verticalsigns d8ae433: AI3D-379 Pydantic config models via iolabs-common ConfigModel

Importance #1: src/iolabs_point_cloud_detection_verticalsigns/config.py @@ -1,13 +1,16 @@

 """Detector configuration.
-The 378-field :class:`DetectorConfig` and its ``from_mapping`` flattener are
+The 379-field :class:`DetectorConfig` and its ``from_mapping`` flattener are
 split by section across the ``_config_<section>`` modules; this module
 recombines them and re-exports every piece, so ``from .config import X``
 keeps working for every name that used to live here.
+``DetectorConfig`` is the FLAT view the detector modules read
+(``config.ground_cell_m``); the NESTED document it is built from is validated
+by the :class:`VerticalSignsConfig` model tree in ``_config_model``.
 """
-from dataclasses import dataclass
 from pathlib import Path
 from typing import Any
 from ._config import load_verticalsigns_config

Importance #2: src/iolabs_point_cloud_detection_verticalsigns/_config_model.py @@ -0,0 +1,45 @@

	1	"""The nested pydantic config model for the vertical-sign detector.
	2
	3	``VerticalSignsConfig`` mirrors ``verticalsigns.default.json`` section for
	4	section and key for key: it is the single source of truth for which config
	5	keys exist and what type each one has. Adding a key means adding a field to
	6	the matching section model and a default to the packaged JSON.
	7	"""
	8
	9	from iolabs.common import config_loader
	10
	11	from . import _model_devices, _model_grid, _model_road, _model_tree
	12
	13
	14	class VerticalSignsConfig(config_loader.ConfigModel):
	15	"""Every configuration section of the vertical-sign detector."""
	16
	17	ground: _model_grid.GroundConfig = _model_grid.GroundConfig()
	18	occupancy: _model_grid.OccupancyConfig = _model_grid.OccupancyConfig()
	19	candidates: _model_grid.CandidatesConfig = _model_grid.CandidatesConfig()
	20	clustering: _model_grid.ClusteringConfig = _model_grid.ClusteringConfig()
	21	classification: _model_grid.ClassificationConfig = _model_grid.ClassificationConfig()
	22	corridor: _model_grid.CorridorConfig = _model_grid.CorridorConfig()
	23	context: _model_grid.ContextConfig = _model_grid.ContextConfig()
	24	delineator: _model_devices.DelineatorConfig = _model_devices.DelineatorConfig()
	25	sign_post: _model_devices.SignPostConfig = _model_devices.SignPostConfig()
	26	panel: _model_devices.PanelConfig = _model_devices.PanelConfig()
	27	gantry: _model_devices.GantryConfig = _model_devices.GantryConfig()
	28	repetitive_row: _model_devices.RepetitiveRowConfig = _model_devices.RepetitiveRowConfig()
	29	road_context: _model_road.RoadContextConfig = _model_road.RoadContextConfig()
	30	edge_line: _model_road.EdgeLineConfig = _model_road.EdgeLineConfig()
	31	field_stake: _model_devices.FieldStakeConfig = _model_devices.FieldStakeConfig()
	32	marker_extract: _model_devices.MarkerExtractConfig = _model_devices.MarkerExtractConfig()
	33	tree: _model_tree.TreeConfig = _model_tree.TreeConfig()
	34	tree_detection: _model_tree.TreeDetectionConfig = _model_tree.TreeDetectionConfig()
	35	chroma_vegetation: _model_tree.ChromaVegetationConfig = _model_tree.ChromaVegetationConfig()
	36	vehicle: _model_grid.VehicleConfig = _model_grid.VehicleConfig()
	37	views: _model_road.ViewsConfig = _model_road.ViewsConfig()
	38	perspective: _model_road.PerspectiveConfig = _model_road.PerspectiveConfig()
	39	tree_instance: _model_tree.TreeInstanceConfig = _model_tree.TreeInstanceConfig()
	40	conic_gate: _model_tree.ConicGateConfig = _model_tree.ConicGateConfig()
	41	conifer_rule: _model_tree.ConiferRuleConfig = _model_tree.ConiferRuleConfig()
	42	radius: _model_grid.RadiusConfig = _model_grid.RadiusConfig()
	43	rail_halfpost: _model_devices.RailHalfpostConfig = _model_devices.RailHalfpostConfig()
	44	reject_rescue: _model_devices.RejectRescueConfig = _model_devices.RejectRescueConfig()
	45	tcs_ground: _model_tree.TcsGroundConfig = _model_tree.TcsGroundConfig()
0

Importance #3: src/iolabs_point_cloud_detection_verticalsigns/_model_devices.py @@ -0,0 +1,140 @@

	1	"""Per-device acceptance gates and the two probe stages.
	2
	3	One slice of the nested :class:`VerticalSignsConfig` model tree; the sections
	4	mirror ``verticalsigns.default.json`` key for key. ``_config_model`` recombines
	5	the slices.
	6	"""
	7
	8	from iolabs.common import config_loader
	9
	10
	11	class DelineatorConfig(config_loader.ConfigModel):
	12	"""Delineator (Leitpfosten) acceptance gates."""
	13
	14	h_min_m: float = 0.7
	15	h_max_m: float = 1.5
	16	max_footprint_m: float = 0.45
	17	relaxed_footprint_m: float = 0.85
	18	relaxed_min_verticality: float = 0.85
	19	relaxed_max_ring_fill_ratio: float = 1.0
	20	relaxed_min_hi_intensity_fraction: float = 0.15
	21	min_hi_intensity_fraction: float = 0.08
	22	min_points: int = 300
	23
	24
	25	class SignPostConfig(config_loader.ConfigModel):
	26	"""Sign-post and plate acceptance gates."""
	27
	28	max_len_minor_m: float = 0.8
	29	h_min_m: float = 1.5
	30	h_max_m: float = 6.0
	31	min_continuity: float = 0.6
	32	plate_hi_intensity_fraction: float = 0.4
	33	plate_hi_intensity_fraction_weak: float = 0.3
	34	plate_upper_surplus_ratio: float = 2.0
	35	min_upper_half_surplus: float = 0.3
	36	plate_min_core_rms_m: float = 0.1
	37	max_plate_thickness_m: float = 0.15
	38	bare_post_min_h_max_m: float = 4.5
	39	bare_post_max_core_rms_m: float = 0.065
	40	bare_post_min_verticality: float = 0.9
	41	bare_post_min_points: int = 450
	42
	43
	44	class PanelConfig(config_loader.ConfigModel):
	45	"""Large panel acceptance gates."""
	46
	47	min_hi: float = 0.4
	48	max_thickness_m: float = 0.2
	49	h_min_m: float = 0.9
	50	len_major_min_m: float = 1.5
	51	len_major_max_m: float = 5.0
	52
	53
	54	class GantryConfig(config_loader.ConfigModel):
	55	"""Gantry leg and pairing gates."""
	56
	57	h_min_m: float = 4.5
	58	len_major_m: float = 8.0
	59	max_len_minor_m: float = 6.0
	60	pair_station_tolerance_m: float = 5.0
	61	pair_min_separation_m: float = 3.0
	62	overhead_h_min_m: float = 4.5
	63	pair_isolation_radius_m: float = 8.0
	64
	65
	66	class RepetitiveRowConfig(config_loader.ConfigModel):
	67	"""Repetitive-row (guardrail post series) grouping."""
	68
	69	min_members: int = 4
	70	max_spacing_m: float = 5.0
	71	max_perp_spread_m: float = 1.5
	72	max_h_max_range_m: float = 0.7
	73	member_max_len_major_m: float = 2.0
	74	member_max_len_minor_m: float = 0.8
	75
	76
	77	class FieldStakeConfig(config_loader.ConfigModel):
	78	"""Field-stake row emission gates."""
	79
	80	row_emit: bool = True
	81	min_members: int = 4
	82	min_spacing_m: float = 2.0
	83	max_spacing_m: float = 10.0
	84	max_spacing_cv: float = 0.35
	85
	86
	87	class MarkerExtractConfig(config_loader.ConfigModel):
	88	"""Bright marker extraction from rejected clusters."""
	89
	90	min_len_major_m: float = 6.0
	91	bright_h_min_m: float = 1.5
	92	min_bright_points: int = 400
	93	window_m: float = 2.5
	94	min_bright_fraction: float = 0.45
	95	min_h_max_m: float = 1.6
	96	min_vertical_span_m: float = 0.5
	97
	98
	99	class RailHalfpostConfig(config_loader.ConfigModel):
	100	"""Guardrail half-post probe stage."""
	101
	102	band_lat_m: float = 0.8
	103	band_z_hi_m: float = 1.5
	104	band_z_lo_m: float = 0.15
	105	cluster_cell_m: float = 0.15
	106	dedupe_m: float = 1.5
	107	enabled: bool = False
	108	ground_cell_m: float = 2.0
	109	ground_percentile: float = 10.0
	110	h_max_m: float = 0.8
	111	h_min_m: float = 0.2
	112	max_lateral_m: float = 0.5
	113	max_width_m: float = 0.2
	114	min_emit_points: int = 8
	115	min_points: int = 15
	116	min_z_extent_m: float = 0.1
	117	models_dir: str = ""
	118	prime_min_records: int = 2
	119	prime_min_sat: int = 1
	120	sample_step_m: float = 0.1
	121	saturation_intensity: float = 55000.0
	122
	123
	124	class RejectRescueConfig(config_loader.ConfigModel):
	125	"""Reject-rescue stage gates."""
	126
	127	accepted_exclusion_m: float = 2.0
	128	enabled: bool = False
	129	h_max_m: float = 1.6
	130	h_min_m: float = 0.85
	131	max_core_rms_m: float = 0.2
	132	merge_radius_m: float = 1.0
	133	min_continuity: float = 0.8
	134	min_decile_fill: float = 0.6
	135	min_h_over_width: float = 1.4
	136	min_points: int = 30
	137	min_records: int = 2
	138	min_roadctx_sat: int = 17
	139	min_verticality: float = 0.9
	140	per_segment_cap: int = 0
0

Importance #4: src/iolabs_point_cloud_detection_verticalsigns/_model_grid.py @@ -0,0 +1,152 @@

	1	"""Grid, candidate, classification, radius and corridor config sections.
	2
	3	One slice of the nested :class:`VerticalSignsConfig` model tree; the sections
	4	mirror ``verticalsigns.default.json`` key for key. ``_config_model`` recombines
	5	the slices.
	6	"""
	7
	8	from iolabs.common import config_loader
	9
	10
	11	class GroundConfig(config_loader.ConfigModel):
	12	"""Ground-model raster cell size and percentile."""
	13
	14	cell_m: float = 0.75
	15	percentile: float = 8.0
	16
	17
	18	class OccupancyConfig(config_loader.ConfigModel):
	19	"""Occupancy grid used to find candidate cells."""
	20
	21	cell_m: float = 0.15
	22
	23
	24	class CandidatesConfig(config_loader.ConfigModel):
	25	"""Height band and seed-cell gates for candidate points."""
	26
	27	min_height_m: float = 0.3
	28	max_height_m: float = 10.0
	29	seed_min_vertical_span_m: float = 0.8
	30	seed_min_h_max_m: float = 0.9
	31	seed_bright_min_vertical_span_m: float = 0.45
	32	seed_bright_min_h_max_m: float = 0.6
	33	seed_bright_min_points: int = 3
	34
	35
	36	class ClusteringConfig(config_loader.ConfigModel):
	37	"""DBSCAN clustering of seed-cell centres."""
	38
	39	eps_m: float = 0.45
	40	min_samples: int = 1
	41	hull_margin_m: float = 0.2
	42
	43
	44	class ClassificationConfig(config_loader.ConfigModel):
	45	"""Cluster-level accept/reject gates and ML verifier wiring."""
	46
	47	continuity_bin_m: float = 0.25
	48	reject_len_major_m: float = 6.0
	49	reject_h_max_with_large_footprint_m: float = 4.5
	50	min_continuity: float = 0.5
	51	min_accept_h_max_m: float = 0.9
	52	core_rms_bin_m: float = 0.25
	53	core_rms_h_min_m: float = 0.3
	54	core_rms_h_cap_m: float = 3.0
	55	hi_intensity_all_points_percentile: float = 98.0
	56	min_volumetric_density: float = 8000.0
	57	pole_floating_min_h_min_m: float = 3.5
	58	pole_isolated_radius_m: float = 8.0
	59	dedup_radius_m: float = 0.8
	60	emit_trees: bool = False
	61	ml_verifier_enabled: bool = True
	62	ml_veto_threshold: float = -1.0
	63	ml_model_path: str = ""
	64	lattice_admission: bool = True
	65	lattice_max_seed_spacing_m: float = 60.0
	66	lattice_max_skip: int = 6
	67	lattice_max_spacing_resid: float = 0.15
	68	lattice_min_anchors: int = 4
	69	lattice_min_seed_spacing_m: float = 15.0
	70	lattice_pool_h_max_max_m: float = 1.4
	71	lattice_pool_h_max_min_m: float = 0.8
	72	lattice_pool_max_len_major_m: float = 1.2
	73	lattice_pool_max_plate_thickness_m: float = 0.05
	74	lattice_pool_min_hi_seed_fraction: float = 0.15
	75	lattice_pool_min_points: int = 20
	76	lattice_pool_min_verticality: float = 0.85
	77	lattice_snap_m: float = 3.0
	78	ml_veto_requires_corridor: bool = True
	79	robust_extent_hi_percentile: float = 99.0
	80	robust_extent_lo_percentile: float = 1.0
	81	robust_extent_stats: bool = True
	82	robust_h_max_percentile: float = 98.0
	83	seed_bright_percentile: float \| None = 95.0
	84	single_record_transient_veto: bool = True
	85	transient_max_h_max_m: float = 2.5
	86	transient_max_verticality: float = 0.3
	87	transient_min_len_major_m: float = 2.0
	88	veg_texture_min_hi_seed_fraction: float = 0.668
	89	veg_texture_min_plate_thickness_m: float = 0.05
	90	veg_texture_veto: bool = True
	91	verticality_sentinel_fix: bool = True
	92
	93
	94	class RadiusConfig(config_loader.ConfigModel):
	95	"""Cylinder-radius fitting and crown-lobe estimation."""
	96
	97	crown_lobe_coverage_target: float = 0.95
	98	crown_lobe_gap_m: float = 0.5
	99	crown_lobe_max_count: int = 8
	100	crown_lobe_min_points: int = 30
	101	crown_lobe_min_samples: int = 10
	102	crown_radius_percentile: float = 95.0
	103	debug_cluster_points: bool = False
	104	fit_bin_m: float = 0.25
	105	fit_divergence_factor: float = 4.0
	106	fit_min_arc_deg: float = 60.0
	107	fit_min_bin_points: int = 8
	108	fit_residual_abs_m: float = 0.03
	109	fit_residual_frac: float = 0.35
	110	pole_radius_max_m: float = 0.5
	111	trunk_radius_max_m: float = 0.8
	112
	113
	114	class CorridorConfig(config_loader.ConfigModel):
	115	"""Road-corridor raster and on-carriageway gates."""
	116
	117	max_dist_to_road_m: float = 10.0
	118	on_carriageway_dist_m: float = 0.25
	119	on_carriageway_exempt_h_max_m: float = 4.5
	120	density_min_points: float = 8.0
	121	density_frac_p95: float = 0.06
	122	density_max_points: float = 150.0
	123	component_min_area_frac: float = 0.15
	124	component_min_area_cells: int = 40
	125	on_carriageway_road_fraction: float = 0.7
	126	on_carriageway_bright_frac: float = 0.5
	127	on_carriageway_delineator_max_len_major_m: float = 0.65
	128	on_carriageway_delineator_min_verticality: float = 0.95
	129
	130
	131	class ContextConfig(config_loader.ConfigModel):
	132	"""Ring and forest neighbourhood context features."""
	133
	134	ring_r_inner_m: float = 0.5
	135	ring_r_outer_m: float = 1.5
	136	ring_h_min_m: float = 0.5
	137	ring_h_max_m: float = 2.5
	138	ring_max_fill_ratio: float = 2.0
	139	ring_min_points: int = 40
	140	forest_min_neighbors: int = 3
	141	forest_radius_m: float = 8.0
	142	forest_neighbor_min_h_max_m: float = 2.0
	143
	144
	145	class VehicleConfig(config_loader.ConfigModel):
	146	"""Vehicle-rejection envelope."""
	147
	148	h_min_m: float = 1.5
	149	h_max_m: float = 4.5
	150	len_major_m: float = 2.5
	151	len_minor_m: float = 1.5
	152	max_hi_intensity_fraction: float = 0.1
0

Importance #5: src/iolabs_point_cloud_detection_verticalsigns/_model_road.py @@ -0,0 +1,99 @@

	1	"""Road-context, edge-line and QC rendering config sections.
	2
	3	One slice of the nested :class:`VerticalSignsConfig` model tree; the sections
	4	mirror ``verticalsigns.default.json`` key for key. ``_config_model`` recombines
	5	the slices.
	6	"""
	7
	8	from iolabs.common import config_loader
	9
	10
	11	class RoadContextConfig(config_loader.ConfigModel):
	12	"""Road-context saturation raster and XML carriageway votes."""
	13
	14	gate_enabled: bool = True
	15	xml_enabled: bool = True
	16	xml_min_agreement: float = 0.6
	17	xml_vote_slack_m: float = 3.0
	18	xml_max_distance_m: float = 60.0
	19	xml_station_tolerance_m: float = 2.0
	20	xml_station_step_m: float = 10.0
	21	min_carriageway_width_m: float = 3.0
	22	max_carriageway_width_m: float = 20.0
	23	paint_fallback_enabled: bool = False
	24	saturation_intensity: float = 55000.0
	25	radius_m: float = 15.0
	26	neighbour_span: int = 1
	27	cache_dir: str = ""
	28	min_neighbourhood_saturated: int = 1000
	29
	30
	31	class EdgeLineConfig(config_loader.ConfigModel):
	32	"""Edge-line paint detection and far-distance filtering."""
	33
	34	gate_enabled: bool = True
	35	paint_max_height_m: float = 0.35
	36	paint_min_height_m: float = -0.25
	37	paint_intensity_percentile: float = 95.0
	38	paint_subsample: int = 20
	39	station_len_m: float = 10.0
	40	min_window_returns: int = 2000
	41	lateral_bin_m: float = 0.1
	42	min_line_points: int = 40
	43	max_line_width_m: float = 1.5
	44	min_line_along_fill: float = 0.4
	45	drive_line_bin_m: float = 0.5
	46	min_band_width_m: float = 2.0
	47	max_band_width_m: float = 9.0
	48	inward_margin_m: float = 0.3
	49	min_coverage_frac: float = 0.6
	50	min_axis_contrast: float = 3.0
	51	axis_search_radius_m: float = 40.0
	52	axis_max_angle_cos: float = 0.8
	53	axis_max_distance_m: float = 150.0
	54	exempt_h_max_m: float = 4.5
	55	reject_requires_transient: bool = True
	56	transient_max_records: int = 1
	57	far_filter_enabled: bool = True
	58	far_max_distance_m: float = 30.0
	59	far_include_lane_lines: bool = True
	60	far_tier2_enabled: bool = True
	61	far_tier2_distance_m: float = 15.0
	62	far_tier2_max_saturation: int = 150
	63	max_carriageway_width_m: float = 20.0
	64	min_carriageway_width_m: float = 3.0
	65	paint_fallback_enabled: bool = False
	66	xml_enabled: bool = True
	67	xml_max_distance_m: float = 60.0
	68	xml_min_agreement: float = 0.6
	69	xml_station_step_m: float = 10.0
	70	xml_station_tolerance_m: float = 2.0
	71	xml_vote_slack_m: float = 3.0
	72
	73
	74	class ViewsConfig(config_loader.ConfigModel):
	75	"""Rendered QC view cameras and image size."""
	76
	77	near_radius_m: float = 45.0
	78	fov_deg: float = 55.0
	79	splat: int = 2
	80	image_width: int = 1100
	81	image_height: int = 750
	82	view_names: tuple[str, ...] = ("back", "side")
	83
	84
	85	class PerspectiveConfig(config_loader.ConfigModel):
	86	"""Perspective-projection QC overlay cameras and tolerances."""
	87
	88	depth_tol_m: float = 0.5
	89	line_samples: int = 20
	90	occluded_alpha: int = 90
	91	solid_width_px: int = 3
	92	halo_width_px: int = 6
	93	base_marker_radius_px: int = 6
	94	back_distance_m: float = 22.0
	95	back_height_m: float = 4.0
	96	context_distance_m: float = 40.0
	97	context_height_m: float = 6.0
	98	share_radius_m: float = 15.0
	99	coverage_tol_m: float = 0.5
0

Importance #6: src/iolabs_point_cloud_detection_verticalsigns/_model_tree.py @@ -0,0 +1,180 @@

