feat: initial commit - massive-cloud point cloud processing system
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alg/delivery_v1.1/underframe_deflection/USAGE.md
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alg/delivery_v1.1/underframe_deflection/USAGE.md
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# underframe_deflection 集成调用指南 (v1.1)
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> 高铁底架纵梁挠度测量算法 —— 基于点云的计算。
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> 本文档面向把本算法集成进上层软件的开发者：怎么准备 **CAD 基准 + 扫描点云 + ROI JSON** 三件套输入、怎么调用、怎么读结果。
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---
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## 0. TL;DR
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```python
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from underframe_deflection import run_measurement_pipeline
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report = run_measurement_pipeline(
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input_cloud="scan.ply",
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cad_path="reference.ply",
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roi_path="roi.json",
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out_dir="out/",
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)
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# 关心的两个核心数值
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print(report["deflection"]["left"]["selected_measurement"]["signed_deflection_mm"])
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print(report["deflection"]["right"]["selected_measurement"]["signed_deflection_mm"])
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```
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落盘后会得到：
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```
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out/
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├── report.json # 完整结果
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├── report.xlsx # 误差报表（Summary + 4 张中间表）
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├── deflection_curves.csv # 沿 X 的挠度曲线
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├── beam_tracks.csv # 候选梁切片诊断
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├── images/ # 5 张诊断 PNG
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└── ply/ # 4 个着色 PLY 叠加图
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```
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---
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## 1. 算法的三个输入
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| 输入 | 类型 | 必需 | 说明 |
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| --- | --- | --- | --- |
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| **扫描点云** | `.ply / .xyz / .txt / .csv` | ✅ 必须 | 现场采集得到的 (N, 3) XYZ 点云，单位 mm |
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| **CAD 基准** | `.ply / .xyz / .stl / .obj` | 可选但强烈建议 | 预先 tessellate / 采样过的 CAD 几何，作为配准基准坐标系；不直接吃 STEP/IGES |
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| **ROI JSON** | `.json` | 可选但强烈建议 | 描述车底测量区域的轴对齐 bbox，坐标基于 CAD 坐标系 |
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> 三者**全给**时走 v1.1 完整流程：去噪 → 配准到 CAD 系 → 按 ROI JSON 截区 → 算挠度 → 出 Excel。
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> 只给扫描点云时走 legacy 流程，与本算法 v1.0 行为完全一致（仅多出 bilateral 这一步可用 `--no-bilateral` 关掉）。
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### 1.1 ROI JSON 文件格式
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固定 6 个 float 字段，单位 mm，CAD 坐标系下：
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```json
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{
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"x_min": -12500.0,
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"x_max": 12500.0,
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"y_min": 150.0,
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"y_max": 4100.0,
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"z_min": -1680.0,
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"z_max": 200.0
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}
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```
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约束：每个轴 `min < max`，且都是有限实数；任一字段缺失或非法 → `InvalidPointCloudError`。
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`test_data/sample_roi.json` 是一个可直接使用的样例。
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### 1.2 CAD 基准
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- **不解析 STEP / IGES**。如果你只有 `.stp`，请用 CAD 软件或离线脚本先把它转成 `.stl` / `.ply` / `.obj`，再喂给本算法。
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- 三角网格的话只读顶点位置，face 信息忽略 —— 算法内部把 CAD 当作"参考点集"用 ICP 对齐。
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- `test_data/m000004802409t02_asm_100000pts.ply` 是从原始 STEP 采样得到的 10 万点 PLY，可直接当 CAD 基准。
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---
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## 2. 三种调用方式
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### 2.1 命令行（最简单的集成方式）
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```bash
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underframe-measure \
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--input-cloud scan.ply \
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--cad reference.ply \
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--roi roi.json \
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--out-dir out/
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```
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可选开关：
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| 开关 | 含义 |
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| --- | --- |
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| `--config CONFIG.json` | 覆盖部分内部超参数（结构同 `DEFAULT_CONFIG`） |
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| `--profile {auto,M,TC,TP,custom}` | 调参档位 |
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| `--skip-registration` | 给了 `--cad` 但不跑 ICP（适合调试或扫描已经预对齐） |
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| `--no-bilateral` | 关闭双边滤波（v1.0 行为） |
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| `--skip-excel` | 不写 `.xlsx` |
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未安装本包时，等价命令：
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```bash
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python -m underframe_deflection.cli --input-cloud scan.ply ...
