lipan
29f1d45d66
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
289 lines
9.7 KiB
Python
289 lines
9.7 KiB
Python
"""High-level convenience: read inputs, compute, write all artifacts.
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The pipeline now accepts three optional companion inputs in addition to
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the scan point cloud:
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- ``cad_path``: CAD reference geometry (PLY/XYZ/STL/OBJ) used as the
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baseline coordinate frame; the scan is rigidly aligned to it.
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- ``roi_path``: JSON file holding an axis-aligned bbox (in the CAD
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frame) that replaces the automatic ROI selection.
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- ``--skip-excel`` / ``skip_excel``: opt out of the Excel artifact.
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When all of these are absent the pipeline behaves like the legacy
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single-cloud workflow.
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"""
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from __future__ import annotations
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import json
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import logging
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from pathlib import Path
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from typing import Any
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import numpy as np
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from .api import DeflectionResult, compute_deflection, load_point_cloud
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from .artifacts import (
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export_overlay_plys,
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render_plots,
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write_beam_tracks_csv,
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write_curves_csv,
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write_report_json,
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)
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from .cad_io import read_cad_reference
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from .config import load_config, resolve_numeric_parameters, cloud_geometry
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from .io import ensure_output_dir
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from .preprocess import estimate_spacing, preprocess_point_cloud
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from .registration import RegistrationResult, apply_transform, register_to_cad
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from .roi import load_roi_aabb
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log = logging.getLogger(__name__)
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def _swap_y_z(points: np.ndarray) -> np.ndarray:
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"""Reorder columns x,y,z -> x,z,y.
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Bridges between the CAD/scan convention (``y`` = height) and the
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algorithm's canonical frame (``z`` = height). Pure column permutation;
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no values are altered.
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"""
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return points[:, [0, 2, 1]]
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def _swap_roi_y_z(roi: dict) -> dict:
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"""Swap the y/z bounds of an AABB to match :func:`_swap_y_z`."""
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return {
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"x_min": roi["x_min"], "x_max": roi["x_max"],
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"y_min": roi["z_min"], "y_max": roi["z_max"],
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"z_min": roi["y_min"], "z_max": roi["y_max"],
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}
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def run_measurement_pipeline(
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input_cloud: str | Path,
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out_dir: str | Path,
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config_path: str | None = None,
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profile: str = "auto",
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*,
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cad_path: str | Path | None = None,
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roi_path: str | Path | None = None,
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skip_registration: bool = False,
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enable_bilateral: bool = True,
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skip_excel: bool = False,
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input_axis: str = "y_up",
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) -> dict:
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"""Read inputs, run the algorithm, and write every artifact.
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Returns the same dict as ``DeflectionResult.to_dict()``; it is also
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persisted to ``<out_dir>/report.json``. CSV files land in
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``out_dir`` directly, plots in ``out_dir/images/``, PLY overlays in
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``out_dir/ply/`` and (unless suppressed) the Excel error report in
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``out_dir/report.xlsx``.
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"""
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out_dir_path = ensure_output_dir(out_dir)
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scan_raw = load_point_cloud(input_cloud)
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user_config: dict[str, Any] = {}
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if config_path:
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user_config = json.loads(Path(config_path).read_text(encoding="utf-8"))
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roi_aabb: dict[str, float] | None = None
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if roi_path is not None:
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roi_aabb = load_roi_aabb(roi_path)
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if not enable_bilateral:
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user_config.setdefault("preprocess", {}).setdefault("bilateral", {})["enabled"] = False
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cad_points: np.ndarray | None = None
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if cad_path is not None:
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cad_points = read_cad_reference(cad_path)
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if input_axis == "y_up":
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# Bridge from the CAD/scan convention (y = height) into the
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# algorithm's canonical frame (z = height). The core algorithm is
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# unchanged; this is a pure input adapter at the I/O boundary.
