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 <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>
// 数据类型
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;
//可视化
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);
}
//打开点云
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函数
pcl::SupervoxelClustering<PointT> 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 <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 ();//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<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
//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<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
//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
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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