Underwater Image Enhancement and Fish/Coral Detection Web Application

Disclaimer

This project is developed for CVPR Lab, IIT Ropar

Project Description

This project provides a comprehensive solution for enhancing the quality of underwater images and performing fish/coral detection. It leverages various deep learning models and image processing techniques to address the challenges posed by underwater environments, such as color distortion, low contrast, and blurriness. The project includes different models for image enhancement and object detection, offering flexibility and options for various underwater imaging tasks.

Features

• Underwater Image Enhancement: Utilizes state-of-the-art models to improve the visual quality of underwater images.
• Fish Detection: Implements object detection models specifically trained for identifying fish in underwater imagery.
• Coral Detection: Implements object detection models specifically trained for identifying corals in underwater imagery.
• Multiple Models: Includes several different models for both enhancement and detection, allowing for experimentation and comparison.
• Modular Design: Organized into components and utility functions for better code structure and reusability.
• Easy Setup: Provides a setup script for quickly getting the project running.

Installation

1. Clone the repository:

   git clone <repository_url>
   cd <project_directory>

2. Set up the environment:

   The project includes a setup.sh script and a requirements.txt file to facilitate the setup process. Execute the setup script:

   bash setup.sh

   This script will likely create a virtual environment and install the necessary dependencies listed in requirements.txt.

   Alternatively, you can manually create a virtual environment and install dependencies:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   pip install -r requirements.txt

Usage

To run the main application, execute the app.py file:

streamlit run app.py

License

This project is licensed under the terms of the .

Contributing

Contributions are welcome! If you'd like to contribute to this project, please follow these steps:

• Fork the repository.
• Create a new branch for your feature or bugfix.
• Make your changes and commit them with clear messages.
• Push your changes to your fork.
• Submit a pull request to the main repository.

Please ensure your code adheres to the project's coding standards and includes appropriate tests.

Contact

If you have any questions, suggestions, or issues, please feel free to open an issue on the GitHub repository or contact the project maintainers at [email protected].