feat: 算法升级、整车配准集成与性能分析文档

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

alg/平整度、平面度、轮廓测量v1.5/archive/relative_code/DBSCAN.py

@@ -0,0 +1,50 @@
+import open3d as o3d
+import numpy as np
+
+
+def dbscan_open3d(pcd, eps=0.1, min_points=10):
+    """
+    使用 Open3D 进行 DBSCAN 聚类
+    Args:
+        pcd: Open3D 点云对象
+        eps: 邻域半径
+        min_points: 最小邻域点数
+    Returns:
+        labels: 每个点的簇标签（-1 表示噪声）
+        clusters: 各簇的点索引列表
+    """
+    # DBSCAN 聚类
+    labels = np.array(pcd.cluster_dbscan(eps=eps, min_points=min_points))
+
+    # 提取各簇索引
+    unique_labels = np.unique(labels)
+    clusters = []
+    for label in unique_labels:
+        if label == -1:
+            continue
+        clusters.append(np.where(labels == label)[0])
+
+    return labels, clusters
+
+
+# 使用示例
+pcd = o3d.io.read_point_cloud("E:\point_distance\data\part_car_head.ply")
+
+# 预处理
+pcd = pcd.voxel_down_sample(voxel_size=0.05)
+pcd, _ = pcd.remove_statistical_outlier(nb_neighbors=20, std_ratio=2.0)
+
+# DBSCAN 聚类
+labels, clusters = dbscan_open3d(pcd, eps=0.1, min_points=50)
+
+print(f"找到 {len(clusters)} 个聚类")
+print(f"噪声点: {np.sum(labels == -1)}")
+
+# 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])