Eye Lab is an open-source tool for creating browser-based eye-tracking usability tests using Webcam + TensorFlow.js, without plugins or native installations.

Originally created by Karine Pistili as a graduation project, Eye Lab has evolved into a full calibration and gaze-tracking microservice used standalone or fully integrated into RUXAILAB.

---

🧩 Architecture Overview

Eye Lab has two repositories:

Component	Description
Frontend (this repo)	Calibration UI + gaze tracking client (Vue.js)
Backend	API handling calibration uploads and optional forwarding to RUXAILAB

Backend repository:
https://github.com/ruxailab/eye-tracker-api

---

👩‍💻 Local Setup

Prerequisites

• Vue CLI 4.x
• Node.js 14.x

---

1. Install dependencies

npm install

2. Run in development mode

npm run serve:development

3. Build

• Build with development env vars:

npm run build:development

• Build with production env vars:

npm run build:production

• Standard production build:

npm run build

---

☁️ Deploying to Firebase Hosting

1. Create .env files for each environment. This project reads environment keys using a suffix of _DEV or _PROD.

.env.development

VUE_APP_ENV=dev
VUE_APP_FIREBASE_API_KEY_DEV='your-dev-api-key'
VUE_APP_FIREBASE_AUTH_DOMAIN_DEV='your-dev-auth-domain'
VUE_APP_FIREBASE_PROJECT_ID_DEV='your-dev-project-id'
VUE_APP_FIREBASE_STORAGE_BUCKET_DEV='your-dev-storage-bucket'
VUE_APP_FIREBASE_MESSAGING_SENDER_ID_DEV='your-dev-sender-id'
VUE_APP_FIREBASE_APP_ID_DEV='your-dev-app-id'
VUE_APP_FIREBASE_MEASUREMENT_ID_DEV='your-dev-measurement-id'
VUE_APP_API_BASE_URL_DEV='https://api-dev.example.com'
VUE_APP_RUXAILAB_URL_DEV='https://ruxailab-dev.example.com'

.env.production

VUE_APP_ENV=prod
VUE_APP_FIREBASE_API_KEY_PROD='your-prod-api-key'
VUE_APP_FIREBASE_AUTH_DOMAIN_PROD='your-prod-auth-domain'
VUE_APP_FIREBASE_PROJECT_ID_PROD='your-prod-project-id'
VUE_APP_FIREBASE_STORAGE_BUCKET_PROD='your-prod-storage-bucket'
VUE_APP_FIREBASE_MESSAGING_SENDER_ID_PROD='your-prod-sender-id'
VUE_APP_FIREBASE_APP_ID_PROD='your-prod-app-id'
VUE_APP_FIREBASE_MEASUREMENT_ID_PROD='your-prod-measurement-id'
VUE_APP_API_BASE_URL_PROD='https://api.example.com'
VUE_APP_RUXAILAB_URL_PROD='https://ruxailab.example.com'

2. Deploy to the development hosting site:

npm run deploy:development

3. Deploy to the production hosting site:

npm run deploy:production

deploy:development uses dev Firebase project alias and the eye-tracking-calib-dev hosting target. deploy:production uses prod Firebase project alias.

---

🔄 GitHub CI/CD

This project includes a Firebase Hosting workflow.
You can customize the settings inside:

.github/workflows/

---

🚀 Eye Lab + RUXAILAB Integration

Eye Lab is used by RUXAILAB as an external calibration and gaze-tracking service.
The workflow below explains the exact communication pipeline.

---

🔗 1. Redirect From RUXAILAB → Eye Lab

During a usability test, RUXAILAB redirects the participant to the Eye Lab URL:

VUE_APP_EYE_LAB_FRONTEND_URL?auth=<userId>&test=<testId>

Eye Lab frontend then:

• sets fromRuxailab = true
• sets calibrationName = userId
• loads DoubleCalibrationRecord.vue
• calls getRuxailabConfig(auth, test)
  → makes a GET request to:

${VUE_APP_RUXAILAB_URL}/getCalibrationConfig

RUXAILAB returns the admin-defined calibration configuration, which is stored in the Eye Lab Vuex store.

---

🎯 2. User Performs Calibration

Eye Lab shows the multi-point calibration interface, collecting:

• iris positions
• timestamps
• fixed-point reference geometry

All data stays local in the browser until calibration is completed.

---

📡 3. Sending Calibration to Eye Lab Backend

When calibration finishes, Eye Lab calls:

POST /api/session/calib_validation

Payload includes (inside FormData):

• from_ruxailab
• file_name (userId)
• fixed circle iris points
• calibrated iris predictions
• screen_height, screen_width
• k (calibration factor)
• full calibration model

---

🔁 4. Forwarding Calibration to RUXAILAB

If from_ruxailab = true, the Eye Lab backend forwards the following JSON to RUXAILAB:

{
  "session_id": "<file_name>",
  "model": { ...calibrationData },
  "screen_height": 1080, 
  "screen_width": 1920,
  "k": number of points
}

Sent to the endpoint:

POST /receiveCalibration

RUXAILAB then stores this calibration inside the user object, making it available for future eye-tracking tests.

---

📋 Integration Diagram

---

📌 Notes

• No webcam video is transmitted or stored in this project — only numeric gaze-related data.
• Accuracy depends on lighting, camera quality and user stability.
• Fully browser-based using TensorFlow.js.

---

🧑‍🤝‍🧑 Contributing

Pull requests are welcome.
Found a bug? Open an issue.

---

📄 License

MIT License
Copyright © Uramaki Lab