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
207 lines
8.2 KiB
Python
207 lines
8.2 KiB
Python
"""train-measurement 算法适配器 (v2.2)。
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读 input.json → 调 TaskManager（来自 alg/train_measurement_system_v2.2）→ 写统一 result.json。
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v2.2 变更：
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- 算法路径从 alg/点云测距v1.1 → alg/train_measurement_system_v2.2
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- 新增孔中心拟合（hole_center_fitter）、测量日志、性能日志
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- 内置配置文件改为 config/measurements_0618.yaml
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- TRAIN_MEAS_ROOT 指向 train_measurement_system_v2.2/（用于定位 config/）
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- 算法内置 _read_binary_ply，直接支持二进制 PLY，无需 ASCII 转换
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约定：
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- 第一个点云路径作为 pointcloud_file 注入到测量配置 YAML 中；
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- 若用户在 UI 选择「自定义 YAML」并提供 customYaml，则使用之；
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否则使用算法仓库自带的 config/measurements_0618.yaml 作为模板。
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- 所有任务结果合成成 measurements[] 列表，附带可视化 PLY 路径到 extra。
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"""
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from __future__ import annotations
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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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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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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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emit_progress(0.05, "加载 train_measurement v2.2 算法包")
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# TRAIN_MEAS_ROOT 由 manifest envExtra 注入，指向 train_measurement_system_v2.2/
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train_root = Path(os.environ.get("TRAIN_MEAS_ROOT") or "")
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if not train_root.exists():
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# 回退：从 ALG_ROOT 推导
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alg_root = Path(os.environ.get("ALG_ROOT") or "/alg")
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train_root = alg_root / "train_measurement_system_v2.2" / "train_measurement_system_v2.2"
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if not train_root.exists():
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raise RuntimeError(
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f"找不到 train_measurement_system 目录：{train_root}。"
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"请确认 TRAIN_MEAS_ROOT 或 ALG_ROOT 环境变量已正确设置。"
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)
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from task_manager import TaskManager # type: ignore
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pc_path = first_pointcloud(payload)
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emit_progress(0.15, f"读取点云：{os.path.basename(pc_path)}")
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# ── 准备 YAML 配置 ────────────────────────────────────────────────────
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config_preset = str(config.get("configPreset") or "builtin")
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custom_yaml_str = str(config.get("customYaml") or "").strip()
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if config_preset == "custom" and custom_yaml_str:
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# 支持 JSON 格式（前端传来）和 YAML 格式
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yaml_path = out_dir / "custom_measurements.yaml"
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try:
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import json as _json
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parsed = _json.loads(custom_yaml_str)
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# JSON 格式：转成 YAML 写盘
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import yaml as _yaml
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yaml_path.write_text(_yaml.dump(parsed, allow_unicode=True, default_flow_style=False), encoding="utf-8")
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except Exception:
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# 已经是 YAML 格式，直接写盘
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yaml_path.write_text(custom_yaml_str, encoding="utf-8")
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else:
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# 使用算法仓库内置的 config/measurements_0618.yaml
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yaml_path = train_root / "config" / "measurements_0618.yaml"
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if not yaml_path.exists():
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raise RuntimeError(f"内置配置文件不存在：{yaml_path}")
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emit_progress(0.20, f"加载配置：{yaml_path.name}")
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# ── 初始化 TaskManager ────────────────────────────────────────────────
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tm = TaskManager()
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# 先注入点云路径，让 load_config 跳过 YAML 里的 pointcloud_file（开发测试路径）
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tm.pointcloud_path_override = pc_path
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# 把输出目录重定向到 outDir，避免写到只读的 /alg 目录
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tm.output_dir_override = str(out_dir)
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if not tm.load_config(str(yaml_path)):
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raise RuntimeError(f"TaskManager 加载配置失败：{yaml_path}")
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# 注入点云路径（覆盖 YAML 中的 pointcloud_file）
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tm.config["pointcloud_file"] = pc_path
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# 注入可视化输出目录
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viz_dir = out_dir / "visualizations"
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viz_dir.mkdir(parents=True, exist_ok=True)
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for task in tm.tasks:
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if hasattr(task, "visualization_dir"):
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task.visualization_dir = str(viz_dir)
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emit_progress(0.30, "加载点云（已在 load_config 中完成）")
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emit_progress(0.40, f"执行 {len(tm.tasks)} 个测量任务")
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results = tm.run_all_tasks()
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emit_progress(0.92, "整理结果")
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measurements: list[dict] = []
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clean_raw: list[dict] = []
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for r in results:
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name = str(r.get("name") or "未命名任务")
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status_raw = r.get("status")
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data = r.get("data") if isinstance(r.get("data"), dict) else {}
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# 剥掉以 '_' 开头的私有 numpy 数组（不可 JSON 序列化）
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clean_data = {k: v for k, v in data.items() if not k.startswith("_")} if data else {}
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clean_raw.append(
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{
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"name": name,
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"type": r.get("type"),
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"status": status_raw,
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"data": clean_data,
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"error": r.get("error"),
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}
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)
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if status_raw == "success":
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value = clean_data.get("mean_distance")
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if value is None:
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value = clean_data.get("distance")
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measurements.append(
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{
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"name": name,
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"value": float(value) if isinstance(value, (int, float)) else float("nan"),
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"unit": "mm",
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"status": "info",
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}
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)
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std = clean_data.get("std_distance")
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if isinstance(std, (int, float)):
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measurements.append(
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{
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"name": f"{name} · 标准差",
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"value": float(std),
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"unit": "mm",
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"status": "info",
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}
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)
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# v1.1 新增字段：min/max distance
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for field_name, label in [
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("min_distance", "最小距离"),
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("max_distance", "最大距离"),
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]:
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v = clean_data.get(field_name)
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if isinstance(v, (int, float)):
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measurements.append(
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{
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"name": f"{name} · {label}",
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"value": float(v),
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"unit": "mm",
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"status": "info",
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}
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)
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else:
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measurements.append(
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{
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"name": name,
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"value": float("nan"),
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"unit": "mm",
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"status": "fail",
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}
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)
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success_count = sum(1 for r in results if r.get("status") == "success")
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payload_out = {
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"schemaVersion": 1,
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"status": "completed",
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"message": f"共 {len(results)} 个任务，成功 {success_count} 个。",
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"measurements": measurements,
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"overlay3d": {"kind": "none"},
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"extra": {
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"rawTaskResults": clean_raw,
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"visualizationDir": "visualizations",
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},
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}
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write_result(out_dir, payload_out)
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emit_progress(1.0, "完成")
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return 0
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except Exception as exc:
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traceback.print_exc()
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write_error_result(
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out_dir,
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f"train-measurement 计算失败：{exc}",
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extra={"traceback": traceback.format_exc()},
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)
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return 1
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if __name__ == "__main__":
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sys.exit(main())
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