video.py

import gc
import math
import os
import re
import warnings
from fractions import Fraction
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import torch

from ..extension import _load_library

from ..utils import _log_api_usage_once
from . import _video_opt

try:
    _load_library("Decoder")
    _HAS_GPU_VIDEO_DECODER = True
except (ImportError, OSError, ModuleNotFoundError):
    _HAS_GPU_VIDEO_DECODER = False

try:
    import av

    av.logging.set_level(av.logging.ERROR)
    if not hasattr(av.video.frame.VideoFrame, "pict_type"):
        av = ImportError(
            """\
Your version of PyAV is too old for the necessary video operations in torchvision.
If you are on Python 3.5, you will have to build from source (the conda-forge
packages are not up-to-date).  See
https://github.com/mikeboers/PyAV#installation for instructions on how to
install PyAV on your system.
"""
        )
except ImportError:
    av = ImportError(
        """\
PyAV is not installed, and is necessary for the video operations in torchvision.
See https://github.com/mikeboers/PyAV#installation for instructions on how to
install PyAV on your system.
"""
    )


def _check_av_available() -> None:
    if isinstance(av, Exception):
        raise av


def _av_available() -> bool:
    return not isinstance(av, Exception)


# PyAV has some reference cycles
_CALLED_TIMES = 0
_GC_COLLECTION_INTERVAL = 10


def write_video(
    filename: str,
    video_array: torch.Tensor,
    fps: float,
    video_codec: str = "libx264",
    options: Optional[Dict[str, Any]] = None,
    audio_array: Optional[torch.Tensor] = None,
    audio_fps: Optional[float] = None,
    audio_codec: Optional[str] = None,
    audio_options: Optional[Dict[str, Any]] = None,
) -> None:
    """
    Writes a 4d tensor in [T, H, W, C] format in a video file

    Args:
        filename (str): path where the video will be saved
        video_array (Tensor[T, H, W, C]): tensor containing the individual frames,
            as a uint8 tensor in [T, H, W, C] format
        fps (Number): video frames per second
        video_codec (str): the name of the video codec, i.e. "libx264", "h264", etc.
        options (Dict): dictionary containing options to be passed into the PyAV video stream
        audio_array (Tensor[C, N]): tensor containing the audio, where C is the number of channels
            and N is the number of samples
        audio_fps (Number): audio sample rate, typically 44100 or 48000
        audio_codec (str): the name of the audio codec, i.e. "mp3", "aac", etc.
        audio_options (Dict): dictionary containing options to be passed into the PyAV audio stream
    """
    if not torch.jit.is_scripting() and not torch.jit.is_tracing():
        _log_api_usage_once(write_video)
    _check_av_available()
    video_array = torch.as_tensor(video_array, dtype=torch.uint8).numpy()

    # PyAV does not support floating point numbers with decimal point
    # and will throw OverflowException in case this is not the case
    if isinstance(fps, float):
        fps = np.round(fps)

    with av.open(filename, mode="w") as container:
        stream = container.add_stream(video_codec, rate=fps)
        stream.width = video_array.shape[2]
        stream.height = video_array.shape[1]
        stream.pix_fmt = "yuv420p" if video_codec != "libx264rgb" else "rgb24"
        stream.options = options or {}

        if audio_array is not None:
            audio_format_dtypes = {
                "dbl": "<f8",
                "dblp": "<f8",
                "flt": "<f4",
                "fltp": "<f4",
                "s16": "<i2",
                "s16p": "<i2",
                "s32": "<i4",
                "s32p": "<i4",
                "u8": "u1",
                "u8p": "u1",
            }
            a_stream = container.add_stream(audio_codec, rate=audio_fps)
            a_stream.options = audio_options or {}

            num_channels = audio_array.shape[0]
            audio_layout = "stereo" if num_channels > 1 else "mono"
            audio_sample_fmt = container.streams.audio[0].format.name

            format_dtype = np.dtype(audio_format_dtypes[audio_sample_fmt])
            audio_array = torch.as_tensor(audio_array).numpy().astype(format_dtype)

            frame = av.AudioFrame.from_ndarray(audio_array, format=audio_sample_fmt, layout=audio_layout)

            frame.sample_rate = audio_fps

            for packet in a_stream.encode(frame):
                container.mux(packet)

            for packet in a_stream.encode():
                container.mux(packet)

        for img in video_array:
            frame = av.VideoFrame.from_ndarray(img, format="rgb24")
            frame.pict_type = "NONE"
            for packet in stream.encode(frame):
                container.mux(packet)

