File size: 8,744 Bytes
e6f80f3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 | """Fail-closed source adapters for the v2 geometry contract.
These read original annotations; they never call v1's geometric fallbacks.
MultiScan is the initially admitted source because it supplies annotated front
and up vectors, local OBB half extents, and independent structural geometry.
"""
from __future__ import annotations
import csv
import json
import math
from collections import Counter, defaultdict
from pathlib import Path
from typing import Iterator
import numpy as np
SOURCE_DIRECTORY = "XXXpilar__multiscan-clean"
AXIS_TOLERANCE = 1e-5
def _array(value, shape, name):
array = np.asarray(value, dtype=np.float64)
if array.shape != shape or not np.isfinite(array).all():
raise ValueError(f"{name} must be finite with shape {shape}")
return array
def canonical_obb(center, extents, axes, front, up, *, half_extents=True, unit_scale=1.0):
"""Source OBB -> local full size, bottom center, semantic yaw.
`axes` are rows containing world directions. Full extents are already
instance-scaled; no asset scale is applied again. Significant pitch/roll,
non-orthogonal frames, and unknown/vertical fronts are refused.
"""
c = _array(center, (3,), "center")
e = _array(extents, (3,), "positive extents")
a = _array(axes, (3, 3), "axes")
f = _array(front, (3,), "front")
u = _array(up, (3,), "up")
if not np.isfinite(unit_scale) or unit_scale <= 0 or not (e > 0).all():
raise ValueError("extents and unit_scale must be strictly positive")
if not np.allclose(a @ a.T, np.eye(3), atol=AXIS_TOLERANCE, rtol=0):
raise ValueError("OBB axes must be orthonormal")
if not np.allclose(u, [0, 0, 1], atol=AXIS_TOLERANCE, rtol=0):
raise ValueError("only upright +Z-up objects are admitted; tilt needs another protocol")
if abs(f[2]) > AXIS_TOLERANCE or not np.isclose(np.linalg.norm(f), 1, atol=AXIS_TOLERANCE):
raise ValueError("annotated front must be a unit horizontal box axis")
if not np.isclose(np.max(np.abs(a @ f)), 1, atol=AXIS_TOLERANCE):
raise ValueError("annotated front must be aligned with an OBB axis")
if not np.isclose(np.max(np.abs(a @ u)), 1, atol=AXIS_TOLERANCE):
raise ValueError("upright up must be aligned with an OBB axis")
basis = np.stack([f, np.cross(u, f), u], axis=1)
full = e * (2 if half_extents else 1) * unit_scale
size = full @ np.abs(a @ basis)
bottom = c * unit_scale - np.array([0, 0, size[2] / 2])
yaw = (math.atan2(f[1], f[0]) + math.pi) % (2 * math.pi) - math.pi
return {"target_size_local_m": size.tolist(), "bottom_center_m": bottom.tolist(), "yaw_rad": yaw}
def _box(row, geometric_front=False):
axes = np.asarray(json.loads(row["obb_axes"]), dtype=float).reshape(3, 3)
front = json.loads(row["front"])
up = json.loads(row["up"])
if geometric_front:
# Fixed obstacles need geometric coverage, not a semantic facing label.
horizontal = [axis for axis in axes if abs(axis[2]) < AXIS_TOLERANCE]
if not horizontal:
raise ValueError("fixed geometry is tilted and cannot be silently projected")
front, up = horizontal[0], [0, 0, 1]
return canonical_obb(json.loads(row["obb_center"]), json.loads(row["obb_half_extents"]),
axes, front, up)
def _room(region):
polygon = _array(json.loads(region["poly_loop"]), (len(json.loads(region["poly_loop"])), 2), "floor polygon")
if len(polygon) < 3:
raise ValueError("independent source floor footprint needs at least three vertices")
floor = float(region["floor_height"])
if not math.isfinite(floor):
raise ValueError("source floor height must be finite")
height = None
if region.get("height_reliable") == "True":
height = float(region["ceiling_height"]) - floor
if not math.isfinite(height) or height <= 0:
raise ValueError("reliable room height must be positive")
return {"frame": "right_handed_z_up", "floor_polygon_xy_m": polygon.tolist(),
"floor_z_m": floor, "height_m": height, "room_type": region.get("room_type") or None,
"boundary_quality": "partial_scanned_floor_convex_hull", "boundary_known": False,
"floor_known": False, "fixed_objects": []}
def multiscan_sample(region, rows, scan):
"""One scan, full requested inventory, with unsupported upright objects fixed.
