K-Means Image Segmentation

This project provides a tool for segmenting images using the K-Means clustering algorithm. It allows users to process images, analyze cluster distributions, merge similar clusters, and generate time-series charts.

Features

• Image Segmentation: Utilizes K-Means clustering to segment images based on color similarity.
• Cluster Analysis: Provides information about the pixel distribution and HSV values for each cluster.
• Cluster Merging: Allows users to merge similar clusters to simplify the segmentation results.

Running the Streamlit App Locally

To run the Streamlit application locally, follow these steps:

1. Install Dependencies: Make sure you have all the required Python packages installed. You can install them using pip:

   pip install -r requirements.txt

2. Run the App: Navigate to the directory containing app.py and run the Streamlit app:

   streamlit run app.py

3. Access the App: Open your web browser and go to the address displayed in the console (usually http://localhost:8501).

File Descriptions

• image_segmentation.py: Contains the core image segmentation functions.
• app.py: Implements the Streamlit application.
• snow_segmentation_notebook.ipynb: A Jupyter Notebook demonstrating the usage of the image segmentation functions.
• requirements.txt: Lists the required Python packages.
• LICENSE: Contains the license information.
• README.md: This file, providing an overview of the project.

License

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