测试环境：
pcl==1.12.1
python-pcl==0.3.1
python==3.7
代码：
# -*- coding: utf-8 -*-
# Euclidean Cluster Extraction
# http://pointclouds.org/documentation/tutorials/cluster_extraction.php#cluster-extraction
import numpy as np
import pcl
def main():
# // Read in the cloud data
# pcl::PCDReader reader;
# pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>), cloud_f (new pcl::PointCloud<pcl::PointXYZ>);
# reader.read ("table_scene_lms400.pcd", *cloud);
# std::cout << "PointCloud before filtering has: " << cloud->points.size () << " data points." << std::endl; //*
cloud = pcl.load('./examples/pcldata/tutorials/table_scene_lms400.pcd')
# // Create the filtering object: downsample the dataset using a leaf size of 1cm
# pcl::VoxelGrid<pcl::PointXYZ> vg;
# pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);
# vg.setInputCloud (cloud);
# vg.setLeafSize (0.01f, 0.01f, 0.01f);
# vg.filter (*cloud_filtered);
# std::cout << "PointCloud after filtering has: " << cloud_filtered->points.size () << " data points." << std::endl; //*
vg = cloud.make_voxel_grid_filter()
vg.set_leaf_size(0.01, 0.01, 0.01)
cloud_filtered = vg.filter()
# // Create the segmentation object for the planar model and set all the parameters
# pcl::SACSegmentation<pcl::PointXYZ> seg;
# pcl::PointIndices::Ptr inliers (new pcl::PointIndices);
# pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients);
# pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_plane (new pcl::PointCloud<pcl::PointXYZ> ());
# pcl::PCDWriter writer;
# seg.setOptimizeCoefficients (true);
# seg.setModelType (pcl::SACMODEL_PLANE);
# seg.setMethodType (pcl::SAC_RANSAC);
# seg.setMaxIterations (100);
# seg.setDistanceThreshold (0.02);
seg = cloud.make_segmenter()
seg.set_optimize_coefficients(True)
seg.set_model_type(pcl.SACMODEL_PLANE)
seg.set_method_type(pcl.SAC_RANSAC)
seg.set_MaxIterations(100)
seg.set_distance_threshold(0.02)
# int i=0, nr_points = (int) cloud_filtered->points.size ();
# while (cloud_filtered->points.size () > 0.3 * nr_points)
# {
# // Segment the largest planar component from the remaining cloud
# seg.setInputCloud (cloud_filtered);
# seg.segment (*inliers, *coefficients);
# if (inliers->indices.size () == 0)
# {
# std::cout << "Could not estimate a planar model for the given dataset." << std::endl;
# break;
# }
# // Extract the planar inliers from the input cloud
# pcl::ExtractIndices<pcl::PointXYZ> extract;
# extract.setInputCloud (cloud_filtered);
# extract.setIndices (inliers);
# extract.setNegative (false);
#
# // Get the points associated with the planar surface
# extract.filter (*cloud_plane);
# std::cout << "PointCloud representing the planar component: " << cloud_plane->points.size () << " data points." << std::endl;
#
# // Remove the planar inliers, extract the rest
# extract.setNegative (true);
# extract.filter (*cloud_f);
# *cloud_filtered = *cloud_f;
# }
i = 0
nr_points = cloud_filtered.size
# while nr_points > 0.3 * nr_points:
# # Segment the largest planar component from the remaining cloud
# [inliers, coefficients] = seg.segment()
# # extract = cloud_filtered.extract()
# # extract = pcl.PointIndices()
# cloud_filtered.extract(extract)
# extract.set_Indices (inliers)
# extract.set_Negative (false)
# cloud_plane = extract.filter ()
#
# extract.set_Negative (True)
# cloud_f = extract.filter ()
# cloud_filtered = cloud_f
# Creating the KdTree object for the search method of the extraction
# pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>);
# tree->setInputCloud (cloud_filtered);
tree = cloud_filtered.make_kdtree()
# tree = cloud_filtered.make_kdtree_flann()
# std::vector<pcl::PointIndices> cluster_indices;
# pcl::EuclideanClusterExtraction<pcl::PointXYZ> ec;
# ec.setClusterTolerance (0.02); // 2cm
# ec.setMinClusterSize (100);
# ec.setMaxClusterSize (25000);
# ec.setSearchMethod (tree);
# ec.setInputCloud (cloud_filtered);
# ec.extract (cluster_indices);
ec = cloud_filtered.make_EuclideanClusterExtraction()
ec.set_ClusterTolerance(0.02)
ec.set_MinClusterSize(100)
ec.set_MaxClusterSize(25000)
ec.set_SearchMethod(tree)
cluster_indices = ec.Extract()
print('cluster_indices : ' + str(cluster_indices.count) + " count.")
# print('cluster_indices : ' + str(cluster_indices.indices.max_size) + " count.")
# int j = 0;
# for (std::vector<pcl::PointIndices>::const_iterator it = cluster_indices.begin (); it != cluster_indices.end (); ++it)
# {
# pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_cluster (new pcl::PointCloud<pcl::PointXYZ>);
# for (std::vector<int>::const_iterator pit = it->indices.begin (); pit != it->indices.end (); ++pit)
# cloud_cluster->points.push_back (cloud_filtered->points[*pit]); //*
# cloud_cluster->width = cloud_cluster->points.size ();
# cloud_cluster->height = 1;
# cloud_cluster->is_dense = true;
#
# std::cout << "PointCloud representing the Cluster: " << cloud_cluster->points.size () << " data points." << std::endl;
# std::stringstream ss;
# ss << "cloud_cluster_" << j << ".pcd";
# writer.write<pcl::PointXYZ> (ss.str (), *cloud_cluster, false); //*
# j++;
# }
#
cloud_cluster = pcl.PointCloud()
for j, indices in enumerate(cluster_indices):
# cloudsize = indices
print('indices = ' + str(len(indices)))
# cloudsize = len(indices)
points = np.zeros((len(indices), 3), dtype=np.float32)
# points = np.zeros((cloudsize, 3), dtype=np.float32)
# for indice in range(len(indices)):
for i, indice in enumerate(indices):
# print('dataNum = ' + str(i) + ', data point[x y z]: ' + str(cloud_filtered[indice][0]) + ' ' + str(cloud_filtered[indice][1]) + ' ' + str(cloud_filtered[indice][2]))
# print('PointCloud representing the Cluster: ' + str(cloud_cluster.size) + " data points.")
points[i][0] = cloud_filtered[indice][0]
points[i][1] = cloud_filtered[indice][1]
points[i][2] = cloud_filtered[indice][2]
cloud_cluster.from_array(points)
ss = "cloud_cluster_" + str(j) + ".pcd"
pcl.save(cloud_cluster, ss)
if __name__ == "__main__":
# import cProfile
# cProfile.run('main()', sort='time')
main()运行结果：
cluster_indices : <built-in method count of list object at 0x00000242906D8B08> count.
indices = 1386
indices = 262
table_scene_lms400.pcd文件需要在这下载：
https://github.com/strawlab/python-pcl/blob/master/examples/pcldata/tutorials/table_scene_lms400.pcd