PCL_超体聚类实现

1.超体聚类——一种来自图像的分割方法

　　超体（supervoxel）是一种集合，集合的元素是“体”。与体素滤波器中的体类似，其本质是一个个的小方块。与之前提到的所有分割手段不同，超体聚类的目的并不是分割出某种特定物体，其对点云实施过分割(over segmentation)，将场景点云化成很多小块，并研究每个小块之间的关系。这种将更小单元合并的分割思路已经出现了有些年份了，在图像分割中，像素聚类形成超像素，以超像素关系来理解图像已经广为研究。本质上这种方法是对局部的一种总结，纹理，材质，颜色类似的部分会被自动的分割成一块，有利于后续识别工作。比如对人的识别，如果能将头发，面部，四肢，躯干分开，则能更好的对各种姿态，性别的人进行识别。

　　点云和图像不一样，其不存在像素邻接关系。所以，超体聚类之前，必须以八叉树对点云进行划分，获得不同点团之间的邻接关系。与图像相似点云的邻接关系也有很多，如面邻接，线邻接，点邻接。其具体解释如下图：

　　基于超体聚类的点云分割，使用点邻接（蓝色）作为相邻判据。

2.超体聚类的实现步骤

　举个简单的例子来体会下超体聚类，其过程和结晶类似。但不是水结晶成冰，而是盐溶液过饱和状态下的多晶核结晶。所有的晶核(seed)同时开始生长，最终填满整个空间，使物质具有晶体结构。　超体聚类实际上是一种特殊的区域生长算法，和无限制的生长不同，超体聚类首先需要规律的布置区域生长“晶核”。晶核在空间中实际上是均匀分布的,并指定晶核距离（Rseed)。再指定粒子距离(Rvoxel)。再指定最小晶粒(MOV)，过小的晶粒需要融入最近的大晶粒。关系如图所示：

　　有了晶粒和结晶范围之后，我们只需要控制结晶过程，就能将整个空间划分开了。结晶过程的本质就是不断吸纳类似的粒子（八分空间）。类似是一个比较模糊的概念，关于类似的定义有以下公式：

 

　　公式中的Dc,表示颜色上的差异，Dn表示法线上的差异，Ds代表点距离上的差异。w_*表示一系列权重。用于控制结晶形状。在晶核周围寻找一圈，D最小的体素被认为是下一个“被发展的党员”。需要注意的是，结晶过程并不是长完一个晶核再长下一个，二是所有的晶核同时开始生长（虽然计算机计算时必然有先后，但从层次上来说是同时的）。其生长顺序如下图所示：

