477 lines
21 KiB
Python
477 lines
21 KiB
Python
"""flatness-suite 算法适配器 (v1.5)。
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v1.5 变更：
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- 算法目录从 alg/point_porj → alg/平整度、平面度、轮廓测量v1.5
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- 新增 car_part_v1/ 多车型 STEP CAD 模板
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- run_contour 新增 Excel 报表输出（excel_output_path）
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- pipeline 新增 result_template_path / combined_context_pointcloud_path 参数
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约定：
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- module=contour：轮廓/平整度，需要 CAD STEP（推荐，算法方基准 3min25s）
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- module=plane：只跑平面拟合
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- module=curvature：只跑曲率估计（大点云极慢，慎用）
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- module=all：plane + curvature + contour（已从前端选项移除，大点云不可用）
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"""
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from __future__ import annotations
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import copy
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import hashlib
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import os
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import sys
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import traceback
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from pathlib import Path
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from typing import Any
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sys.path.insert(0, str(Path(__file__).parent))
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from _plugin_io import (
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emit_progress,
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first_pointcloud,
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load_input,
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write_error_result,
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write_result,
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)
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from flatness_suite_measurements import measurements_from_batch_result
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def _safe_float(v: object) -> float | None:
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try:
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return float(v) # type: ignore[arg-type]
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except (TypeError, ValueError):
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return None
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def _config_bool(config: dict, key: str, default: bool = False) -> bool:
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value = config.get(key, default)
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if isinstance(value, bool):
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return value
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if isinstance(value, str):
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return value.strip().lower() in {"1", "true", "yes", "on"}
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return bool(value)
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def _cleanup_temporary_artifacts(out_dir: Path) -> None:
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# 仅删除适配器自己创建、且不会写入 result.json 的固定临时文件。
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# 不使用通配符，避免误删算法结果或用户要求保留的产物。
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for name in ("_roi_cropped.ply",):
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try:
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(out_dir / name).unlink(missing_ok=True)
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except OSError as exc:
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print(f"[flatness-suite] 清理临时文件失败 {name}: {exc}", file=sys.stderr)
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def _mesh_cache_path(cad_path: str, mesh_deflection: float) -> Path:
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"""根据 STEP 路径 + 文件指纹 + 三角化精度生成缓存路径。
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优先返回配准阶段已生成的 mesh 缓存（_mesh.ply），
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其次返回 flatness 自己的缓存路径，均放在 CAD 同目录的 .cad_sample_cache/ 下。
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"""
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stat = os.stat(cad_path)
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cache_key = "|".join([
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os.path.abspath(cad_path),
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str(stat.st_size),
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str(stat.st_mtime_ns),
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f"{mesh_deflection:.12g}",
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])
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digest = hashlib.sha1(cache_key.encode()).hexdigest()[:16]
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cad_stem = Path(cad_path).stem
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shared_dir = Path(cad_path).parent / ".cad_sample_cache"
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# 配准阶段已缓存的 mesh（与配准采样缓存同目录，命名规则：<stem>_<digest>_mesh.ply）
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registration_mesh = shared_dir / f"{cad_stem}_{digest}_mesh.ply"
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if registration_mesh.exists():
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return registration_mesh
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# flatness 自己写缓存的目标路径
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try:
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shared_dir.mkdir(parents=True, exist_ok=True)
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test = shared_dir / ".write_test"
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test.touch()
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test.unlink()
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return shared_dir / f"{cad_stem}_{digest}_mesh.ply"
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except OSError:
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return Path(cad_path).parent / f"{cad_stem}_{digest}_mesh.ply"
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def _find_alg_dir(alg_root: str) -> Path:
