lerobot-clone/src/lerobot/annotations/steerable_pipeline/frames.py

#!/usr/bin/env python

# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Keyframe extraction for the annotation pipeline.

Modules attach decoded camera frames to their VLM prompts so the model can
ground subtask decomposition, interjection scenarios, and VQA in actual
visual content. The pipeline shares one provider across modules and one
episode at a time, with a small per-episode cache so multiple modules
querying the same timestamp pay decode cost once.
"""

from __future__ import annotations

from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Protocol

from .reader import EpisodeRecord


class FrameProvider(Protocol):
    """Decodes camera frames at episode-relative timestamps."""

    @property
    def camera_keys(self) -> list[str]:
        """All ``observation.images.*`` feature keys this provider can decode."""

    def frames_at(
        self,
        record: EpisodeRecord,
        timestamps: list[float],
        camera_key: str | None = None,
    ) -> list[Any]:
        """Return one PIL.Image per timestamp from ``camera_key`` (or default).

        Empty list if the camera is unavailable. ``camera_key=None`` falls back
        to the provider's default camera so existing single-camera callers
        (Module 1, Module 2) keep working unchanged.
        """

    def video_for_episode(
        self,
        record: EpisodeRecord,
        max_frames: int,
        camera_key: str | None = None,
    ) -> list[Any]:
        """Return up to ``max_frames`` PIL images covering the whole episode.

        Sampling is uniform across the episode duration. The returned list is
        intended to be passed as one ``{"type":"video", "video":<list>}``
        block to a Qwen-VL-compatible model that pools temporally itself.
        Empty list if no camera available.
        """


@dataclass
class _NullProvider:
    """No-op provider used when the dataset has no video keys or in tests."""

    @property
    def camera_keys(self) -> list[str]:
        return []

    def frames_at(
        self,
        record: EpisodeRecord,
        timestamps: list[float],
        camera_key: str | None = None,
    ) -> list[Any]:
        return []

    def video_for_episode(
        self,
        record: EpisodeRecord,
        max_frames: int,
        camera_key: str | None = None,
    ) -> list[Any]:
        return []


def null_provider() -> FrameProvider:
    return _NullProvider()


@dataclass
class VideoFrameProvider:
    """Decodes frames from the dataset's ``observation.images.*`` streams.

    By default the *first* camera key is used for Module 1 (subtask
    decomposition) and Module 2 (interjection scenarios) — those prompts care
    about *what is happening*, not which angle. Module 3 (VQA) instead
    iterates over every camera in :attr:`camera_keys` so each frame's
    grounded answer (bbox/keypoint/...) is tagged with the camera it was
    grounded against.

    ``camera_key`` overrides the default-camera choice but does not restrict
    :attr:`camera_keys`. Pass ``camera_key`` explicitly to ``frames_at`` /
    ``video_for_episode`` to read a non-default stream.

    Caches up to ``cache_size`` decoded frames per process to keep
    co-timestamped Module 2 + Module 1 plan-update calls cheap.
    """

    root: Path
    camera_key: str | None = None
    tolerance_s: float = 1e-2
    cache_size: int = 256
    _meta: Any = field(default=None, init=False, repr=False)
    _cache: dict = field(default_factory=dict, init=False, repr=False)
    _camera_keys: list[str] = field(default_factory=list, init=False, repr=False)

    def __post_init__(self) -> None:
        from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata  # noqa: PLC0415

        self._meta = LeRobotDatasetMetadata(repo_id="local", root=self.root)
        # ``camera_keys`` covers both image- and video-stored cameras
        # (``video_keys`` is video-only). Some datasets declare cameras with
        # ``dtype=image``, which would otherwise look empty here and silently
        # disable Module 3 even though the videos are there.
        keys = list(getattr(self._meta, "camera_keys", None) or self._meta.video_keys or [])
        # Last-resort fallback: if metadata didn't surface anything but the
        # caller explicitly named a camera (``--vlm.camera_key=...``), trust
        # them — the key is by definition known to exist on the dataset.
        if not keys and self.camera_key:
            keys = [self.camera_key]
        self._camera_keys = keys
        if self.camera_key is None:
            self.camera_key = keys[0] if keys else None

