【PCL】（二十八）点云超体素分割

（二十九）点云超体素分割

论文：Voxel Cloud Connectivity Segmentation - Supervoxels for Point Clouds

supervoxel_clustering.cpp

#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>// Types
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;void addSupervoxelConnectionsToViewer (PointT &supervoxel_center,PointCloudT &adjacent_supervoxel_centers,std::string supervoxel_name,pcl::visualization::PCLVisualizer::Ptr & viewer);int main (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 (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);}/*--NT禁用单视图变换（仅影响有组织云）-v设置体素大小，决定基础八叉树结构的叶大小（以米为单位）-s设置种子大小，决定超体素的大小（以米为单位）-c设置颜色影响超体素的形状的权重-z设置空间项的权重-值越高，超体素越规则-n设置曲面法线影响超体素的形状的权重*/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);// 超体素聚类pcl::SupervoxelClustering<PointT> super (voxel_resolution, seed_resolution);if (disable_transform) // 如果收入是有组织的云，而该云的相机坐标不在（0,0,0）且深度不在正Z，则必须将use_transform设置为falsesuper.setUseSingleCameraTransform (false); super.setInputCloud (cloud);super.setColorImportance (color_importance);super.setSpatialImportance (spatial_importance);super.setNormalImportance (normal_importance);std::map <std::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 ());// 超体素可视化pcl::visualization::PCLVisualizer::Ptr viewer (new pcl::visualization::PCLVisualizer ("3D Viewer"));viewer->setBackgroundColor (0, 0, 0);// voxel_centroid_cloud包含由体素质心组成的云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");//labeled_voxel_cloud 是根据其超体素标签（随机颜色）着色的体素。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");// sv_normal_cloud包含一个超体素法线云，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<std::uint32_t, std::uint32_t> supervoxel_adjacency;super.getSupervoxelAdjacency (supervoxel_adjacency);//To make a graph of the supervoxel adjacency, we need to iterate through the supervoxel adjacency multimapfor (auto label_itr = supervoxel_adjacency.cbegin (); label_itr != supervoxel_adjacency.cend (); ){//First get the label  std::uint32_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;for (auto adjacent_itr = supervoxel_adjacency.equal_range (supervoxel_label).first; 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);}void  addSupervoxelConnectionsToViewer (PointT &supervoxel_center,PointCloudT &adjacent_supervoxel_centers,std::string supervoxel_name,pcl::visualization::PCLVisualizer::Ptr & 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 pairfor (auto adjacent_itr = adjacent_supervoxel_centers.begin (); 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);
}

cmake_minimum_required(VERSION 3.5 FATAL_ERROR)project(supervoxel_clustering)find_package(PCL 1.8 REQUIRED)include_directories(${PCL_INCLUDE_DIRS})
link_directories(${PCL_LIBRARY_DIRS})
add_definitions(${PCL_DEFINITIONS})add_executable (supervoxel_clustering supervoxel_clustering.cpp)
target_link_libraries (supervoxel_clustering ${PCL_LIBRARIES})

数据样例

编译并运行：

./supervoxel_clustering milk_cartoon_all_small_clorox.pcd --NT -s 0.47

./supervoxel_clustering milk_cartoon_all_small_clorox.pcd --NT -s 0.1