| """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")) |
| |
| 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() |
|
|
