mirror of
https://github.com/ryantimpe/brickr.git
synced 2026-05-12 22:39:01 -05:00
Ryan Timpe
79 lines
3.1 KiB
R
79 lines
3.1 KiB
R
#' Scale an image raster array to a small number of pixels. Process into a data frame.
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#'
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#' @param image A raster array from an image.
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#' @param img_size Size of output image in pixel, where one pixel = one 'brick'. Use a single value (e.g. \code{48}) for a square image with 48 pixels on each side.
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#' Use an array of two values for a rectangular image \code{c(width, height)}.
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#' @param brightness A value >1 will increase the brightness of the image while a positive value <1 will decrease the brightness.
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#' @param warhol Array of values \code{c(1, 2, 3)} associated with R, G, B color channels. Swap values in array to swap color channels for a fun visual effect.
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#' @return A list with element \code{Img_scaled} containing a data frame of the x- & y-coordinates, R, G, B channels, and hex color of each brick (pixel).
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#' @export
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#'
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scale_image <- function(image, img_size, brightness = 1, warhol = 1:3){
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#Adjust brightness. Max channel value is 1
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if(brightness < 0 ){stop("brightness should be a positive value. Use 1 for no change, >1 for lighter, <1 for darker.")}
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image_b <- image*brightness
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image_b[image_b>1] <- 1
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#Only whole values for image size
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img_size <- round(img_size, 0)
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#RGB channel order as specified with the `warhol` input
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col_chan <- order(warhol[1:3])
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#Convert image to a data frame with RGB values
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img <- dplyr::bind_rows(
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list(
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(as.data.frame(image_b[, , col_chan[1]]) %>%
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dplyr::mutate(y=dplyr::row_number(), channel = "R")),
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(as.data.frame(image_b[, , col_chan[2]]) %>%
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dplyr::mutate(y=dplyr::row_number(), channel = "G")),
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(as.data.frame(image_b[, , col_chan[3]]) %>%
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dplyr::mutate(y=dplyr::row_number(), channel = "B"))
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)
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) %>%
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tidyr::gather(x, value, -y, -channel) %>%
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dplyr::mutate(x = as.numeric(gsub("V", "", x))) %>%
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tidyr::spread(channel, value)
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#Wide or tall image? Shortest side should be `img_size` pixels
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if(max(img$x) > max(img$y)){
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img_scale_x <- max(img$x) / max(img$y)
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img_scale_y <- 1
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} else {
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img_scale_x <- 1
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img_scale_y <- max(img$y) / max(img$x)
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}
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#If only 1 img_size value, create a square image
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if(length(img_size) == 1){
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img_size2 <- c(img_size, img_size)
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} else {
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img_size2 <- img_size[1:2]
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img_scale_x <- 1
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img_scale_y <- 1
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}
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#Rescale the image
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img2 <- img %>%
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dplyr::mutate(y_scaled = (y - min(y))/(max(y)-min(y))*img_size2[2]*img_scale_y + 1,
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x_scaled = (x - min(x))/(max(x)-min(x))*img_size2[1]*img_scale_x + 1) %>%
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dplyr::select(-x, -y) %>%
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dplyr::group_by(y = ceiling(y_scaled), x = ceiling(x_scaled)) %>%
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#Get average R, G, B and convert it to hexcolor
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dplyr::summarize_at(dplyr::vars(R, G, B), dplyr::funs(mean(.))) %>%
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dplyr::rowwise() %>%
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dplyr::mutate(color = rgb(R, G, B)) %>%
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dplyr::ungroup() %>%
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#Center the image
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dplyr::filter(x <= median(x) + img_size2[1]/2, x > median(x) - img_size2[1]/2,
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y <= median(y) + img_size2[2]/2, y > median(y) - img_size2[2]/2) %>%
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#Flip y
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dplyr::mutate(y = (max(y) - y) + 1)
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out_list <- list()
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out_list[["Img_scaled"]] <- img2
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return(out_list)
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} |