Returning an array of colors from a double image

Question

Mark 2022-3-6

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1664934-returning-an-array-of-colors-from-a-double-image

评论： kiana 2024-5-1

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?

2 个评论
显示无隐藏无

Voss 2022-3-6

在 MATLAB Online 中打开

colors = unique(reshape(im,[],3),'rows');

where im is your double image.

DGM 2023-3-5

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

Answer 2

Image Analyst 2022-3-6

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1664934-returning-an-array-of-colors-from-a-double-image#answer_911269

在 MATLAB Online 中打开

rgbImage = imread('peppers.png');
% 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

11 个评论
显示 9更早的评论隐藏 9更早的评论

DGM 2022-3-9

That's what my second example does.

Image Analyst 2022-3-9

在 MATLAB Online 中打开

I think @DGM means to replace

colornames = {'w','r','g','b','y'};

by

colornames = {"white", "red", "gr","blue", "yellow"};

请先登录，再进行评论。

Answer 3

DGM 2022-3-7

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1664934-returning-an-array-of-colors-from-a-double-image#answer_911854

编辑：DGM 2022-3-7

在 MATLAB Online 中打开

You can leverage rgb2ind()'s minimum variance quantization to get a best-fit color table of specified length.

A = imread('https://www.mathworks.com/matlabcentral/answers/uploaded_files/917239/image.png');
[~,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     1
   0     0
   1     0
   1     1
   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

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

Answer 4

DGM 2022-3-7

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1664934-returning-an-array-of-colors-from-a-double-image#answer_912294

编辑：DGM 2022-3-7

在 MATLAB Online 中打开

patchchart.png

Oh okay I totally misunderstood the question. Round 2:

A = imread('patchchart.png');

patchmask = rgb2gray(A)>40;

patchmask = bwareaopen(patchmask,100); % remove positive specks

patchmask = ~bwareaopen(~patchmask,100); % remove negative specks

patchmask = imclearborder(patchmask); % get rid of outer white region

patchmask = imerode(patchmask,ones(10)); % erode to exclude edge effects

imshow(patchmask)

% segment the image

[L N] = bwlabel(patchmask);

% 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'}

17 个评论
显示 15更早的评论隐藏 15更早的评论

DGM 2022-3-10

在 MATLAB Online 中打开

Assuming that your double image is unit-scale (0-1), all you have to do is adjust the threshold and remove the denormalization line.

A = imread('patchchart.png');
A = im2double(A); % <- UNIT-SCALE DOUBLE
patchmask = rgb2gray(A)>0.16; % <- NOTE THRESHOLD 
patchmask = bwareaopen(patchmask,100); % remove positive specks
patchmask = ~bwareaopen(~patchmask,100); % remove negative specks
patchmask = imclearborder(patchmask); % get rid of outer white region
patchmask = imerode(patchmask,ones(10)); % erode to exclude edge effects
imshow(patchmask)
% segment the image
[L N] = bwlabel(patchmask);
% 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 <- THIS IS NOT NEEDED
% 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 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)

DGM 2022-3-11

在 MATLAB Online 中打开

patchchart2.png

Here's this. I had to make the patch ordering a bit more robust in order to get them in the right order. This still isn't enough to keep them in order if there's any significant rotation, but it's enough if the patch sizes vary a bit.

I also didn't use rgb2ind() here. There's something screwy going on internally that's making it not want to process the color table correctly as a whole. The results it gives appear to be dependent on the table order, regardless of dithering options. I don't recall how it does the quantization, but I don't think that really matters since the given method works fine.

A = imread('patchchart2.png');

A = medfilt3(A,[7 7 1]); % median filter to suppress noise

A = imadjust(A,stretchlim(A,0.05)); % increase contrast

patchmask = rgb2gray(A)>40;

patchmask = bwareaopen(patchmask,100); % remove positive specks

patchmask = ~bwareaopen(~patchmask,100); % remove negative specks

patchmask = imclearborder(patchmask); % get rid of outer white region

patchmask = imerode(patchmask,ones(10)); % erode to exclude edge effects

imshow(patchmask)

% segment the image

[L N] = bwlabel(patchmask);

% 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;

% try to snap the centers to a grid

S = regionprops(patchmask,'centroid');

C = vertcat(S.Centroid);

climits = [min(C,[],1); max(C,[],1)];

C = round((C-climits(1,:))./range(climits,1)*3 + 1);

% reorder color samples

idx = sub2ind([4 4],C(:,2),C(:,1));

patchcolors(idx,:) = patchcolors;

% 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'} {'y'} {'b'} {'w'} {'r'} {'y'} {'g'} {'r'} {'y'} {'y'} {'b'} {'g'} {'y'} {'w'} {'r'}

DGM 2022-4-1

在 MATLAB Online 中打开

patchchart4.png

The added noise is one thing, but without the black border, the masking approach needs to be changed anyway.

