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/BF.py

@@ -0,0 +1,95 @@
+import open3d as o3d
+import numpy as np
+from scipy.spatial import KDTree
+import numpy as np
+from scipy.spatial import KDTree
+def chamfer_distance(pcd1, pcd2):
+    """
+    计算两个点云之间的 CD 距离（Chamfer Distance）
+
+    Args:
+        pcd1: Open3D 点云对象1
+        pcd2: Open3D 点云对象2
+
+    Returns:
+        cd: Chamfer Distance 值
+        cd1: 点云1 到 点云2 的平均距离
+        cd2: 点云2 到 点云1 的平均距离
+    """
+    # 获取点坐标
+    points1 = np.asarray(pcd1.points)
+    points2 = np.asarray(pcd2.points)
+
+    # 构建 KD-Tree 加速最近邻搜索
+    tree1 = KDTree(points1)
+    tree2 = KDTree(points2)
+
+    # 计算 points1 中每个点到 points2 的最近距离
+    dist1, _ = tree2.query(points1, k=1)
+    cd1 = np.mean(dist1 ** 2)  # 平方距离的平均值
+
+    # 计算 points2 中每个点到 points1 的最近距离
+    dist2, _ = tree1.query(points2, k=1)
+    cd2 = np.mean(dist2 ** 2)  # 平方距离的平均值
+
+    # 总 CD 距离（双向平均）
+    cd = cd1 + cd2
+
+    return np.sqrt(cd), cd1, cd2
+
+def bilateral_filter(pcd, sigma_s=0.01, sigma_r=0.1):
+    """
+    对点云进行双边滤波
+    Args:
+        pcd: Open3D点云对象
+        sigma_s: 空间标准差
+        sigma_r: 特征（法线）标准差
+    """
+    points = np.asarray(pcd.points)
+    normals = np.asarray(pcd.normals)
+
+    # 构建KD树
+    tree = KDTree(points)
+
+    filtered_points = np.zeros_like(points)
+
+    for i, p in enumerate(points):
+        # 搜索邻域点
+        indices = tree.query_ball_point(p, r=2 * sigma_s)
+        if len(indices) <= 1:
+            filtered_points[i] = p
+            continue
+
+        neigh_points = points[indices]
+        neigh_normals = normals[indices]
+
+        # 计算空间权重
+        spatial_dist = np.linalg.norm(neigh_points - p, axis=1)
+        w_s = np.exp(-spatial_dist ** 2 / (2 * sigma_s ** 2))
+
+        # 计算特征（法线）权重
+        normal_diff = np.linalg.norm(neigh_normals - normals[i], axis=1)
+        w_r = np.exp(-normal_diff ** 2 / (2 * sigma_r ** 2))
+
+        # 双边权重
+        w = w_s * w_r
+        w = w / w.sum()  # 归一化
+
+        # 加权平均
+        filtered_points[i] = np.sum(neigh_points.T * w, axis=1)
+
+    # 更新点云
+    filtered_pcd = o3d.geometry.PointCloud()
+    filtered_pcd.points = o3d.utility.Vector3dVector(filtered_points)
+    filtered_pcd.normals = o3d.utility.Vector3dVector(normals)
+
+    return filtered_pcd
+
+
+# 使用示例
+pcd = o3d.io.read_point_cloud("E:\point_distance\data\part_car_head.ply")
+pcd.estimate_normals()
+pcd_filtered = bilateral_filter(pcd, sigma_s=10, sigma_r=10)
+
+cd=chamfer_distance(pcd,pcd_filtered)
+print(cd)