sustaining_gazes/matlab_version/face_validation/Create_data_66.m

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2016-04-28 21:40:36 +02:00
function Create_data_66()
load '../models/pdm/pdm_66_multi_pie';
load '../models/tri_66.mat';
% This script uses the same format used for patch expert training, and
% expects the data to be there
dataset_loc = '../../../CCNF experiments/clnf/patch training/data_preparation/prepared_data/';
addpath('../PDM_helpers/');
scale = '0.5';
prefix= 'combined_';
% Find the available positive training data
data_files = dir(sprintf('%s/%s%s*.mat', dataset_loc, prefix, scale));
centres_all = [];
for i=1:numel(data_files)
% Load the orientation of the training data
load([dataset_loc, '/', data_files(i).name], 'centres');
centres_all = cat(1, centres_all, centres);
end
label_inds = [1:60,62:64,66:68];
% Construct mirror indices (which views need to be flipped to create other
% profile training data)
mirror_inds = zeros(size(centres_all,1), 1);
for i=1:numel(data_files)
% mirrored image has inverse yaw
mirrored_centre = centres_all(i,:);
mirrored_centre(2) = -mirrored_centre(2);
% if mirrored version has same orientation, do not need mirroring
if(~isequal(mirrored_centre, centres_all(i,:)))
centres_all = cat(1, centres_all, mirrored_centre);
mirror_inds = cat(1, mirror_inds, i);
end
end
outputLocation = './prep_data/';
num_more_neg = 10;
% Make sure same data generated all the time
rng(0);
neg_image_loc = './neg/';
neg_images = cat(1,dir([neg_image_loc, '/*.jpg']),dir([neg_image_loc, '/*.png']));
max_img_used = 1500;
%% do it separately for centers due to memory limitations
for r=1:size(centres_all,1)
a_mod = 0.3;
mirror = false;
if(mirror_inds(r) ~= 0 )
mirror = true;
label_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;...
61,63;66,64];
load([dataset_loc, '/', data_files(mirror_inds(r)).name]);
else
load([dataset_loc, '/', data_files(r).name]);
end
% Convert to 66 point model
landmark_locations = landmark_locations(:,label_inds,:);
visiCurrent = logical(visiIndex);
% Flip the orientation and indices for mirror data
if(mirror)
centres = [centres(1), -centres(2), -centres(3)];
tmp1 = visiCurrent(label_mirror_inds(:,1));
tmp2 = visiCurrent(label_mirror_inds(:,2));
visiCurrent(label_mirror_inds(:,2)) = tmp1;
visiCurrent(label_mirror_inds(:,1)) = tmp2;
end
visibleVerts = 1:numel(visiCurrent);
visibleVerts = visibleVerts(visiCurrent)-1;
% Correct the triangulation to take into account the vertex
% visibilities
triangulation = [];
shape = a_mod * Euler2Rot(centres * pi/180) * reshape(M, numel(M)/3, 3)';
shape = shape';
for i=1:size(T,1)
visib = 0;
for j=1:numel(visibleVerts)
if(T(i,1)==visibleVerts(j))
visib = visib+1;
end
if(T(i,2)==visibleVerts(j))
visib = visib+1;
end
if(T(i,3)==visibleVerts(j))
visib = visib+1;
end
end
% Only if all three of the vertices are visible
if(visib == 3)
% Also want to remove triangles facing the wrong way (self occluded)
v1 = [shape(T(i,1)+1,1), shape(T(i,1)+1,2), shape(T(i,1)+1,3)];
v2 = [shape(T(i,2)+1,1), shape(T(i,2)+1,2), shape(T(i,2)+1,3)];
v3 = [shape(T(i,3)+1,1), shape(T(i,3)+1,2), shape(T(i,3)+1,3)];
normal = cross((v2-v1), v3 - v2);
normal = normal / norm(normal);
direction = normal * [0,0,1]';
% And only if the triangle is facing the camera
if(direction > 0)
triangulation = cat(1, triangulation, T(i,:));
end
end
end
% Initialise the warp
[ alphas, betas, triX, mask, minX, minY, nPix ] = InitialisePieceWiseAffine(triangulation, shape);
imgs_to_use = randperm(size(landmark_locations, 1));
if(size(landmark_locations, 1) > max_img_used)
imgs_to_use = imgs_to_use(1:max_img_used);
end
% Extracting relevant filenames
examples = zeros(numel(imgs_to_use) * (num_more_neg+1), nPix);
errors = zeros(numel(imgs_to_use) * (num_more_neg+1), 1);
unused_pos = 0;
curr_filled = 0;
for j=imgs_to_use
labels = squeeze(landmark_locations(j,:,:));
