PCL学习笔记（五）：PCD文件的IO操作

1、PCD文件读取点云数据

#include<iostream>

#include<pcl/io/pcd_io.h>
#include<pcl/point_types.h>

int 
main(int argc,char **argv)
{
    //声明点云对象指针（点云类型为XYZ点云结构），并初始化该对象，pointcloud是一个泛型类，所以每次使用的记得申明
    //这一句代码的作用是创建一个真是参数为PointXYZ的PointCloud类型的boost共享指针并初始化
    pcl::PiontCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);

    int flatMeathod = 1;
    if(flatMeathod ==0 )
    {
        printf("采用pcl::io::loadPCDFile<pcl::PointXYZ>的方式读取\n");
        //打开本地的点云文件test_pcd.pcd
        //PointCloud类中解析了pcd文件的各字段
        //判断文件是否存在
        if(pcl::io::loadPCDFile<pcl::PointXYZ>("/home/zll/source/test_pcd.pcd",*cloud)== -1)
        {
            PCL_ERROR("could not find test_pcd.pcd");
            return -1;
        }

    }
    else
    {
        printf("采用pcl::PCLPointCloud2 的方式读取");
         //整理需要先转换为PCLPointCloud2类型
        pcl::PCLPointCloud2 cloud2;
        pcl::io::loadPCDFile("/home/zll/source/test_pcd.pcd",cloud2);
        if(pcl::io::loadPCDFile("",cloud2)==-1)
        {
            return -1;
        }
        else{
            pcl::fromPCLPointCloud2(cloud2,*cloud);

        }
    }
    for(size_t i=0;i<cloud->points.size();i++)
    {
        std::cout<<""<<cloud->points[i].x<<""<<cloud->points[i].y<<cloud->points[i].z<<std::endl;   
    }
    return 0;
}

2、向PCD文件写入点云数据

#include<iostream>
#include<pcl/io/pcd_io.h>
#include<pcl/point_types.h>

int 
main(int argc,char ** argv)
{
    //实例化模板类PointCloud,每个点都被设置为pcl::PointXYZ,作为模板类实例化的参数
    pcl::PointCloud<pcl::PointXYZ> cloud;
    //创建点云
    cloud.width=5;
    cloud.height=1;
    cloud.is_dense=false;
    //Point字段=宽*高 
    cloud.points.resize(cloud.width*cloud.height);
    //用随机的值填充PointCloud点云对象
    for(size_t i=0;i<cloud.points.size();++i)
    {
        cloud.points[i].x=1024*rand()/(RAND_MAX+1.0f);
        cloud.points[i].y=1024*rand()/(RAND_MAX+1.0f);
        cloud.points[i].z=1024*rand()/(RAND_MAX+1.0f);
    }
    //把PointCloud对象数据存储在test_pcd.pcd文件中
    pcl::io::savePCDFileASCII("test_pcd.pcd",cloud);
    //打印输出存储的点云数据
    std::cerr<<"Saved"<<cloud.points.size()<<"data points to test_pcd.pcd"<<std::endl;
    for(size_t i=0;i<cloud.points.size();++i)
    {
        std::cerr<<" "<<cloud.poitns[i].x<<cloud.points[i].y<<" "<<cloud.points[i].z<<std::endl;
    }
    return (0);
}

3、两个点云中的字段或数据连接

进行操作前要确保两个数据集中字段的类型相同和维度相等

#include<iostream>
#include<pcl/io/pcd_io.h>
#include<pcl/point_types.h>

int 
main(int argc,char **argv)
{
    if(argc!=2)
    {
        std::cerr<<"please specifty Command line arg '-f' or '-p' "<<std::endl; 
        exit(0);
    }
    pcl::PointCloud<pcl::PointXYZ>cloud_a,cloud_b,cloud_c;
    pcl::PointCloud<pcl::Normal>n_cloud_b;
    pcl::PointCloud<pcl::PointNormal>p_n_cloud_c;

