82 lines
2.9 KiB
Markdown
82 lines
2.9 KiB
Markdown
**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:
|
|
|
|
```bash
|
|
# 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
|
|
|
|
```bash
|
|
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
|
|
|
|
---
|
|
|
|
*This project demonstrates full-stack capabilities: AI/ML integration, real-time systems, cloud-native architecture, and modern web development.* |