Turning images into '9-pan' palettes using KMeans clustering from sklearn.

Overview

img2palette

Turning images into '9-pan' palettes using KMeans clustering from sklearn.

Requirements

We require:

  • Pillow, for opening and processing images
  • Scikit Learn, for clustering

We use numpy. Since it's a dependency of scikit-learn, we're not specifying it; we're going to use the version that comes with our pinned sklearn version.

On Raspberry Pi, we ran into the error

Original error was: libf77blas.so.3: cannot open shared object file: No such file or directory

So we did the following:

sudo apt-get install libatlas-base-dev

The numpy developer documentation recommended either doing that or installing the version of numpy packaged for raspbian. Since we want to use the version of numpy included with sklearn for the least number of dependency headaches, we install libatlas instead.

If you run into additional issues running the script, please add an Issue with your problem or solution to this repository. If you don't have a solution, I'll do my best to come up with one.

Running

We recommend a virtual environment.

~$ python3 -m venv venv
~$ source venv/bin/activate
~$ python3 -m pip install -r requirements.txt

Once that process is complete, run the program:

python3 img2palette.py -i 
   

   

Samples

The output is OK. We should tweak the options in the future.

For this image by Marco Ferrarin:

A Beautiful Mosque.

We receive this palette:

An OK Palette representing the mosque.

Perhaps the clustering could be adjusted for different / better results? But, for a first attempt, I'm pretty happy.

Owner
Samuel Vidovich
Me like program. Me like write code. Me write code all days.
Samuel Vidovich
Model Validation Toolkit is a collection of tools to assist with validating machine learning models prior to deploying them to production and monitoring them after deployment to production.

Model Validation Toolkit is a collection of tools to assist with validating machine learning models prior to deploying them to production and monitoring them after deployment to production.

FINRA 25 Dec 28, 2022
Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)

Little Ball of Fur is a graph sampling extension library for Python. Please look at the Documentation, relevant Paper, Promo video and External Resour

Benedek Rozemberczki 619 Dec 14, 2022
XGBoost + Optuna

AutoXGB XGBoost + Optuna: no brainer auto train xgboost directly from CSV files auto tune xgboost using optuna auto serve best xgboot model using fast

abhishek thakur 517 Dec 31, 2022
AutoOED: Automated Optimal Experiment Design Platform

AutoOED is an optimal experiment design platform powered with automated machine learning to accelerate the discovery of optimal solutions. Our platform solves multi-objective optimization problems an

Yunsheng Tian 107 Jan 03, 2023
Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.

sklearn-evaluation Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking, and Jupyter notebook analysis. Suppo

Eduardo Blancas 354 Dec 31, 2022
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters.

TensorFlowOnSpark TensorFlowOnSpark brings scalable deep learning to Apache Hadoop and Apache Spark clusters. By combining salient features from the T

Yahoo 3.8k Jan 04, 2023
Implementation of linesearch Optimization Algorithms in Python

Nonlinear Optimization Algorithms During my time as Scientific Assistant at the Karlsruhe Institute of Technology (Germany) I implemented various Opti

Paul 3 Dec 06, 2022
Kaggle Tweet Sentiment Extraction Competition: 1st place solution (Dark of the Moon team)

Kaggle Tweet Sentiment Extraction Competition: 1st place solution (Dark of the Moon team)

Artsem Zhyvalkouski 64 Nov 30, 2022
Machine Learning Study 혼자 해보기

Machine Learning Study 혼자 해보기 기여자 (Contributors) ✨ Teddy Lee 🏠 HongJaeKwon 🏠 Seungwoo Han 🏠 Tae Heon Kim 🏠 Steve Kwon 🏠 SW Song 🏠 K1A2 🏠 Wooil

Teddy Lee 1.7k Jan 01, 2023
Combines Bayesian analyses from many datasets.

PosteriorStacker Combines Bayesian analyses from many datasets. Introduction Method Tutorial Output plot and files Introduction Fitting a model to a d

Johannes Buchner 19 Feb 13, 2022
MBTR is a python package for multivariate boosted tree regressors trained in parameter space.

MBTR is a python package for multivariate boosted tree regressors trained in parameter space.

SUPSI-DACD-ISAAC 61 Dec 19, 2022
Crunchdao - Python API for the Crunchdao machine learning tournament

Python API for the Crunchdao machine learning tournament Interact with the Crunc

3 Jan 19, 2022
Credit Card Fraud Detection, used the credit card fraud dataset from Kaggle

Credit Card Fraud Detection, used the credit card fraud dataset from Kaggle

Sean Zahller 1 Feb 04, 2022
Dual Adaptive Sampling for Machine Learning Interatomic potential.

DAS Dual Adaptive Sampling for Machine Learning Interatomic potential. How to cite If you use this code in your research, please cite this using: Hong

6 Jul 06, 2022
A scikit-learn based module for multi-label et. al. classification

scikit-multilearn scikit-multilearn is a Python module capable of performing multi-label learning tasks. It is built on-top of various scientific Pyth

802 Jan 01, 2023
TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.

TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models. The library is a collection of Keras models

538 Jan 01, 2023
Ml based project which uses regression technique to predict the price.

Price-Predictor Ml based project which uses regression technique to predict the price. I have used various regression models and finds the model with

Garvit Verma 1 Jul 09, 2022
XGBoost-Ray is a distributed backend for XGBoost, built on top of distributed computing framework Ray.

XGBoost-Ray is a distributed backend for XGBoost, built on top of distributed computing framework Ray.

92 Dec 14, 2022
Pytools is an open source library containing general machine learning and visualisation utilities for reuse

pytools is an open source library containing general machine learning and visualisation utilities for reuse, including: Basic tools for API developmen

BCG Gamma 26 Nov 06, 2022
Uplift modeling and causal inference with machine learning algorithms

Disclaimer This project is stable and being incubated for long-term support. It may contain new experimental code, for which APIs are subject to chang

Uber Open Source 3.7k Jan 07, 2023