64 lines
2.4 KiB
Markdown
64 lines
2.4 KiB
Markdown
# Real-Time Video AI Analysis Platform
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A production-ready video streaming platform with real-time AI logo detection, demonstrating scalable microservices architecture and modern web technologies.
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## Quick Demo
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```bash
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docker compose up
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```
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**Test the system:**
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1. Open http://localhost:3000 (frontend)
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2. Start webcam stream or use RTMP from OBS
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3. Show logos from `/logos/` folder to camera for real-time detection
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4. Watch live detection results and visual overlays
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## Architecture Overview
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**Key Design Patterns:**
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- **Source Adapters** (`streaming/source_adapters.py`) - Abstract webcam vs RTMP input
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- **Execution Strategies** (`ai_processing/execution_strategies/`) - Local vs distributed processing
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- **Analysis Adapters** (`ai_processing/adapters/`) - Pluggable AI models (CLIP, GCP Vision)
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- **Queue Segregation** - Separate Celery workers for different analysis types
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## Code Organization
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```
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├── backend/
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│ ├── streaming/ # Video ingestion (RTMP/Webcam)
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│ ├── ai_processing/ # AI analysis pipeline
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│ │ ├── adapters/ # Pluggable AI models
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│ │ ├── execution_strategies/ # Local/cloud/distributed
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│ │ └── tasks.py # Celery workers
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│ └── effects/ # Real-time video effects (future)
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├── frontend/ # Angular 17+ SPA
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├── k8s/ # Kubernetes manifests
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└── logos/ # Test images (Apple, Nike, etc.)
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```
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## Tech Stack
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- **Backend**: Django + Channels, Celery, PostgreSQL, Redis
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- **AI/ML**: PyTorch + CLIP, OpenCV, GCP Vision API
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- **Frontend**: Angular 17, WebSockets, HLS.js
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- **Infrastructure**: Docker, Kubernetes, NGINX
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## Features Implemented
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✅ **Real-time logo detection** (CLIP + GCP Vision)
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✅ **Live video streaming** (webcam/RTMP → HLS)
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✅ **WebSocket overlays** (detection boxes, confidence scores)
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✅ **Kubernetes deployment** (auto-scaling, health checks)
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✅ **Modular architecture** (adapters, strategies, queues)
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🔄 **In progress**: Visual properties, audio transcription, distributed processing
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---
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*This project demonstrates full-stack capabilities: AI/ML integration, real-time systems, cloud-native architecture, and modern web development.* |