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"""定位平整度算法目录，支持多个历史版本路径，优先使用最新版。"""
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candidates = [
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Path(alg_root) / "平整度、平面度、轮廓测量v1.5",
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Path(alg_root) / "point_porj",
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Path(alg_root) / "平整度、平面度、轮廓测量v1.2" / "point_porj",
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Path(alg_root) / "平整度、平面度、轮廓测量" / "point_porj",
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]
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for c in candidates:
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if c.is_dir() and (c / "pipeline.py").exists():
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return c
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raise RuntimeError(
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f"找不到平整度算法目录，已尝试：\n"
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+ "\n".join(f" {c}" for c in candidates)
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+ "\n请确认 alg/ 下已解压 平整度、平面度、轮廓测量v1.5/。"
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)
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def _build_pipeline(alg_dir: Path, pc_path: str, out_dir: Path, params: dict) -> Any:
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"""初始化 MeasurementPipeline，注入点云路径和输出路径。"""
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# 把 alg_dir 加到 sys.path，让 pipeline.py 能 import 同级模块
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alg_dir_str = str(alg_dir)
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if alg_dir_str not in sys.path:
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sys.path.insert(0, alg_dir_str)
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from pipeline import MeasurementPipeline # type: ignore
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# 加载内置 config.yaml，然后覆盖关键路径
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pipeline = MeasurementPipeline(config_path=None)
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# 注入点云路径
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pipeline.config["paths"]["pointcloud"] = pc_path
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# 注入输出路径（curvature_output 写到 outDir）
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pipeline.config["paths"]["curvature_output"] = str(out_dir / "curvature.ply")
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# 关闭可视化（无显示器）
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pipeline.config["visualization"]["enabled"] = False
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# 用户参数覆盖
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voxel_size = _safe_float(params.get("voxelSizeMm"))
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if voxel_size is not None:
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pipeline.config["filters"]["voxel_size"] = voxel_size / 1000.0 # mm → m
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threshold_mm = _safe_float(params.get("thresholdMm"))
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if threshold_mm is not None:
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pipeline.config["plane"]["threshold_mm"] = threshold_mm
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return pipeline
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def main() -> int:
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payload = load_input(sys.argv)
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inputs = payload.get("inputs") or {}
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config = inputs.get("config") or {}
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out_dir = Path(payload["outDir"])
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out_dir.mkdir(parents=True, exist_ok=True)
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try:
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alg_root = os.environ.get("ALG_ROOT") or os.environ.get("ALG_ROOT_HOST") or "/alg"
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emit_progress(0.05, "定位算法目录")
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alg_dir = _find_alg_dir(alg_root)
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# ── 判断走单点云模式还是 datasets 批量模式 ──────────────────────────────
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pc_path_raw = first_pointcloud(payload)
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use_batch_mode = bool(config.get("useBatchDatasets", True))
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# ── ROI 裁剪（整车点云场景下必须填写，否则算法极慢）──────────────────────
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def _f(key: str) -> float | None:
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v = config.get(key)
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try:
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return float(v) if v is not None and str(v).strip() != "" else None
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except (TypeError, ValueError):
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return None
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x_min, x_max = _f("roiXMin"), _f("roiXMax")
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y_min, y_max = _f("roiYMin"), _f("roiYMax")
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z_min, z_max = _f("roiZMin"), _f("roiZMax")
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has_roi = any(v is not None for v in (x_min, x_max, y_min, y_max, z_min, z_max))
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if has_roi:
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emit_progress(0.12, "ROI 裁剪中…")
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import numpy as np
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import open3d as o3d # type: ignore
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pcd = o3d.io.read_point_cloud(pc_path_raw)
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pts = np.asarray(pcd.points)
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mask = np.ones(len(pts), dtype=bool)
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if x_min is not None: mask &= pts[:, 0] >= x_min
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if x_max is not None: mask &= pts[:, 0] <= x_max
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if y_min is not None: mask &= pts[:, 1] >= y_min