The footprint and floor slab top are structural inputs. The floor slab top
is a coordinate reference, not verified physical contact. Target furniture
cannot expand the room or estimate its floor. Tilted fixed or requested
objects reject the scene rather than disappear or become yaw-only labels.
"""
room = _room(region)
requests, targets, fixed, source_ids = [], [], [], []
for row in rows:
category = row["category"].replace("_", " ").strip()
structural = row.get("is_architectural") == "True" or row.get("is_opening") == "True"
if structural and category in {"floor", "ceiling"}:
continue
front = np.asarray(json.loads(row["front"]), dtype=float)
use_fixed = structural or abs(front[2]) > AXIS_TOLERANCE
box = _box(row, geometric_front=use_fixed)
if use_fixed:
fixed.append({"id": f"fixed_{len(fixed):04d}", "category": category,
"size_local_m": box["target_size_local_m"],
"bottom_center_m": box["bottom_center_m"], "yaw_rad": box["yaw_rad"]})
continue
if not category:
raise ValueError("requested category missing")
object_id = f"object_{len(requests):04d}"
requests.append({"id": object_id, "category": category, "description": f"a {category}"})
targets.append({"id": object_id, **box})
source_ids.append(row["object_id"])
if not requests:
raise ValueError("scene has no eligible requested objects")
# Requests contain anonymous categories/descriptions only: no relation,
# support, capability, or size role distinguishes repeated instances.
# Groups are supervision bookkeeping (validity, target order), never condition text.
category_count = Counter(request["category"] for request in requests)
groups = [f"anonymous_{request['category']}" if category_count[request["category"]] > 1 else None
for request in requests]
n = len(requests)
return {"schema_version": "fastfill.v2",
"condition": {"schema_version": "fastfill.v2", "room": {**room, "fixed_objects": fixed},
"objects": requests, "constraints": []},
"target": {"schema_version": "fastfill.v2", "objects": targets},
"validity": {"position": [[True] * 3 for _ in range(n)], "size": [[True] * 3 for _ in range(n)],
"yaw": [True] * n, "yaw_symmetry_order": [1] * n, "exchangeable_group": groups},
"provenance": {"source": "MultiScan", "scene_id": region["scan_id"],
"house_id": scan["scene_id"], "source_object_ids": source_ids,
"source_frame": "scan_local_Z_up_meters", "geometry_evidence":
"objects.csv: annotation OBB center/half-extents/axes/front/up; no additional scale",
"room_geometry_evidence": "regions.csv poly_loop and floor slab AABB top",
"semantic_front_evidence": "source annotated front vector",
"upright_axis_tolerance": AXIS_TOLERANCE,
"split": None, "relations_used_as_condition": False,
"mesh_ply": scan.get("mesh_ply"), "mesh_obj": scan.get("mesh_obj")}}
def iter_multiscan(root: Path, diagnostics: dict) -> Iterator[dict]:
"""Yield verified samples; report every rejected source scene."""
base = Path(root) / SOURCE_DIRECTORY
scans, grouped = {}, defaultdict(list)
with (base / "scans.csv").open(newline="") as fh:
scans = {row["scan_id"]: row for row in csv.DictReader(fh)}
with (base / "objects.csv").open(newline="") as fh:
for row in csv.DictReader(fh):
grouped[row["scan_id"]].append(row)
with (base / "regions.csv").open(newline="") as fh:
for region in csv.DictReader(fh):
diagnostics["scenes_inspected"] += 1
try:
sample = multiscan_sample(region, grouped[region["scan_id"]], scans[region["scan_id"]])
except (ValueError, KeyError, TypeError, json.JSONDecodeError) as exc:
diagnostics["rejections"].append({"scene_id": region["scan_id"], "reason": str(exc)})
continue
yield sample
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