Uses the LAB* colorspace to extract "approximate human vision" dominant colors from an image. Optionally, map those dominant colors into preferred "color bins" for a search index facet-by-color solution.
LARGELY based off the neat work of the Miro gem. If you want faster, RGB-based dominant color extraction, use Miro.
Requires Imagemagick. On OSX use homebrew to install: brew install imagemagick
$ gem install kolors
require 'kolors'
# Use path to a local image or URL for remote image
kolors = Kolors::DominantColors.new('../colors/images/QFZMF57HPHVGJ8Z_thumb.png')
# Non-clustered - Color pixel count percentages
kolors.color_bins_result
=> [{"Moss"=>31.785714285714285}, {"Asparagus"=>22.658730158730158}, {"Aluminum"=>7.420634920634921}, {"Tungsten"=>5.396825396825397}, {"Magnesium"=>4.821428571428572}, {"Iron"=>4.424603174603175}, {"Steel"=>4.067460317460317}, {"Silver"=>3.8293650793650795}, {"Tin"=>3.7896825396825395}, {"Mercury"=>3.6904761904761907}, {"Nickel"=>3.5515873015873014}, {"Lead"=>2.380952380952381}, {"Snow"=>2.0634920634920633}, {"Licorice"=>0.11904761904761905}]
- Simplify configuration of "color bins" for facet-by-color mapping
- Nate Vack for help getting this off of the ground.
- Culley Smith for assistance in refactoring towards parallelization.
- Fork it
- Create your feature branch (
git checkout -b my-new-feature) - Commit your changes (
git commit -am 'Add some feature') - Push to the branch (
git push origin my-new-feature) - Create new Pull Request