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```
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### 2.2 一次性 Python 函数（推荐）
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```python
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from underframe_deflection import run_measurement_pipeline
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report = run_measurement_pipeline(
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input_cloud="scan.ply",
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cad_path="reference.ply", # 可选
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roi_path="roi.json", # 可选
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out_dir="out/",
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config_path=None, # 可选 JSON 配置覆盖
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profile="auto", # 可选档位
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skip_registration=False,
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enable_bilateral=True,
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skip_excel=False,
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)
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```
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**返回**：`report` 是一个 `dict`，结构与 `out/report.json` 完全一致。
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### 2.3 纯内存计算（不读写文件）
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如果你已经从其它来源拿到了点云和 ROI（例如直接从相机驱动 / 数据库取数据），可以避开文件 I/O：
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```python
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import numpy as np
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from underframe_deflection import compute_deflection, load_point_cloud
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points = load_point_cloud("scan.ply") # 或自己构造 (N, 3) ndarray
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roi_aabb = { # 与 ROI JSON 同结构的 dict
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"x_min": -12500, "x_max": 12500,
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"y_min": 150, "y_max": 4100,
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"z_min": -1680, "z_max": 200,
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}
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result = compute_deflection(points, roi_aabb=roi_aabb)
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print(result.left.selected_signed_mm, result.right.selected_signed_mm)
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```
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> 注意：`compute_deflection` 本身**不做 CAD 配准**。如果你想自己控制配准过程，先调用 `register_to_cad`，把变换应用到点云后再喂给 `compute_deflection`，配合 `config["frame"]["use_input_axes"]=True`（这本来就是默认值）即可：
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```python
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from underframe_deflection import (
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read_cad_reference, register_to_cad, compute_deflection,
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)
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from underframe_deflection.registration import apply_transform
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scan = load_point_cloud("scan.ply")
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cad = read_cad_reference("reference.ply")
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# 你需要一份预解算好的 config（spacing 之类）。最简单的办法：
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from underframe_deflection.config import load_config, resolve_numeric_parameters, cloud_geometry
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from underframe_deflection.preprocess import estimate_spacing
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cfg = load_config(None, "auto")
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cfg = resolve_numeric_parameters(cfg, cloud_geometry(scan), estimate_spacing(scan))
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reg = register_to_cad(scan, cad, cfg)
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scan_aligned = apply_transform(scan, reg.R, reg.t)
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result = compute_deflection(scan_aligned, roi_aabb=roi_aabb)
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```
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---
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## 3. 读取输出
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### 3.1 核心数值（最常用）
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```python
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left = report["deflection"]["left"]["selected_measurement"]
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right = report["deflection"]["right"]["selected_measurement"]
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if left["available"]:
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x_mm = left["x_mm"]
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signed = left["signed_deflection_mm"] # 带符号，向下为负
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absolute = left["absolute_deflection_mm"]
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type_ = left["selected_type"] # "downward" / "upward"
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beam_pt = left["beam_point"] # [x, y, z]
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ref_pt = left["reference_point"] # [x, y, z]
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```
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如果你用的是 `compute_deflection`，公开数据类更直接：
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```python
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result.left.selected_signed_mm
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result.left.max_downward_mm
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result.left.max_downward_x_mm
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result.left.max_upward_mm
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result.left.reference_confidence
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result.curves.x_mm # ndarray
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result.curves.left_deflection_mm # ndarray, NaN 表示该 X 处无效
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```
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### 3.2 质量警告 `quality_flags`
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**非致命**问题列在这里，调用方应判断是否要重测：
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| Flag | 含义 |
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| --- | --- |
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| `left_track_missing` / `right_track_missing` | 单侧梁追踪失败 |
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| `left_representative_line_missing` / `right_...` | 单侧代表线拟合失败 |
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| `left_reference_unavailable` / `right_...` | 单侧参考线不可用 |
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| `left_reference_low_confidence` / `right_...` | 参考线置信度低 |
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| `left_track_low_coverage` / `right_...` | 单侧追踪覆盖率 < 45% |
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| `left_right_symmetry_warning` | 左右对称性偏差 > 18% |
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| **`registration_not_converged`** | ICP 未收敛到容差内 |
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| **`registration_poor_quality`** | ICP 最终 RMS > max(10 mm, 2×点间距) |
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| **`registration_low_inlier_ratio`** | ICP 内点比例 < 50% |
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| **`passthrough_empty`** | ROI passthrough 后点过少，已自动回退 |
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| **`bilateral_degraded`** | bilateral k 邻域被裁剪 |
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加粗的几项是 v1.1 新增。
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### 3.3 配准信息
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```python
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reg = report["registration"]
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if reg["performed"]:
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reg["rms_mm"] # 最终对齐残差
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reg["initial_rms_mm"] # 粗对齐后的初始残差
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reg["iterations"]
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reg["inlier_ratio"]
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reg["converged"] # bool