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if y_max is not None: mask &= pts[:, 1] <= y_max
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if z_min is not None: mask &= pts[:, 2] >= z_min
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if z_max is not None: mask &= pts[:, 2] <= z_max
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n_total, n_crop = len(pts), int(mask.sum())
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if n_crop == 0:
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raise RuntimeError(
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f"ROI 裁剪后无点（原始 {n_total} 点），请检查坐标范围是否正确。"
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)
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cropped = pcd.select_by_index(np.where(mask)[0])
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roi_ply = out_dir / "_roi_cropped.ply"
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# 二进制写出，算法用 o3d 读所以支持二进制，比 ASCII 快很多
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o3d.io.write_point_cloud(str(roi_ply), cropped, write_ascii=False)
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pc_path_raw = str(roi_ply)
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emit_progress(0.14, f"ROI 裁剪完成：{n_total} → {n_crop} 点")
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# v1.5 用 o3d.io.read_point_cloud 读取，直接支持二进制 PLY，无需转 ASCII
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pc_path = pc_path_raw
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module = str(config.get("module") or "contour")
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emit_progress(0.15, f"初始化 MeasurementPipeline（module={module}）")
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pipeline = _build_pipeline(alg_dir, pc_path if not use_batch_mode else str(alg_dir), out_dir, config)
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# ── datasets 批量模式（useBatchDatasets=true 时） ──────────────────────
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if use_batch_mode and module == "contour":
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emit_progress(0.20, "批量模式：从 datasets/Noise_PointCloud/ 读取所有面板点云")
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data_root = alg_dir / "datasets"
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# combined_contour.ply 可能包含完整整车点云，单次可达十几 GB。
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# 平台页面只使用各面板 *_contour.ply，因此默认从源头跳过组合文件；
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# 仅在用户显式开启时才生成，并作为可下载产物登记。
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keep_combined_contour = _config_bool(config, "keepCombinedContour", False)
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context_pc = None
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if keep_combined_contour:
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context_pc = (config.get("contextPointCloudPath")
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or inputs.get("contextPointCloudPath")
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or pc_path_raw
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or None)
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save_dir = str(out_dir / "contour_batch")
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excel_path = str(out_dir / "contour.xlsx")
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result_template_path = alg_dir / "excel" / "测量结果.xlsx"
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if not result_template_path.is_file():
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raise RuntimeError(f"内置测量结果模板不存在: {result_template_path}")
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selected_task_nos = config.get("demoTaskNos")
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selected_task_ids = (
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[str(int(value)) for value in selected_task_nos if isinstance(value, (int, float)) and float(value).is_integer()]
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if isinstance(selected_task_nos, list)
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else None
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)
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if selected_task_ids == []:
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raise RuntimeError("请至少选择一个轮廓测量任务")
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res = pipeline.run_contour_batch(
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task_ids=selected_task_ids,
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all_tasks=selected_task_ids is None,
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data_root=str(data_root),
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pointcloud_kind="noise",
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point_chunk_size=8192,
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triangle_chunk_size=512,
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save_path=save_dir,
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excel_output_path=excel_path,
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result_template_path=str(result_template_path),
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combined_context_pointcloud_path=context_pc,
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save_combined_visualization=keep_combined_contour,
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no_visualize=True,
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)
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emit_progress(0.95, "整理批量结果")
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measurements = measurements_from_batch_result(res, result_template_path)
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# 各面板热力图 PLY 列表（不含背景点，全部是有意义的颜色）
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panel_plys = sorted([
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f"contour_batch/{p.name}"
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for p in (out_dir / "contour_batch").glob("*_contour.ply")
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if p.name != "combined_contour.ply"
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])
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if panel_plys:
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overlay3d = {"kind": "ply", "file": panel_plys[0], "files": panel_plys}
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else:
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overlay3d = {"kind": "none"}
