function [skin_img, backg_img, clothes_img, outlier_img] = color_segment(img_file_name)
% segment an image into skin, background, and outlier
% Alex Gruenstein, cs223b, Assignment #3
load 'training_data.mat' meanSkin covSkin meanBackg covBackg meanClothes covClothes; %load means and covarss from file

im1 = imread(img_file_name);
im = double(im1);
[rows, cols, depth] = size(im1)

hsv2Data = image_to_hsv2data(im);

P1 = gaussdensity(hsv2Data, meanSkin, covSkin)';
P2 = gaussdensity(hsv2Data, meanBackg, covBackg)';
P4 = gaussdensity(hsv2Data, meanClothes, covClothes)';
P3 = ones(1, rows*cols)/30;


%normalize
S = P1 + P2 + P3 + P4;
P1 = P1 ./ S;
P2 = P2 ./ S;
P3 = P3 ./ S;
P4 = P4 ./ S;

%find max
[C, I] = max([P1; P2; P3; P4]);

Ret = zeros(4,rows*cols);

for i = 1:rows*cols
    Ret(I(i),i) = 1;
end


skin_img = reshape(Ret(1,:), rows, cols);
backg_img = reshape(Ret(2,:), rows, cols);
outlier_img = reshape(Ret(3,:), rows, cols);
clothes_img = reshape(Ret(4,:), rows, cols);

figure(1);
imagesc(im1);
axis image;
figure(2);
imagesc(skin_img);
axis image;
colormap(gray);
figure(3);
imagesc(backg_img);
axis image;
colormap(gray);
figure(4);
imagesc(clothes_img);
axis image;
colormap(gray);
figure(5);
imagesc(outlier_img);
axis image;
colormap(gray);