Finding colors in colorschemes

ColorSchemes.jl provides the function getinverse(cscheme, c) which is the inverse of get(cscheme, x).

This function returns a value between 0 and 1 that tries to place a color within a colorscheme by converting it to a value representing its position on the colorscheme's axis.

Example of using getinverse()

One example use for getinverse() is to convert a heatmap image into an Array of continuous values, e.g. temperature.

In this example, we will convert a heatmap image representing elevation in the United States into an Array of elevation values. The image represents global temperature anomalies averaged from 2008 through 2012, with blue as -2 C and Red as +2 C. Higher than normal temperatures are shown in red (red is +2°C) and lower than normal temperatures are shown in blue (blue is -2°C). The global surface temperature in 2012 was +0.55°C. source.

using Images, FileIO
img = download("https://www.nasa.gov/images/content/719282main_2008_2012_printdata.1462.jpg") |> load
img = imresize(img, Tuple(Int(x) for x in size(img) .* 0.2));
display(img)

temps = [getinverse(ColorSchemes.temperaturemap, pixel) for pixel in img]

432×768 Array{Float64,2}:
 0.975615  0.975615  0.975615  0.975615  …  0.975615  0.975615  0.975615
 0.975484  0.975767  0.975615  0.975615     0.975615  0.975615  0.975767
 0.975615  0.975615  0.975615  0.975615     0.975615  0.975615  0.975615
 0.975615  0.975615  0.975615  0.975615     0.975615  0.975615  0.975615
 0.975615  0.975615  0.975615  0.975615     0.975615  0.975615  0.975615
 0.975615  0.975615  0.975615  0.975615  …  0.975615  0.975615  0.975615
 0.975615  0.975615  0.975615  0.975615     0.975615  0.975615  0.975615
 0.975615  0.975615  0.975615  0.975615     0.975615  0.975615  0.975615
 ⋮                                       ⋱  ⋮                           
 0.820419  0.820084  0.819388  0.819388     0.819977  0.821949  0.81973
 0.816596  0.816055  0.816055  0.816055  …  0.819388  0.819388  0.818957
 0.813865  0.813247  0.813247  0.813247     0.816055  0.815452  0.813865
 0.810015  0.809307  0.809307  0.809307     0.813247  0.812582  0.812582
 0.808566  0.805171  0.805171  0.805171     0.810015  0.810015  0.809307
 0.804418  0.801045  0.80182   0.801045     0.805171  0.805171  0.805171
 0.801045  0.802513  0.802513  0.800252  …  0.804418  0.804308  0.801045
 0.802037  0.798624  0.798624  0.798624     0.802401  0.800252  0.802848

The data has been converted from its original colors to an array of continuous values between 0 and 1, which makes it possible to process as data. For example, we can find the places with the greatest anomalies:

mintemp, maxtemp = argmin(temps), argmax(temps)

 (CartesianIndex(397, 127), CartesianIndex(17, 314))

and these maximum and minimum 'coordinates' can be displayed on the image using, for example, Luxor.jl (or any other package that allows you to mix images and vector graphics easily):

save("/tmp/img.png", img)
using Luxor
pngimg = readpng("/tmp/img.png")

w, h = pngimg.width, pngimg.height

maxpt = Point(maxtemp[2], maxtemp[1]) # image and graphics coords need swapping
minpt = Point(mintemp[2], mintemp[1])

@png begin
    placeimage(pngimg, O, centered=true)
    translate(-w/2, -h/2)
    sethue("mediumseagreen")
    fontsize(20)
    fontface("Avenir-Black")
    setopacity(0.75)
    circle(maxpt, 5, :fill)
    label("largest positive anomaly", :E, maxpt, offset=20)
    circle(minpt, 5, :fill)
    label("largest negative anomaly", :E, minpt, offset=20)
end 800 460

We can display the array of continuous values as a grayscale image, where black is 0.0 and white is 1.0.

Gray.(temps)

getinverse(cscheme::ColorScheme, c, range=(0.0, 1.0))

julia> getinverse(colorschemes[:leonardo], RGB(1, 0, 0))
0.6248997995654847

julia> getinverse(ColorScheme([Colors.RGB(0,0,0), Colors.RGB(1,1,1)]),  Colors.RGB(.5,.5,.5))
0.5432555858022048

julia> cs = ColorScheme(range(Colors.RGB(0,0,0), stop=Colors.RGB(1,1,1), length=5))

julia> getinverse(cs, cs[3])
0.5