Ubuntu 20.04.06 PCL C++学习记录（二十一）【切记使用rm * -rf前先确认是否是对应文件夹】

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@[TOC]PCL中点云分割模块的学习

学习背景

参考书籍：《点云库PCL从入门到精通》以及官方代码PCL官方代码链接,，PCL版本为1.10.0，CMake版本为3.16，测试点云下载地址

学习内容

根据欧几里得距离和需要保持的用户可自定义条件对点进行聚类，点云文件可从上述地址下载。

源代码及所用函数

源代码

#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/console/time.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/features/normal_3d.h>
#include <pcl/segmentation/conditional_euclidean_clustering.h>typedef pcl::PointXYZI PointTypeIO;
typedef pcl::PointXYZINormal PointTypeFull;bool enforceIntensitySimilarity (const PointTypeFull& point_a, const PointTypeFull& point_b, float /*squared_distance*/){if (std::abs (point_a.intensity - point_b.intensity) < 5.0f)return (true);elsereturn (false);
}bool enforceNormalOrIntensitySimilarity (const PointTypeFull& point_a, const PointTypeFull& point_b, float /*squared_distance*/){Eigen::Map<const Eigen::Vector3f> point_a_normal = point_a.getNormalVector3fMap (), point_b_normal = point_b.getNormalVector3fMap ();if (std::abs (point_a.intensity - point_b.intensity) < 5.0f)return (true);if (std::abs (point_a_normal.dot (point_b_normal)) > std::cos (30.0f / 180.0f * static_cast<float> (M_PI)))return (true);return (false);}boolcustomRegionGrowing (const PointTypeFull& point_a, const PointTypeFull& point_b, float squared_distance){Eigen::Map<const Eigen::Vector3f> point_a_normal = point_a.getNormalVector3fMap (), point_b_normal = point_b.getNormalVector3fMap ();if (squared_distance < 10000){if (std::abs (point_a.intensity - point_b.intensity) < 8.0f)return (true);if (std::abs (point_a_normal.dot (point_b_normal)) > std::cos (30.0f / 180.0f * static_cast<float> (M_PI)))return (true);}else{if (std::abs (point_a.intensity - point_b.intensity) < 3.0f)return (true);}return (false);}intmain (){// Data containers usedpcl::PointCloud<PointTypeIO>::Ptr cloud_in (new pcl::PointCloud<PointTypeIO>), cloud_out (new pcl::PointCloud<PointTypeIO>);pcl::PointCloud<PointTypeFull>::Ptr cloud_with_normals (new pcl::PointCloud<PointTypeFull>);pcl::IndicesClustersPtr clusters (new pcl::IndicesClusters), small_clusters (new pcl::IndicesClusters), large_clusters (new pcl::IndicesClusters);pcl::search::KdTree<PointTypeIO>::Ptr search_tree (new pcl::search::KdTree<PointTypeIO>);pcl::console::TicToc tt;// Load the input point cloudstd::cerr << "Loading...\n", tt.tic ();pcl::io::loadPCDFile ("/home/jojo/PointCloud/Statues_4.pcd", *cloud_in);std::cerr << ">> Done: " << tt.toc () << " ms, " << cloud_in->size () << " points\n";// Downsample the cloud using a Voxel Grid classstd::cerr << "Downsampling...\n", tt.tic ();pcl::VoxelGrid<PointTypeIO> vg;vg.setInputCloud (cloud_in);vg.setLeafSize (80.0, 80.0, 80.0);vg.setDownsampleAllData (true);vg.filter (*cloud_out);std::cerr << ">> Done: " << tt.toc () << " ms, " << cloud_out->size () << " points\n";// Set up a Normal Estimation class and merge data in cloud_with_normalsstd::cerr << "Computing normals...\n", tt.tic ();pcl::copyPointCloud (*cloud_out, *cloud_with_normals);pcl::NormalEstimation<PointTypeIO, PointTypeFull> ne;ne.setInputCloud (cloud_out);ne.setSearchMethod (search_tree);ne.setRadiusSearch (300.0);ne.compute (*cloud_with_normals);std::cerr << ">> Done: " << tt.toc () << " ms\n";// Set up a Conditional Euclidean Clustering classstd::cerr << "Segmenting to clusters...\n", tt.tic ();pcl::ConditionalEuclideanClustering<PointTypeFull> cec (true);cec.setInputCloud (cloud_with_normals);cec.setConditionFunction (&customRegionGrowing);cec.setClusterTolerance (500.0);cec.setMinClusterSize (cloud_with_normals->size () / 1000);cec.setMaxClusterSize (cloud_with_normals->size () / 5);cec.segment (*clusters);cec.getRemovedClusters (small_clusters, large_clusters);std::cerr << ">> Done: " << tt.toc () << " ms\n";// Using the intensity channel for lazy visualization of the outputfor (const auto& small_cluster : (*small_clusters))for (const auto& j : small_cluster.indices)(*cloud_out)[j].intensity = -2.0;for (const auto& large_cluster : (*large_clusters))for (const auto& j : large_cluster.indices)(*cloud_out)[j].intensity = +10.0;for (const auto& cluster : (*clusters)){int label = rand () % 8;for (const auto& j : cluster.indices)(*cloud_out)[j].intensity = label;}// Save the output point cloudstd::cerr << "Saving...\n", tt.tic ();pcl::io::savePCDFile ("output.pcd", *cloud_out);std::cerr << ">> Done: " << tt.toc () << " ms\n";return (0);}

CMakeLists.txt

cmake_minimum_required(VERSION 3.16 FATAL_ERROR)#指定CMake的最低版本要求为3.16
project(project)#设置项目名称
find_package(PCL 1.10 REQUIRED)#查找PCL库,要求版本为1.10或更高。
include_directories(${PCL_INCLUDE_DIRS})#将PCL库的头文件目录添加到包含路径中
link_directories(${PCL_LIBRARY_DIRS})#将PCL库的库文件目录添加到链接器搜索路径中。
add_definitions(${PCL_DEFINITIONS})#添加PCL库的编译器定义
add_executable (conditional_euclidean_clustering conditional_euclidean_clustering.cpp)
target_link_libraries (conditional_euclidean_clustering ${PCL_LIBRARIES})#将PCL库链接到可执行文件目标。

运行结果

注意：当使用 PCL 的标准 PCD 查看器打开输出点云时，按“5”将切换到强度通道可视化。 太小的簇将被涂成红色，太大的簇将被涂成蓝色，而实际的簇/感兴趣的物体将被随机着色为黄色和青色， 如果不按则都为一个颜色。

函数

补充内容

• std::cout << “降采样中\n”,tt.tic();和std::cout << “降采样中\n”,tt.tic()<<std::endl;的区别
  1. std::cout << “降采样中\n”, tt.tic();
     这个语句使用了逗号运算符 ,。逗号运算符会按照从左到右的顺序依次计算其左右两侧的表达式，并返回右侧表达式的值。在这个语句中，首先会输出字符串 “降采样中\n”，然后计算 tt.tic()，但是 tt.tic() 的返回值会被丢弃，因为它没有被使用或输出。
  2. std::cout << “降采样中\n”, tt.tic() << std::endl;
     这个语句也使用了逗号运算符 ,。同样地，它会先输出字符串 “降采样中\n”，然后计算 tt.tic()。但是，这里的 tt.tic() 的返回值会被传递给 std::cout，然后再输出一个换行符 std::endl。

第二个语句不仅会输出字符串 “降采样中\n”，还会输出 tt.tic() 的返回值，并在最后添加一个换行符

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