sustaining_gazes/matlab_runners/Action Unit Experiments/run_AU_prediction_Bosphorus.m
2016-12-05 17:28:39 -05:00

195 lines
4.8 KiB
Matlab

% Perform static model prediction using images
clear
addpath('./helpers');
find_Bosphorus;
out_loc = './out_bosph/';
if(~exist(out_loc, 'dir'))
mkdir(out_loc);
end
%%
executable = '"../../x64/Release/FaceLandmarkImg.exe"';
bosph_dirs = dir([Bosphorus_dir, '/BosphorusDB/BosphorusDB/bs*']);
%%
parfor f1=1:numel(bosph_dirs)
command = executable;
input_dir = [Bosphorus_dir, '/BosphorusDB/BosphorusDB/', bosph_dirs(f1).name];
command = cat(2, command, [' -fdir "' input_dir '" -ofdir "' out_loc '"']);
command = cat(2, command, ' -multi_view 1 -wild -q');
dos(command);
end
%%
aus_Bosph = [1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 20, 23, 25, 26, 45];
[ labels_gt, valid_ids, filenames] = extract_Bosphorus_labels(Bosphorus_dir, all_recs, aus_Bosph);
%% Read the predicted values
% First read the first file to get the ids and line numbers
% au occurences
fid = fopen([out_loc, filenames{1}, '_det_0.pts']);
data = fgetl(fid);
ind = 0;
beg_ind = -1;
end_ind = -1;
aus_det = [];
aus_det_id = [];
while ischar(data)
if(~isempty(findstr(data, 'au occurences:')))
num_occurences = str2num(data(numel('au occurences:')+1:end));
% Skip ahead two lines
data = fgetl(fid);
data = fgetl(fid);
ind = ind + 2;
beg_ind = ind;
end
if(beg_ind ~= -1 && end_ind == -1)
if(~isempty(findstr(data, '}')))
end_ind = ind;
else
d = strsplit(data, ' ');
aus_det = cat(1, aus_det, str2num(d{1}(3:end)));
aus_det_id = cat(1, aus_det_id, ind - beg_ind + 1);
end
end
data = fgetl(fid);
ind = ind + 1;
end
fclose(fid);
%%
labels_pred = zeros(size(labels_gt));
for i=1:numel(filenames)
% Will need to read the relevant AUs only
if(exist([out_loc, filenames{i}, '_det_0.pts'], 'file'))
fid = fopen([out_loc, filenames{i}, '_det_0.pts']);
for k=1:beg_ind
data = fgetl(fid);
end
for k=1:num_occurences
data = fgetl(fid);
if(sum(aus_Bosph == aus_det(k))>0)
d = strsplit(data, ' ');
labels_pred(i, aus_Bosph == aus_det(k)) = str2num(d{2});
end
end
fclose(fid);
end
end
%%
f = fopen('results/Bosphorus_res_class.txt', 'w');
labels_gt_bin = labels_gt;
labels_gt_bin(labels_gt_bin > 1) = 1;
f1s_class = zeros(1, numel(aus_Bosph));
for au = 1:numel(aus_Bosph)
tp = sum(labels_gt_bin(:,au) == 1 & labels_pred(:, au) == 1);
fp = sum(labels_gt_bin(:,au) == 0 & labels_pred(:, au) == 1);
fn = sum(labels_gt_bin(:,au) == 1 & labels_pred(:, au) == 0);
tn = sum(labels_gt_bin(:,au) == 0 & labels_pred(:, au) == 0);
precision = tp./(tp+fp);
recall = tp./(tp+fn);
f1 = 2 * precision .* recall ./ (precision + recall);
f1s_class(au) = f1;
fprintf(f, 'AU%d class, Precision - %.3f, Recall - %.3f, F1 - %.3f\n', aus_Bosph(au), precision, recall, f1);
end
fclose(f);
%% Read the predicted values for intensities
% First read the first file to get the ids and line numbers
% au occurences
fid = fopen([out_loc, filenames{1}, '_det_0.pts']);
data = fgetl(fid);
ind = 0;
beg_ind = -1;
end_ind = -1;
aus_det = [];
aus_det_id = [];
while ischar(data)
if(~isempty(findstr(data, 'au intensities:')))
num_occurences = str2num(data(numel('au intensities:')+1:end));
% Skip ahead two lines
data = fgetl(fid);
data = fgetl(fid);
ind = ind + 2;
beg_ind = ind;
end
if(beg_ind ~= -1 && end_ind == -1)
if(~isempty(findstr(data, '}')))
end_ind = ind;
else
d = strsplit(data, ' ');
aus_det = cat(1, aus_det, str2num(d{1}(3:end)));
aus_det_id = cat(1, aus_det_id, ind - beg_ind + 1);
end
end
data = fgetl(fid);
ind = ind + 1;
end
fclose(fid);
%%
labels_pred = zeros(size(labels_gt));
for i=1:numel(filenames)
% Will need to read the relevant AUs only
if(exist([out_loc, filenames{i}, '_det_0.pts'], 'file'))
fid = fopen([out_loc, filenames{i}, '_det_0.pts']);
for k=1:beg_ind
data = fgetl(fid);
end
for k=1:num_occurences
data = fgetl(fid);
if(sum(aus_Bosph == aus_det(k))>0)
d = strsplit(data, ' ');
labels_pred(i, aus_Bosph == aus_det(k)) = str2num(d{2});
end
end
fclose(fid);
end
end
%%
f = fopen('results/Bosphorus_res_int.txt', 'w');
cccs_reg = zeros(1, numel(aus_Bosph));
for au = 1:numel(aus_Bosph)
[ ~, ~, corrs, ccc, rms, ~ ] = evaluate_regression_results( labels_pred(:, au), labels_gt(:, au));
cccs_reg(au) = ccc;
fprintf(f, 'AU%d intensity, Corr - %.3f, RMS - %.3f, CCC - %.3f\n', aus_Bosph(au), corrs, rms, ccc);
end
fclose(f);