function [img, seg, names, counts] = LM2segments(D, imagesize, HOMEIMAGES)
%
% Transforms all the labelme labels into segmentation masks.
% It takes into account the occlusions, removing labeled pixels that are
% occluded by other objects. Depth ordering is achieved using the function
% LMsortlayers.m
%
% 'img' and 'seg' are matrices
%
% HOMEANNOTATIONS = 'http://labelme.csail.mit.edu/Annotations'
% HOMEIMAGES = 'http://labelme.csail.mit.edu/Images'

Nimages = length(D);

% Create list of objects:
[names, counts, imagendx, objectndx, objclass_ndx] = LMobjectnames(D, 'name');
for k = 1:length(objclass_ndx)
    D(imagendx(k)).annotation.object(objectndx(k)).namendx = objclass_ndx(k);
end

% Initalize output variables
seg = zeros([imagesize(1) imagesize(2) Nimages], 'uint16');
img = zeros([imagesize(1) imagesize(2) 3 Nimages], 'uint8');

figure
for ndx = 1:Nimages
    % Load image
    imgtmp = LMimread(D, ndx, HOMEIMAGES);
    annotation = D(ndx).annotation;
    if size(imgtmp,3)==1; imgtmp = repmat(imgtmp, [1 1 3]); end
    [nrows ncols cc] = size(imgtmp);
    
    % Scale image so that image box fits tight in image
    scaling = max(imagesize(1)/nrows, imagesize(2)/ncols);
    [annotationtmp, imgtmp] = LMimscale(annotation, imgtmp, scaling, 'bicubic');

    % Crop image to final size
    [nr nc cc] = size(imgtmp);
    sr = floor((nr-imagesize(1))/2);
    sc = floor((nc-imagesize(2))/2);
    [annotationtmp, imgtmp] = LMimcrop(annotationtmp, imgtmp, [sc+1 sc+imagesize(2) sr+1 sr+imagesize(1)]);

    % Sort layers
    annotationtmp = LMsortlayers(annotationtmp, imgtmp);
    
    % Get segmentation
    [mask, classes] = LMobjectmask(annotationtmp, size(imgtmp));
    classesndx = [annotationtmp.object.namendx];
    area = squeeze(sum(sum(mask,1),2));
    
    j = find(area>2); % remove small objects
    mask = mask(:,:,j);
    classesndx = classesndx(j);

    Mclasses = zeros([imagesize(1) imagesize(2)]);
    for k = size(mask,3):-1:1;
        Mclasses = Mclasses+classesndx(k)*(Mclasses==0).*mask(:,:,k);
    end
    
    % Store values
    seg(:,:,ndx)   = uint16(Mclasses);
    img(:,:,:,ndx) = imgtmp;
    
    % Visualization
    subplot(121)
    image(imgtmp); axis('equal'); axis('tight');
    title(sprintf('%d (out of %d)', ndx, Nimages))
    subplot(122)
    image(mod(Mclasses,128)); axis('equal'); axis('tight');
    drawnow
end

% plot stats
figure
subplot(121)
loglog(sort(counts, 'descend'))
xlabel('count rank')
ylabel('Number of instances')
axis('tight')
pixelcounts = hist(single(seg(:)), 0:single(max(seg(:))));
unlabeled = pixelcounts(1);
pixelcounts = pixelcounts(2:end); % remove unlabeled pixels;
subplot(122)
loglog(sort(pixelcounts, 'descend'))
xlabel('area rank')
ylabel('Number of pixels')
axis('tight')
title(sprintf('%d categories', length(names)))

figure
montage(reshape(uint8(mod(seg(:,:,1:20),256)), [imagesize(1) imagesize(2) 1 20]))