59 lines
1.6 KiB
Mathematica
59 lines
1.6 KiB
Mathematica
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function [ decision ] = face_check_cnn( img, shape, global_params, cnns )
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%FACE_CHECK_CNN Summary of this function goes here
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% Detailed explanation goes here
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%%
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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, cnns.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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mask_small = cnns(view_id).mask(1:size(cnns(view_id).triX,1), 1:size(cnns(view_id).triX,2));
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if(size(cnns(view_id).destination,1) == 66 && size(shape,1) == 68)
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label_inds = [1:60,62:64,66:68];
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shape = shape(label_inds,:);
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end
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img_crop = Crop(img, shape, cnns(view_id).triangulation,...
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cnns(view_id).triX, mask_small,...
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cnns(view_id).alphas, cnns(view_id).betas,...
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cnns(view_id).nPix, cnns(view_id).minX, ...
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cnns(view_id).minY);
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%%
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img_crop = reshape(img_crop(logical(cnns(view_id).mask)), 1, cnns(view_id).nPix);
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img_crop(isnan(img_crop)) = 0;
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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 - cnns(view_id).mean_ex;
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img_crop = img_crop ./ cnns(view_id).std_ex;
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mask = cnns(view_id).mask;
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% Normalisation to 0-1
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% img(mask) = img_crop / 255;
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img = zeros(size(mask));
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img(mask) = img_crop;
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% img = cat(3, img, img);
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% [er, bad] = nntest(nn, test_x, test_y);
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cnn = cnns(view_id).cnn;
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%%
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cnn = cnnff(cnn, img);
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decision = cnn.o(1);
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% normalise decision from ~ 0, 1 to [0,3]
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decision = decision * 3;
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