/artistassistapp

ArtistAssistApp is a FREE web app for artists of all levels and styles for accurate color mixing from a photo based on real paints, tonal values, grid method, simplified sketching, etc. 🧑‍🎨

Primary LanguageTypeScriptApache License 2.0Apache-2.0

ArtistAssistApp

TypeScript React Ant Design npm

ArtistAssistApp logo

ArtistAssistApp is the best ever FREE painting assistant tool that allows artists to see the reference photo more clearly and mix colors more accurately.

ArtistAssistApp provides various tools for artists for accurate color mixing from a photo based on real paints, tonal value study, grid method for drawing, simplified sketching, etc.

Try it now at ArtistAssistApp.com

ArtistAssistApp-Fruits-Demo-10MB.mp4

Getting Started

Screenshots

Accurately mixing a color from a reference photo with real paints

ArtistAssistApp: accurately mixing a color from a reference photo with real paints

Tonal value study

ArtistAssistApp: tonal value study

Drawing a grid over a reference photo

ArtistAssistApp: drawing with a grid method (square grid) ArtistAssistApp: drawing with a grid method (3x3 grid)

Smoothing out a reference photo

ArtistAssistApp: smoothing out a reference photo ArtistAssistApp: smoothing out a reference photo

Previewing a photo painted using a limited palette

ArtistAssistApp: previewing a reference photo in different primary colors ArtistAssistApp: previewing a reference photo in different primary colors

Mixing specific colors of specific brands in any proportions

ArtistAssistApp: mixing specific colors of specific brands in any proportions ArtistAssistApp: mixing specific colors of specific brands in any proportions ArtistAssistApp: mixing specific colors of specific brands in any proportions ArtistAssistApp: spectral reflectance curve ArtistAssistApp: spectral reflectance curve

Implementation details

This project does not use artificial intelligence (AI), but rather mathematics.

The core logic of the project doesn't depend on any math or color library and includes the implementation of the following:

  • converting between color models (e.g. sRGB to CIELAB),
  • sRGB to spectral reflectance curve,
  • subtractive color mixing using weighted geometric mean of reflectance curves,
  • matrix operations,
  • matrix inversion using LU decomposition,
  • solving a system of linear algebraic equations using forward and backward substitution,
  • the average color of the circular area of the image
  • color difference using CIEDE2000,
  • vector operations,
  • fast median blur using sliding window and histogram,
  • median cut for color quantization,
  • RGB to grayscale conversion based on luminance,
  • and more.