网站建设电话邀约话术,无人区在线影院免费高清,有路由器做网站,沈阳计算机培训机构欧式聚类实现方法大致是#xff1a;
1、找到空间中某点 p 1 p_1 p1#xff0c;用KD-Tree找到离他最近的n个点#xff0c;判断这n个点到 p 1 p_1 p1的距离。将距离小于阈值r的点 p 2 、 p 3 、 p 4 p_2、p_3、p_4 p2、p3、p4…放在类Q里
2、在 Q ( p 1 ) Q(p_1…欧式聚类实现方法大致是
1、找到空间中某点 p 1 p_1 p1用KD-Tree找到离他最近的n个点判断这n个点到 p 1 p_1 p1的距离。将距离小于阈值r的点 p 2 、 p 3 、 p 4 p_2、p_3、p_4 p2、p3、p4…放在类Q里
2、在 Q ( p 1 ) Q(p_1) Q(p1)里找到一点 p 2 p_2 p2 ,重复步骤1
3、在 Q ( p 1 , p 2 ) Q(p_1,p_2) Q(p1,p2)找到一点重复步骤1找到 p 22 、 p 23 p_{22}、p_{23} p22、p23… 全部放进Q里
4、当Q再也不能有新点加入了则完成搜索了
使用pcl库的欧式聚类
std::vectorpcl::PointIndices cluster_indices;
pcl::EuclideanClusterExtraction ec;
ec.setClusterTolerance (0.02); //设置近邻搜索的搜索半径为2cm
ec.setMinClusterSize (100);//设置一个聚类需要的最少点数目为100
ec.setMaxClusterSize (25000); //设置一个聚类需要的最大点数目为25000
ec.setSearchMethod (tree);//设置点云的搜索机制
ec.setInputCloud (cloud_filtered);
ec.extract (cluster_indices);//从点云中提取聚类并将点云索引保存在cluster_indices中
//迭代访问点云索引cluster_indices直到分割出所有聚类int j 0;for (std::vectorpcl::PointIndices::const_iterator it cluster_indices.begin (); it ! cluster_indices.end (); it)
{pcl::PointCloudpcl::PointXYZ::Ptr cloud_cluster (new pcl::PointCloudpcl::PointXYZ);//创建新的点云数据集cloud_cluster将所有当前聚类写入到点云数据集中for (std::vectorint::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; }pcl::visualization::CloudViewer viewer(Cloud Viewer);viewer.showCloud(cloud_cluster);pause();
}
pcl实现原理 pcl::extractEuclideanClusters (const PointCloudPointT cloud,const typename search::SearchPointT::Ptr tree,float tolerance, std::vectorPointIndices clusters,unsigned int min_pts_per_cluster,unsigned int max_pts_per_cluster)
{if (tree-getInputCloud ()-points.size () ! cloud.points.size ()) // 点数量检查{PCL_ERROR ([pcl::extractEuclideanClusters] Tree built for a different point cloud dataset (%lu) than the input cloud (%lu)!\n, tree-getInputCloud ()-points.size (), cloud.points.size ());return;}int nn_start_idx tree-getSortedResults () ? 1 : 0;std::vectorbool processed (cloud.points.size (), false);std::vectorint nn_indices;std::vectorfloat nn_distances; for (int i 0; i static_castint (cloud.points.size ()); i) //遍历点云中的每一个点{if (processed[i])continue;std::vectorint seed_queue;int sq_idx 0;seed_queue.push_back (i); processed[i] true;while (sq_idx static_castint (seed_queue.size ())) //遍历每一个种子{if (!tree-radiusSearch (seed_queue[sq_idx], tolerance, nn_indices, nn_distances)) {sq_idx;continue;}for (size_t j nn_start_idx; j nn_indices.size (); j) {if (nn_indices[j] -1 || processed[nn_indices[j]]) continue; seed_queue.push_back (nn_indices[j]); processed[nn_indices[j]] true;}sq_idx;}if (seed_queue.size () min_pts_per_cluster seed_queue.size () max_pts_per_cluster){pcl::PointIndices r;r.indices.resize (seed_queue.size ());for (size_t j 0; j seed_queue.size (); j)r.indices[j] seed_queue[j];std::sort (r.indices.begin (), r.indices.end ());r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()), r.indices.end ());r.header cloud.header;clusters.push_back (r);}}
}
