Datasets:
File size: 2,832 Bytes
64563dd | 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 | """Reference scorer for form-field-v1-benchmark — COCO mAP50-95 (pycocotools), per-variant + per-class.
Predictions: a JSON list of {"image_id": <page_id>, "category_id": 1|2|3, "score": float, "bbox": [x,y,w,h]}
(category 1=Text, 2=ChoiceButton, 3=Signature; bbox in page pixels).
pip install datasets pycocotools
python score_detector.py preds.json # overall + per variant + per class
"""
import sys, json, contextlib, io
from datasets import load_dataset
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
CATS = [(1, "Text"), (2, "ChoiceButton"), (3, "Signature")]
CAT_ID = {"Text": 1, "ChoiceButton": 2, "Signature": 3}
def build_gt(split_rows):
images, anns, aid = [], [], 1
idmap = {}
for i, r in enumerate(split_rows):
idmap[r["page_id"]] = i
images.append({"id": i, "file_name": r["page_id"], "width": r["width"], "height": r["height"],
"variant": r["variant"]})
for f in r["fields"]:
x, y, w, h = f["box"]
anns.append({"id": aid, "image_id": i, "category_id": CAT_ID[f["category"]],
"bbox": [x, y, w, h], "area": w * h, "iscrowd": 0}); aid += 1
coco = COCO(); coco.dataset = {"images": images,
"categories": [{"id": c, "name": n} for c, n in CATS], "annotations": anns}
with contextlib.redirect_stdout(io.StringIO()): coco.createIndex()
return coco, idmap
def score(coco, dets, tag):
with contextlib.redirect_stdout(io.StringIO()):
dt = coco.loadRes(dets)
ev = COCOeval(coco, dt, "bbox"); ev.params.maxDets = [1, 100, 1000]
ev.evaluate(); ev.accumulate(); ev.summarize()
per = []
for cid, nm in CATS:
with contextlib.redirect_stdout(io.StringIO()):
e = COCOeval(coco, dt, "bbox"); e.params.maxDets = [1, 100, 1000]; e.params.catIds = [cid]
e.evaluate(); e.accumulate(); e.summarize()
per.append(f"{nm} {e.stats[0]:.3f}")
print(f"{tag:14s} mAP50-95={ev.stats[0]:.4f} AP50={ev.stats[1]:.4f} ({' / '.join(per)})")
def main():
preds = json.load(open(sys.argv[1]))
rows = list(load_dataset("nutrientdocs/form-field-v1-benchmark", split="test"))
# map page_id (string image_id in preds) -> integer id used by the GT
coco_all, idmap = build_gt(rows)
dets = [{**d, "image_id": idmap[d["image_id"]]} for d in preds if d["image_id"] in idmap]
score(coco_all, dets, "OVERALL")
for variant in ("empty", "filled", "handwritten"):
sub = [r for r in rows if r["variant"] == variant]
coco_v, idm_v = build_gt(sub)
dv = [{**d, "image_id": idm_v[d["image_id"]]} for d in preds if d["image_id"] in idm_v]
score(coco_v, dv, variant)
if __name__ == "__main__":
main()
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