media-analyzer/README.md

If you cloned this repository before August 25, 2025:

The commit history has been cleaned up for better readability. If you have a local clone:

# Fetch latest changes
git fetch --all --prune

# Switch to the new main branch
git switch main || git checkout -b main origin/main
git reset --hard origin/main

# Optional: Clean up old tracking branches
git branch -d webcam  # if you have it locally

Original commit history: Check the webcam branch to see the original development history up to commit e790025.

Real-Time Video AI Analysis Platform

A production-ready video streaming platform with real-time AI logo detection, demonstrating scalable microservices architecture and modern web technologies.

Quick Demo

docker compose up

Test the system:

1. Open http://localhost:3000 (frontend)
2. Start webcam stream or use RTMP from OBS
3. Show logos from /logos/ folder to camera for real-time detection
4. Watch live detection results and visual overlays

Architecture Overview

Key Design Patterns:

• Source Adapters (streaming/source_adapters.py) - Abstract webcam vs RTMP input
• Execution Strategies (ai_processing/execution_strategies/) - Local vs distributed processing
• Analysis Adapters (ai_processing/adapters/) - Pluggable AI models (CLIP, GCP Vision)
• Queue Segregation - Separate Celery workers for different analysis types

Code Organization

├── backend/
│   ├── streaming/           # Video ingestion (RTMP/Webcam)
│   ├── ai_processing/       # AI analysis pipeline
│   │   ├── adapters/        # Pluggable AI models
│   │   ├── execution_strategies/  # Local/cloud/distributed
│   │   └── tasks.py         # Celery workers
│   └── effects/             # Real-time video effects (future)
├── frontend/                # Angular 17+ SPA
├── k8s/                     # Kubernetes manifests
└── logos/                   # Test images (Apple, Nike, etc.)

Tech Stack

• Backend: Django + Channels, Celery, PostgreSQL, Redis
• AI/ML: PyTorch + CLIP, OpenCV, GCP Vision API
• Frontend: Angular 17, WebSockets, HLS.js
• Infrastructure: Docker, Kubernetes, NGINX

Features Implemented

✅ Real-time logo detection (CLIP + GCP Vision)
✅ Live video streaming (webcam/RTMP → HLS)
✅ WebSocket overlays (detection boxes, confidence scores)
✅ Kubernetes deployment (auto-scaling, health checks)
✅ Modular architecture (adapters, strategies, queues)

🔄 In progress: Visual properties, audio transcription, distributed processing

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This project demonstrates full-stack capabilities: AI/ML integration, real-time systems, cloud-native architecture, and modern web development.