PCL边界识别

———————————–【转自：SimpleTriangle】————————————–

#include <iostream>#include <vector>#include <ctime>#include <boost/thread/thread.hpp>#include <pcl/io/pcd_io.h>#include <pcl/visualization/pcl_visualizer.h>#include <pcl/console/parse.h>#include <pcl/features/eigen.h>#include <pcl/features/feature.h>#include <pcl/features/normal_3d.h>#include <pcl/impl/point_types.hpp>#include <pcl/features/boundary.h>#include <pcl/visualization/cloud_viewer.h>using namespace std;int main(int argc, char **argv){        pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);        // if (pcl::io::loadPCDFile<pcl::PointXYZ>("/home/yxg/pcl/pcd/mid.pcd",*cloud) == -1)        if (pcl::io::loadPCDFile<pcl::PointXYZ>(argv[1],*cloud) == -1)        {                PCL_ERROR("COULD NOT READ FILE mid.pcl \n");                return (-1);        }        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(20);  //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::savePCDFileASCII("boudary.pcd", *boundPoints);        pcl::io::savePCDFileASCII("NoBoundpoints.pcd",noBoundPoints);        pcl::visualization::CloudViewer viewer ("test");        viewer.showCloud(boundPoints);        while (!viewer.wasStopped())        {        }        return 0;}