Sorting unique colors from the image (much simplified now)

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Mario Malic 2020-7-29

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https://ww2.mathworks.cn/matlabcentral/answers/572356-sorting-unique-colors-from-the-image-much-simplified-now

评论： Mario Malic 2020-8-2

Hello everyone,

Code uses rgb2hex and hex2rgb from FEX, I might be doing unecessary work with this, but let it be for now - https://ch.mathworks.com/matlabcentral/fileexchange/46289-rgb2hex-and-hex2rgb.

clc;
clear;
fclose all;
File_Name = 'https://upload.wikimedia.org/wikipedia/commons/thumb/e/ec/Mona_Lisa%2C_by_Leonardo_da_Vinci%2C_from_C2RMF_retouched.jpg/161px-Mona_Lisa%2C_by_Leonardo_da_Vinci%2C_from_C2RMF_retouched.jpg'; % Image name
Image_Data = imread(File_Name);
[V_Pixels, H_Pixels, ~] = size(Image_Data);
Num_Points = H_Pixels * V_Pixels; 
Colors = cell(V_Pixels, H_Pixels);
% Getting colors as hex values
for ii = 1 : 1 : V_Pixels
    for jj = 1 : 1 : H_Pixels
        Colors {ii, jj} = rgb2hex([Image_Data(ii,jj,1) Image_Data(ii,jj,2) Image_Data(ii,jj,3)]); % RGB values
    end
end
Color_Vector = (unique(reshape(Colors, [1, Num_Points])))'; %Removes duplicates and sorts
Color_RGB = hex2rgb(Color_Vector);
x = 1:1:length(Color_Vector);
y = ones(1,length(Color_Vector));
figure(1);
hb = bar(x, y);
hb.FaceColor = 'flat';
for ii = 1 : 1 : length(Color_Vector)
    hb.CData(ii,:) = Color_RGB(ii,:);
end

A graph is attached here, the colors are not sorted well. Obviously it means that colors can't be sorted this way.

I found some posts on sorting the image by HSV, but also realised that I am out of depth when it comes to image processing. I found an example here https://stackoverflow.com/a/2246189/14022732, but I am a bit lost when I have to convert back to rgb.

If you could share some tips on this, it would be great.

EDIT: I would like to sort unique colors so that they appear like a gradient, see my reply below.

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Answer 1

Image Analyst 2020-7-30

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Mario:

Here is the color gamut of that image in RGB color space:

and here is the gamut in HSV color space:

Each dot in the conical gamut represents the color of one pixel in the image. You can see that there is no real sensible way to sort that. What you're doing has nothing to do with HSV color space. You're all in RGB color space and you're sorting by Blue first, then green, and finally by red. Not sure that makes any sense as far as sorting goes, and I don't see how sorting by anything in HSV color space makes sense either. Do you just want to see unique colors?

Also I don't know what you mean by converting back to RGB. Not sure exactly what would get converted back - all the values, or just the unique values. If you keep all the values, of course you can convert back but if you throw out some because they're duplicated, then you can convert back to colors but you won't have an image, list a list of RGB values because you threw out some pixels and we don't know what to put there. For example if we have an aray m=[1,2,3,3,4] and we multiply by 2 we get m2 = [2,4,6,6,8]. Now if we throw out duplicates we get m2 = [2,4,6,8]. So you can divide by 2 to get [1,2,3,4] but that is not what you originally started with.

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Image Analyst 2020-8-1

在 MATLAB Online 中打开

Actually I think it's a good discussion that others may learn from. I'm not saying it's a worthless exercise, in fact I think you should try some things. I'm just saying that you should not expect, at least for general images, that you'll get a nice smooth ramp/gradient. So try it out and see what it looks like. It could be fun. Why not just start with the distance of all colors from black? Then if there are multiple pixels with that same distance, just take them as they are or else pick them randomly. The output image might look interesting. Here's a start.

% Demo to find distances.  By Image Analyst, May 5, 2020.
clc;    % Clear the command window.
close all;  % Close all figures (except those of imtool.)
clearvars;
workspace;  % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 16;
fprintf('Beginning to run %s.m ...\n', mfilename);
%-----------------------------------------------------------------------------------------------------------------------------------
% Read in image.
folder = [];
baseFileName = 'peppers.png';
fullFileName = fullfile(folder, baseFileName);
% Check if file exists.
if ~exist(fullFileName, 'file')
	% The file doesn't exist -- didn't find it there in that folder.
	% Check the entire search path (other folders) for the file by stripping off the folder.
	fullFileNameOnSearchPath = baseFileName; % No path this time.
	if ~exist(fullFileNameOnSearchPath, 'file')
		% Still didn't find it.  Alert user.
		errorMessage = sprintf('Error: %s does not exist in the search path folders.', fullFileName);
		uiwait(warndlg(errorMessage));
		return;
	end
end
rgbImage = imread(fullFileName);
[rows, columns, numberOfColorChannels] = size(rgbImage)
% Display the test image full size.
subplot(2, 1, 1);
imshow(rgbImage, []);
axis('on', 'image');
caption = sprintf('Reference Image : "%s"', baseFileName);
title(caption, 'FontSize', fontSize, 'Interpreter', 'None');
drawnow;
hp = impixelinfo(); % Set up status line to see values when you mouse over the image.
% Set up figure properties:
% Enlarge figure to full screen.
hFig1 = gcf;
hFig1.Units = 'Normalized';
hFig1.WindowState = 'maximized';
% Get rid of tool bar and pulldown menus that are along top of figure.
% set(gcf, 'Toolbar', 'none', 'Menu', 'none');
% Give a name to the title bar.
hFig1.Name = 'Demo by Image Analyst';
[r, g, b] = imsplit(double(rgbImage));
distances = sqrt(r(:).^2 + g(:).^2 + b(:).^2);
[sortedDistances, sortIndexes] = sort(distances, 'ascend');
% Sort our RGB arrays the same way.
r = r(sortIndexes);
g = g(sortIndexes);
b = b(sortIndexes);
% Now determine the number of rows and columns that would make a good output size.
% TODO : you do this.  You might use factors or some function to determine two
% integers that are the biggest.  You can use the original image's rows and columns if you want:
[rows, columns, numberOfColorChannels] = size(rgbImage);
% Then use reshape to make r, g, and b into a rectangular matrix of that size.
reshapedR = reshape(r, rows, columns);
reshapedG = reshape(g, rows, columns);
reshapedB = reshape(b, rows, columns);
% Then create the final rgb image like
rgbOut = cat(3, uint8(reshapedR), uint8(reshapedG), uint8(reshapedB));
subplot(2, 1, 2);
imshow(rgbOut);
title('Sorted Colors', 'FontSize', fontSize, 'Interpreter', 'None');
drawnow;
hp = impixelinfo(); % Set up status line to see values when you mouse over the image.

You can see what I mean about it not being smooth and monotonically increasing - that's why there's "noise" in the output image. You can reduce that be doing a further sort of all distances that are the ""same" or close to it.

Image Analyst 2020-8-2

There is no "right" way to do it. There are several ways to do it, according to the respective algorithms, but it's not like one is more correct than another - they're just different.

Mario Malic 2020-8-2