function [] = consensusMasks(locArray)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Generates the consensus masks to be used in our experiment. Uses the IoU
%and the hungarian algorithm in order to make matches
% Inputs:
% 		- locArray: string array of 4 file paths:
% 			1.  output directory [filepath]
% 			2.  directory for first grader [filepath]
% 			3.  directory for second grader [filepath]
% 			4.  directory for third grader [filepath]
% Outputs: (none)
%       -
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

outputdir       = locArray(1);
user1_folder    = locArray(2);
user2_folder    = locArray(3);
user3_folder    = locArray(4);
 
cd(user3_folder)
A = dir('0*');

threshold = 0.5;

for vid = 1:15
cd(user1_folder)
vname = A(vid).name;
greenind = strfind(vname, 'Green');
B = dir(strcat(vname(1:greenind), '*_masks.tif'));
name1 = B(1).name;
masks1 = bigread2(name1);
projname = dir(strcat(vname(1:greenind), '*_projection.tif'));
projection = imread(projname(1).name);
cd(strcat(user2_folder, vname))
maskname = dir('*.tif');
masks2 = bigread2(maskname(1).name);

cd(user3_folder)
fname = dir(strcat(vname(1:greenind), '*'));
cd(fname(1).name)
maskname = dir('*.tif');
masks3 = bigread2(maskname(1).name);
[d1 d2 d3] = size(masks1);
[s1 s2 s3] = size(masks3);
if d1 == s2
    masks1 = permute(masks1, [2 1 3]);
end
M1match = zeros(size(masks1, 3), 2);
M2match = zeros(size(masks2, 3), 2);
M3match = zeros(size(masks3, 3), 2);
M1neurons = [];
M2neurons = [];
M3neurons = [];

[M] = matchNeurons(masks1, masks2, threshold);
M1match(M(:, 1), 1) = M(:, 2);
M2match(M(:, 2), 1) = M(:, 1);
M1neurons = cat(1, M1neurons, M(:, 1));
M2neurons = cat(1, M2neurons, M(:, 2));
[M] = matchNeurons(masks1, masks3, threshold);
M1match(M(:, 1), 2) = M(:, 2);
M3match(M(:, 2), 1) = M(:, 1);
M1neurons = cat(1, M1neurons, M(:, 1));
M3neurons = cat(1, M3neurons, M(:, 2));
[M] = matchNeurons(masks2, masks3, threshold);
M2match(M(:, 1), 2) = M(:, 2);
M3match(M(:, 2), 2) = M(:, 1);
M2neurons = cat(1, M2neurons, M(:, 1));
M3neurons = cat(1, M3neurons, M(:, 2));
M1neurons = unique(M1neurons);
M2neurons = unique(M2neurons);
M3neurons = unique(M3neurons);

sm1 = sum(masks1(:, :, M1neurons),3);
sm2 = sum(masks2(:, :, M2neurons),3);
sm3 = sum(masks3(:, :, M3neurons), 3);
M1use = find(M1match(:, 1));
fmask = [];

%Masks 1 to masks 2
M1use = find(M1match(:, 1) > 0 & M1match(:, 2) == 0);
for nn = 1:length(M1use)
    cm2 = masks1(:, :, M1use(nn));
    cm3 = masks2(:, :, M1match(M1use(nn), 1));
    cummask = logical(cm2)+logical(cm3);
    maskind = find(cummask >= 2);
    blank = zeros(size(cm2));
    blank(maskind) = 1;
    fmask = cat(3, fmask, blank);
end

%Masks 1 to masks 3
M1use = find(M1match(:, 2) > 0 & M1match(:, 1) == 0);
for nn = 1:length(M1use)
    cm2 = masks1(:, :, M1use(nn));
    cm3 = masks3(:, :, M1match(M1use(nn), 2));
    cummask = logical(cm2)+logical(cm3);
    maskind = find(cummask >= 2);
    blank = zeros(size(cm2));
    blank(maskind) = 1;
    fmask = cat(3, fmask, blank);
    nn
end

%All matching
M1use = find(M1match(:, 2) > 0 & M1match(:, 1) > 0);
for nn = 1:length(M1use)
    cm1 = masks1(:, :, M1use(nn));
    cm2 = masks2(:, :, M1match(M1use(nn), 1));
    cm3 = masks3(:, :, M1match(M1use(nn), 2));
    cummask = logical(cm1)+logical(cm2)+logical(cm3);
    maskind = find(cummask >= 2);
    blank = zeros(size(cm2));
    blank(maskind) = 1;
    fmask = cat(3, fmask, blank);
end

%Masks 2 to 3
M1use = find(M2match(:, 2) > 0 & M2match(:, 1) == 0);
for nn = 1:length(M1use)
    cm2 = masks2(:, :, M1use(nn));
    cm3 = masks3(:, :, M2match(M1use(nn), 2));
    cummask = logical(cm2)+logical(cm3);
    maskind = find(cummask >= 2);
    blank = zeros(size(cm1));
    blank(maskind) = 1;
    fmask = cat(3, fmask, blank);
end
    fmask = removeDuplicates(fmask);
    projection = double(projection);
    mask2image(fmask, projection/max(projection(:)))
    axis equal
    F = getframe(gcf);
    [X, Map] = frame2im(F);
    cd(outputdir)
    imwrite(X, strcat(vname, '_consensusmasks.jpg'));
    masks = fmask;
    save(strcat(vname, '_consensusmasks.mat'), 'masks');
end