【PCL】（二十六）自定义条件的欧几里得聚类分割点云-编程知识

【PCL】（二十六）自定义条件的欧几里得聚类分割点云

（二十六）自定义条件的欧几里得聚类分割点云

以下代码实现自定义条件对点进行欧几里得聚类分割。

conditional_euclidean_clustering.cpp

#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;/*条件函数的格式是固定的：
前两个输入参数的类型必须与pcl::ConditionalEuclideanClustering类中使用的模板化类型相同：包括当前种子点（第一个参数）和当前候选点（第二个参数）的点信息。
第三个输入参数必须是浮点值：为种子点和候选点之间的平方距离。
输出参数必须是布尔值。返回TRUE将把候选点合并到种子点的簇中，否则不会。*/// 条件函数示例1
bool enforceIntensitySimilarity (const PointTypeFull& point_a, const PointTypeFull& point_b, float /*squared_distance*/)
{// 根据强度值相近程度进行聚类if (std::abs (point_a.intensity - point_b.intensity) < 5.0f)return (true);elsereturn (false);
}
// 条件函数示例2
bool enforceNormalOrIntensitySimilarity (const PointTypeFull& point_a, const PointTypeFull& point_b, float /*squared_distance*/)
{// 强度值相似或法线方向相似归为一类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);
}
// 条件函数示例3
bool customRegionGrowing (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 ();// 与第二个条件函数相似，但根据点之间的距离具有不同的条件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);
}int main ()
{// 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;  // 用于输出计时结果。// Load the input point cloudstd::cerr << "Loading...\n", tt.tic ();pcl::io::loadPCDFile ("Statues_5.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);  // 类初始化为TRUE。这将允许提取太小或太大的簇cec.setInputCloud (cloud_with_normals);cec.setConditionFunction (&customRegionGrowing);  //   指定条件函数cec.setClusterTolerance (100.0);cec.setMinClusterSize (cloud_with_normals->size () / 1000);   // 占云总点不到0.1%的簇被认为太小。cec.setMaxClusterSize (cloud_with_normals->size () / 5);   // 占云总点20%以上的簇被认为太大。cec.segment (*clusters);// 太小的集群或太大的集群不会传递到主输出，而是可以在单独的pcl::Indices据容器中检索，但前提是类已用TRUE初始化。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);
}

编译

cmake_minimum_required(VERSION 3.5 FATAL_ERROR)project(conditional_euclidean_clustering)find_package(PCL 1.7 REQUIRED)include_directories(${PCL_INCLUDE_DIRS})
link_directories(${PCL_LIBRARY_DIRS})
add_definitions(${PCL_DEFINITIONS})add_executable (conditional_euclidean_clustering conditional_euclidean_clustering.cpp)
target_link_libraries (conditional_euclidean_clustering ${PCL_LIBRARIES})