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/PF.py

@@ -0,0 +1,94 @@
+import open3d as o3d
+import numpy as np
+from scipy.spatial import KDTree
+
+
+def compute_keep_ratio_and_density(pcd, filtered_pcd, sample_size=1000):
+    """
+    计算直通滤波的保留率和密度变化
+
+    Args:
+        pcd: 原始点云
+        filtered_pcd: 滤波后点云
+        sample_size: 密度计算采样点数（加速计算）
+
+    Returns:
+        keep_ratio: 保留率 (%)
+        density_change: 密度变化 (%)
+    """
+    n_orig = len(pcd.points)
+    n_filt = len(filtered_pcd.points)
+
+    # 1. 保留率
+    keep_ratio = n_filt / n_orig * 100
+
+    # 2. 密度变化（基于平均最近邻距离）
+    points_orig = np.asarray(pcd.points)
+    points_filt = np.asarray(filtered_pcd.points)
+
+    # 采样（点数太多时加速）
+    if len(points_orig) > sample_size:
+        idx_orig = np.random.choice(len(points_orig), sample_size, replace=False)
+        points_orig_sample = points_orig[idx_orig]
+    else:
+        points_orig_sample = points_orig
+
+    if len(points_filt) > sample_size:
+        idx_filt = np.random.choice(len(points_filt), sample_size, replace=False)
+        points_filt_sample = points_filt[idx_filt]
+    else:
+        points_filt_sample = points_filt
+
+    # 计算平均最近邻距离（密度指标）
+    if len(points_orig_sample) > 5:
+        tree_orig = KDTree(points_orig_sample)
+        dist_orig, _ = tree_orig.query(points_orig_sample, k=2)  # k=2 排除自身
+        avg_dist_orig = np.mean(dist_orig[:, 1])
+    else:
+        avg_dist_orig = 0
+
+    if len(points_filt_sample) > 5:
+        tree_filt = KDTree(points_filt_sample)
+        dist_filt, _ = tree_filt.query(points_filt_sample, k=2)
+        avg_dist_filt = np.mean(dist_filt[:, 1])
+    else:
+        avg_dist_filt = 0
+
+    # 密度变化（距离增大 = 密度减小）
+    if avg_dist_orig > 0:
+        density_change = (avg_dist_filt - avg_dist_orig) / avg_dist_orig * 100
+    else:
+        density_change = 0
+
+    return keep_ratio, density_change
+
+
+# ========== 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")
+
+    # 直通滤波（Z轴）
+    points = np.asarray(pcd.points)
+    mask = (points[:, 2] >= 0.0) & (points[:, 2] <= 2.0)
+    filtered_points = points[mask]
+
+    filtered_pcd = o3d.geometry.PointCloud()
+    filtered_pcd.points = o3d.utility.Vector3dVector(filtered_points)
+
+    # 计算指标
+    keep_ratio, density_change = compute_keep_ratio_and_density(pcd, filtered_pcd)
+
+    print("=" * 40)
+    print("直通滤波指标")
+    print("=" * 40)
+    print(f"保留率: {keep_ratio:.2f}%")
+    print(f"密度变化: {density_change:+.2f}%")
+
+    # 解释
+    if density_change > 0:
+        print(f"  → 点云变稀疏了 {density_change:.2f}%")
+    elif density_change < 0:
+        print(f"  → 点云变密集了 {abs(density_change):.2f}%")
+    else:
+        print(f"  → 密度基本不变")