511 lines
20 KiB
Python
511 lines
20 KiB
Python
"""任务管理器 - 负责加载配置、调度任务、汇总结果"""
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import yaml
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import numpy as np
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import time
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from pathlib import Path
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from typing import List, Dict, Any
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from datetime import datetime
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from pointcloud_loader import PointCloudLoader
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from measurements.base import MeasurementResult
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from measurements.plane_distance import PlaneToPlaneDistance
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from measurements.point_to_plane import PointToPlaneDistance
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from measurements.point_to_point import PointToPointDistance
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from measurements.plane_with_holes import PlaneToPlaneWithHoles
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from measurements.point_to_plane_with_holes import PointToPlaneWithHoles
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from measurements.point_to_point_with_holes import PointToPointWithHoles
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from measurements.line_to_line import LineToLineDistance
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from measurements.line_to_plane import LineToPlaneDistance
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from excel_exporter import ExcelExporter
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from performance_logger import PerformanceLogger
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from measurement_logger import MeasurementLogger
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from spatial_index import LinearScanIndex, GridIndex, GridIndexOptimized, OctreeIndex, KDTreeIndex
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class TaskManager:
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"""测量任务管理器"""
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def __init__(self):
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"""初始化任务管理器"""
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self.config = None
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self.point_cloud = None
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self.spatial_index = None # 空间索引
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self.tasks = []
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self.results = []
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self.pointcloud_path_override = None
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self.output_dir_override = None
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def load_config(self, config_path: str) -> bool:
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"""
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加载配置文件
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Args:
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config_path: 配置文件路径
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Returns:
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bool: 是否加载成功
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"""
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try:
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with open(config_path, 'r', encoding='utf-8') as f:
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self.config = yaml.safe_load(f)
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# 加载点云（支持相对路径）
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pointcloud_path = self.pointcloud_path_override or self.config.get('pointcloud_file')
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if not pointcloud_path:
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print("错误: 配置文件中未指定点云文件路径")
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return False
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# 处理相对路径
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pointcloud_path = self._resolve_path(pointcloud_path, config_path)
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# 加载点云
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print(f"\n{'='*70}")
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print("阶段1: 加载点云")
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print("="*70)
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print(f" 文件: {Path(pointcloud_path).name}")
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load_start = time.time()
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self.point_cloud = PointCloudLoader.load_ply(pointcloud_path)
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load_time = time.time() - load_start
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print(f" 点云数量: {len(self.point_cloud):,} 个点")
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print(f" 加载耗时: {load_time:.2f} 秒")
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# 保存点云加载信息供性能日志使用
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self.pointcloud_load_time = load_time
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self.pointcloud_filename = Path(pointcloud_path).name
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# 构建空间索引
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print(f"\n{'='*70}")
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print("阶段2: 构建空间索引")
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print("="*70)
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index_start = time.time()
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self._build_spatial_index()
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self.index_build_time = time.time() - index_start
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# 解析任务
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print(f"\n{'='*70}")
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print("阶段3: 解析测量任务")
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print("="*70)
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tasks_config = self.config.get('tasks', [])
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if not tasks_config:
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print("警告: 配置文件中没有定义任务")
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return True
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print(f"\n找到 {len(tasks_config)} 个测量任务")
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for task_config in tasks_config:
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task = self._create_task(task_config)
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if task:
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self.tasks.append(task)
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print(f" - {task.name} ({task.task_type})")
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return True
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except FileNotFoundError:
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print(f"错误: 配置文件不存在: {config_path}")
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return False
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except yaml.YAMLError as e:
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print(f"错误: 配置文件格式错误: {e}")
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return False
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except Exception as e:
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print(f"错误: 加载配置失败: {e}")
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return False
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def _resolve_path(self, path: str, config_path: str) -> str:
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"""
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解析路径（支持相对路径和绝对路径）
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Args:
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path: 待解析的路径
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config_path: 配置文件路径（用于相对路径的基准）
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Returns:
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str: 解析后的绝对路径
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"""
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path_obj = Path(path)
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if path_obj.is_absolute():
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return str(path_obj)
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else:
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# 相对于配置文件所在目录
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config_dir = Path(config_path).parent
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return str(config_dir / path_obj)
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def override_pointcloud_path(self, path: str):
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"""
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覆盖点云文件路径（用于命令行参数）
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Args:
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path: 点云文件路径