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combined_rel = "contour_batch/combined_contour.ply"
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result_files = [combined_rel] if keep_combined_contour and (out_dir / combined_rel).is_file() else []
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write_result(out_dir, {
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"status": "completed",
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"message": f"批量完成 {len(res.get('rows') or [])} 个面板测量",
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"measurements": measurements,
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"overlay3d": overlay3d,
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"files": result_files,
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"extra": {
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"module": "contour_batch",
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"excelPath": "contour.xlsx",
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"combinedContourKept": bool(result_files),
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},
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})
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return 0
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measurements: list[dict] = []
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extra: dict[str, Any] = {"module": module}
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skipped: list[str] = []
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# ── plane ──────────────────────────────────────────────────────────────
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if module in ("all", "plane"):
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emit_progress(0.30, "平面拟合 (RANSAC)")
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try:
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res = pipeline.run_plane(
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pointcloud_path=pc_path,
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threshold_mm=_safe_float(config.get("thresholdMm")),
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no_visualize=True,
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)
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pr = res.get("plane_result")
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if pr is not None:
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rmse_mm = float(pr.rmse) * 1000.0 # m → mm
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measurements += [
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{
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"name": "平面 RMSE",
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"value": round(rmse_mm, 4),
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"unit": "mm",
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"status": "pass" if rmse_mm <= 1.0 else "warning" if rmse_mm <= 2.0 else "fail",
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},
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{
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"name": "内点率",
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"value": round(float(pr.inlier_ratio), 2),
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"unit": "%",
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"status": "pass" if pr.inlier_ratio >= 80 else "warning" if pr.inlier_ratio >= 60 else "fail",
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},
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{
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"name": "法向角误差",
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"value": round(float(pr.normal_angle_deg), 4),
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"unit": "deg",
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"status": "info",
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},
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]
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extra["planeModel"] = list(pr.model) if pr.model else None
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except Exception as exc:
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skipped.append(f"plane: {exc}")
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# ── curvature ──────────────────────────────────────────────────────────
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if module in ("all", "curvature"):
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emit_progress(0.55, "曲率估计")
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try:
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res = pipeline.run_curvature(
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pointcloud_path=pc_path,
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no_visualize=True,
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)
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curvatures = res.get("curvatures")
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if curvatures is not None:
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import numpy as np
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measurements += [
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{
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"name": "曲率均值",
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"value": round(float(np.mean(curvatures)), 6),
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"unit": "1/mm",
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"status": "info",
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},
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{
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"name": "曲率最大值",
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"value": round(float(np.max(curvatures)), 6),
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"unit": "1/mm",
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"status": "info",
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},
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]
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# 保存曲率点云路径
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curv_ply = out_dir / "curvature.ply"
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if curv_ply.exists():
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extra["curvaturePly"] = "curvature.ply"
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except Exception as exc:
