Relabelling with bwlabel()

Hi, I want my code to label the images in the collage by row instead of by column. I have come pretty close to doing this. The images I have attached are the original image and the labeled image. In the labeled image you can see in the first row the first image is labeled as 2 and if you follow that the image labeled 1 is the 10th position. I am not sure how to fix it. Below is my code...any insight?

My code is formatted to do this for multiple images at a time, I want to automate this process.

% Read in folder of binary images
image_folder_binary = ' folder '; % Enter name of folder from which you want to upload pictures with full path
filenames_bin = dir(fullfile(image_folder_binary, '*.tif')); % read all images with a sppecified extention, its tif in our case
binary_images = numel(filenames_bin); % count total number of photos present in that folder
%%
for n = 1:binary_images
     binary_Images=fullfile(image_folder_binary, filenames_bin(n).name) ; % its will specify images names with full path and extension
       our_images_binary   = imread(binary_Images); % read images
       L = bwlabel(our_images_binary,8)
       L = bwlabel(L')'
       blobMeasurements {n}  = regionprops(L, our_images_binary, 'all');
       numberOfBlobs(n) = size(blobMeasurements{n}, 1);
       
       % bwboundaries() returns a cell array, where each cell contains the row/column coordinates for an object in the image.
       % Plot the borders of all the zoop on the original grayscale image using the coordinates returned by bwboundaries.
       boundaries = bwboundaries(our_images_binary);
       numberOfBoundaries(n) = size(boundaries, 1);
end
%%
for n = 1 : binary_images
% Loop over all blobs printing their measurements to the command window.
    for k = 1 : numberOfBlobs(n)      % Loop through all blobs.
    	% Find the mean of each blob.  (R2008a has a better way where you can pass the original image
    	% directly into regionprops.  The way below works for all versions including earlier versions.)
    	thisBlobsPixels = blobMeasurements{n}(k).PixelIdxList;  % Get list of pixels in current blob.
    	meanGL = mean(our_images_binary(thisBlobsPixels)); % Find mean intensity (in original image!)
    	meanGL2008a = blobMeasurements{n}(k).MeanIntensity; % Mean again, but only for version >= R2008a
    	
    	blobArea = blobMeasurements{n}(k).Area;		% Get area.
    	blobPerimeter = blobMeasurements{n}(k).Perimeter;		% Get perimeter.
    	blobCentroid = blobMeasurements{n}(k).Centroid;		% Get centroid one at a time
    	blobECD{n}(k) = sqrt(4 * blobArea / pi);					% Compute ECD - Equivalent Circular Diameter.
	fprintf(1,'image #%03d blob #%03d %17.1f %11.1f %8.1f %8.1f %8.1f % 8.1f\n', n, k, meanGL, blobArea, blobPerimeter, blobCentroid, blobECD{n}(k));
   end
end
%%
for n = 1 : binary_images
    for k = 1 : numberOfBlobs(n)
        boxes = cat(1, blobMeasurements{n}.BoundingBox);
        left_edge = boxes(:,1);
        [sorted, sort_order] = sort(left_edge);
        s2 = blobMeasurements{n}(sort_order);
    end
end
%%
for n = 1 : binary_images
    I = im2uint8(our_images_binary);
    I(~our_images_binary) = 200;
end
%% option 1, which is like 99% good
extrema = cat(1, blobMeasurements{n}.Extrema);
left_most_top = extrema(1:8:end, :);
[sorted, sort_order] = sortrows(fliplr(left_most_top));
s2 = blobMeasurements{n}(sort_order);
imshow(I, 'InitialMag', 'fit')
hold on
for k = 1:numel(s2)
   centroid = s2(k).Centroid;
   text(centroid(1), centroid(2), sprintf('%d', k));
end
hold off

Answer 1

[Image Analyst]

You need to call sort(). Instead of

       L = bwlabel(our_images_binary,8)
       L = bwlabel(L')'
       blobMeasurements {n}  = regionprops(L, our_images_binary, 'all');
       numberOfBlobs(n) = size(blobMeasurements{n}, 1);

have this

       L = bwlabel(our_images_binary,8)
       props = regionprops(L, our_images_binary, 'all');
       xyCentroids = vertcat(props.Centroid);
       y = xyCentroids(:, 2); % Get all the y values (row centroids of blobs)
       [sortedY, sortOrder] = sort(y, 'ascend'); % Do the sort.
       % Re-sort props in order of increasing y.
       props = props(sortOrder);
       numberOfBlobs(n) = length(props);
       blobMeasurements{n}  = props;

[no zoop]

Hi, would I continue with the rest of my code? and how exactly would I visualize this new sort order to confirm if it is correct?

