Ex5 Done

ex4/GaussLikelihood.m

@@ -5,4 +5,5 @@
     detSigma = det(Sigma);
     for i = 1:size(xs,1)
         lvals(i) = exp(-0.5*(xs2(i,:)-mu)/Sigma*transpose(xs2(i,:)-mu))/((2*pi)^(size(xs2,2)/2)*sqrt(abs(detSigma)));
-    end
+    end
+end

ex4/GrabCenterPixels.m

@@ -1,4 +1,5 @@
 function cim = GrabCenterPixels(im_fname, p)
     im = imread(im_fname);
     [H, W, dummy] = size(im);
-    cim = im(floor(H*(1-p)/2)+1:floor(H*(1+p)/2), floor(W*(1-p)/2)+1:floor(W*(1+p)/2), :);
+    cim = im(floor(H*(1-p)/2)+1:floor(H*(1+p)/2), floor(W*(1-p)/2)+1:floor(W*(1+p)/2), :);
+end

ex4/SkinClassifier.m

@@ -1,4 +1,5 @@
 function bim = SkinClassifier(im, hsv)
+    % Skin Classifier returns the binary im of classified pixels
     % hsv = 0 => rgb
     % hsv = 1 => hsv
 
@@ -8,6 +9,7 @@
         xs = rgb2hsv(xs);
         xs = [cos(xs(:,1)), sin(xs(:,1)), xs(:,2:3)];
     end
+
     lvals = GaussLikelihood(xs, mu, sig);
     prob = reshape(lvals, size(im,1), size(im,2), 1);
 
@@ -17,4 +19,6 @@
 
     ratio = prob./prob_back;
     bim = zeros(size(ratio));
-    bim(find(ratio > 1)) = 1;
+    bim(find(ratio > 1)) = 1;
+
+end

ex4/TrainColourModel.m

@@ -1,23 +1,26 @@
 function [mu, Sigma] = TrainColourModel(DirName, p, n, m)
+    % Train the model bt reading the pictures located in DirName
+    % Compute mu and sigma as the trained parameters
     % m = 0: RGB
     % m = 1: HSV
     ls = dir(strcat(DirName,'*.jpg'));
 
     datas = [];
     for i = 1:n
         if i < size(ls,1)
-            cim = GrabCenterPixels(ls(i).name, p);
+            cim = GrabCenterPixels(strcat(DirName, ls(i).name), p);
             rgb_data = reshape(cim, [size(cim,1)*size(cim,2), 3]);
             datas = [datas; rgb_data];
             size(datas);
         end
     end
 
+    % Use HSV
     if m > 0
-        % Use HSV colour model
     	datas = rgb2hsv(datas);
         datas = [cos(datas(:,1)), sin(datas(:,1)), datas(:,2:3)];
     end
 
     mu = sum(datas, 1)/size(datas,1);
-    Sigma = cov(double(datas));
+    Sigma = cov(double(datas));
+end

ex4/arthur/GaussLikelihood.m

@@ -0,0 +1,9 @@
+function lvals = GaussLikelihood(xs, mu, Sigma)
+    xs2 = double(xs);
+    lvals =  zeros(size(xs,1),1);
+    iSigma = inv(Sigma);
+    detSigma = det(Sigma);
+    for i = 1:size(xs,1)
+        lvals(i) = exp(-0.5*(xs2(i,:)-mu)/Sigma*transpose(xs2(i,:)-mu))/((2*pi)^(size(xs2,2)/2)*sqrt(abs(detSigma)));
+    end
+end

ex4/arthur/GrabCenterPixels.m

@@ -0,0 +1,5 @@
+function cim = GrabCenterPixels(im_fname, p)
+    im = imread(im_fname);
+    [H, W, dummy] = size(im);
+    cim = im(floor(H*(1-p)/2)+1:floor(H*(1+p)/2), floor(W*(1-p)/2)+1:floor(W*(1+p)/2), :);
+end

ex4/arthur/SkinClassifier.m

@@ -0,0 +1,24 @@
+function bim = SkinClassifier(im, hsv)
+    % Skin Classifier returns the binary im of classified pixels
+    % hsv = 0 => rgb
+    % hsv = 1 => hsv
+
+    [mu sig] = TrainColourModel('lfw/George_W_Bush/', 0.2, 20, hsv);
+    xs = reshape(im, size(im,1)*size(im,2), size(im,3));
+    if hsv > 0
+        xs = rgb2hsv(xs);
+        xs = [cos(xs(:,1)), sin(xs(:,1)), xs(:,2:3)];
+    end
+
+    lvals = GaussLikelihood(xs, mu, sig);
+    prob = reshape(lvals, size(im,1), size(im,2), 1);
+
+    [mu_back sig_back] = TrainColourModel('BackgroundImages/', 1, 22, hsv);
+    lvals = GaussLikelihood(xs, mu_back, sig_back);
+    prob_back = reshape(lvals, size(im,1), size(im,2), 1);
+
+    ratio = prob./prob_back;
+    bim = zeros(size(ratio));
+    bim(find(ratio > 1)) = 1;
+
+end

ex4/arthur/TrainColourModel.m

@@ -0,0 +1,26 @@
+function [mu, Sigma] = TrainColourModel(DirName, p, n, m)
+    % Train the model bt reading the pictures located in DirName
+    % Compute mu and sigma as the trained parameters
+    % m = 0: RGB
+    % m = 1: HSV
+    ls = dir(strcat(DirName,'*.jpg'));
+
+    datas = [];
+    for i = 1:n
+        if i < size(ls,1)
+            cim = GrabCenterPixels(strcat(DirName, ls(i).name), p);
+            rgb_data = reshape(cim, [size(cim,1)*size(cim,2), 3]);
+            datas = [datas; rgb_data];
+            size(datas);
+        end
+    end
+
+    % Use HSV
+    if m > 0
+    	datas = rgb2hsv(datas);
+        datas = [cos(datas(:,1)), sin(datas(:,1)), datas(:,2:3)];
+    end
+
+    mu = sum(datas, 1)/size(datas,1);
+    Sigma = cov(double(datas));
+end