Files
wifi-densepose/v1/docs/user-guide/getting-started.md
Claude 6ed69a3d48 feat: Complete Rust port of WiFi-DensePose with modular crates
Major changes:
- Organized Python v1 implementation into v1/ subdirectory
- Created Rust workspace with 9 modular crates:
  - wifi-densepose-core: Core types, traits, errors
  - wifi-densepose-signal: CSI processing, phase sanitization, FFT
  - wifi-densepose-nn: Neural network inference (ONNX/Candle/tch)
  - wifi-densepose-api: Axum-based REST/WebSocket API
  - wifi-densepose-db: SQLx database layer
  - wifi-densepose-config: Configuration management
  - wifi-densepose-hardware: Hardware abstraction
  - wifi-densepose-wasm: WebAssembly bindings
  - wifi-densepose-cli: Command-line interface

Documentation:
- ADR-001: Workspace structure
- ADR-002: Signal processing library selection
- ADR-003: Neural network inference strategy
- DDD domain model with bounded contexts

Testing:
- 69 tests passing across all crates
- Signal processing: 45 tests
- Neural networks: 21 tests
- Core: 3 doc tests

Performance targets:
- 10x faster CSI processing (~0.5ms vs ~5ms)
- 5x lower memory usage (~100MB vs ~500MB)
- WASM support for browser deployment
2026-01-13 03:11:16 +00:00

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# Getting Started with WiFi-DensePose
## Overview
WiFi-DensePose is a revolutionary privacy-preserving human pose estimation system that transforms commodity WiFi infrastructure into a powerful human sensing platform. This guide will help you install, configure, and start using the system.
## Table of Contents
1. [System Requirements](#system-requirements)
2. [Installation](#installation)
3. [Quick Start](#quick-start)
4. [Basic Configuration](#basic-configuration)
5. [First Pose Detection](#first-pose-detection)
6. [Troubleshooting](#troubleshooting)
7. [Next Steps](#next-steps)
## System Requirements
### Hardware Requirements
#### WiFi Router Requirements
- **Compatible Hardware**: Atheros-based routers (TP-Link Archer series, Netgear Nighthawk), Intel 5300 NIC-based systems, or ASUS RT-AC68U series
- **Antenna Configuration**: Minimum 3×3 MIMO antenna configuration
- **Frequency Bands**: 2.4GHz and 5GHz support
- **Firmware**: OpenWRT firmware compatibility with CSI extraction patches
#### Processing Hardware
- **CPU**: Multi-core processor (4+ cores recommended)
- **RAM**: 8GB minimum, 16GB recommended
- **Storage**: 50GB available space
- **Network**: Gigabit Ethernet for CSI data streams
- **GPU** (Optional): NVIDIA GPU with CUDA capability and 4GB+ memory for real-time processing
### Software Requirements
#### Operating System
- **Primary**: Linux (Ubuntu 20.04+, CentOS 8+)
- **Secondary**: Windows 10/11 with WSL2
- **Container**: Docker support for deployment
#### Runtime Dependencies
- Python 3.8+
- PyTorch (GPU-accelerated recommended)
- OpenCV
- FFmpeg
- FastAPI
## Installation
### Method 1: Docker Installation (Recommended)
#### Prerequisites
```bash
# Install Docker and Docker Compose
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
sudo usermod -aG docker $USER
# Install Docker Compose
sudo curl -L "https://github.com/docker/compose/releases/latest/download/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
sudo chmod +x /usr/local/bin/docker-compose
```
#### Download and Setup
```bash
# Clone the repository
git clone https://github.com/your-org/wifi-densepose.git
cd wifi-densepose
# Copy environment configuration
cp .env.example .env
# Edit configuration (see Configuration section)
nano .env
# Start the system
docker-compose up -d
```
### Method 2: Native Installation
#### Install System Dependencies
```bash
# Ubuntu/Debian
sudo apt update
sudo apt install -y python3.9 python3.9-pip python3.9-venv
sudo apt install -y build-essential cmake
sudo apt install -y libopencv-dev ffmpeg
# CentOS/RHEL
sudo yum update
sudo yum install -y python39 python39-pip
sudo yum groupinstall -y "Development Tools"
sudo yum install -y opencv-devel ffmpeg
```
#### Install Python Dependencies
```bash
# Create virtual environment
python3.9 -m venv venv
source venv/bin/activate
# Install requirements
pip install -r requirements.txt
# Install PyTorch with CUDA support (if GPU available)
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
```
#### Install WiFi-DensePose
```bash
# Install in development mode
pip install -e .