        # Flush stream
        for packet in stream.encode():
            container.mux(packet)


def _read_from_stream(
    container: "av.container.Container",
    start_offset: float,
    end_offset: float,
    pts_unit: str,
    stream: "av.stream.Stream",
    stream_name: Dict[str, Optional[Union[int, Tuple[int, ...], List[int]]]],
) -> List["av.frame.Frame"]:
    global _CALLED_TIMES, _GC_COLLECTION_INTERVAL
    _CALLED_TIMES += 1
    if _CALLED_TIMES % _GC_COLLECTION_INTERVAL == _GC_COLLECTION_INTERVAL - 1:
        gc.collect()

    if pts_unit == "sec":
        # TODO: we should change all of this from ground up to simply take
        # sec and convert to MS in C++
        start_offset = int(math.floor(start_offset * (1 / stream.time_base)))
        if end_offset != float("inf"):
            end_offset = int(math.ceil(end_offset * (1 / stream.time_base)))
    else:
        warnings.warn("The pts_unit 'pts' gives wrong results. Please use pts_unit 'sec'.")

    frames = {}
    should_buffer = True
    max_buffer_size = 5
    if stream.type == "video":
        # DivX-style packed B-frames can have out-of-order pts (2 frames in a single pkt)
        # so need to buffer some extra frames to sort everything
        # properly
        extradata = stream.codec_context.extradata
        # overly complicated way of finding if `divx_packed` is set, following
        # https://github.com/FFmpeg/FFmpeg/commit/d5a21172283572af587b3d939eba0091484d3263
        if extradata and b"DivX" in extradata:
            # can't use regex directly because of some weird characters sometimes...
            pos = extradata.find(b"DivX")
            d = extradata[pos:]
            o = re.search(rb"DivX(\d+)Build(\d+)(\w)", d)
            if o is None:
                o = re.search(rb"DivX(\d+)b(\d+)(\w)", d)
            if o is not None:
                should_buffer = o.group(3) == b"p"
    seek_offset = start_offset
    # some files don't seek to the right location, so better be safe here
    seek_offset = max(seek_offset - 1, 0)
    if should_buffer:
        # FIXME this is kind of a hack, but we will jump to the previous keyframe
        # so this will be safe
        seek_offset = max(seek_offset - max_buffer_size, 0)
    try:
        # TODO check if stream needs to always be the video stream here or not
        container.seek(seek_offset, any_frame=False, backward=True, stream=stream)
    except av.AVError:
        # TODO add some warnings in this case
        # print("Corrupted file?", container.name)
        return []
    buffer_count = 0
    try:
        for _idx, frame in enumerate(container.decode(**stream_name)):
            frames[frame.pts] = frame
            if frame.pts >= end_offset:
                if should_buffer and buffer_count < max_buffer_size:
                    buffer_count += 1
                    continue
                break
    except av.AVError:
        # TODO add a warning
        pass
    # ensure that the results are sorted wrt the pts
    result = [frames[i] for i in sorted(frames) if start_offset <= frames[i].pts <= end_offset]
    if len(frames) > 0 and start_offset > 0 and start_offset not in frames:
        # if there is no frame that exactly matches the pts of start_offset
        # add the last frame smaller than start_offset, to guarantee that
        # we will have all the necessary data. This is most useful for audio
        preceding_frames = [i for i in frames if i < start_offset]
        if len(preceding_frames) > 0:
            first_frame_pts = max(preceding_frames)
            result.insert(0, frames[first_frame_pts])
    return result


def _align_audio_frames(
    aframes: torch.Tensor, audio_frames: List["av.frame.Frame"], ref_start: int, ref_end: float
) -> torch.Tensor:
    start, end = audio_frames[0].pts, audio_frames[-1].pts
    total_aframes = aframes.shape[1]
    step_per_aframe = (end - start + 1) / total_aframes
    s_idx = 0
    e_idx = total_aframes
    if start < ref_start:
        s_idx = int((ref_start - start) / step_per_aframe)
    if end > ref_end:
        e_idx = int((ref_end - end) / step_per_aframe)
    return aframes[:, s_idx:e_idx]


def read_video(
    filename: str,
    start_pts: Union[float, Fraction] = 0,
    end_pts: Optional[Union[float, Fraction]] = None,
    pts_unit: str = "pts",
    output_format: str = "THWC",
) -> Tuple[torch.Tensor, torch.Tensor, Dict[str, Any]]:
    """
    Reads a video from a file, returning both the video frames as well as
    the audio frames

    Args:
        filename (str): path to the video file
        start_pts (int if pts_unit = 'pts', float / Fraction if pts_unit = 'sec', optional):
            The start presentation time of the video
        end_pts (int if pts_unit = 'pts', float / Fraction if pts_unit = 'sec', optional):
            The end presentation time
        pts_unit (str, optional): unit in which start_pts and end_pts values will be interpreted,
            either 'pts' or 'sec'. Defaults to 'pts'.
        output_format (str, optional): The format of the output video tensors. Can be either "THWC" (default) or "TCHW".