A = imread('patchchart4.png');

A = imerode(A,ones(3));

Ay = medfilt3(A,[101 1 1]); % use a long median filter

Ax = medfilt3(A,[1 101 1]); % both directions

A = max(Ax,Ay); % combine to keep patches from getting connected

A = imadjust(A,stretchlim(A,0.05));

patchmask = rgb2gray(A)<230;

patchmask = bwareaopen(patchmask,100); % remove positive specks

patchmask = ~bwareaopen(~patchmask,100); % remove negative specks

oamask = imdilate(patchmask,ones(10)); % dilate to merge patches

oamask = bwareafilt(oamask,1); % select largest blob

patchmask = patchmask & oamask; % exclude fiducials and any border noise

patchmask = imerode(patchmask,ones(10)); % erode to exclude edge effects

imshow(patchmask)

% segment the image

[L N] = bwlabel(patchmask);

% 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;

% try to snap the centers to a grid

S = regionprops(patchmask,'centroid');

C = vertcat(S.Centroid);

climits = [min(C,[],1); max(C,[],1)];

C = round((C-climits(1,:))./range(climits,1)*3 + 1);

% reorder color samples

idx = sub2ind([4 4],C(:,2),C(:,1));

patchcolors(idx,:) = patchcolors;

% 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);

% alternatively:

% find index of closest match for each patch

idx = rgb2ind(permute(patchcolors,[1 3 2]),colorrefs,'nodither') + 1;

% look up color names

patchnames = reshape(colornames(idx),4,4)

patchnames = 4×4 cell array

{'b'} {'y'} {'g'} {'r'} {'g'} {'r'} {'r'} {'b'} {'r'} {'r'} {'g'} {'r'} {'b'} {'y'} {'y'} {'y'}

imshow(A)

kiana 2024-4-22

在 MATLAB Online 中打开

Hello

thanks for the codes and explanation

I have a problem similar to this one

I tried this code for normal photos and it works but with photos with noise it does not work well

For Denoise I use

A = imerode(A,ones(3));
Ay = medfilt3(A,[101 1 1]); % use a long median filter
Ax = medfilt3(A,[1 101 1]); % both directions
A = max(Ax,Ay); % combine to keep patches from getting connected
A = imadjust(A,stretchlim(A,0.05));
patchmask = rgb2gray(A)<230;
patchmask = bwareaopen(patchmask,100); % remove positive specks
patchmask = ~bwareaopen(~patchmask,100); % remove negative specks
oamask = imdilate(patchmask,ones(10)); % dilate to merge patches
oamask = bwareafilt(oamask,1); % select largest blob
patchmask = patchmask & oamask; % exclude fiducials and any border noise
patchmask = imerode(patchmask,ones(10)); % erode to exclude edge effects

filter as you sent but it does not solve the problem

i upload the result and my photo

can you help me to solve this issue ?

DGM 2024-4-23

在 MATLAB Online 中打开

That example was intended for a type of image without a black border. The prior versions may work, but for this example, I'm going to stop doing everything strictly with base/IPT tools.

A = imread('noise_1.png');

% A = amedfilt(A,5,1); % more robust against JPG-compressed impulse noise

A = fmedfiltforum(A,5); % fixed median noise reduction filter (get rid of SNP noise)

A = imerode(A,ones(5)); % this may no longer be strictly necessary, but it's safer

A = medfilt3(A,[11 11 1]); % this is not ideal, but it's succinct

A = imadjust(A,stretchlim(A,0.05));

patchmask = rgb2gray(A)>20; % threshold

patchmask = bwareaopen(patchmask,100); % remove positive specks

patchmask = ~bwareaopen(~patchmask,100); % remove negative specks

patchmask = imclearborder(patchmask); % get rid of outer white region

patchmask = imerode(patchmask,ones(10)); % erode to exclude edge effects

imshow(patchmask)

% segment the image

[L N] = bwlabel(patchmask);

% 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;

% try to snap the centers to a grid

S = regionprops(patchmask,'centroid');

