| |
| """Extract GT labels at exactly the frames used by Robotwin temporal features.""" |
| import argparse |
| import json |
| from pathlib import Path |
|
|
| import h5py |
| import numpy as np |
|
|
|
|
| def feature_indices(total, n=33, stride=4): |
| if total <= 0: |
| raise ValueError("empty episode") |
| ids = np.arange(n, dtype=np.int64) * stride |
| if ids[-1] >= total: |
| ids = np.linspace(0, total - 1, n, dtype=np.int64) |
| return ids |
|
|
|
|
| def main(): |
| p = argparse.ArgumentParser() |
| p.add_argument("--manifest", type=Path, required=True) |
| p.add_argument("--gt-root", type=Path, required=True, |
| help="dataset_gt/<embodiment>, containing task/gt_export/data") |
| p.add_argument("--output", type=Path, required=True) |
| p.add_argument("--camera", default="head_camera") |
| p.add_argument("--num-frames", type=int, default=33) |
| p.add_argument("--frame-stride", type=int, default=4) |
| args = p.parse_args() |
| args.output.mkdir(parents=True, exist_ok=True) |
| rows = [] |
| for line in args.manifest.read_text().splitlines(): |
| if not line.strip(): |
| continue |
| row = json.loads(line) |
| task, episode = row["task"], row["episode"] |
| src = args.gt_root / task / "gt_export" / "data" / f"{episode}.hdf5" |
| out = args.output / f"{task}__{episode}.npz" |
| if not src.exists(): |
| rows.append({"task": task, "episode": episode, "status": "missing", "path": str(src)}) |
| continue |
| with h5py.File(src, "r") as f: |
| prefix = f"observation/{args.camera}" |
| total = len(f[f"{prefix}/rgb"]) |
| ids = feature_indices(total, args.num_frames, args.frame_stride) |
| data = {"frame_indices": ids, "total_frames": total} |
| for name in ("depth", "actor_segmentation", "mesh_segmentation", |
| "intrinsic_cv", "extrinsic_cv", "cam2world_gl"): |
| key = f"{prefix}/{name}" |
| if key in f: |
| data[name] = f[key][ids] |
| if "pointcloud" in f: |
| |
| data["pointcloud"] = f["pointcloud"][ids] |
| np.savez_compressed(out, **data, task=task, episode=episode, |
| camera=args.camera, num_frames=args.num_frames, |
| frame_stride=args.frame_stride) |
| rows.append({"task": task, "episode": episode, "status": "ok", |
| "total_frames": total, "indices": ids.tolist(), "path": str(out)}) |
| (args.output / "index.json").write_text(json.dumps(rows, indent=2)) |
| print(f"ok={sum(x['status']=='ok' for x in rows)} missing={sum(x['status']=='missing' for x in rows)}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
