PCL超體聚類

超體聚類是一種圖像的分割方法。

超體（supervoxel）是一種集合，集合的元素是「體」。與體素濾波器中的體相似，其本質是一個個的小方塊。與大部分的分割手段不一樣，超體聚 類的目的並非分割出某種特定物體，超體是對點雲實施過度割(over segmentation)，將場景點雲化成不少小塊，並研究每一個小塊之間的關係。這種將更小單元合併的分割思路已經出現了有些年份了，在圖像分割中，像 素聚類造成超像素，以超像素關係來理解圖像已經廣爲研究。本質上這種方法是對局部的一種總結，紋理，材質，顏色相似的部分會被自動的分割成一塊，有利於後 續識別工做。好比對人的識別，若是能將頭髮，面部，四肢，軀幹分開，則能更好的對各類姿態，性別的人進行識別。

點雲和圖像不同，其不存在像素鄰接關係。因此，超體聚類以前，必須以八叉樹對點雲進行劃分，得到不一樣點團之間的鄰接關係。與圖像類似點雲的鄰接關係也有不少，如面鄰接，線鄰接，點鄰接。

超體聚類其實是一種特殊的區域生長算法，和無限制的生長不一樣，超體聚類首先須要規律的佈置區域生長「晶核」。晶核在空間中其實是均勻分佈的,並指定晶核距離（Rseed)。再指定粒子距離(Rvoxel)。再指定最小晶粒(MOV)，太小的晶粒須要融入最近的大晶粒。

這些基本參數在接下來的參數中會有設置

#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>

// 數據類型
typedef 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;

//可視化
void addSupervoxelConnectionsToViewer (PointT &supervoxel_center,
                                       PointCloudT &adjacent_supervoxel_centers,
                                       std::string supervoxel_name,
                                       boost::shared_ptr<pcl::visualization::PCLVisualizer> & viewer);


int
main (int argc, char ** argv)
{
//解析命令行
  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);
  }

  //打開點雲
  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);
  }


  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函數
  pcl::SupervoxelClustering<PointT> super (voxel_resolution, seed_resolution);
  if (disable_transform)//若是設置的是參數--NT  就用默認的參數
  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 <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);

  PointCloudT::Ptr voxel_centroid_cloud = super.getVoxelCentroidCloud ();//得到體素中心的點雲
  viewer->addPointCloud (voxel_centroid_cloud, "voxel centroids");
  viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE,2.0, "voxel centroids");     //渲染點雲
  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<PointNormal> (sv_normal_cloud,1,0.05f, "supervoxel_normals");

  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 multimap
  //爲了使整個超體造成衣服圖，咱們須要遍歷超體的每一個臨近的個體
  std::multimap<uint32_t,uint32_t>::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<PointT>::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<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 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
//從給定的點雲中生成一個星型的多邊形，
    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可視化構成的聚類圖
void
addSupervoxelConnectionsToViewer (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 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<vtkPolyData> polyData = vtkSmartPointer<vtkPolyData>::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);
}

可執行文件生成後的圖像顯示以下

固然也能夠本身設定參數生成本身想要的效果。同時在不一樣的場景中，使用的參數是十分重要的，

只是先了解超體的概念，若是想應用到實際的應用中，還須要不少其餘的知識 ，因此這裏只是基本的學習

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