	1	"""Tree, vegetation and ground-filter config sections.
	2
	3	One slice of the nested :class:`VerticalSignsConfig` model tree; the sections
	4	mirror ``verticalsigns.default.json`` key for key. ``_config_model`` recombines
	5	the slices.
	6	"""
	7
	8	from iolabs.common import config_loader
	9
	10
	11	class TreeConfig(config_loader.ConfigModel):
	12	"""Legacy tree crown hints."""
	13
	14	crown_h_min_m: float = 2.0
	15	crown_max_area_m2: float = 4.0
	16	isotropy_ratio: float = 0.75
	17	greenness_hint: float = 0.45
	18
	19
	20	class TreeDetectionConfig(config_loader.ConfigModel):
	21	"""Tree detection stage: which blobs are emitted as trees."""
	22
	23	enabled: bool = False
	24	max_dist_to_road_m: float = 20.0
	25	seed_min_vertical_span_m: float = 1.5
	26	seed_points_above_m: float = 2.0
	27	eps_m: float = 1.5
	28	min_samples: int = 3
	29	hull_margin_m: float = 0.5
	30	min_points: int = 60
	31	bridge_max_on_road_fraction: float = 0.6
	32	dedup_radius_m: float = 2.0
	33	min_confidence: float = -1.0
	34	model_path: str = ""
	35	hedge_split_enabled: bool = False
	36
	37
	38	class TreeInstanceConfig(config_loader.ConfigModel):
	39	"""Tree instance splitting: how one blob is cut into instances."""
	40
	41	enabled: bool = False
	42	local_ground_footprint_m: float = 15.0
	43	local_ground_cell_m: float = 2.0
	44	local_ground_percentile: float = 5.0
	45	local_ground_window_m: float = 6.0
	46	crown_base_bin_m: float = 0.25
	47	crown_base_density_frac: float = 0.35
	48	crown_base_run_bins: int = 3
	49	crown_base_min_m: float = 1.2
	50	stem_band_low_m: float = 0.5
	51	stem_band_cap_m: float = 4.0
	52	stem_band_min_thickness_m: float = 0.7
	53	stem_eps_m: float = 0.35
	54	stem_min_samples: int = 20
	55	stem_max_diameter_m: float = 1.2
	56	stem_min_vertical_reach: float = 0.5
	57	stem_min_verticality: float = 0.6
	58	stem_min_score: float = 0.45
	59	stem_exg_bonus: float = 0.1
	60	stem_merge_dist_m: float = 1.2
	61	stem_uncertain_dist_m: float = 2.0
	62	apex_fallback_enabled: bool = True
	63	apex_cell_m: float = 0.5
	64	apex_smooth_sigma_m: float = 0.7
	65	apex_min_separation_m: float = 2.5
	66	apex_min_prominence_m: float = 0.8
	67	apex_min_height_m: float = 2.0
	68	apex_trigger_span_m: float = 8.0
	69	apex_seed_radius_m: float = 0.6
	70	apex_confidence_scale: float = 0.6
	71	min_points_per_instance: int = 1200
	72	seedless_single_max_footprint_m: float = 10.0
	73	seedless_single_min_height_m: float = 1.5
	74	seedless_single_max_height_m: float = 25.0
	75	seedless_single_confidence: float = 0.35
	76	seedless_min_p95_h_m: float = 2.0
	77	seedless_max_aspect: float = 2.5
	78	seedless_min_points: int = 800
	79	float_fragment_min_h_m: float = 3.0
	80	float_fragment_p25_h_m: float = 4.0
	81	min_tree_footprint_m: float = 1.5
	82	max_tree_footprint_m: float = 60.0
	83	megacluster_points: int = 1000000
	84	planar_min_footprint_m: float = 12.0
	85	planar_cell_m: float = 1.0
	86	planar_max_spread_m: float = 0.3
	87	planar_fraction_min: float = 0.55
	88	hedge_max_ground_gap_m: float = 2.0
	89	hedge_max_height_m: float = 7.5
	90	hedge_min_length_m: float = 8.0
	91	hedge_min_area_m2: float = 20.0
	92	hedge_min_continuity: float = 0.75
	93	hedge_continuity_bin_m: float = 1.0
	94	hedge_max_top_relief_m: float = 1.5
	95	hedge_max_seed_per_10m: float = 1.0
	96	hedge_stem_score_min: float = 0.6
	97	assign_voxel_m: float = 0.3
	98	assign_max_gap_m: float = 1.25
	99	assign_max_graph_dist_m: float = 30.0
	100	max_claim_radius_m: float = 9.0
	101	low_evidence_margin: float = 0.05
	102	low_evidence_abstain: bool = False
	103	min_cluster_points: int = 150
	104	single_tree_footprint_m: float = 8.0
	105	partial_abstain_fraction: float = 0.2
	106	min_instance_points: int = 120
	107	min_instance_fraction: float = 0.01
	108	instance_max_linearity: float = 0.92
	109	instance_min_minor_m: float = 1.0
	110	instance_min_vertical_m: float = 1.5
	111	instance_min_thickness_share: float = 0.02
	112	confidence_seed_weight: float = 0.6
	113	confidence_size_ref_points: float = 2000.0
	114	confidence_max: float = 0.95
	115	confidence_fallback_max: float = 0.9
	116
	117
	118	class ChromaVegetationConfig(config_loader.ConfigModel):
	119	"""ExG chromaticity vegetation veto."""
	120
	121	enabled: bool = False
	122	exg_min: float = 0.155
	123	exg_iqr_min: float = 0.21
	124	max_hi_intensity_fraction: float = 0.08
	125	min_change_of_curvature: float = 0.2
	126	min_plate_thickness_m: float = 0.175
	127
	128
	129	class TcsGroundConfig(config_loader.ConfigModel):
	130	"""Tablecloth (TCS) ground pre-filter."""
	131
	132	cache_dir: str = ""
	133	cell_m: float = 0.2
	134	elev_scalar: float = 0.0
	135	enabled: bool = False
	136	max_elev_diff_m: float = 0.15
	137	mechanism: str = "smrf_numpy"
	138	pit_fill_enabled: bool = True
	139	slope_threshold: float = 0.3
	140	smrf_max_window_m: float = 6.0
	141
	142
	143	class ConicGateConfig(config_loader.ConfigModel):
	144	"""Conic-shape gate for cone/tree separation."""
	145
	146	apex_deg_max: float = 35.0
	147	apex_deg_min: float = 5.0
	148	change_of_curvature_min: float = 0.06
	149	enabled: bool = False
	150	h_max_min_m: float = 2.5
	151	h_over_width_max: float = 12.0
	152	h_over_width_min: float = 1.5
	153	max_hi_intensity_fraction: float = 0.2
	154	max_on_road_fraction: float = 0.6
	155	min_crown_area_m2: float = 0.3
	156	min_decile_fill_fraction: float = 0.8
	157	omnivariance_min: float = 0.1
	158	taper_slope_max: float = -0.4
	159	taper_slope_robust_max: float = -0.3
	160	texture_cue_enabled: bool = True
	161
	162
	163	class ConiferRuleConfig(config_loader.ConfigModel):
	164	"""Conifer acceptance rule."""
	165
	166	enabled: bool = False
	167	h_max_min_m: float = 2.0
	168	h_over_width_max: float = 15.0
	169	h_over_width_min: float = 2.0
	170	max_apex_ratio: float = 0.75
	171	max_crown_base_frac: float = 0.55
	172	max_crown_taper: float = -0.1
	173	max_hi_intensity_fraction: float = 0.2
	174	max_on_road_fraction: float = 0.6
	175	max_stem_ratio: float = 2.2
	176	max_volumetric_density: float = 380.0
	177	min_change_of_curvature: float = 0.04
	178	min_crown_area_m2: float = 0.2
	179	min_decile_fill_fraction: float = 0.8
	180	min_volumetric_density: float = 140.0
0

Importance #7: src/iolabs_point_cloud_detection_verticalsigns/_config_conic.py @@ -1,17 +1,17 @@

1	"""The colour-free conic gate and the conifer rule that rides on it.	1	"""The colour-free conic gate and the conifer rule that rides on it.
2		2
3	One slice of the flat 372-field ``DetectorConfig``, moved out of	3	One slice of the flat ``DetectorConfig``, moved out of
4	``config.py`` verbatim. ``config.py`` recombines the slices and	4	``config.py`` verbatim. ``config.py`` recombines the slices and
5	re-exports both names defined here.	5	re-exports both names defined here.
6	"""	6	"""
7		7
8	from dataclasses import dataclass
9	from typing import Any	8	from typing import Any
10		9
		10	from iolabs.common import config_loader
11		11
12	@dataclass(frozen=True)	12
13	class ConicFields:	13	class ConicFields(config_loader.ConfigModel):
14	"""The colour-free conic gate and the conifer rule that rides on it.	14	"""The colour-free conic gate and the conifer rule that rides on it.
15		15
16	Metres unless stated otherwise.	16	Metres unless stated otherwise.
17	"""	17	"""

Importance #8: src/iolabs_point_cloud_detection_verticalsigns/_config_corridor.py @@ -1,19 +1,19 @@

1	"""Road corridor rasterization and on-carriageway rejection.	1	"""Road corridor rasterization and on-carriageway rejection.
2		2
3	Also plate planarity, the bright-panel class and the free-space ring.	3	Also plate planarity, the bright-panel class and the free-space ring.
4		4
5	One slice of the flat 372-field ``DetectorConfig``, moved out of	5	One slice of the flat ``DetectorConfig``, moved out of
6	``config.py`` verbatim. ``config.py`` recombines the slices and	6	``config.py`` verbatim. ``config.py`` recombines the slices and
7	re-exports both names defined here.	7	re-exports both names defined here.
8	"""	8	"""
9		9
10	from dataclasses import dataclass
11	from typing import Any	10	from typing import Any
12		11
		12	from iolabs.common import config_loader
13		13
14	@dataclass(frozen=True)	14
15	class CorridorFields:	15	class CorridorFields(config_loader.ConfigModel):
16	"""Road corridor rasterization and on-carriageway rejection.	16	"""Road corridor rasterization and on-carriageway rejection.
17		17
18	Also plate planarity, the bright-panel class and the free-space ring.	18	Also plate planarity, the bright-panel class and the free-space ring.
19		19

Importance #9: src/iolabs_point_cloud_detection_verticalsigns/_config_devices.py @@ -1,19 +1,19 @@

1	"""Per-device thresholds for delineators, sign posts and gantries.	1	"""Per-device thresholds for delineators, sign posts and gantries.
2		2
3	Also isolated-floating-pole rejection and duplicate suppression.	3	Also isolated-floating-pole rejection and duplicate suppression.
4		4
5	One slice of the flat 372-field ``DetectorConfig``, moved out of	5	One slice of the flat ``DetectorConfig``, moved out of
6	``config.py`` verbatim. ``config.py`` recombines the slices and	6	``config.py`` verbatim. ``config.py`` recombines the slices and
7	re-exports both names defined here.	7	re-exports both names defined here.
8	"""	8	"""
9		9
10	from dataclasses import dataclass
11	from typing import Any	10	from typing import Any
12		11
		12	from iolabs.common import config_loader
13		13
14	@dataclass(frozen=True)	14
15	class DeviceFields:	15	class DeviceFields(config_loader.ConfigModel):
16	"""Per-device thresholds for delineators, sign posts and gantries.	16	"""Per-device thresholds for delineators, sign posts and gantries.
17		17
18	Also isolated-floating-pole rejection and duplicate suppression.	18	Also isolated-floating-pole rejection and duplicate suppression.
19		19

Importance #10: src/iolabs_point_cloud_detection_verticalsigns/_config_evidence.py @@ -3,19 +3,19 @@

3	Covers the verticality sentinel, tier-2 robust extent statistics,	3	Covers the verticality sentinel, tier-2 robust extent statistics,
4	retroreflectivity references, the single-record transient and	4	retroreflectivity references, the single-record transient and
5	vegetation-texture vetoes, the delineator lattice and tree emission.	5	vegetation-texture vetoes, the delineator lattice and tree emission.
6		6
7	One slice of the flat 372-field ``DetectorConfig``, moved out of	7	One slice of the flat ``DetectorConfig``, moved out of
8	``config.py`` verbatim. ``config.py`` recombines the slices and	8	``config.py`` verbatim. ``config.py`` recombines the slices and
9	re-exports both names defined here.	9	re-exports both names defined here.
10	"""	10	"""
11		11
12	from dataclasses import dataclass
13	from typing import Any	12	from typing import Any
14		13
		14	from iolabs.common import config_loader
15		15
16	@dataclass(frozen=True)	16
17	class EvidenceFields:	17	class EvidenceFields(config_loader.ConfigModel):
18	"""Evidence-level thresholds: sentinels, vetoes and reference percentiles.	18	"""Evidence-level thresholds: sentinels, vetoes and reference percentiles.
19		19
20	Covers the verticality sentinel, tier-2 robust extent statistics,	20	Covers the verticality sentinel, tier-2 robust extent statistics,
21	retroreflectivity references, the single-record transient and	21	retroreflectivity references, the single-record transient and

Importance #11: src/iolabs_point_cloud_detection_verticalsigns/_config_grid.py @@ -1,19 +1,19 @@

1	"""Ground, occupancy grid, candidate band and clustering thresholds.	1	"""Ground, occupancy grid, candidate band and clustering thresholds.
2		2
3	Also the first classification gates and vehicle rejection.	3	Also the first classification gates and vehicle rejection.
4		4
5	One slice of the flat 372-field ``DetectorConfig``, moved out of	5	One slice of the flat ``DetectorConfig``, moved out of
6	``config.py`` verbatim. ``config.py`` recombines the slices and	6	``config.py`` verbatim. ``config.py`` recombines the slices and
7	re-exports both names defined here.	7	re-exports both names defined here.
8	"""	8	"""
9		9
10	from dataclasses import dataclass
11	from typing import Any	10	from typing import Any
12		11
		12	from iolabs.common import config_loader
13		13
14	@dataclass(frozen=True)	14
15	class GridFields:	15	class GridFields(config_loader.ConfigModel):
16	"""Ground, occupancy grid, candidate band and clustering thresholds.	16	"""Ground, occupancy grid, candidate band and clustering thresholds.
17		17
18	Also the first classification gates and vehicle rejection.	18	Also the first classification gates and vehicle rejection.
19		19

Importance #12: src/iolabs_point_cloud_detection_verticalsigns/_config_perspective.py @@ -1,17 +1,17 @@

1	"""Perspective-projection QC overlay cameras and coverage tolerances.	1	"""Perspective-projection QC overlay cameras and coverage tolerances.
2		2
3	One slice of the flat 372-field ``DetectorConfig``, moved out of	3	One slice of the flat ``DetectorConfig``, moved out of
4	``config.py`` verbatim. ``config.py`` recombines the slices and	4	``config.py`` verbatim. ``config.py`` recombines the slices and
5	re-exports both names defined here.	5	re-exports both names defined here.
6	"""	6	"""
7		7
8	from dataclasses import dataclass
9	from typing import Any	8	from typing import Any
10		9
		10	from iolabs.common import config_loader
11		11
12	@dataclass(frozen=True)	12
13	class PerspectiveFields:	13	class PerspectiveFields(config_loader.ConfigModel):
14	"""Perspective-projection QC overlay cameras and coverage tolerances.	14	"""Perspective-projection QC overlay cameras and coverage tolerances.
15		15
16	Metres unless stated otherwise.	16	Metres unless stated otherwise.
17	"""	17	"""

Importance #13: src/iolabs_point_cloud_detection_verticalsigns/_config_roadcontext.py @@ -1,19 +1,19 @@

1	"""Road-context gate, driven-lane band and repetitive-row rejection.	1	"""Road-context gate, driven-lane band and repetitive-row rejection.
2		2
3	Also field-stake rows and embedded-marker extraction.	3	Also field-stake rows and embedded-marker extraction.
4		4
5	One slice of the flat 378-field ``DetectorConfig``, moved out of	5	One slice of the flat ``DetectorConfig``, moved out of
6	``config.py`` verbatim. ``config.py`` recombines the slices and	6	``config.py`` verbatim. ``config.py`` recombines the slices and
7	re-exports both names defined here.	7	re-exports both names defined here.
8	"""	8	"""
9		9
10	from dataclasses import dataclass
11	from typing import Any	10	from typing import Any
12		11
		12	from iolabs.common import config_loader
13		13
14	@dataclass(frozen=True)	14
15	class RoadContextFields:	15	class RoadContextFields(config_loader.ConfigModel):
16	"""Road-context gate, driven-lane band and repetitive-row rejection.	16	"""Road-context gate, driven-lane band and repetitive-row rejection.
17		17
18	Also field-stake rows and embedded-marker extraction.	18	Also field-stake rows and embedded-marker extraction.
19		19

Importance #14: src/iolabs_point_cloud_detection_verticalsigns/_config_stages.py @@ -2,19 +2,19 @@

2		2
3	The rail-relative half-post pass, the reject-rescue second look and	3	The rail-relative half-post pass, the reject-rescue second look and
4	the ML verifier.	4	the ML verifier.
5		5
6	One slice of the flat 372-field ``DetectorConfig``, moved out of	6	One slice of the flat ``DetectorConfig``, moved out of
7	``config.py`` verbatim. ``config.py`` recombines the slices and	7	``config.py`` verbatim. ``config.py`` recombines the slices and
8	re-exports both names defined here.	8	re-exports both names defined here.
9	"""	9	"""
10		10
11	from dataclasses import dataclass
12	from typing import Any	11	from typing import Any
13		12
		13	from iolabs.common import config_loader
14		14
15	@dataclass(frozen=True)	15
16	class StageFields:	16	class StageFields(config_loader.ConfigModel):
17	"""Opt-in post-classification stages.	17	"""Opt-in post-classification stages.
18		18
19	The rail-relative half-post pass, the reject-rescue second look and	19	The rail-relative half-post pass, the reject-rescue second look and
20	the ML verifier.	20	the ML verifier.

Importance #15: src/iolabs_point_cloud_detection_verticalsigns/_config_treedetect.py @@ -1,17 +1,17 @@

1	"""Experimental tree detection and TCS ground filtering of the DEM input.	1	"""Experimental tree detection and TCS ground filtering of the DEM input.
2		2
3	One slice of the flat 372-field ``DetectorConfig``, moved out of	3	One slice of the flat ``DetectorConfig``, moved out of
4	``config.py`` verbatim. ``config.py`` recombines the slices and	4	``config.py`` verbatim. ``config.py`` recombines the slices and
5	re-exports both names defined here.	5	re-exports both names defined here.
6	"""	6	"""
7		7
8	from dataclasses import dataclass
9	from typing import Any	8	from typing import Any
10		9
		10	from iolabs.common import config_loader
11		11
12	@dataclass(frozen=True)	12
13	class TreeDetectionFields:	13	class TreeDetectionFields(config_loader.ConfigModel):
14	"""Experimental tree detection and TCS ground filtering of the DEM input.	14	"""Experimental tree detection and TCS ground filtering of the DEM input.
15		15
16	Metres unless stated otherwise.	16	Metres unless stated otherwise.
17	"""	17	"""

Importance #16: src/iolabs_point_cloud_detection_verticalsigns/_config_treeinstance.py @@ -1,16 +1,16 @@

1	"""Per-point tree instance splitting of merged canopy blobs.	1	"""Per-point tree instance splitting of merged canopy blobs.
2		2
3	One slice of the flat 372-field ``DetectorConfig``. ``config.py``	3	One slice of the flat ``DetectorConfig``. ``config.py``
4	recombines the slices and re-exports both names defined here.	4	recombines the slices and re-exports both names defined here.
5	"""	5	"""
6		6
7	from dataclasses import dataclass
8	from typing import Any	7	from typing import Any
9		8
		9	from iolabs.common import config_loader
10		10
11	@dataclass(frozen=True)	11
12	class TreeInstanceFields:	12	class TreeInstanceFields(config_loader.ConfigModel):
13	"""Stem-seeded instance splitting of a single ``type: "tree"`` detection.	13	"""Stem-seeded instance splitting of a single ``type: "tree"`` detection.
14		14
15	Metres unless stated otherwise.	15	Metres unless stated otherwise.
16	"""	16	"""

Importance #17: src/iolabs_point_cloud_detection_verticalsigns/_config_vegetation.py @@ -1,19 +1,19 @@

1	"""Tree rejection, chromaticity vegetation reject and radius fitting.	1	"""Tree rejection, chromaticity vegetation reject and radius fitting.
2		2
3	Also core compactness and the crown-circle overlay knobs.	3	Also core compactness and the crown-circle overlay knobs.
4		4
5	One slice of the flat 372-field ``DetectorConfig``, moved out of	5	One slice of the flat ``DetectorConfig``, moved out of
6	``config.py`` verbatim. ``config.py`` recombines the slices and	6	``config.py`` verbatim. ``config.py`` recombines the slices and
7	re-exports both names defined here.	7	re-exports both names defined here.
8	"""	8	"""
9		9
10	from dataclasses import dataclass
11	from typing import Any	10	from typing import Any
12		11
		12	from iolabs.common import config_loader
13		13
14	@dataclass(frozen=True)	14
15	class VegetationFields:	15	class VegetationFields(config_loader.ConfigModel):
16	"""Tree rejection, chromaticity vegetation reject and radius fitting.	16	"""Tree rejection, chromaticity vegetation reject and radius fitting.
17		17
18	Also core compactness and the crown-circle overlay knobs.	18	Also core compactness and the crown-circle overlay knobs.
19		19

Importance #18: src/iolabs_point_cloud_detection_verticalsigns/config.py @@ -1,13 +1,16 @@

 """Detector configuration.
-The 378-field :class:`DetectorConfig` and its ``from_mapping`` flattener are
+The 379-field :class:`DetectorConfig` and its ``from_mapping`` flattener are
 split by section across the ``_config_<section>`` modules; this module
 recombines them and re-exports every piece, so ``from .config import X``
 keeps working for every name that used to live here.
+``DetectorConfig`` is the FLAT view the detector modules read
+(``config.ground_cell_m``); the NESTED document it is built from is validated
+by the :class:`VerticalSignsConfig` model tree in ``_config_model``.
 """
-from dataclasses import dataclass
 from pathlib import Path
 from typing import Any
 from ._config import load_verticalsigns_config

Importance #19: src/iolabs_point_cloud_detection_verticalsigns/config.py @@ -49,16 +52,15 @@

49	"perspective_kwargs",	52	"perspective_kwargs",
50	]	53	]
51		54
52		55
53	@dataclass(frozen=True)
54	class DetectorConfig( # noqa: D101 - docstring below, after the base list	56	class DetectorConfig( # noqa: D101 - docstring below, after the base list
55	# The bases are listed in REVERSE section order ON PURPOSE: dataclasses	57	# The bases are listed in REVERSE section order ON PURPOSE: both
56	# collects fields by walking the MRO backwards, so this ordering	58	# dataclasses and pydantic collect fields by walking the MRO backwards, so
57	# reproduces the original single-class field order exactly (ground first,	59	# this ordering reproduces the original single-class field order exactly
58	# then perspective, then the slices added since). Reordering these lines	60	# (ground first, then perspective, then the slices added since).
59	# reorders the fields, so a NEW slice goes at the TOP of this list to have	61	# Reordering these lines reorders the fields, so a NEW slice goes at the
60	# its fields appended at the end.	62	# TOP of this list to have its fields appended at the end.
61	TreeInstanceFields,	63	TreeInstanceFields,
62	PerspectiveFields,	64	PerspectiveFields,
63	ConicFields,	65	ConicFields,
64	TreeDetectionFields,	66	TreeDetectionFields,

Importance #20: src/iolabs_point_cloud_detection_verticalsigns/config.py @@ -75,9 +77,9 @@

75	@classmethod	77	@classmethod
76	def from_mapping(cls, config: dict[str, Any]) -> "DetectorConfig":	78	def from_mapping(cls, config: dict[str, Any]) -> "DetectorConfig":
77	"""Builds a DetectorConfig by flattening the nested config sections.	79	"""Builds a DetectorConfig by flattening the nested config sections.
78		80
79	Only keys present in a section override the corresponding dataclass	81	Only keys present in a section override the corresponding model
80	default, so a partial (or default) config reproduces the built-in	82	default, so a partial (or default) config reproduces the built-in
81	thresholds exactly.	83	thresholds exactly.
82		84
83	Args:	85	Args:

Importance #21: src/iolabs_point_cloud_detection_verticalsigns/config.py @@ -101,8 +103,30 @@

101	**perspective_kwargs(config, defaults),	103	**perspective_kwargs(config, defaults),
102	**tree_instance_kwargs(config, defaults),	104	**tree_instance_kwargs(config, defaults),
103	)	105	)
104		106
		107	def with_overrides(self, **overrides: Any) -> "DetectorConfig":
		108	"""Return a copy of this config with overrides applied.
		109
		110	``model_copy(update=...)`` skips validation, so a misspelled name would
		111	be attached as a new attribute and the intended threshold would keep
		112	its default. The names are therefore checked here, reproducing the
		113	``TypeError`` that ``dataclasses.replace`` used to raise.
		114
		115	Args:
		116	overrides: Field name to new value, e.g. ``cluster_eps_m=0.9``.
		117
		118	Returns:
		119	A new frozen config carrying overrides.
		120
		121	Raises:
		122	ValueError: An override names a field this config does not declare.
		123	"""
		124	unknown = sorted(set(overrides) - set(type(self).model_fields))
		125	if unknown:
		126	raise ValueError(f"Unknown DetectorConfig field(s): {', '.join(unknown)}")
		127	return self.model_copy(update=overrides)
		128
105	@classmethod	129	@classmethod
106	def load(cls, config_path: str \| Path \| None = None) -> "DetectorConfig":	130	def load(cls, config_path: str \| Path \| None = None) -> "DetectorConfig":
107	"""Load config from the packaged defaults merged with an optional user JSON."""	131	"""Load config from the packaged defaults merged with an optional user JSON."""
108	return cls.from_mapping(load_verticalsigns_config(config_path))	132	return cls.from_mapping(load_verticalsigns_config(config_path))

