def align_multi(cfg, image, min_confidence=0.97, limits=None):
boxes = []
landmarks = []
detector = MTCNN()
faces = detector.detect_faces(image)
refrence = get_reference_facial_points(default_square=True)
for face in faces:
if face['confidence'] < min_confidence:
continue
boxes.append(face['box'])
landmark = []
for name, points in face['keypoints'].items():
landmark.append(list(points))
landmarks.append(landmark)
if limits:
boxes = boxes[:limits]
landmarks = landmarks[:limits]
faces = []
for landmark in landmarks:
warped_face = warp_and_crop_face(image,
landmark,
reference_pts=refrence,
crop_size=(cfg['input_size'],
cfg['input_size']))
faces.append(warped_face)
return np.array(boxes), np.array(landmarks), np.array(faces)
def align_face(img_path, resize_face_size=112):
img = Image.open(img_path)
bounding_boxes, landmarks = detect_faces(img)
scale = resize_face_size / 112.
reference = get_reference_facial_points(default_square = True) * scale
facial5points = [[landmarks[0][j], landmarks[0][j + 5]] for j in range(5)]
warped_face = warp_and_crop_face(np.array(img), facial5points, reference, crop_size=(resize_face_size, resize_face_size))
return warped_face
def prepare_facebank(cfg, model):
names = ['Unknown']
embeddings = []
detector = MTCNN()
for name in os.listdir(cfg['face_bank']):
if os.path.isfile(name):
continue
else:
emb = []
for file in tqdm(os.listdir(os.path.join(cfg['face_bank'], name))):
if not os.path.isfile(
os.path.join(cfg['face_bank'], name, file)):
continue
else:
image = cv2.imread(
os.path.join(cfg['face_bank'], name, file))
image = cv2.resize(image,
(cfg['input_size'], cfg['input_size']))
face = detector.detect_faces(image)
if len(face) > 0:
face = face[0]
refrence = get_reference_facial_points(
default_square=True)
landmark = []
for _, points in face['keypoints'].items():
landmark.append(list(points))
warped_face = warp_and_crop_face(
image,
landmark,
reference_pts=refrence,
crop_size=(cfg['input_size'], cfg['input_size']))
image = np.array(warped_face)
image = image.astype(np.float32) / 255.
if len(image.shape) == 3:
image = np.expand_dims(image, 0)
emb.append(l2_norm(model(image)).numpy())
if len(emb) == 0:
continue
emb = np.array(emb)
mean = np.mean(emb, axis=0)
embeddings.append(mean)
names.append(name)
embeddings = np.array(embeddings)
names = np.array(names)
np.save(os.path.join('data', 'facebank.npy'), embeddings)
np.save(os.path.join('data', 'names.npy'), names)
return embeddings, names
def align(cfg, image):
detector = MTCNN()
face = detector.detect_faces(image)[0]
refrence = get_reference_facial_points(default_square=True)
landmark = []
for name, points in face['keypoints'].items():
landmark.append(list(points))
warped_face = warp_and_crop_face(image,
landmark,
reference_pts=refrence,
crop_size=(cfg['input_size'],
cfg['input_size']))
return warped_face
help="specify your destination dir",
default="./data/test_Aligned",
type=str)
parser.add_argument(
"-crop_size",
"--crop_size",
help="specify size of aligned faces, align and crop with padding",
default=112,
type=int)
args = parser.parse_args()
source_root = args.source_root # specify your source dir
dest_root = args.dest_root # specify your destination dir
crop_size = args.crop_size # specify size of aligned faces, align and crop with padding
scale = crop_size / 112.
reference = get_reference_facial_points(default_square=True) * scale
cwd = os.getcwd() # delete '.DS_Store' existed in the source_root
os.chdir(source_root)
os.system("find . -name '*.DS_Store' -type f -delete")
os.system("find . -name '*.ipynb_checkpoints' -prune -exec rm -rf {} \;")
os.chdir(cwd)
if not os.path.isdir(dest_root):
os.mkdir(dest_root)
for subfolder in tqdm(os.listdir(source_root)):
if not os.path.isdir(os.path.join(dest_root, subfolder)):
os.mkdir(os.path.join(dest_root, subfolder))
for image_name in os.listdir(os.path.join(source_root, subfolder)):
print("Processing\t{}".format(
default="/media/hyo/文档/Dataset/face_dataset_v2/lfw_aligned",
type=str)
parser.add_argument(
"-crop_size",
"--crop_size",
help="specify size of aligned faces, align and crop with padding",
default=224,
type=int)
args = parser.parse_args()
source_root = args.source_root # specify your source dir
dest_root = args.dest_root # specify your destination dir
crop_size = args.crop_size # specify size of aligned faces, align and crop with padding
scale = crop_size / 112.
# reference = get_reference_facial_points(default_square = True) * scale
original_5_points = get_reference_facial_points(default_square=True)
# original_5_points[:, 1] = original_5_points[:, 1] - 15
reference = (original_5_points) * scale
cwd = os.getcwd() # delete '.DS_Store' existed in the source_root
os.chdir(source_root)
os.system("find . -name '*.DS_Store' -type f -delete")
os.chdir(cwd)
if not os.path.isdir(dest_root):
os.mkdir(dest_root)
# for image_name in tqdm(os.listdir(source_root)):
# if not os.path.isdir(os.path.join(dest_root, subfolder)):
# os.mkdir(os.path.join(dest_root, subfolder))
# # for image_name in os.listdir(os.path.join(source_root, subfolder)):
from tqdm import tqdm
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser(description = "face alignment")
parser.add_argument("-source_root", "--source_root", help = "specify your source dir", default = "./data/test", type = str)
parser.add_argument("-dest_root", "--dest_root", help = "specify your destination dir", default = "./data/test_Aligned", type = str)
parser.add_argument("-crop_size", "--crop_size", help = "specify size of aligned faces, align and crop with padding", default = 112, type = int)
args = parser.parse_args()
source_root = args.source_root # specify your source dir
dest_root = args.dest_root # specify your destination dir
crop_size = args.crop_size # specify size of aligned faces, align and crop with padding
scale = crop_size / 112.
reference = get_reference_facial_points(default_square = True) * scale
cwd = os.getcwd() # delete '.DS_Store' existed in the source_root
os.chdir(source_root)
os.system("find . -name '*.DS_Store' -type f -delete")
os.chdir(cwd)
if not os.path.isdir(dest_root):
os.mkdir(dest_root)
for subfolder in tqdm(os.listdir(source_root)):
if not os.path.isdir(os.path.join(dest_root, subfolder)):
os.mkdir(os.path.join(dest_root, subfolder))
for image_name in os.listdir(os.path.join(source_root, subfolder)):
print("Processing\t{}".format(os.path.join(source_root, subfolder, image_name)))
img = Image.open(os.path.join(source_root, subfolder, image_name))
