open source pkg v1

pkg/OpenFace/matlab_version/fitting/PatchResponseCCNF.m

@@ -0,0 +1,77 @@
+function [ responses ] = PatchResponseCCNF(patches, patch_experts_class, visibilities, patchExperts, window_size)
+%PATCHRESPONSESVM Summary of this function goes here
+%   Detailed explanation goes here
+
+    normalisationOptions = patchExperts.normalisationOptionsCol;
+    patchSize = normalisationOptions.patchSize;
+                  
+    responses = cell(size(patches, 1), 1);
+    empty = zeros(window_size(1)-patchSize(1)+1, window_size(2)-patchSize(2)+1);
+    
+    for i = 1:numel(patches(:,1))
+        responses{i} = empty;
+        if visibilities(i)
+            
+            col_norm = normalisationOptions.useNormalisedCrossCorr == 1;
+
+            b = zeros(numel(empty), 1);
+
+            num_hl = size(patch_experts_class{i}.thetas,1);
+            
+            smallRegionVec = patches(i,:);
+            smallRegion = reshape(smallRegionVec, window_size(1), window_size(2));
+
+            for hls = 1:num_hl
+
+                w = patch_experts_class{i}.w{hls};
+                
+                % normalisation needed per each response
+                norm_w = patch_experts_class{i}.norm_w{hls};
+
+                response = -norm_w * SVMresponse(smallRegion, w, col_norm, patchSize) - patch_experts_class{i}.thetas(hls, 1);
+
+                % here we include the bias term as well, as it wasn't added
+                % during the response calculation
+                h1 = 1./(1 + exp(response(:)));
+                b = b + (2 * patch_experts_class{i}.alphas(hls) * h1);
+
+            end
+            
+            % Sigma will be dependent on the size of the patch find the
+            % needed precomputed Sigma
+            rel_sigma = 1;
+            if(numel(patch_experts_class{i}.Sigma) > 1)
+                for sig=1:numel(patch_experts_class{i}.Sigma)
+                    if(size(patch_experts_class{i}.Sigma{sig},2)==numel(b))
+                        rel_sigma = sig;
+                        break;
+                    end
+                end
+            end
+
+            response = patch_experts_class{i}.Sigma{rel_sigma} * b;      
+            
+            % make sure we have no negative responses
+            response = response - min(response);
+            
+            responses{i}(:) = response;
+            
+        end
+    end
+    
+end
+
+function response = SVMresponse(region, patchExpert, normalise_x_corr,patchSize)
+
+    if(normalise_x_corr)
+        
+        % the fast mex convolution
+        [response] = normxcorr2_mex(patchExpert, region);
+
+        response = response(patchSize(1):end-patchSize(1)+1,patchSize(2):end-patchSize(2)+1);       
+    else
+        % this assumes that the patch is already normed
+        template = rot90(patchExpert,2);
+        response = conv2(region, template, 'valid');  
+    end
+end