欧式聚类提取-------PCL-编程知识

欧式聚类提取-------PCL

欧式聚类

std::vector<pcl::PointCloud<pcl::PointXYZ>::Ptr> PclTool::euclideanClustering(const pcl::PointCloud<pcl::PointXYZ>::Ptr& cloud)
{std::vector<pcl::PointCloud<pcl::PointXYZ>::Ptr> clustered_clouds;// 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);// 创建平面模型分割的对象并设置参数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>());seg.setOptimizeCoefficients(true);seg.setModelType(pcl::SACMODEL_PLANE);  // 分割模型seg.setMethodType(pcl::SAC_RANSAC);     // 随机参数估计方法seg.setMaxIterations(100);              // 最大的迭代的次数seg.setDistanceThreshold(0.02);         // 设置阀值pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_f(new pcl::PointCloud<pcl::PointXYZ>);int i = 0, nr_points = (int)cloud_filtered->points.size();while (cloud_filtered->points.size() > 0.3 * nr_points)  // 滤波停止条件{// Segment the largest planar component from the remaining cloudseg.setInputCloud(cloud_filtered);  // 输入seg.segment(*inliers, *coefficients);if (inliers->indices.size() == 0){std::cout << "Could not estimate a planar model for the given dataset." << std::endl;break;}pcl::ExtractIndices<pcl::PointXYZ> extract;extract.setInputCloud(cloud_filtered);extract.setIndices(inliers);extract.setNegative(false);// Get the points associated with the planar surfaceextract.filter(*cloud_plane);  // [平面std::cout << "PointCloud representing the planar component: " << cloud_plane->points.size() << " data points." << std::endl;//  // 移去平面局内点，提取剩余点云extract.setNegative(true);extract.filter(*cloud_f);*cloud_filtered = *cloud_f;}// 创建KdTree对象用于欧式聚类的搜索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;  // 欧式聚类对象ec.setClusterTolerance(0.02);                       // 设置聚类容差为2cmec.setMinClusterSize(100);                          // 设置一个聚类的最小点数为100ec.setMaxClusterSize(25000);                        // 设置一个聚类的最大点数为25000ec.setSearchMethod(tree);                           // 设置搜索方法ec.setInputCloud(cloud_filtered);ec.extract(cluster_indices);  // 从点云中提取聚类// 迭代聚类索引并创建每个聚类的点云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;clustered_clouds.push_back(cloud_cluster);}return clustered_clouds;
}