3D视觉学习计划之点云的读取和保存

一、QT配置PCL

1.Pro文件
这里先贴出我的Pro文件：

TEMPLATE = app
CONFIG += console c++14
CONFIG -= app_bundle
CONFIG -= qtSOURCES += \main.cppINCLUDEPATH += .\/usr/local/include/pcl-1.13 \/usr/include/eigen3 \/usr/include/vtk-7.1 \/usr/include/boostLIBS += /usr/local/lib/lib* \/usr/lib/x86_64-linux-gnu/libpcl_*.so \/usr/lib/x86_64-linux-gnu/libvtk*.so \/usr/lib/x86_64-linux-gnu/libboost_*.so

注意点如下：
1.CONFIG += console c++14这个地方需要改成c++14,之前是11就可以
2.其他的依赖文件和包含路径这些，需要开启自己的路径进行参考。
2.测试代码
测试代码如下：

#include <pcl/point_cloud.h>   //点云头文件
#include <pcl/octree/octree.h> //八叉树头文件#include <iostream>
#include <vector>
#include <ctime>int main(int argc, char **argv)
{srand((unsigned int)time(NULL)); //用系统时间初始化随机种子与 srand (time (NULL))的区别pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);// 创建点云数据cloud->width = 1000;cloud->height = 1; //无序cloud->points.resize(cloud->width * cloud->height);for (size_t i = 0; i < cloud->points.size(); ++i) //随机循环产生点云的坐标值{cloud->points[i].x = 1024.0f * rand() / (RAND_MAX + 1.0f);cloud->points[i].y = 1024.0f * rand() / (RAND_MAX + 1.0f);cloud->points[i].z = 1024.0f * rand() / (RAND_MAX + 1.0f);}/****************************************************************************创建一个octree实例，用设置分辨率进行初始化，该octree用它的页节点存放点索引向量，分辨率参数描述最低一级octree的最小体素的尺寸，因此octree的深度是分辨率和点云空间维度的函数，如果知道点云的边界框，应该用defineBoundingbox方法把它分配给octree然后通过点云指针把所有点增加到ctree中。*****************************************************************************/float resolution = 128.0f;pcl::octree::OctreePointCloudSearch<pcl::PointXYZ> octree(resolution); //初始化Octreeoctree.setInputCloud(cloud);      //设置输入点云    这两句是最关键的建立PointCloud和octree之间的联系octree.addPointsFromInputCloud(); //构建octreepcl::PointXYZ searchPoint; //设置searchPointsearchPoint.x = 1024.0f * rand() / (RAND_MAX + 1.0f);searchPoint.y = 1024.0f * rand() / (RAND_MAX + 1.0f);searchPoint.z = 1024.0f * rand() / (RAND_MAX + 1.0f);/*************************************************************************************一旦PointCloud和octree联系一起，就能进行搜索操作，这里使用的是“体素近邻搜索”，把查询点所在体素中其他点的索引作为查询结果返回，结果以点索引向量的形式保存，因此搜索点和搜索结果之间的距离取决于octree的分辨率参数
*****************************************************************************************/std::vector<int> pointIdxVec; //存储体素近邻搜索结果向量if (octree.voxelSearch(searchPoint, pointIdxVec)) //执行搜索，返回结果到pointIdxVec{std::cout << "Neighbors within voxel search at (" << searchPoint.x<< " " << searchPoint.y<< " " << searchPoint.z << ")"<< std::endl;for (size_t i = 0; i < pointIdxVec.size(); ++i) //打印结果点坐标std::cout << "    " << cloud->points[pointIdxVec[i]].x<< " " << cloud->points[pointIdxVec[i]].y<< " " << cloud->points[pointIdxVec[i]].z << std::endl;}/**********************************************************************************K 被设置为10 ，K近邻搜索  方法把搜索结果写到两个分开的向量，第一个pointIdxNKNSearch包含搜索结果（结果点的索引的向量）  第二个向量pointNKNSquaredDistance存储搜索点与近邻之间的距离的平方。
*************************************************************************************///K 近邻搜索int K = 10;std::vector<int> pointIdxNKNSearch;         //结果点的索引的向量std::vector<float> pointNKNSquaredDistance; //搜索点与近邻之间的距离的平方std::cout << "K nearest neighbor search at (" << searchPoint.x<< " " << searchPoint.y<< " " << searchPoint.z<< ") with K=" << K << std::endl;if (octree.nearestKSearch(searchPoint, K, pointIdxNKNSearch, pointNKNSquaredDistance) > 0){for (size_t i = 0; i < pointIdxNKNSearch.size(); ++i)std::cout << "    " << cloud->points[pointIdxNKNSearch[i]].x<< " " << cloud->points[pointIdxNKNSearch[i]].y<< " " << cloud->points[pointIdxNKNSearch[i]].z<< " (squared distance: " << pointNKNSquaredDistance[i] << ")" << std::endl;}// 半径内近邻搜索std::vector<int> pointIdxRadiusSearch;std::vector<float> pointRadiusSquaredDistance;float radius = 256.0f * rand() / (RAND_MAX + 1.0f);std::cout << "Neighbors within radius search at (" << searchPoint.x<< " " << searchPoint.y<< " " << searchPoint.z<< ") with radius=" << radius << std::endl;if (octree.radiusSearch(searchPoint, radius, pointIdxRadiusSearch, pointRadiusSquaredDistance) > 0){for (size_t i = 0; i < pointIdxRadiusSearch.size(); ++i)std::cout << "    " << cloud->points[pointIdxRadiusSearch[i]].x<< " " << cloud->points[pointIdxRadiusSearch[i]].y<< " " << cloud->points[pointIdxRadiusSearch[i]].z<< " (squared distance: " << pointRadiusSquaredDistance[i] << ")" << std::endl;}
}

输出结果如下图：
在这里插入图片描述

二、C++版本读取文件

#include <pcl/io/pcd_io.h>int main()
{// 读取点云pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);pcl::io::loadPCDFile<pcl::PointXYZ>("path/to/pointcloud.pcd", *cloud);// 对点云进行处理// ...// 保存点云pcl::io::savePCDFile("path/to/new/pointcloud.pcd", *cloud);return 0;
}

二、python 版本

import pclpy
from pclpy import pcl
import numpy as np# 读取点云
cloud = pcl.PointCloud.PointXYZ()
pcl.io.loadPCDFile("path/to/pointcloud.pcd", cloud)# 对点云进行处理
# ...# 保存点云
pcl.io.savePCDFileASCII("path/to/new/pointcloud.pcd", cloud)