import open3d as o3d
import numpy as np


def euclidean_clustering_open3d(pcd, radius=0.1, min_cluster_size=50, max_cluster_size=10000):
    """
    使用 Open3D 进行欧几里得聚类
    Args:
        pcd: Open3D 点云对象
        radius: 聚类半径
        min_cluster_size: 最小簇大小
        max_cluster_size: 最大簇大小
    Returns:
        labels: 每个点的簇标签（-1 表示噪声/无效簇）
        clusters: 各簇的点索引列表
    """
    # 欧几里得聚类
    labels = np.array(pcd.cluster_dbscan(eps=radius, min_points=min_cluster_size))

    # 统计簇信息
    unique_labels = np.unique(labels)
    clusters = []

    for label in unique_labels:
        if label == -1:  # -1 表示噪声点
            continue
        cluster_indices = np.where(labels == label)[0]
        if min_cluster_size <= len(cluster_indices) <= max_cluster_size:
            clusters.append(cluster_indices)

    return labels, clusters


# 使用示例
pcd = o3d.io.read_point_cloud("E:\point_distance\data\part_car_left_side.ply")
labels, clusters = euclidean_clustering_open3d(pcd, radius=4, min_cluster_size=100)

print(f"找到 {len(clusters)} 个聚类")

# 为每个聚类着色
colors = np.random.rand(len(clusters), 3)
colored_points = np.zeros((len(pcd.points), 3))
for i, cluster in enumerate(clusters):
    colored_points[cluster] = colors[i]

pcd.colors = o3d.utility.Vector3dVector(colored_points)
o3d.visualization.draw_geometries([pcd])