# Or install from PyPI (when available)
pip install wifi-densepose
```
## Quick Start
### 1. Environment Configuration
Create and configure your environment file:
```bash
# Copy the example configuration
cp .env.example .env
```
Edit the `.env` file with your settings:
```bash
# Application settings
APP_NAME="WiFi-DensePose API"
VERSION="1.0.0"
ENVIRONMENT="development"
DEBUG=true
# Server settings
HOST="0.0.0.0"
PORT=8000
# Security settings (CHANGE IN PRODUCTION!)
SECRET_KEY="your-secret-key-here"
JWT_EXPIRE_HOURS=24
# Hardware settings
WIFI_INTERFACE="wlan0"
CSI_BUFFER_SIZE=1000
MOCK_HARDWARE=true # Set to false when using real hardware
# Pose estimation settings
POSE_CONFIDENCE_THRESHOLD=0.5
POSE_MAX_PERSONS=5
# Storage settings
DATA_STORAGE_PATH="./data"
MODEL_STORAGE_PATH="./models"
```
### 2. Start the System
#### Using Docker
```bash
# Start all services
docker-compose up -d
# Check service status
docker-compose ps
# View logs
docker-compose logs -f
```
#### Using Native Installation
```bash
# Activate virtual environment
source venv/bin/activate
# Start the API server
python -m src.api.main
# Or use uvicorn directly
uvicorn src.api.main:app --host 0.0.0.0 --port 8000 --reload
```
### 3. Verify Installation
Check that the system is running:
```bash
# Check API health
curl http://localhost:8000/health
# Expected response:
# {"status": "healthy", "timestamp": "2025-01-07T10:00:00Z"}
```
Access the web interface:
- **API Documentation**: http://localhost:8000/docs
- **Alternative Docs**: http://localhost:8000/redoc
- **Health Check**: http://localhost:8000/health
## Basic Configuration
### Domain Configuration
WiFi-DensePose supports different domain-specific configurations:
#### Healthcare Domain
```bash
# Set healthcare-specific settings
export DOMAIN="healthcare"
export POSE_CONFIDENCE_THRESHOLD=0.8
export ENABLE_FALL_DETECTION=true
export ALERT_SENSITIVITY=0.9
```
#### Retail Domain
```bash
# Set retail-specific settings
export DOMAIN="retail"
export POSE_CONFIDENCE_THRESHOLD=0.7
export ENABLE_TRAFFIC_ANALYTICS=true
export ZONE_TRACKING=true
```
#### Security Domain
```bash
# Set security-specific settings
export DOMAIN="security"
export POSE_CONFIDENCE_THRESHOLD=0.9
export ENABLE_INTRUSION_DETECTION=true
export ALERT_IMMEDIATE=true
```
### Router Configuration
#### Configure WiFi Routers for CSI Extraction
1. **Flash OpenWRT Firmware**:
```bash
# Download OpenWRT firmware for your router model
wget https://downloads.openwrt.org/releases/22.03.0/targets/...
# Flash firmware (router-specific process)
# Follow your router's flashing instructions
```
2. **Install CSI Extraction Patches**:
```bash
# SSH into router
ssh root@192.168.1.1
# Install CSI tools
opkg update
opkg install csi-tools
# Configure CSI extraction
echo "csi_enable=1" >> /etc/config/wireless
echo "csi_rate=30" >> /etc/config/wireless
```
3. **Configure Network Settings**:
```bash
# Set router to monitor mode
iwconfig wlan0 mode monitor
# Start CSI data streaming
csi_tool -i wlan0 -d 192.168.1.100 -p 5500
```
### Database Configuration
#### SQLite (Development)
```bash
# Default SQLite database (no additional configuration needed)
DATABASE_URL="sqlite:///./data/wifi_densepose.db"
```
#### PostgreSQL (Production)
```bash
# Install PostgreSQL with TimescaleDB extension
sudo apt install postgresql-14 postgresql-14-timescaledb
# Configure database
DATABASE_URL="postgresql://user:password@localhost:5432/wifi_densepose"
DATABASE_POOL_SIZE=10
DATABASE_MAX_OVERFLOW=20
```
#### Redis (Caching)
```bash
# Install Redis
sudo apt install redis-server
# Configure Redis
REDIS_URL="redis://localhost:6379/0"
REDIS_PASSWORD="" # Set password for production
```
## First Pose Detection
### 1. Start the System
```bash
# Using Docker
docker-compose up -d
# Using native installation
python -m src.api.main
```
### 2. Initialize Hardware
```bash
# Check system status
curl http://localhost:8000/api/v1/system/status
# Start pose estimation system
curl -X POST http://localhost:8000/api/v1/system/start \