Importance #22: dev/out_eval/pass8/p8_edgeline_diag.py @@ -11,9 +11,8 @@

11		11
12	from __future__ import annotations	12	from __future__ import annotations
13		13
14	import argparse	14	import argparse
15	import dataclasses
16	from pathlib import Path	15	from pathlib import Path
17		16
18	import numpy as np	17	import numpy as np
19		18

Importance #23: dev/out_eval/pass8/p8_edgeline_diag.py @@ -217,10 +216,10 @@

217	header = "variant".ljust(20) + "".join(s.rjust(9) for s in segments)	216	header = "variant".ljust(20) + "".join(s.rjust(9) for s in segments)
218	print(header)	217	print(header)
219	for name, overrides in variants.items():	218	for name, overrides in variants.items():
220	try:	219	try:
221	cfg = dataclasses.replace(config, **overrides)	220	cfg = config.with_overrides(**overrides)
222	except TypeError as exc:	221	except ValueError as exc:
223	print(f"{name.ljust(20)} SKIP ({exc})")	222	print(f"{name.ljust(20)} SKIP ({exc})")
224	continue	223	continue
225	cells = []	224	cells = []
226	for segment in segments:	225	for segment in segments:

Importance #24: dev/out_eval/pass9/p10/p10_yield.py @@ -80,40 +80,36 @@

 def fix_delineator_h_ceiling(h):
     def _f(f, cfg):
-        return f, dataclasses.replace(cfg, delineator_h_max_m=h)
+        return f, cfg.with_overrides(delineator_h_max_m=h)
     return _f
 def fix_min_points(n):
     def _f(f, cfg):
-        return f, dataclasses.replace(cfg, delineator_min_points=n)
+        return f, cfg.with_overrides(delineator_min_points=n)
     return _f
 def fix_relaxed_footprint(m):
     def _f(f, cfg):
-        return f, dataclasses.replace(cfg, delineator_relaxed_footprint_m=m)
+        return f, cfg.with_overrides(delineator_relaxed_footprint_m=m)
     return _f
 def fix_combo_sentinel_hceiling(f, cfg):
-    cfg2 = dataclasses.replace(cfg, delineator_h_max_m=1.8)
+    cfg2 = cfg.with_overrides(delineator_h_max_m=1.8)
     if f.len_minor > SENTINEL and f.verticality == 0.0:
         return dataclasses.replace(f, verticality=1.0), cfg2
     return f, cfg2
 def fix_all(f, cfg):
-    cfg2 = dataclasses.replace(
+    cfg2 = cfg.with_overrides(delineator_h_max_m=1.8, delineator_min_points=150)
-        cfg,
-        delineator_h_max_m=1.8,
-        delineator_min_points=150,
-    )
     if f.len_minor > SENTINEL and f.verticality == 0.0:
         return dataclasses.replace(f, verticality=1.0), cfg2
     return f, cfg2

Importance #25: src/iolabs_point_cloud_detection_verticalsigns/ml.py @@ -680,10 +680,8 @@

     forest stand merges into one large low-curvature canopy blob that the
     fine-DBSCAN sign-path trees never produce, so the classifier must see it.
     Lazy imports avoid a heavy import chain at module load.
     """
-    from dataclasses import replace
     import numpy as np
     from . import detect as _detect
     from .corridor import build_road_corridor

Importance #26: src/iolabs_point_cloud_detection_verticalsigns/ml.py @@ -715,10 +713,10 @@

715	)	713	)
716	mask = select_tree_seed_cells(counts, vspan, hmax, config)	714	mask = select_tree_seed_cells(counts, vspan, hmax, config)
717	if not np.any(mask):	715	if not np.any(mask):
718	return []	716	return []
719	tcfg = replace(	717	tcfg = config.with_overrides(
720	config, cluster_eps_m=config.tree_eps_m,	718	cluster_eps_m=config.tree_eps_m,
721	cluster_min_samples=config.tree_min_samples,	719	cluster_min_samples=config.tree_min_samples,
722	cluster_hull_margin_m=config.tree_hull_margin_m,	720	cluster_hull_margin_m=config.tree_hull_margin_m,
723	)	721	)
724	clusters = cluster_candidates(	722	clusters = cluster_candidates(

Importance #27: src/iolabs_point_cloud_detection_verticalsigns/trees.py @@ -19,10 +19,8 @@

19	"""	19	"""
20		20
21	from __future__ import annotations	21	from __future__ import annotations
22		22
23	from dataclasses import replace
24
25	import numpy as np	23	import numpy as np
26	from iolabs.logstash import get_props_logger	24	from iolabs.logstash import get_props_logger
27		25
28	from ._log_props import LOG_PROPS	26	from ._log_props import LOG_PROPS

Importance #28: src/iolabs_point_cloud_detection_verticalsigns/trees.py @@ -117,10 +115,9 @@

     if not np.any(seed_mask):
         return []
     # Coarser DBSCAN via a shallow config clone (own eps / min_samples / margin).
-    tree_cfg = replace(
+    tree_cfg = config.with_overrides(
-        config,
         cluster_eps_m=config.tree_eps_m,
         cluster_min_samples=config.tree_min_samples,
         cluster_hull_margin_m=config.tree_hull_margin_m,
     )

Importance #29: tests/conftest.py @@ -0,0 +1,29 @@

	1	"""Shared fixtures for the vertical-sign detector tests."""
	2
	3	from collections.abc import Callable
	4	from typing import Any
	5
	6	import pytest
	7	from iolabs.common import config_loader
	8
	9
	10	def _section_values(model: type[config_loader.ConfigModel]) -> dict[str, Any]:
	11	"""Return one valid non-default value per field of model."""
	12	values: dict[str, Any] = {}
	13	for name, field in model.model_fields.items():
	14	annotation = field.annotation
	15	if annotation is bool:
	16	values[name] = not field.default
	17	elif annotation is int:
	18	values[name] = int(field.default) + 1
	19	elif annotation is str:
	20	values[name] = f"{field.default}_x"
	21	else:
	22	values[name] = 0.5
	23	return values
	24
	25
	26	@pytest.fixture
	27	def section_values() -> Callable[[type[config_loader.ConfigModel]], dict[str, Any]]:
	28	"""Return a builder for a full override of one config section."""
	29	return _section_values
0

Importance #30: tests/test_chroma_vegetation.py @@ -8,15 +8,14 @@

 """
 from __future__ import annotations
-import dataclasses
 import json
 import numpy as np
 import pytest
-from iolabs_point_cloud_detection_verticalsigns import _config
+from iolabs_point_cloud_detection_verticalsigns import _config, _model_tree
 from iolabs_point_cloud_detection_verticalsigns.classify import (
     CHROMA_VETOABLE_TYPES,
     apply_tree_emission,
     classify_cluster,

Importance #31: tests/test_chroma_vegetation.py @@ -77,9 +76,9 @@

77	return ClusterFeatures(**base)	76	return ClusterFeatures(**base)
78		77
79		78
80	def _enabled(**overrides) -> DetectorConfig:	79	def _enabled(**overrides) -> DetectorConfig:
81	return dataclasses.replace(DetectorConfig(), chroma_veg_enabled=True, **overrides)	80	return DetectorConfig().with_overrides(chroma_veg_enabled=True, **overrides)
82		81
83		82
84	# --- the rule does its job -------------------------------------------------	83	# --- the rule does its job -------------------------------------------------
85		84

Importance #32: tests/test_chroma_vegetation.py @@ -272,9 +271,9 @@

272	# --- wiring ----------------------------------------------------------------	271	# --- wiring ----------------------------------------------------------------
273		272
274		273
275	def test_emit_trees_promotes_the_reason() -> None:	274	def test_emit_trees_promotes_the_reason() -> None:
276	config = dataclasses.replace(DetectorConfig(), emit_trees=True)	275	config = DetectorConfig().with_overrides(emit_trees=True)
277	assert apply_tree_emission(None, "chroma_vegetation", config) == (	276	assert apply_tree_emission(None, "chroma_vegetation", config) == (
278	"tree",	277	"tree",
279	"chroma_vegetation",	278	"chroma_vegetation",
280	)	279	)

Importance #33: tests/test_chroma_vegetation.py @@ -290,23 +289,23 @@

Importance #34: tests/test_config_split.py @@ -6,16 +6,18 @@

 things must stay true for that split to be invisible to callers:
 * every field is still reachable from the nested config document,
 * the slices partition the fields (no field lost, none declared twice),
-* an absent key still falls back to the dataclass default.
+* an absent key still falls back to the model default.
 """
-import dataclasses
 import json
 import re
 from pathlib import Path
+from iolabs.common import config_loader
+from iolabs_point_cloud_detection_verticalsigns import _config_model
 from iolabs_point_cloud_detection_verticalsigns._config import load_default_config
 from iolabs_point_cloud_detection_verticalsigns.config import DetectorConfig
 CONFIG_PY = (

Importance #35: tests/test_config_split.py @@ -45,9 +47,9 @@

45	"""	47	"""
46	section_locals: dict[str, str] = {}	48	section_locals: dict[str, str] = {}
47	document: dict[str, dict] = {}	49	document: dict[str, dict] = {}
48	expected: dict[str, object] = {}	50	expected: dict[str, object] = {}
49	fields = {f.name: f for f in dataclasses.fields(DetectorConfig)}	51	fields = DetectorConfig.model_fields
50		52
51	for path in sorted(CONFIG_PY.parent.glob("_config_*.py")):	53	for path in sorted(CONFIG_PY.parent.glob("_config_*.py")):
52	text = path.read_text()	54	text = path.read_text()
53	section_locals.update(	55	section_locals.update(

Importance #36: tests/test_config_split.py @@ -69,9 +71,9 @@

69	wrong = {n: (getattr(built, n), v) for n, v in expected.items() if getattr(built, n) != v}	71	wrong = {n: (getattr(built, n), v) for n, v in expected.items() if getattr(built, n) != v}
70	assert not wrong	72	assert not wrong
71		73
72		74
73	def test_absent_sections_fall_back_to_the_dataclass_defaults() -> None:	75	def test_absent_sections_fall_back_to_the_model_defaults() -> None:
74	assert DetectorConfig.from_mapping({}) == DetectorConfig()	76	assert DetectorConfig.from_mapping({}) == DetectorConfig()
75		77
76		78
77	def test_a_partial_section_only_overrides_the_keys_it_carries() -> None:	79	def test_a_partial_section_only_overrides_the_keys_it_carries() -> None:

Importance #37: tests/test_config_split.py @@ -80,8 +82,46 @@

80	assert built.ground_percentile == DetectorConfig().ground_percentile	82	assert built.ground_percentile == DetectorConfig().ground_percentile
81	assert built.perspective_coverage_tol_m == DetectorConfig().perspective_coverage_tol_m	83	assert built.perspective_coverage_tol_m == DetectorConfig().perspective_coverage_tol_m
82		84
83		85
		86	def test_every_mapped_key_exists_in_the_nested_model() -> None:
		87	"""A flat field wired to a section key the model does not declare is dead.
		88
		89	``load_verticalsigns_config`` validates against the model, so such a key is
		90	rejected for a user config and can only ever hold its flat default.
		91	"""
		92	document, _ = _saturating_config()
		93	merged = config_loader.deep_merge_dicts(load_default_config(), document)
		94	assert _config_model.VerticalSignsConfig.model_validate(merged)
		95
		96
84	def test_the_packaged_defaults_round_trip() -> None:	97	def test_the_packaged_defaults_round_trip() -> None:
85	packaged = load_default_config()	98	packaged = load_default_config()
86	assert json.dumps(packaged) # it is a plain JSON document	99	assert json.dumps(packaged) # it is a plain JSON document
87	assert DetectorConfig.from_mapping(packaged) == DetectorConfig.load()	100	assert DetectorConfig.from_mapping(packaged) == DetectorConfig.load()
		101
		102
		103	def test_the_packaged_defaults_equal_the_flat_defaults() -> None:
		104	"""The nested model and the flat slices must not drift apart.
		105
		106	The nested :class:`VerticalSignsConfig` sections and the flat
		107	``DetectorConfig`` slices declare the same numbers twice, so a value
		108	changed on one side only is a silent config bug: ``DetectorConfig()`` (what
		109	tests and ad-hoc calls build) would disagree with ``DetectorConfig.load()``
		110	(what the detector runs).
		111	"""
		112	assert DetectorConfig.from_mapping(load_default_config()) == DetectorConfig()
		113
		114
		115	def test_with_overrides_rejects_a_misspelled_field() -> None:
		116	"""A typo must not become a new attribute while the threshold keeps its default.
		117
		118	``model_copy(update=...)`` skips validation, so this is the only thing
		119	standing between a misspelled override and a silently ignored threshold.
		120	"""
		121	assert DetectorConfig().with_overrides(cluster_eps_m=0.9).cluster_eps_m == 0.9
		122	try:
		123	DetectorConfig().with_overrides(cluster_eps=0.9)
		124	except ValueError as exc:
		125	assert "cluster_eps" in str(exc)
		126	else: # pragma: no cover - the failure the test exists to catch
		127	raise AssertionError("a misspelled field name was accepted")

Importance #38: tests/test_detect.py @@ -1,6 +1,5 @@

1	import csv	1	import csv
2	import dataclasses
3	import io	2	import io
4		3
5	import numpy as np	4	import numpy as np
6		5

Importance #39: tests/test_detect.py @@ -14,9 +13,9 @@

14	assign_record_coverage,	13	assign_record_coverage,
15	)	14	)
16	from iolabs_point_cloud_detection_verticalsigns.features import ClusterFeatures	15	from iolabs_point_cloud_detection_verticalsigns.features import ClusterFeatures
17		16
18	_CFG = dataclasses.replace(DetectorConfig(), tree_crown_h_min_m=2.5)	17	_CFG = DetectorConfig().with_overrides(tree_crown_h_min_m=2.5)
19		18
20		19
21	def _feat(**overrides) -> ClusterFeatures:	20	def _feat(**overrides) -> ClusterFeatures:
22	base = dict(	21	base = dict(

Importance #40: tests/test_edgeline.py @@ -8,10 +8,8 @@

8	"""	8	"""
9		9
10	from __future__ import annotations	10	from __future__ import annotations
11		11
12	import dataclasses
13
14	import numpy as np	12	import numpy as np
15		13
16	from iolabs_point_cloud_detection_verticalsigns.classify import (	14	from iolabs_point_cloud_detection_verticalsigns.classify import (
17	apply_edge_line_gate,	15	apply_edge_line_gate,

Importance #41: tests/test_edgeline.py @@ -279,12 +277,10 @@

     axis = _axis()
     coverages = []
     for pct in (93.0, 95.0, 97.0):
         for fill in (0.3, 0.4, 0.5):
-            config = dataclasses.replace(
+            config = DetectorConfig().with_overrides(
-                DetectorConfig(),
+                edgeline_paint_intensity_percentile=pct, edgeline_min_line_along_fill=fill
-                edgeline_paint_intensity_percentile=pct,
-                edgeline_min_line_along_fill=fill,
             )
             coverages.append(build_edge_lines(xy, intensity, axis, config).coverage)
     assert min(coverages) >= 0.6, coverages

Importance #42: tests/test_tcs_ground.py @@ -5,9 +5,8 @@

5	"""	5	"""
6		6
7	from __future__ import annotations	7	from __future__ import annotations
8		8
9	import dataclasses
10	import os	9	import os
11	from pathlib import Path	10	from pathlib import Path
12		11
13	import numpy as np	12	import numpy as np

Importance #43: tests/test_tcs_ground.py @@ -94,9 +93,9 @@

94	return _write_record(tmp_path / name, points)	93	return _write_record(tmp_path / name, points)
95		94
96		95
97	def _config(**overrides: object) -> DetectorConfig:	96	def _config(**overrides: object) -> DetectorConfig:
98	return dataclasses.replace(DetectorConfig(), **overrides)	97	return DetectorConfig().with_overrides(**overrides)
99		98
100		99
101	def _dem(files: list[Path]) -> np.ndarray:	100	def _dem(files: list[Path]) -> np.ndarray:
102	model = build_ground_model(	101	model = build_ground_model(

Importance #44: tests/test_tree_instances.py @@ -12,15 +12,14 @@

 """
 from __future__ import annotations
-import dataclasses
 import json
 import numpy as np
 import pytest
-from iolabs_point_cloud_detection_verticalsigns import _config
+from iolabs_point_cloud_detection_verticalsigns import _config, _model_tree
 from iolabs_point_cloud_detection_verticalsigns.config import DetectorConfig
 from iolabs_point_cloud_detection_verticalsigns.tree_instances import (
     ABSTAIN_ASSIGNED,
     ABSTAIN_HEDGE,

Importance #45: tests/test_tree_instances.py @@ -363,9 +362,9 @@

363	_canopy_ball(rng, radius=1.5, centre_h=3.0, centre_xy=(0.0, 0.0)),	362	_canopy_ball(rng, radius=1.5, centre_h=3.0, centre_xy=(0.0, 0.0)),
364	_canopy_ball(rng, radius=1.5, centre_h=3.0, centre_xy=(18.0, 0.0)),	363	_canopy_ball(rng, radius=1.5, centre_h=3.0, centre_xy=(18.0, 0.0)),
365	]	364	]
366	)	365	)
367	config = dataclasses.replace(DetectorConfig(), ti_apex_fallback_enabled=False)	366	config = DetectorConfig().with_overrides(ti_apex_fallback_enabled=False)
368		367
369	result = split_tree_cluster(xyz, 0.0, None, config)	368	result = split_tree_cluster(xyz, 0.0, None, config)
370		369
371	assert result.seeds == []	370	assert result.seeds == []

Importance #46: tests/test_tree_instances.py @@ -464,10 +463,10 @@

464	)	463	)
465	# The apex fallback is switched off here on purpose: it would recover the	464	# The apex fallback is switched off here on purpose: it would recover the
466	# far trees from their crowns (that is exactly what it is for) and so hide	465	# far trees from their crowns (that is exactly what it is for) and so hide
467	# the failure this counterfactual exists to display.	466	# the failure this counterfactual exists to display.
468	config = dataclasses.replace(	467	config = DetectorConfig().with_overrides(
469	DetectorConfig(), ti_local_ground_footprint_m=1e6, ti_apex_fallback_enabled=False	468	ti_local_ground_footprint_m=1e6, ti_apex_fallback_enabled=False
470	)	469	)
471		470
472	result = split_tree_cluster(xyz, 0.0, None, config)	471	result = split_tree_cluster(xyz, 0.0, None, config)
473		472

Importance #47: tests/test_tree_instances.py @@ -492,9 +491,9 @@

492	# Stem-only: with the apex fallback on, the fragment is a crown of its own	491	# Stem-only: with the apex fallback on, the fragment is a crown of its own
493	# and legitimately becomes its own instance. What must never happen — and	492	# and legitimately becomes its own instance. What must never happen — and
494	# is what this test pins — is the fragment being ANNEXED by the tree next	493	# is what this test pins — is the fragment being ANNEXED by the tree next
495	# to it through the graph.	494	# to it through the graph.
496	config = dataclasses.replace(DetectorConfig(), ti_apex_fallback_enabled=False)	495	config = DetectorConfig().with_overrides(ti_apex_fallback_enabled=False)
497		496
498	result = split_tree_cluster(xyz, 0.0, None, config)	497	result = split_tree_cluster(xyz, 0.0, None, config)
499		498
500	assert len(result.seeds) == 1	499	assert len(result.seeds) == 1

Importance #48: tests/test_tree_instances.py @@ -510,10 +509,10 @@

510	which is right for some callers and wrong for most, so it is a switch.	509	which is right for some callers and wrong for most, so it is a switch.
511	"""	510	"""
512	rng = np.random.default_rng(47)	511	rng = np.random.default_rng(47)
513	xyz = np.vstack([_cone(rng, 0.0, 0.0), _cone(rng, 3.0, 0.0)])	512	xyz = np.vstack([_cone(rng, 0.0, 0.0), _cone(rng, 3.0, 0.0)])
514	strict = dataclasses.replace(	513	strict = DetectorConfig().with_overrides(
515	DetectorConfig(), ti_low_evidence_abstain=True, ti_low_evidence_margin=0.05	514	ti_low_evidence_abstain=True, ti_low_evidence_margin=0.05
516	)	515	)
517		516
518	lenient = split_tree_cluster(xyz, 0.0, None, DetectorConfig())	517	lenient = split_tree_cluster(xyz, 0.0, None, DetectorConfig())
519	result = split_tree_cluster(xyz, 0.0, None, strict)	518	result = split_tree_cluster(xyz, 0.0, None, strict)

Importance #49: tests/test_tree_instances.py @@ -748,9 +747,9 @@

748	[r * np.cos(ang), r * np.sin(ang), rng.uniform(0.0, 2.2, 800)]	747	[r * np.cos(ang), r * np.sin(ang), rng.uniform(0.0, 2.2, 800)]
749	)	748	)
750	xyz = np.vstack([band, trunk])	749	xyz = np.vstack([band, trunk])
751	# Stem-only, so the one seed is unambiguous and the cap is what is measured.	750	# Stem-only, so the one seed is unambiguous and the cap is what is measured.
752	config = dataclasses.replace(DetectorConfig(), ti_apex_fallback_enabled=False)	751	config = DetectorConfig().with_overrides(ti_apex_fallback_enabled=False)
753		752
754	result = split_tree_cluster(xyz, 0.0, None, config)	753	result = split_tree_cluster(xyz, 0.0, None, config)
755	far = xyz[:, 0] > 15.0	754	far = xyz[:, 0] > 15.0
756	near = xyz[:, 0] < 5.0	755	near = xyz[:, 0] < 5.0

Importance #50: tests/test_tree_instances.py @@ -764,10 +763,9 @@

     uncapped = split_tree_cluster(
         xyz,
         0.0,
         None,
-        dataclasses.replace(
+        config.with_overrides(
-            config,
             ti_max_claim_radius_m=40.0,
             ti_instance_max_linearity=1.01,
             ti_instance_min_thickness_share=0.0,
         ),

Importance #51: tests/test_tree_instances.py @@ -791,9 +789,9 @@

791		789
792	result = split_tree_cluster(xyz, 0.0, None, DetectorConfig())	790	result = split_tree_cluster(xyz, 0.0, None, DetectorConfig())
793	# The v2 semantics, reconstructed: the same cap applied to the PATH.	791	# The v2 semantics, reconstructed: the same cap applied to the PATH.
794	v2 = split_tree_cluster(	792	v2 = split_tree_cluster(
795	xyz, 0.0, None, dataclasses.replace(DetectorConfig(), ti_assign_max_graph_dist_m=9.0)	793	xyz, 0.0, None, DetectorConfig().with_overrides(ti_assign_max_graph_dist_m=9.0)
796	)	794	)
797		795
798	assert len(result.seeds) == 1	796	assert len(result.seeds) == 1
799	assert float(np.mean(result.labels[canopy] >= 0)) > 0.95	797	assert float(np.mean(result.labels[canopy] >= 0)) > 0.95

