File size: 2,767 Bytes
876f3c3 | 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 | #!/usr/bin/env python3
"""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:
# Pointcloud is stored at the root and is frame-aligned.
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()
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