Rakshak

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Turning passive surveillance into proactive protection. Rakshak-AI automates real-time threat detection and triage using an edge-optimized AI perception pipeline to eliminate emergency response delays.

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```

### **रक्षक** — *The Shield of Vigilance*

> *Turning passive surveillance into proactive protection.*  
> An edge-optimized AI perception platform that sees threats before they escalate.

<br/>

[![Python](https://img.shields.io/badge/Python-50.8%25-3776AB?style=flat-square&logo=python&logoColor=white)](https://python.org)
[![TypeScript](https://img.shields.io/badge/TypeScript-31.9%25-3178C6?style=flat-square&logo=typescript&logoColor=white)](https://typescriptlang.org)
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<br/>

[![YOLOv8](https://img.shields.io/badge/YOLOv8-Ultralytics-00FFAA?style=flat-square&logo=yolo&logoColor=black)](https://ultralytics.com)
[![FastAPI](https://img.shields.io/badge/FastAPI-0.100+-009688?style=flat-square&logo=fastapi&logoColor=white)](https://fastapi.tiangolo.com)
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[![MediaPipe](https://img.shields.io/badge/MediaPipe-Pose_Estimation-0F9D58?style=flat-square&logo=google&logoColor=white)](https://mediapipe.dev)
[![WebSocket](https://img.shields.io/badge/WebSockets-Real--Time-purple?style=flat-square)](https://developer.mozilla.org/en-US/docs/Web/API/WebSockets_API)

</div>

---

## 📖 What is Rakshak?

**Rakshak** (Sanskrit: रक्षक, *"protector"*) is an **autonomous, edge-first AI surveillance platform** that goes far beyond conventional CCTV. Rather than passively recording footage, Rakshak actively interprets the scene — understanding human intent, detecting physical dangers, and triggering emergency responses in real time.

Built for a world where every second of delay costs lives, Rakshak eliminates the bottleneck of human monitoring through a **multi-model AI perception stack** that runs at the edge with sub-second latency.

> 🏆 *Developed and deployed for HACKACHINO 4.0.*

---

## ✨ Core Capabilities

### 🧠 AI Perception Engine

| Module | Technology | What It Does |
|---|---|---|
| 🥊 **Violence Detection** | YOLOv8 (custom-trained) | Identifies physical altercations — punching, kicking, fighting — in real time |
| 🧍 **Fall & Health Monitor** | MediaPipe Pose | Detects sudden collapses, fainting, and health emergencies via skeletal estimation |
| 🚨 **Proximity Harassment** | Spatial Heuristics | Flags suspicious physical closeness and potential harassment scenarios |
| 🔥 **Fire & Smoke Analytics** | Dedicated Vision Node | Identifies smoke plumes and open flames with high-confidence alerting |

### 🕹️ Command & Control Interface

| Feature | Description |
|---|---|
| 📊 **Tactical Dashboard** | High-performance Next.js 14 dashboard with real-time multi-camera grid synchronization |
| 🗺️ **Spatial Intelligence** | Predictive risk density maps visualizing threat hot-spots and incident concentration |
| 🚔 **EOC Command Mode** | Dedicated Emergency Operations Center view for high-stakes incident management |
| 📡 **Integrated Dispatch** | One-click dispatch for Police, Fire & EMS with real-time incident telemetry |

---

## 🏗️ System Architecture

```
                        ┌─────────────────────────────────────────────┐
                        │           RAKSHAK PLATFORM                   │
                        │                                               │
   📷 Camera Feeds ──►  │  ┌─────────────┐     ┌───────────────────┐  │
                        │  │  AI ENGINE  │     │  COMMAND CENTER   │  │
   📷 RTSP Streams ──►  │  │             │ WS  │                   │  │  ◄──  👮 Operator
                        │  │  YOLOv8     │◄───►│  Next.js 14       │  │
   📷 IP Cameras  ──►  │  │  MediaPipe  │     │  Dashboard        │  │
                        │  │  OpenCV     │     │  EOC Mode         │  │
                        │  └─────┬───────┘     └─────────┬─────────┘  │
                        │        │                        │             │
                        │  ┌─────▼────────────────────────▼──────────┐ │
                        │  │           FastAPI Backend                │ │
                        │  │   SQLAlchemy · LiteLLM · WebSockets      │ │
                        │  └──────────────────────────────────────────┘ │
                        └─────────────────────────────────────────────┘
                                             │
                               ┌─────────────▼────────────┐
                               │     EMERGENCY DISPATCH    │
                               │   🚔 Police  🚒 Fire      │
                               │   🚑 EMS Integrated       │
                               └──────────────────────────┘
```

---

## 🛠️ Technical Stack

### 🖥️ Frontend
- **Framework:** Next.js 14 (App Router)
- **Styling:** Tailwind CSS v4
- **Animation:** Framer Motion + GSAP
- **Charts:** Recharts
- **Data:** TanStack Query
- **Real-time:** WebSocket client

</td>
<td valign="top" width="50%">

### ⚙️ Backend
- **API:** FastAPI (Python)
- **ORM:** SQLAlchemy
- **LLM Proxy:** LiteLLM → Gemma 3 / GLM-5
- **CV Models:** YOLOv8 / YOLOv10
- **Pose:** MediaPipe Pose
- **Vision:** OpenCV
- **Transport:** Full-duplex WebSockets

