Unsupervised Segmentation App Using Deep Learning

Table of Contents

1. Introduction
2. Acknowledgments
3. Requirements
4. Installation
5. How to Run
6. Code Explanation
7. Contributing
8. License

Introduction 🌟

This project is a web application built using Streamlit and Tensorflow. It performs unsupervised segmentation on uploaded images. The segmented image can be downloaded, and the colors of the segments can be customized.

Acknowledgments

This code is inspired from the project pytorch-unsupervised-segmentation by kanezaki. The original project is based on the paper “Unsupervised Image Segmentation by Backpropagation” presented at IEEE ICASSP 2018. The code is optimized for thin section images and microscopy analysis.

Requirements

• Streamlit
• OpenCV
• NumPy
• Tensorflow
• scikit-image
• PIL
• base64

Installation

1. Clone the repository

   git clone https://github.com/your-repo/unsupervised-segmentation.git
   

2. Navigate to the project directory

   cd PetroSEG-v2
   

3. Install the required packages

   pip install -r requirements.txt
   

How to Run

1. Navigate to the project directory

   cd PetroSEG-v2
   

2. Run the Streamlit app

   streamlit run app.py
   

Contributing

Feel free to open issues and pull requests!

License

This project is licensed under the MIT License.