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scan_raw = _swap_y_z(scan_raw)
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if cad_points is not None:
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cad_points = _swap_y_z(cad_points)
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if roi_aabb is not None:
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roi_aabb = _swap_roi_y_z(roi_aabb)
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elif input_axis != "z_up":
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raise ValueError(f"input_axis must be 'z_up' or 'y_up', got {input_axis!r}")
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registration_meta: dict[str, Any] = {
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"enabled": cad_points is not None and not skip_registration,
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"skipped": cad_points is not None and skip_registration,
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"performed": False,
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}
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registration_result: RegistrationResult | None = None
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scan_for_compute = scan_raw
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if cad_points is not None and not skip_registration:
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registration_result, scan_for_compute = _register_scan(
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scan_raw,
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cad_points,
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profile,
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user_config,
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roi_aabb,
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)
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registration_meta.update(
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performed=True,
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rms_mm=registration_result.rms_mm,
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initial_rms_mm=registration_result.initial_rms_mm,
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iterations=registration_result.iterations,
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inlier_ratio=registration_result.inlier_ratio,
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converged=registration_result.converged,
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R=registration_result.R.tolist(),
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t=registration_result.t.tolist(),
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)
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result = compute_deflection(
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scan_for_compute,
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config=user_config,
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profile=profile,
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roi_aabb=roi_aabb,
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)
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_augment_report(
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result,
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input_cloud=input_cloud,
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cad_path=cad_path,
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roi_path=roi_path,
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roi_aabb=roi_aabb,
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registration_meta=registration_meta,
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)
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_apply_registration_quality_flags(result, registration_result)
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write_report_json(result, out_dir_path / "report.json")
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write_beam_tracks_csv(result, out_dir_path / "beam_tracks.csv")
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write_curves_csv(result, out_dir_path / "deflection_curves.csv")
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ply_exports = export_overlay_plys(result, out_dir_path / "ply")
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result.report["ply_exports"] = ply_exports
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write_report_json(result, out_dir_path / "report.json")
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render_plots(result, out_dir_path / "images")
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if not skip_excel:
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_write_excel_safely(
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result,
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out_dir_path / "report.xlsx",
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inputs={
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"scan_path": str(Path(input_cloud).resolve()),
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"cad_path": str(Path(cad_path).resolve()) if cad_path else "",
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"roi_path": str(Path(roi_path).resolve()) if roi_path else "",
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"config_path": str(Path(config_path).resolve()) if config_path else "",
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"profile": profile,
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},
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registration_info=registration_meta,
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)
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return result.report
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# ---------------------------------------------------------------------------
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# Internals
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# ---------------------------------------------------------------------------
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def _register_scan(
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scan_raw: np.ndarray,
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cad: np.ndarray,
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profile: str,
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user_config: dict[str, Any],
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roi_aabb: dict[str, float] | None,
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) -> tuple[RegistrationResult, np.ndarray]:
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"""Run a lightweight preprocess + ICP and apply the transform."""
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from .config import deep_merge
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base_config = load_config(None, profile)
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if user_config:
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base_config = deep_merge(base_config, user_config)
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if roi_aabb is not None:
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# Avoid letting the passthrough crop the cloud before registration
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# (the AABB is expressed in the *post-registration* frame).
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base_config["preprocess"]["passthrough"] = {"enabled": False, "aabb": None}
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base_config["preprocess"]["bilateral"] = dict(
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base_config["preprocess"].get("bilateral", {})
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)
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base_config["preprocess"]["bilateral"]["enabled"] = False
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geometry = cloud_geometry(scan_raw)
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spacing = estimate_spacing(
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scan_raw,
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sample_size=base_config["preprocess"]["spacing_sample_size"],
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)
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resolved = resolve_numeric_parameters(base_config, geometry, spacing)
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work = preprocess_point_cloud(scan_raw, resolved)
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reg = register_to_cad(work.work_points, cad, resolved)
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scan_aligned = apply_transform(scan_raw, reg.R, reg.t)
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return reg, scan_aligned
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def _augment_report(
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result: DeflectionResult,
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*,
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input_cloud: str | Path,
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cad_path: str | Path | None,
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roi_path: str | Path | None,
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roi_aabb: dict[str, float] | None,
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registration_meta: dict[str, Any],
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) -> None:
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result.report["input_cloud"] = str(Path(input_cloud).resolve())
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result.report["cad_reference"] = str(Path(cad_path).resolve()) if cad_path else None
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result.report["roi_source"] = str(Path(roi_path).resolve()) if roi_path else None
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result.report["roi_aabb"] = roi_aabb
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result.report["registration"] = registration_meta
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def _apply_registration_quality_flags(
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result: DeflectionResult,
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registration: RegistrationResult | None,
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) -> None:
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if registration is None:
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return
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if not registration.converged:
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result.quality_flags.append("registration_not_converged")
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# Use spacing-relative threshold: NN distance between sparse clouds is
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# bounded below by half the spacing, so an absolute 10 mm gate
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# misfires on coarsely-sampled scans. Treat >2x spacing as poor.
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spacing_mm = float(
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(result.report.get("preprocess") or {}).get("spacing_mm", 0.0) or 0.0
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)
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poor_threshold = max(10.0, 2.0 * spacing_mm)
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if registration.rms_mm > poor_threshold:
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result.quality_flags.append("registration_poor_quality")
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if registration.inlier_ratio < 0.5:
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result.quality_flags.append("registration_low_inlier_ratio")
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result.report["quality_flags"] = list(result.quality_flags)
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def _write_excel_safely(
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result: DeflectionResult,
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path: Path,
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*,
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inputs: dict[str, Any],
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registration_info: dict[str, Any],
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) -> None:
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try:
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from .excel_report import write_excel_report
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except ImportError as exc:
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log.warning("Skipping Excel report — %s", exc)
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return
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try:
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write_excel_report(
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result,
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path,
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inputs=inputs,
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registration_info=registration_info,
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)
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except ImportError as exc:
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log.warning("Skipping Excel report — openpyxl missing (%s)", exc)
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except Exception as exc: # pragma: no cover - defensive
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log.warning("Skipping Excel report — %s", exc)
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__all__ = ["run_measurement_pipeline"]
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