    @property
    def camera_keys(self) -> list[str]:
        """All ``observation.images.*`` keys available on this dataset."""
        return list(self._camera_keys)

    def frames_at(
        self,
        record: EpisodeRecord,
        timestamps: list[float],
        camera_key: str | None = None,
    ) -> list[Any]:
        target = camera_key if camera_key is not None else self.camera_key
        if not timestamps or target is None:
            return []

        out: list[Any] = []
        misses: list[float] = []
        miss_indices: list[int] = []
        for i, ts in enumerate(timestamps):
            key = (record.episode_index, target, round(float(ts), 6))
            cached = self._cache.get(key)
            if cached is not None:
                out.append(cached)
            else:
                out.append(None)
                misses.append(float(ts))
                miss_indices.append(i)

        if misses:
            decoded = self._decode(record.episode_index, misses, target)
            # decoder may return fewer frames than requested when some
            # timestamps fall outside the video; pair what we have and
            # leave the rest as None to be filtered below.
            for i, img in zip(miss_indices, decoded):
                out[i] = img
                key = (record.episode_index, target, round(float(timestamps[i]), 6))
                if len(self._cache) >= self.cache_size:
                    self._cache.pop(next(iter(self._cache)))
                self._cache[key] = img
        # filter out any None left over from decode failures
        return [img for img in out if img is not None]

    def _decode(
        self, episode_index: int, timestamps: list[float], camera_key: str
    ) -> list[Any]:
        import os as _os  # noqa: PLC0415

        from PIL import Image  # noqa: PLC0415

        from lerobot.datasets.video_utils import decode_video_frames  # noqa: PLC0415

        ep = self._meta.episodes[episode_index]
        from_timestamp = ep[f"videos/{camera_key}/from_timestamp"]
        shifted = [from_timestamp + ts for ts in timestamps]
        video_path = self.root / self._meta.get_video_file_path(episode_index, camera_key)
        # ``torchcodec`` import currently bad-allocs on cu128/torch-2.8 in
        # some environments; default to ``pyav`` (always available via
        # the ``av`` package) and let users override with
        # LEROBOT_VIDEO_BACKEND=torchcodec when their stack supports it.
        backend = _os.environ.get("LEROBOT_VIDEO_BACKEND", "pyav")
        try:
            frames = decode_video_frames(
                video_path,
                shifted,
                self.tolerance_s,
                backend=backend,
                return_uint8=True,
            )
        except Exception as exc:
            # Log loudly the first time decoding fails so silent
            # Module-3-no-op (every prompt skipped because frames_at returned
            # []) is debuggable from the job log instead of post-hoc parquet
            # inspection. Subsequent failures stay quiet.
            if not getattr(self, "_warned_decode_fail", False):
                import logging  # noqa: PLC0415

                logging.getLogger(__name__).warning(
                    "VideoFrameProvider._decode failed for episode=%s camera=%s "
                    "video_path=%s backend=%s: %s",
                    episode_index,
                    camera_key,
                    video_path,
                    backend,
                    exc,
                    exc_info=True,
                )
                self._warned_decode_fail = True
            return []
        # frames: [N, C, H, W] uint8, RGB
        out: list[Any] = []
        arr = frames.cpu().numpy() if hasattr(frames, "cpu") else frames
        for i in range(arr.shape[0]):
            chw = arr[i]
            hwc = chw.transpose(1, 2, 0)
            out.append(Image.fromarray(hwc, mode="RGB"))
        return out

    def video_for_episode(
        self,
        record: EpisodeRecord,
        max_frames: int,
        camera_key: str | None = None,
    ) -> list[Any]:
        """Return up to ``max_frames`` images uniformly sampled across the episode.

        The whole episode duration is covered; the model picks subtask
        boundaries from the temporal pooling it does internally.
        """
        target = camera_key if camera_key is not None else self.camera_key
        if max_frames <= 0 or target is None or not record.frame_timestamps:
            return []
        n_frames = min(max_frames, len(record.frame_timestamps))
        if n_frames == len(record.frame_timestamps):
            timestamps = list(record.frame_timestamps)
        else:
            t0 = record.frame_timestamps[0]
            t_last = record.frame_timestamps[-1]
            if t_last <= t0:
                timestamps = [float(t0)] * n_frames
            else:
                step = (t_last - t0) / (n_frames - 1) if n_frames > 1 else 0.0
                timestamps = [float(t0 + i * step) for i in range(n_frames)]
        return self.frames_at(record, timestamps, camera_key=target)


def make_frame_provider(root: Path, camera_key: str | None = None) -> FrameProvider:
    """Build a :class:`VideoFrameProvider` if videos are present, else null."""
    try:
        provider = VideoFrameProvider(root=root, camera_key=camera_key)
    except Exception:
        return null_provider()
    if provider.camera_key is None:
        return null_provider()
    return provider


def to_image_blocks(images: list[Any]) -> list[dict[str, Any]]:
    """Convert PIL images to Qwen-VL-compatible content blocks."""
    return [{"type": "image", "image": img} for img in images]


def to_video_block(images: list[Any]) -> list[dict[str, Any]]:
    """Wrap a list of PIL images as one Qwen-VL video block.

    Returns ``[]`` when the list is empty, so the caller can splat the result
    into a content array without a separate emptiness check.
    """
    if not images:
        return []
    return [{"type": "video", "video": list(images)}]


def to_video_url_block(url: str | None, fps: float = 2.0) -> list[dict[str, Any]]:
    """Wrap a video file URL as one ``video_url`` block.

    Used by the ``openai`` backend (transformers serve / vllm serve /
    ktransformers serve), where the server handles frame sampling.
    Returns ``[]`` when ``url`` is ``None`` so the caller can splat.
    """
    if not url:
        return []
    return [{"type": "video_url", "video_url": {"url": url}, "fps": fps}]


def episode_clip_path(
    record: EpisodeRecord,
    provider: "VideoFrameProvider",
    cache_dir: Path,
) -> Path | None:
    """Extract the episode's subclip to ``cache_dir/ep_{idx:06d}.mp4``.

    Returns ``None`` if the dataset has no video tracks. Skips re-extract
    when the cached clip already exists. Re-encodes to H.264
    (libx264) so the resulting mp4 is decodable by every downstream
    video processor — stream-copy would inherit the source codec
    (often AV1 in modern LeRobot datasets), which vllm's libav build
    cannot decode.
    """
    import subprocess  # noqa: PLC0415

    if provider.camera_key is None:
        return None
    cache_dir.mkdir(parents=True, exist_ok=True)
    out_path = cache_dir / f"ep_{record.episode_index:06d}.mp4"
    if out_path.exists() and out_path.stat().st_size > 0:
        return out_path
    ep = provider._meta.episodes[record.episode_index]
    from_timestamp = float(ep[f"videos/{provider.camera_key}/from_timestamp"])
    to_timestamp = float(ep[f"videos/{provider.camera_key}/to_timestamp"])
    src = provider.root / provider._meta.get_video_file_path(
        record.episode_index, provider.camera_key
    )
    cmd = [
        "ffmpeg",
        "-y",
        "-loglevel",
        "error",
        "-ss",
        f"{from_timestamp:.3f}",
        "-to",
        f"{to_timestamp:.3f}",
        "-i",
        str(src),
        "-c:v",
        "libx264",
        "-preset",
        "ultrafast",
        "-crf",
        "23",
        "-pix_fmt",
        "yuv420p",
        "-an",
        str(out_path),
    ]
    try:
        subprocess.run(cmd, check=True, timeout=300)
    except (subprocess.CalledProcessError, subprocess.TimeoutExpired, FileNotFoundError):
        return None
    return out_path if out_path.exists() and out_path.stat().st_size > 0 else None