Help Vectorizing For-Loop with Complex Statements

% size of image
imageSize = 564;

% generate the color range used in the colormap
colormap_used_in_image = jet(256^3); % for perfect results use 256^3, 256^2 is good estimator
colormap_used_in_image = colormap_used_in_image * 255; % convert to actual RGB values

% example image where some of the data is in the jet colormap, some isn't
data = randi(255,564,564,3);

% convert data to doubles for math
data = double(data);

% empty array to be filled with our jet mapped data
jetmappedData = nan(imageSize,imageSize);

% this loop calculates which value from the colormap best fits the
% image
wb = waitbar(0,append('Colormapping Values for Image # ',string(1)));
for r = 1:imageSize % for each row in the image
    for c = 1:imageSize % for each column in the image
 
            diff1 = abs(data(r,c,1) - colormap_used_in_image(:,1)); % difference in R value
            diff2 = abs(data(r,c,2) - colormap_used_in_image(:,2)); % difference in G value
            diff3 = abs(data(r,c,3) - colormap_used_in_image(:,3)); % difference in B value
            diffTotal = diff1 + diff2 + diff3; % difference in all the values
            [val,loc] = min(diffTotal);
            if val <= 3 % if the value isn't found in jet
                jetmappedData(r,c) = loc;
            else
                jetmappedData(r,c) = NaN;
            end
    end
    waitbar(r/imageSize,wb,append('Colormapping Values for Image # ',string(1)));
end

I have a lossless image (564x564) generated from MATLAB with a jet-colorbar from a range of [0,1]. The original data is lost and I'm trying to recover it from the image. Here I am converting RGB values to their original datapoints. Over sixteen trillion operations need to be performed per photo... far too many. How can I vectorize this for loop to increase efficiency? (I also have a 3070ti GPU if it would help to utilize).

Thanks!

[Matt J]

Your code cannot be run. Input data has not been attached.

[Parker McLeod]

Updated with sample data - good call :)

Answer 1

[Matt J]

Eliminating one of the loops should help.

cmap=reshape(colormap_used_in_image',1,3,[]);

for c = 1:imageSize % for each row in the image
    
   D= reshape(data(:,c,:),[],3);
   
   idx=circlePixels(:,c);
   
   D=sum( abs(D(idx,:)-cmap),2);
   
   [val,loc]=min(D,[],3);
   loc(val<120)=nan;
   
   jetmappedData(idx,c)=loc(:);
   
    
end

[Parker McLeod]

Requested 564x3x16777216 (211.5GB) array exceeds maximum array size preference (56.9GB). This might cause MATLAB to become unresponsive.

Error in Image2DataTemp (line 31)

D=sum(abs(D(idx,:)-cmap),2);

Any ideas?

[Parker McLeod]

I broke down your loop futher into even smaller parts (so it could fit into memory), but the runtime is still over 20 hours for a single 564x564 image. Any further optimization I could try?

[Matt J]

In your code comments, you remark that jet(256^2) might be good enough I would suggest that. Also, convert data to single floats instead of double.

Also, converting the data to gpuArrays (if you have the Parallel Comp. Toolbox) should indeed accelerate things.

[Parker McLeod]

I'll look into those - thanks!

[Matt J]

You're welcome, but if they work, please Accept-click the answer.