    Returns:
        vframes (Tensor[T, H, W, C] or Tensor[T, C, H, W]): the `T` video frames
        aframes (Tensor[K, L]): the audio frames, where `K` is the number of channels and `L` is the number of points
        info (Dict): metadata for the video and audio. Can contain the fields video_fps (float) and audio_fps (int)
    """
    if not torch.jit.is_scripting() and not torch.jit.is_tracing():
        _log_api_usage_once(read_video)

    output_format = output_format.upper()
    if output_format not in ("THWC", "TCHW"):
        raise ValueError(f"output_format should be either 'THWC' or 'TCHW', got {output_format}.")

    from torchvision import get_video_backend

    if not os.path.exists(filename):
        raise RuntimeError(f"File not found: {filename}")

    if get_video_backend() != "pyav":
        vframes, aframes, info = _video_opt._read_video(filename, start_pts, end_pts, pts_unit)
    else:
        _check_av_available()

        if end_pts is None:
            end_pts = float("inf")

        if end_pts < start_pts:
            raise ValueError(
                f"end_pts should be larger than start_pts, got start_pts={start_pts} and end_pts={end_pts}"
            )

        info = {}
        video_frames = []
        audio_frames = []
        audio_timebase = _video_opt.default_timebase

        try:
            with av.open(filename, metadata_errors="ignore") as container:
                if container.streams.audio:
                    audio_timebase = container.streams.audio[0].time_base
                if container.streams.video:
                    video_frames = _read_from_stream(
                        container,
                        start_pts,
                        end_pts,
                        pts_unit,
                        container.streams.video[0],
                        {"video": 0},
                    )
                    video_fps = container.streams.video[0].average_rate
                    # guard against potentially corrupted files
                    if video_fps is not None:
                        info["video_fps"] = float(video_fps)

                if container.streams.audio:
                    audio_frames = _read_from_stream(
                        container,
                        start_pts,
                        end_pts,
                        pts_unit,
                        container.streams.audio[0],
                        {"audio": 0},
                    )
                    info["audio_fps"] = container.streams.audio[0].rate

        except av.AVError:
            # TODO raise a warning?
            pass

        vframes_list = [frame.to_rgb().to_ndarray() for frame in video_frames]
        aframes_list = [frame.to_ndarray() for frame in audio_frames]

        if vframes_list:
            vframes = torch.as_tensor(np.stack(vframes_list))
        else:
            vframes = torch.empty((0, 1, 1, 3), dtype=torch.uint8)

        if aframes_list:
            aframes = np.concatenate(aframes_list, 1)
            aframes = torch.as_tensor(aframes)
            if pts_unit == "sec":
                start_pts = int(math.floor(start_pts * (1 / audio_timebase)))
                if end_pts != float("inf"):
                    end_pts = int(math.ceil(end_pts * (1 / audio_timebase)))
            aframes = _align_audio_frames(aframes, audio_frames, start_pts, end_pts)
        else:
            aframes = torch.empty((1, 0), dtype=torch.float32)

    if output_format == "TCHW":
        # [T,H,W,C] --> [T,C,H,W]
        vframes = vframes.permute(0, 3, 1, 2)

    return vframes, aframes, info


def _can_read_timestamps_from_packets(container: "av.container.Container") -> bool:
    extradata = container.streams[0].codec_context.extradata
    if extradata is None:
        return False
    if b"Lavc" in extradata:
        return True
    return False


def _decode_video_timestamps(container: "av.container.Container") -> List[int]:
    if _can_read_timestamps_from_packets(container):
        # fast path
        return [x.pts for x in container.demux(video=0) if x.pts is not None]
    else:
        return [x.pts for x in container.decode(video=0) if x.pts is not None]


def read_video_timestamps(filename: str, pts_unit: str = "pts") -> Tuple[List[int], Optional[float]]:
    """
    List the video frames timestamps.

    Note that the function decodes the whole video frame-by-frame.

    Args:
        filename (str): path to the video file
        pts_unit (str, optional): unit in which timestamp values will be returned
            either 'pts' or 'sec'. Defaults to 'pts'.

    Returns:
        pts (List[int] if pts_unit = 'pts', List[Fraction] if pts_unit = 'sec'):
            presentation timestamps for each one of the frames in the video.
        video_fps (float, optional): the frame rate for the video

    """
    if not torch.jit.is_scripting() and not torch.jit.is_tracing():
        _log_api_usage_once(read_video_timestamps)
    from torchvision import get_video_backend

    if get_video_backend() != "pyav":
        return _video_opt._read_video_timestamps(filename, pts_unit)

    _check_av_available()

    video_fps = None
    pts = []

    try:
        with av.open(filename, metadata_errors="ignore") as container:
            if container.streams.video:
                video_stream = container.streams.video[0]
                video_time_base = video_stream.time_base
                try:
                    pts = _decode_video_timestamps(container)
                except av.AVError:
                    warnings.warn(f"Failed decoding frames for file {filename}")
                video_fps = float(video_stream.average_rate)
    except av.AVError as e:
        msg = f"Failed to open container for {filename}; Caught error: {e}"
        warnings.warn(msg, RuntimeWarning)

    pts.sort()

    if pts_unit == "sec":
        pts = [x * video_time_base for x in pts]

    return pts, video_fps