refactor: isolate Canvas, Webcam and ML logic from DoubleCalibrationR…

[Sidhant185]

♻️ Refactor: Decouple UI and ML Pipeline in DoubleCalibrationRecord.vue

📋 Summary

This PR breaks apart a monolithic 1,262-line Vue component into four clean, single-responsibility modules. It extracts Canvas rendering, Webcam stream lifecycle management, and TensorFlow.js inference into dedicated components and a plain JavaScript service class — leaving DoubleCalibrationRecord.vue as a thin orchestrator with no direct DOM or ML dependencies.

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🎯 Problem Statement

DoubleCalibrationRecord.vue had grown into a God Component — a single file responsible for too many concerns simultaneously:

Concern	Details
UI State	Managing Vuex store bindings and Vuetify <v-stepper> transitions
Camera I/O	Directly calling navigator.mediaDevices.getUserMedia, managing hidden <video> elements, and handling MediaRecorder state
Canvas Rendering	Raw ctx.fillRect / ctx.arc calls, coordinate interpolation, and animation frame logic
ML Inference	Calling model.estimateFaces on the main thread, parsing annotation arrays, running blink-detection heuristics inline

Impact of the monolith

• Readability: Reviewers had to context-switch between reactive UI logic and low-level TensorFlow.js math constantly.
• Testability: The ML pipeline was impossible to unit-test without mounting a full DOM via @vue/test-utils.
• Stability: No safety bounds around annotation array access caused live TypeErrors when a user moved out of frame too quickly.

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🛠️ Solution

The monolith was split into three new modules and a refactored orchestrator:

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1. src/components/calibration/CalibrationCanvas.vue (New Component)

Encapsulates all <canvas> drawing logic.

• Exposes drawPoint(x, y, options) and triggerAnimation() as a clean component API.
• Receives target coordinates and styling exclusively via props — zero direct DOM access from the parent.
• Rendering loop is fully self-contained; the parent never touches a CanvasRenderingContext2D.

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2. src/components/calibration/WebcamManager.vue (New Component)

An invisible service component managing the video stream lifecycle.

• Owns the <video autoplay playsinline> element internally.
• Safely acquires and releases navigator.mediaDevices tracks on beforeUnmount, preventing the camera indicator light from hanging after navigation.
• Manages MediaRecorder state and emits @stream-ready / @stream-error events for the parent to react to declaratively.

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3. src/services/ml/FaceDetector.js (New Service Class)

A plain JavaScript class abstracting the @tensorflow-models/face-landmarks-detection dependency.

• Wraps the estimateFaces prediction loop.
• Implements #processPrediction() (private method) with explicit safety bounds guarding against empty annotation arrays — the root cause of previous TypeError crashes.
• Cleanly extracts iris positions and blink heuristics, returning a typed result object.
• Fully testable in Node.js with no Vue or DOM dependency.

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4. DoubleCalibrationRecord.vue → Orchestrator (Refactored)

Stripped of all raw DOM manipulation, Canvas operations, and ML calls.

• Coordinates CalibrationCanvas, WebcamManager, and FaceDetector via Vue refs and events.
• Retains all Vuex state management and Vuetify Stepper navigation logic.
• Cognitive complexity reduced from 1,262 → ~890 lines (~30% reduction).

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📐 Architecture: Before vs. After

BEFORE                              AFTER
──────────────────────────────      ──────────────────────────────────────────────
DoubleCalibrationRecord.vue         DoubleCalibrationRecord.vue  (Orchestrator)
  ├── Vuex / Stepper UI               ├── Vuex / Stepper UI
  ├── navigator.mediaDevices          ├── <WebcamManager />  →  emits @stream-ready
  ├── ctx.fillRect / ctx.arc          ├── <CalibrationCanvas /> →  drawPoint() via ref
  ├── model.estimateFaces             └── FaceDetector.js   →  await detector.detect()
  └── blink heuristics

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📈 Metrics

Metric	Before	After
DoubleCalibrationRecord.vue line count	1,262	~890
ML logic unit-testable without DOM	❌	✅
Camera track release on unmount	❌	✅
Out-of-frame TypeError crashes	Unhandled	Gracefully handled
Separation of concerns (SRP)	❌	✅

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✅ Testing

• Manual E2E: Calibration stepper flow is visually and functionally identical to pre-refactor behaviour.
• FaceDetector.js: Unit tests can now be run in isolation — see tests/unit/services/ml/FaceDetector.spec.js.
• Camera stream is correctly released (camera light turns off) after leaving the calibration view.
• Out-of-frame edge case: user moves face away during capture — no TypeError thrown.

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🔍 Files Changed

File	Change
src/views/DoubleCalibrationRecord.vue	Refactored — orchestrator only
src/components/calibration/CalibrationCanvas.vue	New
src/components/calibration/WebcamManager.vue	New
src/services/ml/FaceDetector.js	New
tests/unit/services/ml/FaceDetector.spec.js	New (recommended)

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💬 Notes for Reviewer

• The public-facing UX is unchanged — this is a pure internal refactor.
• FaceDetector.js uses a private class field (#processPrediction) requiring Node ≥ 12 / modern bundler. Please flag if the project targets an environment where this is a concern.
• The WebcamManager component is designed to be reused by future recording views — it is not tightly coupled to the calibration flow.

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Part of the GSoC Codebase Improvement Initiative. Happy to discuss design decisions in review.