使用PCL进行法向量可视化

1、使用PCL进行法向量可视化

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <boost/thread/thread.hpp>using namespace std;int
main(int argc, char** argv)
{pcl::PointCloud<pcl::PointNormal>::Ptr cloud(new pcl::PointCloud<pcl::PointNormal>);if (pcl::io::loadPCDFile<pcl::PointNormal>("bunny.pcd", *cloud) == -1){PCL_ERROR("Could not read file\n");}//---------------------可视化（含法线）-----------------------------boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer("CloudCompare-XYZNormal viewer"));viewer->setWindowName("CloudCompare-XYZNormal");viewer->addText("CloudCompare-PointNormal", 50, 50, 0, 1, 0, "v1_text");viewer->addPointCloud<pcl::PointNormal>(cloud, "CloudCompare-XYZNormal");viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 0, 1, 0, "CloudCompare-XYZNormal");viewer->addPointCloudNormals<pcl::PointNormal>(cloud, 20, 0.02, "normals");while (!viewer->wasStopped()){viewer->spinOnce(100);boost::this_thread::sleep(boost::posix_time::microseconds(100000));}return 0;
}

2、计算所有点的法线并显示

#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/kdtree/kdtree_flann.h>
//#include <pcl/features/normal_3d.h>
#include <pcl/features/normal_3d_omp.h>//使用OMP需要添加的头文件
#include <pcl/visualization/pcl_visualizer.h>
#include <boost/thread/thread.hpp>
using namespace std;
int main()
{//------------------加载点云数据-------------------pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);if (pcl::io::loadPCDFile<pcl::PointXYZ>("车载点云.pcd", *cloud) == -1){PCL_ERROR("Could not read file\n");}//------------------计算法线----------------------pcl::NormalEstimationOMP<pcl::PointXYZ, pcl::Normal> n;//OMP加速pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>);//建立kdtree来进行近邻点集搜索pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>());n.setNumberOfThreads(10);//设置openMP的线程数//n.setViewPoint(0,0,0);//设置视点，默认为（0，0，0）n.setInputCloud(cloud);n.setSearchMethod(tree);n.setKSearch(10);//点云法向计算时，需要所搜的近邻点大小//n.setRadiusSearch(0.03);//半径搜素n.compute(*normals);//开始进行法向计//----------------可视化--------------boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer("Normal viewer"));//viewer->initCameraParameters();//设置照相机参数，使用户从默认的角度和方向观察点云//设置背景颜色viewer->setBackgroundColor(0.3, 0.3, 0.3);viewer->addText("faxian", 10, 10, "text");//设置点云颜色pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> single_color(cloud, 0, 225, 0);//添加坐标系viewer->addCoordinateSystem(0.1);viewer->addPointCloud<pcl::PointXYZ>(cloud, single_color, "sample cloud");//添加需要显示的点云法向。cloud为原始点云模型，normal为法向信息，20表示需要显示法向的点云间隔，即每20个点显示一次法向，0.02表示法向长度。viewer->addPointCloudNormals<pcl::PointXYZ, pcl::Normal>(cloud, normals, 20, 0.02, "normals");//设置点云大小viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "sample cloud");while (!viewer->wasStopped()){viewer->spinOnce(100);boost::this_thread::sleep(boost::posix_time::microseconds(100000));}return 0;
}

3、计算一个子集的法线

#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/kdtree/kdtree_flann.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <boost/thread/thread.hpp>
#include <pcl/features/normal_3d_omp.h>
using namespace std;
int main()
{//---------------------加载点云数据----------------------pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);if (pcl::io::loadPCDFile<pcl::PointXYZ>("车载点云.pcd", *cloud) == -1){PCL_ERROR("Could not read file\n");}//--------------计算云中前10%的点法线-----------------------vector<int> point_indices(floor(cloud->points.size() / 10));for (size_t i = 0; i < point_indices.size(); ++i) {point_indices[i] = i;	}//-------------------传递索引----------------------------pcl::IndicesPtr indices(new vector <int>(point_indices));//-------------------计算法线----------------------------pcl::NormalEstimationOMP<pcl::PointXYZ, pcl::Normal> n;//OMP加速n.setInputCloud(cloud);n.setIndices(indices);// 创建一个kd树，方便搜索；并将它传递给上面创建的法线估算类对象pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>());n.setSearchMethod(tree);n.setRadiusSearch(0.01);pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>);//----------------估算特征---------------n.compute(*normals);//-------------为方便可视化，将前10%点云提出-------------------------------pcl::PointCloud<pcl::PointXYZ>::Ptr cloud1(new pcl::PointCloud<pcl::PointXYZ>);pcl::copyPointCloud(*cloud, point_indices, *cloud1);//------------------可视化-----------------------boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer("Normal viewer"));//设置背景颜色viewer->setBackgroundColor(0.3, 0.3, 0.3);viewer->addText("faxian", 10, 10, "text");//设置点云颜色pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> single_color1(cloud1, 0, 225, 0);pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> single_color(cloud, 255, 0, 0);//添加坐标系//viewer->addCoordinateSystem(0.1);viewer->addPointCloud<pcl::PointXYZ>(cloud, single_color, "sample cloud");viewer->addPointCloud<pcl::PointXYZ>(cloud1, single_color1, "sample cloud1");viewer->addPointCloudNormals<pcl::PointXYZ, pcl::Normal>(cloud1, normals, 20, 0.02, "normals");//设置点云大小viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "sample cloud1");while (!viewer->wasStopped()){viewer->spinOnce(100);boost::this_thread::sleep(boost::posix_time::microseconds(100000));}return 0;
}