Real-Time Object Detection Application (CDN Version)

This is a modified version of my original Object Detection App that uses CDN-based dependencies instead of npm packages. This approach helps avoid dependency conflicts that can occur with npm installations.

Key Changes

Architecture Change: NPM → CDN

• Removed npm package dependencies in favor of loading libraries via CDN
• Eliminated the need for Node.js, npm, and package.json
• Static HTML/JS application that works directly in the browser without build steps

Implementation Details

1. Dependency Management:

   • TensorFlow.js, COCO-SSD, and BlazeFace models loaded from CDN
   • React and ReactDOM loaded from CDN
   • Tailwind CSS loaded from CDN using their in-browser build system

2. Structure:

   • Single HTML file loads all dependencies and scripts
   • JavaScript files are organized in a modular structure
   • React components defined in separate files using Babel for JSX transformation

3. File Organization:

   object-detection-app/
   ├── index.html               # Main HTML file with CDN imports
   ├── js/                      # JavaScript files
   │   ├── constants.js         # Application constants
   │   ├── detectionService.js  # Object detection logic
   │   ├── drawingUtils.js      # Canvas drawing utilities
   │   ├── modelLoader.js       # Model loading utilities
   │   ├── index.js             # Application initialization
   │   ├── components/          # React components
   │       ├── App.js           # Main App component
   │       ├── DetectionView.js # Detection visualization component
   │       ├── ImageUploader.js # Image upload component
   │       ├── ModelSelector.js # Model selection component
   │       └── ResultsPanel.js  # Detection results display component
   

Features

All existing features from the original application have been preserved:

• 📷 Image upload and object detection
• 🎥 Real-time webcam object detection
• 🔍 Support for multiple pre-trained models (COCO-SSD, BlazeFace)
• 📊 Detection results visualization with bounding boxes
• 📱 Responsive design for desktop and mobile devices

Benefits of This Approach

1. No Build Process Required: The application can be run directly in a browser without npm install or build steps
2. Reduced Dependencies: Eliminates local node_modules and dependency conflicts
3. Easy Deployment: Can be deployed on any static file hosting service
4. Simplified Development: No need for a Node.js environment to develop or run the application

Getting Started

Simply open the index.html file in a modern web browser to run the application. No server or installation required!

Browser Support

This application requires a modern browser with support for:

• ES6+ JavaScript
• WebGL (for TensorFlow.js)
• WebRTC (for webcam access)
• Canvas API

Performance Considerations

When using CDN-based dependencies:

• Initial load may be slower than optimized bundled applications
• TensorFlow.js model files are loaded over the network rather than bundled
• Consider adding CDN caching headers when deploying to production

License

This project is licensed under the MIT License - see the original repository for details.