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Consider using release 0.3.18 instead.
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EV-Utils

Overview

EV-Utils (evutils) is a performant collection of utilities for working with event-based vision data. Built with minimal dependencies, it relies on compiled C backed for speed while offering a clean, modular Python interface.

Inspirations & Related Work

This project draws inspiration from several excellent libraries in the event-based vision ecosystem and attempts to fill in their shortcomings:

Installation

We recommend installing evutils using uv.

From PyPi

uv add evutils[all]

From Git

git clone --recurse-submodules https://github.com/mandulaj/evutils.git
cd evutils

uv pip install -e ".[dev]"

Note: You can also install specific optional dependency groups like uv add evutils[torch,hdf5].

Architecture

The library is divided into several discrete modules. Many can be used independently without installing the full suite of dependencies:

└── augment     - Event augmentations
└── dataset     - Wrappers for various dataset loaders
└── events      - Core event handling logic
└── io          - Event reading and writing interfaces
    ├── reader 
    └── writer
└── random      - Random event generation and noise injection
└── torch       - PyTorch integration (requires evutils[torch])
└── types       - Standard types for representing Events in NumPy arrays
└── vis         - Visualization methods
    ├── histogram
    └── reconstructor

Quick API overview

`io`: Reading and Writing Events

The io module provides methods for reading and writing events into various event formats. It provides a simple .read() and .write() interface as well as more advanced interfaces using iterators and slicing.

from evutils.io import EventReader


ev_file = EventReader("raw_file.raw", delta_t=10e3)

events = ev_file.read()

`utils`

Various utility functions

`random`

Generating random events and adding noise to event recordings

`types`

This provides several standard types for representing Events in numpy arrays

`vis`

The vis moduels provides several methods for visualizing the events (for example as histograms), but also provides a streamlined interface for more complex visualization techneques, such as using the E2Vid reconstructor.

from evutils.vis.reconstructor import RPG_Reconstructor

reconstructor = RPG_Reconstructor(1280, 720)

img = reconstructor.gen_frame(events)

Running tests

Tests are managed via pytest. If you installed the package with the [dev] or [test] flag, you can run them via:

uv run pytest -s

Benchmarks

Read/write throughput benchmarks (using pytest-benchmark) live in benchmarks/ and are kept out of the normal test run. Run them explicitly:

uv run pytest benchmarks/                                   # evutils only
uv run pytest benchmarks/ --benchmark-group-by=param:fmt    # compare libraries per format

The benchmarks download a real Prophesee recording on first use. Optional cross-library comparisons run automatically once the libraries are installed (uv pip install -e ".[compare]"); OpenEB/Metavision is compared via the Docker image in benchmarks/docker/. See benchmarks/README.md for details.

Decode/encode throughput on a 12th Gen Intel Core i7-1280P (millions of events per second, higher is better):

Read (full-file decode)

Format	evutils	expelliarmus	evlib
EVT2	91	105	5
EVT2.1	72	–	–
EVT3	53	41	5

Write (evutils; expelliarmus/evlib are read-only for these formats)

Format	evutils
EVT2	146
EVT2.1	110
EVT3	74

(– = format not supported by that library.)

Acknowledgements

Thanks to all the contributors for supporting this project:

Elia Franc
Jakub Mandula

Cite

@PhDThesis{2024mandula_evutils,
  author        = {Jakub Mandula},
  title         = {EV-Utils: collection of utilities for working with event-based vision data},
  school        = {Dept. of Information Technology and Electrical Engineering, ETH Zurich},
  year          = 2024
}

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evutils 0.3.7

EV-Utils

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`io`: Reading and Writing Events

`utils`

`random`

`types`

`vis`