Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/simonarvin/eyeloop
EyeLoop is a Python 3-based eye-tracker tailored specifically to dynamic, closed-loop experiments on consumer-grade hardware.
https://github.com/simonarvin/eyeloop
eye-tracking neurology neuroscience psychology senses video-oculography visual
Last synced: about 1 month ago
JSON representation
EyeLoop is a Python 3-based eye-tracker tailored specifically to dynamic, closed-loop experiments on consumer-grade hardware.
- Host: GitHub
- URL: https://github.com/simonarvin/eyeloop
- Owner: simonarvin
- License: gpl-3.0
- Archived: true
- Created: 2020-07-02T08:35:39.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2022-10-20T20:51:11.000Z (about 2 years ago)
- Last Synced: 2024-08-01T22:55:19.213Z (4 months ago)
- Topics: eye-tracking, neurology, neuroscience, psychology, senses, video-oculography, visual
- Language: Python
- Homepage:
- Size: 256 MB
- Stars: 480
- Watchers: 20
- Forks: 67
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- awesome-psychology-projects - simonarvin/eyeloop - Python 3-based eye-tracker tailored specifically to dynamic, closed-loop experiments (Psychometrics / Tools for tests and experiments)
README
# EyeLoop [](https://www.gnu.org/licenses/gpl-3.0) [](https://github.com/simonarvin/eyeloop/issues) [](https://travis-ci.com/simonarvin/eyeloop)   
EyeLoop is a Python 3-based eye-tracker tailored specifically to dynamic, closed-loop experiments on consumer-grade hardware. Users are encouraged to contribute to EyeLoop's development.
## Features ##
- [x] **High-speed** > 1000 Hz on non-specialized hardware (no dedicated processing units necessary).
- [x] Modular, readable, **customizable**.
- [x] **Open-source**, and entirely Python 3.
- [x] **Works on any platform**, easy installation.
## Overview ##
- [How it works](#how-it-works)
- [Getting started](#getting-started)
- [Your first experiment](#designing-your-first-experiment)
- [Data](#data)
- [User interface](#graphical-user-interface)
- [Authors](#authors)
- [Examples](https://github.com/simonarvin/eyeloop/blob/master/examples)
- [*EyeLoop Playground*](https://github.com/simonarvin/eyeloop_playground)
## How it works ##
EyeLoop consists of two functional domains: the engine and the optional modules. The engine performs the eye-tracking, whereas the modules perform optional tasks, such as:
- Experiments
- Data acquisition
- Importing video sequences to the engine
> The modules import or extract data from the engine, and are therefore called *Importers* and *Extractors*, respectively.
One of EyeLoop's most appealing features is its modularity: Experiments are built simply by combining modules with the core Engine. Thus, the Engine has one task only: to compute eye-tracking data based on an *imported* sequence, and offer the generated data for *extraction*.
> How does [the Engine](https://github.com/simonarvin/eyeloop/blob/master/eyeloop/engine/README.md) work?\
> How does [the Importer](https://github.com/simonarvin/eyeloop/blob/master/eyeloop/importers/README.md) work?\
> How does [the Extractor](https://github.com/simonarvin/eyeloop/blob/master/eyeloop/extractors/README.md) work?
## Getting started ##
### Installation ###
Install EyeLoop by cloning the repository:
```
git clone https://github.com/simonarvin/eyeloop.git
```
>Dependencies: ```python -m pip install -r requirements.txt```
>Using pip:
> ```pip install .```
You may want to use a Conda or Python virtual environment when
installing `eyeloop`, to avoid mixing up with your system dependencies.
>Using pip and a virtual environment:
> ```python -m venv venv```
> ```source venv/bin/activate```
> ```(venv) pip install .```
Alternatively:
>- numpy: ```python pip install numpy```
>- opencv: ```python pip install opencv-python```
To download full examples with footage, check out EyeLoop's playground repository:
```
git clone https://github.com/simonarvin/eyeloop_playground.git
```
---
### Initiation ###
EyeLoop is initiated through the command-line utility `eyeloop`.
```
eyeloop
```
To access the video sequence, EyeLoop must be connected to an appropriate *importer class* module. Usually, the default opencv importer class (*cv*) is sufficient. For some machine vision cameras, however, a vimba-based importer (*vimba*) is neccessary.
```
eyeloop --importer cv/vimba
```
> [Click here](https://github.com/simonarvin/eyeloop/blob/master/eyeloop/importers/README.md) for more information on *importers*.
To perform offline eye-tracking, we pass the video argument ```--video``` with the path of the video sequence:
```
eyeloop --video [file]/[folder]
```
EyeLoop can be used on a multitude of eye types, including rodents, human and non-human primates. Specifically, users can suit their eye-tracking session to any species using the ```--model``` argument.
