R/canvas_mosaic.R

In aRtsy: Generative Art with 'ggplot2'

# Copyright (C) 2021-2023 Koen Derks

# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.

# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.

#' Draw Moisaics
#'
#' @description This function draws the predictions from a k-nearest neighbors algorithm trained on randomly generated categorical data.
#'
#' @usage canvas_mosaic(
#'   colors,
#'   n = 1000,
#'   resolution = 500
#' )
#'
#' @param colors      a string or character vector specifying the color(s) used for the artwork.
#' @param n           a positive integer specifying the number of random data points to generate.
#' @param resolution  resolution of the artwork in pixels per row/column. Increasing the resolution increases the quality of the artwork but also increases the computation time exponentially.
#'
#' @return A \code{ggplot} object containing the artwork.
#'
#' @references \url{https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm}
#'
#' @author Koen Derks, \email{koen-derks@hotmail.com}
#'
#' @keywords artwork canvas
#'
#' @seealso \code{colorPalette}
#'
#' @examples
#' \donttest{
#' set.seed(1)
#'
#' # Simple example
#' canvas_mosaic(colors = colorPalette("retro2"))
#' }
#'
#' @export

canvas_mosaic <- function(colors,
                          n = 1000,
                          resolution = 500) {
  .checkUserInput(resolution = resolution)
  train <- data.frame(
    x = stats::runif(n, 0, resolution),
    y = stats::runif(n, 0, resolution),
    z = factor(sample(colors, size = n, replace = TRUE))
  )
  fit <- kknn::train.kknn(formula = z ~ x + y, data = train, kmax = 1)
  sequence <- seq(0, resolution, by = 1)
  canvas <- expand.grid(sequence, sequence)
  colnames(canvas) <- c("x", "y")
  z <- predict(fit, newdata = canvas)
  full_canvas <- data.frame(x = canvas[["x"]], y = canvas[["y"]], z = z)
  artwork <- ggplot2::ggplot(data = full_canvas, mapping = ggplot2::aes(x = x, y = y, fill = z)) +
    ggplot2::geom_raster(interpolate = TRUE) +
    ggplot2::xlim(c(-1, resolution + 1)) +
    ggplot2::ylim(c(-1, resolution + 1)) +
    ggplot2::scale_fill_manual(values = colors)
  artwork <- theme_canvas(artwork)
  return(artwork)
}