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general_align.m
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general_align.m
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folder = 'E:\datasets\discomfort';
addpath('..');
image_list = get_image_list_in_folder(folder);
target_folder = 'H:\datasets\discomfort-align';
if exist(target_folder, 'dir')==0
mkdir(target_folder);
end;
pdollar_toolbox_path='D:/face project/pdollar-toolbox';
addpath(genpath(pdollar_toolbox_path));
MTCNN_path = 'D:\face project\MTCNN_face_detection_alignment\code\codes\MTCNNv2';
caffe_model_path=[MTCNN_path , '/model'];
addpath(genpath(MTCNN_path));
coord5points = [30.2946, 65.5318, 48.0252, 33.5493, 62.7299; ...
51.6963, 51.5014, 71.7366, 92.3655, 92.2041];
imgSize = [112, 96];
align_method = 'yandong';% wuxiang or yandong
%caffe.set_mode_cpu();
gpu_id=0;
caffe.set_mode_gpu();
caffe.set_device(gpu_id);
caffe.reset_all();
%three steps's threshold
threshold=[0.6 0.7 0.7]
minsize = 100;
%scale factor
factor=0.709;
%load caffe models
prototxt_dir =strcat(caffe_model_path,'/det1.prototxt');
model_dir = strcat(caffe_model_path,'/det1.caffemodel');
PNet=caffe.Net(prototxt_dir,model_dir,'test');
prototxt_dir = strcat(caffe_model_path,'/det2.prototxt');
model_dir = strcat(caffe_model_path,'/det2.caffemodel');
RNet=caffe.Net(prototxt_dir,model_dir,'test');
prototxt_dir = strcat(caffe_model_path,'/det3.prototxt');
model_dir = strcat(caffe_model_path,'/det3.caffemodel');
ONet=caffe.Net(prototxt_dir,model_dir,'test');
prototxt_dir = strcat(caffe_model_path,'/det4.prototxt');
model_dir = strcat(caffe_model_path,'/det4.caffemodel');
LNet=caffe.Net(prototxt_dir,model_dir,'test');
faces=cell(0);
for image_id = 1:length(image_list);
[file_folder, file_name, file_ext] = fileparts(image_list{image_id});
target_filename = strrep(image_list{image_id},folder, target_folder);
if exist(target_filename, 'file')
continue;
end;
img = imread(image_list{image_id});
if size(img, 3) < 3
img(:,:,2) = img(:,:,1);
img(:,:,3) = img(:,:,1);
end
assert(strcmp(target_filename, image_list{image_id})==0);
[file_folder, file_name, file_ext] = fileparts(target_filename);
if exist(file_folder,'dir')==0
mkdir(file_folder);
end;
disp([num2str(image_id) '/' num2str(length(image_list)) ' ' target_filename]);
[boundingboxes points]=detect_face(img,min([minsize size(img,1)/2 size(img,2)/2]),PNet,RNet,ONet,LNet,threshold,false,factor);
if isempty(boundingboxes)
continue;
end;
default_face = 1;
if size(boundingboxes,1) > 1
for bb=2:size(boundingboxes,1)
if abs((boundingboxes(bb,1) + boundingboxes(bb,3))/2 - size(img,2) / 2) + abs((boundingboxes(bb,2) + boundingboxes(bb,4))/2 - size(img,1) / 2) < ...
abs((boundingboxes(default_face,1) + boundingboxes(default_face,3))/2 - size(img,2) / 2) + abs((boundingboxes(default_face,2) + boundingboxes(default_face,4))/2 - size(img,1) / 2)
default_face = bb;
end;
end;
end;
facial5points = double(reshape(points(:,default_face),[5 2])');
if strcmp(align_method, 'wuxiang') > 0
[res, eyec2, cropImg, resize_scale] = align_face_WX(img,facial5points',144,48,48);
cropImg = uint8(cropImg);
else
Tfm = cp2tform(facial5points', coord5points', 'similarity');
cropImg = imtransform(img, Tfm, 'XData', [1 imgSize(2)],...
'YData', [1 imgSize(1)], 'Size', imgSize);
end;
imwrite(cropImg, target_filename);
% show detection result
% numbox=size(boundingboxes,1);
% figure(1);
% imshow(img)
% hold on;
% for j=1:numbox
% plot(points(1:5,j),points(6:10,j),'g.','MarkerSize',10);
% r=rectangle('Position',[boundingboxes(j,1:2) boundingboxes(j,3:4)-boundingboxes(j,1:2)],'Edgecolor','g','LineWidth',3);
% end;
% hold off;
% figure(2);
% imshow(cropImg);
% pause
end;