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"""
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self.pointcloud_path_override = path
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def override_output_dir(self, output_dir: str):
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"""
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覆盖输出目录（用于命令行参数）
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Args:
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output_dir: 输出目录路径
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"""
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self.output_dir_override = output_dir
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def _create_task(self, task_config: Dict[str, Any]):
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"""
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根据配置创建测量任务
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Args:
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task_config: 任务配置字典
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Returns:
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BaseMeasurement: 测量任务实例
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"""
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task_type = task_config.get('type')
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task_name = task_config.get('name', 'Unnamed Task')
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# 处理带有孔中心检测的任务类型
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if '4_hole_center' in task_type:
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if 'plane_to_plane' in task_type:
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return PlaneToPlaneWithHoles(
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name=task_name,
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point_cloud=self.point_cloud,
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config=task_config,
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spatial_index=self.spatial_index
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)
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elif 'point_to_plane' in task_type:
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return PointToPlaneWithHoles(
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name=task_name,
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point_cloud=self.point_cloud,
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config=task_config,
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spatial_index=self.spatial_index
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)
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elif 'hole_center' in task_type and 'point_to_point' in task_type:
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return PointToPointWithHoles(
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name=task_name,
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point_cloud=self.point_cloud,
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config=task_config,
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spatial_index=self.spatial_index
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)
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elif task_type == 'plane_to_plane':
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return PlaneToPlaneDistance(
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name=task_name,
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point_cloud=self.point_cloud,
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config=task_config,
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spatial_index=self.spatial_index
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)
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elif task_type == 'point_to_plane':
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return PointToPlaneDistance(
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name=task_name,
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point_cloud=self.point_cloud,
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config=task_config,
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spatial_index=self.spatial_index
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)
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elif task_type == 'point_to_point':
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return PointToPointDistance(
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name=task_name,
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point_cloud=self.point_cloud,
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config=task_config,
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spatial_index=self.spatial_index
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)
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elif task_type == 'line_to_line':
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return LineToLineDistance(
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name=task_name,
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point_cloud=self.point_cloud,
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config=task_config,
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spatial_index=self.spatial_index
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)
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elif task_type == 'line_to_plane':
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return LineToPlaneDistance(
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name=task_name,
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point_cloud=self.point_cloud,
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config=task_config,
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spatial_index=self.spatial_index
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)
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else:
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print(f"警告: 未知的任务类型: {task_type}")
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return None
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def run_all_tasks(self) -> List[Dict[str, Any]]:
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"""
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执行所有测量任务
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Returns:
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List[Dict]: 任务结果列表
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"""
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import time
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self.results = []
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# 创建性能日志记录器
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if self.output_dir_override:
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output_dir = Path(self.output_dir_override)
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else:
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output_dir = Path(__file__).parent.parent / "results"
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perf_logger = PerformanceLogger(str(output_dir))
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# 记录初始化阶段性能
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if hasattr(self, 'pointcloud_load_time'):
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perf_logger.log_pointcloud_loading(
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self.pointcloud_load_time,
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len(self.point_cloud),
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self.pointcloud_filename
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)
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if hasattr(self, 'index_build_time'):
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index_info = self.spatial_index.get_info() if self.spatial_index else {}
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index_method = self.config.get('roi_extraction', {}).get('method', 'unknown')
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perf_logger.log_spatial_index(
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self.index_build_time,
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index_method,
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index_info
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)
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# 创建详细测量日志记录器
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detail_logger = MeasurementLogger(str(output_dir))
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for i, task in enumerate(self.tasks, 1):
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print(f"\n[{i}/{len(self.tasks)}] 执行任务: {task.name}")
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print("-" * 60)
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# 记录任务开始到详细日志
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detail_logger.log_task_start(i, len(self.tasks), task.name)
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# 记录开始时间
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start_time = time.time()
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# 将详细日志记录器传递给任务
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task.detail_logger = detail_logger
