+-----------------------------------------------------------------------------------------+
|                  Cloud Video Processing vs In-Browser WebGPU Vision                     |
+-----------------------------------------------------------------------------------------+

CLOUD VIDEO PROCESSING (Heavy Bandwidth, High Latency, Privacy Nightmare):
[User Camera] ──(Continuous 1080p Video Stream)──> [INTERNET] ──> [Cloud GPU Server (PyTorch)]
                                                                        │ (250ms Roundtrip)
                                                                        ▼
* Heavy cloud GPU costs; Violates biometric privacy laws; Lags behind live user movement!

IN-BROWSER WEBGPU VISION (2026 Standard - 100% Private, 60 FPS Instant):
[User Camera (navigator.mediaDevices.getUserMedia)]
  │
  ▼ (Zero-Copy Frame Texture Bind)
[LOCAL CLIENT WEBGPU COMPUTE SHADERS (MediaPipe / ONNX Runtime Web)]:
  ├── 1. Extracts 33 3D Pose Keypoints in < 12 milliseconds!
  ├── 2. Calculates joint angles and fitness repetition metrics in RAM.
  └── 3. Overlays real-time skeleton on HTML5 Canvas at locked 60 FPS!
* Zero Video Packets Sent Over Internet! 100% Private! $0.00 Server Invoices!

+-----------------------------------------------------------------------------------------+
|                  In-Browser Computer Vision Framework Matrix                            |
+-----------------------------------------------------------------------------------------+

GOOGLE MEDIAPIPE (Tasks-Vision Web SDK)
- Specialization: Human-centric vision: 33-point 3D Pose, 478-point Face Mesh, 21-point Hands.
- Engine: Highly optimized C++ WebAssembly + WebGPU Shader Kernels.
- Best for: Fitness apps, virtual try-on, hand gesture controllers, interactive webcam games.

ONNX RUNTIME WEB (YOLOv10 / YOLO11 / Depth Anything)
- Specialization: General multi-class object detection, instance segmentation, depth estimation.
- Engine: WebGPU Execution Provider (`device: "webgpu"`).
- Best for: Security object tracking, retail shelf inspection, AR depth sensing, autonomous drones.

// components/LivePoseTracker.tsx
"use client";

import React, { useEffect, useRef, useState } from "react";
import { FilesetResolver, PoseLandmarker, DrawingUtils } from "@google/mediapipe-tasks-vision";

export function LivePoseTracker() {
  const videoRef = useRef<HTMLVideoElement>(null);
  const canvasRef = useRef<HTMLCanvasElement>(null);
  const [isModelReady, setIsModelReady] = useState(false);
  const [fps, setFps] = useState(0);

  useEffect(() => {
    let poseLandmarker: PoseLandmarker | null = null;
    let animationFrameId: number;
    let lastTime = performance.now();
    let frameCount = 0;

    async function initMediaPipeVision() {
      // 1. Load WebAssembly and WebGPU Vision Binaries
      const vision = await FilesetResolver.forVisionTasks(
        "https://cdn.jsdelivr.net/npm/@google/mediapipe-tasks-vision@latest/wasm"
      );

      // 2. Initialize GPU-Accelerated Pose Landmarker
      poseLandmarker = await PoseLandmarker.createFromOptions(vision, {
        baseOptions: {
          modelAssetPath: "https://storage.googleapis.com/mediapipe-models/pose_landmarker/pose_landmarker_lite/float16/latest/pose_landmarker_lite.task",
          delegate: "GPU", // Enforces Hardware WebGPU Acceleration!
        },
        runningMode: "VIDEO",
        numPoses: 1,
        minPoseDetectionConfidence: 0.65,
        minTrackingConfidence: 0.65,
      });

      setIsModelReady(true);
      startCamera();
    }

    async function startCamera() {
      const stream = await navigator.mediaDevices.getUserMedia({
        video: { width: 1280, height: 720, frameRate: { ideal: 60 } },
        audio: false,
      });

      if (videoRef.current) {
        videoRef.current.srcObject = stream;
        videoRef.current.onloadeddata = () => {
          videoRef.current?.play();
          renderLoop();
        };
      }
    }

