PCL点云聚类及可视化C++代码详解

PCL点云聚类及可视化C++代码详解

这篇博客将介绍如何使用PCL库对点云数据进行聚类分析，并通过代码示例演示如何可视化聚类结果。

代码示例cpp#include #include <pcl/point_types.h>#include <pcl/features/normal_3d.h>#include <pcl/io/ply_io.h>#include <pcl/kdtree/kdtree.h>#include <pcl/segmentation/extract_clusters.h>#include <pcl/kdtree/kdtree_flann.h>#include <pcl/visualization/cloud_viewer.h>

using namespace pcl;

int main(){ // 读取点云数据 pcl::PointCloudpcl::PointXYZ::Ptr cloud(new pcl::PointCloudpcl::PointXYZ); pcl::PointCloudpcl::PointXYZ::Ptr clouds(new pcl::PointCloudpcl::PointXYZ); pcl::io::loadPLYFilepcl::PointXYZ('E:\dianyun\09.ply', *cloud); //将骨架点都添加到原始点云内 for (const auto& point : clouds->points) { cloud->push_back(point); } //pcl::io::savePLYFileBinary('merged_cloud.ply', *cloud);

// 创建kdtree对象    pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>);    tree->setInputCloud(cloud);

// 设置已知点p为聚类起点    int p_index = 640; // 假设已知点p的索引为100

// 定义聚类对象    pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;    ec.setClusterTolerance(0.008); // 设置聚类的容差    ec.setMinClusterSize(100); // 设置聚类的最小点数    ec.setMaxClusterSize(5000); // 设置聚类的最大点数    ec.setSearchMethod(tree);    ec.setInputCloud(cloud);

// 设置聚类的种子点    pcl::PointIndices::Ptr seed_indices(new pcl::PointIndices);

seed_indices->indices.push_back(p_index);    ec.setIndices(seed_indices);

// 执行聚类    std::vector<pcl::PointIndices> cluster_indices;    ec.extract(cluster_indices);

// 输出聚类结果    for (const auto &indices : cluster_indices)    {        std::cout << 'Cluster with ' << indices.indices.size() << ' points:' << std::endl;        for (const auto &index : indices.indices)        {            std::cout << '  (' << cloud->points[index].x << ', '                << cloud->points[index].y << ', '                << cloud->points[index].z << ')' << std::endl;        }        std::cout << std::endl;    }    // 可视化聚类结果    pcl::visualization::CloudViewer viewer('Cluster Viewer');    viewer.showCloud(cloud);    while (!viewer.wasStopped())    {

}

return 0