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reg["R"] # 3x3 list
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reg["t"] # length-3 list
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```
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### 3.4 Excel 误差报表
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`out/report.xlsx` 5 个 sheet：
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| Sheet | 内容 |
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| --- | --- |
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| **Summary** | key/value 形式：版本、输入路径、配准统计、预处理各阶段计数、左右挠度核心数值、质量警告 |
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| **Deflection_Curves** | 沿 X 的逐点表：x / 左右 beam_y, beam_z / 左右 ref_y, ref_z / 左右 deflection / 左右 confidence |
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| **Beam_Tracks** | 每片候选条带：slice_index、y_center、z_low、width、score、selected_rank 等 |
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| **Reference_Lines** | 左右参考线 front/back 端点、direction、residual、confidence |
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| **Representative_Points** | 左右每个代表点的 x/y/z/width/residual/confidence |
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> 用 `openpyxl`、`pandas.read_excel`、Excel/LibreOffice 都可以直接读。
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### 3.5 异常
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| 异常类 | 触发条件 |
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| --- | --- |
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| `InvalidPointCloudError` | 点云 / CAD / ROI JSON 文件格式非法、字段缺失、AABB 反转 |
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| `UnsupportedFormatError` | 文件后缀不在支持列表 |
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| `InsufficientPointsError` | 点数过少（点云 < 256）或 ROI 过滤后无重叠 |
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| `FrameInferenceError` | 自动坐标系推断失败（仅 legacy 路径） |
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集成方应在外层 `try / except DeflectionError` 兜底，并把 `quality_flags` 落入日志或上层告警。
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---
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## 4. 配置覆盖（高级）
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绝大多数场景不需要碰配置。需要微调时，通过 `--config my.json` 或 `config=` 传入嵌套 dict，与 `DEFAULT_CONFIG` 深度合并：
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```python
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config = {
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"preprocess": {
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"bilateral": {"enabled": True, "k": 32, "sigma_n_deg": 10.0},
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# 也可以从外部传 passthrough，但通常用 ROI JSON 即可
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},
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"registration": {
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"icp_max_iter": 80,
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"trim_ratio": 0.15,
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},
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"reference": {
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"low_confidence_threshold": 0.5,
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},
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"deflection": {
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"evaluation_grid_factor": 0.5, # 更密的挠度采样
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},
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}
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```
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完整字段定义见 `underframe_deflection/config.py: DEFAULT_CONFIG`。
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---
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## 5. 进度回调
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`compute_deflection` 支持 `progress` 回调：
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```python
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def on_progress(stage: str, fraction: float) -> None:
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print(f"[{fraction:5.1%}] {stage}")
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result = compute_deflection(points, roi_aabb=roi_aabb, progress=on_progress)
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```
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阶段顺序：`preprocess → frame → beam_tracking → line_fitting → reference → deflection`。
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`run_measurement_pipeline` 当前未透传 progress 回调，需要时请直接调 `compute_deflection`。
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---
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## 6. 坐标系约定
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输入点云**必须**满足以下约定，否则结果无意义：
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- **X 轴**：纵向（车长方向）
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- **Y 轴**：横向（车宽方向）
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- **Z 轴**：高度方向，**向上为正**
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- 单位：毫米
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CAD 基准也必须遵循同样的轴向；ROI JSON 中的 bbox 字段在 CAD 坐标系下。
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扫描点云轴向不同 → 调用前自行做置换 / 旋转。
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---
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## 7. 版本兼容性
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- `__version__` —— 包版本
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- `ALGORITHM_VERSION` —— 算法版本（升级会改变数值结果）
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- `SCHEMA_VERSION` —— `report.json` 结构版本（仅在破坏性变更时递增；新字段不递增）
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v1.1 新增了非破坏性的可选字段（`cad_reference / roi_source / roi_aabb / registration / preprocess.passthrough / preprocess.bilateral / roi.source / quality_flags 新值`），`SCHEMA_VERSION` 保持 `"1.0"` 不变。
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---
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## 8. 集成模板
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```python
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import logging
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from underframe_deflection import (
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run_measurement_pipeline,
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DeflectionError,
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)
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log = logging.getLogger(__name__)
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def measure(scan_path: str, cad_path: str, roi_path: str, out_dir: str) -> dict | None:
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try:
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report = run_measurement_pipeline(
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input_cloud=scan_path,
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cad_path=cad_path,
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roi_path=roi_path,
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out_dir=out_dir,
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)
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except DeflectionError as exc:
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log.error("deflection failed: %s", exc)
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return None
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flags = report.get("quality_flags") or []
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if flags:
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log.warning("quality flags: %s", flags)
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return {
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"schema_version": report["schema_version"],
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"algorithm_version": report["algorithm_version"],
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"left_signed_mm": report["deflection"]["left"].get("selected_measurement", {}).get("signed_deflection_mm"),
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"right_signed_mm": report["deflection"]["right"].get("selected_measurement", {}).get("signed_deflection_mm"),
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"registration_rms_mm": report["registration"].get("rms_mm"),
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"flags": flags,
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}
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```
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