evt3 — fast event camera decoder for Python

PyPI Python versions CI License: MIT

Read Prophesee and Metavision .raw event camera recordings directly into NumPy arrays. evt3 decodes the EVT3 (EVT 3.0) encoding used by Prophesee event-based vision sensors. The decoder is written in Rust, releases the GIL, and writes straight into NumPy's columnar layout, so there is no Python-level parsing loop and no per-event object.

Use it for event-based vision, neuromorphic research, and DVS data analysis when you need .raw files as x, y, p, t arrays without installing a full camera SDK.

  • Fast — 55M events/second decode-only; the companion CLI is 1.62x faster than the optimized C++ reference on full CSV output, with byte-identical results.
  • NumPy-native — zero-copy uint16/uint8/uint64 arrays, stable across repeated access.
  • Bounded memory — batch iterators for recordings larger than RAM.
  • Streaming — a stateful decoder for live camera byte chunks.
  • No SDK required — pure wheel, only NumPy at runtime.

Installation

pip install evt3

Wheels are published for CPython 3.9 through 3.14 on Linux, macOS, and Windows, including the free-threaded CPython 3.14 build.

The wheel supports .raw files. HDF5 (.h5/.hdf5) input needs the native libhdf5 library and must be built from source — see the HDF5 documentation.

Quick start

import evt3

events = evt3.decode_file("recording.raw")

print(f"Decoded {len(events):,} events")
print(f"Sensor: {events.sensor_width}x{events.sensor_height}")

x = events.x          # np.ndarray[uint16]
y = events.y          # np.ndarray[uint16]
p = events.polarity   # np.ndarray[uint8]  (0=OFF, 1=ON)
t = events.timestamp  # np.ndarray[uint64] (microseconds)

# Short aliases also work
p = events.p
t = events.t

duration_s = (t[-1] - t[0]) / 1e6
print(f"Duration: {duration_s:.2f} s, rate: {len(events) / duration_s:,.0f} ev/s")

Convert to pandas

import pandas as pd

df = pd.DataFrame(events.to_dict())

Large recordings — bounded memory

decode_file holds the whole recording. For files larger than RAM, iterate batches instead:

for batch in evt3.decode_file_batches("recording.raw", batch_bytes=8 << 20):
    analyze(batch.x, batch.y, batch.p, batch.t)

# Including external trigger edges
for events, triggers in evt3.decode_file_batches_with_triggers("recording.raw"):
    analyze(events, triggers.timestamp, triggers.id, triggers.value)

Live input — stateful streaming decoder

The decoder keeps its state across arbitrary chunk borders, so USB packets do not need to align to EVT3 word or time boundaries:

decoder = evt3.Decoder(sensor_width=1280, sensor_height=720)
for raw_chunk in camera_chunks:
    analyze(decoder.feed(raw_chunk))
decoder.finish()

External triggers

events, triggers = evt3.decode_file_with_triggers("recording.raw")
trigger_times = triggers.timestamp
trigger_values = triggers.value

Decode from bytes

with open("recording.raw", "rb") as f:
    raw_bytes = f.read()

events = evt3.decode_bytes(raw_bytes, sensor_width=1280, sensor_height=720)

Build Events from your own arrays

events = evt3.Events.from_arrays(
    x=x, y=y, p=p, t=t,
    geometry=(1280, 720),
    copy=False,
)

Publish to AugurRS

Send decoded or filtered arrays into a running AugurRS session for interactive preview, 3D raw-event inspection, and viewer tools:

evt3.augur.publish_events(events, name="recording-analysis-window")

with evt3.augur.connect() as augur:
    augur.publish_events(events, name="raw")
    augur.publish_events(filtered_events, name="filtered")

Command-line tool

The same decoder ships as a standalone CLI that converts .raw to CSV or a packed binary format:

cargo install evt3-cli
evt3 recording.raw events.csv

Pre-built binaries for Linux, macOS, and Windows are on the releases page. The decoder is also available as a Rust library with cargo add evt3.

Performance notes

The decoder is implemented in Rust with attention to throughput:

  • Streaming buffer decoding for large files
  • Columnar layout for cache-efficient NumPy access
  • Minimal allocation during decoding
  • The GIL is released while Rust reads and decodes

decode_file, decode_file_with_triggers, and decode_bytes stay source-compatible. decode_file writes directly into the final NumPy columns. decode_file_batches and decode_file_batches_with_triggers are opt-in and keep event storage bounded; during iteration the consumer's current arrays and the next batch being built can briefly coexist.

Links

License

MIT — see LICENSE-MIT.

Project links

Data verified by PyPI on Aug 12, 2026
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Credits

Author: Uthmann

GitHub Statistics

Data verified by PyPI on Aug 12, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT)

Requires

Python >=3.9

Provides Extra

dev

Tags

event-camera evt3 prophesee decoder neuromorphic dvs

Classifiers

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