Importance #52: tests/test_tree_instances.py @@ -812,9 +810,9 @@

812	rng = np.random.default_rng(97)	810	rng = np.random.default_rng(97)
813	big = _cone(rng, 0.0, 0.0, n_crown=3000, n_trunk=400)	811	big = _cone(rng, 0.0, 0.0, n_crown=3000, n_trunk=400)
814	small = _cone(rng, 4.0, 0.0, top=4.0, crown_r=1.0, n_crown=300, n_trunk=120)	812	small = _cone(rng, 4.0, 0.0, top=4.0, crown_r=1.0, n_crown=300, n_trunk=120)
815	xyz = np.vstack([big, small])	813	xyz = np.vstack([big, small])
816	config = dataclasses.replace(DetectorConfig(), ti_min_instance_points=1000)	814	config = DetectorConfig().with_overrides(ti_min_instance_points=1000)
817		815
818	kept = split_tree_cluster(xyz, 0.0, None, DetectorConfig())	816	kept = split_tree_cluster(xyz, 0.0, None, DetectorConfig())
819	result = split_tree_cluster(xyz, 0.0, None, config)	817	result = split_tree_cluster(xyz, 0.0, None, config)
820		818

Importance #53: tests/test_tree_instances.py @@ -873,10 +871,9 @@

     kept = split_tree_cluster(
         xyz,
         0.0,
         None,
-        dataclasses.replace(
+        DetectorConfig().with_overrides(
-            DetectorConfig(),
             ti_instance_max_linearity=1.01,
             ti_instance_min_minor_m=0.0,
             ti_instance_min_vertical_m=0.0,
             ti_instance_min_thickness_share=0.0,

Importance #54: tests/test_tree_instances.py @@ -910,9 +907,9 @@

910	)	907	)
911		908
912	result = split_tree_cluster(xyz, 0.0, None, DetectorConfig())	909	result = split_tree_cluster(xyz, 0.0, None, DetectorConfig())
913	undamped = split_tree_cluster(	910	undamped = split_tree_cluster(
914	xyz, 0.0, None, dataclasses.replace(DetectorConfig(), ti_min_points_per_instance=1)	911	xyz, 0.0, None, DetectorConfig().with_overrides(ti_min_points_per_instance=1)
915	)	912	)
916		913
917	assert len(undamped.seeds) >= 3	914	assert len(undamped.seeds) >= 3
918	assert len(result.seeds) <= 1	915	assert len(result.seeds) <= 1

Importance #55: tests/test_tree_instances.py @@ -931,16 +928,16 @@

931	[rng.uniform(0.0, 7.0, n), rng.uniform(-0.3, 0.3, n), rng.uniform(0.0, 3.0, n)]	928	[rng.uniform(0.0, 7.0, n), rng.uniform(-0.3, 0.3, n), rng.uniform(0.0, 3.0, n)]
932	)	929	)
933	# Stem-only: the seedless verdict is what is being measured, and an apex	930	# Stem-only: the seedless verdict is what is being measured, and an apex
934	# seed would answer the question before the gate is reached.	931	# seed would answer the question before the gate is reached.
935	config = dataclasses.replace(DetectorConfig(), ti_apex_fallback_enabled=False)	932	config = DetectorConfig().with_overrides(ti_apex_fallback_enabled=False)
936		933
937	result = split_tree_cluster(xyz, 0.0, None, config)	934	result = split_tree_cluster(xyz, 0.0, None, config)
938	lenient = split_tree_cluster(	935	lenient = split_tree_cluster(
939	xyz,	936	xyz,
940	0.0,	937	0.0,
941	None,	938	None,
942	dataclasses.replace(config, ti_seedless_max_aspect=100.0, ti_seedless_min_points=1),	939	config.with_overrides(ti_seedless_max_aspect=100.0, ti_seedless_min_points=1),
943	)	940	)
944		941
945	assert result.seeds == []	942	assert result.seeds == []
946	assert result.split_quality == "uncertain"	943	assert result.split_quality == "uncertain"

Importance #56: tests/test_tree_instances.py @@ -974,9 +971,9 @@

974	xyz = _foliage_band(rng)	971	xyz = _foliage_band(rng)
975		972
976	result = split_tree_cluster(xyz, 0.0, None, DetectorConfig())	973	result = split_tree_cluster(xyz, 0.0, None, DetectorConfig())
977	blind = split_tree_cluster(	974	blind = split_tree_cluster(
978	xyz, 0.0, None, dataclasses.replace(DetectorConfig(), ti_hedge_max_height_m=4.5)	975	xyz, 0.0, None, DetectorConfig().with_overrides(ti_hedge_max_height_m=4.5)
979	)	976	)
980		977
981	assert result.is_hedge and result.split_quality == "hedge"	978	assert result.is_hedge and result.split_quality == "hedge"
982	assert result.seeds == []	979	assert result.seeds == []

Importance #57: tests/test_tree_instances.py @@ -1051,9 +1048,9 @@

1051		1048
1052	# --- config section --------------------------------------------------------	1049	# --- config section --------------------------------------------------------
1053		1050
1054		1051
1055	def test_overrides_reach_the_dataclass() -> None:	1052	def test_overrides_reach_the_model() -> None:
1056	config = DetectorConfig.from_mapping(	1053	config = DetectorConfig.from_mapping(
1057	{"tree_instance": {"enabled": True, "stem_eps_m": 0.5}}	1054	{"tree_instance": {"enabled": True, "stem_eps_m": 0.5}}
1058	)	1055	)
1059	assert config.tree_instance_enabled is True	1056	assert config.tree_instance_enabled is True

Importance #58: tests/test_tree_instances.py @@ -1061,21 +1058,21 @@

Importance #59: pyproject.toml @@ -1,7 +1,7 @@

1	[project]	1	[project]
2	name = "iolabs-point-cloud-detection-verticalsigns"	2	name = "iolabs-point-cloud-detection-verticalsigns"
3	version = "0.2.0"	3	version = "0.2.1"
4	description = "Classical geometric vertical sign and gate detection in MLS LiDAR point clouds"	4	description = "Classical geometric vertical sign and gate detection in MLS LiDAR point clouds"
5	readme = "README.md"	5	readme = "README.md"
6	requires-python = ">=3.11,<3.13"	6	requires-python = ">=3.11,<3.13"
7	dependencies = [	7	dependencies = [

Importance #60: pyproject.toml @@ -9,10 +9,11 @@

   "pillow>=10.0",
   "scikit-learn>=1.5",
   "scipy>=1.13",
   "joblib>=1.3",
+  "pydantic>=2.7",
   "iolabs-logstash>=0.4.0",
-  "iolabs-common>=0.7.0",
+  "iolabs-common>=0.9.0",
   "iolabs-geometry-geometry>=0.11.0",
   "iolabs-geometry-raster>=0.2.0",
   "iolabs-point-cloud-tablecloth>=0.2.0",
 ]

Importance #61: BRIEF.md @@ -94,10 +94,12 @@

   plus the packaged `verticalsigns.default.json`, and `tests/` with fast
   synthetic unit tests (a fake pole, a fake wall, a fake tree → correct
   classification; world_to_pixel round-trip). Config thresholds live in
   `verticalsigns.default.json`; `_config.load_verticalsigns_config` deep-merges
-  a user `--config` JSON over the defaults (ALLOWED_* frozensets reject unknown
+  a user `--config` JSON over the defaults and validates the result against the
-  keys). Logging uses `iolabs.logstash.get_props_logger(__name__, LOG_PROPS)`.
+  `VerticalSignsConfig` pydantic model tree (`_config_model.py` +
+  `_model_<slice>.py`), which rejects unknown keys and bad values. Logging
+  uses `iolabs.logstash.get_props_logger(__name__, LOG_PROPS)`.
 - CLI: `uv run verticalsigns-detect --data-dir ... --segments 000,012 --out out/ [--config overrides.json]`
   (segment IDs zero-padded to 3); `python -m
   iolabs_point_cloud_detection_verticalsigns.detect ...` is equivalent.
 - Performance: a segment has ~2–8 M points across its run3 files; use

Importance #62: README.md @@ -23,13 +23,23 @@

23	`ground`, `occupancy`, `candidates`, `clustering`, `classification`, `corridor`,	23	`ground`, `occupancy`, `candidates`, `clustering`, `classification`, `corridor`,
24	`context`, `delineator`, `sign_post`, `panel`, `gantry`, `repetitive_row`,	24	`context`, `delineator`, `sign_post`, `panel`, `gantry`, `repetitive_row`,
25	`marker_extract`, `rail_halfpost`, `reject_rescue`, `tree`, `tree_detection`,	25	`marker_extract`, `rail_halfpost`, `reject_rescue`, `tree`, `tree_detection`,
26	`chroma_vegetation`, `vehicle`, `views`, `perspective`). Pass `--config`	26	`chroma_vegetation`, `vehicle`, `views`, `perspective`). Pass `--config`
27	to deep-merge a partial JSON over those defaults; unknown keys are rejected.	27	to deep-merge a partial JSON over those defaults; unknown keys and bad values
28		28	are rejected.
29	The 378-field `DetectorConfig` is declared across the `_config_<section>`	29
30	modules and recombined in `config.py`, which re-exports every name — import	30	The schema of that JSON is the `VerticalSignsConfig` pydantic model tree
31	from `...verticalsigns.config` exactly as before.	31	(`_config_model.py` plus the `_model_<slice>.py` sections, built on
		32	`iolabs.common.config_loader.ConfigModel`): one nested model per JSON section,
		33	one field per key. **Adding a config key = add the field to its section model
		34	and the default to `verticalsigns.default.json`, nothing else.**
		35
		36	The 379-field `DetectorConfig` is the FLAT view the detector modules read
		37	(`config.ground_cell_m`): it is declared across the `_config_<section>` modules
		38	and recombined in `config.py`, which re-exports every name — import from
		39	`...verticalsigns.config` exactly as before. A new key that the detector reads
		40	also needs its flat field and the `*_kwargs` line that maps the section key
		41	onto it.
32		42
33	## QC rendering is an optional extra	43	## QC rendering is an optional extra
34		44
35	`verticalsigns-views` and `verticalsigns-perspective` render QC imagery and need	45	`verticalsigns-views` and `verticalsigns-perspective` render QC imagery and need

Importance #63: README.md @@ -23,13 +23,23 @@

23	`ground`, `occupancy`, `candidates`, `clustering`, `classification`, `corridor`,	23	`ground`, `occupancy`, `candidates`, `clustering`, `classification`, `corridor`,
24	`context`, `delineator`, `sign_post`, `panel`, `gantry`, `repetitive_row`,	24	`context`, `delineator`, `sign_post`, `panel`, `gantry`, `repetitive_row`,
25	`marker_extract`, `rail_halfpost`, `reject_rescue`, `tree`, `tree_detection`,	25	`marker_extract`, `rail_halfpost`, `reject_rescue`, `tree`, `tree_detection`,
26	`chroma_vegetation`, `vehicle`, `views`, `perspective`). Pass `--config`	26	`chroma_vegetation`, `vehicle`, `views`, `perspective`). Pass `--config`
27	to deep-merge a partial JSON over those defaults; unknown keys are rejected.	27	to deep-merge a partial JSON over those defaults; unknown keys and bad values
28		28	are rejected.
29	The 378-field `DetectorConfig` is declared across the `_config_<section>`	29
30	modules and recombined in `config.py`, which re-exports every name — import	30	The schema of that JSON is the `VerticalSignsConfig` pydantic model tree
31	from `...verticalsigns.config` exactly as before.	31	(`_config_model.py` plus the `_model_<slice>.py` sections, built on
		32	`iolabs.common.config_loader.ConfigModel`): one nested model per JSON section,
		33	one field per key. **Adding a config key = add the field to its section model
		34	and the default to `verticalsigns.default.json`, nothing else.**
		35
		36	The 379-field `DetectorConfig` is the FLAT view the detector modules read
		37	(`config.ground_cell_m`): it is declared across the `_config_<section>` modules
		38	and recombined in `config.py`, which re-exports every name — import from
		39	`...verticalsigns.config` exactly as before. A new key that the detector reads
		40	also needs its flat field and the `*_kwargs` line that maps the section key
		41	onto it.
32		42
33	## QC rendering is an optional extra	43	## QC rendering is an optional extra
34		44
35	`verticalsigns-views` and `verticalsigns-perspective` render QC imagery and need	45	`verticalsigns-views` and `verticalsigns-perspective` render QC imagery and need

Importance #64: dev/out_eval/pass8/p8_edgeline_diag.py @@ -11,9 +11,8 @@

11		11
12	from __future__ import annotations	12	from __future__ import annotations
13		13
14	import argparse	14	import argparse
15	import dataclasses
16	from pathlib import Path	15	from pathlib import Path
17		16
18	import numpy as np	17	import numpy as np
19		18

Importance #65: dev/out_eval/pass8/p8_edgeline_diag.py @@ -217,10 +216,10 @@

217	header = "variant".ljust(20) + "".join(s.rjust(9) for s in segments)	216	header = "variant".ljust(20) + "".join(s.rjust(9) for s in segments)
218	print(header)	217	print(header)
219	for name, overrides in variants.items():	218	for name, overrides in variants.items():
220	try:	219	try:
221	cfg = dataclasses.replace(config, **overrides)	220	cfg = config.with_overrides(**overrides)
222	except TypeError as exc:	221	except ValueError as exc:
223	print(f"{name.ljust(20)} SKIP ({exc})")	222	print(f"{name.ljust(20)} SKIP ({exc})")
224	continue	223	continue
225	cells = []	224	cells = []
226	for segment in segments:	225	for segment in segments:

Importance #66: dev/out_eval/pass9/p10/p10_yield.py @@ -80,40 +80,36 @@

 def fix_delineator_h_ceiling(h):
     def _f(f, cfg):
-        return f, dataclasses.replace(cfg, delineator_h_max_m=h)
+        return f, cfg.with_overrides(delineator_h_max_m=h)
     return _f
 def fix_min_points(n):
     def _f(f, cfg):
-        return f, dataclasses.replace(cfg, delineator_min_points=n)
+        return f, cfg.with_overrides(delineator_min_points=n)
     return _f
 def fix_relaxed_footprint(m):
     def _f(f, cfg):
-        return f, dataclasses.replace(cfg, delineator_relaxed_footprint_m=m)
+        return f, cfg.with_overrides(delineator_relaxed_footprint_m=m)
     return _f
 def fix_combo_sentinel_hceiling(f, cfg):
-    cfg2 = dataclasses.replace(cfg, delineator_h_max_m=1.8)
+    cfg2 = cfg.with_overrides(delineator_h_max_m=1.8)
     if f.len_minor > SENTINEL and f.verticality == 0.0:
         return dataclasses.replace(f, verticality=1.0), cfg2
     return f, cfg2
 def fix_all(f, cfg):
-    cfg2 = dataclasses.replace(
+    cfg2 = cfg.with_overrides(delineator_h_max_m=1.8, delineator_min_points=150)
-        cfg,
-        delineator_h_max_m=1.8,
-        delineator_min_points=150,
-    )
     if f.len_minor > SENTINEL and f.verticality == 0.0:
         return dataclasses.replace(f, verticality=1.0), cfg2
     return f, cfg2

Importance #67: pyproject.toml @@ -1,7 +1,7 @@

1	[project]	1	[project]
2	name = "iolabs-point-cloud-detection-verticalsigns"	2	name = "iolabs-point-cloud-detection-verticalsigns"
3	version = "0.2.0"	3	version = "0.2.1"
4	description = "Classical geometric vertical sign and gate detection in MLS LiDAR point clouds"	4	description = "Classical geometric vertical sign and gate detection in MLS LiDAR point clouds"
5	readme = "README.md"	5	readme = "README.md"
6	requires-python = ">=3.11,<3.13"	6	requires-python = ">=3.11,<3.13"
7	dependencies = [	7	dependencies = [

Importance #68: pyproject.toml @@ -9,10 +9,11 @@

   "pillow>=10.0",
   "scikit-learn>=1.5",
   "scipy>=1.13",
   "joblib>=1.3",
+  "pydantic>=2.7",
   "iolabs-logstash>=0.4.0",
-  "iolabs-common>=0.7.0",
+  "iolabs-common>=0.9.0",
   "iolabs-geometry-geometry>=0.11.0",
   "iolabs-geometry-raster>=0.2.0",
   "iolabs-point-cloud-tablecloth>=0.2.0",
 ]

Importance #69: src/iolabs_point_cloud_detection_verticalsigns/_config.py @@ -1,630 +1,81 @@

