44 lines
1.2 KiB
Matlab
44 lines
1.2 KiB
Matlab
function [ decision ] = face_check_nn( img, shape, global_params, nns )
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%FACE_CHECK_NN Summary of this function goes here
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% Detailed explanation goes here
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if(size(img,3) == 3)
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img = rgb2gray(img);
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end
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% first need to determine the view
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centres = cat(1, nns.centres);
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dists = centres*pi/180 - repmat(global_params(2:4)',size(centres,1),1);
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[~,view_id] = min(sum(dists.^2,2));
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img_crop = Crop(img, shape, nns(view_id).triangulation,...
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nns(view_id).triX, nns(view_id).mask,...
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nns(view_id).alphas, nns(view_id).betas,...
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nns(view_id).nPix, nns(view_id).minX, ...
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nns(view_id).minY);
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%%
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img_crop = reshape(img_crop(logical(nns(view_id).mask)), 1, nns(view_id).nPix);
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img_crop(isnan(img_crop)) = 0;
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%%
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% normalisation (local)
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img_crop = (img_crop - mean(img_crop));
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norms = std(img_crop);
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if(norms==0)
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norms = 1;
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end
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img_crop = img_crop / norms;
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% normalisation (global)
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img_crop = img_crop - nns(view_id).mean_ex;
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img_crop = img_crop ./ nns(view_id).std_ex;
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nn = nns(view_id).nn;
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% [er, bad] = nntest(nn, test_x, test_y);
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nn = nnff(nn, img_crop, zeros(size(img_crop,1), nn.size(end)));
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decision = nn.a{end};
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%%
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% normalise decision from ~ 0, 1 to [0,3]
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decision = decision * 3; |