lipan
cec0766e67
- 算法包替换为 delivery_v1.2（coupler_panel 新增 autotune/layer_select，underframe_deflection 新增 single_beam 管线），删除 delivery_v1.1 - manifests/adapters 的 PYTHONPATH 与版本说明同步更新到 v1.2 - 新增任务级 CAD 文件支持（task_cad_files 表、上传/读取路由、TaskManager 全选与加载显示） - 新增 train-measurement 自定义前端面板（YAML 编辑器 + 可视化 ROI 配置 + 3D 取点） - PointCloudViewer 支持 PLY 叠加层渲染与 ROI 拾取 - Dockerfile 改用 Miniforge 安装 pythonocc-core，修复 libgomp/libstdc++ 依赖
341 lines
11 KiB
Python
341 lines
11 KiB
Python
"""Direct single-beam deflection measurement.
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This module is an independent measurement path for the case where the input
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ROI **already isolates one longitudinal beam** (left or right). It does not
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use the dual-beam tracking algorithm in :mod:`beams` / :mod:`reference`; that
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algorithm expects a car-bottom slab containing both beams and cannot resolve a
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single, laterally-continuous beam surface.
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Geometry convention (CAD / scan native frame):
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x = longitudinal (car length)
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y = vertical (height, up)
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z = lateral (car width)
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Deflection is the vertical (y) departure of the beam underside from a straight
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reference line along x. For a nominally straight beam the deflection is ~0.
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Method:
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1. Crop the cloud to the ROI (axis-aligned bbox).
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2. Slice along x.
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3. Per slice, estimate the underside as the *dominant lower plane* height
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(histogram peak of the lower half), robust to hangers / cables / noise
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that hang below the real surface and to the upper flange above it.
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4. Fit a robust straight reference line through (x, underside).
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5. deflection(x) = underside(x) - reference(x).
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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import numpy as np
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from .exceptions import InsufficientPointsError, InvalidPointCloudError
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from .roi import normalize_aabb
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# ---------------------------------------------------------------------------
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# Result types
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# ---------------------------------------------------------------------------
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@dataclass
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class SingleBeamResult:
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"""Result of :func:`measure_single_beam`.
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All distances are millimetres. ``deflection_mm`` is the underside height
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minus the fitted straight reference, sampled at ``x_mm`` (slice centres).
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``selected_*`` is the point of maximum absolute deflection on the kept
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(spike-rejected) profile.
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"""
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side: str
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available: bool
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x_mm: np.ndarray
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underside_height_mm: np.ndarray
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reference_height_mm: np.ndarray
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deflection_mm: np.ndarray
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kept_mask: np.ndarray
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selected_x_mm: float | None
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selected_signed_mm: float | None
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selected_absolute_mm: float | None
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max_downward_mm: float | None
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max_downward_x_mm: float | None
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max_upward_mm: float | None
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max_upward_x_mm: float | None
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rms_mm: float | None
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reference_slope_mm_per_m: float | None
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n_slices: int
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n_kept: int
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roi_point_count: int
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quality_flags: list[str] = field(default_factory=list)
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report: dict = field(default_factory=dict)
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def to_dict(self) -> dict:
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return self.report
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# ---------------------------------------------------------------------------
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# Public entry point
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# ---------------------------------------------------------------------------
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def measure_single_beam(
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points: np.ndarray,
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roi_aabb: dict,
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*,
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side: str = "beam",
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config: dict | None = None,
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) -> SingleBeamResult:
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"""Measure the vertical deflection of a single isolated beam.
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Args:
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points: ``(N, 3)`` XYZ array, native frame (x=length, y=height,
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z=width), millimetres.
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roi_aabb: axis-aligned bbox dict with ``x_min/x_max/y_min/y_max/
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z_min/z_max`` (mm, native frame). Should isolate one beam.
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side: label carried into the result / report (e.g. ``"left"``).
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config: optional overrides; see :data:`DEFAULT_SINGLE_BEAM_CONFIG`.
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Returns:
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:class:`SingleBeamResult`.
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Raises:
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InvalidPointCloudError: ``points`` is not ``(N, 3+)``.
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InsufficientPointsError: too few points in the ROI to measure.
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"""
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cfg = dict(DEFAULT_SINGLE_BEAM_CONFIG)
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if config:
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cfg.update(config)
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array = np.asarray(points, dtype=np.float64)
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if array.ndim != 2 or array.shape[1] < 3:
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raise InvalidPointCloudError(
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f"Point cloud must be a 2D (N, 3+) array, got shape {array.shape}."
