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https://github.com/aestream/faery

Sends event camera data from A to B. Supports live cameras and dead recordings

https://github.com/aestream/faery

Science Score: 26.0%

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    Scientific vocabulary similarity
    Low similarity (13.4%) to scientific vocabulary

Keywords

drivers event-camera neuromorphic-engineering neuromorphic-vision rust video-processing video-streaming
Last synced: 5 months ago · JSON representation

Repository

Sends event camera data from A to B. Supports live cameras and dead recordings

Basic Info
Statistics
  • Stars: 14
  • Watchers: 2
  • Forks: 1
  • Open Issues: 15
  • Releases: 7
Topics
drivers event-camera neuromorphic-engineering neuromorphic-vision rust video-processing video-streaming
Created over 1 year ago · Last pushed 6 months ago
Metadata Files
Readme License

README.md

faery logo

Faery: A Stream Processing Library for Neuromorphic Event-Based Data

PyPI - Downloads GitHub Tag Discord Neuromorphic Computing

Faery converts neuromorphic event-based data between formats. It can also generate videos, spectrograms, and event rate curves.

📄 Read more in our documentation.

Installation

Using pip: pip install faery.

We recommend using a virtual environment to install Faery. More information in the installation instructions.

Usage

Faery can be used as a command line tool or as a Python library.

Command line tool: Faery can convert data between formats, render videos, and analyze event-based data. It is particularly useful for quick conversions and batch processing of multiple files. Here are three examples:

  1. Convert a Prophesee raw file to AEDAT format:

sh faery input file input.raw output file output.aedat4

  1. Create an MP4 video from your event data with temporal filtering:

sh faery input file events.aedat4 filter temporal --window-size 1000us output mp4 output.mp4 --frame-rate 30

  1. Stream data from an Inivation camera to a UDP socket (note: requires event camera drivers):

sh faery input inivation camera output udp localhost 7777

Python library: Faery provides a set of input functions to read event data from files, UDP streams, or other sources. You can chain methods to filter, render, or analyze the data. For example, to render an AEDAT4 event file as a real-time MP4 video:

python import faery faery.events_stream_from_file("input.aedat4") \ .regularize(frequency_hz=60.0) \ .render(exponential_decay=0.2, style="starry_night") \ .to_mp4("output.mp4")

More information is available in the command line usage documentation, the Python library documentation, and the examples directory.

Acknowledgements

Faery was initiated at the 2024 Telluride neuromorphic workshop by

License: LGPLv3.0

Owner

  • Name: AEStream
  • Login: aestream
  • Kind: organization
  • Email: jens@jepedersen.dk

Efficient streaming of sparse event data supporting files, network I/O, GPU peripherals (via Torch) and neuromorphic protocols

GitHub Events

Total
  • Create event: 16
  • Release event: 8
  • Issues event: 15
  • Watch event: 5
  • Delete event: 14
  • Issue comment event: 21
  • Push event: 149
  • Pull request review event: 43
  • Pull request review comment event: 55
  • Pull request event: 17
  • Fork event: 1
Last Year
  • Create event: 16
  • Release event: 8
  • Issues event: 15
  • Watch event: 5
  • Delete event: 14
  • Issue comment event: 21
  • Push event: 149
  • Pull request review event: 43
  • Pull request review comment event: 55
  • Pull request event: 17
  • Fork event: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 8
  • Total pull requests: 6
  • Average time to close issues: 5 months
  • Average time to close pull requests: 9 days
  • Total issue authors: 3
  • Total pull request authors: 2
  • Average comments per issue: 0.25
  • Average comments per pull request: 0.67
  • Merged pull requests: 4
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 7
  • Pull requests: 6
  • Average time to close issues: 4 months
  • Average time to close pull requests: 9 days
  • Issue authors: 3
  • Pull request authors: 2
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.67
  • Merged pull requests: 4
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • Jegp (10)
  • aMarcireau (6)
  • hovren (1)
Pull Request Authors
  • aMarcireau (6)
  • Jegp (5)
  • andreapignaz (1)
Top Labels
Issue Labels
enhancement (7) help wanted (6) bug (1)
Pull Request Labels
documentation (1) bug (1) enhancement (1)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 209 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 9
  • Total maintainers: 1
pypi.org: faery

Ferries event-based data from A to B

  • Versions: 9
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 209 Last month
Rankings
Dependent packages count: 10.0%
Average: 38.1%
Dependent repos count: 66.2%
Maintainers (1)
aMarcireau
Last synced: 6 months ago

Dependencies

Cargo.lock cargo
  • aho-corasick 1.1.3
  • autocfg 1.3.0
  • bindgen 0.69.4
  • bitflags 1.3.2
  • bitflags 2.6.0
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  • matrixmultiply 0.2.4
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  • minimal-lexical 0.2.1
  • ndarray 0.13.1
  • neuromorphic-types 0.4.0
  • nom 7.1.3
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  • shlex 1.3.0
  • smallvec 1.13.2
  • syn 2.0.71
  • target-lexicon 0.12.15
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  • unicode-ident 1.0.12
  • unindent 0.2.3
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  • zstd 0.13.2
  • zstd-safe 7.2.0
  • zstd-sys 2.0.12+zstd.1.5.6
Cargo.toml cargo
pyproject.toml pypi
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  • numpy >=2
.github/workflows/ci-build.yml actions
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  • hecrj/setup-rust-action v2 composite