置默认输入参数  
    pcl::search::Search<pcl::PointXYZ>::Ptr tree = boost::shared_ptr<pcl::search::Search<pcl::PointXYZ> >(new pcl::search::KdTree<pcl::PointXYZ>);//创建一个指向kd树搜索对象的共享指针  
    pcl::PointCloud <pcl::Normal>::Ptr normals(new pcl::PointCloud <pcl::Normal>);  
    pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normal_estimator;//创建法线估计对象  
    normal_estimator.setSearchMethod(tree);//设置搜索方法  
    normal_estimator.setInputCloud(cloud);//设置法线估计对象输入点集  
    normal_estimator.setKSearch(KN_normal);// 设置用于法向量估计的k近邻数目  
    normal_estimator.compute(*normals);//计算并输出法向量  
    // 区域生长算法的5个参数  
    pcl::RegionGrowing<pcl::PointXYZ, pcl::Normal> reg;//创建区域生长分割对象  
    reg.setMinClusterSize(50);//设置一个聚类需要的最小点数  
    reg.setMaxClusterSize(1000000);//设置一个聚类需要的最大点数  
    reg.setSearchMethod(tree);//设置搜索方法  
    reg.setNumberOfNeighbours(30);//设置搜索的临近点数目  
    reg.setInputCloud(cloud);//设置输入点云  
    reg.setInputNormals(normals);//设置输入点云的法向量  
    reg.setSmoothnessThreshold(SmoothnessThreshold / 180.0 * M_PI);//设置平滑阈值  
    reg.setCurvatureThreshold(CurvatureThreshold);//设置曲率阈值  
    std::vector <pcl::PointIndices> clusters;//保存每一种聚类，每一种聚类下面还有具体的点  
    reg.extract(clusters);//获取聚类的结果，分割结果保存在点云索引的向量中。  
    //创建一个文件夹来放点云  
    ccHObject* CloudGroup = new ccHObject(QString("SegmentGroup"));  
    for (size_t i = 0; i < clusters.size(); i++)  
    {
      
        pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_seg1(new pcl::PointCloud<pcl::PointXYZ>);  
        for (std::vector<int>::const_iterator pit = clusters[i].indices.begin(); pit != clusters[i].indices.end(); pit++)//创建一个迭代器pit以访问第一个聚类的每一个点  
        {
      
            cloud_seg1->points.push_back(cloud->points[*pit]);//迭代器pit类似于一个指针，将第一个聚类分割中的每一个点进行强制类型转换，并放置在points中  
        }  
        cloud_seg1->width = cloud_seg1->points.size();  
        cloud_seg1->height = 1;  
        cloud_seg1->is_dense = false;  
    ccPointCloud* newPointCloud = new ccPointCloud(QString::number(i + 1) + ".Region_Seg");  
    for (int i = 0; i < cloud_seg1->size(); ++i)  
    {
      
        double x = cloud_seg1->points[i].x;  
        double y = cloud_seg1->points[i].y;  
        double z = cloud_seg1->points[i].z;  
        newPointCloud->addPoint(CCVector3(x, y, z));  
    }  
    //newPointCloud->setRGBColor(ccColor::Rgba(100, 255, 100, 255));  
    newPointCloud->setRGBColor(ccColor::Rgba(rand() % 105 + 150, rand() % 155 + 100, rand() % 205 + 50, 255));  
    newPointCloud->showColors(true);  
    CloudGroup->addChild(newPointCloud);  
    //CloudGroup->getLastChild()->setEnabled(false);  
    addToDB(newPointCloud);  
    }  
    m_ccRoot->addElement(CloudGroup);  
}

  （2）分割结果
  ①分割前
  

  ②分割后
  

参考资料：
[1] 来吧!我在未来等你!. CloudCompare二次开发之如何配置PCL点云库？; 2023-05-15 [accessed 2023-05-17].
[2] Tech沉思录. PCL 【点云分割】; 2019-08-17 [accessed 2023-05-17].
[3] 步子小不扯淡. PCL: Segmentation模块之SACSegmentation点云分割; 2014-09-07 [accessed 2023-05-17].
[4] SOC罗三炮. PCL教程-点云分割之区域生长分割算法; 2023-01-08 [accessed 2023-05-17].
[5] 悠缘之空. PCL函数库摘要——点云分割; 2021-11-07 [accessed 2023-05-17].