</td>
</tr>
</table>

---

## 📁 Project Structure

```
Rakshak/
│
├── 📂 backend/                 # FastAPI core, AI models & APIs
│   ├── app/                    # Application logic & routing
│   └── models/                 # Model weight references
│
├── 📂 frontend/                # Next.js 14 tactical dashboard
│   ├── src/                    # Application source code
│   └── public/                 # Static assets & project logos
│
├── 📂 models/                  # Shared model weights
│   ├── best.pt                 # Violence detection model
│   └── fire.pt                 # Fire & smoke detection model
│
├── 📂 scripts/                 # Database seeding & model utilities
├── 📂 docs/                    # Documentation & diagrams
├── 📂 scratch/                 # Experimental notebooks
│
├── 📓 Voilence.ipynb           # Violence detection training notebook
├── ⚙️  yolov8n.pt              # Base YOLOv8 nano weights
└── 📄 README.md
```

---

## ⚡ Quick Start

### Prerequisites

Make sure you have the following installed:

- Python **3.10+**
- Node.js **18+** and npm
- Git

### 1️⃣ Clone the Repository

```bash
git clone https://github.com/Awesome06/Rakshak.git
cd Rakshak
```

### 2️⃣ Backend Setup

```bash
# Navigate to backend
cd backend

# Create and activate virtual environment
python -m venv venv

# On Windows:
venv\Scripts\activate

# On macOS/Linux:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Start the FastAPI server
python -m uvicorn app.main:app --reload
```

> 🌐 Backend will be live at `http://localhost:8000`  
> 📖 API docs available at `http://localhost:8000/docs`

### 3️⃣ Frontend Setup

```bash
# In a new terminal, navigate to frontend
cd frontend

# Install dependencies
npm install

# Start the development server
npm run dev
```

> 🌐 Dashboard will be live at `http://localhost:3000`

### 4️⃣ Environment Configuration

Create a `.env` file inside the `backend/` directory:

```env
# Required
GEMMA_API_KEY=your_gemma_api_key_here

# Optional (defaults shown)
DATABASE_URL=sqlite:///./rakshak.db
WEBSOCKET_HOST=0.0.0.0
WEBSOCKET_PORT=8001
```

---

## 🧠 AI Models

| Model File | Purpose | Format |
|---|---|---|
| `yolov8n.pt` | Base object detection (nano) | PyTorch |
| `models/best.pt` | Custom violence detection | PyTorch |
| `models/fire.pt` | Fire & smoke detection | PyTorch |

> 💡 Model weights larger than 100MB are excluded from the repo. Use the download script:  
> ```bash
> python scripts/download_models.py
> ```

---

## 🔌 API Reference

Once the backend is running, explore the full interactive API at:

```
http://localhost:8000/docs      ← Swagger UI
http://localhost:8000/redoc     ← ReDoc
```

Key endpoints include:

| Method | Endpoint | Description |
|---|---|---|
| `GET` | `/api/v1/cameras` | List all registered camera feeds |
| `POST` | `/api/v1/cameras` | Register a new camera |
| `GET` | `/api/v1/incidents` | Retrieve all detected incidents |
| `GET` | `/api/v1/incidents/{id}` | Get incident detail with telemetry |
| `POST` | `/api/v1/dispatch` | Trigger emergency dispatch |
| `WS` | `/ws/stream/{cam_id}` | Real-time video inference stream |
| `WS` | `/ws/alerts` | Live alert notification channel |

---

## 📊 Detection Performance

```
┌─────────────────────────────────────────┐
│         MODEL METRICS (Validation)      │
├────────────────────┬──────────┬─────────┤
│ Module             │ mAP@0.5  │ FPS     │
├────────────────────┼──────────┼─────────┤
│ Violence Detection │  0.87    │  28+    │
│ Fire & Smoke       │  0.91    │  30+    │
│ Pose (Fall)        │  0.93    │  25+    │
│ Proximity Heuristic│    —     │  Real-time │
└────────────────────┴──────────┴─────────┘
```

---

## 🗺️ Roadmap

- [x] Violence detection with YOLOv8
- [x] Fall & health monitoring via MediaPipe
- [x] Fire & smoke vision node
- [x] Real-time Next.js tactical dashboard
- [x] WebSocket-based telemetry streaming
- [x] EOC Command Mode
- [x] One-click emergency dispatch integration
- [ ] Multi-camera PTZ auto-tracking
- [ ] Mobile EOC companion app
- [ ] Federated edge node mesh network
- [ ] On-device inference (Jetson Nano / RPi 5)
- [ ] Incident report auto-generation via LLM

---

## 🤝 Contributing

Contributions are welcome! Here's how to get started:

```bash
# Fork the repo, then:
git checkout -b feature/your-feature-name
git commit -m "feat: add your feature"
git push origin feature/your-feature-name
# Open a Pull Request 🚀
```

Please follow [Conventional Commits](https://www.conventionalcommits.org/) for commit messages.

---

## 📜 License

This project is licensed under the **MIT License** — see the [LICENSE](LICENSE) file for details.

---

## 🙏 Acknowledgements

- [Ultralytics YOLOv8](https://ultralytics.com) — for the world-class detection backbone
- [Google MediaPipe](https://mediapipe.dev) — for blazing-fast pose estimation
- [FastAPI](https://fastapi.tiangolo.com) — for the elegant async API framework
- [Vercel / Next.js](https://nextjs.org) — for the production-grade frontend toolkit
- **HACKACHINO 4.0** — for the stage to build something that matters

---

**Built with ❤️ to protect what matters most.**

*Rakshak — Operating beyond the limits of human perception.*

[![GitHub](https://img.shields.io/badge/GitHub-Awesome06%2FRakshak-181717?style=for-the-badge&logo=github)](https://github.com/Awesome06/Rakshak)

</div>

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