File size: 4,099 Bytes
bf5ab25 | 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 | #!/usr/bin/env python3
"""Run the Tibetan modern-book layout detector (RF-DETR-L fine-tune) on one or
more page images, applying the recommended *per-class* confidence thresholds.
The model is a 4-class RF-DETR-L (header, text-area, footnote, footer),
fine-tuned on the same `tam2col` labels as BDRC's primary RT-DETR-l release
(see the model card / blog post). Like that model, the detector is
deliberately recall-happy on the small marginal header/footer boxes, so the
single best operating point differs by class. The thresholds below are each
class's own max-F1 confidence from a native per-class sweep on the held-out
test set (same methodology as the primary RT-DETR-l release); a single global
0.30 is the best compromise if you need one number for all classes.
Usage:
python infer.py --checkpoint rfdetr_tibetan_book_layout.pth --source page.jpg
python infer.py --checkpoint rfdetr_tibetan_book_layout.pth --source pages/ --out preds
"""
from __future__ import annotations
import argparse
from pathlib import Path
IMG_EXTS = {".jpg", ".jpeg", ".png", ".webp", ".bmp", ".tif", ".tiff"}
# Per-class max-F1 operating points (see model card). Use --global-conf 0.30
# instead if you prefer one number for all classes.
CLASS_THRESHOLDS = {0: 0.46, 1: 0.32, 2: 0.26, 3: 0.52}
CONF_FLOOR = min(CLASS_THRESHOLDS.values())
def main() -> int:
ap = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("--checkpoint", required=True, help="path to the .pth checkpoint")
ap.add_argument("--source", required=True, help="image file or folder")
ap.add_argument("--out", default=None,
help="optional folder to write YOLO-format .txt labels")
ap.add_argument("--shape", type=int, default=1024)
ap.add_argument("--global-conf", type=float, default=None,
help="use ONE threshold for all classes instead of per-class")
args = ap.parse_args()
from rfdetr import RFDETRLarge
from PIL import Image
thresholds = ({c: args.global_conf for c in CLASS_THRESHOLDS}
if args.global_conf is not None else CLASS_THRESHOLDS)
floor = min(thresholds.values())
model = RFDETRLarge.from_checkpoint(args.checkpoint)
# class_names on the checkpoint: ['none', 'header', 'text-area', 'footnote', 'footer']
names = {0: "header", 1: "text-area", 2: "footnote", 3: "footer"}
src = Path(args.source)
imgs = sorted(p for p in src.iterdir() if p.suffix.lower() in IMG_EXTS) \
if src.is_dir() else [src]
out_dir = Path(args.out) if args.out else None
if out_dir:
out_dir.mkdir(parents=True, exist_ok=True)
n_img = n_kept = 0
for ip in imgs:
n_img += 1
with Image.open(ip) as im:
W, H = im.size
det = model.predict(str(ip), threshold=floor, shape=(args.shape, args.shape))
lines = []
if det is not None and len(det) > 0:
for box, cls_id, score in zip(det.xyxy, det.class_id, det.confidence):
cls = int(cls_id) - 1 # class 0 on the checkpoint is background
if cls < 0 or cls > 3 or score < thresholds.get(cls, floor):
continue
x1, y1, x2, y2 = box.tolist()
cx, cy = ((x1 + x2) / 2) / W, ((y1 + y2) / 2) / H
w, h = (x2 - x1) / W, (y2 - y1) / H
lines.append((cls, float(score), cx, cy, w, h))
n_kept += len(lines)
print(f"{ip.stem}: {len(lines)} boxes")
for cls, score, cx, cy, w, h in lines:
print(f" {names[cls]:10} conf={score:.3f} "
f"cx={cx:.3f} cy={cy:.3f} w={w:.3f} h={h:.3f}")
if out_dir:
(out_dir / f"{ip.stem}.txt").write_text(
"".join(f"{c} {cx:.6f} {cy:.6f} {w:.6f} {h:.6f}\n"
for c, _, cx, cy, w, h in lines))
print(f"\n{n_img} images, {n_kept} boxes kept (thresholds: {thresholds})")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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