1	"""Packaged-default configuration loading and validation for the detector.	1	"""Packaged-default configuration loading and validation for the detector.
2		2
3	The canonical configuration lives in ``verticalsigns.default.json`` packaged	3	The canonical configuration lives in ``verticalsigns.default.json`` packaged
4	next to this module. ``load_verticalsigns_config`` returns a validated dict that	4	next to this module, and its schema is the :class:`VerticalSignsConfig` pydantic
5	deep-merges an optional user JSON over those defaults, rejecting unknown keys	5	model tree in ``_config_model``. ``load_verticalsigns_config`` returns a
6	(per section) with a clear error. The internal :class:`DetectorConfig` dataclass	6	validated plain dict that deep-merges an optional user JSON over those defaults,
7	is constructed from that dict via ``DetectorConfig.from_mapping``.	7	rejecting unknown keys (per section) and bad values with a clear error. The
8		8	internal :class:`DetectorConfig` model is built from that dict via
9	Deep-merge, default-path resolution, and allowed-key validation are provided by	9	``DetectorConfig.from_mapping``.
10	``iolabs.common.config_loader``; section allowlists and entrypoints stay here.	10
		11	Loading, deep-merge and validation are provided by
		12	``iolabs.common.config_loader``; the schema and entrypoints stay here.
11	"""	13	"""
12		14
13	from __future__ import annotations	15	from __future__ import annotations
14		16
15	import json	17	import json
		18	import logging
16	from pathlib import Path	19	from pathlib import Path
17	from typing import Any	20	from typing import Any
18		21
19	from iolabs.common.config_loader import (	22	from iolabs.common import config_loader
20	ConfigError,
21	deep_merge_dicts,
22	load_packaged_json,
23	validate_allowed_keys,
24	)
25
26	_PACKAGE_NAME = "iolabs_point_cloud_detection_verticalsigns"
27	_DEFAULT_RESOURCE = "verticalsigns.default.json"
28
29	ALLOWED_TOP_LEVEL_KEYS = frozenset(
30	{
31	"ground",
32	"occupancy",
33	"candidates",
34	"clustering",
35	"classification",
36	"radius",
37	"corridor",
38	"context",
39	"delineator",
40	"sign_post",
41	"panel",
42	"gantry",
43	"tree",
44	"tree_detection",
45	"tree_instance",
46	"chroma_vegetation",
47	"tcs_ground",
48	"conic_gate",
49	"conifer_rule",
50	"vehicle",
51	"repetitive_row",
52	"road_context",
53	"edge_line",
54	"field_stake",
55	"marker_extract",
56	"rail_halfpost",
57	"reject_rescue",
58	"views",
59	"perspective",
60	}
61	)
62		23
63	ALLOWED_GROUND_KEYS = frozenset({"cell_m", "percentile"})	24	from ._config_model import VerticalSignsConfig
64	ALLOWED_OCCUPANCY_KEYS = frozenset({"cell_m"})
65	ALLOWED_CANDIDATES_KEYS = frozenset(
66	{
67	"min_height_m",
68	"max_height_m",
69	"seed_min_vertical_span_m",
70	"seed_min_h_max_m",
71	"seed_bright_min_vertical_span_m",
72	"seed_bright_min_h_max_m",
73	"seed_bright_min_points",
74	}
75	)
76	ALLOWED_CLUSTERING_KEYS = frozenset({"eps_m", "min_samples", "hull_margin_m"})
77	ALLOWED_CLASSIFICATION_KEYS = frozenset(
78	{
79	"continuity_bin_m",
80	"reject_len_major_m",
81	"reject_h_max_with_large_footprint_m",
82	"min_continuity",
83	"min_accept_h_max_m",
84	"core_rms_bin_m",
85	"core_rms_h_min_m",
86	"core_rms_h_cap_m",
87	"hi_intensity_all_points_percentile",
88	"seed_bright_percentile",
89	"verticality_sentinel_fix",
90	"robust_extent_stats",
91	"robust_h_max_percentile",
92	"robust_extent_lo_percentile",
93	"robust_extent_hi_percentile",
94	"single_record_transient_veto",
95	"transient_max_verticality",
96	"transient_min_len_major_m",
97	"transient_max_h_max_m",
98	"veg_texture_veto",
99	"veg_texture_min_plate_thickness_m",
100	"veg_texture_min_hi_seed_fraction",
101	"lattice_admission",
102	"lattice_min_anchors",
103	"lattice_snap_m",
104	"lattice_max_skip",
105	"lattice_min_seed_spacing_m",
106	"lattice_max_seed_spacing_m",
107	"lattice_max_spacing_resid",
108	"lattice_pool_h_max_min_m",
109	"lattice_pool_h_max_max_m",
110	"lattice_pool_max_len_major_m",
111	"lattice_pool_min_verticality",
112	"lattice_pool_min_points",
113	"lattice_pool_max_plate_thickness_m",
114	"lattice_pool_min_hi_seed_fraction",
115	"min_volumetric_density",
116	"pole_floating_min_h_min_m",
117	"pole_isolated_radius_m",
118	"dedup_radius_m",
119	"emit_trees",
120	"ml_verifier_enabled",
121	"ml_veto_threshold",
122	"ml_model_path",
123	"ml_veto_requires_corridor",
124	}
125	)
126	ALLOWED_RADIUS_KEYS = frozenset(
127	{
128	"fit_bin_m",
129	"fit_min_bin_points",
130	"fit_min_arc_deg",
131	"fit_residual_frac",
132	"fit_residual_abs_m",
133	"fit_divergence_factor",
134	"pole_radius_max_m",
135	"trunk_radius_max_m",
136	"crown_radius_percentile",
137	"crown_lobe_gap_m",
138	"crown_lobe_min_samples",
139	"crown_lobe_min_points",
140	"crown_lobe_coverage_target",
141	"crown_lobe_max_count",
142	"debug_cluster_points",
143	}
144	)
145	ALLOWED_CORRIDOR_KEYS = frozenset(
146	{
147	"max_dist_to_road_m",
148	"on_carriageway_dist_m",
149	"on_carriageway_exempt_h_max_m",
150	"density_min_points",
151	"density_frac_p95",
152	"density_max_points",
153	"component_min_area_frac",
154	"component_min_area_cells",
155	"on_carriageway_road_fraction",
156	"on_carriageway_bright_frac",
157	"on_carriageway_delineator_max_len_major_m",
158	"on_carriageway_delineator_min_verticality",
159	}
160	)
161	ALLOWED_CONTEXT_KEYS = frozenset(
162	{
163	"ring_r_inner_m",
164	"ring_r_outer_m",
165	"ring_h_min_m",
166	"ring_h_max_m",
167	"ring_max_fill_ratio",
168	"ring_min_points",
169	"forest_min_neighbors",
170	"forest_radius_m",
171	"forest_neighbor_min_h_max_m",
172	}
173	)
174	ALLOWED_DELINEATOR_KEYS = frozenset(
175	{
176	"h_min_m",
177	"h_max_m",
178	"max_footprint_m",
179	"relaxed_footprint_m",
180	"relaxed_min_verticality",
181	"relaxed_max_ring_fill_ratio",
182	"relaxed_min_hi_intensity_fraction",
183	"min_hi_intensity_fraction",
184	"min_points",
185	}
186	)
187	ALLOWED_SIGN_POST_KEYS = frozenset(
188	{
189	"max_len_minor_m",
190	"h_min_m",
191	"h_max_m",
192	"min_continuity",
193	"plate_hi_intensity_fraction",
194	"plate_hi_intensity_fraction_weak",
195	"plate_upper_surplus_ratio",
196	"min_upper_half_surplus",
197	"plate_min_core_rms_m",
198	"max_plate_thickness_m",
199	"bare_post_min_h_max_m",
200	"bare_post_max_core_rms_m",
201	"bare_post_min_verticality",
202	"bare_post_min_points",
203	}
204	)
205	ALLOWED_PANEL_KEYS = frozenset(
206	{"min_hi", "max_thickness_m", "h_min_m", "len_major_min_m", "len_major_max_m"}
207	)
208	ALLOWED_GANTRY_KEYS = frozenset(
209	{
210	"h_min_m",
211	"len_major_m",
212	"max_len_minor_m",
213	"pair_station_tolerance_m",
214	"pair_min_separation_m",
215	"overhead_h_min_m",
216	"pair_isolation_radius_m",
217	}
218	)
219	ALLOWED_REPETITIVE_ROW_KEYS = frozenset(
220	{
221	"min_members",
222	"max_spacing_m",
223	"max_perp_spread_m",
224	"max_h_max_range_m",
225	"member_max_len_major_m",
226	"member_max_len_minor_m",
227	}
228	)
229	ALLOWED_ROAD_CONTEXT_KEYS = frozenset(
230	{
231	"gate_enabled",
232	"xml_enabled",
233	"xml_min_agreement",
234	"xml_vote_slack_m",
235	"xml_max_distance_m",
236	"xml_station_tolerance_m",
237	"xml_station_step_m",
238	"min_carriageway_width_m",
239	"max_carriageway_width_m",
240	"paint_fallback_enabled",
241	"saturation_intensity",
242	"radius_m",
243	"neighbour_span",
244	"cache_dir",
245	"min_neighbourhood_saturated",
246	}
247	)
248	ALLOWED_EDGE_LINE_KEYS = frozenset(
249	{
250	"gate_enabled",
251	"paint_max_height_m",
252	"paint_min_height_m",
253	"paint_intensity_percentile",
254	"paint_subsample",
255	"station_len_m",
256	"min_window_returns",
257	"lateral_bin_m",
258	"min_line_points",
259	"max_line_width_m",
260	"min_line_along_fill",
261	"drive_line_bin_m",
262	"min_band_width_m",
263	"max_band_width_m",
264	"inward_margin_m",
265	"min_coverage_frac",
266	"min_axis_contrast",
267	"axis_search_radius_m",
268	"axis_max_angle_cos",
269	"axis_max_distance_m",
270	"exempt_h_max_m",
271	"reject_requires_transient",
272	"transient_max_records",
273	"far_filter_enabled",
274	"far_max_distance_m",
275	"far_include_lane_lines",
276	"far_tier2_enabled",
277	"far_tier2_distance_m",
278	"far_tier2_max_saturation",
279	# Read from the edge_line section by DetectorConfig.from_mapping but
280	# historically listed only under road_context, which the loader never
281	# consults for them — they were unsettable until added here.
282	"xml_enabled",
283	"xml_min_agreement",
284	"xml_vote_slack_m",
285	"xml_max_distance_m",
286	"xml_station_tolerance_m",
287	"xml_station_step_m",
288	"min_carriageway_width_m",
289	"max_carriageway_width_m",
290	"paint_fallback_enabled",
291	}
292	)
293	ALLOWED_FIELD_STAKE_KEYS = frozenset(
294	{
295	"row_emit",
296	"min_members",
297	"min_spacing_m",
298	"max_spacing_m",
299	"max_spacing_cv",
300	}
301	)
302	ALLOWED_MARKER_EXTRACT_KEYS = frozenset(
303	{
304	"min_len_major_m",
305	"bright_h_min_m",
306	"min_bright_points",
307	"window_m",
308	"min_bright_fraction",
309	"min_h_max_m",
310	"min_vertical_span_m",
311	}
312	)
313	# Own sections rather than more classification keys: both are STAGES with their
314	# own frozen constant sets and their own provenance (see railpost.py /
315	# rescue.py), and folding a dozen probe constants into `classification` would
316	# make it impossible to see at a glance which knobs belong to which stage.
317	ALLOWED_RAIL_HALFPOST_KEYS = frozenset(
318	{
319	"enabled",
320	"models_dir",
321	"band_lat_m",
322	"band_z_lo_m",
323	"band_z_hi_m",
324	"sample_step_m",
325	"cluster_cell_m",
326	"min_emit_points",
327	"ground_cell_m",
328	"ground_percentile",
329	"saturation_intensity",
330	"h_min_m",
331	"h_max_m",
332	"max_lateral_m",
333	"max_width_m",
334	"min_points",
335	"min_z_extent_m",
336	"dedupe_m",
337	"prime_min_sat",
338	"prime_min_records",
339	}
340	)
341	ALLOWED_REJECT_RESCUE_KEYS = frozenset(
342	{
343	"enabled",
344	"h_min_m",
345	"h_max_m",
346	"min_verticality",
347	"max_core_rms_m",
348	"min_h_over_width",
349	"min_records",
350	"min_roadctx_sat",
351	"min_continuity",
352	"min_decile_fill",
353	"min_points",
354	"merge_radius_m",
355	"accepted_exclusion_m",
356	"per_segment_cap",
357	}
358	)
359	ALLOWED_TREE_KEYS = frozenset(
360	{"crown_h_min_m", "crown_max_area_m2", "isotropy_ratio", "greenness_hint"}
361	)
362	ALLOWED_TREE_DETECTION_KEYS = frozenset(
363	{
364	"enabled",
365	"max_dist_to_road_m",
366	"seed_min_vertical_span_m",
367	"seed_points_above_m",
368	"eps_m",
369	"min_samples",
370	"hull_margin_m",
371	"min_points",
372	"bridge_max_on_road_fraction",
373	"dedup_radius_m",
374	"min_confidence",
375	"model_path",
376	"hedge_split_enabled",
377	}
378	)
379	# Post-detection instance splitting, a separate section from "tree_detection":
380	# those keys decide WHICH blobs are emitted as trees, these decide how ONE
381	# emitted blob is cut into per-point instances. Both carry an "enabled" and
382	# several metre thresholds with similar names, so merging them would make a
383	# hand-written override ambiguous about which stage it is tuning.
384	ALLOWED_TREE_INSTANCE_KEYS = frozenset(
385	{
386	"enabled",
387	"local_ground_footprint_m",
388	"local_ground_cell_m",
389	"local_ground_percentile",
390	"local_ground_window_m",
391	"crown_base_bin_m",
392	"crown_base_density_frac",
393	"crown_base_run_bins",
394	"crown_base_min_m",
395	"stem_band_low_m",
396	"stem_band_cap_m",
397	"stem_band_min_thickness_m",
398	"stem_eps_m",
399	"stem_min_samples",
400	"stem_max_diameter_m",
401	"stem_min_vertical_reach",
402	"stem_min_verticality",
403	"stem_min_score",
404	"stem_exg_bonus",
405	"stem_merge_dist_m",
406	"stem_uncertain_dist_m",
407	"hedge_max_ground_gap_m",
408	"hedge_max_height_m",
409	"hedge_min_length_m",
410	"hedge_min_area_m2",
411	"hedge_max_top_relief_m",
412	"hedge_max_seed_per_10m",
413	"hedge_stem_score_min",
414	"assign_voxel_m",
415	"assign_max_gap_m",
416	"assign_max_graph_dist_m",
417	"low_evidence_margin",
418	"low_evidence_abstain",
419	"min_cluster_points",
420	"single_tree_footprint_m",
421	"partial_abstain_fraction",
422	"confidence_seed_weight",
423	"apex_fallback_enabled",
424	"apex_cell_m",
425	"apex_smooth_sigma_m",
426	"apex_min_separation_m",
427	"apex_min_prominence_m",
428	"apex_min_height_m",
429	"apex_trigger_span_m",
430	"apex_seed_radius_m",
431	"apex_confidence_scale",
432	"seedless_single_max_footprint_m",
433	"seedless_single_min_height_m",
434	"seedless_single_max_height_m",
435	"seedless_single_confidence",
436	"seedless_min_p95_h_m",
437	"seedless_max_aspect",
438	"seedless_min_points",
439	"min_points_per_instance",
440	"float_fragment_min_h_m",
441	"float_fragment_p25_h_m",
442	"min_tree_footprint_m",
443	"max_tree_footprint_m",
444	"megacluster_points",
445	"planar_min_footprint_m",
446	"planar_cell_m",
447	"planar_max_spread_m",
448	"planar_fraction_min",
449	"hedge_min_continuity",
450	"hedge_continuity_bin_m",
451	"max_claim_radius_m",
452	"min_instance_points",
453	"min_instance_fraction",
454	"instance_max_linearity",
455	"instance_min_minor_m",
456	"instance_min_thickness_share",
457	"instance_min_vertical_m",
458	"confidence_size_ref_points",
459	"confidence_max",
460	"confidence_fallback_max",
461	}
462	)
463	# Deliberately its OWN section rather than more keys under "tree": the tree
464	# section's greenness_hint (0.45) is calibrated against the legacy segment-max
465	# normalized greenness, while these thresholds are ExG chromaticity in a
466	# different range entirely. Separate sections make the two impossible to
467	# confuse in a hand-written config override.
468	ALLOWED_CHROMA_VEGETATION_KEYS = frozenset(
469	{
470	"enabled",
471	"exg_min",
472	"exg_iqr_min",
473	"max_hi_intensity_fraction",
474	"min_change_of_curvature",
475	"min_plate_thickness_m",
476	}
477	)
478	ALLOWED_VEHICLE_KEYS = frozenset(
479	{"h_min_m", "h_max_m", "len_major_m", "len_minor_m", "max_hi_intensity_fraction"}
480	)
481	ALLOWED_VIEWS_KEYS = frozenset(
482	{"near_radius_m", "fov_deg", "splat", "image_width", "image_height", "view_names"}
483	)
484	ALLOWED_PERSPECTIVE_KEYS = frozenset(
485	{
486	"depth_tol_m",
487	"line_samples",
488	"occluded_alpha",
489	"solid_width_px",
490	"halo_width_px",
491	"base_marker_radius_px",
492	"back_distance_m",
493	"back_height_m",
494	"context_distance_m",
495	"context_height_m",
496	"share_radius_m",
497	"coverage_tol_m",
498	}
499	)
500		25
501	ALLOWED_TCS_GROUND_KEYS = frozenset(	26	logger = logging.getLogger(__name__)
502	{
503	"enabled",
504	"mechanism",
505	"cell_m",
506	"slope_threshold",
507	"max_elev_diff_m",
508	"smrf_max_window_m",
509	"elev_scalar",
510	"pit_fill_enabled",
511	"cache_dir",
512	}
513	)
514	ALLOWED_CONIC_GATE_KEYS = frozenset(
515	{
516	"enabled",
517	"taper_slope_max",
518	"taper_slope_robust_max",
519	"apex_deg_min",
520	"apex_deg_max",
521	"h_over_width_min",
522	"h_over_width_max",
523	"texture_cue_enabled",
524	"change_of_curvature_min",
525	"omnivariance_min",
526	"max_hi_intensity_fraction",
527	"h_max_min_m",
528	"max_on_road_fraction",
529	"min_decile_fill_fraction",
530	"min_crown_area_m2",
531	}
532	)
533	ALLOWED_CONIFER_RULE_KEYS = frozenset(
534	{
535	"enabled",
536	"max_stem_ratio",
537	"min_volumetric_density",
538	"max_volumetric_density",
539	"max_apex_ratio",
540	"max_crown_taper",
541	"max_crown_base_frac",
542	"h_over_width_min",
543	"h_over_width_max",
544	"h_max_min_m",
545	"min_change_of_curvature",
546	"max_hi_intensity_fraction",
547	"max_on_road_fraction",
548	"min_decile_fill_fraction",
549	"min_crown_area_m2",
550	}
551	)
552		27
553	_ALLOWED_BY_SECTION: dict[str, frozenset[str]] = {	28	_PACKAGE_NAME = "iolabs_point_cloud_detection_verticalsigns"
554	"ground": ALLOWED_GROUND_KEYS,	29	_DEFAULT_RESOURCE = "verticalsigns.default.json"
555	"occupancy": ALLOWED_OCCUPANCY_KEYS,	30	_CONTEXT = "verticalsigns config"
556	"candidates": ALLOWED_CANDIDATES_KEYS,
557	"clustering": ALLOWED_CLUSTERING_KEYS,
558	"classification": ALLOWED_CLASSIFICATION_KEYS,
559	"radius": ALLOWED_RADIUS_KEYS,
560	"corridor": ALLOWED_CORRIDOR_KEYS,
561	"context": ALLOWED_CONTEXT_KEYS,
562	"delineator": ALLOWED_DELINEATOR_KEYS,
563	"sign_post": ALLOWED_SIGN_POST_KEYS,
564	"panel": ALLOWED_PANEL_KEYS,
565	"gantry": ALLOWED_GANTRY_KEYS,
566	"tree": ALLOWED_TREE_KEYS,
567	"tree_detection": ALLOWED_TREE_DETECTION_KEYS,
568	"tree_instance": ALLOWED_TREE_INSTANCE_KEYS,
569	"chroma_vegetation": ALLOWED_CHROMA_VEGETATION_KEYS,
570	"tcs_ground": ALLOWED_TCS_GROUND_KEYS,
571	"conic_gate": ALLOWED_CONIC_GATE_KEYS,
572	"conifer_rule": ALLOWED_CONIFER_RULE_KEYS,
573	"vehicle": ALLOWED_VEHICLE_KEYS,
574	"repetitive_row": ALLOWED_REPETITIVE_ROW_KEYS,
575	"road_context": ALLOWED_ROAD_CONTEXT_KEYS,
576	"edge_line": ALLOWED_EDGE_LINE_KEYS,
577	"field_stake": ALLOWED_FIELD_STAKE_KEYS,
578	"marker_extract": ALLOWED_MARKER_EXTRACT_KEYS,
579	"rail_halfpost": ALLOWED_RAIL_HALFPOST_KEYS,
580	"reject_rescue": ALLOWED_REJECT_RESCUE_KEYS,
581	"views": ALLOWED_VIEWS_KEYS,
582	"perspective": ALLOWED_PERSPECTIVE_KEYS,
583	}
584		31
585		32
586	class VerticalSignsConfigError(ConfigError):	33	class VerticalSignsConfigError(config_loader.ConfigError):
587	"""Raised when the vertical sign detector config contains unsupported keys."""	34	"""Raised when the vertical sign detector config contains unsupported keys."""
588		35
589		36
590	def _validate_config(config: dict[str, Any]) -> None:
591	validate_allowed_keys(
592	config,
593	ALLOWED_TOP_LEVEL_KEYS,
594	context="verticalsigns config",
595	error_cls=VerticalSignsConfigError,
596	)
597	for section, allowed_keys in _ALLOWED_BY_SECTION.items():
598	raw_section = config.get(section)
599	if raw_section is None:
600	continue
601	if not isinstance(raw_section, dict):
602	raise VerticalSignsConfigError(
603	f"verticalsigns config section '{section}' must be a mapping"
604	)
605	validate_allowed_keys(
606	raw_section,
607	allowed_keys,
608	context=f"verticalsigns {section}",
609	error_cls=VerticalSignsConfigError,
610	)
611
612
613	def load_default_config() -> dict[str, Any]:	37	def load_default_config() -> dict[str, Any]:
614	"""Return a fresh copy of the packaged default configuration."""	38	"""Return a fresh copy of the packaged default configuration."""
615	return load_packaged_json(_PACKAGE_NAME, _DEFAULT_RESOURCE)	39	return load_verticalsigns_config()
616		40
617		41
618	def load_verticalsigns_config(config_path: str \| Path \| None = None) -> dict[str, Any]:	42	def load_verticalsigns_config(config_path: str \| Path \| None = None) -> dict[str, Any]:
619	"""Load the detector config, deep-merging an optional user JSON over the defaults.	43	"""Load the detector config, deep-merging an optional user JSON over the defaults.
620		44
621	Unknown top-level sections or per-section keys raise :class:`VerticalSignsConfigError`.	45	Args:
		46	config_path: Optional user JSON merged over the packaged defaults. Only
		47	the keys it carries are overridden.
		48
		49	Returns:
		50	The validated config document, every section present.
		51
		52	Raises:
		53	VerticalSignsConfigError: The user JSON is malformed, or the merged
		54	config holds an unknown section/key or an invalid value.
622	"""	55	"""
623	config = load_default_config()	56	overrides = _read_user_config(config_path) if config_path is not None else None
624	if config_path is not None:	57	config = config_loader.load_config(
625	with Path(config_path).open("r", encoding="utf-8") as handle:	58	VerticalSignsConfig,
626	user_config: dict[str, Any] = json.load(handle)	59	package=_PACKAGE_NAME,
627	_validate_config(user_config)	60	filename=_DEFAULT_RESOURCE,
628	config = deep_merge_dicts(config, user_config)	61	overrides=overrides,
629	_validate_config(config)	62	context=_CONTEXT,
630	return config	63	error_cls=VerticalSignsConfigError,
		64	)
		65	return config.model_dump()
		66
		67
		68	def _read_user_config(config_path: str \| Path) -> dict[str, Any]:
		69	"""Read a user config JSON, wrapping decode errors in the package error."""
		70	path = Path(config_path)
		71	try:
		72	with path.open("r", encoding="utf-8") as handle:
		73	user_config: Any = json.load(handle)
		74	except json.JSONDecodeError as exc:
		75	raise VerticalSignsConfigError(f"Invalid JSON in {path}: {exc}") from exc
		76	if not isinstance(user_config, dict):
		77	raise VerticalSignsConfigError(
		78	f"{path} must hold a JSON object, not a {type(user_config).__name__}"
		79	)
		80	logger.debug("Loaded %s overrides from %s", _CONTEXT, path)
		81	return user_config

Importance #70: src/iolabs_point_cloud_detection_verticalsigns/_config_conic.py @@ -1,17 +1,17 @@

1	"""The colour-free conic gate and the conifer rule that rides on it.	1	"""The colour-free conic gate and the conifer rule that rides on it.
2		2
3	One slice of the flat 372-field ``DetectorConfig``, moved out of	3	One slice of the flat ``DetectorConfig``, moved out of
4	``config.py`` verbatim. ``config.py`` recombines the slices and	4	``config.py`` verbatim. ``config.py`` recombines the slices and
5	re-exports both names defined here.	5	re-exports both names defined here.
6	"""	6	"""
7		7
8	from dataclasses import dataclass
9	from typing import Any	8	from typing import Any
10		9
		10	from iolabs.common import config_loader
11		11
12	@dataclass(frozen=True)	12
13	class ConicFields:	13	class ConicFields(config_loader.ConfigModel):
14	"""The colour-free conic gate and the conifer rule that rides on it.	14	"""The colour-free conic gate and the conifer rule that rides on it.
15		15
16	Metres unless stated otherwise.	16	Metres unless stated otherwise.
17	"""	17	"""

Importance #71: src/iolabs_point_cloud_detection_verticalsigns/_config_corridor.py @@ -1,19 +1,19 @@

1	"""Road corridor rasterization and on-carriageway rejection.	1	"""Road corridor rasterization and on-carriageway rejection.
2		2
3	Also plate planarity, the bright-panel class and the free-space ring.	3	Also plate planarity, the bright-panel class and the free-space ring.
4		4
5	One slice of the flat 372-field ``DetectorConfig``, moved out of	5	One slice of the flat ``DetectorConfig``, moved out of
6	``config.py`` verbatim. ``config.py`` recombines the slices and	6	``config.py`` verbatim. ``config.py`` recombines the slices and
7	re-exports both names defined here.	7	re-exports both names defined here.
8	"""	8	"""
9		9
10	from dataclasses import dataclass
11	from typing import Any	10	from typing import Any
12		11
		12	from iolabs.common import config_loader
13		13
14	@dataclass(frozen=True)	14
15	class CorridorFields:	15	class CorridorFields(config_loader.ConfigModel):
16	"""Road corridor rasterization and on-carriageway rejection.	16	"""Road corridor rasterization and on-carriageway rejection.
17		17
18	Also plate planarity, the bright-panel class and the free-space ring.	18	Also plate planarity, the bright-panel class and the free-space ring.
19		19

Importance #72: src/iolabs_point_cloud_detection_verticalsigns/_config_devices.py @@ -1,19 +1,19 @@

1	"""Per-device thresholds for delineators, sign posts and gantries.	1	"""Per-device thresholds for delineators, sign posts and gantries.
2		2
3	Also isolated-floating-pole rejection and duplicate suppression.	3	Also isolated-floating-pole rejection and duplicate suppression.
4		4
5	One slice of the flat 372-field ``DetectorConfig``, moved out of	5	One slice of the flat ``DetectorConfig``, moved out of
6	``config.py`` verbatim. ``config.py`` recombines the slices and	6	``config.py`` verbatim. ``config.py`` recombines the slices and
7	re-exports both names defined here.	7	re-exports both names defined here.
8	"""	8	"""
9		9
10	from dataclasses import dataclass
11	from typing import Any	10	from typing import Any
12		11
		12	from iolabs.common import config_loader
13		13
14	@dataclass(frozen=True)	14
15	class DeviceFields:	15	class DeviceFields(config_loader.ConfigModel):
16	"""Per-device thresholds for delineators, sign posts and gantries.	16	"""Per-device thresholds for delineators, sign posts and gantries.
17		17
18	Also isolated-floating-pole rejection and duplicate suppression.	18	Also isolated-floating-pole rejection and duplicate suppression.
19		19

Importance #73: src/iolabs_point_cloud_detection_verticalsigns/_config_evidence.py @@ -3,19 +3,19 @@

3	Covers the verticality sentinel, tier-2 robust extent statistics,	3	Covers the verticality sentinel, tier-2 robust extent statistics,
4	retroreflectivity references, the single-record transient and	4	retroreflectivity references, the single-record transient and
5	vegetation-texture vetoes, the delineator lattice and tree emission.	5	vegetation-texture vetoes, the delineator lattice and tree emission.
6		6
7	One slice of the flat 372-field ``DetectorConfig``, moved out of	7	One slice of the flat ``DetectorConfig``, moved out of
8	``config.py`` verbatim. ``config.py`` recombines the slices and	8	``config.py`` verbatim. ``config.py`` recombines the slices and
9	re-exports both names defined here.	9	re-exports both names defined here.
10	"""	10	"""
11		11
12	from dataclasses import dataclass
13	from typing import Any	12	from typing import Any
14		13
		14	from iolabs.common import config_loader
15		15
16	@dataclass(frozen=True)	16
17	class EvidenceFields:	17	class EvidenceFields(config_loader.ConfigModel):
18	"""Evidence-level thresholds: sentinels, vetoes and reference percentiles.	18	"""Evidence-level thresholds: sentinels, vetoes and reference percentiles.
19		19
20	Covers the verticality sentinel, tier-2 robust extent statistics,	20	Covers the verticality sentinel, tier-2 robust extent statistics,
21	retroreflectivity references, the single-record transient and	21	retroreflectivity references, the single-record transient and

Importance #74: src/iolabs_point_cloud_detection_verticalsigns/_config_grid.py @@ -1,19 +1,19 @@

