• PCL 点云超体素分割


    一、概述

      PCL中点云超体素分割的简单使用案例。

    二、代码

    supervoxel_clustering.cpp

    #include 
    #include 
    #include 
    #include 
    #include 
    #include 
    
    //VTK include needed for drawing graph lines
    #include 
    
    // 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  \n "
                                    "--NT Dsables the single cloud transform \n"
                                    "-v \n-s \n"
                                    "-c  \n-z  \n"
                                    "-n \n", argv[0]);
        return (1);
      }*/
    
    
      PointCloudT::Ptr cloud (new PointCloudT);
      pcl::console::print_highlight ("Loading point cloud...\n");
      if (pcl::io::loadPCDFile<PointT>("table_scene_lms400.pcd", *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;
      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
      //  //
    
      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);
    
      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 ());
    
      pcl::visualization::PCLVisualizer::Ptr viewer (new pcl::visualization::PCLVisualizer ("3D Viewer"));
      viewer->setBackgroundColor (0, 0, 0);
    
      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");
    
      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<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 multimap
      for (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 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;
        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 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
        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);
    }
    
    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 pair
      for (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 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);
    }
    
    
    
    
    • 1
    • 2
    • 3
    • 4
    • 5
    • 6
    • 7
    • 8
    • 9
    • 10
    • 11
    • 12
    • 13
    • 14
    • 15
    • 16
    • 17
    • 18
    • 19
    • 20
    • 21
    • 22
    • 23
    • 24
    • 25
    • 26
    • 27
    • 28
    • 29
    • 30
    • 31
    • 32
    • 33
    • 34
    • 35
    • 36
    • 37
    • 38
    • 39
    • 40
    • 41
    • 42
    • 43
    • 44
    • 45
    • 46
    • 47
    • 48
    • 49
    • 50
    • 51
    • 52
    • 53
    • 54
    • 55
    • 56
    • 57
    • 58
    • 59
    • 60
    • 61
    • 62
    • 63
    • 64
    • 65
    • 66
    • 67
    • 68
    • 69
    • 70
    • 71
    • 72
    • 73
    • 74
    • 75
    • 76
    • 77
    • 78
    • 79
    • 80
    • 81
    • 82
    • 83
    • 84
    • 85
    • 86
    • 87
    • 88
    • 89
    • 90
    • 91
    • 92
    • 93
    • 94
    • 95
    • 96
    • 97
    • 98
    • 99
    • 100
    • 101
    • 102
    • 103
    • 104
    • 105
    • 106
    • 107
    • 108
    • 109
    • 110
    • 111
    • 112
    • 113
    • 114
    • 115
    • 116
    • 117
    • 118
    • 119
    • 120
    • 121
    • 122
    • 123
    • 124
    • 125
    • 126
    • 127
    • 128
    • 129
    • 130
    • 131
    • 132
    • 133
    • 134
    • 135
    • 136
    • 137
    • 138
    • 139
    • 140
    • 141
    • 142
    • 143
    • 144
    • 145
    • 146
    • 147
    • 148
    • 149
    • 150
    • 151
    • 152
    • 153
    • 154
    • 155
    • 156
    • 157
    • 158
    • 159
    • 160
    • 161
    • 162
    • 163
    • 164
    • 165
    • 166
    • 167
    • 168
    • 169
    • 170

    三、结果

    在这里插入图片描述

  • 相关阅读:
    电吉他学习笔记
    整理最新java面试宝典2019
    四年时间,从一个浑浑噩噩的程序员到csdn博客专家的成长之路
    前端面试题之——说一说深浅拷贝,都是怎么实现的?
    Linux开发工具:vim的介绍和用法及其简单配置
    DM3730 X-load 分析
    博客主题 “Text“ 夏日清新特别版
    易点易动固定资产管理系统:提升企业固定资产领用效率的智能选择
    【开发规范】持续更新中......
    R语言获取data.table数据中指定数据列的最小值所在的数据行(minimum)
  • 原文地址:https://blog.csdn.net/m0_51204289/article/details/126915270