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
370 lines
12 KiB
Python
370 lines
12 KiB
Python
from __future__ import annotations
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import copy
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import json
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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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DEFAULT_CONFIG: dict[str, Any] = {
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"preprocess": {
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"stat_k": 18,
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"stat_std_ratio": 2.8,
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"radius_spacing_multiplier": 2.8,
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"radius_min_mm": 12.0,
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"radius_min_neighbors": 4,
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"voxel_spacing_multiplier": 1.5,
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"voxel_min_mm": 8.0,
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"normal_k": 20,
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"spacing_sample_size": 4000,
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# Optional passthrough crop performed before any other filter.
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# ``aabb`` is populated from the external ROI JSON when supplied.
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"passthrough": {
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"enabled": False,
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"aabb": None,
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},
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# Point-cloud bilateral filter applied after radius filtering.
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"bilateral": {
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"enabled": True,
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"k": 24,
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"sigma_d_spacing_multiplier": 2.0,
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"sigma_d_min_mm": 2.0,
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"sigma_n_deg": 15.0,
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"iterations": 1,
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},
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},
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"frame": {
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"use_input_axes": True,
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"coarse_slice_count": 28,
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"coarse_y_bins": 30,
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"bottom_quantile": 0.14,
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"low_band_margin_spacing_multiplier": 6.0,
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"candidate_bin_min_points": 12,
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"normal_alignment_min": 0.55,
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"flatness_spacing_multiplier": 4.0,
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"flatness_min_mm": 8.0,
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"symmetry_weight": 1.25,
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"bottom_weight": 1.3,
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"flatness_weight": 1.0,
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"hole_weight": 0.55,
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},
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"roi": {
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"x_trim_quantile": 0.004,
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"bottom_height_ratio": 0.24,
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"bottom_quantile": 0.18,
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"z_margin_spacing_multiplier": 3.0,
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"min_z_ceiling_mm": 1500.0,
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# External axis-aligned bbox supplied via JSON.
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# Fields: x_min, x_max, y_min, y_max, z_min, z_max (mm, CAD frame).
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"aabb": None,
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},
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"beam": {
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"slice_thickness_length_ratio": 0.012,
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"slice_thickness_spacing_multiplier": 22.0,
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"slice_step_ratio": 0.5,
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"slice_min_points": 80,
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"y_bin_width_width_ratio": 0.03,
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"y_bin_width_spacing_multiplier": 3.5,
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"low_quantile": 0.18,
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"low_core_quantile": 0.28,
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"min_bin_points": 10,
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"width_min_ratio": 0.018,
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"width_max_ratio": 0.18,
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"width_preferred_ratio": 0.065,
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"flatness_spacing_multiplier": 4.5,
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"flatness_min_mm": 10.0,
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"normal_alignment_min": 0.6,
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"low_band_margin_spacing_multiplier": 8.0,
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"midline_trim_quantile": 0.02,
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"edge_preference_weight": 3.2,
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"strong_edge_ratio": 0.58,
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"inner_reject_ratio": 0.32,
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"edge_keep_min_ratio": 0.72,
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"relative_edge_window": 0.18,
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"drop_weak_edge_slices": True,
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},
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"tracking": {
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"top_k": 3,
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"beam_width": 24,
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"min_coverage_ratio": 0.28,
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"weights": {
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"coverage_reward": 2.2,
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"node_reward": 1.0,
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"y_penalty": 1.6,
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"z_penalty": 1.4,
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"width_penalty": 0.7,
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"normal_penalty": 0.8,
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"gap_penalty": 2.0,
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"gap_growth_penalty": 0.7,
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"jitter_penalty": 0.9,
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},
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},
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"representative": {
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"band_y_margin_spacing_multiplier": 4.0,
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"band_z_margin_spacing_multiplier": 10.0,
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"core_quantile": 0.28,
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"core_margin_spacing_multiplier": 5.0,
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"underside_profile_bin_count": 14,
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"underside_quantile": 0.2,
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"underside_outer_fraction": 0.4,
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"underside_rise_spacing_multiplier": 0.55,
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"underside_core_spacing_multiplier": 0.7,
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"underside_profile_margin_spacing_multiplier": 1.0,
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"underside_min_bin_points": 18,
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"max_iter": 5,
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"spline_smoothing_y_spacing_multiplier": 3.0,
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"spline_smoothing_z_spacing_multiplier": 8.0,
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"max_gap_slices": 4,
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"grid_step_slice_ratio": 1.0,
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# Robust rejection of representative points contaminated by gussets /
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# cross-members before the spline fit. Window is in units of beam
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# slice steps; sigma is the robust (MAD-based) rejection threshold.
