DressGuard AI

A computer-vision-powered clothing compliance detection system built with YOLOv8 for real-time dress-code validation.

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Table of Contents

• Overview
• Features
• Architecture
• Prerequisites
• Installation
• Running the Application
• API Endpoints
• Configuration
• Network Access
• Detection Flow
• Troubleshooting
• Development
• Performance Optimization
• Security Considerations
• Additional Resources
• License
• Contributors
• Contributing

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Overview

DressGuard is a full-stack system designed to detect clothing items and validate them against predefined compliance rules.

It consists of:

• FastAPI backend
• React (Vite) frontend
• YOLOv8-based object detection engine

Supports real-time detection via:

• Webcam
• Image upload
• Video file
• IP cameras

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Features

• Multiple Detection Modes (Image, Webcam, Video, IP Camera)
• Multi-Model Support (Switch between YOLO models dynamically)
• Compliance Checking (Configurable validation rules)
• Distance Checking (Ensures correct user positioning)
• Real-Time Visualization (Bounding boxes and confidence scores)
• Structured Rotating Logs
• RESTful API with OpenAPI documentation
• Modern Responsive UI (React + Vite)
• Performance-Optimized Frame Handling

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Architecture

DressGuard/
├── backend/ (FastAPI + YOLOv8)
│   ├── main.py
│   ├── detector.py
│   ├── config.py
│   └── utils/
│       ├── compliance.py
│       └── logger.py
│
└── frontend/ (React + Vite)
    ├── src/
    │   ├── components/
    │   └── utils/
    └── public/

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Prerequisites

Backend

• Python 3.8+
• pip
• CUDA 12.x (optional, for GPU acceleration)

Frontend

• Node.js 16+
• Modern browser

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Installation

1. Clone Repository

git clone <repository-url>
cd DressGuard

2. Backend Setup

pip install -r requirements.txt
mkdir models
cp .env.example .env

Add your YOLO model files inside the models/ directory.

3. Frontend Setup

cd frontend
npm install
cp .env.example .env
cd ..

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Running the Application

Start Backend

uvicorn main:app --reload

For network access:

uvicorn main:app --reload --host 0.0.0.0 --port 8000

Backend URLs:

• http://localhost:8000
• http://localhost:8000/docs

Start Frontend

cd frontend
npm run dev

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API Endpoints

Method Endpoint Description

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GET / API information GET /health Health check GET /models List available YOLO models GET /current-model Get active model POST /detect Detect clothing in image POST /switch-model Switch YOLO model

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Configuration

Compliance Rules (config.py)

COMPLIANT_CLOTHES = {"full sleeve shirt", "pants", "id card"}
NON_COMPLIANT_CLOTHES = {"t-shirt", "shorts"}

COMPLIANCE_RULES = {
    "min_confidence": 0.5,
    "require_all_compliant": True
}

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Troubleshooting

Model File Missing

Ensure .pt files are located in the models/ directory.

CUDA Out of Memory

Disable GPU in config.py:

ENABLE_GPU = False

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Security Considerations

• Validate upload file types
• Use environment variables for sensitive information
• Employ HTTPS in production
• Restrict CORS origins
• Consider implementing rate limiting

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License

MIT License

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Contributors

Alston Daniel Mendonca Reevan D Mello

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Contributing

1. Fork the repository
2. Create a new branch
3. Commit your changes
4. Open a Pull Request