Installation Guide

Prerequisites

Before installing ANAS, ensure your system meets these requirements:

System Requirements

  • Operating System: Windows 10+, Ubuntu 18.04+, macOS 10.14+, or Raspberry Pi OS
  • Python Version: 3.8 or higher
  • RAM: Minimum 4GB (8GB recommended)
  • Storage: 2GB free space
  • Camera: Webcam or USB camera (required for real-time operation)

Hardware Requirements (Optional)

  • Raspberry Pi: Model 3B+ or newer for embedded deployment
  • GPIO Access: For haptic feedback motor
  • Audio Output: Speakers or audio jack for voice feedback

Quick Installation

1. Clone the Repository

git clone https://github.com/Demonking09/Stage2_Advance_navigation_System.git
cd Stage2_Advance_navigation_System

2. Create Virtual Environment

# Windows
python -m venv .venv
.venv\Scripts\activate

# Linux/macOS
python -m venv .venv
source .venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Download Pre-trained Models

The system requires pre-trained models. Download them from the releases page or train your own.

Platform-Specific Setup

Windows Setup

# Install PyTorch (CPU version)
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

# Install remaining dependencies
pip install -r requirements.txt

Linux Setup

# Install system dependencies
sudo apt-get update
sudo apt-get install python3-dev python3-pip

# Install PyTorch
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

# Install remaining dependencies
pip install -r requirements.txt

macOS Setup

# Install PyTorch
pip install torch torchvision torchaudio

# Install remaining dependencies
pip install -r requirements.txt

Raspberry Pi Setup

# Update system
sudo apt-get update && sudo apt-get upgrade

# Install system dependencies
sudo apt-get install python3-dev python3-pip libatlas-base-dev

# Install PyTorch (ARM version)
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

# Install GPIO support
pip install RPi.GPIO

# Install remaining dependencies
pip install -r requirements.txt

Model Setup

Download Pre-trained Models

  1. Go to Releases
  2. Download the latest model files:
    • yolov8n.pt - Obstacle detection model
    • texture_model.pth - Surface classification model
  3. Place them in the project root directory

Training Your Own Models (Optional)

# Train surface texture classifier
python train_with_validation_confusion.py

# Train with custom dataset
python train_texture_cnn.py

Hardware Configuration

GPIO Setup (Raspberry Pi)

# Edit hardware_interface.py
HAPTIC_GPIO_PIN = 17  # Change to your GPIO pin
SPEAKER_DEVICE = "alsa"  # or "pulseaudio"

Audio Setup

# Test audio output
python hardware_interface.py

Verification

Run Quick Tests

# Test all components
python quick_test.py

# Test proximity tracking
python test_proximity_tracker.py

# Test hardware interface
python hardware_interface.py

Expected Output

============================================================
QUICK TEST: All Components
============================================================
✅ YOLO Model: Loaded successfully
✅ Texture Model: Loaded successfully
✅ Hardware Interface: Initialized
✅ Proximity Tracker: Ready
✅ Camera: Accessible

All tests passed! System ready for operation.

Troubleshooting

Common Issues

Import Errors

# Reinstall dependencies
pip uninstall torch torchvision torchaudio
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
pip install -r requirements.txt

Camera Not Found

# Check camera access
python -c "import cv2; print(cv2.VideoCapture(0).isOpened())"

GPIO Permission Denied (Raspberry Pi)

# Add user to gpio group
sudo usermod -a -G gpio $USER
# Reboot required
sudo reboot

Audio Issues

# Install audio dependencies
sudo apt-get install alsa-utils pulseaudio

Next Steps

Once installation is complete:

  1. Run the main system
  2. Configure hardware
  3. Start field testing

For detailed usage instructions, see the Usage Guide.