sustaining_gazes/matlab_version/demo/face_video_demo.m
Tadas Baltrusaitis 305af01326 Auto stash before merge of "develop" and "origin/develop"
- Adding new license files
- Replacing images with more suitable CC ones
2017-05-08 21:36:23 -04:00

208 lines
No EOL
7.1 KiB
Matlab

clear
addpath('../PDM_helpers/');
addpath(genpath('../fitting/'));
addpath('../models/');
addpath(genpath('../face_detection'));
addpath('../CCNF/');
%%
vid_dir = '../../samples/';
vids = cat(1, dir([vid_dir, '*.avi']), dir([vid_dir, '*.wmv']));
%%
verbose = true;
record = true;
%% loading the patch experts
[clmParams, pdm] = Load_CLM_params_vid();
% 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/wild/', 'svr_patches_*_wild.mat', [], [], clmParams);
% A general SVR
% [patches] = Load_Patch_Experts( '../models/general/', 'svr_patches_*_general.mat', [], [], clmParams);
clmParams.multi_modal_types = patches(1).multi_modal_types;
% load the face validator and add its dependency
load('../face_validation/trained/face_check_cnn_68.mat', 'face_check_cnns');
addpath(genpath('../face_validation'));
%%
for v=1:numel(vids)
% load the video
vr = VideoReader([vid_dir, vids(v).name]);
[~,fname,~] = fileparts(vids(v).name);
if(record)
if(~exist('./tracked_vids', 'file'))
mkdir('tracked_vids');
end
writerObj = VideoWriter(sprintf('./tracked_vids/%s.avi', fname));
open(writerObj);
end
det = false;
initialised = false;
nFrames = vr.NumberOfFrames;
% Read one frame at a time.
all_local_params = zeros(nFrames, numel(pdm.E));
all_global_params = zeros(nFrames,6);
for i = 1 : nFrames
% if this version throws a "Dot name reference on non-scalar structure"
% error change obj.NumberOfFrames to obj(1).NumberOfFrames (in two
% places in read function) or surround it with an empty try catch
% statement
image_orig = read(vr, i);
if((~det && mod(i,4) == 0) || ~initialised)
% 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'});
if(~isempty(bboxs))
% Pick the biggest face for tracking
[~,ind] = max(bboxs(3,:) - bboxs(1,:));
bbox = bboxs(:,ind);
% Discard overly small detections
if(bbox(3) - bbox(1) > 40)
% Either infer the local and global shape parameters
% from the detected landmarks or just using the
% bounding box
if(~isempty(det_shapes))
shape = det_shapes(:,:,ind);
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] = fit_PDM_ortho_proj_to_2D(M, E, V, shape);
g_param_n = [a; Rot2Euler(R)'; T];
l_param_n = params;
else
num_points = numel(pdm.M) / 3;
M = reshape(pdm.M, num_points, 3);
width_model = max(M(:,1)) - min(M(:,1));
height_model = max(M(:,2)) - min(M(:,2));
a = (((bbox(3) - bbox(1)) / width_model) + ((bbox(4) - bbox(2))/ height_model)) / 2;
tx = (bbox(3) + bbox(1))/2;
ty = (bbox(4) + bbox(2))/2;
% correct it so that the bounding box is just around the minimum
% and maximum point in the initialised face
tx = tx - a*(min(M(:,1)) + max(M(:,1)))/2;
ty = ty + a*(min(M(:,2)) + max(M(:,2)))/2;
% visualisation
g_param_n = [a, 0, 0, 0, tx, ty]';
l_param_n = zeros(size(pdm.E));
end
% If tracking has not started trust the detection
if(~initialised)
g_param = g_param_n;
l_param = l_param_n;
det = true;
initialised = true;
else
% If tracking has already started double check the
% detection
shape_new = GetShapeOrtho(pdm.M, pdm.V, params, g_param_n);
dec = face_check_cnn(image, shape_new, g_param, face_check_cnns);
if(dec < 0.5)
det = true;
g_param = g_param_n;
l_param = l_param_n;
else
det = false;
end
end
end
end
end
if(size(image_orig,3) == 3)
image = rgb2gray(image_orig);
else
image = image_orig;
end
d_image = [];
if(initialised)
[shape,g_param,l_param,lhood,lmark_lhood,view_used] = Fitting_from_bb(image, d_image, bbox, pdm, patches, clmParams, 'gparam', g_param, 'lparam', l_param);
all_local_params(i,:) = l_param;
all_global_params(i,:) = g_param;
dec = face_check_cnn(image, shape, g_param, face_check_cnns);
if(dec < 0.5)
clmParams.window_size = [19,19; 17,17;];
clmParams.numPatchIters = 2;
det = true;
else
clmParams.window_size = [21,21; 19,19; 17,17;];
clmParams.numPatchIters = 3;
det = false;
end
end
if(verbose)
try
if(max(image_orig(:)) > 1)
imshow(double(image_orig)/255, 'Border', 'tight');
else
imshow(double(image_orig), 'Border', 'tight');
end
axis equal;
hold on;
if(initialised)
plot(shape(:,1), shape(:,2),'.r','MarkerSize',20);
plot(shape(:,1), shape(:,2),'.b','MarkerSize',10);
end
hold off;
drawnow expose;
pause(0.05);
if(record)
frame = getframe;
writeVideo(writerObj,frame);
end
catch warn
fprintf('%s', warn.message);
end
end
end
if(record)
close(writerObj);
end
close all;
experiments.local_params = all_local_params;
experiments.global_params = all_global_params;
end