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/distance_mesh.py

@@ -0,0 +1,141 @@
+import open3d as o3d
+import numpy as np
+from scipy.spatial import KDTree
+import copy
+
+
+def compute_point_to_mesh_distance(point_cloud, mesh):
+    """
+    计算点云到 Mesh 的距离
+
+    Args:
+        point_cloud: Open3D 点云对象
+        mesh: Open3D 网格对象
+
+    Returns:
+        distances: 每个点到最近三角形面的距离
+    """
+    # 方法1：使用 Open3D 内置函数（新版本）
+    try:
+        distances = mesh.compute_point_cloud_distance(point_cloud)
+        return np.asarray(distances)
+    except AttributeError:
+        pass
+
+    # 方法2：使用 mesh 顶点最近邻（近似）
+    mesh_points = np.asarray(mesh.vertices)
+    points = np.asarray(point_cloud.points)
+
+    tree = KDTree(mesh_points)
+    distances, indices = tree.query(points)
+
+    return distances
+
+
+def compute_statistics(distances):
+    """计算距离统计信息"""
+    stats = {
+        'mean': np.mean(distances),
+        'rmse': np.sqrt(np.mean(distances ** 2)),
+        'max': np.max(distances),
+        'min': np.min(distances),
+        'std': np.std(distances),
+        'median': np.median(distances),
+    }
+
+    # 分位数
+    percentiles = [50, 75, 90, 95, 99]
+    for p in percentiles:
+        stats[f'percentile_{p}'] = np.percentile(distances, p)
+
+    return stats
+
+
+def visualize_distance(point_cloud, mesh, distances, max_dist=None):
+    """可视化距离（按距离着色）"""
+    pcd_colored = copy.deepcopy(point_cloud)
+
+    if max_dist is None:
+        max_dist = np.percentile(distances, 95)
+
+    # 生成颜色（红：远，蓝：近）
+    colors = np.zeros((len(distances), 3))
+    norm_dist = np.clip(distances / (max_dist + 1e-8), 0, 1)
+    colors[:, 0] = norm_dist  # 红色通道（远）
+    colors[:, 2] = 1 - norm_dist  # 蓝色通道（近）
+
+    pcd_colored.colors = o3d.utility.Vector3dVector(colors)
+
+    # Mesh 着色为灰色
+    mesh_colored = copy.deepcopy(mesh)
+    mesh_colored.paint_uniform_color([0.5, 0.5, 0.5])
+
+    # 可视化
+    o3d.visualization.draw_geometries(
+        [mesh_colored, pcd_colored],
+        window_name="Distance to Mesh (Blue=Near, Red=Far)",
+        width=1024,
+        height=768
+    )
+
+
+def print_statistics(stats):
+    """打印统计信息"""
+    print("=" * 60)
+    print("点云到 Mesh 距离统计")
+    print("=" * 60)
+    print(f"平均距离: {stats['mean']:.6f} m ({stats['mean'] * 1000:.3f} mm)")
+    print(f"RMSE:     {stats['rmse']:.6f} m ({stats['rmse'] * 1000:.3f} mm)")
+    print(f"中位数:   {stats['median']:.6f} m ({stats['median'] * 1000:.3f} mm)")
+    print(f"标准差:   {stats['std']:.6f} m ({stats['std'] * 1000:.3f} mm)")
+    print(f"最大距离: {stats['max']:.6f} m ({stats['max'] * 1000:.3f} mm)")
+    print(f"最小距离: {stats['min']:.6f} m ({stats['min'] * 1000:.3f} mm)")
+    print("\n距离分位数:")
+    print(f"  50%:  {stats['percentile_50']:.6f} m ({stats['percentile_50'] * 1000:.3f} mm)")
+    print(f"  75%:  {stats['percentile_75']:.6f} m ({stats['percentile_75'] * 1000:.3f} mm)")
+    print(f"  90%:  {stats['percentile_90']:.6f} m ({stats['percentile_90'] * 1000:.3f} mm)")
+    print(f"  95%:  {stats['percentile_95']:.6f} m ({stats['percentile_95'] * 1000:.3f} mm)")
+    print(f"  99%:  {stats['percentile_99']:.6f} m ({stats['percentile_99'] * 1000:.3f} mm)")
+
+
+# ========== 主程序 ==========
+if __name__ == "__main__":
+    # 读取文件（使用原始字符串避免转义问题）
+    file_path = r"E:\point_distance\data\40_noisy.ply"
+    mesh_path = r"E:\point_distance\data\40.stl"
+
+    print("读取点云...")
+    source_pcd = o3d.io.read_point_cloud(file_path)
+
+    print("读取 Mesh...")
+    mesh = o3d.io.read_triangle_mesh(mesh_path)
+
+    # 检查读取是否成功
+    if len(source_pcd.points) == 0:
+        print("错误：点云读取失败，请检查文件路径")
+        exit()
+
+    if len(mesh.vertices) == 0:
+        print("错误：Mesh 读取失败，请检查文件路径")
+        exit()
+
+    print(f"\n点云点数: {len(source_pcd.points)}")
+    print(f"Mesh 顶点数: {len(mesh.vertices)}")
+    print(f"Mesh 面数: {len(mesh.triangles)}")
+
+    # 可选：Mesh 法线计算（用于更好的可视化）
+    mesh.compute_vertex_normals()
+
+    # 计算距离
+    print("\n计算点云到 Mesh 的距离...")
+    distances = compute_point_to_mesh_distance(source_pcd, mesh)
+
+    # 计算统计信息
+    stats = compute_statistics(distances)
+
+    # 打印统计信息
+    print_statistics(stats)
+
+    # 可视化
+    print("\n可视化...")
+    visualize_distance(source_pcd, mesh, distances)