lerobot/lerobot/common/datasets/video_utils.py

#!/usr/bin/env python

# Copyright 2024 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.
import importlib
import json
import logging
import subprocess
import warnings
from collections import OrderedDict
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, ClassVar

import pyarrow as pa
import torch
import torchvision
from datasets.features.features import register_feature
from PIL import Image


def get_safe_default_codec():
    if importlib.util.find_spec("torchcodec"):
        return "torchcodec"
    else:
        logging.warning(
            "'torchcodec' is not available in your platform, falling back to 'pyav' as a default decoder"
        )
        return "pyav"


def decode_video_frames(
    video_path: Path | str,
    timestamps: list[float],
    tolerance_s: float,
    backend: str | None = None,
) -> torch.Tensor:
    """
    Decodes video frames using the specified backend.

    Args:
        video_path (Path): Path to the video file.
        timestamps (list[float]): List of timestamps to extract frames.
        tolerance_s (float): Allowed deviation in seconds for frame retrieval.
        backend (str, optional): Backend to use for decoding. Defaults to "torchcodec" when available in the platform; otherwise, defaults to "pyav"..

    Returns:
        torch.Tensor: Decoded frames.

    Currently supports torchcodec on cpu and pyav.
    """
    if backend is None:
        backend = get_safe_default_codec()
    if backend == "torchcodec":
        return decode_video_frames_torchcodec(video_path, timestamps, tolerance_s)
    elif backend in ["pyav", "video_reader"]:
        return decode_video_frames_torchvision(video_path, timestamps, tolerance_s, backend)
    else:
        raise ValueError(f"Unsupported video backend: {backend}")


def decode_video_frames_torchvision(
    video_path: Path | str,
    timestamps: list[float],
    tolerance_s: float,
    backend: str = "pyav",
    log_loaded_timestamps: bool = False,
) -> torch.Tensor:
    """Loads frames associated to the requested timestamps of a video

    The backend can be either "pyav" (default) or "video_reader".
    "video_reader" requires installing torchvision from source, see:
    https://github.com/pytorch/vision/blob/main/torchvision/csrc/io/decoder/gpu/README.rst
    (note that you need to compile against ffmpeg<4.3)

    While both use cpu, "video_reader" is supposedly faster than "pyav" but requires additional setup.
    For more info on video decoding, see `benchmark/video/README.md`

    See torchvision doc for more info on these two backends:
    https://pytorch.org/vision/0.18/index.html?highlight=backend#torchvision.set_video_backend

    Note: Video benefits from inter-frame compression. Instead of storing every frame individually,
    the encoder stores a reference frame (or a key frame) and subsequent frames as differences relative to
    that key frame. As a consequence, to access a requested frame, we need to load the preceding key frame,
    and all subsequent frames until reaching the requested frame. The number of key frames in a video
    can be adjusted during encoding to take into account decoding time and video size in bytes.
    """
    video_path = str(video_path)

    # set backend
    keyframes_only = False
    torchvision.set_video_backend(backend)
    if backend == "pyav":
        keyframes_only = True  # pyav doesnt support accuracte seek

    # set a video stream reader
    # TODO(rcadene): also load audio stream at the same time
    reader = torchvision.io.VideoReader(video_path, "video")

    # set the first and last requested timestamps
    # Note: previous timestamps are usually loaded, since we need to access the previous key frame
    first_ts = min(timestamps)
    last_ts = max(timestamps)

    # access closest key frame of the first requested frame
    # Note: closest key frame timestamp is usually smaller than `first_ts` (e.g. key frame can be the first frame of the video)
    # for details on what `seek` is doing see: https://pyav.basswood-io.com/docs/stable/api/container.html?highlight=inputcontainer#av.container.InputContainer.seek
    reader.seek(first_ts, keyframes_only=keyframes_only)

    # load all frames until last requested frame
    loaded_frames = []
    loaded_ts = []
    for frame in reader:
        current_ts = frame["pts"]
        if log_loaded_timestamps:
            logging.info(f"frame loaded at timestamp={current_ts:.4f}")
        loaded_frames.append(frame["data"])
        loaded_ts.append(current_ts)
        if current_ts >= last_ts:
            break

    if backend == "pyav":
        reader.container.close()

    reader = None

    query_ts = torch.tensor(timestamps)
    loaded_ts = torch.tensor(loaded_ts)

    # compute distances between each query timestamp and timestamps of all loaded frames
    dist = torch.cdist(query_ts[:, None], loaded_ts[:, None], p=1)
    min_, argmin_ = dist.min(1)

    is_within_tol = min_ < tolerance_s
    assert is_within_tol.all(), (
        f"One or several query timestamps unexpectedly violate the tolerance ({min_[~is_within_tol]} > {tolerance_s=})."
        "It means that the closest frame that can be loaded from the video is too far away in time."
        "This might be due to synchronization issues with timestamps during data collection."
        "To be safe, we advise to ignore this item during training."
        f"\nqueried timestamps: {query_ts}"
        f"\nloaded timestamps: {loaded_ts}"
        f"\nvideo: {video_path}"
        f"\nbackend: {backend}"
    )

