File size: 2,615 Bytes
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 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 | """Pseudo ground truth from two teacher detectors — the 2026-10-07 method.
"Two large reference detectors (YOLO26-X at 1280 px and RT-DETR-X at 640 px)
are run on every frame; an object counts when both agree (or one is very
confident). Uncertain boxes are don't care." The original scripts were lost
with a session scratchpad; the thresholds below are the documented SHAPE of
the rule with defaults chosen here, not the exact values of that run.
Detections must already be in macro classes (`classes.to_macro`).
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import Sequence
from .metrics import MATCH_IOU, Detection, GroundTruthBox, iou
@dataclass(frozen=True)
class PseudoGtPolicy:
#: Both teachers at or above this, same class, IoU >= 0.5 ⇒ positive.
agree_score: float = 0.3
#: One teacher alone at or above this ⇒ positive.
solo_score: float = 0.8
#: A box a teacher holds at or above this that is not a positive ⇒ don't care.
care_score: float = 0.3
def pseudo_ground_truth(
teacher_a: Sequence[Detection],
teacher_b: Sequence[Detection],
policy: PseudoGtPolicy = PseudoGtPolicy(),
) -> tuple[list[GroundTruthBox], list[GroundTruthBox]]:
positives: list[GroundTruthBox] = []
used_b: set[int] = set()
leftovers: list[Detection] = []
for a in sorted(teacher_a, key=lambda d: -d.score):
partner = -1
if a.score >= policy.agree_score:
for j, b in enumerate(teacher_b):
if j in used_b or b.cls != a.cls or b.score < policy.agree_score:
continue
if iou(a.bbox, b.bbox) >= MATCH_IOU:
partner = j
break
if partner >= 0:
used_b.add(partner)
positives.append(GroundTruthBox(a.cls, a.bbox))
elif a.score >= policy.solo_score:
positives.append(GroundTruthBox(a.cls, a.bbox))
else:
leftovers.append(a)
for j, b in enumerate(teacher_b):
if j in used_b:
continue
duplicate = any(p.cls == b.cls and iou(p.bbox, b.bbox) >= MATCH_IOU for p in positives)
if duplicate:
continue
if b.score >= policy.solo_score:
positives.append(GroundTruthBox(b.cls, b.bbox))
else:
leftovers.append(b)
dont_care = [
GroundTruthBox(d.cls, d.bbox)
for d in leftovers
if d.score >= policy.care_score
and not any(p.cls == d.cls and iou(p.bbox, d.bbox) >= MATCH_IOU for p in positives)
]
return positives, dont_care
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