In [1]:

using Clustering, Images, ColorSchemes
using Plots
plotlyjs()

function image2zvals(img::Union{Matrix{RGB{T}}, Matrix{RGBA{T}}};  
               n_colors=64, maxiter=200, tol=1e-04) where T<:Real
    
    rows, cols = size(img)
    observations = reshape(img, rows*cols)
    kmres = kmeans(observations, n_colors; maxiter = maxiter, tol=tol)
    nclust = nclusters(kmres)
    a = assignments(kmres)/nclust
    z_data = reshape(a, rows, cols)
   
    hcolors =  kmres.centers # kmres.centers is the codebook
    if fieldcount(eltype(img)) == 3
        cscheme = ColorScheme([RGB(c...) for c in eachcol(hcolors)])
    else
         cscheme = ColorScheme([RGB(c[1:3]...) for c in eachcol(hcolors)]) 
    end    
    return z_data, cscheme
end

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Out[1]:

image2zvals (generic function with 1 method)

In [2]:

img = load("heatmap-forum.jpeg");

In [3]:

zdata, cscheme = image2zvals(img; n_colors=64,  maxiter=200, tol=1e-04);

Reconstructed heatmap:

In [5]:

plt = heatmap(zdata[end:-1:1,:], c=cgrad(cscheme), cbar=false, size=(375, 300))

Out[5]:

In [6]:

zdata

Out[6]:

240×320 Matrix{Float64}:
 0.0625    0.0625    0.0625    0.0625    …  0.84375   0.828125  0.9375
 0.0625    0.0625    0.0625    0.0625       0.234375  0.8125    0.9375
 0.0625    0.0625    0.0625    0.0625       0.828125  0.0625    0.59375
 0.0625    0.0625    0.0625    0.0625       0.828125  0.59375   0.0625
 0.0625    0.0625    0.0625    0.0625       0.328125  0.328125  0.828125
 0.0625    0.0625    0.0625    0.0625    …  0.734375  0.328125  0.828125
 0.0625    0.0625    0.0625    0.0625       0.59375   0.0625    0.59375
 0.0625    0.0625    0.0625    0.0625       0.703125  0.859375  0.9375
 0.0625    0.0625    0.0625    0.0625       0.703125  0.9375    0.9375
 0.8125    0.8125    0.0625    0.0625       0.703125  0.9375    0.0625
 0.8125    0.8125    0.8125    0.0625    …  0.0625    0.59375   0.0625
 0.8125    0.8125    0.8125    0.0625       0.0625    0.59375   0.0625
 0.8125    0.8125    0.0625    0.8125       0.9375    0.59375   0.0625
 ⋮                                       ⋱                      
 0.703125  0.859375  0.859375  0.34375      0.453125  0.15625   0.421875
 0.859375  0.859375  0.59375   0.828125     0.359375  0.453125  0.453125
 0.703125  0.859375  0.703125  0.59375   …  0.421875  0.453125  0.3125
 0.859375  0.859375  0.859375  0.859375     0.421875  0.359375  0.40625
 0.59375   0.59375   0.8125    0.8125       0.90625   0.453125  0.3125
 0.59375   0.34375   0.8125    0.328125     0.453125  0.359375  0.109375
 0.59375   0.59375   0.34375   0.59375      0.3125    0.453125  0.359375
 0.859375  0.859375  0.34375   0.59375   …  0.890625  0.3125    0.453125
 0.859375  0.859375  0.859375  0.859375     0.578125  0.890625  0.890625
 0.859375  0.859375  0.859375  0.859375     0.671875  0.671875  0.671875
 0.859375  0.859375  0.859375  0.859375     0.40625   0.890625  0.890625
 0.859375  0.859375  0.859375  0.859375     0.3125    0.40625   0.671875

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