PCL超体聚类-白红宇

超体聚类是一种图像的分割方法。

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。与大部分的分割手段不同，超体聚类的目的并不是分割出某种特定物体，超体是对点云实施过分割(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。

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。

这些基本参数在接下来的参数中会有设置

#include 
     
      #include 
      
       #include 
       
        #include 
        
         #include 
         
          #include 
          
           //VTK include needed for drawing graph lines#include 
           
            // 数据类型typedef pcl::PointXYZRGBA PointT;typedef pcl::PointCloud
            
              PointCloudT;typedef pcl::PointNormal PointNT;typedef pcl::PointCloud
             
               PointNCloudT;typedef pcl::PointXYZL PointLT;typedef pcl::PointCloud
              
                PointLCloudT;//可视化void addSupervoxelConnectionsToViewer (PointT &supervoxel_center, PointCloudT &adjacent_supervoxel_centers, std::string supervoxel_name, boost::shared_ptr
               
                 & viewer);intmain (int argc, char ** argv){//解析命令行 if (argc < 2) { pcl::console::print_error ("Syntax is: %s 
                
                  \n " "--NT Dsables the single cloud transform \n" "-v 
                 
                  \n-s 
                  
                   \n" "-c 
                   
                     \n-z 
                    
                      \n" "-n 
                     
                      \n", argv[0]); return (1); } //打开点云 PointCloudT::Ptr cloud = boost::shared_ptr 
                      
                        (new PointCloudT ()); pcl::console::print_highlight ("Loading point cloud...\n"); if (pcl::io::loadPCDFile
                       
                         (argv[1], *cloud)) { pcl::console::print_error ("Error loading cloud file!\n"); return (1); } bool disable_transform = pcl::console::find_switch (argc, argv, "--NT"); float voxel_resolution = 0.008f; //分辨率 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);//如何使用SupervoxelClustering函数 pcl::SupervoxelClustering
                        
                          super (voxel_resolution, seed_resolution); if (disable_transform)//如果设置的是参数--NT 就用默认的参数 super.setUseSingleCameraTransform (false); super.setInputCloud (cloud); super.setColorImportance (color_importance); //0.2f super.setSpatialImportance (spatial_importance); //0.4f super.setNormalImportance (normal_importance); //1.0f std::map 
                         
                          ::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
                          
                            viewer (new pcl::visualization::PCLVisualizer ("3D Viewer")); viewer->setBackgroundColor (0, 0, 0); PointCloudT::Ptr voxel_centroid_cloud = super.getVoxelCentroidCloud ();//获得体素中心的点云 viewer->addPointCloud (voxel_centroid_cloud, "voxel centroids"); viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE,2.0, "voxel centroids"); //渲染点云 viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_OPACITY,0.95, "voxel centroids"); PointLCloudT::Ptr labeled_voxel_cloud = super.getLabeledVoxelCloud (); viewer->addPointCloud (labeled_voxel_cloud, "labeled voxels"); viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_OPACITY,0.8, "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
                           
                             (sv_normal_cloud,1,0.05f, "supervoxel_normals"); pcl::console::print_highlight ("Getting supervoxel adjacency\n"); std::multimap
                            
                              supervoxel_adjacency; super.getSupervoxelAdjacency (supervoxel_adjacency); //To make a graph of the supervoxel adjacency, we need to iterate through the supervoxel adjacency multimap //为了使整个超体形成衣服图，我们需要遍历超体的每个临近的个体 std::multimap
                             
                              ::iterator label_itr = supervoxel_adjacency.begin (); for ( ; label_itr != supervoxel_adjacency.end (); ) { //First get the label uint32_t supervoxel_label = label_itr->first; //Now get the supervoxel corresponding to the label pcl::Supervoxel
                              
                               ::Ptr supervoxel = supervoxel_clusters.at (supervoxel_label); //Now we need to iterate through the adjacent supervoxels and make a point cloud of them PointCloudT adjacent_supervoxel_centers; std::multimap
                               
                                ::iterator adjacent_itr = supervoxel_adjacency.equal_range (supervoxel_label).first; for ( ; adjacent_itr!=supervoxel_adjacency.equal_range (supervoxel_label).second; ++adjacent_itr) { pcl::Supervoxel
                                
                                 ::Ptr neighbor_supervoxel = supervoxel_clusters.at (adjacent_itr->second); adjacent_supervoxel_centers.push_back (neighbor_supervoxel->centroid_); } //Now we make a name for this polygon std::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 given//从给定的点云中生成一个星型的多边形， addSupervoxelConnectionsToViewer (supervoxel->centroid_, adjacent_supervoxel_centers, ss.str (), viewer); //Move iterator forward to next label label_itr = supervoxel_adjacency.upper_bound (supervoxel_label); } while (!viewer->wasStopped ()) { viewer->spinOnce (100); } return (0);}//VTK可视化构成的聚类图voidaddSupervoxelConnectionsToViewer (PointT &supervoxel_center, PointCloudT &adjacent_supervoxel_centers, std::string supervoxel_name, boost::shared_ptr
                                 
                                   & viewer){ vtkSmartPointer
                                  
                                    points = vtkSmartPointer
                                   
                                    ::New (); vtkSmartPointer
                                    
                                      cells = vtkSmartPointer
                                     
                                      ::New (); vtkSmartPointer
                                      
                                        polyLine = vtkSmartPointer
                                       
                                        ::New (); //Iterate through all adjacent points, and add a center point to adjacent point pair PointCloudT::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 in vtkSmartPointer
                                        
                                          polyData = vtkSmartPointer
                                         
                                          ::New (); // Add the points to the dataset polyData->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 dataset polyData->SetLines (cells); viewer->addModelFromPolyData (polyData,supervoxel_name);}

可执行文件生成后的图像显示如下

当然也可以自己设定参数生成自己想要的效果。同时在不同的场景中，使用的参数是十分重要的，

只是先了解超体的概念，如果想应用到实际的应用中，还需要很多其他的知识，所以这里只是基本的学习

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