C = vertcat(S.Centroid);

climits = [min(C,[],1); max(C,[],1)];

C = round((C-climits(1,:))./range(climits,1)*3 + 1);

% reorder color samples

idx = sub2ind([4 4],C(:,2),C(:,1));

patchcolors(idx,:) = patchcolors;

% 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 = 4x4 cell array

{'b'} {'w'} {'r'} {'g'} {'w'} {'b'} {'r'} {'b'} {'b'} {'r'} {'b'} {'y'} {'g'} {'y'} {'g'} {'g'}

Doing an actual noise-removal median filter instead of trying to suppress everything with just simple median filters and morphological operations should be a lot more robust against impulse noise. The attached file fmedfiltforum() is a slower, version-dependent copy of MIMT fmedfilt(). It doesn't require MIMT, but it does require IPT and R2019b or newer. MIMT amedfilt() would potentially be quite a bit more robust (especially if the image is compressed) but it's slower, and probably overkill.

We have two types of images: ones with a contiguous white background, and ones with a black frame around the tiles. Consequently, I'm using two different ways to create the mask. Is it possible to write one function/script which can handle both types of images? What if we use saturation to find the foreground instead of looking at luma alone? Maybe, but relying on chroma/saturation will be problematic for two reasons. FIrst, the existence of white tiles means that you're going to have to extrapolate the location of tiles which the segmentation will miss. Second, the use of any JPG compression will probably ruin saturation data and risk creating extra problems.

Is there a better way? Probably. I'm just adapting ad-hoc scripts as people keep giving me slightly different versions. I don't have an understanding of the full scope of the problem requirements. If the images are grossly transformed or changed in other unexpected ways, the given examples will probably break.

kiana 2024-4-25

Thanks alot

That was a great help.

kiana 2024-5-1

在 MATLAB Online 中打开

Hi

I want have the matrix color of proj_1 so i correct it first first i find circles in proj_1 and org_1 and then correct the proj_1

but when I want to find colors I come up with a problem i it gives the wrong colors

can you help me to solve this problem ?

    if contains(filename, 'proj')
    image_db = loadImage_proj(filename);
    % Find circle centers in the projection image
    circle_centres = findCircles_proj(image_db, filename);
    % Display circle centerscv 
    %figure(1), imshow(circle_centres);
    % Correct image distortion based on the found circle centers
    corrected = correctImage_proj(circle_centres, image_db, filename);
    rgb = corrected;
    %saturationIncrease = 0.01; % Increase saturation by 0.3
    %valueAdjustment = 100;  % Decrease value by 0.2 to make it darker
    
    %rgb = enhanceYellowColor(rgb, saturationIncrease, valueAdjustment);        
    %rgb = imerode(rgb,ones(5)); 
    %rgb = medfilt3(rgb,[11 11 1]);
    %rgb = imadjust(rgb,stretchlim(rgb,0.05));    
    rgb = medfilt3(rgb,[7 7 1]); % median filter to suppress noise
    rgb = imadjust(rgb,stretchlim(rgb,0.05)); % increase contrast
        
    patchmask = rgb2gray(rgb)>0.101;
    patchmask = bwareaopen(patchmask,100); % remove positive specks
    patchmask = ~bwareaopen(~patchmask,100); % remove negative specks
    patchmask = imclearborder(patchmask); % get rid of outer white region
    patchmask = imerode(patchmask,ones(10)); % erode to exclude edge effects
        
    imshow(patchmask)
        
        % segment the image
     [L N] = bwlabel(patchmask);
        
        % get average color in each mask region
      patchcolors = zeros(N,3);
        for p = 1:N % step through patches
            patchmk = L==p;
            Apatch = rgb(patchmk(:,:,[1 1 1]));
            patchcolors(p,:) = mean(reshape(Apatch,[],3),1);
        end
       patchcolors = patchcolors./255;
        
        % try to snap the centers to a grid
       S = regionprops(patchmask,'centroid');
       C = vertcat(S.Centroid);
       climits = [min(C,[],1); max(C,[],1)];
       C = round((C-climits(1,:))./range(climits,1)*3 + 1);
        
        % reorder color samples
       idx = sub2ind([4 4],C(:,2),C(:,1));
       patchcolors(idx,:) = patchcolors;
        
        % 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)        
        %figure(3);
        %subplot(1, 2, 1), imshow(rgb), title('corrected Image');
        %subplot(1, 2, 2), imshow(patchmask), title('color Image');      
    end    