sustaining_gazes/matlab_version/face_validation/Test_face_checker.m

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2016-04-28 19:40:36 +00:00
function [ predictions, gts, rmse, corr_coeff ] = Test_face_checker( face_check_fun )
addpath('../PDM_helpers');
% Read in the face images
[image_locs, ~, landmark_locations] = Collect_wild_imgs('C:/Users/Tadas/Dropbox/AAM/test data/');
load '../models/pdm/pdm_68_aligned_wild.mat';
load '../models/tri_68.mat';
% Make sure same data generated all the time
rng(0);
neg_image_loc = 'E:/datasets/detection_validation/neg/';
neg_images = cat(1,dir([neg_image_loc, '/*.jpg']),dir([neg_image_loc, '/*.png']));
curr_filled = 0;
num_imgs = numel(image_locs);
more_neg = 1;
errors_all = zeros(num_imgs + num_imgs*more_neg, 1);
predictions_all = zeros(num_imgs + num_imgs*more_neg, 1);
img_used = cell(size(errors_all));
labels_used = cell(size(errors_all));
for j=1:num_imgs
labels = squeeze(landmark_locations(j,:,:));
% Extract the correct PDM parameters for the model and evaluate how
% well the checker detects correct detections
curr_filled = curr_filled + 1;
img = imread(image_locs(j).img);
img_used(curr_filled) = {image_locs(j).img};
[ a_orig, R_orig, trans_orig, ~, params_orig, err, shape] = fit_PDM_ortho_proj_to_2D(M, E, V, labels);
eul_orig = Rot2Euler(R_orig);
p_global = [a_orig; eul_orig'; trans_orig];
labels_used(curr_filled) = {shape};
% Test the positive example
predictions_all(curr_filled) = face_check_fun(img, shape, p_global);
error = norm(shape(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors_all(curr_filled) = error;
curr_filled = curr_filled + 1;
img_used(curr_filled) = {image_locs(j).img};
if(mod(j, 6) == 0)
% 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];
elseif(mod(j,6) == 1)
% 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];
elseif(mod(j,6) == 2)
% A somewhat offset example
trans_mod = trans_orig + randn(2,1) * 10;
p_global = [a_orig; eul_orig'; trans_mod];
elseif(mod(j,6) == 3)
% A rotated sample
eul_mod = eul_orig + randn(1,3)*0.2;
p_global = [a_orig; eul_mod'; trans_orig];
elseif(mod(j,6) == 4)
% A sample with modified shape parameters
p_global = [a_orig; eul_orig'; trans_orig];
params_orig = params_orig + randn(size(params_orig)).*sqrt(E);
elseif(mod(j,6) == 5)
% pick a random image from negative inriaperson dataset, use original location if
% first, otherwhise resize it to fit
n_img = randi(numel(neg_images));
neg_image = imread([neg_image_loc, neg_images(n_img).name]);
img_used(curr_filled) = {[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
img = neg_image;
end
labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
labels_mod = labels_mod(:,1:2);
labels_used(curr_filled) = {labels_mod};
predictions_all(curr_filled) = face_check_fun(img, labels_mod, p_global);
% Compute the badness of fit
error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
errors_all(curr_filled,:) = error;
if(mod(j,6) == 5)
% Set high error to 3, if inriaperson used
errors_all(curr_filled,:) = 3;
end
if(mod(curr_filled, 10) == 0)
fprintf('%d/%d done\n', j, num_imgs);
end
end
predictions = predictions_all(1:curr_filled,:);
gts = errors_all(1:curr_filled);
gts(gts > 3) = 3;
rmse = sqrt(mean((predictions - gts).^2));
corr_coeff = corr(predictions, gts);
[~, inds] = sort((predictions - gts).^2, 'descend');
%% Visualise the worst errors
num_to_vis = 20;
samples_per_row = 4;
num_cols = ceil(num_to_vis / samples_per_row);
figure;
for i=1:num_to_vis
subplot(num_cols, samples_per_row, i);
curr_img = imread(img_used{inds(i)});
curr_labels = labels_used{inds(i)};
min_x = min(curr_labels(:,1));
min_y = min(curr_labels(:,2));
max_x = max(curr_labels(:,1));
max_y = max(curr_labels(:,2));
curr_img = imcrop(curr_img, [min_x, min_y, max_x - min_x, max_y - min_y]);
imshow(curr_img);
hold on;
plot(curr_labels(:,1) - min_x, curr_labels(:,2) - min_y, '.');
text(20, 20, 0, sprintf('Pred:%f, Actual:%f', predictions(inds(i)), gts(inds(i))));
end
end