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Make palette from image7/14/2023 ![]() Larger k may allow for better palette construction under some conditions, but takes longer to run. It is limited by the number of unique colors in the image. This is different from n, the number of colors are desired in the derived palette. The number of k-means centers k defines the maximum number of unique colors to consider in the image for color binning prior to palette construction. There is also variation in possible palettes from a given image, depending on the image complexity and other properties, though you can set the random seed for reproducibility. This can include trimming the extreme values of the color distribution in terms of brightness, saturation and presence of near-black/white colors as pre-processing steps. This function does a decent job of creating qualitative, sequential and divergent palettes from images, but additional tweaking of function arguments is needed on a case by case basis. There are many ways to do it none are perfect.Ĭolor is a multi-dimensional property any reduction to a a one dimensional color spectrum necessarily removes information.Ĭreating a sequential palette from an arbitrary image that contains several hues, at different saturation and brightness levels, and making a palette that looks sequential is particularly problematic. Ordering colors is a challenging problem. ![]() Logical, quantize the reference thumbnail image in the plot using the derived color palette. Logical, adjust rectangles in plot to use the image aspect ratio. Numeric, set the seed for reproducible results. ![]() See details.Ĭharacter, color used for divergent palette center, defaults to white. See details.Ī numeric vector of length two giving the lower and upper quantiles to trim trim near-black and near-white colors in RGB space.Īs above, trim possible colors based on brightness in HSV space.Īs above, trim possible colors based on saturation in HSV space.Ĭharacter, sort sequential palette by HSV dimensions in a specific order, e.g., "hsv", "svh". Integer, the number of k-means cluster centers to consider in the image. Image_pal ( file, n = 9, type = c ( "qual", "seq", "div" ), k = 100, bw = c ( 0, 1 ), brightness = c ( 0, 1 ), saturation = c ( 0, 1 ), seq_by = "hsv", div_center = "#FFFFFF", seed = NULL, plot = FALSE, labels = TRUE, label_size = 1, label_color = "#000000", keep_asp = TRUE, quantize = FALSE )Ĭharacter, type of palette: qualitative, sequential or divergent ( "qual", "seq", or "div"). ![]()
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