EyeTrax

EyeTrax is a Python library that provides webcam-based eye tracking. Extract facial features, train a model and predict gaze with an easy‑to‑use interface.

Features

• Real‑time gaze estimation
• Multiple calibration workflows
• Optional filtering (Kalman / Kalman+EMA / KDE)
• Model persistence – save / load a trained GazeEstimator
• Virtual-camera overlay that integrates with streaming software (e.g., OBS) via the bundled eyetrax-virtualcam CLI

Installation

From PyPI

pip install eyetrax

From source

git clone https://github.com/ck-zhang/eyetrax && cd eyetrax

# editable install — pick one
python -m pip install -e .
pip install uv && uv sync

Demo

The EyeTrax package provides two command‑line entry points

| Command | Purpose | |---------|---------| | eyetrax-demo | Run an on‑screen gaze overlay demo | | eyetrax-virtualcam | Stream the overlay to a virtual webcam |

Options

| Flag | Values | Default | Description | |------|--------|---------|-------------| | --filter | kalman, kalman_ema, kde, none | none | Smoothing filter | | --ema-alpha (kalman_ema only) | 0–1 | 0.25 | EMA smoothing strength | | --camera | int | 0 | Physical webcam index | | --calibration | 9p, 5p, lissajous, dense | 9p | Calibration routine | | --grid-rows (dense only) | int | 5 | Calibration grid rows | | --grid-cols (dense only) | int | 5 | Calibration grid columns | | --grid-margin (dense only) | float | 0.10 | Margin ratio from edges | | --background (demo only) | path | — | Background image | | --confidence (KDE only) | 0–1 | 0.5 | Contour probability |

Quick Examples

eyetrax-demo --filter kalman

# Kalman + EMA smoothing (tune EMA strength)
eyetrax-demo --filter kalman_ema --ema-alpha 0.5

# Dense grid calibration (higher spatial coverage)
eyetrax-demo --calibration dense --grid-rows 7 --grid-cols 7

eyetrax-virtualcam --filter kde --calibration 5p

Virtual camera demo

https://github.com/user-attachments/assets/de4a0b63-8631-4c16-9901-9f83bc0bb766

Library Usage

from eyetrax import GazeEstimator, run_9_point_calibration
import cv2

# Create estimator and calibrate
estimator = GazeEstimator()
run_9_point_calibration(estimator)

# Save model
estimator.save_model("gaze_model.pkl")

# Load model
estimator = GazeEstimator()
estimator.load_model("gaze_model.pkl")

cap = cv2.VideoCapture(0)

while True:
    # Extract features from frame
    ret, frame = cap.read()
    features, blink = estimator.extract_features(frame)

    # Predict screen coordinates
    if features is not None and not blink:
        x, y = estimator.predict([features])[0]
        print(f"Gaze: ({x:.0f}, {y:.0f})")

More

If you find EyeTrax useful, consider starring the repo or contributing. If you use it in your research, please cite it. The project is available under the MIT license.

BibTeX

@software{Zhang2025_EyeTrax,
  author       = {Chenkai Zhang},
  title        = {EyeTrax},
  version      = {0.2.2},
  date         = {2025-04-23},
  url          = {https://pypi.org/project/eyetrax/},
  repository   = {https://github.com/ck-zhang/EyeTrax},
  doi          = {10.5281/zenodo.17188537},
  keywords     = {eye tracking, computer vision}
}

APA style

Zhang, C. (2025). EyeTrax (0.2.2) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.17188537