1	"""Ground, occupancy grid, candidate band and clustering thresholds.	1	"""Ground, occupancy grid, candidate band and clustering thresholds.
2		2
3	Also the first classification gates and vehicle rejection.	3	Also the first classification gates and vehicle rejection.
4		4
5	One slice of the flat 372-field ``DetectorConfig``, moved out of	5	One slice of the flat ``DetectorConfig``, moved out of
6	``config.py`` verbatim. ``config.py`` recombines the slices and	6	``config.py`` verbatim. ``config.py`` recombines the slices and
7	re-exports both names defined here.	7	re-exports both names defined here.
8	"""	8	"""
9		9
10	from dataclasses import dataclass
11	from typing import Any	10	from typing import Any
12		11
		12	from iolabs.common import config_loader
13		13
14	@dataclass(frozen=True)	14
15	class GridFields:	15	class GridFields(config_loader.ConfigModel):
16	"""Ground, occupancy grid, candidate band and clustering thresholds.	16	"""Ground, occupancy grid, candidate band and clustering thresholds.
17		17
18	Also the first classification gates and vehicle rejection.	18	Also the first classification gates and vehicle rejection.
19		19

Importance #75: src/iolabs_point_cloud_detection_verticalsigns/_config_model.py @@ -0,0 +1,45 @@

	1	"""The nested pydantic config model for the vertical-sign detector.
	2
	3	``VerticalSignsConfig`` mirrors ``verticalsigns.default.json`` section for
	4	section and key for key: it is the single source of truth for which config
	5	keys exist and what type each one has. Adding a key means adding a field to
	6	the matching section model and a default to the packaged JSON.
	7	"""
	8
	9	from iolabs.common import config_loader
	10
	11	from . import _model_devices, _model_grid, _model_road, _model_tree
	12
	13
	14	class VerticalSignsConfig(config_loader.ConfigModel):
	15	"""Every configuration section of the vertical-sign detector."""
	16
	17	ground: _model_grid.GroundConfig = _model_grid.GroundConfig()
	18	occupancy: _model_grid.OccupancyConfig = _model_grid.OccupancyConfig()
	19	candidates: _model_grid.CandidatesConfig = _model_grid.CandidatesConfig()
	20	clustering: _model_grid.ClusteringConfig = _model_grid.ClusteringConfig()
	21	classification: _model_grid.ClassificationConfig = _model_grid.ClassificationConfig()
	22	corridor: _model_grid.CorridorConfig = _model_grid.CorridorConfig()
	23	context: _model_grid.ContextConfig = _model_grid.ContextConfig()
	24	delineator: _model_devices.DelineatorConfig = _model_devices.DelineatorConfig()
	25	sign_post: _model_devices.SignPostConfig = _model_devices.SignPostConfig()
	26	panel: _model_devices.PanelConfig = _model_devices.PanelConfig()
	27	gantry: _model_devices.GantryConfig = _model_devices.GantryConfig()
	28	repetitive_row: _model_devices.RepetitiveRowConfig = _model_devices.RepetitiveRowConfig()
	29	road_context: _model_road.RoadContextConfig = _model_road.RoadContextConfig()
	30	edge_line: _model_road.EdgeLineConfig = _model_road.EdgeLineConfig()
	31	field_stake: _model_devices.FieldStakeConfig = _model_devices.FieldStakeConfig()
	32	marker_extract: _model_devices.MarkerExtractConfig = _model_devices.MarkerExtractConfig()
	33	tree: _model_tree.TreeConfig = _model_tree.TreeConfig()
	34	tree_detection: _model_tree.TreeDetectionConfig = _model_tree.TreeDetectionConfig()
	35	chroma_vegetation: _model_tree.ChromaVegetationConfig = _model_tree.ChromaVegetationConfig()
	36	vehicle: _model_grid.VehicleConfig = _model_grid.VehicleConfig()
	37	views: _model_road.ViewsConfig = _model_road.ViewsConfig()
	38	perspective: _model_road.PerspectiveConfig = _model_road.PerspectiveConfig()
	39	tree_instance: _model_tree.TreeInstanceConfig = _model_tree.TreeInstanceConfig()
	40	conic_gate: _model_tree.ConicGateConfig = _model_tree.ConicGateConfig()
	41	conifer_rule: _model_tree.ConiferRuleConfig = _model_tree.ConiferRuleConfig()
	42	radius: _model_grid.RadiusConfig = _model_grid.RadiusConfig()
	43	rail_halfpost: _model_devices.RailHalfpostConfig = _model_devices.RailHalfpostConfig()
	44	reject_rescue: _model_devices.RejectRescueConfig = _model_devices.RejectRescueConfig()
	45	tcs_ground: _model_tree.TcsGroundConfig = _model_tree.TcsGroundConfig()
0

Importance #76: src/iolabs_point_cloud_detection_verticalsigns/_config_perspective.py @@ -1,17 +1,17 @@

1	"""Perspective-projection QC overlay cameras and coverage tolerances.	1	"""Perspective-projection QC overlay cameras and coverage tolerances.
2		2
3	One slice of the flat 372-field ``DetectorConfig``, moved out of	3	One slice of the flat ``DetectorConfig``, moved out of
4	``config.py`` verbatim. ``config.py`` recombines the slices and	4	``config.py`` verbatim. ``config.py`` recombines the slices and
5	re-exports both names defined here.	5	re-exports both names defined here.
6	"""	6	"""
7		7
8	from dataclasses import dataclass
9	from typing import Any	8	from typing import Any
10		9
		10	from iolabs.common import config_loader
11		11
12	@dataclass(frozen=True)	12
13	class PerspectiveFields:	13	class PerspectiveFields(config_loader.ConfigModel):
14	"""Perspective-projection QC overlay cameras and coverage tolerances.	14	"""Perspective-projection QC overlay cameras and coverage tolerances.
15		15
16	Metres unless stated otherwise.	16	Metres unless stated otherwise.
17	"""	17	"""

Importance #77: src/iolabs_point_cloud_detection_verticalsigns/_config_roadcontext.py @@ -1,19 +1,19 @@

1	"""Road-context gate, driven-lane band and repetitive-row rejection.	1	"""Road-context gate, driven-lane band and repetitive-row rejection.
2		2
3	Also field-stake rows and embedded-marker extraction.	3	Also field-stake rows and embedded-marker extraction.
4		4
5	One slice of the flat 378-field ``DetectorConfig``, moved out of	5	One slice of the flat ``DetectorConfig``, moved out of
6	``config.py`` verbatim. ``config.py`` recombines the slices and	6	``config.py`` verbatim. ``config.py`` recombines the slices and
7	re-exports both names defined here.	7	re-exports both names defined here.
8	"""	8	"""
9		9
10	from dataclasses import dataclass
11	from typing import Any	10	from typing import Any
12		11
		12	from iolabs.common import config_loader
13		13
14	@dataclass(frozen=True)	14
15	class RoadContextFields:	15	class RoadContextFields(config_loader.ConfigModel):
16	"""Road-context gate, driven-lane band and repetitive-row rejection.	16	"""Road-context gate, driven-lane band and repetitive-row rejection.
17		17
18	Also field-stake rows and embedded-marker extraction.	18	Also field-stake rows and embedded-marker extraction.
19		19

Importance #78: src/iolabs_point_cloud_detection_verticalsigns/_config_stages.py @@ -2,19 +2,19 @@

2		2
3	The rail-relative half-post pass, the reject-rescue second look and	3	The rail-relative half-post pass, the reject-rescue second look and
4	the ML verifier.	4	the ML verifier.
5		5
6	One slice of the flat 372-field ``DetectorConfig``, moved out of	6	One slice of the flat ``DetectorConfig``, moved out of
7	``config.py`` verbatim. ``config.py`` recombines the slices and	7	``config.py`` verbatim. ``config.py`` recombines the slices and
8	re-exports both names defined here.	8	re-exports both names defined here.
9	"""	9	"""
10		10
11	from dataclasses import dataclass
12	from typing import Any	11	from typing import Any
13		12
		13	from iolabs.common import config_loader
14		14
15	@dataclass(frozen=True)	15
16	class StageFields:	16	class StageFields(config_loader.ConfigModel):
17	"""Opt-in post-classification stages.	17	"""Opt-in post-classification stages.
18		18
19	The rail-relative half-post pass, the reject-rescue second look and	19	The rail-relative half-post pass, the reject-rescue second look and
20	the ML verifier.	20	the ML verifier.

Importance #79: src/iolabs_point_cloud_detection_verticalsigns/_config_treedetect.py @@ -1,17 +1,17 @@

1	"""Experimental tree detection and TCS ground filtering of the DEM input.	1	"""Experimental tree detection and TCS ground filtering of the DEM input.
2		2
3	One slice of the flat 372-field ``DetectorConfig``, moved out of	3	One slice of the flat ``DetectorConfig``, moved out of
4	``config.py`` verbatim. ``config.py`` recombines the slices and	4	``config.py`` verbatim. ``config.py`` recombines the slices and
5	re-exports both names defined here.	5	re-exports both names defined here.
6	"""	6	"""
7		7
8	from dataclasses import dataclass
9	from typing import Any	8	from typing import Any
10		9
		10	from iolabs.common import config_loader
11		11
12	@dataclass(frozen=True)	12
13	class TreeDetectionFields:	13	class TreeDetectionFields(config_loader.ConfigModel):
14	"""Experimental tree detection and TCS ground filtering of the DEM input.	14	"""Experimental tree detection and TCS ground filtering of the DEM input.
15		15
16	Metres unless stated otherwise.	16	Metres unless stated otherwise.
17	"""	17	"""

Importance #80: src/iolabs_point_cloud_detection_verticalsigns/_config_treeinstance.py @@ -1,16 +1,16 @@

1	"""Per-point tree instance splitting of merged canopy blobs.	1	"""Per-point tree instance splitting of merged canopy blobs.
2		2
3	One slice of the flat 372-field ``DetectorConfig``. ``config.py``	3	One slice of the flat ``DetectorConfig``. ``config.py``
4	recombines the slices and re-exports both names defined here.	4	recombines the slices and re-exports both names defined here.
5	"""	5	"""
6		6
7	from dataclasses import dataclass
8	from typing import Any	7	from typing import Any
9		8
		9	from iolabs.common import config_loader
10		10
11	@dataclass(frozen=True)	11
12	class TreeInstanceFields:	12	class TreeInstanceFields(config_loader.ConfigModel):
13	"""Stem-seeded instance splitting of a single ``type: "tree"`` detection.	13	"""Stem-seeded instance splitting of a single ``type: "tree"`` detection.
14		14
15	Metres unless stated otherwise.	15	Metres unless stated otherwise.
16	"""	16	"""

Importance #81: src/iolabs_point_cloud_detection_verticalsigns/_config_vegetation.py @@ -1,19 +1,19 @@

1	"""Tree rejection, chromaticity vegetation reject and radius fitting.	1	"""Tree rejection, chromaticity vegetation reject and radius fitting.
2		2
3	Also core compactness and the crown-circle overlay knobs.	3	Also core compactness and the crown-circle overlay knobs.
4		4
5	One slice of the flat 372-field ``DetectorConfig``, moved out of	5	One slice of the flat ``DetectorConfig``, moved out of
6	``config.py`` verbatim. ``config.py`` recombines the slices and	6	``config.py`` verbatim. ``config.py`` recombines the slices and
7	re-exports both names defined here.	7	re-exports both names defined here.
8	"""	8	"""
9		9
10	from dataclasses import dataclass
11	from typing import Any	10	from typing import Any
12		11
		12	from iolabs.common import config_loader
13		13
14	@dataclass(frozen=True)	14
15	class VegetationFields:	15	class VegetationFields(config_loader.ConfigModel):
16	"""Tree rejection, chromaticity vegetation reject and radius fitting.	16	"""Tree rejection, chromaticity vegetation reject and radius fitting.
17		17
18	Also core compactness and the crown-circle overlay knobs.	18	Also core compactness and the crown-circle overlay knobs.
19		19

Importance #82: src/iolabs_point_cloud_detection_verticalsigns/_model_devices.py @@ -0,0 +1,140 @@

	1	"""Per-device acceptance gates and the two probe stages.
	2
	3	One slice of the nested :class:`VerticalSignsConfig` model tree; the sections
	4	mirror ``verticalsigns.default.json`` key for key. ``_config_model`` recombines
	5	the slices.
	6	"""
	7
	8	from iolabs.common import config_loader
	9
	10
	11	class DelineatorConfig(config_loader.ConfigModel):
	12	"""Delineator (Leitpfosten) acceptance gates."""
	13
	14	h_min_m: float = 0.7
	15	h_max_m: float = 1.5
	16	max_footprint_m: float = 0.45
	17	relaxed_footprint_m: float = 0.85
	18	relaxed_min_verticality: float = 0.85
	19	relaxed_max_ring_fill_ratio: float = 1.0
	20	relaxed_min_hi_intensity_fraction: float = 0.15
	21	min_hi_intensity_fraction: float = 0.08
	22	min_points: int = 300
	23
	24
	25	class SignPostConfig(config_loader.ConfigModel):
	26	"""Sign-post and plate acceptance gates."""
	27
	28	max_len_minor_m: float = 0.8
	29	h_min_m: float = 1.5
	30	h_max_m: float = 6.0
	31	min_continuity: float = 0.6
	32	plate_hi_intensity_fraction: float = 0.4
	33	plate_hi_intensity_fraction_weak: float = 0.3
	34	plate_upper_surplus_ratio: float = 2.0
	35	min_upper_half_surplus: float = 0.3
	36	plate_min_core_rms_m: float = 0.1
	37	max_plate_thickness_m: float = 0.15
	38	bare_post_min_h_max_m: float = 4.5
	39	bare_post_max_core_rms_m: float = 0.065
	40	bare_post_min_verticality: float = 0.9
	41	bare_post_min_points: int = 450
	42
	43
	44	class PanelConfig(config_loader.ConfigModel):
	45	"""Large panel acceptance gates."""
	46
	47	min_hi: float = 0.4
	48	max_thickness_m: float = 0.2
	49	h_min_m: float = 0.9
	50	len_major_min_m: float = 1.5
	51	len_major_max_m: float = 5.0
	52
	53
	54	class GantryConfig(config_loader.ConfigModel):
	55	"""Gantry leg and pairing gates."""
	56
	57	h_min_m: float = 4.5
	58	len_major_m: float = 8.0
	59	max_len_minor_m: float = 6.0
	60	pair_station_tolerance_m: float = 5.0
	61	pair_min_separation_m: float = 3.0
	62	overhead_h_min_m: float = 4.5
	63	pair_isolation_radius_m: float = 8.0
	64
	65
	66	class RepetitiveRowConfig(config_loader.ConfigModel):
	67	"""Repetitive-row (guardrail post series) grouping."""
	68
	69	min_members: int = 4
	70	max_spacing_m: float = 5.0
	71	max_perp_spread_m: float = 1.5
	72	max_h_max_range_m: float = 0.7
	73	member_max_len_major_m: float = 2.0
	74	member_max_len_minor_m: float = 0.8
	75
	76
	77	class FieldStakeConfig(config_loader.ConfigModel):
	78	"""Field-stake row emission gates."""
	79
	80	row_emit: bool = True
	81	min_members: int = 4
	82	min_spacing_m: float = 2.0
	83	max_spacing_m: float = 10.0
	84	max_spacing_cv: float = 0.35
	85
	86
	87	class MarkerExtractConfig(config_loader.ConfigModel):
	88	"""Bright marker extraction from rejected clusters."""
	89
	90	min_len_major_m: float = 6.0
	91	bright_h_min_m: float = 1.5
	92	min_bright_points: int = 400
	93	window_m: float = 2.5
	94	min_bright_fraction: float = 0.45
	95	min_h_max_m: float = 1.6
	96	min_vertical_span_m: float = 0.5
	97
	98
	99	class RailHalfpostConfig(config_loader.ConfigModel):
	100	"""Guardrail half-post probe stage."""
	101
	102	band_lat_m: float = 0.8
	103	band_z_hi_m: float = 1.5
	104	band_z_lo_m: float = 0.15
	105	cluster_cell_m: float = 0.15
	106	dedupe_m: float = 1.5
	107	enabled: bool = False
	108	ground_cell_m: float = 2.0
	109	ground_percentile: float = 10.0
	110	h_max_m: float = 0.8
	111	h_min_m: float = 0.2
	112	max_lateral_m: float = 0.5
	113	max_width_m: float = 0.2
	114	min_emit_points: int = 8
	115	min_points: int = 15
	116	min_z_extent_m: float = 0.1
	117	models_dir: str = ""
	118	prime_min_records: int = 2
	119	prime_min_sat: int = 1
	120	sample_step_m: float = 0.1
	121	saturation_intensity: float = 55000.0
	122
	123
	124	class RejectRescueConfig(config_loader.ConfigModel):
	125	"""Reject-rescue stage gates."""
	126
	127	accepted_exclusion_m: float = 2.0
	128	enabled: bool = False
	129	h_max_m: float = 1.6
	130	h_min_m: float = 0.85
	131	max_core_rms_m: float = 0.2
	132	merge_radius_m: float = 1.0
	133	min_continuity: float = 0.8
	134	min_decile_fill: float = 0.6
	135	min_h_over_width: float = 1.4
	136	min_points: int = 30
	137	min_records: int = 2
	138	min_roadctx_sat: int = 17
	139	min_verticality: float = 0.9
	140	per_segment_cap: int = 0
0

Importance #83: src/iolabs_point_cloud_detection_verticalsigns/_model_grid.py @@ -0,0 +1,152 @@

	1	"""Grid, candidate, classification, radius and corridor config sections.
	2
	3	One slice of the nested :class:`VerticalSignsConfig` model tree; the sections
	4	mirror ``verticalsigns.default.json`` key for key. ``_config_model`` recombines
	5	the slices.
	6	"""
	7
	8	from iolabs.common import config_loader
	9
	10
	11	class GroundConfig(config_loader.ConfigModel):
	12	"""Ground-model raster cell size and percentile."""
	13
	14	cell_m: float = 0.75
	15	percentile: float = 8.0
	16
	17
	18	class OccupancyConfig(config_loader.ConfigModel):
	19	"""Occupancy grid used to find candidate cells."""
	20
	21	cell_m: float = 0.15
	22
	23
	24	class CandidatesConfig(config_loader.ConfigModel):
	25	"""Height band and seed-cell gates for candidate points."""
	26
	27	min_height_m: float = 0.3
	28	max_height_m: float = 10.0
	29	seed_min_vertical_span_m: float = 0.8
	30	seed_min_h_max_m: float = 0.9
	31	seed_bright_min_vertical_span_m: float = 0.45
	32	seed_bright_min_h_max_m: float = 0.6
	33	seed_bright_min_points: int = 3
	34
	35
	36	class ClusteringConfig(config_loader.ConfigModel):
	37	"""DBSCAN clustering of seed-cell centres."""
	38
	39	eps_m: float = 0.45
	40	min_samples: int = 1
	41	hull_margin_m: float = 0.2
	42
	43
	44	class ClassificationConfig(config_loader.ConfigModel):
	45	"""Cluster-level accept/reject gates and ML verifier wiring."""
	46
	47	continuity_bin_m: float = 0.25
	48	reject_len_major_m: float = 6.0
	49	reject_h_max_with_large_footprint_m: float = 4.5
	50	min_continuity: float = 0.5
	51	min_accept_h_max_m: float = 0.9
	52	core_rms_bin_m: float = 0.25
	53	core_rms_h_min_m: float = 0.3
	54	core_rms_h_cap_m: float = 3.0
	55	hi_intensity_all_points_percentile: float = 98.0
	56	min_volumetric_density: float = 8000.0
	57	pole_floating_min_h_min_m: float = 3.5
	58	pole_isolated_radius_m: float = 8.0
	59	dedup_radius_m: float = 0.8
	60	emit_trees: bool = False
	61	ml_verifier_enabled: bool = True
	62	ml_veto_threshold: float = -1.0
	63	ml_model_path: str = ""
	64	lattice_admission: bool = True
	65	lattice_max_seed_spacing_m: float = 60.0
	66	lattice_max_skip: int = 6
	67	lattice_max_spacing_resid: float = 0.15
	68	lattice_min_anchors: int = 4
	69	lattice_min_seed_spacing_m: float = 15.0
	70	lattice_pool_h_max_max_m: float = 1.4
	71	lattice_pool_h_max_min_m: float = 0.8
	72	lattice_pool_max_len_major_m: float = 1.2
	73	lattice_pool_max_plate_thickness_m: float = 0.05
	74	lattice_pool_min_hi_seed_fraction: float = 0.15
	75	lattice_pool_min_points: int = 20
	76	lattice_pool_min_verticality: float = 0.85
	77	lattice_snap_m: float = 3.0
	78	ml_veto_requires_corridor: bool = True
	79	robust_extent_hi_percentile: float = 99.0
	80	robust_extent_lo_percentile: float = 1.0
	81	robust_extent_stats: bool = True
	82	robust_h_max_percentile: float = 98.0
	83	seed_bright_percentile: float \| None = 95.0
	84	single_record_transient_veto: bool = True
	85	transient_max_h_max_m: float = 2.5
	86	transient_max_verticality: float = 0.3
	87	transient_min_len_major_m: float = 2.0
	88	veg_texture_min_hi_seed_fraction: float = 0.668
	89	veg_texture_min_plate_thickness_m: float = 0.05
	90	veg_texture_veto: bool = True
	91	verticality_sentinel_fix: bool = True
	92
	93
	94	class RadiusConfig(config_loader.ConfigModel):
	95	"""Cylinder-radius fitting and crown-lobe estimation."""
	96
	97	crown_lobe_coverage_target: float = 0.95
	98	crown_lobe_gap_m: float = 0.5
	99	crown_lobe_max_count: int = 8
	100	crown_lobe_min_points: int = 30
	101	crown_lobe_min_samples: int = 10
	102	crown_radius_percentile: float = 95.0
	103	debug_cluster_points: bool = False
	104	fit_bin_m: float = 0.25
	105	fit_divergence_factor: float = 4.0
	106	fit_min_arc_deg: float = 60.0
	107	fit_min_bin_points: int = 8
	108	fit_residual_abs_m: float = 0.03
	109	fit_residual_frac: float = 0.35
	110	pole_radius_max_m: float = 0.5
	111	trunk_radius_max_m: float = 0.8
	112
	113
	114	class CorridorConfig(config_loader.ConfigModel):
	115	"""Road-corridor raster and on-carriageway gates."""
	116
	117	max_dist_to_road_m: float = 10.0
	118	on_carriageway_dist_m: float = 0.25
	119	on_carriageway_exempt_h_max_m: float = 4.5
	120	density_min_points: float = 8.0
	121	density_frac_p95: float = 0.06
	122	density_max_points: float = 150.0
	123	component_min_area_frac: float = 0.15
	124	component_min_area_cells: int = 40
	125	on_carriageway_road_fraction: float = 0.7
	126	on_carriageway_bright_frac: float = 0.5
	127	on_carriageway_delineator_max_len_major_m: float = 0.65
	128	on_carriageway_delineator_min_verticality: float = 0.95
	129
	130
	131	class ContextConfig(config_loader.ConfigModel):
	132	"""Ring and forest neighbourhood context features."""
	133
	134	ring_r_inner_m: float = 0.5
	135	ring_r_outer_m: float = 1.5
	136	ring_h_min_m: float = 0.5
	137	ring_h_max_m: float = 2.5
	138	ring_max_fill_ratio: float = 2.0
	139	ring_min_points: int = 40
	140	forest_min_neighbors: int = 3
	141	forest_radius_m: float = 8.0
	142	forest_neighbor_min_h_max_m: float = 2.0
	143
	144
	145	class VehicleConfig(config_loader.ConfigModel):
	146	"""Vehicle-rejection envelope."""
	147
	148	h_min_m: float = 1.5
	149	h_max_m: float = 4.5
	150	len_major_m: float = 2.5
	151	len_minor_m: float = 1.5
	152	max_hi_intensity_fraction: float = 0.1
0

