PCL-点云边界提取

问题描述：继续更新功能

实现代码

输入的是被提取边界的点云

void border(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud){



    std::cout << "points sieze is:" << cloud->size() << std::endl;
    pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>);
    pcl::PointCloud<pcl::Boundary> boundaries;
    pcl::BoundaryEstimation<pcl::PointXYZ, pcl::Normal, pcl::Boundary> est;
    pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>());

    pcl::KdTreeFLANN<pcl::PointXYZ> kdtree;  //创建一个快速k近邻查询,查询的时候若该点在点云中，则第一个近邻点是其本身
    kdtree.setInputCloud(cloud);
    int k =2;
    float everagedistance =0;
    for (int i =0; i < cloud->size()/2;i++)
    {
            vector<int> nnh ;
            vector<float> squaredistance;
            //  pcl::PointXYZ p;
            //   p = cloud->points[i];
            kdtree.nearestKSearch(cloud->points[i],k,nnh,squaredistance);
            everagedistance += sqrt(squaredistance[1]);
            //   cout<<everagedistance<<endl;
    }
    everagedistance = everagedistance/(cloud->size()/2);
    cout<<"everage distance is : "<<everagedistance<<endl;


    pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> normEst;  //其中pcl::PointXYZ表示输入类型数据，pcl::Normal表示输出类型,且pcl::Normal前三项是法向，最后一项是曲率
      normEst.setInputCloud(cloud);
      normEst.setSearchMethod(tree);
      // normEst.setRadiusSearch(2);  //法向估计的半径
      normEst.setKSearch(9);  //法向估计的点数
      normEst.compute(*normals);
      cout << "normal size is " << normals->size() << endl;

      //normal_est.setViewPoint(0,0,0); //这个应该会使法向一致
      est.setInputCloud(cloud);
      est.setInputNormals(normals);
      //  est.setAngleThreshold(90);
      //   est.setSearchMethod (pcl::search::KdTree<pcl::PointXYZ>::Ptr (new pcl::search::KdTree<pcl::PointXYZ>));
      est.setSearchMethod(tree);
      est.setKSearch(50);  //一般这里的数值越高，最终边界识别的精度越好
      //  est.setRadiusSearch(everagedistance);  //搜索半径
      est.compute(boundaries);

      //  pcl::PointCloud<pcl::PointXYZ> boundPoints;
      pcl::PointCloud<pcl::PointXYZ>::Ptr boundPoints(new               pcl::PointCloud<pcl::PointXYZ>);
      pcl::PointCloud<pcl::PointXYZ> noBoundPoints;
      int countBoundaries = 0;
      for (int i = 0; i < cloud->size(); i++) {
          uint8_t x = (boundaries.points[i].boundary_point);
          int a = static_cast<int>(x); //该函数的功能是强制类型转换
          if (a == 1)
          {
              //  boundPoints.push_back(cloud->points[i]);
              (*boundPoints).push_back(cloud->points[i]);
              countBoundaries++;
          }
          else
              noBoundPoints.push_back(cloud->points[i]);

      }
      std::cout << "boudary size is：" << countBoundaries << std::endl;
      //  pcl::io::savePCDFileASCII("boudary.pcd",boundPoints);

      pcl::io::savePLYFileASCII("/home/wxw/桌面/Boundpoints.ply", *boundPoints);
     // pcl::io::savePLYFileASCII("C:\\Users\\fhlhc\\Desktop\\NoBoundpoints.ply", noBoundPoints);


      //双视口
      boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer("test Viewer"));
      viewer->initCameraParameters();
      int v1(0), v2(0);
      //原始点云窗口
      viewer->createViewPort(0.0, 0.0, 0.5, 1.0, v1);
      viewer->setBackgroundColor(0, 0, 0, v1);
      viewer->addText("original", 10, 10, "v1 text", v1);
      viewer->addPointCloud<pcl::PointXYZ>(cloud, "sample cloud1", v1);
      viewer->addCoordinateSystem(1.0);
      viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "sample cloud1");
      //滤波窗口
      viewer->createViewPort(0.5, 0.0, 1.0, 1.0, v2);
      viewer->setBackgroundColor(0, 0, 0, v2);
      viewer->addText("提取边界", 10, 10, "v2 text", v2);
      viewer->addPointCloud<pcl::PointXYZ>(boundPoints, "sample cloud2", v2);
      viewer->addCoordinateSystem(1.0);
      viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "sample cloud2");
      while (!viewer->wasStopped())
      {
          viewer->spinOnce(100);  //刷新
          std::this_thread::sleep_for(100ms);      }

}

效果