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# 执行任务
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result = task.execute()
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# 记录结束时间
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end_time = time.time()
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execution_time = end_time - start_time
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# 将执行时间添加到结果数据中
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if result.data is None:
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result.data = {}
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result.data['execution_time'] = execution_time
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# 获取详细的时间分布
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timing_breakdown = result.data.get('timing_breakdown', {})
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# 记录到性能日志
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perf_logger.log_task(task.name, execution_time, timing_breakdown)
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# 记录结果
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result_dict = {
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'name': result.task_name,
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'type': result.task_type,
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'status': 'success' if result.success else 'failed',
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'data': result.data if result.success else None,
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'error': result.error_message if not result.success else None,
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'execution_time': execution_time
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}
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self.results.append(result_dict)
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# 检查是否有宽度差值检查结果，如果有则生成额外的任务19结果
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if result.success and result.data and 'width_variation' in result.data:
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width_var = result.data['width_variation']
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# 创建任务19的结果
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task19_name = task.name.replace("任务18", "任务19").replace("门口宽度", "门口宽度差值")
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task19_result_dict = {
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'name': task19_name,
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'type': result.task_type,
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'status': 'success',
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'data': {
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'upper_width': width_var['upper_width'],
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'middle_width': width_var['middle_width'],
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'lower_width': width_var['lower_width'],
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'diff_upper_middle': width_var['diff_upper_middle'],
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'diff_middle_lower': width_var['diff_middle_lower'],
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'diff_upper_lower': width_var['diff_upper_lower'],
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'max_diff': max(abs(width_var['diff_upper_middle']),
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abs(width_var['diff_middle_lower']),
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abs(width_var['diff_upper_lower'])),
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'tolerance': width_var['tolerance'],
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'pass': width_var['pass'],
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'execution_time': execution_time
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},
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'error': None,
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'execution_time': execution_time
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}
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self.results.append(task19_result_dict)
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# 打印任务19结果
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print(f"\n [自动生成] {task19_name}")
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print(f" 上部宽度: {width_var['upper_width']:.4f} mm")
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print(f" 中部宽度: {width_var['middle_width']:.4f} mm")
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print(f" 下部宽度: {width_var['lower_width']:.4f} mm")
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print(f" 最大差值: {task19_result_dict['data']['max_diff']:.4f} mm")
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print(f" 公差要求: ±{width_var['tolerance']:.1f} mm")
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status = "✅ 通过" if width_var['pass'] else "❌ 超差"
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print(f" 检查结果: {status}")
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# 打印结果
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if result.success:
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print(f"[成功] 任务完成 (用时: {execution_time:.2f} 秒)")
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if result.data:
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print(f" 平均距离: {result.data.get('mean_distance', 0):.2f} mm")
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print(f" 标准差: {result.data.get('std_distance', 0):.2f} mm")
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print(f" 范围: [{result.data.get('min_distance', 0):.2f}, {result.data.get('max_distance', 0):.2f}] mm")
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print(f" 测量点数: {result.data.get('point_count', result.data.get('sample_count', 0))}")
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if 'source_inlier_ratio' in result.data:
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print(f" 源平面内点率: {result.data.get('source_inlier_ratio', 0):.1%}")
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if 'target_inlier_ratio' in result.data:
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print(f" 目标平面内点率: {result.data.get('target_inlier_ratio', 0):.1%}")
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if 'plane_inlier_ratio' in result.data:
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print(f" 平面内点率: {result.data.get('plane_inlier_ratio', 0):.1%}")
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# 记录成功到详细日志
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detail_logger.log_task_success(execution_time)
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if result.data:
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# 记录测量结果到详细日志
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if 'mean_distance' in result.data:
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detail_logger.log_distance_result(
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result.data.get('mean_distance', 0),
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result.data.get('std_distance', 0),
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result.data.get('min_distance', 0),
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result.data.get('max_distance', 0)
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)
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else:
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print(f"[失败] {result.error_message} (用时: {execution_time:.2f} 秒)")
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# 记录失败到详细日志
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detail_logger.log_task_failure(result.error_message, execution_time)
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# 可视化（如果配置中启用）
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if result.success and task.config.get('visualization', False):
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self._visualize_result(task, result)
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# 保存性能日志
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perf_logger.save()
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# 关闭详细测量日志
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detail_logger.close()
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# 导出Excel结果
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self._export_results()
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return self.results
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def _visualize_result(self, task, result: MeasurementResult):
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"""
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可视化测量结果
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Args:
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task: 测量任务
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result: 测量结果
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"""
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try:
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import open3d as o3d
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import matplotlib.pyplot as plt
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print(" 生成可视化...")
