sustaining_gazes/matlab_runners/Feature Point Experiments/Run_CLM_fitting_on_images.m
Tadas Baltrusaitis 2128589309 A lot of changes:
- New AU recognition models trained on extra datasets - Bosphorus, UNBC, FERA2011
- Cleaner and clearer separation of static and dynamic AU models
- AU training code cleaned up and instructions added
- bug fixes with median feature computation
- AU prediction correction (smoothing and shifting) with post processing
2016-07-22 09:35:50 -04:00

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3.4 KiB
Matlab

function [ err_outline, err_no_outline ] = Run_CLM_fitting_on_images(output_loc, database_root, varargin)
%RUN_CLM_FITTING_ON_IMAGES Summary of this function goes here
% Detailed explanation goes here
dataset_dirs = {};
if(any(strcmp(varargin, 'use_afw')))
afw_loc = [database_root '/AFW/'];
dataset_dirs = cat(1, dataset_dirs, afw_loc);
end
if(any(strcmp(varargin, 'use_lfpw')))
lfpw_loc = [database_root 'lfpw/testset/'];
dataset_dirs = cat(1, dataset_dirs, lfpw_loc);
end
if(any(strcmp(varargin, 'use_ibug')))
ibug_loc = [database_root 'ibug/'];
dataset_dirs = cat(1, dataset_dirs, ibug_loc);
end
if(any(strcmp(varargin, 'use_helen')))
helen_loc = [database_root '/helen/testset/'];
dataset_dirs = cat(1, dataset_dirs, helen_loc);
end
if(any(strcmp(varargin, 'verbose')))
verbose = true;
else
verbose = false;
end
command = '"../../x64/Release/FaceLandmarkImg.exe" ';
if(any(strcmp(varargin, 'model')))
model = varargin{find(strcmp(varargin, 'model')) + 1};
else
% the default model is the 68 point in the wild one
model = '"model/main_wild.txt"';
end
if(any(strcmp(varargin, 'multi_view')))
multi_view = varargin{find(strcmp(varargin, 'multi_view')) + 1};
else
multi_view = 0;
end
command = cat(2, command, [' -mloc ' model ' ']);
command = cat(2, command, [' -multi_view ' num2str(multi_view) ' ']);
tic
parfor i=1:numel(dataset_dirs)
input_loc = ['-fdir "', dataset_dirs{i}, '" '];
command_c = cat(2, command, input_loc);
out_loc = ['-ofdir "', output_loc, '" '];
command_c = cat(2, command_c, out_loc);
if(verbose)
out_im_loc = ['-oidir "', output_loc, '" '];
command_c = cat(2, command_c, out_im_loc);
end
command_c = cat(2, command_c, ' -wild ');
dos(command_c);
end
toc
%%
% Extract the error sizes
dirs = {[database_root '/AFW/'];
[database_root '/ibug/'];
[database_root '/helen/testset/'];
[database_root 'lfpw/testset/'];};
landmark_dets = dir([output_loc '/*.pts']);
landmark_det_dir = [output_loc '/'];
num_imgs = size(landmark_dets,1);
labels = zeros(68,2,num_imgs);
shapes = zeros(68,2,num_imgs);
curr = 0;
for i=1:numel(dirs)
gt_labels = dir([dirs{i}, '*.pts']);
for g=1:numel(gt_labels)
curr = curr+1;
gt_landmarks = dlmread([dirs{i}, gt_labels(g).name], ' ', 'A4..B71');
% find the corresponding detection
landmark_det = dlmread([landmark_det_dir, gt_labels(g).name], ' ', 'A4..B71');
labels(:,:,curr) = gt_landmarks;
if(size(landmark_det,1) == 66)
inds_66 = [[1:60],[62:64],[66:68]];
shapes(inds_66,:,curr) = landmark_det;
else
shapes(:,:,curr) = landmark_det;
end
end
end
% Convert to correct format, so as to have same feature points in ground
% truth and detections
if(size(shapes,2) == 66 && size(labels,2) == 68)
inds_66 = [[1:60],[62:64],[66:68]];
labels = labels(inds_66,:,:);
shapes = shapes(inds_66,:,:);
end
% Center the pixel
labels = labels - 0.5;
err_outline = compute_error(labels, shapes);
labels_no_out = labels(18:end,:,:);
shapes_no_out = shapes(18:end,:,:);
err_no_outline = compute_error(labels_no_out, shapes_no_out);
%%
save([output_loc, 'res.mat'], 'labels', 'shapes', 'err_outline', 'err_no_outline');
end