[Image Analyst]

Yes, just replace the lines of code with the new ones I gave.

You could use ismember() to loop through the blobs to get each blob one at a time and them show it with imshow() and you should see them marching down the screen from top to bottom.

[no zoop]

Unfortunatley it still doesn't order them correctly. The end game is if I can order them along a row than I can crop them along the wrong (by taking measurements along the row). Below is my code in what I think you are trying to explain to me.

for n = 1:binary_images
     binary_Images=fullfile(image_folder_binary, filenames_bin(n).name) ; % its will specify images names with full path and extension
       our_images_binary   = imread(binary_Images); % read images
       
       L = bwlabel(our_images_binary,8)
       props = regionprops(L, our_images_binary, 'all');
       xyCentroids = vertcat(props.Centroid);
       y = xyCentroids(:, 2); % Get all the y values (row centroids of blobs)
       [sortedY, sortOrder] = sort(y, 'ascend'); % Do the sort.
       % Re-sort props in order of increasing y.
       props = props(sortOrder);
       numberOfBlobs(n) = length(props);
       blobMeasurements{n}  = props;
       
       % bwboundaries() returns a cell array, where each cell contains the row/column coordinates for an object in the image.
       % Plot the borders of all the zoop on the original grayscale image using the coordinates returned by bwboundaries.
       boundaries = bwboundaries(our_images_binary);
       numberOfBoundaries(n) = size(boundaries, 1);
end

%%
for n = 1 : binary_images
% Loop over all blobs printing their measurements to the command window.
    for k = 1 : numberOfBlobs(n)      % Loop through all blobs.
    	% Find the mean of each blob.  (R2008a has a better way where you can pass the original image
    	% directly into regionprops.  The way below works for all versions including earlier versions.)
    	thisBlobsPixels = blobMeasurements{n}(k).PixelIdxList;  % Get list of pixels in current blob.
    	meanGL = mean(our_images_binary(thisBlobsPixels)); % Find mean intensity (in original image!)
    	meanGL2008a = blobMeasurements{n}(k).MeanIntensity; % Mean again, but only for version >= R2008a
    	
    	blobArea = blobMeasurements{n}(k).Area;		% Get area.
    	blobPerimeter = blobMeasurements{n}(k).Perimeter;		% Get perimeter.
    	blobCentroid = blobMeasurements{n}(k).Centroid;		% Get centroid one at a time
    	blobECD{n}(k) = sqrt(4 * blobArea / pi);					% Compute ECD - Equivalent Circular Diameter.
	fprintf(1,'image #%03d blob #%03d %17.1f %11.1f %8.1f %8.1f %8.1f % 8.1f\n', n, k, meanGL, blobArea, blobPerimeter, blobCentroid, blobECD{n}(k));
   end
end

%%
message = sprintf('Would you like to crop out and save each individual images?');
reply = questdlg(message, 'Extract Individual Images?', 'Yes', 'No', 'Yes');
% Note: reply will = '' for Upper right X, 'Yes' for Yes, and 'No' for No.
if strcmpi(reply, 'Yes')
   for n = 1 : original_images
       for k = 1 : numberOfBlobs(n)      % Loop through all blobs.
           % Find the bounding box of each blob.
           thisBlobsBoundingBox  = s2(k).BoundingBox;  % Get list of pixels in current blob.
           % Extract out this zoop into it's own image.
           subImage = imcrop(OG_images{j}, thisBlobsBoundingBox); 
           % Saving the cropped image
           folder = 'folder';
           thisBaseFileName = sprintf('Image%03d_%03d.tif', n, k);
           thisFullFileName = fullfile(folder, thisBaseFileName);
           imwrite(subImage, thisFullFileName,'tif');   
       end
   end
end