    // 3. High-Speed 60 FPS Render Loop
    function renderLoop() {
      if (videoRef.current && canvasRef.current && poseLandmarker) {
        const video = videoRef.current;
        const canvas = canvasRef.current;
        const ctx = canvas.getContext("2d")!;
        const drawingUtils = new DrawingUtils(ctx);

        canvas.width = video.videoWidth;
        canvas.height = video.videoHeight;

        const startTimeMs = performance.now();
        // Hardware-Accelerated 3D Landmark Extraction in &lt; 10ms!
        const results = poseLandmarker.detectForVideo(video, startTimeMs);

        ctx.clearRect(0, 0, canvas.width, canvas.height);

        // Draw Skeleton Overlays
        if (results.landmarks && results.landmarks.length > 0) {
          for (const landmark of results.landmarks) {
            drawingUtils.drawConnectors(landmark, PoseLandmarker.POSE_CONNECTIONS, {
              color: "#ff0055", // Crimson Red Skeleton
              lineWidth: 3,
            });
            drawingUtils.drawLandmarks(landmark, {
              color: "#00f0ff", // Neon Cyan Joints
              lineWidth: 1,
              radius: 4,
            });
          }
        }

        // Calculate Real-Time FPS
        frameCount++;
        const now = performance.now();
        if (now - lastTime >= 1000) {
          setFps(frameCount);
          frameCount = 0;
          lastTime = now;
        }
      }
      animationFrameId = requestAnimationFrame(renderLoop);
    }

    initMediaPipeVision();

    return () => {
      cancelAnimationFrame(animationFrameId);
      poseLandmarker?.close();
    };
  }, []);

  return (
    <div className="relative p-6 bg-neutral-900 border border-neutral-800 rounded-xl text-white max-w-3xl mx-auto">
      <div className="flex justify-between items-center mb-4">
        <h3 className="text-xl font-bold">⚡ 60 FPS In-Browser 3D Pose Tracking</h3>
        <span className="px-3 py-1 text-xs font-mono font-bold bg-neutral-800 text-emerald-400 rounded-full border border-neutral-700">
          {isModelReady ? `${fps} FPS (WebGPU)` : "Loading Vision Model..."}
        </span>
      </div>

      <div className="relative w-full aspect-video rounded-lg overflow-hidden bg-black">
        `<video ref={videoRef} className="absolute inset-0 w-full h-full object-cover" muted playsInline />`
        `<canvas ref={canvasRef} className="absolute inset-0 w-full h-full object-cover z-10" />`
      </div>
    </div>
  );
}

// services/yoloDetector.ts
import * as ort from "onnxruntime-web/webgpu";

export class YOLODetector {
  private session: ort.InferenceSession | null = null;

  async loadModel() {
    console.log("🚀 Loading YOLOv10-Nano ONNX model with WebGPU backend...");
    // 1. Initialize ONNX Session with WebGPU Execution Provider
    this.session = await ort.InferenceSession.create(
      "https://cdn.mojostudio.in/models/yolov10n-int8.onnx",
      {
        executionProviders: ["webgpu"],
        graphOptimizationLevel: "all",
      }
    );
    console.log("✅ YOLOv10 WebGPU session initialized!");
  }

  async detectObjects(preprocessedTensor: ort.Tensor): Promise<any> {
    if (!this.session) throw new Error("YOLO Session not initialized");

    // 2. Execute GPU Compute Shader Matrix Inference in &lt; 18ms!
    const feeds = { images: preprocessedTensor };
    const results = await this.session.run(feeds);
    
    return results.output0; // Returns bounding boxes [x, y, w, h, confidence, classId]
  }
}

       +-------------------------------------------------------------+
       |             End-to-End Pose Detection Latency (ms)          |
       +-------------------------------------------------------------+
 Cloud Video Stream (AWS EC2 g5.xlarge) | ==================================== [265.0 ms]
 In-Browser WebAssembly (CPU Wasm)      | ================== [48.0 ms]
 In-Browser MediaPipe WebGPU (M4 / RTX) | === [8.5 ms] (30x Faster Response!)
                                        +-------------------------------------+
                                        0ms     60ms    120ms   180ms   240ms

       +-------------------------------------------------------------+
       |             Monthly Cloud GPU Infrastructure Cost ($)       |
       +-------------------------------------------------------------+
 Cloud Hosted Vision API (100k Users)   | ==================================== [$38,000.00]
 In-Browser WebGPU Local Inference      | = [$0.00] (100% Free Scaling!)
                                        +-------------------------------------+
                                        $0      $10000  $20000  $30000  $40000