TorchCodec 0.0.1

A video decoder for PyTorch

Installation | Simple Example | Detailed Example | Documentation | Contributing | License

TorchCodec

TorchCodec is a Python package with a goal to provide useful and fast APIs to decode video frames to PyTorch Tensors.

[!NOTE] ⚠️ TorchCodec is still in early development stage and some APIs may be updated in future versions without a deprecation cycle, depending on user feedback. If you have any suggestions or issues, please let us know by opening an issue!

Using TorchCodec

Here's a condensed summary of what you can do with TorchCodec. For a more detailed example, check out our documentation!

from torchcodec.decoders import SimpleVideoDecoder

decoder = SimpleVideoDecoder("path/to/video.mp4")

decoder.metadata
# VideoStreamMetadata:
#   num_frames: 250
#   duration_seconds: 10.0
#   bit_rate: 31315.0
#   codec: h264
#   average_fps: 25.0
#   ... (truncated output)

len(decoder)  # == decoder.metadata.num_frames!
# 250
decoder.metadata.average_fps  # Note: instantaneous fps can be higher or lower
# 25.0

# Simple Indexing API
decoder[0]  # uint8 tensor of shape [C, H, W]
decoder[0 : -1 : 20]  # uint8 stacked tensor of shape [N, C, H, W]


# Iterate over frames:
for frame in decoder:
    pass

# Indexing, with PTS and duration info
decoder.get_frame_at(len(decoder) - 1)
# Frame:
#   data (shape): torch.Size([3, 400, 640])
#   pts_seconds: 9.960000038146973
#   duration_seconds: 0.03999999910593033

decoder.get_frames_at(start=10, stop=30, step=5)
# FrameBatch:
#   data (shape): torch.Size([4, 3, 400, 640])
#   pts_seconds: tensor([0.4000, 0.6000, 0.8000, 1.0000])
#   duration_seconds: tensor([0.0400, 0.0400, 0.0400, 0.0400])

# Time-based indexing with PTS and duration info
decoder.get_frame_displayed_at(pts_seconds=2)
# Frame:
#   data (shape): torch.Size([3, 400, 640])
#   pts_seconds: 2.0
#   duration_seconds: 0.03999999910593033

You can use the following snippet to generate a video with FFmpeg and tryout TorchCodec:

fontfile=/usr/share/fonts/dejavu-sans-mono-fonts/DejaVuSansMono-Bold.ttf
output_video_file=/tmp/output_video.mp4

ffmpeg -f lavfi -i \
    color=size=640x400:duration=10:rate=25:color=blue \
    -vf "drawtext=fontfile=${fontfile}:fontsize=30:fontcolor=white:x=(w-text_w)/2:y=(h-text_h)/2:text='Frame %{frame_num}'" \
    ${output_video_file}

Installing TorchCodec

We'll be providing wheels in the coming days so that you can just install torchcodec using pip. For now, you can just build from source. You will need the following dependencies:

• A C++ compiler+linker. This is typically available on a baseline Linux installation already.
• cmake
• pkg-config
• FFmpeg
• PyTorch nightly

Start by installing PyTorch following the official instructions.

Then, the easiest way to install the rest of the dependencies is to run:

conda install cmake pkg-config ffmpeg -c conda-forge

To clone and install the repo, run:

git clone [e-mail]:pytorch/torchcodec.git
# Or, using https instead of ssh: git clone https://github.com/pytorch/torchcodec.git
cd torchcodec

pip install -e ".[dev]" --no-build-isolation -vv

TorchCodec supports all major FFmpeg version in [4, 7].

Planned future work

We are actively working on the following features:

• Ship wheels for Linux, so that Linux users can pip install torchcodec.
• Ship wheels for MacOS, so that MacOS users can pip install torchcodec.
• GPU decoding
• Audio decoding

Let us know if you have any feature requests by opening an issue!

Contributing

We welcome contributions to TorchCodec! Please see our contributing guide for more details.

License

TorchCodec is released under the BSD 3 license.