Importance #84: src/iolabs_point_cloud_detection_verticalsigns/_model_road.py @@ -0,0 +1,99 @@

	1	"""Road-context, edge-line and QC rendering config sections.
	2
	3	One slice of the nested :class:`VerticalSignsConfig` model tree; the sections
	4	mirror ``verticalsigns.default.json`` key for key. ``_config_model`` recombines
	5	the slices.
	6	"""
	7
	8	from iolabs.common import config_loader
	9
	10
	11	class RoadContextConfig(config_loader.ConfigModel):
	12	"""Road-context saturation raster and XML carriageway votes."""
	13
	14	gate_enabled: bool = True
	15	xml_enabled: bool = True
	16	xml_min_agreement: float = 0.6
	17	xml_vote_slack_m: float = 3.0
	18	xml_max_distance_m: float = 60.0
	19	xml_station_tolerance_m: float = 2.0
	20	xml_station_step_m: float = 10.0
	21	min_carriageway_width_m: float = 3.0
	22	max_carriageway_width_m: float = 20.0
	23	paint_fallback_enabled: bool = False
	24	saturation_intensity: float = 55000.0
	25	radius_m: float = 15.0
	26	neighbour_span: int = 1
	27	cache_dir: str = ""
	28	min_neighbourhood_saturated: int = 1000
	29
	30
	31	class EdgeLineConfig(config_loader.ConfigModel):
	32	"""Edge-line paint detection and far-distance filtering."""
	33
	34	gate_enabled: bool = True
	35	paint_max_height_m: float = 0.35
	36	paint_min_height_m: float = -0.25
	37	paint_intensity_percentile: float = 95.0
	38	paint_subsample: int = 20
	39	station_len_m: float = 10.0
	40	min_window_returns: int = 2000
	41	lateral_bin_m: float = 0.1
	42	min_line_points: int = 40
	43	max_line_width_m: float = 1.5
	44	min_line_along_fill: float = 0.4
	45	drive_line_bin_m: float = 0.5
	46	min_band_width_m: float = 2.0
	47	max_band_width_m: float = 9.0
	48	inward_margin_m: float = 0.3
	49	min_coverage_frac: float = 0.6
	50	min_axis_contrast: float = 3.0
	51	axis_search_radius_m: float = 40.0
	52	axis_max_angle_cos: float = 0.8
	53	axis_max_distance_m: float = 150.0
	54	exempt_h_max_m: float = 4.5
	55	reject_requires_transient: bool = True
	56	transient_max_records: int = 1
	57	far_filter_enabled: bool = True
	58	far_max_distance_m: float = 30.0
	59	far_include_lane_lines: bool = True
	60	far_tier2_enabled: bool = True
	61	far_tier2_distance_m: float = 15.0
	62	far_tier2_max_saturation: int = 150
	63	max_carriageway_width_m: float = 20.0
	64	min_carriageway_width_m: float = 3.0
	65	paint_fallback_enabled: bool = False
	66	xml_enabled: bool = True
	67	xml_max_distance_m: float = 60.0
	68	xml_min_agreement: float = 0.6
	69	xml_station_step_m: float = 10.0
	70	xml_station_tolerance_m: float = 2.0
	71	xml_vote_slack_m: float = 3.0
	72
	73
	74	class ViewsConfig(config_loader.ConfigModel):
	75	"""Rendered QC view cameras and image size."""
	76
	77	near_radius_m: float = 45.0
	78	fov_deg: float = 55.0
	79	splat: int = 2
	80	image_width: int = 1100
	81	image_height: int = 750
	82	view_names: tuple[str, ...] = ("back", "side")
	83
	84
	85	class PerspectiveConfig(config_loader.ConfigModel):
	86	"""Perspective-projection QC overlay cameras and tolerances."""
	87
	88	depth_tol_m: float = 0.5
	89	line_samples: int = 20
	90	occluded_alpha: int = 90
	91	solid_width_px: int = 3
	92	halo_width_px: int = 6
	93	base_marker_radius_px: int = 6
	94	back_distance_m: float = 22.0
	95	back_height_m: float = 4.0
	96	context_distance_m: float = 40.0
	97	context_height_m: float = 6.0
	98	share_radius_m: float = 15.0
	99	coverage_tol_m: float = 0.5
0

Importance #85: src/iolabs_point_cloud_detection_verticalsigns/_model_tree.py @@ -0,0 +1,180 @@

	1	"""Tree, vegetation and ground-filter config sections.
	2
	3	One slice of the nested :class:`VerticalSignsConfig` model tree; the sections
	4	mirror ``verticalsigns.default.json`` key for key. ``_config_model`` recombines
	5	the slices.
	6	"""
	7
	8	from iolabs.common import config_loader
	9
	10
	11	class TreeConfig(config_loader.ConfigModel):
	12	"""Legacy tree crown hints."""
	13
	14	crown_h_min_m: float = 2.0
	15	crown_max_area_m2: float = 4.0
	16	isotropy_ratio: float = 0.75
	17	greenness_hint: float = 0.45
	18
	19
	20	class TreeDetectionConfig(config_loader.ConfigModel):
	21	"""Tree detection stage: which blobs are emitted as trees."""
	22
	23	enabled: bool = False
	24	max_dist_to_road_m: float = 20.0
	25	seed_min_vertical_span_m: float = 1.5
	26	seed_points_above_m: float = 2.0
	27	eps_m: float = 1.5
	28	min_samples: int = 3
	29	hull_margin_m: float = 0.5
	30	min_points: int = 60
	31	bridge_max_on_road_fraction: float = 0.6
	32	dedup_radius_m: float = 2.0
	33	min_confidence: float = -1.0
	34	model_path: str = ""
	35	hedge_split_enabled: bool = False
	36
	37
	38	class TreeInstanceConfig(config_loader.ConfigModel):
	39	"""Tree instance splitting: how one blob is cut into instances."""
	40
	41	enabled: bool = False
	42	local_ground_footprint_m: float = 15.0
	43	local_ground_cell_m: float = 2.0
	44	local_ground_percentile: float = 5.0
	45	local_ground_window_m: float = 6.0
	46	crown_base_bin_m: float = 0.25
	47	crown_base_density_frac: float = 0.35
	48	crown_base_run_bins: int = 3
	49	crown_base_min_m: float = 1.2
	50	stem_band_low_m: float = 0.5
	51	stem_band_cap_m: float = 4.0
	52	stem_band_min_thickness_m: float = 0.7
	53	stem_eps_m: float = 0.35
	54	stem_min_samples: int = 20
	55	stem_max_diameter_m: float = 1.2
	56	stem_min_vertical_reach: float = 0.5
	57	stem_min_verticality: float = 0.6
	58	stem_min_score: float = 0.45
	59	stem_exg_bonus: float = 0.1
	60	stem_merge_dist_m: float = 1.2
	61	stem_uncertain_dist_m: float = 2.0
	62	apex_fallback_enabled: bool = True
	63	apex_cell_m: float = 0.5
	64	apex_smooth_sigma_m: float = 0.7
	65	apex_min_separation_m: float = 2.5
	66	apex_min_prominence_m: float = 0.8
	67	apex_min_height_m: float = 2.0
	68	apex_trigger_span_m: float = 8.0
	69	apex_seed_radius_m: float = 0.6
	70	apex_confidence_scale: float = 0.6
	71	min_points_per_instance: int = 1200
	72	seedless_single_max_footprint_m: float = 10.0
	73	seedless_single_min_height_m: float = 1.5
	74	seedless_single_max_height_m: float = 25.0
	75	seedless_single_confidence: float = 0.35
	76	seedless_min_p95_h_m: float = 2.0
	77	seedless_max_aspect: float = 2.5
	78	seedless_min_points: int = 800
	79	float_fragment_min_h_m: float = 3.0
	80	float_fragment_p25_h_m: float = 4.0
	81	min_tree_footprint_m: float = 1.5
	82	max_tree_footprint_m: float = 60.0
	83	megacluster_points: int = 1000000
	84	planar_min_footprint_m: float = 12.0
	85	planar_cell_m: float = 1.0
	86	planar_max_spread_m: float = 0.3
	87	planar_fraction_min: float = 0.55
	88	hedge_max_ground_gap_m: float = 2.0
	89	hedge_max_height_m: float = 7.5
	90	hedge_min_length_m: float = 8.0
	91	hedge_min_area_m2: float = 20.0
	92	hedge_min_continuity: float = 0.75
	93	hedge_continuity_bin_m: float = 1.0
	94	hedge_max_top_relief_m: float = 1.5
	95	hedge_max_seed_per_10m: float = 1.0
	96	hedge_stem_score_min: float = 0.6
	97	assign_voxel_m: float = 0.3
	98	assign_max_gap_m: float = 1.25
	99	assign_max_graph_dist_m: float = 30.0
	100	max_claim_radius_m: float = 9.0
	101	low_evidence_margin: float = 0.05
	102	low_evidence_abstain: bool = False
	103	min_cluster_points: int = 150
	104	single_tree_footprint_m: float = 8.0
	105	partial_abstain_fraction: float = 0.2
	106	min_instance_points: int = 120
	107	min_instance_fraction: float = 0.01
	108	instance_max_linearity: float = 0.92
	109	instance_min_minor_m: float = 1.0
	110	instance_min_vertical_m: float = 1.5
	111	instance_min_thickness_share: float = 0.02
	112	confidence_seed_weight: float = 0.6
	113	confidence_size_ref_points: float = 2000.0
	114	confidence_max: float = 0.95
	115	confidence_fallback_max: float = 0.9
	116
	117
	118	class ChromaVegetationConfig(config_loader.ConfigModel):
	119	"""ExG chromaticity vegetation veto."""
	120
	121	enabled: bool = False
	122	exg_min: float = 0.155
	123	exg_iqr_min: float = 0.21
	124	max_hi_intensity_fraction: float = 0.08
	125	min_change_of_curvature: float = 0.2
	126	min_plate_thickness_m: float = 0.175
	127
	128
	129	class TcsGroundConfig(config_loader.ConfigModel):
	130	"""Tablecloth (TCS) ground pre-filter."""
	131
	132	cache_dir: str = ""
	133	cell_m: float = 0.2
	134	elev_scalar: float = 0.0
	135	enabled: bool = False
	136	max_elev_diff_m: float = 0.15
	137	mechanism: str = "smrf_numpy"
	138	pit_fill_enabled: bool = True
	139	slope_threshold: float = 0.3
	140	smrf_max_window_m: float = 6.0
	141
	142
	143	class ConicGateConfig(config_loader.ConfigModel):
	144	"""Conic-shape gate for cone/tree separation."""
	145
	146	apex_deg_max: float = 35.0
	147	apex_deg_min: float = 5.0
	148	change_of_curvature_min: float = 0.06
	149	enabled: bool = False
	150	h_max_min_m: float = 2.5
	151	h_over_width_max: float = 12.0
	152	h_over_width_min: float = 1.5
	153	max_hi_intensity_fraction: float = 0.2
	154	max_on_road_fraction: float = 0.6
	155	min_crown_area_m2: float = 0.3
	156	min_decile_fill_fraction: float = 0.8
	157	omnivariance_min: float = 0.1
	158	taper_slope_max: float = -0.4
	159	taper_slope_robust_max: float = -0.3
	160	texture_cue_enabled: bool = True
	161
	162
	163	class ConiferRuleConfig(config_loader.ConfigModel):
	164	"""Conifer acceptance rule."""
	165
	166	enabled: bool = False
	167	h_max_min_m: float = 2.0
	168	h_over_width_max: float = 15.0
	169	h_over_width_min: float = 2.0
	170	max_apex_ratio: float = 0.75
	171	max_crown_base_frac: float = 0.55
	172	max_crown_taper: float = -0.1
	173	max_hi_intensity_fraction: float = 0.2
	174	max_on_road_fraction: float = 0.6
	175	max_stem_ratio: float = 2.2
	176	max_volumetric_density: float = 380.0
	177	min_change_of_curvature: float = 0.04
	178	min_crown_area_m2: float = 0.2
	179	min_decile_fill_fraction: float = 0.8
	180	min_volumetric_density: float = 140.0
0

Importance #86: src/iolabs_point_cloud_detection_verticalsigns/config.py @@ -1,13 +1,16 @@

 """Detector configuration.
-The 378-field :class:`DetectorConfig` and its ``from_mapping`` flattener are
+The 379-field :class:`DetectorConfig` and its ``from_mapping`` flattener are
 split by section across the ``_config_<section>`` modules; this module
 recombines them and re-exports every piece, so ``from .config import X``
 keeps working for every name that used to live here.
+``DetectorConfig`` is the FLAT view the detector modules read
+(``config.ground_cell_m``); the NESTED document it is built from is validated
+by the :class:`VerticalSignsConfig` model tree in ``_config_model``.
 """
-from dataclasses import dataclass
 from pathlib import Path
 from typing import Any
 from ._config import load_verticalsigns_config

Importance #87: src/iolabs_point_cloud_detection_verticalsigns/config.py @@ -49,16 +52,15 @@

49	"perspective_kwargs",	52	"perspective_kwargs",
50	]	53	]
51		54
52		55
53	@dataclass(frozen=True)
54	class DetectorConfig( # noqa: D101 - docstring below, after the base list	56	class DetectorConfig( # noqa: D101 - docstring below, after the base list
55	# The bases are listed in REVERSE section order ON PURPOSE: dataclasses	57	# The bases are listed in REVERSE section order ON PURPOSE: both
56	# collects fields by walking the MRO backwards, so this ordering	58	# dataclasses and pydantic collect fields by walking the MRO backwards, so
57	# reproduces the original single-class field order exactly (ground first,	59	# this ordering reproduces the original single-class field order exactly
58	# then perspective, then the slices added since). Reordering these lines	60	# (ground first, then perspective, then the slices added since).
59	# reorders the fields, so a NEW slice goes at the TOP of this list to have	61	# Reordering these lines reorders the fields, so a NEW slice goes at the
60	# its fields appended at the end.	62	# TOP of this list to have its fields appended at the end.
61	TreeInstanceFields,	63	TreeInstanceFields,
62	PerspectiveFields,	64	PerspectiveFields,
63	ConicFields,	65	ConicFields,
64	TreeDetectionFields,	66	TreeDetectionFields,

Importance #88: src/iolabs_point_cloud_detection_verticalsigns/config.py @@ -75,9 +77,9 @@

75	@classmethod	77	@classmethod
76	def from_mapping(cls, config: dict[str, Any]) -> "DetectorConfig":	78	def from_mapping(cls, config: dict[str, Any]) -> "DetectorConfig":
77	"""Builds a DetectorConfig by flattening the nested config sections.	79	"""Builds a DetectorConfig by flattening the nested config sections.
78		80
79	Only keys present in a section override the corresponding dataclass	81	Only keys present in a section override the corresponding model
80	default, so a partial (or default) config reproduces the built-in	82	default, so a partial (or default) config reproduces the built-in
81	thresholds exactly.	83	thresholds exactly.
82		84
83	Args:	85	Args:

Importance #89: src/iolabs_point_cloud_detection_verticalsigns/config.py @@ -101,8 +103,30 @@

101	**perspective_kwargs(config, defaults),	103	**perspective_kwargs(config, defaults),
102	**tree_instance_kwargs(config, defaults),	104	**tree_instance_kwargs(config, defaults),
103	)	105	)
104		106
		107	def with_overrides(self, **overrides: Any) -> "DetectorConfig":
		108	"""Return a copy of this config with overrides applied.
		109
		110	``model_copy(update=...)`` skips validation, so a misspelled name would
		111	be attached as a new attribute and the intended threshold would keep
		112	its default. The names are therefore checked here, reproducing the
		113	``TypeError`` that ``dataclasses.replace`` used to raise.
		114
		115	Args:
		116	overrides: Field name to new value, e.g. ``cluster_eps_m=0.9``.
		117
		118	Returns:
		119	A new frozen config carrying overrides.
		120
		121	Raises:
		122	ValueError: An override names a field this config does not declare.
		123	"""
		124	unknown = sorted(set(overrides) - set(type(self).model_fields))
		125	if unknown:
		126	raise ValueError(f"Unknown DetectorConfig field(s): {', '.join(unknown)}")
		127	return self.model_copy(update=overrides)
		128
105	@classmethod	129	@classmethod
106	def load(cls, config_path: str \| Path \| None = None) -> "DetectorConfig":	130	def load(cls, config_path: str \| Path \| None = None) -> "DetectorConfig":
107	"""Load config from the packaged defaults merged with an optional user JSON."""	131	"""Load config from the packaged defaults merged with an optional user JSON."""
108	return cls.from_mapping(load_verticalsigns_config(config_path))	132	return cls.from_mapping(load_verticalsigns_config(config_path))

Importance #90: src/iolabs_point_cloud_detection_verticalsigns/ml.py @@ -680,10 +680,8 @@

     forest stand merges into one large low-curvature canopy blob that the
     fine-DBSCAN sign-path trees never produce, so the classifier must see it.
     Lazy imports avoid a heavy import chain at module load.
     """
-    from dataclasses import replace
     import numpy as np
     from . import detect as _detect
     from .corridor import build_road_corridor

Importance #91: src/iolabs_point_cloud_detection_verticalsigns/ml.py @@ -715,10 +713,10 @@

715	)	713	)
716	mask = select_tree_seed_cells(counts, vspan, hmax, config)	714	mask = select_tree_seed_cells(counts, vspan, hmax, config)
717	if not np.any(mask):	715	if not np.any(mask):
718	return []	716	return []
719	tcfg = replace(	717	tcfg = config.with_overrides(
720	config, cluster_eps_m=config.tree_eps_m,	718	cluster_eps_m=config.tree_eps_m,
721	cluster_min_samples=config.tree_min_samples,	719	cluster_min_samples=config.tree_min_samples,
722	cluster_hull_margin_m=config.tree_hull_margin_m,	720	cluster_hull_margin_m=config.tree_hull_margin_m,
723	)	721	)
724	clusters = cluster_candidates(	722	clusters = cluster_candidates(

Importance #92: src/iolabs_point_cloud_detection_verticalsigns/trees.py @@ -19,10 +19,8 @@

19	"""	19	"""
20		20
21	from __future__ import annotations	21	from __future__ import annotations
22		22
23	from dataclasses import replace
24
25	import numpy as np	23	import numpy as np
26	from iolabs.logstash import get_props_logger	24	from iolabs.logstash import get_props_logger
27		25
28	from ._log_props import LOG_PROPS	26	from ._log_props import LOG_PROPS

Importance #93: src/iolabs_point_cloud_detection_verticalsigns/trees.py @@ -117,10 +115,9 @@

     if not np.any(seed_mask):
         return []
     # Coarser DBSCAN via a shallow config clone (own eps / min_samples / margin).
-    tree_cfg = replace(
+    tree_cfg = config.with_overrides(
-        config,
         cluster_eps_m=config.tree_eps_m,
         cluster_min_samples=config.tree_min_samples,
         cluster_hull_margin_m=config.tree_hull_margin_m,
     )

Importance #94: tests/conftest.py @@ -0,0 +1,29 @@

	1	"""Shared fixtures for the vertical-sign detector tests."""
	2
	3	from collections.abc import Callable
	4	from typing import Any
	5
	6	import pytest
	7	from iolabs.common import config_loader
	8
	9
	10	def _section_values(model: type[config_loader.ConfigModel]) -> dict[str, Any]:
	11	"""Return one valid non-default value per field of model."""
	12	values: dict[str, Any] = {}
	13	for name, field in model.model_fields.items():
	14	annotation = field.annotation
	15	if annotation is bool:
	16	values[name] = not field.default
	17	elif annotation is int:
	18	values[name] = int(field.default) + 1
	19	elif annotation is str:
	20	values[name] = f"{field.default}_x"
	21	else:
	22	values[name] = 0.5
	23	return values
	24
	25
	26	@pytest.fixture
	27	def section_values() -> Callable[[type[config_loader.ConfigModel]], dict[str, Any]]:
	28	"""Return a builder for a full override of one config section."""
	29	return _section_values
0

Importance #95: tests/test_chroma_vegetation.py @@ -8,15 +8,14 @@

 """
 from __future__ import annotations
-import dataclasses
 import json
 import numpy as np
 import pytest
-from iolabs_point_cloud_detection_verticalsigns import _config
+from iolabs_point_cloud_detection_verticalsigns import _config, _model_tree
 from iolabs_point_cloud_detection_verticalsigns.classify import (
     CHROMA_VETOABLE_TYPES,
     apply_tree_emission,
     classify_cluster,

Importance #96: tests/test_chroma_vegetation.py @@ -77,9 +76,9 @@

77	return ClusterFeatures(**base)	76	return ClusterFeatures(**base)
78		77
79		78
80	def _enabled(**overrides) -> DetectorConfig:	79	def _enabled(**overrides) -> DetectorConfig:
81	return dataclasses.replace(DetectorConfig(), chroma_veg_enabled=True, **overrides)	80	return DetectorConfig().with_overrides(chroma_veg_enabled=True, **overrides)
82		81
83		82
84	# --- the rule does its job -------------------------------------------------	83	# --- the rule does its job -------------------------------------------------
85		84

Importance #97: tests/test_chroma_vegetation.py @@ -272,9 +271,9 @@

272	# --- wiring ----------------------------------------------------------------	271	# --- wiring ----------------------------------------------------------------
273		272
274		273
275	def test_emit_trees_promotes_the_reason() -> None:	274	def test_emit_trees_promotes_the_reason() -> None:
276	config = dataclasses.replace(DetectorConfig(), emit_trees=True)	275	config = DetectorConfig().with_overrides(emit_trees=True)
277	assert apply_tree_emission(None, "chroma_vegetation", config) == (	276	assert apply_tree_emission(None, "chroma_vegetation", config) == (
278	"tree",	277	"tree",
279	"chroma_vegetation",	278	"chroma_vegetation",
280	)	279	)

Importance #98: tests/test_chroma_vegetation.py @@ -290,23 +289,23 @@

Importance #99: tests/test_config_split.py @@ -6,16 +6,18 @@

 things must stay true for that split to be invisible to callers:
 * every field is still reachable from the nested config document,
 * the slices partition the fields (no field lost, none declared twice),
-* an absent key still falls back to the dataclass default.
+* an absent key still falls back to the model default.
 """
-import dataclasses
 import json
 import re
 from pathlib import Path
+from iolabs.common import config_loader
+from iolabs_point_cloud_detection_verticalsigns import _config_model
 from iolabs_point_cloud_detection_verticalsigns._config import load_default_config
 from iolabs_point_cloud_detection_verticalsigns.config import DetectorConfig
 CONFIG_PY = (

Importance #100: tests/test_config_split.py @@ -45,9 +47,9 @@

45	"""	47	"""
46	section_locals: dict[str, str] = {}	48	section_locals: dict[str, str] = {}
47	document: dict[str, dict] = {}	49	document: dict[str, dict] = {}
48	expected: dict[str, object] = {}	50	expected: dict[str, object] = {}
49	fields = {f.name: f for f in dataclasses.fields(DetectorConfig)}	51	fields = DetectorConfig.model_fields
50		52
51	for path in sorted(CONFIG_PY.parent.glob("_config_*.py")):	53	for path in sorted(CONFIG_PY.parent.glob("_config_*.py")):
52	text = path.read_text()	54	text = path.read_text()
53	section_locals.update(	55	section_locals.update(

