sustaining_gazes/matlab_runners/Head Pose Experiments/calcBiwiError.m

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Matlab

function [meanError, all_rot_preds, all_rot_gts, meanErrors, all_errors, rels_all, seq_ids] = calcBiwiError(resDir, gtDir)
seqNames = {'01','02','03','04','05','06','07','08','09', ...
'10', '11','12','13','14','15','16','17','18','19', ...
'20', '21','22','23','24'};
rotMeanErr = zeros(numel(seqNames),3);
rotRMS = zeros(numel(seqNames),3);
rot = cell(1,numel(seqNames));
rotg = cell(1,numel(seqNames));
rels_all = [];
seq_ids = {};
for i=1:numel(seqNames)
posesGround = load ([gtDir '/' seqNames{i} '/groundTruthPose.txt']);
fname = [resDir seqNames{i} '.csv'];
if(i == 1)
% First read in the column names
tab = readtable(fname);
column_names = tab.Properties.VariableNames;
confidence_id = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'confidence'));
rot_ids = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'pose_R'));
t_ids = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'pose_T'));
end
all_params = dlmread(fname, ',', 1, 0);
T = all_params(:,t_ids);
tx = T(:,1);
ty = T(:,2);
tz = T(:,3);
rot{i} = all_params(:, rot_ids);
rels = all_params(:, confidence_id);
% the reliabilities of head pose
rels_all = cat(1, rels_all, rels);
rotg{i} = posesGround(:,[5 6 7]);
% Correct the first frame so it corresponds to (0,0,0), as slightly
% different pose might be assumed frontal and this corrects for
% that
% Work out the correction matrix for ground truth
rot_corr_gt = Euler2Rot(rotg{i}(1,:));
for r_e = 1:size(rotg{i},1)
rot_curr_gt = Euler2Rot(rotg{i}(r_e,:));
rot_new_gt = rot_corr_gt' * rot_curr_gt;
rotg{i}(r_e,:) = Rot2Euler(rot_new_gt);
end
% First move the orientation to camera space
zx = sqrt(tx.^2 + tz.^2);
eul_x = atan2(ty, zx);
zy = sqrt(ty.^2 + tz.^2);
eul_y = -atan2(tx, zy);
for r_e = 1:size(rot{i},1)
cam_rot = Euler2Rot([eul_x(r_e), eul_y(r_e), 0]);
h_rot = Euler2Rot(rot{i}(r_e,:));
c_rot = cam_rot * h_rot;
rot{i}(r_e,:) = Rot2Euler(c_rot);
end
% Work out the correction matrix for estimates
rot_corr_est = Euler2Rot(rot{i}(1,:));
for r_e = 1:size(rot{i},1)
rot_curr_est = Euler2Rot(rot{i}(r_e,:));
rot_new_est = rot_corr_est' * rot_curr_est;
rot{i}(r_e,:) = Rot2Euler(rot_new_est);
end
rotg{i} = rotg{i} * 180 / pi;
rot{i} = rot{i} * 180 / pi;
rotMeanErr(i,:) = mean(abs((rot{i}(:,:)-rotg{i}(:,:))));
rotRMS(i,:) = sqrt(mean(((rot{i}(:,:)-rotg{i}(:,:))).^2));
seq_ids = cat(1, seq_ids, repmat(seqNames(i), size(rot{i},1), 1));
end
%%
meanErrors = rotMeanErr;
allRot = cell2mat(rot');
allRotg = cell2mat(rotg');
meanError = mean(abs((allRot(:,:)-allRotg(:,:))));
all_errors = abs(allRot-allRotg);
rmsError = sqrt(mean(((allRot(:,:)-allRotg(:,:))).^2));
errorVariance = std(abs((allRot(:,:)-allRotg(:,:))));
all_rot_preds = allRot;
all_rot_gts = allRotg;