# Returning an array of colors from a double image

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Mark on 6 Mar 2022
Commented: DGM on 26 Apr 2023
I am trying to write a function that takes a type double image as input and returns an array of the colors in that image. The returned colors are supposed to be in a matrix form. The colors in my existing image are red, green, blue, white, and yellow. I can't get my head around this. Any suggestions?
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DGM on 5 Mar 2023

Voss on 6 Mar 2022
% create an image with colors r,g,b,w,y
im = ones(2,4,3);
im(1,1,:) = [1 0 0];
im(1,3,:) = [0 1 0];
im(2,2,:) = [0 0 1];
im(2,4,:) = [1 1 0];
imshow(im); % get the set of unique colors in the image:
colors = unique(reshape(im,[],3),'rows')
colors = 5×3
0 0 1 0 1 0 1 0 0 1 1 0 1 1 1

Image Analyst on 6 Mar 2022
% Call the function:
colors = GetUniqueColors(rgbImage)
% Define the function:
function colors = GetUniqueColors(rgbImage)
[r, g, b] = imsplit(rgbImage);
colors = unique([r(:), g(:), b(:)], "rows")
end
Image Analyst on 9 Mar 2022
I think @DGM means to replace
colornames = {'w','r','g','b','y'};
by
colornames = {"white", "red", "gr","blue", "yellow"};

DGM on 7 Mar 2022
Edited: DGM on 7 Mar 2022
You can leverage rgb2ind()'s minimum variance quantization to get a best-fit color table of specified length.
[~,CT] = rgb2ind(A,6) % get a color table of at most 6 colors
CT = 6×3
0.0392 0.0392 0.0392 0.2039 0.1686 0.9569 0.9569 0.0549 0.0392 0.8471 0.9569 0.0588 0.9569 0.9569 0.9569 0.0863 0.9569 0.2235
Bear in mind that since these colors were originally close to the extremes of the data range, truncation means that the addition of zero-mean gaussian noise will indeed shift the mean colors of the image, even if the noise mean is zero. I should point out that it's pretty clear the blue, green and yellow patches weren't on their corners to begin with.
If you know that you only want primary + secondary + neutral colors, you can just round the result.
CTrounded = round(CT)
CTrounded = 6×3
0 0 0 0 0 1 1 0 0 1 1 0 1 1 1 0 1 0
Otherwise, you can try to renormalize the values to correct for the inward shift caused by the noise. This assumes that the colors in the image nominally spanned the data range before the noise was added.
CTnormalized = mat2gray(CT)
CTnormalized = 6×3
0 0 0 0.1795 0.1410 1.0000 1.0000 0.0171 0 0.8803 1.0000 0.0214 1.0000 1.0000 1.0000 0.0513 1.0000 0.2009

DGM on 7 Mar 2022
Edited: DGM on 7 Mar 2022
Oh okay I totally misunderstood the question. Round 2: % segment the image
% get average color in each mask region
patchcolors = zeros(N,3);
for p = 1:N % step through patches
patchmk = L==p;
Apatch = A(patchmk(:,:,[1 1 1]));
patchcolors(p,:) = mean(reshape(Apatch,[],3),1);
end
patchcolors = patchcolors./255; % normalize
% specify a correlated list of colors and color names
colornames = {'w','r','g','b','y'};
colorrefs = [1 1 1; 1 0 0; 0 1 0; 0 0 1; 1 1 0];
% find color distances in RGB
D = patchcolors - permute(colorrefs,[3 2 1]);
D = squeeze(sum(D.^2,2));
% find index of closest match for each patch
[~,idx] = min(D,[],2);
% look up color names
patchnames = reshape(colornames(idx),4,4)
patchnames = 4×4 cell array
{'b'} {'y'} {'w'} {'y'} {'y'} {'w'} {'w'} {'r'} {'w'} {'y'} {'r'} {'r'} {'g'} {'w'} {'w'} {'r'}
Alternatively, instead of doing the distance minimization the long way, you could just use rgb2ind() to do that work:
% find index of closest match for each patch
idx = rgb2ind(permute(patchcolors,[1 3 2]),colorrefs) + 1;
% look up color names
patchnames = reshape(colornames(idx),4,4)
patchnames = 4×4 cell array
{'b'} {'y'} {'w'} {'y'} {'y'} {'w'} {'w'} {'r'} {'w'} {'y'} {'r'} {'r'} {'g'} {'w'} {'w'} {'r'}
DGM on 26 Apr 2023
I posted several versions of code, so I don't know which one you tried, and you didn't post your image, so I don't know what you tried it on.