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)
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array = array[:, :3]
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aabb = normalize_aabb(roi_aabb)
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mask = (
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(array[:, 0] >= aabb["x_min"]) & (array[:, 0] <= aabb["x_max"])
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& (array[:, 1] >= aabb["y_min"]) & (array[:, 1] <= aabb["y_max"])
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& (array[:, 2] >= aabb["z_min"]) & (array[:, 2] <= aabb["z_max"])
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)
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beam = array[mask]
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if len(beam) < cfg["min_roi_points"]:
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raise InsufficientPointsError(
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f"ROI for side '{side}' contains {len(beam)} points; need at least "
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f"{cfg['min_roi_points']}."
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)
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x = beam[:, 0]
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x_min, x_max = float(x.min()), float(x.max())
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slice_mm = float(cfg["slice_mm"])
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edges = np.arange(x_min, x_max + slice_mm, slice_mm)
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xs: list[float] = []
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under: list[float] = []
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support: list[int] = []
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for i in range(len(edges) - 1):
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s = beam[(x >= edges[i]) & (x < edges[i + 1])]
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if len(s) < cfg["min_slice_points"]:
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continue
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height = _dominant_underside_height(s[:, 1], cfg)
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if height is None:
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continue
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xs.append(float(0.5 * (edges[i] + edges[i + 1])))
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under.append(height)
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support.append(int(np.sum(np.abs(s[:, 1] - height) <= cfg["support_tol_mm"])))
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flags: list[str] = []
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if len(xs) < cfg["min_valid_slices"]:
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return _empty_result(side, len(beam), flags + ["insufficient_valid_slices"])
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x_arr = np.asarray(xs, dtype=np.float64)
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under_arr = np.asarray(under, dtype=np.float64)
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# Reject residual spikes on the underside profile before / via the fit.
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slope, intercept, kept = _robust_line_fit(x_arr, under_arr, cfg)
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reference = slope * x_arr + intercept
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deflection = under_arr - reference
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n_kept = int(kept.sum())
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if n_kept < cfg["min_valid_slices"]:
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return _empty_result(side, len(beam), flags + ["fit_unstable"])
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dev_kept = deflection[kept]
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x_kept = x_arr[kept]
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down_i = int(np.argmin(dev_kept))
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up_i = int(np.argmax(dev_kept))
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abs_i = int(np.argmax(np.abs(dev_kept)))
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selected_signed = float(dev_kept[abs_i])
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rms = float(np.sqrt(np.mean(dev_kept ** 2)))
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if int((~kept).sum()) > cfg["spike_warn_count"]:
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flags.append("many_underside_spikes")
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if np.median(support) < cfg["low_support_threshold"]:
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flags.append("low_surface_support")
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report = {
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"mode": "single_beam",
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"side": side,
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"available": True,
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"roi_aabb": aabb,
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"roi_point_count": int(len(beam)),
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"n_slices": int(len(xs)),
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"n_kept": n_kept,
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"reference_slope_mm_per_m": float(slope * 1000.0),
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"deflection": {
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"selected_x_mm": float(x_kept[abs_i]),
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"selected_signed_mm": selected_signed,
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"selected_absolute_mm": float(abs(selected_signed)),
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"max_downward_mm": float(dev_kept[down_i]),
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"max_downward_x_mm": float(x_kept[down_i]),
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"max_upward_mm": float(dev_kept[up_i]),
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"max_upward_x_mm": float(x_kept[up_i]),
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"rms_mm": rms,
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},
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"quality_flags": flags,
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"curve": {
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"x_mm": x_arr.tolist(),
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"underside_height_mm": under_arr.tolist(),
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"reference_height_mm": reference.tolist(),
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"deflection_mm": deflection.tolist(),
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"kept": kept.tolist(),
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},
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}
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return SingleBeamResult(
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side=side,
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available=True,
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x_mm=x_arr,
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underside_height_mm=under_arr,
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reference_height_mm=reference,
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deflection_mm=deflection,
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kept_mask=kept,
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selected_x_mm=float(x_kept[abs_i]),
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selected_signed_mm=selected_signed,
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selected_absolute_mm=float(abs(selected_signed)),
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max_downward_mm=float(dev_kept[down_i]),
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max_downward_x_mm=float(x_kept[down_i]),
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max_upward_mm=float(dev_kept[up_i]),
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max_upward_x_mm=float(x_kept[up_i]),
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rms_mm=rms,
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reference_slope_mm_per_m=float(slope * 1000.0),
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n_slices=int(len(xs)),
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n_kept=n_kept,
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roi_point_count=int(len(beam)),
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quality_flags=flags,
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report=report,
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)
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# ---------------------------------------------------------------------------
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# Internals
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# ---------------------------------------------------------------------------
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DEFAULT_SINGLE_BEAM_CONFIG: dict = {
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"slice_mm": 200.0,
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"min_roi_points": 2000,
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"min_slice_points": 30,
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"min_valid_slices": 8,
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# dominant-plane extraction
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"hist_bin_mm": 3.0,
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"plane_refine_mm": 8.0,
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"support_tol_mm": 3.0,
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# robust line fit
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"fit_iters": 5,
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"fit_tukey_c": 4.685,
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"fit_reject_sigma": 4.0,
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# warnings: flag only when a large *fraction* of slices are spikes
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"spike_warn_count": 40,
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"low_support_threshold": 30,
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}
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def _dominant_underside_height(height: np.ndarray, cfg: dict) -> float | None:
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"""Height of the dominant plane in the lower half of a slice.
