44 lines
No EOL
1.2 KiB
Matlab
44 lines
No EOL
1.2 KiB
Matlab
function [ decision ] = face_check_nn( img, shape, global_params, nns )
|
|
%FACE_CHECK_NN Summary of this function goes here
|
|
% Detailed explanation goes here
|
|
|
|
if(size(img,3) == 3)
|
|
img = rgb2gray(img);
|
|
end
|
|
% first need to determine the view
|
|
centres = cat(1, nns.centres);
|
|
|
|
dists = centres*pi/180 - repmat(global_params(2:4)',size(centres,1),1);
|
|
[~,view_id] = min(sum(dists.^2,2));
|
|
|
|
img_crop = Crop(img, shape, nns(view_id).triangulation,...
|
|
nns(view_id).triX, nns(view_id).mask,...
|
|
nns(view_id).alphas, nns(view_id).betas,...
|
|
nns(view_id).nPix, nns(view_id).minX, ...
|
|
nns(view_id).minY);
|
|
|
|
%%
|
|
img_crop = reshape(img_crop(logical(nns(view_id).mask)), 1, nns(view_id).nPix);
|
|
img_crop(isnan(img_crop)) = 0;
|
|
|
|
%%
|
|
% normalisation (local)
|
|
img_crop = (img_crop - mean(img_crop));
|
|
norms = std(img_crop);
|
|
if(norms==0)
|
|
norms = 1;
|
|
end
|
|
img_crop = img_crop / norms;
|
|
|
|
% normalisation (global)
|
|
img_crop = img_crop - nns(view_id).mean_ex;
|
|
img_crop = img_crop ./ nns(view_id).std_ex;
|
|
nn = nns(view_id).nn;
|
|
% [er, bad] = nntest(nn, test_x, test_y);
|
|
nn = nnff(nn, img_crop, zeros(size(img_crop,1), nn.size(end)));
|
|
|
|
decision = nn.a{end};
|
|
|
|
%%
|
|
% normalise decision from ~ 0, 1 to [0,3]
|
|
decision = decision * 3; |