    # get closest frames to the query timestamps
    closest_frames = torch.stack([loaded_frames[idx] for idx in argmin_])
    closest_ts = loaded_ts[argmin_]

    if log_loaded_timestamps:
        logging.info(f"{closest_ts=}")

    # convert to the pytorch format which is float32 in [0,1] range (and channel first)
    closest_frames = closest_frames.type(torch.float32) / 255

    assert len(timestamps) == len(closest_frames)
    return closest_frames


def decode_video_frames_torchcodec(
    video_path: Path | str,
    timestamps: list[float],
    tolerance_s: float,
    device: str = "cpu",
    log_loaded_timestamps: bool = False,
) -> torch.Tensor:
    """Loads frames associated with the requested timestamps of a video using torchcodec.

    Note: Setting device="cuda" outside the main process, e.g. in data loader workers, will lead to CUDA initialization errors.

    Note: Video benefits from inter-frame compression. Instead of storing every frame individually,
    the encoder stores a reference frame (or a key frame) and subsequent frames as differences relative to
    that key frame. As a consequence, to access a requested frame, we need to load the preceding key frame,
    and all subsequent frames until reaching the requested frame. The number of key frames in a video
    can be adjusted during encoding to take into account decoding time and video size in bytes.
    """

    if importlib.util.find_spec("torchcodec"):
        from torchcodec.decoders import VideoDecoder
    else:
        raise ImportError("torchcodec is required but not available.")

    # initialize video decoder
    decoder = VideoDecoder(video_path, device=device, seek_mode="approximate")
    loaded_frames = []
    loaded_ts = []
    # get metadata for frame information
    metadata = decoder.metadata
    average_fps = metadata.average_fps

    # convert timestamps to frame indices
    frame_indices = [round(ts * average_fps) for ts in timestamps]

    # retrieve frames based on indices
    frames_batch = decoder.get_frames_at(indices=frame_indices)

    for frame, pts in zip(frames_batch.data, frames_batch.pts_seconds, strict=False):
        loaded_frames.append(frame)
        loaded_ts.append(pts.item())
        if log_loaded_timestamps:
            logging.info(f"Frame loaded at timestamp={pts:.4f}")

    query_ts = torch.tensor(timestamps)
    loaded_ts = torch.tensor(loaded_ts)

    # compute distances between each query timestamp and loaded timestamps
    dist = torch.cdist(query_ts[:, None], loaded_ts[:, None], p=1)
    min_, argmin_ = dist.min(1)

    is_within_tol = min_ < tolerance_s
    assert is_within_tol.all(), (
        f"One or several query timestamps unexpectedly violate the tolerance ({min_[~is_within_tol]} > {tolerance_s=})."
        "It means that the closest frame that can be loaded from the video is too far away in time."
        "This might be due to synchronization issues with timestamps during data collection."
        "To be safe, we advise to ignore this item during training."
        f"\nqueried timestamps: {query_ts}"
        f"\nloaded timestamps: {loaded_ts}"
        f"\nvideo: {video_path}"
    )

    # get closest frames to the query timestamps
    closest_frames = torch.stack([loaded_frames[idx] for idx in argmin_])
    closest_ts = loaded_ts[argmin_]

    if log_loaded_timestamps:
        logging.info(f"{closest_ts=}")

    # convert to float32 in [0,1] range (channel first)
    closest_frames = closest_frames.type(torch.float32) / 255

    assert len(timestamps) == len(closest_frames)
    return closest_frames


def encode_video_frames(
    imgs_dir: Path | str,
    video_path: Path | str,
    fps: int,
    vcodec: str = "libsvtav1",
    pix_fmt: str = "yuv420p",
    g: int | None = 2,
    crf: int | None = 30,
    fast_decode: int = 0,
    log_level: str | None = "error",
    overwrite: bool = False,
) -> None:
    """More info on ffmpeg arguments tuning on `benchmark/video/README.md`"""
    video_path = Path(video_path)
    imgs_dir = Path(imgs_dir)
    video_path.parent.mkdir(parents=True, exist_ok=True)

    ffmpeg_args = OrderedDict(
        [
            ("-f", "image2"),
            ("-r", str(fps)),
            ("-i", str(imgs_dir / "frame_%06d.png")),
            ("-vcodec", vcodec),
            ("-pix_fmt", pix_fmt),
        ]
    )

    if g is not None:
        ffmpeg_args["-g"] = str(g)

    if crf is not None:
        ffmpeg_args["-crf"] = str(crf)

    if fast_decode:
        key = "-svtav1-params" if vcodec == "libsvtav1" else "-tune"
        value = f"fast-decode={fast_decode}" if vcodec == "libsvtav1" else "fastdecode"
        ffmpeg_args[key] = value

    if log_level is not None:
        ffmpeg_args["-loglevel"] = str(log_level)

    ffmpeg_args = [item for pair in ffmpeg_args.items() for item in pair]
    if overwrite:
        ffmpeg_args.append("-y")

    ffmpeg_cmd = ["ffmpeg"] + ffmpeg_args + [str(video_path)]
    # redirect stdin to subprocess.DEVNULL to prevent reading random keyboard inputs from terminal
    subprocess.run(ffmpeg_cmd, check=True, stdin=subprocess.DEVNULL)

    if not video_path.exists():
        raise OSError(
            f"Video encoding did not work. File not found: {video_path}. "
            f"Try running the command manually to debug: `{''.join(ffmpeg_cmd)}`"
        )


