evt3 — fast event camera decoder for Python
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/uint64arrays, 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
.rawfiles. HDF5 (.h5/.hdf5) input needs the nativelibhdf5library 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.
Metadata
Release files for evt3 0.4.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
Total release size: 5.1 MB
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