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ebeed67 | 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 | """COCO → CamStack macro classes, as the pipeline maps them
(`packages/types/src/catalogs/coco-classmap.ts` `COCO_TO_MACRO`). The floors
and the comparison are per macro class, because that is what the pipeline
gates on. `package` (COCO luggage proxies) is mapped but not evaluated by
default: it was too rare to calibrate (D739 keeps it at 0.50)."""
from __future__ import annotations
from typing import Optional
from .metrics import Detection
COCO_TO_MACRO: dict[str, str] = {
"person": "person",
**{k: "vehicle" for k in ("bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat")},
**{
k: "animal"
for k in ("bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe")
},
**{k: "package" for k in ("suitcase", "backpack", "handbag")},
}
#: Macro classes a root detector is evaluated on.
DETECTOR_CLASSES: tuple[str, ...] = ("person", "vehicle", "animal")
def macro_of(label: str) -> Optional[str]:
"""A label's macro class; a label that already IS a macro maps to itself."""
if label in DETECTOR_CLASSES or label == "package":
return label
return COCO_TO_MACRO.get(label)
def to_macro(detections: list[Detection]) -> list[Detection]:
"""Map detections to macro classes; a class the pipeline drops is dropped."""
out = []
for d in detections:
macro = macro_of(d.cls)
if macro is not None:
out.append(Detection(macro, d.score, d.bbox))
return out
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