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"outlier_rejection_enabled": True,
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"outlier_window_slices": 2.5,
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"outlier_sigma": 2.5,
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},
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"reference": {
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"mode": "line_endpoints",
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"stable_region_fraction": 0.14,
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"search_extension_spacing_multiplier": 12.0,
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"search_extension_min_mm": 650.0,
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"lateral_margin_width_ratio": 0.11,
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"vertical_margin_height_ratio": 0.22,
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"underside_lower_margin_spacing_multiplier": 1.2,
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"underside_upper_margin_spacing_multiplier": 3.8,
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"circle_trials": 360,
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"circle_sample_size": 700,
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"normal_axis_max_alignment": 0.65,
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"preferred_axes": ["y"],
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"radius_min_mm": 35.0,
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"radius_max_width_ratio": 0.04,
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"fit_tolerance_spacing_multiplier": 0.38,
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"coverage_bin_count": 20,
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"min_inliers": 18,
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"cluster_eps_spacing_multiplier": 2.5,
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"cluster_min_samples": 2,
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"x_window_count": 6,
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"x_window_overlap_ratio": 0.35,
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"axis_bonus": {
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"y": 0.42,
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"z": 0.18,
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"x": -0.85
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},
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"extremity_reward": 2.2,
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"underside_penalty_weight": 1.2,
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"vertical_center_penalty_weight": 0.25,
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"lateral_penalty_weight": 0.55,
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"low_confidence_threshold": 0.6,
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},
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"deflection": {
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"evaluation_grid_factor": 1.0,
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},
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"registration": {
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"enabled": True,
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"skip": False,
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"icp_max_iter": 60,
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"icp_tol_mm": 0.05,
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"icp_tol_rel": 1.0e-4,
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"icp_max_corr_spacing_multiplier": 6.0,
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"icp_max_corr_min_mm": 25.0,
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"icp_downsample_target": 30000,
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"trim_ratio": 0.2,
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"pca_sign_disambiguation": True,
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# If the recovered transform is within these tolerances of identity
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# (and snapping does not worsen correspondence), return exact identity
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# so an already-aligned scan is left untouched.
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"identity_rot_tol_deg": 0.5,
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"identity_trans_tol_mm": 5.0,
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},
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"cad": {
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"path": None,
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"downsample_voxel_mm": 4.0,
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},
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"visualization": {
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"max_scatter_points": 40000,
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"dpi": 180,
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},
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}
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PROFILE_OVERRIDES: dict[str, dict[str, Any]] = {
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"auto": {},
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"M": {
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"beam": {
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"slice_thickness_length_ratio": 0.011,
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"width_preferred_ratio": 0.06,
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}
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},
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"TC": {
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"beam": {
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"slice_thickness_length_ratio": 0.014,
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"width_preferred_ratio": 0.07,
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},
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"reference": {"stable_region_fraction": 0.16},
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},
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"TP": {
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"beam": {
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"slice_thickness_length_ratio": 0.013,
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"width_preferred_ratio": 0.072,
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},
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"reference": {"vertical_margin_height_ratio": 0.24},
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},
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"custom": {},
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}
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def deep_merge(base: dict[str, Any], override: dict[str, Any]) -> dict[str, Any]:
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merged = copy.deepcopy(base)
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for key, value in override.items():
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if isinstance(value, dict) and isinstance(merged.get(key), dict):
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merged[key] = deep_merge(merged[key], value)
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else:
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merged[key] = copy.deepcopy(value)
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return merged
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def load_config(config_path: str | None, profile: str) -> dict[str, Any]:
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profile_name = profile if profile in PROFILE_OVERRIDES else "auto"
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config = deep_merge(DEFAULT_CONFIG, PROFILE_OVERRIDES[profile_name])
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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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config = deep_merge(config, user_config)
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return config
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def cloud_geometry(points: np.ndarray) -> dict[str, float]:
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mins = points.min(axis=0)
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maxs = points.max(axis=0)
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spans = maxs - mins
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return {
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"x_min": float(mins[0]),
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"x_max": float(maxs[0]),
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"y_min": float(mins[1]),
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"y_max": float(maxs[1]),
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"z_min": float(mins[2]),
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"z_max": float(maxs[2]),
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"length": float(spans[0]),
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"width": float(spans[1]),