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data = result.data
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if not data:
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return
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# 创建可视化窗口
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vis = o3d.visualization.Visualizer()
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vis.create_window(window_name=f"测量结果: {task.name}", width=1200, height=800)
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# 显示源平面点云（蓝色）
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source_pcd = o3d.geometry.PointCloud()
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source_pcd.points = o3d.utility.Vector3dVector(data['_source_points'])
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source_pcd.paint_uniform_color([0.2, 0.4, 0.8])
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vis.add_geometry(source_pcd)
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# 显示目标平面点云（绿色）
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target_pcd = o3d.geometry.PointCloud()
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target_pcd.points = o3d.utility.Vector3dVector(data['_target_points'])
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target_pcd.paint_uniform_color([0.2, 0.8, 0.4])
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vis.add_geometry(target_pcd)
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# 显示采样点（红色）
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sampled_pcd = o3d.geometry.PointCloud()
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sampled_pcd.points = o3d.utility.Vector3dVector(data['_sampled_points'])
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sampled_pcd.paint_uniform_color([0.9, 0.2, 0.2])
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vis.add_geometry(sampled_pcd)
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# 运行可视化
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vis.run()
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vis.destroy_window()
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except Exception as e:
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print(f" 可视化失败: {e}")
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def _export_results(self):
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"""导出结果到Excel"""
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try:
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# 转换为MeasurementResult对象列表
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measurement_results = []
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for result_dict in self.results:
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measurement_results.append(MeasurementResult(
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task_name=result_dict['name'],
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task_type=result_dict['type'],
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success=result_dict['status'] == 'success',
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data=result_dict.get('data'),
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error_message=result_dict.get('error')
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))
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# 导出Excel（支持命令行覆盖输出目录）
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if self.output_dir_override:
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output_dir = Path(self.output_dir_override)
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else:
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output_dir = Path(__file__).parent.parent / "results"
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exporter = ExcelExporter(str(output_dir))
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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filename = f"measurement_results_{timestamp}"
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# 传递任务配置信息到导出器
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excel_path = exporter.export(measurement_results, filename, self.config.get('tasks', []))
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print(f"\n结果已导出到: {excel_path}")
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except Exception as e:
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print(f"\n导出Excel失败: {e}")
|
||
|
||
def _build_spatial_index(self):
|
||
"""根据配置构建空间索引"""
|
||
index_config = self.config.get('roi_extraction', {})
|
||
index_method = index_config.get('method', 'linear').lower()
|
||
|
||
if index_method == 'linear':
|
||
print(" 方法: 线性扫描（无索引）")
|
||
self.spatial_index = LinearScanIndex(self.point_cloud)
|
||
|
||
elif index_method == 'grid':
|
||
grid_size = index_config.get('grid_size', 100.0)
|
||
use_optimized = index_config.get('use_optimized', True) # 默认使用优化版
|
||
|
||
if use_optimized:
|
||
print(f" 方法: Grid网格索引（优化版 - numpy向量化）")
|
||
self.spatial_index = GridIndexOptimized(self.point_cloud, grid_size=grid_size)
|
||
else:
|
||
print(f" 方法: Grid网格索引（原始版 - Python循环）")
|
||
self.spatial_index = GridIndex(self.point_cloud, grid_size=grid_size)
|
||
|
||
elif index_method == 'octree':
|
||
max_depth = index_config.get('max_depth', 8)
|
||
max_points = index_config.get('max_points_per_node', 1000)
|
||
print(f" 方法: Octree八叉树索引")
|
||
self.spatial_index = OctreeIndex(self.point_cloud, max_depth=max_depth, max_points_per_node=max_points)
|
||
|
||
elif index_method == 'kdtree':
|
||
print(f" 方法: KDTree索引")
|
||
self.spatial_index = KDTreeIndex(self.point_cloud)
|
||
|
||
else:
|
||
print(f" 警告: 未知的索引方法 '{index_method}'，使用线性扫描")
|
||
self.spatial_index = LinearScanIndex(self.point_cloud)
|
||
|
||
# 显示索引信息
|
||
info = self.spatial_index.get_info()
|
||
if 'grid_count' in info:
|
||
print(f" 详细信息: {info['grid_count']:,} 个网格, 平均 {info['avg_points_per_grid']:.1f} 点/格")
|
||
elif 'node_count' in info:
|
||
print(f" 详细信息: {info['node_count']:,} 个节点")
|