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/VGF.py

@@ -0,0 +1,96 @@
+import open3d as o3d
+import numpy as np
+from scipy.spatial import KDTree
+
+
+def evaluate_voxel_filter(pcd, leaf_size=0.05):
+    """
+    评估体素滤波效果，计算各项指标
+
+    Args:
+        pcd: 原始点云
+        leaf_size: 体素边长
+
+    Returns:
+        metrics: 指标字典
+        filtered_pcd: 滤波后点云
+    """
+    # 执行体素滤波
+    filtered_pcd = pcd.voxel_down_sample(leaf_size)
+
+    n_orig = len(pcd.points)
+    n_filt = len(filtered_pcd.points)
+
+    # 1. 基础统计指标
+    keep_ratio = n_filt / n_orig * 100
+    downsample_rate = (n_orig - n_filt) / n_orig * 100
+    compression_ratio = n_orig / n_filt if n_filt > 0 else 0
+
+    # 2. 密度变化指标
+    points_orig = np.asarray(pcd.points)
+    points_filt = np.asarray(filtered_pcd.points)
+
+    # 计算平均最近邻距离（采样加速）
+    sample_size = min(1000, n_filt)
+    if n_filt > sample_size:
+        idx = np.random.choice(n_filt, sample_size, replace=False)
+        points_sample = points_filt[idx]
+    else:
+        points_sample = points_filt
+
+    if len(points_sample) > 5:
+        tree = KDTree(points_sample)
+        dist, _ = tree.query(points_sample, k=2)
+        avg_distance = np.mean(dist[:, 1])
+    else:
+        avg_distance = 0
+
+    # 3. 输出指标
+    metrics = {
+        'leaf_size': leaf_size,
+        'n_orig': n_orig,
+        'n_filt': n_filt,
+        'keep_ratio': keep_ratio,
+        'downsample_rate': downsample_rate,
+        'compression_ratio': compression_ratio,
+        'avg_point_distance': avg_distance,
+        'estimated_density': 1 / (avg_distance ** 3) if avg_distance > 0 else 0
+    }
+
+    return metrics, filtered_pcd
+
+
+# ========== 使用示例 ==========
+if __name__ == "__main__":
+    # 读取点云
+    pcd = o3d.io.read_point_cloud(r"E:\point_distance\data\part_car_head.ply")
+
+    # 测试不同体素大小
+    leaf_sizes = [2, 15, 20, 2]
+
+    print("=" * 60)
+    print("体素滤波指标评估")
+    print("=" * 60)
+    print(f"{'体素边长':<10} {'保留率':<12} {'下采样率':<12} {'压缩比':<10} {'平均间距':<12}")
+    print("-" * 60)
+
+    for leaf_size in leaf_sizes:
+        metrics, filtered = evaluate_voxel_filter(pcd, leaf_size)
+        print(f"{leaf_size:<10} {metrics['keep_ratio']:>8.2f}%    "
+              f"{metrics['downsample_rate']:>8.2f}%    "
+              f"{metrics['compression_ratio']:>8.1f}x    "
+              f"{metrics['avg_point_distance']:>8.4f}m")
+
+    # 可视化对比
+    print("\n可视化对比...")
+    orig_colored = o3d.geometry.PointCloud()
+    orig_colored.points = pcd.points
+    orig_colored.paint_uniform_color([0, 0, 1])  # 蓝色
+    # o3d.visualization.draw_geometries([orig_colored],window_name=f"orignal (blue,{len(pcd.points)}")
+    metrics, filtered = evaluate_voxel_filter(pcd, leaf_size=20)
+    filtered.paint_uniform_color([1, 0, 0])  # 红色
+    # o3d.visualization.draw_geometries([filtered], window_name=f"Voxel (red,{len(filtered.points)}")
+    o3d.visualization.draw_geometries(
+        [orig_colored, filtered],
+        window_name=f"Original (Blue, {len(pcd.points)} pts) vs Voxel (Red, {len(filtered.points)} pts)"
+    )