README.md

---
license: mit
title: PetroSeg
sdk: streamlit
emoji: 👀
colorFrom: red
colorTo: yellow
---
# Unsupervised Segmentation App with Streamlit and PyTorch

## Table of Contents
1. [Introduction](#introduction)
2. [Acknowledgments](#acknowledgments)
3. [Requirements](#requirements)
4. [Installation](#installation)
5. [How to Run](#how-to-run)
6. [Code Explanation](#code-explanation)
7. [Contributing](#contributing)
8. [License](#license)

---

## Introduction 🌟
This project is a web application built using Streamlit and PyTorch. 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](https://github.com/kanezaki/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 📋
- Python 3.x
- Streamlit
- PyTorch
- OpenCV
- NumPy
- scikit-image
- PIL
- base64

---

## Installation 🛠️

1. **Clone the repository**
    ```bash
    git clone https://github.com/your-repo/unsupervised-segmentation.git
    ```
2. **Navigate to the project directory**
    ```bash
    cd unsupervised-segmentation
    ```
3. **Install the required packages**
    ```bash
    pip install -r requirements.txt
    ```

---

## How to Run 🚀

1. **Navigate to the project directory**
    ```bash
    cd unsupervised-segmentation
    ```
2. **Run the Streamlit app**
    ```bash
    streamlit run app.py
    ```

---
![Streamlit App Screenshot](https://github.com/fazzam12345/Unsupervised-Segmentation-App/blob/master/Streamlit_app.png?raw=true)


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## Contributing 🤝
Feel free to open issues and pull requests!

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## License 📜
This project is licensed under the MIT License.