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Robust to (a) hangers / cables / noise that float *below* the real
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underside and (b) the upper flange / deck plate *above* it. We look only
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at the lower half (<= median), take the densest histogram bin (the real
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surface has the most points), then refine with the median of nearby
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points.
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"""
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if len(height) < 10:
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return None
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lo = float(height.min())
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mid = float(np.median(height))
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if mid - lo < cfg["hist_bin_mm"]:
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return float(mid)
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lower = height[height <= mid]
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bins = np.arange(lo, mid + cfg["hist_bin_mm"], cfg["hist_bin_mm"])
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if len(bins) < 2:
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return float(mid)
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hist, edges = np.histogram(lower, bins=bins)
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if hist.sum() == 0:
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return None
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k = int(np.argmax(hist))
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peak = 0.5 * (edges[k] + edges[k + 1])
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near = height[np.abs(height - peak) <= cfg["plane_refine_mm"]]
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return float(np.median(near)) if len(near) else float(peak)
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def _robust_line_fit(
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x: np.ndarray, y: np.ndarray, cfg: dict
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) -> tuple[float, float, np.ndarray]:
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"""Tukey-reweighted straight-line fit; returns (slope, intercept, keep)."""
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w = np.ones_like(y)
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coef = np.array([0.0, float(np.median(y))])
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for _ in range(int(cfg["fit_iters"])):
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A = np.vstack([x, np.ones_like(x)]).T
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coef = np.linalg.lstsq(A * w[:, None], y * w, rcond=None)[0]
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r = y - (coef[0] * x + coef[1])
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scale = float(np.median(np.abs(r - np.median(r))) * 1.4826) + 1e-6
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u = np.clip(np.abs(r) / (cfg["fit_tukey_c"] * scale), 0.0, 1.0)
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w = (1.0 - u ** 2) ** 2
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r = y - (coef[0] * x + coef[1])
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med = float(np.median(r))
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mad = float(np.median(np.abs(r - med)) * 1.4826) + 1e-6
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keep = np.abs(r - med) <= cfg["fit_reject_sigma"] * mad
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return float(coef[0]), float(coef[1]), keep
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def _empty_result(side: str, roi_count: int, flags: list[str]) -> SingleBeamResult:
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empty = np.zeros(0, dtype=np.float64)
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return SingleBeamResult(
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side=side,
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available=False,
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x_mm=empty,
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underside_height_mm=empty,
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reference_height_mm=empty,
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deflection_mm=empty,
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kept_mask=np.zeros(0, dtype=bool),
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selected_x_mm=None,
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selected_signed_mm=None,
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selected_absolute_mm=None,
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max_downward_mm=None,
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max_downward_x_mm=None,
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max_upward_mm=None,
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max_upward_x_mm=None,
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rms_mm=None,
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reference_slope_mm_per_m=None,
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n_slices=0,
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n_kept=0,
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roi_point_count=roi_count,
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quality_flags=flags,
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report={
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"mode": "single_beam",
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"side": side,
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"available": False,
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"roi_point_count": roi_count,
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"quality_flags": flags,
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},
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)
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__all__ = ["SingleBeamResult", "measure_single_beam", "DEFAULT_SINGLE_BEAM_CONFIG"]
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