| from __future__ import annotations |
|
|
| import argparse |
| from pathlib import Path |
|
|
| from common import ROOT, load_config |
| from dynafall.data import load_pickle, save_pickle, split_video_records |
| from dynafall.features import make_clips, normalize_pose |
|
|
|
|
| def main() -> None: |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--dataset", required=True) |
| ap.add_argument("--config", default="configs/default.yaml") |
| ap.add_argument("--seed", type=int, default=None) |
| ap.add_argument("--group-key", choices=["video", "scenario"], default="video") |
| ap.add_argument("--output-name", default=None) |
| args = ap.parse_args() |
| cfg = load_config(args.config) |
| seed = args.seed if args.seed is not None else cfg["seed"] |
| records = load_pickle(ROOT / "data/poses" / f"{args.dataset}_keypoints.pkl") |
| splits = split_video_records(records, cfg["splits"], seed, group_key=args.group_key) |
| out_dir = ROOT / "data/processed" / (args.output_name or args.dataset) |
| buckets = {k: [] for k in ["train", "val", "test"]} |
| for rec in records: |
| split = next(k for k, ids in splits.items() if rec["video_id"] in ids) |
| norm = normalize_pose(rec["keypoints"]) |
| for i, clip in enumerate(make_clips(norm, cfg["clip_len"], cfg["stride"])): |
| buckets[split].append({"video_id": rec["video_id"], "clip_id": i, "label": int(rec["label"]), "joint": clip}) |
| for split, rows in buckets.items(): |
| save_pickle(rows, out_dir / f"{split}.pkl") |
| print(f"{split}: {len(rows)} clips") |
| save_pickle( |
| {"seed": seed, "group_key": args.group_key, "splits": {k: sorted(v) for k, v in splits.items()}}, |
| out_dir / "video_splits.pkl", |
| ) |
| print(f"Wrote {out_dir}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
