Files
OpenFace/matlab_version/models/cen/create_cen_experts_menpo.m
2017-05-08 13:09:29 -04:00

115 lines
4.5 KiB
Matlab

clear;
load('../general/ccnf_patches_0.25_general.mat', 'centers', 'visiIndex', 'normalisationOptions');
mirrorInds = [1,17;2,16;3,15;4,14;5,13;6,12;7,11;8,10;18,27;19,26;20,25;21,24;22,23;...
32,36;33,35;37,46;38,45;39,44;40,43;41,48;42,47;49,55;50,54;51,53;60,56;59,57;...
61,65;62,64;68,66];
% For mirroring
frontalView = 1;
profileViewInds = [2,3,4];
% Grab all related experts and mirror them appropriatelly, just need to
% mirror the first layer
non_mirrored = mirrorInds(:,1);
normalisationOptions = rmfield(normalisationOptions, 'ccnf_ratio');
normalisationOptions.dccnf = true;
n_landmarks = size(visiIndex, 2);
n_views = size(visiIndex, 1);
patch_experts.correlations = zeros(n_views, n_landmarks);
patch_experts.rms_errors = zeros(n_views, n_landmarks);
patch_experts.types = {'reg'};
patch_experts.patch_experts = cell(n_views, n_landmarks);
scales = {'0.25', '0.35', '0.50', '1.00'};
visiIndex = zeros(7, 68);
root = 'D:/deep_experts/menpo/rmses/';
for s=scales
for c=1:n_views
if(c == frontalView || sum(profileViewInds==c)> 0)
for i=1:n_landmarks
mirror = false;
% Find the relevant file
if(c == frontalView)
rel_file = sprintf([root, '/%s_frontal_%d_512.mat'], s{1}, i);
else
rel_file = sprintf([root, '/%s_profile%d_%d_512.mat'], s{1}, c-1, i);
end
if(exist(rel_file, 'file'))
visiIndex(c,i) = 1;
load(rel_file);
else
rel_id = mirrorInds(mirrorInds(:,2)==i,1);
if(isempty(rel_id))
rel_id = mirrorInds(mirrorInds(:,1)==i,2);
end
if(c == frontalView)
rel_file = sprintf([root, '/%s_frontal_%d_512.mat'], s{1}, rel_id);
mirror = true;
visiIndex(c,i) = 1;
load(rel_file);
end
end
patch_experts.correlations(c, i) = correlation_2;
patch_experts.rms_errors(c, i) = rmse;
if(~mirror)
patch_experts.patch_experts{c, i} = weights;
else
flips = fliplr(reshape([1:121]', 11, 11));
weights_flipped = weights;
weights_flipped{1}(2:end,:) = weights{1}(flips+1,:);
patch_experts.patch_experts{c,i} = weights_flipped;
end
end
else
swap_id = find(centers(:,2) == -centers(c,2));
corr_T = patch_experts.correlations(swap_id,:);
% Appending a mirror view instead, based on the profile view
corr_T = swap(corr_T, mirrorInds(:,1), mirrorInds(:,2));
patch_experts.correlations(c,:) = corr_T;
vis_T = visiIndex(swap_id,:);
% Appending a mirror view instead, based on the profile view
vis_T = swap(vis_T, mirrorInds(:,1), mirrorInds(:,2));
visiIndex(c,:) = vis_T;
rmsT = patch_experts.rms_errors(swap_id,:);
rmsT = swap(rmsT, mirrorInds(:,1), mirrorInds(:,2));
patch_experts.rms_errors(c,:) = rmsT;
patchExpertMirror = patch_experts.patch_experts(swap_id,:);
patchExpertMirrorT1 = patchExpertMirror(1,mirrorInds(:,1),:);
patchExpertMirrorT2 = patchExpertMirror(1,mirrorInds(:,2),:);
patchExpertMirror(1,mirrorInds(:,2),:) = patchExpertMirrorT1;
patchExpertMirror(1,mirrorInds(:,1),:) = patchExpertMirrorT2;
% To flip a patch expert it
for p=1:size(patchExpertMirror,2)
if(visiIndex(c, p))
weights = patchExpertMirror{p};
flips = fliplr(reshape([1:121]', 11, 11));
weights_flipped = weights;
weights_flipped{1}(2:end,:) = weights{1}(flips+1,:);
patch_experts.patch_experts{c,p} = weights_flipped;
end
end
end
end
trainingScale = str2num(s{1});
save(['cen_patches_', s{1} '_menpo.mat'], 'trainingScale', 'centers', 'visiIndex', 'patch_experts', 'normalisationOptions');
write_patch_expert_bin(['cen_patches_', s{1} '_menpo.dat'], trainingScale, centers, visiIndex, patch_experts);
end