open source pkg v1

pkg/OpenFace/matlab_version/fitting/PatchResponseCEN_sparse.m

@@ -0,0 +1,70 @@
+function [ responses ] = PatchResponseCEN_sparse(rois, patch_experts, visibilities, window_size, support_size)
+%PatchResponseCEN_sparse computing response maps on the areas of interest
+                  
+    responses = cell(size(rois, 1), 1);
+    empty = zeros(window_size(1)-support_size(1)+1, window_size(2)-support_size(2)+1);
+
+    % Compute which indices to keep and which to remove
+    to_rem_ids = [1:2:numel(empty)];
+    to_keep_ids = setdiff(1:numel(empty), to_rem_ids);
+
+    % Prepare the interpolation back to dense response
+    [x_scattered, y_scattered] = ind2sub(size(empty), to_keep_ids);
+    F = scatteredInterpolant(cat(2,y_scattered', x_scattered'), zeros(size(x_scattered))', 'natural');
+    [yq, xq] = ind2sub(size(empty), 1:numel(empty));
+
+    % The actual response accross patches
+    for i = 1:numel(rois(:,1))
+        responses{i} = empty;
+        if visibilities(i)
+                 
+            smallRegionVec = rois(i,:);
+            smallRegion = reshape(smallRegionVec, window_size(1), window_size(2));
+
+            roi = im2col_mine(smallRegion, support_size)';
+            
+            % Normalize
+            mean_curr = mean(roi, 2);
+            roi_normed = roi - repmat(mean_curr, 1, support_size(1) * support_size(2));
+
+            % Normalising the patches using the L2 norm
+            scaling = sqrt(sum(roi_normed.^2,2));
+            scaling(scaling == 0) = 1;
+
+            roi_normed = roi_normed ./ repmat(scaling, 1, 11 * 11);
+
+            roi = roi_normed;
+
+            roi = roi';
+            
+            % Discard the support regions on which we will not evaluate
+            roi = roi(:,to_keep_ids);
+
+            % Add bias
+            roi_normed = cat(1, ones(1, size(roi,2)), roi);
+            output = roi_normed;
+            weights = patch_experts{i};
+
+            % Where forward pass of CEN happens
+            for w =1:numel(weights)/2
+                
+                % mult and bias
+                output = weights{(w-1)*2+1}' * output + repmat(weights{(w-1)*2+2}', 1, size(roi_normed,2));
+
+                if w < 3
+                   output = max(0, output);
+                else
+                   output = 1./(1+exp(-output));
+                end
+
+            end
+            
+            vals = double(output);
+            
+            % Now interpolate
+            F.Values = vals';            
+            responses{i}(:) = F(xq,yq);
+        end
+    end
+    
+end