156 lines
4.1 KiB
Matlab
156 lines
4.1 KiB
Matlab
clear;
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addpath('../PDM_helpers/');
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%% Create the PDM using PCA, from the recovered 3D data
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clear
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load('Torr_wild');
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% need to still perform procrustes though
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x = P3(1:end/3,:);
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y = P3(end/3+1:2*end/3,:);
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% To make sure that PDM faces the right way (positive Z towards the screen)
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z = -P3(2*end/3+1:end,:);
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%% Replacing and augmenting eye models with synth
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num_samples = size(x, 1);
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to_augment_ratio = 0.2;
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rng(0);
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% Take half of the samples and replace either their left or right eye with
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% a synthetic one from an eye PDM but with same alignment and scaling
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to_augment = randperm(num_samples);
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to_augment = to_augment(1:round(end * to_augment_ratio));
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left_eye_inds = [37,38,39,40,41,42];
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right_eye_inds = [43,44,45,46,47,48];
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load('pdm_l_eye.mat');
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M_l_eye = M;
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V_l_eye = V;
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E_l_eye = E;
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load('pdm_r_eye.mat');
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M_r_eye = M;
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V_r_eye = V;
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E_r_eye = E;
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num_points = numel(M) / 3;
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for i = to_augment
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shape_3D = cat(1, x(i,:), y(i,:), z(i,:))';
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eye_shape_l = shape_3D(left_eye_inds, :);
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eye_shape_r = shape_3D(right_eye_inds, :);
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[ R_l, T_l ] = AlignShapesWithScale ( reshape(M_l_eye, num_points, 3), eye_shape_l );
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[ R_r, T_r ] = AlignShapesWithScale ( reshape(M_r_eye, num_points, 3), eye_shape_r );
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params_left = randn(size(E)) .* sqrt(E);
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params_right = params_left;
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% Third of samples with left and right eye done independently
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if(mod(i,3) == 0)
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params_right = randn(size(E)) .* sqrt(E);
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end
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shape_l_unaligned = M_l_eye + V_l_eye * params_left;
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shape_r_unaligned = M_r_eye + V_r_eye * params_right;
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shape_l = (R_l * reshape(shape_l_unaligned, num_points, 3)' + repmat(T_l', 1, num_points))';
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shape_r = (R_r * reshape(shape_r_unaligned, num_points, 3)' + repmat(T_r', 1, num_points))';
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shape_3D(left_eye_inds, :) = shape_l;
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shape_3D(right_eye_inds, :) = shape_r;
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x(i,:) = shape_3D(:,1);
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y(i,:) = shape_3D(:,2);
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z(i,:) = shape_3D(:,3);
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end
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%%
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[ normX, normY, normZ, meanShape, Transform ] = ProcrustesAnalysis3D(x,y,z, true);
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observations = [normX normY normZ];
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[princComp, score, eigenVals] = princomp(observations,'econ');
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% Keeping most variation
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totalSum = sum(eigenVals);
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count = numel(eigenVals);
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for i=1:numel(eigenVals)
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if ((sum(eigenVals(1:i)) / totalSum) >= 0.999)
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count = i;
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break;
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end
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end
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V = princComp(:,1:count);
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E = eigenVals(1:count);
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M = meanShape(:);
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% Now normalise it to have actual world scale
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lPupil = [(M(37) + M(40))/2; (M(37 + 68) + M(40 + 68))/2];
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rPupil = [(M(43) + M(46))/2; (M(43 + 68) + M(46 + 68))/2];
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dist = norm(lPupil - rPupil,2);
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% average human interocular distance is 65mm
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scaling = 65 / dist;
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% normalise the mean values as well
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M(1:end/3) = M(1:end/3) - mean(M(1:end/3));
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M(end/3+1:2*end/3) = M(end/3+1:2*end/3) - mean(M(end/3+1:2*end/3));
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M(2*end/3+1:end) = M(2*end/3+1:end) - mean(M(2*end/3+1:end));
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M = M * scaling;
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E = E * scaling .^ 2;
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% orthonormalise V
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scalingV = sqrt(sum(V.^2));
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V = V ./ repmat(scalingV, numel(M),1);
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E = E .* (scalingV') .^ 2;
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%% Also align the model to a Multi-PIE one for accurate head orientation
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% also align the mean shape model (an aligned 68 point PDM from Multi-PIE dataset)
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M_wild = M;
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load('pdm_68_multi_pie.mat', 'M');
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M_align = M;
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n_points = numel(M)/3;
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M_wild = reshape(M_wild, n_points, 3);
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% work out the translation and rotation needed
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[ R, T ] = AlignShapesKabsch(M_wild, reshape(M_align, n_points,3));
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% Transform the wild one to be in the same reference as the Multi-PIE one
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M_aligned = (R * M_wild')';
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M_aligned(:,1) = M_aligned(:,1) + T(1);
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M_aligned(:,2) = M_aligned(:,2) + T(2);
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M_aligned(:,3) = M_aligned(:,3) + T(3);
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M_aligned = M_aligned(:);
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V_aligned = V;
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% need to align the principal components as well
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for i=1:size(V,2)
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V_aligned_pie_curr = (R * reshape(V(:,i), n_points, 3)')';
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V_aligned(:,i) = V_aligned_pie_curr(:);
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end
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V = V_aligned;
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M = M_aligned;
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save('pdm_68_aligned_wild_eyes.mat', 'E', 'M', 'V');
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writePDM(V, E, M, 'pdm_68_aligned_wild_eyes.txt');
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% also save this to model location
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if(exist('../../models/pdm/', 'file'))
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save('../../models/pdm/pdm_68_aligned_wild_eyes.mat', 'E', 'M', 'V');
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end
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