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
127 lines
2.6 KiB
Python
127 lines
2.6 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any
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import numpy as np
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@dataclass(slots=True)
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class FrameHypothesis:
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name: str
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axes: np.ndarray
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origin: np.ndarray
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score: float
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bottom_score: float
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symmetry_score: float
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flatness_score: float
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hole_score: float
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continuity_score: float
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@dataclass(slots=True)
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class CandidateBand:
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candidate_id: str
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slice_index: int
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x_start: float
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x_end: float
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x_center: float
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y_min: float
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y_max: float
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y_center: float
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z_low: float
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width: float
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point_count: int
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low_point_count: int
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flatness: float
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normal_alignment: float
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score: float
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edge_ratio: float
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side: str
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y_mid: float
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bin_start: int
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bin_end: int
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@dataclass(slots=True)
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class SliceResult:
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slice_index: int
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x_start: float
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x_end: float
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x_center: float
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y_mid: float
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candidate_bands: list[CandidateBand] = field(default_factory=list)
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left_candidates: list[CandidateBand] = field(default_factory=list)
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right_candidates: list[CandidateBand] = field(default_factory=list)
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point_count: int = 0
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@dataclass(slots=True)
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class TrackSolution:
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side: str
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rank: int
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score: float
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coverage_ratio: float
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coverage_count: int
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candidate_ids: list[str | None]
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candidates: list[CandidateBand]
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score_margin: float | None
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confidence: float
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@dataclass(slots=True)
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class RepresentativePoint:
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side: str
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slice_index: int
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x: float
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y: float
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z: float
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width: float
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point_count: int
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residual: float
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confidence: float
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x_start: float
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x_end: float
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@dataclass(slots=True)
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class FittedLine:
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side: str
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x_support: np.ndarray
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y_support: np.ndarray
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z_support: np.ndarray
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y_spline: Any
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z_spline: Any
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y_residual_rms: float
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z_residual_rms: float
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low_confidence_segments: list[tuple[float, float]]
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gaps: list[tuple[float, float]]
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@dataclass(slots=True)
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class HoleCandidate:
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side: str
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region: str
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axis: str
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center: np.ndarray
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radius: float
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score: float
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inlier_count: int
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coverage_ratio: float
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residual: float
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confidence: float
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@dataclass(slots=True)
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class ReferenceLine:
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side: str
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available: bool
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point_front: np.ndarray | None
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point_back: np.ndarray | None
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direction: np.ndarray | None
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confidence: float
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residual: float | None
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candidates_front: list[HoleCandidate] = field(default_factory=list)
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candidates_back: list[HoleCandidate] = field(default_factory=list)
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alternates: list[dict[str, Any]] = field(default_factory=list)
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