| """Thin loading helpers for the React video-format dataset. |
| |
| Decoded frames are RGB uint8 (T, H, W, 3) — standard decoder convention. |
| """ |
| from __future__ import annotations |
|
|
| from pathlib import Path |
|
|
| import numpy as np |
| import pyarrow.parquet as pq |
|
|
| try: |
| import av |
| _BACKEND = "av" |
| except Exception: |
| import cv2 |
| _BACKEND = "cv2" |
|
|
|
|
| def load_video(mp4_path, frames=None): |
| """Decode an MP4 to (N, H, W, 3) uint8 RGB. |
| |
| frames=None -> all frames; else an iterable of frame indices. |
| """ |
| mp4_path = str(mp4_path) |
| want = None if frames is None else sorted(set(int(i) for i in frames)) |
| if _BACKEND == "av": |
| c = av.open(mp4_path) |
| out = [] |
| idxset = set(want) if want is not None else None |
| for i, fr in enumerate(c.decode(c.streams.video[0])): |
| if idxset is None or i in idxset: |
| out.append(fr.to_ndarray(format="rgb24")) |
| if idxset is not None and i >= max(idxset): |
| break |
| c.close() |
| return np.stack(out) |
| cap = cv2.VideoCapture(mp4_path) |
| out = [] |
| if want is None: |
| ok, fr = cap.read() |
| while ok: |
| out.append(fr[..., ::-1]); ok, fr = cap.read() |
| else: |
| for i in want: |
| cap.set(cv2.CAP_PROP_POS_FRAMES, i) |
| ok, fr = cap.read() |
| out.append(fr[..., ::-1] if ok else np.zeros((480, 640, 3), np.uint8)) |
| cap.release() |
| return np.stack(out) |
|
|
|
|
| def episode_paths(task_root, episode): |
| """Resolve an episode key '<date>/episode_NNN' to its file paths.""" |
| root = Path(task_root) |
| date, ep = episode.split("/") |
| vd = root / "videos" / date / ep |
| return { |
| "view_left": vd / "view_left.mp4", "view_middle": vd / "view_middle.mp4", |
| "view_right": vd / "view_right.mp4", |
| "tactile_left": vd / "tactile_left.mp4", "tactile_right": vd / "tactile_right.mp4", |
| "depth_dir": root / "depth" / date / ep, |
| "parquet": root / "meta" / date / f"{ep}.parquet", |
| } |
|
|
|
|
| def load_meta(parquet_path, columns=None): |
| """Load per-frame metadata as a dict of numpy arrays.""" |
| tbl = pq.read_table(str(parquet_path), columns=columns) |
| out = {} |
| for c in tbl.column_names: |
| col = tbl.column(c).to_pylist() |
| out[c] = np.array(col) |
| return out |
|
|
