def __init__(self,
model_name: str = 'arcface_r100_v1',
epoch_num: int = 0,
use_flip: bool = False,
ctx: mx.Context = mx.cpu()):
self.use_flip = use_flip
self.embeddings = dict()
if model_name == 'arcface_r100_v1':
model = model_zoo.get_model(model_name)
if ctx.device_type.startswith('cpu'):
ctx_id = -1
else:
ctx_id = ctx.device_id
model.prepare(ctx_id=ctx_id)
self.model = model.model
else:
sym, arg_params, aux_params = mx.model.load_checkpoint(
model_name, epoch_num)
sym = sym.get_internals()['fc1_output']
model = mx.mod.Module(symbol=sym, context=ctx, label_names=None)
data_shape = (2 if use_flip else 1, 3, 112, 112)
model.bind(data_shapes=[('data', data_shape)], for_training=False)
model.set_params(arg_params, aux_params)
#warmup
data = mx.nd.zeros(shape=data_shape)
db = mx.io.DataBatch(data=(data, ))
model.forward(db, is_train=False)
embedding = model.get_outputs()[0].asnumpy()
self.model = model
self.metric = metrics.cosine
def detect(img):
model = get_model('retinaface_r50_v1')
model.prepare(ctx_id=-1, nms=0.4)
bbox, feature_pnts = model.detect(img, threshold=0.5, scale=1.0)
if bbox.shape[0] == 0:
return None
bbox = bbox[0, 0:4]
feature_pnts = feature_pnts[0, :].reshape((2, 5)).T
nimg = preprocess(img, bbox, feature_pnts, image_size='112,112')
nimg = cvtColor(nimg, COLOR_BGR2RGB)
aligned = transpose(nimg, (2, 0, 1))
return aligned
def prepare_images(root_dir, output_dir):
whitelist_dir = 'MID'
detector = model_zoo.get_model('retinaface_r50_v1')
detector.prepare(ctx_id=-1, nms=0.4)
for family_path in tqdm(root_dir.iterdir()):
for person_path in family_path.iterdir():
if not person_path.is_dir() or not person_path.name.startswith(whitelist_dir):
continue
output_person = ensure_path(output_dir / person_path.relative_to(root_dir))
for img_path in person_path.iterdir():
img = cv2.imread(str(img_path))
bbox, landmarks = detector.detect(img, threshold=0.5, scale=1.0)
output_path = output_person / img_path.name
if len(landmarks) < 1:
print(f'smth wrong with {img_path}')
continue
warped_img = norm_crop(img, landmarks[0])
cv2.imwrite(str(output_path), warped_img)
def get_detector_with_backup() -> Detector:
detector = get_retina_resnet50(resolution=512, ctx=mx.gpu(0), batch_size=8, num_workers=6)
backup = model_zoo.get_model('retinaface_r50_v1')
backup.prepare(ctx_id=0, nms=0.4)
def detect(img_paths: Sequence[Path]) -> Generator[Detections, None, None]:
for img_path, (scores, bboxes, landmarks) in zip(img_paths, detector(img_paths)):
if len(landmarks) < 1:
img = cv2.imread(str(img_path))
if img is None:
logging.warning(f'cannot load an image: {img_path}')
yield [], [], []
continue
dets, landmarks = backup.detect(img)
scores = dets[:, -1]
bboxes = dets[:, :4]
yield scores, bboxes, landmarks
return detect
return normed_embedding
config = Configs()
model_name = 'arcface_r100_v1'
engine = config.build_model_paths(model_name, 'plan')[1]
model_onnx = config.build_model_paths(model_name, 'onnx')[1]
model = InsightTRT(rec_name=engine)
#model = InsightTriton(rec_name=engine)
#model = InsightORT(rec_name=model_onnx)
model.prepare()
model_orig = model_zoo.get_model(model_name, root=config.mxnet_models_dir)
model_orig.prepare(-1)
im = cv2.imread('test_images/crop.jpg', cv2.IMREAD_COLOR)
iters = 100
t0 = time.time()
for i in range(iters):
emb = model.get_embedding(im)
t1 = time.time()
print(f'Took {t1 - t0} s. ({iters/(t1 - t0)} faces/sec)')
#
emb1 = model.get_embedding(im)[0]
emb2 = model_orig.get_embedding(im)[0]
import numpy as np
from insightface.model_zoo import get_model
from server.face_processing import face_align
model = get_model('arcface_r100_v1') # init model get_emmberding_face
ctx_id = -1
model.prepare(ctx_id=ctx_id)
from mtcnn.mtcnn import MTCNN
detector = MTCNN()
def get_face(Img):
faces = detector.detect_faces(Img)
if len(faces) != 1:
return None
else:
box = faces[0]['box']
keypoints = faces[0]['keypoints']
left_eye = keypoints['left_eye']
right_eye = keypoints['right_eye']
nose = keypoints['nose']
mouth_left = keypoints['mouth_left']
mouth_right = keypoints['mouth_right']
points = np.asarray(
[left_eye, right_eye, nose, mouth_left, mouth_right])
face_extracted = face_align.preprocess(Img,
box,
points,
def __init__(self, model_name='retinaface_r50_v1', gpu=False):
from insightface.model_zoo import get_model
self._model = get_model(model_name)
_ctx_id = 1 if gpu else -1
self._model.prepare(ctx_id=_ctx_id, nms=0.4)
super(RetinaFaceDetector, self).__init__()
def __init__(self):
self.model = get_model('arcface_r100_v1')
self.model.prepare(ctx_id=-1)
