torchvision.io

The torchvision.io package provides functions for performing IO operations. They are currently specific to reading and writing video and images.

Video

torchvision.io.read_video(filename: str, start_pts: int = 0, end_pts: Union[float, NoneType] = None, pts_unit: str = 'pts') → 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

Parameters:

• filename (str) – path to the video file
• start_pts (int if pts_unit = 'pts', optional) – float / Fraction if pts_unit = ‘sec’, optional the start presentation time of the video
• end_pts (int if pts_unit = 'pts', optional) – 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’.

Returns:

• vframes (Tensor[T, H, W, C]) – 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)

torchvision.io.read_video_timestamps(filename: str, pts_unit: str = 'pts') → Tuple[List[int], Union[float, NoneType]]

List the video frames timestamps.

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

Parameters:

• 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

torchvision.io.write_video(filename: str, video_array: torch.Tensor, fps: float, video_codec: str = 'libx264', options: Union[Dict[str, Any], NoneType] = None) → None

Writes a 4d tensor in [T, H, W, C] format in a video file

Parameters:

• 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) – frames per second

Fine-grained video API

In addition to the read_video function, we provide a high-performance lower-level API for more fine-grained control compared to the read_video function. It does all this whilst fully supporting torchscript.

class torchvision.io.VideoReader(path, stream='video')

Fine-grained video-reading API. Supports frame-by-frame reading of various streams from a single video container.

Example

The following examples creates a VideoReader object, seeks into 2s point, and returns a single frame:

import torchvision
video_path = "path_to_a_test_video"

reader = torchvision.io.VideoReader(video_path, "video")
reader.seek(2.0)
frame = next(reader)

VideoReader implements the iterable API, which makes it suitable to using it in conjunction with itertools for more advanced reading. As such, we can use a VideoReader instance inside for loops:

reader.seek(2)
for frame in reader:
    frames.append(frame['data'])
# additionally, `seek` implements a fluent API, so we can do
for frame in reader.seek(2):
    frames.append(frame['data'])

With itertools, we can read all frames between 2 and 5 seconds with the following code:

for frame in itertools.takewhile(lambda x: x['pts'] <= 5, reader.seek(2)):
    frames.append(frame['data'])

and similarly, reading 10 frames after the 2s timestamp can be achieved as follows:

for frame in itertools.islice(reader.seek(2), 10):
    frames.append(frame['data'])

Note

Each stream descriptor consists of two parts: stream type (e.g. ‘video’) and a unique stream id (which are determined by the video encoding). In this way, if the video contaner contains multiple streams of the same type, users can acces the one they want. If only stream type is passed, the decoder auto-detects first stream of that type.

Parameters:

• path (string) – Path to the video file in supported format
• stream (string, optional) – descriptor of the required stream, followed by the stream id, in the format {stream_type}:{stream_id}. Defaults to "video:0". Currently available options include ['video', 'audio']

__next__()

Decodes and returns the next frame of the current stream

Returns:a dictionary with fields data and pts containing decoded frame and corresponding timestamp Return type:(dict)

get_metadata()

Returns video metadata

Returns:dictionary containing duration and frame rate for every stream Return type:(dict)

seek(time_s: float)

Seek within current stream.

Parameters:time_s (float) – seek time in seconds

Note

Current implementation is the so-called precise seek. This means following seek, call to next() will return the frame with the exact timestamp if it exists or the first frame with timestamp larger than time_s.

set_current_stream(stream: str)

Set current stream. Explicitly define the stream we are operating on.

Parameters:stream (string) – descriptor of the required stream. Defaults to "video:0" Currently available stream types include ['video', 'audio']. Each descriptor consists of two parts: stream type (e.g. ‘video’) and a unique stream id (which are determined by video encoding). In this way, if the video contaner contains multiple streams of the same type, users can acces the one they want. If only stream type is passed, the decoder auto-detects first stream of that type and returns it. Returns:True on succes, False otherwise Return type:(bool)

Example of inspecting a video:

import torchvision
video_path = "path to a test video"
# Constructor allocates memory and a threaded decoder
# instance per video. At the momet it takes two arguments:
# path to the video file, and a wanted stream.
reader = torchvision.io.VideoReader(video_path, "video")

# The information about the video can be retrieved using the
# `get_metadata()` method. It returns a dictionary for every stream, with
# duration and other relevant metadata (often frame rate)
reader_md = reader.get_metadata()

# metadata is structured as a dict of dicts with following structure
# {"stream_type": {"attribute": [attribute per stream]}}
#
# following would print out the list of frame rates for every present video stream
print(reader_md["video"]["fps"])

# we explicitly select the stream we would like to operate on. In
# the constructor we select a default video stream, but
# in practice, we can set whichever stream we would like
video.set_current_stream("video:0")

Image

torchvision.io.read_image(path: str) → torch.Tensor

Reads a JPEG or PNG image into a 3 dimensional RGB Tensor. The values of the output tensor are uint8 between 0 and 255.

Parameters:path (str) – path of the JPEG or PNG image. Returns:output Return type:Tensor[3, image_height, image_width]

torchvision.io.decode_image(input: torch.Tensor) → torch.Tensor

Detects whether an image is a JPEG or PNG and performs the appropriate operation to decode the image into a 3 dimensional RGB Tensor.

The values of the output tensor are uint8 between 0 and 255.

Parameters:input (Tensor) – a one dimensional uint8 tensor containing the raw bytes of the PNG or JPEG image. Returns:output Return type:Tensor[3, image_height, image_width]

torchvision.io.encode_jpeg(input: torch.Tensor, quality: int = 75) → torch.Tensor

Takes an input tensor in CHW layout and returns a buffer with the contents of its corresponding JPEG file.

Parameters:

• input (Tensor[channels, image_height, image_width])) – int8 image tensor of c channels, where c must be 1 or 3.
• quality (int) – Quality of the resulting JPEG file, it must be a number between 1 and 100. Default: 75

Returns:

output – A one dimensional int8 tensor that contains the raw bytes of the JPEG file.

Return type:

Tensor[1]

torchvision.io.write_jpeg(input: torch.Tensor, filename: str, quality: int = 75)

Takes an input tensor in CHW layout and saves it in a JPEG file.

Parameters:

• input (Tensor[channels, image_height, image_width]) – int8 image tensor of c channels, where c must be 1 or 3.
• filename (str) – Path to save the image.
• quality (int) – Quality of the resulting JPEG file, it must be a number between 1 and 100. Default: 75

torchvision.io.encode_png(input: torch.Tensor, compression_level: int = 6) → torch.Tensor

Takes an input tensor in CHW layout and returns a buffer with the contents of its corresponding PNG file.

Parameters:

• input (Tensor[channels, image_height, image_width]) – int8 image tensor of c channels, where c must 3 or 1.
• compression_level (int) – Compression factor for the resulting file, it must be a number between 0 and 9. Default: 6

Returns:

output – A one dimensional int8 tensor that contains the raw bytes of the PNG file.

Return type:

Tensor[1]

torchvision.io.write_png(input: torch.Tensor, filename: str, compression_level: int = 6)

Takes an input tensor in CHW layout (or HW in the case of grayscale images) and saves it in a PNG file.

Parameters:

• input (Tensor[channels, image_height, image_width]) – int8 image tensor of c channels, where c must be 1 or 3.
• filename (str) – Path to save the image.
• compression_level (int) – Compression factor for the resulting file, it must be a number between 0 and 9. Default: 6