@dataclass
class VideoFrame:
    # TODO(rcadene, lhoestq): move to Hugging Face `datasets` repo
    """
    Provides a type for a dataset containing video frames.

    Example:

    ```python
    data_dict = [{"image": {"path": "videos/episode_0.mp4", "timestamp": 0.3}}]
    features = {"image": VideoFrame()}
    Dataset.from_dict(data_dict, features=Features(features))
    ```
    """

    pa_type: ClassVar[Any] = pa.struct({"path": pa.string(), "timestamp": pa.float32()})
    _type: str = field(default="VideoFrame", init=False, repr=False)

    def __call__(self):
        return self.pa_type


with warnings.catch_warnings():
    warnings.filterwarnings(
        "ignore",
        "'register_feature' is experimental and might be subject to breaking changes in the future.",
        category=UserWarning,
    )
    # to make VideoFrame available in HuggingFace `datasets`
    register_feature(VideoFrame, "VideoFrame")


def get_audio_info(video_path: Path | str) -> dict:
    ffprobe_audio_cmd = [
        "ffprobe",
        "-v",
        "error",
        "-select_streams",
        "a:0",
        "-show_entries",
        "stream=channels,codec_name,bit_rate,sample_rate,bit_depth,channel_layout,duration",
        "-of",
        "json",
        str(video_path),
    ]
    result = subprocess.run(ffprobe_audio_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
    if result.returncode != 0:
        raise RuntimeError(f"Error running ffprobe: {result.stderr}")

    info = json.loads(result.stdout)
    audio_stream_info = info["streams"][0] if info.get("streams") else None
    if audio_stream_info is None:
        return {"has_audio": False}

    # Return the information, defaulting to None if no audio stream is present
    return {
        "has_audio": True,
        "audio.channels": audio_stream_info.get("channels", None),
        "audio.codec": audio_stream_info.get("codec_name", None),
        "audio.bit_rate": int(audio_stream_info["bit_rate"]) if audio_stream_info.get("bit_rate") else None,
        "audio.sample_rate": int(audio_stream_info["sample_rate"])
        if audio_stream_info.get("sample_rate")
        else None,
        "audio.bit_depth": audio_stream_info.get("bit_depth", None),
        "audio.channel_layout": audio_stream_info.get("channel_layout", None),
    }


def get_video_info(video_path: Path | str) -> dict:
    ffprobe_video_cmd = [
        "ffprobe",
        "-v",
        "error",
        "-select_streams",
        "v:0",
        "-show_entries",
        "stream=r_frame_rate,width,height,codec_name,nb_frames,duration,pix_fmt",
        "-of",
        "json",
        str(video_path),
    ]
    result = subprocess.run(ffprobe_video_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
    if result.returncode != 0:
        raise RuntimeError(f"Error running ffprobe: {result.stderr}")

    info = json.loads(result.stdout)
    video_stream_info = info["streams"][0]

    # Calculate fps from r_frame_rate
    r_frame_rate = video_stream_info["r_frame_rate"]
    num, denom = map(int, r_frame_rate.split("/"))
    fps = num / denom

    pixel_channels = get_video_pixel_channels(video_stream_info["pix_fmt"])

    video_info = {
        "video.fps": fps,
        "video.height": video_stream_info["height"],
        "video.width": video_stream_info["width"],
        "video.channels": pixel_channels,
        "video.codec": video_stream_info["codec_name"],
        "video.pix_fmt": video_stream_info["pix_fmt"],
        "video.is_depth_map": False,
        **get_audio_info(video_path),
    }

    return video_info


def get_video_pixel_channels(pix_fmt: str) -> int:
    if "gray" in pix_fmt or "depth" in pix_fmt or "monochrome" in pix_fmt:
        return 1
    elif "rgba" in pix_fmt or "yuva" in pix_fmt:
        return 4
    elif "rgb" in pix_fmt or "yuv" in pix_fmt:
        return 3
    else:
        raise ValueError("Unknown format")


def get_image_pixel_channels(image: Image):
    if image.mode == "L":
        return 1  # Grayscale
    elif image.mode == "LA":
        return 2  # Grayscale + Alpha
    elif image.mode == "RGB":
        return 3  # RGB
    elif image.mode == "RGBA":
        return 4  # RGBA
    else:
        raise ValueError("Unknown format")