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085f6c4 | 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 | """Align extracted entities with synthgen ground-truth records (T020/T022).
synthgen writes entities-vN.json per revision: {uid, kind, semantic, bbox,
text}. Extraction produces one DrawingEntity per synthetic primitive, so a
deterministic one-to-one nearest-center assignment within the same structural
kind recovers labels (semantic → classifier targets) and cross-revision
correspondence (shared uid → matcher positives).
"""
from __future__ import annotations
import json
from pathlib import Path
from typing import Any
from cadmorph.models import DrawingGraph
ALIGN_TOL_REL = 0.02 # max center distance for a label assignment, rel. to diagonal
def load_records(path: str | Path) -> list[dict[str, Any]]:
return json.loads(Path(path).read_text(encoding="utf-8"))
def _center(bbox: list[float] | tuple[float, ...]) -> tuple[float, float]:
return ((bbox[0] + bbox[2]) / 2.0, (bbox[1] + bbox[3]) / 2.0)
def align_records(graph: DrawingGraph, records: list[dict[str, Any]]) -> dict[str, dict[str, Any]]:
"""entity_id -> ground-truth record, one-to-one, globally nearest-first.
Only same-kind pairs within ALIGN_TOL_REL of each other are eligible;
entities/records without a counterpart stay unassigned.
"""
tol = graph.revision.sheet_diagonal * ALIGN_TOL_REL
candidates: list[tuple[float, str, int]] = []
for entity in graph.entities:
ex, ey = _center(entity.bbox)
for i, record in enumerate(records):
if record["kind"] != entity.kind:
continue
rx, ry = _center(record["bbox"])
dist = ((ex - rx) ** 2 + (ey - ry) ** 2) ** 0.5
if dist <= tol:
candidates.append((dist, entity.entity_id, i))
assignment: dict[str, dict[str, Any]] = {}
used_records: set[int] = set()
for dist, entity_id, i in sorted(candidates, key=lambda c: (c[0], c[1], c[2])):
if entity_id in assignment or i in used_records:
continue
assignment[entity_id] = records[i]
used_records.add(i)
return assignment
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