lipan
29f1d45d66
算法更新: delivery v1.2.1、平整度v1.5、测距v2.2、整车配准v1.3 适配器: vehicle-registration 新增、各算法 manifest 和 adapter 更新 后端: taskCadFiles/source 字段迁移、derivedPointClouds 注册、取消任务联动 前端: ElapsedTime 时长显示、两步算法流程、canRun 逻辑修复 文档: 新增5份算法性能分析报告
47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
import open3d as o3d
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import numpy as np
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def euclidean_clustering_open3d(pcd, radius=0.1, min_cluster_size=50, max_cluster_size=10000):
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"""
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使用 Open3D 进行欧几里得聚类
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Args:
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pcd: Open3D 点云对象
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radius: 聚类半径
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min_cluster_size: 最小簇大小
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max_cluster_size: 最大簇大小
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Returns:
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labels: 每个点的簇标签（-1 表示噪声/无效簇）
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clusters: 各簇的点索引列表
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"""
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# 欧几里得聚类
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labels = np.array(pcd.cluster_dbscan(eps=radius, min_points=min_cluster_size))
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# 统计簇信息
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unique_labels = np.unique(labels)
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clusters = []
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for label in unique_labels:
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if label == -1: # -1 表示噪声点
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continue
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cluster_indices = np.where(labels == label)[0]
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if min_cluster_size <= len(cluster_indices) <= max_cluster_size:
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clusters.append(cluster_indices)
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return labels, clusters
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# 使用示例
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pcd = o3d.io.read_point_cloud("E:\point_distance\data\part_car_left_side.ply")
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labels, clusters = euclidean_clustering_open3d(pcd, radius=4, min_cluster_size=100)
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print(f"找到 {len(clusters)} 个聚类")
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# 为每个聚类着色
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colors = np.random.rand(len(clusters), 3)
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colored_points = np.zeros((len(pcd.points), 3))
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for i, cluster in enumerate(clusters):
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colored_points[cluster] = colors[i]
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pcd.colors = o3d.utility.Vector3dVector(colored_points)
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o3d.visualization.draw_geometries([pcd]) |