Roodaki/Kmeans-Color-Quantization
Utilizing K-means clustering to reduce color complexity while maintaining visual quality by grouping similar pixels into clusters, with each cluster's centroid representing all pixels within.
This tool helps graphic designers, web developers, or digital artists reduce the number of colors in an image without losing its visual appeal. You provide an image file and choose how many colors you want in the final output. The tool then gives you a new, color-optimized image file.
No commits in the last 6 months.
Use this if you need to optimize images for web performance, reduce file sizes, or achieve a specific artistic style by limiting the color palette.
Not ideal if you need advanced image editing features beyond color reduction, like detailed retouching, layering, or complex transformations.
Stars
10
Forks
1
Language
Python
License
—
Category
Last pushed
Feb 24, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Roodaki/Kmeans-Color-Quantization"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
scikit-learn-contrib/hdbscan
A high performance implementation of HDBSCAN clustering.
annoviko/pyclustering
pyclustering is a Python, C++ data mining library.
panagiotisanagnostou/HiPart
Hierarchical divisive clustering algorithm execution, visualization and Interactive visualization.
erdogant/clusteval
Clusteval provides methods for unsupervised cluster validation
mqcomplab/MDANCE
MDANCE: O(N) clustering for molecular dynamics. Process 1.5M frames in 40min. 8 specialized algorithms.