Image Compression with K-Means Clustering

This project is a simple image compression application using the K-Means clustering method. Users can upload an image, choose the number of colors (clusters) for compression, and view/download the compressed image.

How to Run?

Locally

1. Clone the repository:

   git clone https://github.com/Kunal-Kumar-Sahoo/k-means-compressor.git

2. Navigate to the project directory:

   cd k-means-compressor/

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the streamlit app:

   streamlit run app.py

5. Access the application in your browser at http://localhost:8501

Using Docker

1. Build the docker image:

   docker build -t kmeanscompressor

2. Run the docker container:

   docker run -p 8501:8501 kmeanscompressor

3. Access the application in your browser at http://localhost:8501

Project Structure

• image_compression.py: Contains the ImageCompression class for handling image compression using K-means clustering.
• streamlit_app.py: Defines the StreamlitApp class for the Streamlit web application.

Requirements

• Python 3.10
• Streamlit
• scikit-learn
• scikit-image
• seaborn
• matplotlib
• Pillow

License

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

Acknowledgments

• Inspired by the power of image compression algorithms.

Feel free to contribute and open issues if you encounter any problems!