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/Eucilid.py

@@ -0,0 +1,47 @@
+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])