img = squeeze(all_images(j,:,:));
if(mirror)
img = fliplr(img);
imgSize = size(img);
flippedLbls = labels;
flippedLbls(:,1) = imgSize(1) - flippedLbls(:,1);
tmp1 = flippedLbls(label_mirror_inds(:,1),:);
tmp2 = flippedLbls(label_mirror_inds(:,2),:);
flippedLbls(label_mirror_inds(:,2),:) = tmp1;
flippedLbls(label_mirror_inds(:,1),:) = tmp2;
labels = flippedLbls;
end
% If for some reason some of the labels are not visible in the
% current sample skip this label
non_existent_labels = labels(:,1)==0 | labels(:,2)==0;
non_existent_inds = find(non_existent_labels)-1;
if(numel(intersect(triangulation(:), non_existent_inds)) > 0)
unused_pos = unused_pos + 1;
continue;
end
curr_filled = curr_filled + 1;
[features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
examples(curr_filled,:) = features;
% Extract the correct PDM parameters for the model (we will perturb
% them for some negative examples)
[ a_orig, R_orig, trans_orig, ~, params_orig] = fit_PDM_ortho_proj_to_2D(M, E, V, labels);
eul_orig = Rot2Euler(R_orig);
% a slightly perturbed example, too tight
% from 0.3 to 0.9
a_mod = a_orig * (0.6 + (randi(7) - 4)*0.1);
p_global = [a_mod; eul_orig'; trans_orig];
labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
% Compute the badness of fit
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% a slightly perturbed example, too broad
% from 1.2 to 0.6
a_mod = a_orig * (1.4 + (randi(5) - 3)*0.1);
p_global = [a_mod; eul_orig'; trans_orig];
labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% A somewhat offset example
trans_mod = trans_orig + randn(2,1) * 10;
p_global = [a_orig; eul_orig'; trans_mod];
labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% A rotated sample
eul_mod = eul_orig + randn(1,3)*0.2;
p_global = [a_orig; eul_mod'; trans_orig];
labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% A sample with modified shape parameters
p_global = [a_orig; eul_orig'; trans_orig];
params_mod = params_orig + randn(size(params_orig)).*sqrt(E);
labels_mod = GetShapeOrtho(M, V, params_mod, p_global);
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors(curr_filled,:) = error;
% pick a random image from negative inriaperson dataset, use original location if
% first, otherwhise resize it to fit
for n=6:num_more_neg
n_img = randi(numel(neg_images));
neg_image = imread([neg_image_loc, neg_images(n_img).name]);
if(size(neg_image,3) == 3)
neg_image = rgb2gray(neg_image);
end
[h_neg, w_neg] = size(neg_image);
% if the current labels fit just use them, if not, then resize
% to fit
max_x = max(labels(:,1));
max_y = max(labels(:,2));
if(max_x > w_neg || max_y > h_neg)
neg_image = imresize(neg_image, [max_y, max_x]);
end
[features] = ExtractFaceFeatures(neg_image, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
% Set high error to 3
errors(curr_filled,:) = 3;
end
if(mod(curr_filled, 10) == 0)
fprintf('%d/%d done\n', curr_filled/(num_more_neg+1), numel(imgs_to_use));
end
% add the pos example to the background
end
examples = examples(1:curr_filled,:);
errors = errors(1:curr_filled);
% svm training
filename = sprintf('%s/face_checker_general_training_66_%d.mat', outputLocation, r);
save(filename, 'examples', 'errors', 'alphas', 'betas', 'triangulation', 'minX', 'minY', 'nPix', 'shape', 'triX', 'mask', 'centres');
end
end
function [features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY)
% Make sure labels are within range
[hRes, wRes] = size(img);
labels(labels(:,1) < 1,1) = 1;
labels(labels(:,2) < 1,2) = 1;
labels(labels(:,1) > wRes-1,1) = wRes-1;
labels(labels(:,2) > hRes-1,2) = hRes-1;
crop_img = Crop(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
crop_img(isnan(crop_img)) = 0;
% vectorised version
features = reshape(crop_img(logical(mask)), 1, nPix);
% normalisations
features = (features - mean(features));
norms = std(features);
if(norms==0)
norms = 1;
end
features = features / norms;
end