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skipped.append(f"curvature: {exc}")
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# ── contour ────────────────────────────────────────────────────────────
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if module in ("all", "contour"):
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emit_progress(0.75, "轮廓-平整度（需 CAD STEP）")
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cad_path = inputs.get("cadPath") or None
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mesh_path = inputs.get("meshPath") or None
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if not cad_path and not mesh_path:
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skipped.append("contour: 未提供 CAD STL/STEP，跳过")
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else:
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try:
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save_path = str(out_dir / "contour.ply")
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excel_path = str(out_dir / "contour.xlsx")
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# STEP mesh 缓存：避免每次重新三角化 442 MB+ 整车 STEP
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cached_mesh_ply: str | None = None
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if cad_path and not mesh_path:
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_deflection = float(
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_safe_float(config.get("meshDeflection")) or 0.1
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)
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_cache_ply = _mesh_cache_path(cad_path, _deflection)
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if _cache_ply.exists():
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emit_progress(0.76, "使用已缓存 CAD mesh，跳过 STEP 三角化")
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mesh_path = str(_cache_ply)
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cad_path = None
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else:
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# 先单独做三角化，完成后立即缓存，再跑点距计算
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# 这样即使点距阶段 OOM，下次也不用重新三角化
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emit_progress(0.76, "首次三角化 CAD STEP，结果将缓存供后续复用…")
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try:
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import open3d as o3d # type: ignore
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_orig_cad = cad_path
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sys.path.insert(0, str(alg_dir))
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from cad_converter import CADConverter # type: ignore
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_converter = CADConverter()
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_verts, _faces = _converter.step_to_numpy_mesh(
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_orig_cad, mesh_deflection=_deflection
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)
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_mesh_o3d = o3d.geometry.TriangleMesh()
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_mesh_o3d.vertices = o3d.utility.Vector3dVector(_verts)
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_mesh_o3d.triangles = o3d.utility.Vector3iVector(_faces.astype(int))
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o3d.io.write_triangle_mesh(str(_cache_ply), _mesh_o3d)
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emit_progress(0.80, f"CAD mesh 已缓存：{_cache_ply.name}，开始点距计算…")
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mesh_path = str(_cache_ply)
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cad_path = None
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except Exception as _tri_exc:
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emit_progress(0.76, f"三角化缓存写入失败（{_tri_exc}），继续直接跑…")
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context_pc_path = (inputs.get("contextPointCloudPath") or None)
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res = pipeline.run_contour(
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pointcloud_path=pc_path,
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cad_path=cad_path,
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mesh_path=mesh_path,
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point_chunk_size=8192,
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triangle_chunk_size=512,
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save_path=save_path,
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excel_output_path=excel_path,
|
||
combined_context_pointcloud_path=context_pc_path,
|
||
no_visualize=True,
|
||
)
|
||
|
||
stats = res.get("distance_stats")
|
||
for key, label, unit in [
|
||
("mean", "轮廓偏差均值", "mm"),
|
||
("max", "轮廓偏差最大值", "mm"),
|
||
("std", "轮廓偏差标准差", "mm"),
|
||
]:
|
||
v = _safe_float(getattr(stats, key, None) if stats is not None else None)
|
||
if v is not None:
|
||
measurements.append({
|
||
"name": label,
|
||
"value": round(v, 4),
|
||
"unit": unit,
|
||
"status": "info",
|
||
})
|
||
if Path(save_path).exists():
|
||
extra["contourPly"] = "contour.ply"
|
||
except Exception as exc:
|
||
skipped.append(f"contour: {exc}")
|
||
|
||
emit_progress(0.95, "整理结果")
|
||
|
||
# 3D 叠加层：优先 contour PLY，其次 curvature PLY
|
||
overlay3d: dict = {"kind": "none"}
|
||
for ply_key in ("contourPly", "curvaturePly"):
|
||
if ply_key in extra:
|
||
overlay3d = {"kind": "ply", "file": extra[ply_key]}
|
||
break
|
||
|
||
if skipped:
|
||
extra["skipped"] = skipped
|
||
|
||
status_vals = [m.get("status") for m in measurements]
|
||
overall = (
|
||
"fail" if "fail" in status_vals
|
||
else "warning" if "warning" in status_vals
|
||
else "completed"
|
||
)
|
||
|
||
payload_out = {
|
||
"schemaVersion": 1,
|
||
"status": overall,
|
||
"message": (
|
||
f"完成 {len(measurements)} 项测量。"
|
||
+ (f" 跳过：{'; '.join(skipped)}" if skipped else "")
|
||
),
|
||
"measurements": measurements,
|
||
"overlay3d": overlay3d,
|
||
"extra": extra,
|
||
}
|
||
write_result(out_dir, payload_out)
|
||
emit_progress(1.0, "完成")
|
||
return 0
|
||
|
||
except Exception as exc:
|
||
traceback.print_exc()
|
||
write_error_result(
|
||
out_dir,
|
||
f"flatness-suite 计算失败：{exc}",
|
||
extra={"traceback": traceback.format_exc()},
|
||
)
|
||
return 1
|
||
finally:
|
||
_cleanup_temporary_artifacts(out_dir)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
sys.exit(main())
|