def __init__(self, model_path, epoch_num='0000', image_size=(112, 112),
no_face_raise=True):
self.model_path = ','.join([model_path, epoch_num])
self.no_face_raise = no_face_raise
args = argparse.Namespace()
args.model = self.model_path
args.det = 0
args.flip = 0
args.threshold = 1.24
args.ga_model = ''
args.image_size = ",".join([str(i) for i in image_size])
self.model = face_model.FaceModel(args)
def get_identity_descriptor(images, default_bbox):
"""
Compute an identity vector (by the ArcFace face recognition system) for each image in `images`.
Args:
images:
iterable of `numpy.ndarray`, dtype == uint8, shape == (256, 256, 3)
Images to compute identity descriptors for.
default_bbox:
`tuple` of `int`, length == 4
See `get_default_bbox()`.
Returns:
descriptors:
`numpy.ndarray`, dtype == float32, shape == (`len(images)`, `FACE_DESCRIPTOR_DIM`)
num_bad_images:
int
For how many images face detection failed.
"""
global arcface_model
# Load the model if it hasn't been loaded yet
if arcface_model is None:
from insightface import face_model
arcface_model = face_model.FaceModel(
image_size='112,112',
model=
"/Vol0/user/e.burkov/Projects/insightface/models/model-r100-ii/model,0000",
ga_model="",
det=0,
flip=1,
threshold=1.24,
gpu=0)
num_bad_images = 0
images_cropped = []
for image in images:
image_cropped = arcface_model.get_input(image)
if image_cropped is None: # no faces found
num_bad_images += 1
t, l, b, r = default_bbox
image_cropped = cv2.resize(image[t:256 - b, l:256 - r], (112, 112),
interpolation=cv2.INTER_CUBIC)
image_cropped = image_cropped.transpose(2, 0, 1)
images_cropped.append(image_cropped)
return arcface_model.get_feature(np.stack(images_cropped)), num_bad_images
parser.add_argument('--gpu', default=0, type=int, help='gpu id')
parser.add_argument(
'--det',
default=0,
type=int,
help='mtcnn option, 1 means using R+O, 0 means detect from begining')
parser.add_argument('--flip',
default=0,
type=int,
help='whether do lr flip aug')
parser.add_argument('--threshold',
default=1.24,
type=float,
help='ver dist threshold')
args = parser.parse_args()
model = face_model.FaceModel(args)
def test():
img = cv2.imread(
'/home/lyk/machine_learning/competition/iqiyi/take_face/test_data_jpg_face/IQIYI_VID_DATA_Part1/IQIYI_VID_TRAIN/IQIYI_VID_TRAIN_0000001.mp4/IQIYI_VID_TRAIN_0000001.mp4_003.jpg/IQIYI_VID_TRAIN_0000001.mp4_003.jpg_0.jpg'
)
img = model.get_input(img)
f1 = model.get_feature(img)
#print(f1[0:10])
img = cv2.imread(
'/home/lyk/machine_learning/competition/iqiyi/take_face/test_data_jpg_face/IQIYI_VID_DATA_Part1/IQIYI_VID_TRAIN/IQIYI_VID_TRAIN_0000001.mp4/IQIYI_VID_TRAIN_0000001.mp4_002.jpg/IQIYI_VID_TRAIN_0000001.mp4_002.jpg_0.jpg'
)
img = model.get_input(img)
f2 = model.get_feature(img)