sustaining_gazes/matlab_version/demo/face_image_demo_eyes.m

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2016-04-28 19:40:36 +00:00
clear
addpath('../PDM_helpers/');
addpath(genpath('../fitting/'));
addpath('../models/');
addpath(genpath('../face_detection'));
addpath('../CCNF/');
%% loading the patch experts
[clmParams, pdm] = Load_CLM_params_wild();
[clmParams_eye, pdm_eye] = Load_CLM_params_eye();
% An accurate CCNF (or CLNF) model
% [patches] = Load_Patch_Experts( '../models/general/', 'ccnf_patches_*_general.mat', [], [], clmParams);
% A simpler (but less accurate SVR)
[patches] = Load_Patch_Experts( '../models/general/', 'svr_patches_*_general.mat', [], [], clmParams);
[patches_eye] = Load_Patch_Experts( 'C:\Users\Tadas\Dropbox\AAM\patch_experts_eyes\svr_training\trained/', 'svr_patches_*_synth.mat', [], [], clmParams);
clmParams.multi_modal_types = patches(1).multi_modal_types;
clmParams_eye.multi_modal_types = patches_eye(1).multi_modal_types;
%%
root_dir = 'C:\Users\Tadas\Dropbox\AAM\test data\gaze_original\p00/';
images = dir([root_dir, '*.jpg']);
verbose = true;
for img=1:numel(images)
image_orig = imread([root_dir images(img).name]);
% First attempt to use the Matlab one (fastest but not as accurate, if not present use yu et al.)
% [bboxs, det_shapes] = detect_faces(image_orig, {'cascade', 'yu'});
% Zhu and Ramanan and Yu et al. are slower, but also more accurate
% and can be used when vision toolbox is unavailable
% [bboxs, det_shapes] = detect_faces(image_orig, {'yu', 'zhu'});
% The complete set that tries all three detectors starting with fastest
% and moving onto slower ones if fastest can't detect anything
[bboxs, det_shapes] = detect_faces(image_orig, {'cascade', 'yu', 'zhu'});
if(size(image_orig,3) == 3)
image = rgb2gray(image_orig);
end
%%
if(verbose)
f = figure;
if(max(image(:)) > 1)
imshow(double(image_orig)/255, 'Border', 'tight');
else
imshow(double(image_orig), 'Border', 'tight');
end
axis equal;
hold on;
end
for i=1:size(bboxs,2)
% Convert from the initial detected shape to CLM model parameters,
% if shape is available
bbox = bboxs(:,i);
if(~isempty(det_shapes))
shape = det_shapes(:,:,i);
inds = [1:60,62:64,66:68];
M = pdm.M([inds, inds+68, inds+68*2]);
E = pdm.E;
V = pdm.V([inds, inds+68, inds+68*2],:);
[ a, R, T, ~, params, err, shapeOrtho] = fit_PDM_ortho_proj_to_2D(M, E, V, shape);
g_param = [a; Rot2Euler(R)'; T];
l_param = params;
% Use the initial global and local params for clm fitting in the image
[shape,~,~,lhood,lmark_lhood,view_used] = Fitting_from_bb(image, [], bbox, pdm, patches, clmParams, 'gparam', g_param, 'lparam', l_param);
else
[shape,~,~,lhood,lmark_lhood,view_used] = Fitting_from_bb(image, [], bbox, pdm, patches, clmParams);
end
% shape correction for matlab format
shape = shape + 1;
if(verbose)
% valid points to draw (not to draw self-occluded ones)
v_points = logical(patches(1).visibilities(view_used,:));
try
plot(shape(v_points,1), shape(v_points',2),'.r','MarkerSize',20);
plot(shape(v_points,1), shape(v_points',2),'.b','MarkerSize',10);
catch warn
end
end
% Map from detected landmarks to eye params
shape_r_eye = zeros(20,2);
shape_r_eye([9,11,13,15,17,19],:) = shape([43,44,45,46,47,48], :);
[ a, R, T, ~, params, err, shapeOrtho] = fit_PDM_ortho_proj_to_2D(pdm_eye.M, pdm_eye.E, pdm_eye.V, shape_r_eye);
g_param = [a; Rot2Euler(R)'; T];
l_param = params;
% Use the initial global and local params for clm fitting in the image
patches_eye(1).visibilities(1:8) = 0;
patches_eye(2).visibilities(1:8) = 0;
patches_eye(3).visibilities(1:8) = 0;
[shape_eye,~,~,lhood,lmark_lhood,view_used] = Fitting_from_bb(image, [], bbox, pdm_eye, patches_eye, clmParams_eye, 'gparam', g_param, 'lparam', l_param);
plot(shape_eye(:,1), shape_eye(:,2), '.g', 'MarkerSize',15);
% % Now do the eyes
% min_x = shape(43,1);
% max_x = shape(43,1);
% bbox_eye = shape(43,1)
end
hold off;
end