Importance #101: tests/test_config_split.py @@ -69,9 +71,9 @@

69	wrong = {n: (getattr(built, n), v) for n, v in expected.items() if getattr(built, n) != v}	71	wrong = {n: (getattr(built, n), v) for n, v in expected.items() if getattr(built, n) != v}
70	assert not wrong	72	assert not wrong
71		73
72		74
73	def test_absent_sections_fall_back_to_the_dataclass_defaults() -> None:	75	def test_absent_sections_fall_back_to_the_model_defaults() -> None:
74	assert DetectorConfig.from_mapping({}) == DetectorConfig()	76	assert DetectorConfig.from_mapping({}) == DetectorConfig()
75		77
76		78
77	def test_a_partial_section_only_overrides_the_keys_it_carries() -> None:	79	def test_a_partial_section_only_overrides_the_keys_it_carries() -> None:

Importance #102: tests/test_config_split.py @@ -80,8 +82,46 @@

80	assert built.ground_percentile == DetectorConfig().ground_percentile	82	assert built.ground_percentile == DetectorConfig().ground_percentile
81	assert built.perspective_coverage_tol_m == DetectorConfig().perspective_coverage_tol_m	83	assert built.perspective_coverage_tol_m == DetectorConfig().perspective_coverage_tol_m
82		84
83		85
		86	def test_every_mapped_key_exists_in_the_nested_model() -> None:
		87	"""A flat field wired to a section key the model does not declare is dead.
		88
		89	``load_verticalsigns_config`` validates against the model, so such a key is
		90	rejected for a user config and can only ever hold its flat default.
		91	"""
		92	document, _ = _saturating_config()
		93	merged = config_loader.deep_merge_dicts(load_default_config(), document)
		94	assert _config_model.VerticalSignsConfig.model_validate(merged)
		95
		96
84	def test_the_packaged_defaults_round_trip() -> None:	97	def test_the_packaged_defaults_round_trip() -> None:
85	packaged = load_default_config()	98	packaged = load_default_config()
86	assert json.dumps(packaged) # it is a plain JSON document	99	assert json.dumps(packaged) # it is a plain JSON document
87	assert DetectorConfig.from_mapping(packaged) == DetectorConfig.load()	100	assert DetectorConfig.from_mapping(packaged) == DetectorConfig.load()
		101
		102
		103	def test_the_packaged_defaults_equal_the_flat_defaults() -> None:
		104	"""The nested model and the flat slices must not drift apart.
		105
		106	The nested :class:`VerticalSignsConfig` sections and the flat
		107	``DetectorConfig`` slices declare the same numbers twice, so a value
		108	changed on one side only is a silent config bug: ``DetectorConfig()`` (what
		109	tests and ad-hoc calls build) would disagree with ``DetectorConfig.load()``
		110	(what the detector runs).
		111	"""
		112	assert DetectorConfig.from_mapping(load_default_config()) == DetectorConfig()
		113
		114
		115	def test_with_overrides_rejects_a_misspelled_field() -> None:
		116	"""A typo must not become a new attribute while the threshold keeps its default.
		117
		118	``model_copy(update=...)`` skips validation, so this is the only thing
		119	standing between a misspelled override and a silently ignored threshold.
		120	"""
		121	assert DetectorConfig().with_overrides(cluster_eps_m=0.9).cluster_eps_m == 0.9
		122	try:
		123	DetectorConfig().with_overrides(cluster_eps=0.9)
		124	except ValueError as exc:
		125	assert "cluster_eps" in str(exc)
		126	else: # pragma: no cover - the failure the test exists to catch
		127	raise AssertionError("a misspelled field name was accepted")

Importance #103: tests/test_detect.py @@ -1,6 +1,5 @@

1	import csv	1	import csv
2	import dataclasses
3	import io	2	import io
4		3
5	import numpy as np	4	import numpy as np
6		5

Importance #104: tests/test_detect.py @@ -14,9 +13,9 @@

14	assign_record_coverage,	13	assign_record_coverage,
15	)	14	)
16	from iolabs_point_cloud_detection_verticalsigns.features import ClusterFeatures	15	from iolabs_point_cloud_detection_verticalsigns.features import ClusterFeatures
17		16
18	_CFG = dataclasses.replace(DetectorConfig(), tree_crown_h_min_m=2.5)	17	_CFG = DetectorConfig().with_overrides(tree_crown_h_min_m=2.5)
19		18
20		19
21	def _feat(**overrides) -> ClusterFeatures:	20	def _feat(**overrides) -> ClusterFeatures:
22	base = dict(	21	base = dict(

Importance #105: tests/test_edgeline.py @@ -8,10 +8,8 @@

8	"""	8	"""
9		9
10	from __future__ import annotations	10	from __future__ import annotations
11		11
12	import dataclasses
13
14	import numpy as np	12	import numpy as np
15		13
16	from iolabs_point_cloud_detection_verticalsigns.classify import (	14	from iolabs_point_cloud_detection_verticalsigns.classify import (
17	apply_edge_line_gate,	15	apply_edge_line_gate,

Importance #106: tests/test_edgeline.py @@ -279,12 +277,10 @@

     axis = _axis()
     coverages = []
     for pct in (93.0, 95.0, 97.0):
         for fill in (0.3, 0.4, 0.5):
-            config = dataclasses.replace(
+            config = DetectorConfig().with_overrides(
-                DetectorConfig(),
+                edgeline_paint_intensity_percentile=pct, edgeline_min_line_along_fill=fill
-                edgeline_paint_intensity_percentile=pct,
-                edgeline_min_line_along_fill=fill,
             )
             coverages.append(build_edge_lines(xy, intensity, axis, config).coverage)
     assert min(coverages) >= 0.6, coverages

Importance #107: tests/test_tcs_ground.py @@ -5,9 +5,8 @@

5	"""	5	"""
6		6
7	from __future__ import annotations	7	from __future__ import annotations
8		8
9	import dataclasses
10	import os	9	import os
11	from pathlib import Path	10	from pathlib import Path
12		11
13	import numpy as np	12	import numpy as np

Importance #108: tests/test_tcs_ground.py @@ -94,9 +93,9 @@

94	return _write_record(tmp_path / name, points)	93	return _write_record(tmp_path / name, points)
95		94
96		95
97	def _config(**overrides: object) -> DetectorConfig:	96	def _config(**overrides: object) -> DetectorConfig:
98	return dataclasses.replace(DetectorConfig(), **overrides)	97	return DetectorConfig().with_overrides(**overrides)
99		98
100		99
101	def _dem(files: list[Path]) -> np.ndarray:	100	def _dem(files: list[Path]) -> np.ndarray:
102	model = build_ground_model(	101	model = build_ground_model(

Importance #109: tests/test_tree_instances.py @@ -12,15 +12,14 @@

 """
 from __future__ import annotations
-import dataclasses
 import json
 import numpy as np
 import pytest
-from iolabs_point_cloud_detection_verticalsigns import _config
+from iolabs_point_cloud_detection_verticalsigns import _config, _model_tree
 from iolabs_point_cloud_detection_verticalsigns.config import DetectorConfig
 from iolabs_point_cloud_detection_verticalsigns.tree_instances import (
     ABSTAIN_ASSIGNED,
     ABSTAIN_HEDGE,

Importance #110: tests/test_tree_instances.py @@ -363,9 +362,9 @@

363	_canopy_ball(rng, radius=1.5, centre_h=3.0, centre_xy=(0.0, 0.0)),	362	_canopy_ball(rng, radius=1.5, centre_h=3.0, centre_xy=(0.0, 0.0)),
364	_canopy_ball(rng, radius=1.5, centre_h=3.0, centre_xy=(18.0, 0.0)),	363	_canopy_ball(rng, radius=1.5, centre_h=3.0, centre_xy=(18.0, 0.0)),
365	]	364	]
366	)	365	)
367	config = dataclasses.replace(DetectorConfig(), ti_apex_fallback_enabled=False)	366	config = DetectorConfig().with_overrides(ti_apex_fallback_enabled=False)
368		367
369	result = split_tree_cluster(xyz, 0.0, None, config)	368	result = split_tree_cluster(xyz, 0.0, None, config)
370		369
371	assert result.seeds == []	370	assert result.seeds == []

Importance #111: tests/test_tree_instances.py @@ -464,10 +463,10 @@

464	)	463	)
465	# The apex fallback is switched off here on purpose: it would recover the	464	# The apex fallback is switched off here on purpose: it would recover the
466	# far trees from their crowns (that is exactly what it is for) and so hide	465	# far trees from their crowns (that is exactly what it is for) and so hide
467	# the failure this counterfactual exists to display.	466	# the failure this counterfactual exists to display.
468	config = dataclasses.replace(	467	config = DetectorConfig().with_overrides(
469	DetectorConfig(), ti_local_ground_footprint_m=1e6, ti_apex_fallback_enabled=False	468	ti_local_ground_footprint_m=1e6, ti_apex_fallback_enabled=False
470	)	469	)
471		470
472	result = split_tree_cluster(xyz, 0.0, None, config)	471	result = split_tree_cluster(xyz, 0.0, None, config)
473		472

Importance #112: tests/test_tree_instances.py @@ -492,9 +491,9 @@

492	# Stem-only: with the apex fallback on, the fragment is a crown of its own	491	# Stem-only: with the apex fallback on, the fragment is a crown of its own
493	# and legitimately becomes its own instance. What must never happen — and	492	# and legitimately becomes its own instance. What must never happen — and
494	# is what this test pins — is the fragment being ANNEXED by the tree next	493	# is what this test pins — is the fragment being ANNEXED by the tree next
495	# to it through the graph.	494	# to it through the graph.
496	config = dataclasses.replace(DetectorConfig(), ti_apex_fallback_enabled=False)	495	config = DetectorConfig().with_overrides(ti_apex_fallback_enabled=False)
497		496
498	result = split_tree_cluster(xyz, 0.0, None, config)	497	result = split_tree_cluster(xyz, 0.0, None, config)
499		498
500	assert len(result.seeds) == 1	499	assert len(result.seeds) == 1

Importance #113: tests/test_tree_instances.py @@ -510,10 +509,10 @@

510	which is right for some callers and wrong for most, so it is a switch.	509	which is right for some callers and wrong for most, so it is a switch.
511	"""	510	"""
512	rng = np.random.default_rng(47)	511	rng = np.random.default_rng(47)
513	xyz = np.vstack([_cone(rng, 0.0, 0.0), _cone(rng, 3.0, 0.0)])	512	xyz = np.vstack([_cone(rng, 0.0, 0.0), _cone(rng, 3.0, 0.0)])
514	strict = dataclasses.replace(	513	strict = DetectorConfig().with_overrides(
515	DetectorConfig(), ti_low_evidence_abstain=True, ti_low_evidence_margin=0.05	514	ti_low_evidence_abstain=True, ti_low_evidence_margin=0.05
516	)	515	)
517		516
518	lenient = split_tree_cluster(xyz, 0.0, None, DetectorConfig())	517	lenient = split_tree_cluster(xyz, 0.0, None, DetectorConfig())
519	result = split_tree_cluster(xyz, 0.0, None, strict)	518	result = split_tree_cluster(xyz, 0.0, None, strict)

Importance #114: tests/test_tree_instances.py @@ -748,9 +747,9 @@

748	[r * np.cos(ang), r * np.sin(ang), rng.uniform(0.0, 2.2, 800)]	747	[r * np.cos(ang), r * np.sin(ang), rng.uniform(0.0, 2.2, 800)]
749	)	748	)
750	xyz = np.vstack([band, trunk])	749	xyz = np.vstack([band, trunk])
751	# Stem-only, so the one seed is unambiguous and the cap is what is measured.	750	# Stem-only, so the one seed is unambiguous and the cap is what is measured.
752	config = dataclasses.replace(DetectorConfig(), ti_apex_fallback_enabled=False)	751	config = DetectorConfig().with_overrides(ti_apex_fallback_enabled=False)
753		752
754	result = split_tree_cluster(xyz, 0.0, None, config)	753	result = split_tree_cluster(xyz, 0.0, None, config)
755	far = xyz[:, 0] > 15.0	754	far = xyz[:, 0] > 15.0
756	near = xyz[:, 0] < 5.0	755	near = xyz[:, 0] < 5.0

Importance #115: tests/test_tree_instances.py @@ -764,10 +763,9 @@

     uncapped = split_tree_cluster(
         xyz,
         0.0,
         None,
-        dataclasses.replace(
+        config.with_overrides(
-            config,
             ti_max_claim_radius_m=40.0,
             ti_instance_max_linearity=1.01,
             ti_instance_min_thickness_share=0.0,
         ),

Importance #116: tests/test_tree_instances.py @@ -791,9 +789,9 @@

791		789
792	result = split_tree_cluster(xyz, 0.0, None, DetectorConfig())	790	result = split_tree_cluster(xyz, 0.0, None, DetectorConfig())
793	# The v2 semantics, reconstructed: the same cap applied to the PATH.	791	# The v2 semantics, reconstructed: the same cap applied to the PATH.
794	v2 = split_tree_cluster(	792	v2 = split_tree_cluster(
795	xyz, 0.0, None, dataclasses.replace(DetectorConfig(), ti_assign_max_graph_dist_m=9.0)	793	xyz, 0.0, None, DetectorConfig().with_overrides(ti_assign_max_graph_dist_m=9.0)
796	)	794	)
797		795
798	assert len(result.seeds) == 1	796	assert len(result.seeds) == 1
799	assert float(np.mean(result.labels[canopy] >= 0)) > 0.95	797	assert float(np.mean(result.labels[canopy] >= 0)) > 0.95

Importance #117: tests/test_tree_instances.py @@ -812,9 +810,9 @@

812	rng = np.random.default_rng(97)	810	rng = np.random.default_rng(97)
813	big = _cone(rng, 0.0, 0.0, n_crown=3000, n_trunk=400)	811	big = _cone(rng, 0.0, 0.0, n_crown=3000, n_trunk=400)
814	small = _cone(rng, 4.0, 0.0, top=4.0, crown_r=1.0, n_crown=300, n_trunk=120)	812	small = _cone(rng, 4.0, 0.0, top=4.0, crown_r=1.0, n_crown=300, n_trunk=120)
815	xyz = np.vstack([big, small])	813	xyz = np.vstack([big, small])
816	config = dataclasses.replace(DetectorConfig(), ti_min_instance_points=1000)	814	config = DetectorConfig().with_overrides(ti_min_instance_points=1000)
817		815
818	kept = split_tree_cluster(xyz, 0.0, None, DetectorConfig())	816	kept = split_tree_cluster(xyz, 0.0, None, DetectorConfig())
819	result = split_tree_cluster(xyz, 0.0, None, config)	817	result = split_tree_cluster(xyz, 0.0, None, config)
820		818

Importance #118: tests/test_tree_instances.py @@ -873,10 +871,9 @@

     kept = split_tree_cluster(
         xyz,
         0.0,
         None,
-        dataclasses.replace(
+        DetectorConfig().with_overrides(
-            DetectorConfig(),
             ti_instance_max_linearity=1.01,
             ti_instance_min_minor_m=0.0,
             ti_instance_min_vertical_m=0.0,
             ti_instance_min_thickness_share=0.0,

Importance #119: tests/test_tree_instances.py @@ -910,9 +907,9 @@

910	)	907	)
911		908
912	result = split_tree_cluster(xyz, 0.0, None, DetectorConfig())	909	result = split_tree_cluster(xyz, 0.0, None, DetectorConfig())
913	undamped = split_tree_cluster(	910	undamped = split_tree_cluster(
914	xyz, 0.0, None, dataclasses.replace(DetectorConfig(), ti_min_points_per_instance=1)	911	xyz, 0.0, None, DetectorConfig().with_overrides(ti_min_points_per_instance=1)
915	)	912	)
916		913
917	assert len(undamped.seeds) >= 3	914	assert len(undamped.seeds) >= 3
918	assert len(result.seeds) <= 1	915	assert len(result.seeds) <= 1

Importance #120: tests/test_tree_instances.py @@ -931,16 +928,16 @@

931	[rng.uniform(0.0, 7.0, n), rng.uniform(-0.3, 0.3, n), rng.uniform(0.0, 3.0, n)]	928	[rng.uniform(0.0, 7.0, n), rng.uniform(-0.3, 0.3, n), rng.uniform(0.0, 3.0, n)]
932	)	929	)
933	# Stem-only: the seedless verdict is what is being measured, and an apex	930	# Stem-only: the seedless verdict is what is being measured, and an apex
934	# seed would answer the question before the gate is reached.	931	# seed would answer the question before the gate is reached.
935	config = dataclasses.replace(DetectorConfig(), ti_apex_fallback_enabled=False)	932	config = DetectorConfig().with_overrides(ti_apex_fallback_enabled=False)
936		933
937	result = split_tree_cluster(xyz, 0.0, None, config)	934	result = split_tree_cluster(xyz, 0.0, None, config)
938	lenient = split_tree_cluster(	935	lenient = split_tree_cluster(
939	xyz,	936	xyz,
940	0.0,	937	0.0,
941	None,	938	None,
942	dataclasses.replace(config, ti_seedless_max_aspect=100.0, ti_seedless_min_points=1),	939	config.with_overrides(ti_seedless_max_aspect=100.0, ti_seedless_min_points=1),
943	)	940	)
944		941
945	assert result.seeds == []	942	assert result.seeds == []
946	assert result.split_quality == "uncertain"	943	assert result.split_quality == "uncertain"

Importance #121: tests/test_tree_instances.py @@ -974,9 +971,9 @@

974	xyz = _foliage_band(rng)	971	xyz = _foliage_band(rng)
975		972
976	result = split_tree_cluster(xyz, 0.0, None, DetectorConfig())	973	result = split_tree_cluster(xyz, 0.0, None, DetectorConfig())
977	blind = split_tree_cluster(	974	blind = split_tree_cluster(
978	xyz, 0.0, None, dataclasses.replace(DetectorConfig(), ti_hedge_max_height_m=4.5)	975	xyz, 0.0, None, DetectorConfig().with_overrides(ti_hedge_max_height_m=4.5)
979	)	976	)
980		977
981	assert result.is_hedge and result.split_quality == "hedge"	978	assert result.is_hedge and result.split_quality == "hedge"
982	assert result.seeds == []	979	assert result.seeds == []

Importance #122: tests/test_tree_instances.py @@ -1051,9 +1048,9 @@

1051		1048
1052	# --- config section --------------------------------------------------------	1049	# --- config section --------------------------------------------------------
1053		1050
1054		1051
1055	def test_overrides_reach_the_dataclass() -> None:	1052	def test_overrides_reach_the_model() -> None:
1056	config = DetectorConfig.from_mapping(	1053	config = DetectorConfig.from_mapping(
1057	{"tree_instance": {"enabled": True, "stem_eps_m": 0.5}}	1054	{"tree_instance": {"enabled": True, "stem_eps_m": 0.5}}
1058	)	1055	)
1059	assert config.tree_instance_enabled is True	1056	assert config.tree_instance_enabled is True

Importance #123: tests/test_tree_instances.py @@ -1061,21 +1058,21 @@

290	assert config.chroma_veg_exg_iqr_min == DetectorConfig().chroma_veg_exg_iqr_min	289	assert config.chroma_veg_exg_iqr_min == DetectorConfig().chroma_veg_exg_iqr_min
291		290
292		291
293	def test_config_rejects_an_unknown_key_in_the_new_section(tmp_path) -> None:	292	def test_config_rejects_an_unknown_key_in_the_new_section(tmp_path) -> None:
294	"""Pins the _ALLOWED_BY_SECTION wiring.	293	"""Pins the ChromaVegetationConfig wiring.
295		294
296	Without this, deleting the "chroma_vegetation" entry from that mapping	295	Without this, dropping the "chroma_vegetation" field from the model tree
297	leaves the whole suite green while silently disabling validation for the	296	leaves the whole suite green while silently disabling validation for the
298	section -- a typo'd threshold would then be accepted and ignored.	297	section -- a typo'd threshold would then be accepted and ignored.
299	"""	298	"""
300	path = tmp_path / "override.json"	299	path = tmp_path / "override.json"
301	path.write_text(json.dumps({"chroma_vegetation": {"exg_minimum": 0.2}}))	300	path.write_text(json.dumps({"chroma_vegetation": {"exg_minimum": 0.2}}))
302	with pytest.raises(_config.ConfigError):	301	with pytest.raises(_config.VerticalSignsConfigError):
303	_config.load_verticalsigns_config(path)	302	_config.load_verticalsigns_config(path)
304		303
305		304
306	def test_config_accepts_every_documented_key(tmp_path) -> None:	305	def test_config_accepts_every_documented_key(tmp_path, section_values) -> None:
307	"""The other half: no allowlisted key is rejected."""	306	"""The other half: no modelled key is rejected."""
308	section = {k: 0.1 for k in _config.ALLOWED_CHROMA_VEGETATION_KEYS}	307	section = section_values(_model_tree.ChromaVegetationConfig)
309	section["enabled"] = True	308	section["enabled"] = True
310	path = tmp_path / "override.json"	309	path = tmp_path / "override.json"
311	path.write_text(json.dumps({"chroma_vegetation": section}))	310	path.write_text(json.dumps({"chroma_vegetation": section}))
312	assert _config.load_verticalsigns_config(path)["chroma_vegetation"]["enabled"]	311	assert _config.load_verticalsigns_config(path)["chroma_vegetation"]["enabled"]

1061	assert config.ti_stem_min_samples == DetectorConfig().ti_stem_min_samples	1058	assert config.ti_stem_min_samples == DetectorConfig().ti_stem_min_samples
1062		1059
1063		1060
1064	def test_config_rejects_an_unknown_key_in_the_new_section(tmp_path) -> None:	1061	def test_config_rejects_an_unknown_key_in_the_new_section(tmp_path) -> None:
1065	"""Pins the _ALLOWED_BY_SECTION wiring.	1062	"""Pins the TreeInstanceConfig wiring.
1066		1063
1067	Without it the section validates against nothing, and a typo'd threshold	1064	Without it the section validates against nothing, and a typo'd threshold
1068	is accepted and then silently ignored.	1065	is accepted and then silently ignored.
1069	"""	1066	"""
1070	path = tmp_path / "override.json"	1067	path = tmp_path / "override.json"
1071	path.write_text(json.dumps({"tree_instance": {"stem_eps": 0.5}}))	1068	path.write_text(json.dumps({"tree_instance": {"stem_eps": 0.5}}))
1072	with pytest.raises(_config.ConfigError):	1069	with pytest.raises(_config.VerticalSignsConfigError):
1073	_config.load_verticalsigns_config(path)	1070	_config.load_verticalsigns_config(path)
1074		1071
1075		1072
1076	def test_config_accepts_every_documented_key(tmp_path) -> None:	1073	def test_config_accepts_every_documented_key(tmp_path, section_values) -> None:
1077	section = {k: 0.5 for k in _config.ALLOWED_TREE_INSTANCE_KEYS}	1074	section = section_values(_model_tree.TreeInstanceConfig)
1078	section["enabled"] = True	1075	section["enabled"] = True
1079	path = tmp_path / "override.json"	1076	path = tmp_path / "override.json"
1080	path.write_text(json.dumps({"tree_instance": section}))	1077	path.write_text(json.dumps({"tree_instance": section}))
1081	assert _config.load_verticalsigns_config(path)["tree_instance"]["enabled"]	1078	assert _config.load_verticalsigns_config(path)["tree_instance"]["enabled"]