Painted_Surface_Defect_Detection_App

A user-friendly web application for detecting defects on painted surfaces using live webcam input and image uploads.

📋 Table of Contents

• 📖 Introduction
• ✨ Features
• 💻 Tech Stack
• 🚀 Setup
• 🔧 Usage
• 🤝 Contributing
• 📜 License

📖 Introduction

The Painted Surface Defect Detection App offers real-time defect detection for painted surfaces using a webcam, as well as the ability to upload images for defect detection and severity classification. The application is hosted on PythonAnywhere and utilizes both Django and Flask to provide a comprehensive solution.

✨ Features

• Live Defect Detection: Start and stop real-time defect detection using your webcam.
• Image Upload Detection: Upload an image to detect defects and classify their severity.
• Real-Time Feedback: Instant feedback on detected defects.
• User-Friendly Interface: Simple and intuitive controls for both live detection and image uploads.
• Integration: Combines Django for the main application with Flask for live detection functionality.

💻 Tech Stack

• Frontend:
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• Backend:
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• Tools & Libraries:
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• Database: SQLite (default for Django applications).

🚀 Setup

Prerequisites

• Python 3.x
• Django
• Flask
• Virtual environment tools (virtualenv, pipenv, etc.)

Installation

1. Clone the repository:

   git clone https://github.com/NakulLimbani/Painted_Surface_Defect_Detection_App.git
   cd Painted_Surface_Defect_Detection_App

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

4. Run database migrations:

   python manage.py migrate

5. Start the development server:

   python manage.py runserver

6. Start the Flask server:

   python app_flask.py  # Ensure this script starts the Flask server

🔧 Usage

• Live Detection:

  • Navigate to the homepage to access live detection.
  • Click on the Start Detection button to begin real-time defect detection using your webcam.
  • Click on the Stop Detection button to halt the live defect detection.

• Image Upload Detection:

  • Use the Upload Image section on the homepage to select and upload an image.
  • The application will detect defects in the uploaded image and classify their severity.
  • Review the results displayed on the page after the analysis is complete.

🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature-name).
3. Commit your changes (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/your-feature-name).
5. Open a pull request.

📜 License

This project is licensed under the MIT License. See the LICENSE file for details.