K-Means Color Quantization

This project implements K-Means Clustering for color quantization in images, allowing you to reduce the number of colors while preserving visual quality. The implementation is currently in NumPy, and a CUDA-accelerated version is in progress.

Features

• Perform K-Means clustering on images to reduce colors.
• Adjustable number of clusters (K).
• Supports custom images via GUI.
• Tracks execution time for performance evaluation.
• CUDA acceleration (WIP) to improve performance on large images.

Installation

1. Clone the Repository

git clone https://github.com/The-Silly-Glitch/Color-Quantization-using-K-means-Clustering.git
cd Color-Quantization-using-K-means-Clustering

Usage

Run the GUI

python main.py

• Load an image.
• Set the number of clusters (K).
• Click Quantize to apply K-Means color reduction.

Example Output

Original Image	Quantized Image (K=10)

CUDA Acceleration (WIP)

• The CUDA implementation is under development using CuPy.
• Future improvements include faster distance calculations and parallel K-Means iterations.