YuNet face detection, head pose, gaze, prohibited objects, and temporal fusion engine compiled to WebAssembly with WebGPU acceleration.
Choose between the CPU build (WASM SIMD) or GPU build (WebGPU hardware acceleration):
bun add vigilo-wasmbun add vigilo-wasm-gpunpm install vigilo-wasmnpm install vigilo-wasm-gpu# CPU build
pnpm add vigilo-wasm
# GPU build
pnpm add vigilo-wasm-gpuimport { initVigilo, CameraSource, loadModels, VigiloBrowser } from 'vigilo-wasm';
// 1. Initialize WebAssembly module (idempotent)
await initVigilo();
// 2. Open camera (ideal 1280x720, unbuffered drop-not-queue)
const camera = await CameraSource.open();
// 3. Load ONNX models (cached automatically in Cache API)
const models = await loadModels({
face: '/models/face_detection_yunet_2023mar.onnx',
pose: '/models/headpose_mobilenetv3_small.onnx',
gaze: '/models/mobileone_s0_gaze.onnx',
objects: '/models/yolox_nano.onnx',
}, {
executionProviders: ['webgpu', 'wasm'],
});
// 4. Create browser runtime and listen for events
const vigilo = await VigiloBrowser.create({
camera,
models,
faceHz: 10, // 10 Hz face pacing
objectHz: 1, // 1 Hz background object scan
gazeEvery: 2, // Gaze evaluation every 2nd face frame
});
vigilo.onViolationStarted((v) => {
console.warn(`[VIOLATION START] ${v.kind}: ${v.subject ?? ''} (confidence: ${v.confidence})`);
});
vigilo.onViolationEnded((v) => {
console.info(`[VIOLATION END] ${v.kind} lasted ${v.t_end_ms! - v.t_start_ms}ms`);
});
vigilo.onFrame(({ signals, active, total_ms, stages }) => {
// Draw bounding boxes, 3D pose gizmos, gaze rays, and violation pills
drawHud(signals, active, stages);
});
// Start detection loop
vigilo.start();
// Later when exam finishes, resolve remaining open violations:
// const finalEvents = vigilo.finish();