Source code for

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 ..utils import _log_api_usage_once
from . import _video_opt

    import av

    if not hasattr(, "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 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 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

[docs]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, 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 =[0] 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: "", 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 # 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 ="DivX(\d+)Build(\d+)(\w)", d) if o is None: o ="DivX(\d+)b(\d+)(\w)", d) if o is not None: should_buffer = == 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, any_frame=False, backward=True, stream=stream) except av.AVError: # TODO add some warnings in this case # print("Corrupted file?", 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]
[docs]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 and 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, metadata_errors="ignore") as container: if audio_timebase =[0].time_base if video_frames = _read_from_stream( container, start_pts, end_pts, pts_unit,[0], {"video": 0}, ) video_fps =[0].average_rate # guard against potentially corrupted files if video_fps is not None: info["video_fps"] = float(video_fps) if audio_frames = _read_from_stream( container, start_pts, end_pts, pts_unit,[0], {"audio": 0}, ) info["audio_fps"] =[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]
[docs]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, metadata_errors="ignore") as container: if video_stream =[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


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources