ArtBooster.jl

ArtBooster.jl turns images into abstract figures by predicting their features with a gradient booster. The package renders the booster's depiction of the image in real-time, using whatever model hyperparameters the user provides. Fitting is done using the XGBoost package, which supports Linux and Mac OS X.

Installation

Add the package to Julia with:

Pkg.clone("https://github.com/Allardvm/ArtBooster.jl.git")

To test the package, use Julia's native package testing functions:

Pkg.test("ArtBooster")

Getting started

using Images
using ArtBooster

img = load(Pkg.dir("ArtBooster") * "/test/julia-logo.jpg")

param = Dict("max_depth" => 2,
             "eta" => .5,
             "objective" => "reg:linear")

boostimage(img, param, iterations = 150, res_x = 1260, res_y = 852)

Resulting in:

Exports

Functions

boostimage(img, param; [iterations = 1, res_x = 500, res_y = 500])

Turns an image into an abstract figure by predicting its features with a gradient booster.

Arguments

• img::Matrix{T<:AbstractRGB}: the image to boost.
• param::Dict: a dictionary with the XGBoost parameters to use in the prediction.
• iterations::Int: keyword argument that sets the number of boosting iterations. Defaults to 1.
• res_x::Int: keyword argument that sets the horizontal resolution of the rendered abstract figure. Defaults to 500.
• res_y::Int: keyword argument that sets the vertical resolution of the rendered abstract figure. Defaults to 500.