• Installation
• Usage
• Render event data
• Render frames

Installation

Warning

The Python version that comes from Microsoft's Windows Store does not work with PySide6 (specifically, PySide6 complains that it cannot find qtquick2plugin.dll even though the file exists). Make sure to install Python from the official website https://www.python.org.

1. Clone this repository (git clone https://github.com/neuromorphicsystems/davis346-recorder) or download and unzip a copy (<> Code button). Open a terminal and cd to the repository's directory.

2. Create a virtual environment

   # Linux, macOS
   python3 -m venv .venv
   
   # Windows
   python -m venv .venv

3. Activate the virtual environment

   # Linux, macOS
   source .venv/bin/activate
   
   # Windows (cmd.exe)
   .venv\Scripts\activate.bat
   
   # Windows (PowerShell)
   .venv\Scripts\Activate.ps1

4. Install dependencies

   pip install neuromorphic-drivers faery PySide6 pillow

Usage

1. Activate the virtual environment (skip this if you just went through the installation steps)

   # Linux, macOS
   source .venv/bin/activate
   
   # Windows (cmd.exe)
   .venv\Scripts\activate.bat
   
   # Windows (PowerShell)
   .venv\Scripts\Activate.ps1

2. Run the application

   python davis346_recorder.py

See https://docs.inivation.com/hardware/hardware-advanced-usage/biasing.html for a guide on bias tuning.

Render event data

1. Create a directory called recordings

2. Move event files of interest (.csv) in the directory recordings

3. Run faery init

4. Faery cannot automatically tell the sensor width and height from a CSV file. In faery_script.py (created by the previous command), find all the calls to faery.events_stream_from_file and add the argument dimensions_fallback=(346, 260). The modified calls should look as follows:

   faery.events_stream_from_file(input, dimensions_fallback=(346, 260))

5. Run faery run

See https://aestream.github.io/faery/batch/ for documentation and an example on how to generate slow-motion videos.

Render frames

The Davis346 ADC has a precision of 10 bits, hence the frame values are in the range [0, 1023].

Neuromorphic-drivers multiplies the raw values by 64, making the effective range [0, 65472], to avoid display issues (16 bits images look very dark if only the range [0, 1023] is used).

The frames' timestamps (same clock as the events) are stored in frames_metadata.csv. The raw frames (native endian u16) are stored in frames/dddddd.raw. They can be loaded in Python as follows.

# read the frame as a numpy array
import numpy

with open("data/<recording name>/frames/000000.raw", "rb") as frame_file:
    array = numpy.frombuffer(frame_file.read(), dtype=numpy.uint16).reshape((260, 346))

# save the frame a 16-bit PNG image
import PIL.Image

PIL.Image.fromarray(array).save("test.png")