PCL边界识别

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

#include 
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using namespace std;int main(int argc, char **argv)
{pcl::PointCloud::Ptr cloud (new pcl::PointCloud);// if (pcl::io::loadPCDFile("/home/yxg/pcl/pcd/mid.pcd",*cloud) == -1)if (pcl::io::loadPCDFile(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::Ptr normals (new pcl::PointCloud);pcl::PointCloud boundaries;pcl::BoundaryEstimation est;pcl::search::KdTree::Ptr tree(new pcl::search::KdTree());/*pcl::KdTreeFLANN 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 nnh ;vector squaredistance;//  pcl::PointXYZ p;//   p = cloud->points[i];kdtree.nearestKSearch(cloud->points[i],k,nnh,squaredistance);everagedistance += sqrt(squaredistance[1]);//   cout<size()/2);cout<<"everage distance is : "<pcl::NormalEstimation 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()<//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::Ptr (new pcl::search::KdTree));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 boundPoints;pcl::PointCloud::Ptr boundPoints (new               pcl::PointCloud);pcl::PointCloud noBoundPoints;int countBoundaries = 0;for (int i=0; isize(); 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++;}elsenoBoundPoints.push_back(cloud->points[i]);}std::cout<<"boudary size is：" <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;
}

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