Eye Tracker

A low-cost, high-precision, low-latency eye tracker prototype. A head-mounted rig pairs an IR eye camera with a forward-facing scene camera. A Python pipeline detects the pupil, calibrates a polynomial mapping from pupil pixels to scene-camera pixels, and renders the gaze locally (OpenCV).

Install

Python (3.11+ recommended):

brew install eigen opencv                       # macOS system deps for pupil-detectors
git clone https://github.com/pupil-labs/pupil-detectors.git ../pupil-detectors
pip install -r requirements.txt                 # installs the local pupil-detectors clone

requirements.txt references ../pupil-detectors as a local path; adjust the clone location or edit the path if your layout differs. pye3d ships from PyPI.

Hardware — head-mounted rig with an IR eye camera and a forward-facing scene camera (USB UVC). Calibration draws four ArUco markers (DICT_4X4_50, IDs 0/1/2/3) directly onto the laptop screen.

Running

py -m scripts.eyetracker

In-app controls (eye tracker window)

Key	Action
c	Quick calibration (4×3 grid, degree-2 polynomial)
d	Detailed calibration (5×4 grid, degree-3 polynomial, with worst-point recapture)
l	Load most recent saved calibration
r	Reset the pye3d 3D pupil model (give it ~30 s to reconverge)
space	Pause
q	Quit

Repo layout

scripts/eyetracker/         # Main Python package — `py -m scripts.eyetracker`
    __main__.py             # Composition root: wires concrete classes into App
    app.py                  # Main loop, frame routing, key dispatch
    config.py               # All tunables (camera, calibration grid, smoother, ArUco)
    cameras/                # OpenCV camera sources + discovery
    pupil/                  # Pupil Labs detector + confidence/jump gates
    scene/                  # ArUco detection and screen→scene homography
    gaze/                   # Polynomial mapper, 1€ smoother
    calibration/            # State machine, sample collector, persistence
    dataset.py              # Load per-session labels into one frame (+ Parquet cache)
    display/                # Tk calibration overlay, cv2 windows

scripts/extras/             # Standalone utilities
    record.py                       # Sync-recorded eye + scene MP4s
    analyze_recordings.py           # Per-file stats on a recording dir
    calibrate_scene_intrinsics.py   # ChArUco intrinsics for the scene camera
    generate_charuco_board.py       # Prints the board PNG used above
    gaze_emulator.py                # Synthetic gaze stream for dashboard dev
    measure_gaze_accuracy.py        # Post-hoc accuracy on a labeled session
    heatmap.py, camera_test.py, linux_cam_stream.py

experimental/               # Paused / on-hold work, kept for reference
    frontend/               # Next.js 16 / React 19 dashboard (Supabase auth) — see its README

docs/                       # Implementation notes, citations, architecture
    polynomial_gaze_mapping.md      # How the pupil→scene fit works end-to-end
    data_collection.md              # Fields the data-collection pipeline captures
    dataset_format.md               # On-disk format for sessions + calibration artifacts
    citations/                      # references.bib + references.tex
    architecture/workspace.dsl      # Structurizr C4 model (C1 / C2 / C3)

data/                       # Recorded MP4s + per-session calibration dumps
3d-files/                   # STLs for the headset mounts
requirements.txt            # Python deps (OpenCV, numpy, pupil-detectors)

Pipeline

graph TD
    subgraph Hardware ["Head-mounted rig"]
        EYE[IR eye camera] --> CAP[OpenCV capture]
        SCN[Scene camera] --> CAP
    end

    subgraph Pipeline ["Python (scripts/eyetracker)"]
        CAP --> PD[Pupil Labs 2D detector + pye3d]
        PD --> GATE[Confidence + jump gates]
        GATE --> POLY[Polynomial gaze mapper]
        CAP --> ARUCO[ArUco screen-corner detection]
        ARUCO -. calibration only .-> CAL[Calibration routine<br/>screen→scene homography]
        CAL --> POLY
        POLY --> SMOOTH[1€ smoother]
    end

    subgraph Output
        SMOOTH -->|cv2 windows| CV[Display]
    end

Loading

See docs/polynomial_gaze_mapping.md for the math behind the pupil→scene fit and why the homography only shows up during calibration.

Code style

• Python — PEP 8. Enforced by flake8 in CI (.github/workflows/linter.yml): blocking on E9/F63/F7/F82 (syntax errors, undefined names), with line length 127 and McCabe complexity 10 as non-blocking warnings. Public-facing modules, classes, and functions carry docstrings.

Design & architecture

• External references — catalogued in docs/citations/references.bib

Maintainer

Daniel Kaijzer

Prior contributors

Cody Lam, Ethan Shim, Harwin He, Roselio Ortega