　　接下来所有晶核继续公平竞争，发展第二个“党员”，以此循环，最终所有晶体应该几乎同时完成生长。整个点云也被晶格所分割开来。并且保证了一个晶包里的粒子都是类似的。

#include <pcl/console/parse.h>#include <pcl/point_cloud.h>#include <pcl/point_types.h>#include <pcl/io/pcd_io.h>#include <pcl/visualization/pcl_visualizer.h>#include <pcl/segmentation/supervoxel_clustering.h>//VTK include needed for drawing graph lines#include <vtkPolyLine.h>// Typestypedef pcl::PointXYZRGBA PointT;typedef pcl::PointCloud<PointT> PointCloudT;typedef pcl::PointNormal PointNT;typedef pcl::PointCloud<PointNT> PointNCloudT;typedef pcl::PointXYZL PointLT;typedef pcl::PointCloud<PointLT> PointLCloudT;void addSupervoxelConnectionsToViewer(PointT &supervoxel_center,PointCloudT &adjacent_supervoxel_centers,std::string supervoxel_name,boost::shared_ptr<pcl::visualization::PCLVisualizer> & viewer);intmain(int argc, char ** argv){if (argc < 2){pcl::console::print_error("Syntax is: %s <pcd-file> \n ""--NT Dsables the single cloud transform \n""-v <voxel resolution>\n-s <seed resolution>\n""-c <color weight> \n-z <spatial weight> \n""-n <normal_weight>\n", argv[0]);return (1);}PointCloudT::Ptr cloud = boost::shared_ptr <PointCloudT>(new PointCloudT());pcl::console::print_highlight("Loading point cloud...\n");if (pcl::io::loadPCDFile<PointT>(argv[1], *cloud)){pcl::console::print_error("Error loading cloud file!\n");return (1);}//设定结晶参数bool disable_transform = pcl::console::find_switch(argc, argv, "--NT");float voxel_resolution = 0.008f;bool voxel_res_specified = pcl::console::find_switch(argc, argv, "-v");if (voxel_res_specified)pcl::console::parse(argc, argv, "-v", voxel_resolution);float seed_resolution = 0.1f;bool seed_res_specified = pcl::console::find_switch(argc, argv, "-s");if (seed_res_specified)pcl::console::parse(argc, argv, "-s", seed_resolution);float color_importance = 0.2f;if (pcl::console::find_switch(argc, argv, "-c"))pcl::console::parse(argc, argv, "-c", color_importance);float spatial_importance = 0.4f;if (pcl::console::find_switch(argc, argv, "-z"))pcl::console::parse(argc, argv, "-z", spatial_importance);float normal_importance = 1.0f;if (pcl::console::find_switch(argc, argv, "-n"))pcl::console::parse(argc, argv, "-n", normal_importance);//////////////////////////////  //////////////////////////////////// This is how to use supervoxels//////////////////////////////  ////////////////////////////////输入点云及结晶参数pcl::SupervoxelClustering<PointT> super(voxel_resolution, seed_resolution);if (disable_transform)super.setUseSingleCameraTransform(false);super.setInputCloud(cloud);super.setColorImportance(color_importance);super.setSpatialImportance(spatial_importance);super.setNormalImportance(normal_importance);//输出结晶分割结果：结果是一个映射表std::map <uint32_t, pcl::Supervoxel<PointT>::Ptr > supervoxel_clusters;pcl::console::print_highlight("Extracting supervoxels!\n");super.extract(supervoxel_clusters);pcl::console::print_info("Found %d supervoxels\n", supervoxel_clusters.size());boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer("3D Viewer"));viewer->setBackgroundColor(0, 0, 0);//获得晶体中心PointCloudT::Ptr voxel_centroid_cloud = super.getVoxelCentroidCloud();viewer->addPointCloud(voxel_centroid_cloud, "voxel centroids");viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2.0, "voxel centroids");viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_OPACITY, 0.95, "voxel centroids");//获得晶体PointLCloudT::Ptr labeled_voxel_cloud = super.getLabeledVoxelCloud();viewer->addPointCloud(labeled_voxel_cloud, "labeled voxels");viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_OPACITY, 1, "labeled voxels");PointNCloudT::Ptr sv_normal_cloud = super.makeSupervoxelNormalCloud(supervoxel_clusters);//We have this disabled so graph is easy to see, uncomment to see supervoxel normals//viewer->addPointCloudNormals<PointNT> (sv_normal_cloud,1,0.05f, "supervoxel_normals");//将相连的晶体中心连起来并显示 pcl::console::print_highlight("Getting supervoxel adjacency\n");std::multimap<uint32_t, uint32_t> supervoxel_adjacency;super.getSupervoxelAdjacency(supervoxel_adjacency);//To make a graph of the supervoxel adjacency, we need to iterate through the supervoxel adjacency multimapstd::multimap<uint32_t, uint32_t>::iterator label_itr = supervoxel_adjacency.begin();for (; label_itr != supervoxel_adjacency.end(); ){//First get the labeluint32_t supervoxel_label = label_itr->first;//Now get the supervoxel corresponding to the labelpcl::Supervoxel<PointT>::Ptr supervoxel = supervoxel_clusters.at(supervoxel_label);//Now we need to iterate through the adjacent supervoxels and make a point cloud of themPointCloudT adjacent_supervoxel_centers;std::multimap<uint32_t, uint32_t>::iterator adjacent_itr = supervoxel_adjacency.equal_range(supervoxel_label).first;for (; adjacent_itr != supervoxel_adjacency.equal_range(supervoxel_label).second; ++adjacent_itr){pcl::Supervoxel<PointT>::Ptr neighbor_supervoxel = supervoxel_clusters.at(adjacent_itr->second);adjacent_supervoxel_centers.push_back(neighbor_supervoxel->centroid_);}//Now we make a name for this polygonstd::stringstream ss;ss << "supervoxel_" << supervoxel_label;//This function is shown below, but is beyond the scope of this tutorial - basically it just generates a "star" polygon mesh from the points givenaddSupervoxelConnectionsToViewer(supervoxel->centroid_, adjacent_supervoxel_centers, ss.str(), viewer);//Move iterator forward to next labellabel_itr = supervoxel_adjacency.upper_bound(supervoxel_label);}while (!viewer->wasStopped()){viewer->spinOnce(100);}return (0);}voidaddSupervoxelConnectionsToViewer(PointT &supervoxel_center,PointCloudT &adjacent_supervoxel_centers,std::string supervoxel_name,boost::shared_ptr<pcl::visualization::PCLVisualizer> & viewer){vtkSmartPointer<vtkPoints> points = vtkSmartPointer<vtkPoints>::New();vtkSmartPointer<vtkCellArray> cells = vtkSmartPointer<vtkCellArray>::New();vtkSmartPointer<vtkPolyLine> polyLine = vtkSmartPointer<vtkPolyLine>::New();//Iterate through all adjacent points, and add a center point to adjacent point pairPointCloudT::iterator adjacent_itr = adjacent_supervoxel_centers.begin();for (; adjacent_itr != adjacent_supervoxel_centers.end(); ++adjacent_itr){points->InsertNextPoint(supervoxel_center.data);points->InsertNextPoint(adjacent_itr->data);}// Create a polydata to store everything invtkSmartPointer<vtkPolyData> polyData = vtkSmartPointer<vtkPolyData>::New();// Add the points to the datasetpolyData->SetPoints(points);polyLine->GetPointIds()->SetNumberOfIds(points->GetNumberOfPoints());for (unsigned int i = 0; i < points->GetNumberOfPoints(); i++)polyLine->GetPointIds()->SetId(i, i);cells->InsertNextCell(polyLine);// Add the lines to the datasetpolyData->SetLines(cells);viewer->addModelFromPolyData(polyData, supervoxel_name);}