GitHub topics: complementary-colors

Repositories

tlux/tint

An Elixir library to work with colors, convert between different colorspaces and calculate color distances.

Language: Elixir - Size: 181 KB - Last synced at: 17 days ago - Pushed at: 3 months ago - Stars: 11 - Forks: 5

ArefAghajani/opposite_color

a simple falsk app to generate the oposite color

Language: HTML - Size: 9.77 KB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

marzapower/MPColorTools

A collection of tools for handling colors on iOS SDK

Language: Objective-C - Size: 145 KB - Last synced at: 23 days ago - Pushed at: about 9 years ago - Stars: 125 - Forks: 9

Amexio Colors Autogenerates Material Design based Themes. Out of the box, there are 80+ Material Design based themes available. Amexio Colors API is available for the developers to build their on themes or use the API to handle various color combinations and derive new colors based on the fundamental colors. Amexio Colors CLI is for the Angular developers to create Material Design based themes. CLI can be installed using npm.

Language: Java - Size: 1.1 MB - Last synced at: 6 days ago - Pushed at: about 6 years ago - Stars: 6 - Forks: 2

phiyodr/theoryofcolors

Functionalities to work with colors. Cast colors types, find closest color using text-embeddings, find color complement.

Language: Python - Size: 12.7 KB - Last synced at: about 1 year ago - Pushed at: over 2 years ago - Stars: 2 - Forks: 0

mster/prismaek

Generate color complements, shades, tints, and tones. Convert between color spaces.

Language: JavaScript - Size: 64.5 KB - Last synced at: about 1 year ago - Pushed at: almost 4 years ago - Stars: 3 - Forks: 1

LeonOrou/ML_Image_Complementer

ML Project from 2nd semester AI studies for predicting missing pixels in image files Contains a Convolutional Neural Network (CNN) that is trained to predict missing pixels from images. The missing pixels are stand-alone seperated by an x- and y-spacing which have for the training values between 2 and inlcuding 6 pixels. The missing pixels are starting at an x- and y-offset which have for training values between 0 and 8 pixels. The final accuracy measured as the root mean squared error of the predicted RGB values were around 18.42 pixels.

Language: Python - Size: 53.2 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 2 - Forks: 0