```
eyeloop --model ellipsoid/circular
```
> In general, the ellipsoid pupil model is best suited for rodents, whereas the circular model is best suited for primates.
To learn how to optimize EyeLoop for your video material, see [*EyeLoop Playground*](https://github.com/simonarvin/eyeloop_playground).
To see all command-line arguments, pass:
```
eyeloop --help
```
## Designing your first experiment ##
In EyeLoop, experiments are built by stacking modules. By default, EyeLoop imports two base *extractors*, namely a FPS-counter and a data acquisition tool. To add custom extractors, e.g., for experimental purposes, use the argument tag ```--extractors```:
```
eyeloop --extractors [file_path]/p (where p = file prompt)
```
Inside the *extractor* file, or a composite python file containing several *extractors*, define the list of *extractors* to be added:
```python
extractors_add = [extractor1, extractor2, etc]
```
*Extractors* are instantiated by EyeLoop at start-up. Then, at every subsequent time-step, the *extractor's* ```fetch()``` function is called by the engine.
```python
class Extractor:
def __init__(self) -> None:
...
def fetch(self, core) -> None:
...
```
```fetch()``` gains access to all eye-tracking data in real-time via the *core* pointer.
> [Click here](https://github.com/simonarvin/eyeloop/blob/master/eyeloop/extractors/README.md) for more information on *extractors*.
### Open-loop example ###
As an example, we'll here design a simple *open-loop* experiment where the brightness of a PC monitor is linked to the phase of the sine wave function. We create anew python-file, say "*test_ex.py*", and in it define the sine wave frequency and phase using the instantiator:
```python
class Experiment:
def __init__(self) -> None:
self.frequency = ...
self.phase = 0
```
Then, by using ```fetch()```, we shift the phase of the sine wave function at every time-step, and use this to control the brightness of a cv-render.
```python
...
def fetch(self, engine) -> None:
self.phase += self.frequency
sine = numpy.sin(self.phase) * .5 + .5
brightness = numpy.ones((height, width), dtype=float) * sine
cv2.imshow("Experiment", brightness)
```
To add our test extractor to EyeLoop, we'll need to define an extractors_add array:
```python
extractors_add = [Experiment()]
```
Finally, we test the experiment by running command:
```
eyeloop --extractors path/to/test_ex.py
```
> See [Examples](https://github.com/simonarvin/eyeloop/blob/master/examples) for demo recordings and experimental designs.
> For extensive test data, see [*EyeLoop Playground*](https://github.com/simonarvin/eyeloop_playground)
## Data ##
EyeLoop produces a json-datalog for each eye-tracking session. The datalog's first column is the timestamp.
The next columns define the pupil (if tracked):
```((center_x, center_y), radius1, radius2, angle)```
The next columns define the corneal reflection (if tracked):
```((center_x, center_y), radius1, radius2, angle)```
The next columns contain any data produced by custom Extractor modules
## Graphical user interface ##
The default graphical user interface in EyeLoop is [*minimum-gui*.](https://github.com/simonarvin/eyeloop/blob/master/eyeloop/guis/minimum/README.md)
> EyeLoop is compatible with custom graphical user interfaces through its modular logic. [Click here](https://github.com/simonarvin/eyeloop/blob/master/eyeloop/guis/README.md) for instructions on how to build your own.
## Running unit tests ##
Install testing requirements by running in a terminal:
`pip install -r requirements_testing.txt`
Then run tox: `tox`
Reports and results will be outputted to `/tests/reports`
## Known issues ##
- [ ] Respawning/freezing windows when running *minimum-gui* in Ubuntu.
## References ##
If you use any of this code or data, please cite [Arvin et al. 2021] ([article](https://www.frontiersin.org/articles/10.3389/fncel.2021.779628/full)).
```latex
@ARTICLE{Arvin2021-tg,
title = "{EyeLoop}: An open-source system for high-speed, closed-loop
eye-tracking",
author = "Arvin, Simon and Rasmussen, Rune and Yonehara, Keisuke",
journal = "Front. Cell. Neurosci.",
volume = 15,
pages = "494",
year = 2021
}
```
## License ##
This project is licensed under the GNU General Public License v3.0. Note that the software is provided "as is", without warranty of any kind, express or implied.
## Authors ##
**Lead Developer:**
Simon Arvin, [email protected]
**Researchers:**
- Simon Arvin, [email protected]
- Rune Rasmussen, [email protected]
- Keisuke Yonehara, [email protected]
**Corresponding Author:**
Keisuke Yonehera, [email protected]
---
