어노테이션 품질

from itertools import combinations
from pathlib import Path

from ultralytics.data.utils import check_det_dataset

def iou(a, b):
    """IoU of two normalized (x_center, y_center, width, height) boxes."""
    ax1, ay1, ax2, ay2 = a[0] - a[2] / 2, a[1] - a[3] / 2, a[0] + a[2] / 2, a[1] + a[3] / 2
    bx1, by1, bx2, by2 = b[0] - b[2] / 2, b[1] - b[3] / 2, b[0] + b[2] / 2, b[1] + b[3] / 2
    inter = max(0, min(ax2, bx2) - max(ax1, bx1)) * max(0, min(ay2, by2) - max(ay1, by1))
    return inter / (a[2] * a[3] + b[2] * b[3] - inter)

def check_labels(text, names):
    """Return a list of problems in one YOLO detection label file."""
    problems, boxes = [], []
    for i, row in enumerate(text.split("\n"), 1):
        values = row.split()
        if not values:
            continue
        if len(values) != 5:
            problems.append(f"row {i}: {len(values)} values, expected 5")
        elif int(values[0]) not in names:
            problems.append(f"row {i}: unknown class {values[0]}")
        elif not all(0 <= float(v) <= 1 for v in values[1:]):
            problems.append(f"row {i}: coordinates outside 0-1")
        else:
            boxes.append((i, int(values[0]), [float(v) for v in values[1:]]))
    for (i, ca, a), (j, cb, b) in combinations(boxes, 2):
        if ca == cb and iou(a, b) > 0.8:
            problems.append(f"row {j}: duplicate of row {i} (IoU {iou(a, b):.2f})")
    return problems

broken = "0 0.50 0.50 0.20 0.30\n0 0.51 0.50 0.20 0.30\n3 0.40 0.40 0.10 0.10\n0 1.20 0.50 0.10 0.10\n0 0.50 0.50 0.10"
print("\n".join(check_labels(broken, {0: "pill"})))

data = check_det_dataset("medical-pills.yaml")
files = sorted(Path(data["path"], "labels").rglob("*.txt"))
found = sum(len(check_labels(f.read_text(), data["names"])) for f in files)
print(f"medical-pills: {found} problems in {len(files)} label files")

row 3: unknown class 3
row 4: coordinates outside 0-1
row 5: 4 values, expected 5
row 2: duplicate of row 1 (IoU 0.90)
medical-pills: 0 problems in 115 label files

## Class: forklift

Include:
- Forklifts with at least 30% of the body visible, moving or parked, any size or color.

Do not label:
- Pallet jacks (no overhead mast) → class `pallet_jack`.
- Reflections in glass or polished floors, and forklifts on posters or screens.

Box rules:
- Include the mast and the full visible body.
- Exclude the load (pallets, boxes) — those have their own classes.
- For partly occluded forklifts, box the visible part only.

def iou(a, b):
    """IoU of two (x1, y1, x2, y2) pixel boxes."""
    w = max(0, min(a[2], b[2]) - max(a[0], b[0]))
    h = max(0, min(a[3], b[3]) - max(a[1], b[1]))
    inter = w * h
    return inter / ((a[2] - a[0]) * (a[3] - a[1]) + (b[2] - b[0]) * (b[3] - b[1]) - inter)

def agreement(boxes_a, boxes_b, threshold=0.5):
    """Share of objects both annotators drew with the same class and IoU >= threshold."""
    unmatched, matched = list(boxes_b), 0
    for cls_a, box_a in boxes_a:
        best = max(unmatched, key=lambda b: iou(box_a, b[1]) if b[0] == cls_a else -1, default=None)
        if best and best[0] == cls_a and iou(box_a, best[1]) >= threshold:
            unmatched.remove(best)
            matched += 1
    return matched / max(len(boxes_a), len(boxes_b))

# Two annotators label the same calibration image
alice = [("forklift", (100, 200, 400, 600)), ("person", (450, 220, 520, 480)), ("person", (600, 230, 660, 470))]
bob = [("forklift", (100, 120, 400, 600)), ("person", (452, 218, 522, 478)), ("pallet_jack", (600, 230, 660, 470))]
print(f"agreement: {agreement(alice, bob):.0%}")

agreement: 67%