def save_to_list_file(allfaces, lst_file, image_style_dir, annotation_dir, face_indexes, use_front, use_box):
save_faces = []
for index in face_indexes:
face = allfaces[index]
if use_front == False or face.check_front():
save_faces.append( face )
print ('Prepare to save {} face images into {}'.format(len(save_faces), lst_file))
lst_file = open(lst_file, 'w')
all_face_sizes = []
for face in save_faces:
image_path = face.image_path
sub_dir, base_name = image_path.split('/')
cannot_dir = osp.join(annotation_dir, sub_dir)
cannot_path = osp.join(cannot_dir, base_name.split('.')[0] + '-{}.pts'.format(face.face_id))
if not osp.isdir(cannot_dir): os.makedirs(cannot_dir)
image_path = osp.join(image_style_dir, image_path)
assert osp.isfile(image_path), 'The image [{}/{}] {} does not exsit'.format(index, len(save_faces), image_path)
pts_str = datasets.PTSconvert2str( face.landmarks.T )
pts_file = open(cannot_path, 'w')
pts_file.write('{}'.format(pts_str))
pts_file.close()
box_str, face_size = face.get_face_size(use_box)
lst_file.write('{} {} {} {}\n'.format(image_path, cannot_path, box_str, face_size))
all_face_sizes.append( face_size )
lst_file.close()
all_faces = np.array( all_face_sizes )
print ('all faces : mean={}, std={}'.format(all_faces.mean(), all_faces.std()))
def main():
#Open the sqlite database
conn = sqlite3.connect(sqlit_path)
c = conn.cursor()
#Creating the query string for retriving: roll, pitch, yaw and faces position
#Change it according to what you want to retrieve
select_string = "faceimages.filepath, faces.face_id, facerect.x, facerect.y, facerect.w, facerect.h"
from_string = "faceimages, faces, facerect"
where_string = "faces.file_id = faceimages.file_id and faces.face_id = facerect.face_id"
query_string = "SELECT " + select_string + " FROM " + from_string + " WHERE " + where_string
tables_string = "SELECT name FROM sqlite_master WHERE type='table'"
table_list = list(c.execute(tables_string))
answers = list(c.execute(query_string))
all_faces = []
for query in c.execute(query_string):
all_faces.append(AFLWFace(query))
print('Finish load all faces : {}'.format(len(all_faces)))
for face in all_faces:
faceid = face.face_id
select_string = "featurecoords.face_id, featurecoords.feature_id, featurecoords.x, featurecoords.y, featurecoords.annot_type_id"
from_string = "featurecoords"
where_string = "featurecoords.face_id = {}".format(faceid)
query_string = "SELECT " + select_string + " FROM " + from_string + " WHERE " + where_string
landmarks = list(c.execute(query_string))
face.set_landmarks(landmarks)
c.close()
print('Finish load all facial landmarks : {}'.format(len(all_faces)))
front_faces, other_faces = [], []
for index, face in enumerate(all_faces):
image_path = face.image_path
image_path = osp.join(image_dir, image_path)
face.from21to19()
if osp.isfile(image_path):
if face.check_front():
front_faces.append(face)
else:
other_faces.append(face)
print('Front faces : {}, Other faces : {}'.format(len(front_faces),
len(other_faces)))
front_length = 1165
train_length, test_length = 20000, 4396
np.random.seed(10)
random_indexes = np.random.permutation(len(front_faces))
# Get the testing frontal faces
indicators = np.zeros(len(front_faces), dtype='bool')
indicators[random_indexes[:front_length]] = True
test_fron_faces, others = [], []
for index, face in enumerate(front_faces):
if indicators[index]:
test_fron_faces.append(face)
else:
others.append(face)
new_test_length = len(others) + len(other_faces) - train_length
np.random.seed(19)
random_indexes = np.random.permutation(len(other_faces))
indicators = np.zeros(len(other_faces), dtype='bool')
indicators[random_indexes[:new_test_length]] = True
train_faces, test_other_faces = [], []
for index, face in enumerate(other_faces):
if indicators[index] == False:
train_faces.append(face)
else:
test_other_faces.append(face)
train_faces = train_faces + others
print('Train : {}, test front : {}, others : {}'.format(
len(train_faces), len(test_fron_faces), len(test_other_faces)))
np.random.seed(10)
all_face_sizes = []
train_save_list = open(osp.join(SAVE_DIR, 'train.' + SUFFIX), 'w')
for face in train_faces:
image_path = face.image_path
sub_dir, base_name = image_path.split('/')
cannot_dir = osp.join(annot_dir, sub_dir)
cannot_path = osp.join(
cannot_dir,
base_name.split('.')[0] + '-{}.pts'.format(face.face_id))
if not osp.isdir(cannot_dir): os.makedirs(cannot_dir)
image_path = osp.join(image_dir, image_path)
assert osp.isfile(
image_path), 'The image [{}/{}] {} does not exsit'.format(
index, len(all_faces), image_path)
pts_str = datasets.PTSconvert2str(face.landmarks.T)
pts_file = open(cannot_path, 'w')
pts_file.write('{}'.format(pts_str))
pts_file.close()
box_str = return_box(cannot_path, face.face_rect)
train_save_list.write('{} {} {} {}\n'.format(image_path, cannot_path,
box_str, face.face_size))
all_face_sizes.append(face.face_size)
train_save_list.close()
test_save_list = open(osp.join(SAVE_DIR, 'test.front.' + SUFFIX), 'w')
for face in test_fron_faces:
image_path = face.image_path
sub_dir, base_name = image_path.split('/')
cannot_dir = osp.join(annot_dir, sub_dir)
cannot_path = osp.join(
cannot_dir,
base_name.split('.')[0] + '-{}.pts'.format(face.face_id))
if not osp.isdir(cannot_dir): os.makedirs(cannot_dir)
image_path = osp.join(image_dir, image_path)
assert osp.isfile(
image_path), 'The image [{}/{}] {} does not exsit'.format(
index, len(all_faces), image_path)
pts_str = datasets.PTSconvert2str(face.landmarks.T)
pts_file = open(cannot_path, 'w')
pts_file.write('{}'.format(pts_str))
pts_file.close()
box_str = return_box(cannot_path, face.face_rect)
test_save_list.write('{} {} {} {}\n'.format(image_path, cannot_path,
box_str, face.face_size))
all_face_sizes.append(face.face_size)
test_save_list.close()
test_other_faces = test_other_faces + test_fron_faces
test_save_list = open(osp.join(SAVE_DIR, 'test.' + SUFFIX), 'w')
for face in test_other_faces:
image_path = face.image_path
sub_dir, base_name = image_path.split('/')
cannot_dir = osp.join(annot_dir, sub_dir)
cannot_path = osp.join(
cannot_dir,
base_name.split('.')[0] + '-{}.pts'.format(face.face_id))
if not osp.isdir(cannot_dir): os.makedirs(cannot_dir)
image_path = osp.join(image_dir, image_path)
assert osp.isfile(
image_path), 'The image [{}/{}] {} does not exsit'.format(
index, len(all_faces), image_path)
pts_str = datasets.PTSconvert2str(face.landmarks.T)
pts_file = open(cannot_path, 'w')
pts_file.write('{}'.format(pts_str))
pts_file.close()
box_str = return_box(cannot_path, face.face_rect)
test_save_list.write('{} {} {} {}\n'.format(image_path, cannot_path,
box_str, face.face_size))
all_face_sizes.append(face.face_size)
test_save_list.close()
all_faces = np.array(all_face_sizes)
print('all faces : mean={}, std={}'.format(all_faces.mean(),
all_faces.std()))