'use strict';
let face = null, hands = null, vision = null;
let lastTime = 0;
async function prepare(c) {
  if (!vision) {
    const module = await import('/vendor/mediapipe/vision_bundle.mjs');
    const files = await module.FilesetResolver.forVisionTasks('/vendor/mediapipe/wasm');
    vision = {module,files};
  }
  if (c.rosto && !face) face = await vision.module.FaceDetector.createFromOptions(vision.files, {
    baseOptions:{modelAssetPath:'/vendor/mediapipe/face.tflite',delegate:'CPU'},runningMode:'VIDEO',minDetectionConfidence:.6});
  if (c.maos && !hands) hands = await vision.module.HandLandmarker.createFromOptions(vision.files, {
    baseOptions:{modelAssetPath:'/vendor/mediapipe/hands.task',delegate:'CPU'},runningMode:'VIDEO',numHands:2,
    minHandDetectionConfidence:.6,minHandPresenceConfidence:.6,minTrackingConfidence:.5});
}
self.onmessage = async ({data}) => {
  const bitmap = data.bitmap;
  try {
    await prepare(data.config);
    if (!bitmap) { self.postMessage({tipo:'pronto'}); return; }
    const began = performance.now();
    const stamp = Math.max(data.timestamp,lastTime+1); lastTime=stamp;
    const faces = data.config.rosto ? face.detectForVideo(bitmap,stamp).detections.map(d=>({
      x:d.boundingBox.originX/bitmap.width,y:d.boundingBox.originY/bitmap.height,
      w:d.boundingBox.width/bitmap.width,h:d.boundingBox.height/bitmap.height,
      score:d.categories[0]?.score || 0,pontos:d.keypoints.map(p=>({x:p.x,y:p.y}))
    })) : [];
    const result = data.config.maos ? hands.detectForVideo(bitmap,stamp) : {landmarks:[]};
    self.postMessage({tipo:'resultado',faces,hands:result.landmarks.map(h=>h.map(p=>({x:p.x,y:p.y}))),
      ms:performance.now()-began,timestamp:data.timestamp});
  } catch(e) { self.postMessage({tipo:'erro',erro:e.message || String(e)}); }
  finally { if (bitmap) bitmap.close(); }
};