-H "Content-Type: application/json" \
-d '{
"configuration": {
"domain": "general",
"environment_id": "room_001",
"calibration_required": true
}
}'
```
### 3. Get Pose Data
#### REST API
```bash
# Get latest pose data
curl http://localhost:8000/api/v1/pose/latest
# Get historical data
curl "http://localhost:8000/api/v1/pose/history?limit=10"
```
#### WebSocket Streaming
```javascript
// Connect to WebSocket
const ws = new WebSocket('ws://localhost:8000/ws/pose');
// Subscribe to pose updates
ws.onopen = function() {
ws.send(JSON.stringify({
type: 'subscribe',
channel: 'pose_updates',
filters: {
min_confidence: 0.7
}
}));
};
// Handle pose data
ws.onmessage = function(event) {
const data = JSON.parse(event.data);
console.log('Pose data:', data);
};
```
### 4. View Results
Access the web dashboard:
- **Main Dashboard**: http://localhost:8000/dashboard
- **Real-time View**: http://localhost:8000/dashboard/live
- **Analytics**: http://localhost:8000/dashboard/analytics
## Troubleshooting
### Common Issues
#### 1. System Won't Start
```bash
# Check logs
docker-compose logs
# Common solutions:
# - Verify port 8000 is available
# - Check environment variables
# - Ensure sufficient disk space
```
#### 2. No Pose Data
```bash
# Check hardware status
curl http://localhost:8000/api/v1/system/status
# Verify router connectivity
ping 192.168.1.1
# Check CSI data reception
netstat -an | grep 5500
```
#### 3. Poor Detection Accuracy
```bash
# Adjust confidence threshold
curl -X PUT http://localhost:8000/api/v1/config \
-H "Content-Type: application/json" \
-d '{"detection": {"confidence_threshold": 0.6}}'
# Recalibrate environment
curl -X POST http://localhost:8000/api/v1/system/calibrate
```
#### 4. High CPU/Memory Usage
```bash
# Check resource usage
docker stats
# Optimize settings
export POSE_PROCESSING_BATCH_SIZE=16
export STREAM_FPS=15
```
### Getting Help
#### Log Analysis
```bash
# View application logs
docker-compose logs wifi-densepose-api
# View system logs
journalctl -u wifi-densepose
# Enable debug logging
export LOG_LEVEL="DEBUG"
```
#### Health Checks
```bash
# Comprehensive system check
curl http://localhost:8000/api/v1/system/status
# Component-specific checks
curl http://localhost:8000/api/v1/hardware/status
curl http://localhost:8000/api/v1/processing/status
```
#### Support Resources
- **Documentation**: [docs/](../README.md)
- **API Reference**: [api-reference.md](api-reference.md)
- **Troubleshooting Guide**: [troubleshooting.md](troubleshooting.md)
- **GitHub Issues**: https://github.com/your-org/wifi-densepose/issues
## Next Steps
### 1. Configure for Your Domain
- Review [configuration.md](configuration.md) for domain-specific settings
- Set up alerts and notifications
- Configure external integrations
### 2. Integrate with Your Applications
- Review [API Reference](api-reference.md)
- Set up webhooks for events
- Configure MQTT for IoT integration
### 3. Deploy to Production
- Review [deployment guide](../developer/deployment-guide.md)
- Set up monitoring and alerting
- Configure backup and recovery
### 4. Optimize Performance
- Tune processing parameters
- Set up GPU acceleration
- Configure load balancing
## Security Considerations
### Development Environment
- Use strong secret keys
- Enable authentication
- Restrict network access
### Production Environment
- Use HTTPS/TLS encryption
- Configure firewall rules
- Set up audit logging
- Regular security updates
## Performance Tips
### Hardware Optimization
- Use SSD storage for better I/O performance
- Ensure adequate cooling for continuous operation
- Use dedicated network interface for CSI data
### Software Optimization
- Enable GPU acceleration when available
- Tune batch sizes for your hardware
- Configure appropriate worker processes
- Use Redis for caching frequently accessed data
---
**Congratulations!** You now have WiFi-DensePose up and running. Continue with the [Configuration Guide](configuration.md) to customize the system for your specific needs.