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"height": float(spans[2]),
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}
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def resolve_numeric_parameters(
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config: dict[str, Any],
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geometry: dict[str, float],
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spacing: float,
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) -> dict[str, Any]:
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resolved = copy.deepcopy(config)
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length = geometry["length"]
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width = geometry["width"]
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height = geometry["height"]
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preprocess = resolved["preprocess"]
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preprocess["radius_mm"] = max(
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preprocess["radius_min_mm"],
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preprocess["radius_spacing_multiplier"] * spacing,
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)
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preprocess["voxel_mm"] = max(
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preprocess["voxel_min_mm"],
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preprocess["voxel_spacing_multiplier"] * spacing,
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)
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bilateral = preprocess.get("bilateral")
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if isinstance(bilateral, dict):
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bilateral["sigma_d_mm"] = max(
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float(bilateral.get("sigma_d_min_mm", 2.0)),
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float(bilateral.get("sigma_d_spacing_multiplier", 2.0)) * spacing,
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)
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bilateral["sigma_n_rad"] = float(
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np.deg2rad(float(bilateral.get("sigma_n_deg", 15.0)))
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)
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frame = resolved["frame"]
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frame["flatness_mm"] = max(
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frame["flatness_min_mm"],
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frame["flatness_spacing_multiplier"] * spacing,
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)
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frame["low_band_margin_mm"] = (
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frame["low_band_margin_spacing_multiplier"] * spacing
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)
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roi = resolved["roi"]
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roi["z_margin_mm"] = roi["z_margin_spacing_multiplier"] * spacing
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beam = resolved["beam"]
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beam["slice_thickness_mm"] = max(
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beam["slice_thickness_length_ratio"] * length,
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beam["slice_thickness_spacing_multiplier"] * spacing,
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)
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beam["slice_step_mm"] = beam["slice_thickness_mm"] * beam["slice_step_ratio"]
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beam["y_bin_width_mm"] = max(
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beam["y_bin_width_width_ratio"] * width,
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beam["y_bin_width_spacing_multiplier"] * spacing,
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)
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beam["width_min_mm"] = beam["width_min_ratio"] * width
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beam["width_max_mm"] = beam["width_max_ratio"] * width
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beam["width_preferred_mm"] = beam["width_preferred_ratio"] * width
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beam["flatness_mm"] = max(
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beam["flatness_min_mm"],
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beam["flatness_spacing_multiplier"] * spacing,
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)
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beam["low_band_margin_mm"] = (
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beam["low_band_margin_spacing_multiplier"] * spacing
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)
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representative = resolved["representative"]
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representative["band_y_margin_mm"] = (
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representative["band_y_margin_spacing_multiplier"] * spacing
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)
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representative["band_z_margin_mm"] = (
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representative["band_z_margin_spacing_multiplier"] * spacing
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)
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representative["core_margin_mm"] = (
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representative["core_margin_spacing_multiplier"] * spacing
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)
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representative["grid_step_mm"] = (
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representative["grid_step_slice_ratio"] * beam["slice_step_mm"]
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)
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reference = resolved["reference"]
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reference["lateral_margin_mm"] = (
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reference["lateral_margin_width_ratio"] * width
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)
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reference["vertical_margin_mm"] = (
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reference["vertical_margin_height_ratio"] * height
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)
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reference["search_extension_mm"] = max(
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reference["search_extension_min_mm"],
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reference["search_extension_spacing_multiplier"] * spacing,
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)
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reference["underside_lower_margin_mm"] = (
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reference["underside_lower_margin_spacing_multiplier"] * spacing
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)
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reference["underside_upper_margin_mm"] = (
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reference["underside_upper_margin_spacing_multiplier"] * spacing
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)
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reference["radius_max_mm"] = max(
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reference["radius_min_mm"] * 1.5,
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reference["radius_max_width_ratio"] * width,
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)
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reference["fit_tolerance_mm"] = (
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reference["fit_tolerance_spacing_multiplier"] * spacing
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)
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reference["cluster_eps_mm"] = (
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reference["cluster_eps_spacing_multiplier"] * spacing
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)
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registration = resolved.get("registration")
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if isinstance(registration, dict):
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registration["icp_max_corr_mm"] = max(
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float(registration.get("icp_max_corr_min_mm", 25.0)),
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float(registration.get("icp_max_corr_spacing_multiplier", 6.0)) * spacing,
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)
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resolved["runtime"] = {
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"spacing_mm": float(spacing),
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"geometry": geometry,
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}
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return resolved
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