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open source pkg v1

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This commit is contained in:
Vijay Yadev
2020-08-04 19:12:31 -04:00
parent bef213dba9
commit c389fc2c47
3708 changed files with 1624220 additions and 1 deletions
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84
pkg/OpenFace/matlab_version/fitting/PatchResponseCEN_mirror.m Normal file
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function [ responses ] = PatchResponseCEN_mirror(patches, patch_experts_class, visibilities, patchExperts, window_size)
% As frontal faces are roughly symmetrical can compute the responses for
% two patches at the same time using only one of the landmark patch experts
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);
% These landmark responses can be computed together
mirror_inds = [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 i = 1:numel(patches(:,1))
if visibilities(i)
% Do it only if not mirrored
if(isempty(find(mirror_inds(:,2)==i, 1)))
responses{i} = empty;
col_norm = normalisationOptions.useNormalisedCrossCorr == 1;
smallRegionVec = patches(i,:);
smallRegion = reshape(smallRegionVec, window_size(1), window_size(2));
patch = im2col_mine(smallRegion, patchSize)';
% Add the mirrored version as well (it will be applied the
% same way)
mirr_id = mirror_inds(find(mirror_inds(:,1)==i,1),2);
if(~isempty(mirr_id))
responses{mirr_id} = empty;
smallRegionVec_mirr = patches(mirr_id,:);
smallRegion_mirr = reshape(smallRegionVec_mirr, window_size(1), window_size(2));
patch_mirr = im2col_mine(fliplr(smallRegion_mirr), patchSize)';
patch = cat(1, patch, patch_mirr);
end
% Normalize
if(col_norm)
mean_curr = mean(patch, 2);
patch_normed = patch - repmat(mean_curr, 1, patchSize(1)* patchSize(2));
% Normalising the patches using the L2 norm
scaling = sqrt(sum(patch_normed.^2,2));
scaling(scaling == 0) = 1;
patch_normed = patch_normed ./ repmat(scaling, 1, 11 * 11);
patch = patch_normed;
end
patch = patch';
% Add bias
patch_normed = cat(1, ones(1, size(patch,2)), patch);
weights = patch_experts_class{i};
% Where DNN will happen
for w =1:numel(weights)/2
% mult and bias
patch_normed = weights{(w-1)*2+1}' * patch_normed + repmat(weights{(w-1)*2+2}', 1, size(patch_normed,2));
if w < 3
% patch_normed(patch_normed < 0) = 0;
patch_normed = max(0, patch_normed);
else
patch_normed = 1./(1+exp(-patch_normed));
end
end
% If no mirroring took place
if(isempty(mirr_id))
responses{i}(:) = reshape(patch_normed', window_size(1)-patchSize(1)+1, window_size(2)-patchSize(2)+1);
else
patch_normed_1 = patch_normed(1:end/2);
patch_normed_2 = patch_normed(end/2+1:end);
responses{i}(:) = reshape(patch_normed_1', window_size(1)-patchSize(1)+1, window_size(2)-patchSize(2)+1);
responses{mirr_id}(:) = fliplr(reshape(patch_normed_2', window_size(1)-patchSize(1)+1, window_size(2)-patchSize(2)+1));
end
end
end
end
end