    //三个输入（cloud_a,cloud_b和n_cloud_b），两个输出(cloud_c和p_n_cloud_c)
    cloud_a.width=5;
    cloud_a.height=cloud_b.height=n_cloud_b.height=1;
    cloud_a.points.resize(cloud_a.width*cloud_a.height);
    if(strcmp(argv[1],"-p")==0)//判断是否为连接a+b=c
    {
        cloud_b.width=3;
        cloud_b.points.resize(n_cloud_b.width*n_cloud_b.height);
    }
    else{//如果是连接xyz与normal，则生成5个法线
        n_cloud_b.width=5;
        n_cloud_b.points.resize(n_cloud_b.width*n_cloud_b.height);
    }
    //循环生成无序点云，填充上面定义的两种类型点云对象
    for(size_t i=0;i<cloud_a.points.size();++i)
    {
        cloud_a.points[i].x=1024*rand()/(RAND_MAX+1.0f);
        cloud_a.points[i].y=1024*rand()/(RAND_MAX+1.0f);
        cloud_a.points[i].z=1024*rand()/(RAND_MAX+1.0f);
    }
    if(strcmp(argv[1],"-p")==0)
    {
        //如果连接a+b=c，则cloud_b用两个点作为xyz数据
        for(size_t i=0;i<cloud_b.points.size();++i)
    {
        cloud_b.points[i].x=1024*rand()/(RAND_MAX+1.0f);
        cloud_b.points[i].y=1024*rand()/(RAND_MAX+1.0f);
        cloud_b.points[i].z=1024*rand()/(RAND_MAX+1.0f);
    }

    }
    else
    {
        //如果连接xyz+normal=xyznormal，则n_cloud_b用5个点作为normal数据
        for(size_t i=0;i<cloud_b.points.size();++i)
        {
            n_cloud_b.points[i].normal[0]=1024*rand()/(RAND_MAX+1.0f);
            n_cloud_b.points[i].normal[1]=1024*rand()/(RAND_MAX+1.0f);
            n_cloud_b.points[i].normal[2]=1024*rand()/(RAND_MAX+1.0f);
        }
    }
    //将cloud_a和cloud_b或n_cloud_b的数据输出
    std::cerr<<"Cloud A:"<<std::endl;
    for(size_t i=0;i<cloud_a.points.size();++i)
    {
        std::cerr<<" "<<cloud_a.points[i].x<<" "<<cloud_a.points[i].y<<" "<<cloud_a.points[i].z<<std::endl;
    }
    std::cerr<<"Cloud B:"<<std::endl;
    for(size_t i=0;i<cloud_b.points.size();++i)
    {
        std::cerr<<" "<<cloud_b.points[i].x<<" "<<cloud_b.points[i].y<<" "<<cloud_b.points[i].z<<std::endl;
    }
    if(strcmp(argv[1],"-p")==0)
    {
        for(size_t i=0;i<cloud_b.points.size();++i)
        {
            std::cerr<<" "<<cloud_b.points[i]<<" "<<cloud_b.points[i]<<" "<<cloud_b.points[i]<<std::endl;
        }
    }
    else
    {
        for(size_t i=0;i<n_cloud_b.points.size();++i)
        {
            std::cerr<<" "<<n_cloud_b.points[i].normal[0]<<" "<<n_cloud_b.points[i].normal[1]<<" "<<n_cloud_b.points[i].normal[2]<<std::endl;
        }
    }

    if(strcmp(argv[1],"-p")==0)
    {
        //把cloud_a和cloud_b连接在一起创建了cloud_c
        cloud_c=cloud_a;
        cloud_c+=cloud_b;
        std::cerr<<"Cloud C： "<<std::endl;
        //输出
        for(size_t i=0;i<cloud_c.points.size();++i)
        {
            std::cerr<<" "<<cloud_c.points[i].x<<" "<<cloud_c.points[i].y<<" "<<cloud_c.points[i].z<<std::endl;
        }
    }
    else
    {
        //把cloud_a和n_cloud_b字段连接在一起创建p_n_cloud_c
        pcl::concatenateFields(cloud_a,n_cloud_b,p_n_cloud_c);
        std::cerr<<"Cloud C: "<<std::endl;
        //输出
        for(size_t i=0;i<p_n_cloud_c.points.size();++i)
        {
            std::cerr<< " "<<p_n_cloud_c.points[i].x<<" "<<p_n_cloud_c.points[i].y<<" "<<p_n_cloud_c.points[i].z<<" "
            <<p_n_cloud_c.points[i].normal[0]<<" "<<p_n_cloud_c.points[i].normal[1]<<" "<<p_n_cloud_c.points[i].normal[2]<<std::endl;

        }
    }
    return (0);
}

参考：

https://www.cnblogs.com/codeAndlearn/p/11609364.html