Metadata-Version: 2.1
Name: iromeku
Version: 0.0.1
Summary: A package for extracting colours from images.
Home-page: https://github.com/siawyoung/iromeku
Author: Lau Siaw Young
Author-email: sy@siawyoung.com
License: UNKNOWN
Description: # Iromeku
        
        [![Build Status](https://travis-ci.com/carousell/DataAccessLayer.svg?token=EF9qHkSmyt2BrPXZdP6q&branch=master)](https://travis-ci.com/carousell/DataAccessLayer)
        
        > 色めく (hiragana いろめく, rōmaji iromeku) 1. to become lively 2. to become roused 3. to look arousing
        
        Iromeku is a library to extract a colour palette from a given image.
        
        The implementation is heavily inspired by the Stack Overflow answer here: ![How does
        the algorithm to color the song list in iTunes 11
        work?](https://stackoverflow.com/questions/13637892/how-does-the-algorithm-to-color-the-song-list-in-itunes-11-work#answer-13675803)
        
        The way it works is by clustering similar colours together, based on the
        Euclidean distances of the pixel's value in the YUV colour space, which more
        closely approximates colour perception.
        
        ## Getting Started
        
        ``` shell
        $ pip install iromeku
        ```
        
        ``` python
        from iromeku import load_image, generate_clusters, get_dominant_colour
        
        rgb_values = load_image("test.jpg")
        clusters = generate_clusters(rgb_values, 0.1)
        colour = get_dominant_colour(clusters)
        print(colour.r, colour.g, colour.b)
        ```
        
        `0.1` in the second argument of `generate_clusters` refers to the threshold under which we consider two colours to be similar. Try adjusting the threshold for different results.
        
        ## Contributing
        
        The library is type hinted using the comment-based syntax for backward compatibility with Python 2. Tests are run using tox.
        
        ## TODO
        
        - [ ] Add example images
        - [ ] Add support for generating complimentary colours
        - [ ] Add support for selective sampling (e.g borders + center)
        - [ ] Improve clustering algorithm
        
        ## License
        
        MIT License
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
