async def embedding_loop(preload):
# =================== FR MODEL ====================
mlp, class_names = read_pkl_model('./model-mlp/mlp.pkl')
embedding = face_embedding.EmbeddingModel(preload)
while True:
ip, img = suspicion_face_queue.get()
dt = time.strftime('%m-%d %H:%M:%S')
predict = mlp.predict_proba([embedding.get_one_feature(img)])
prob = predict.max(1)[0]
name = class_names[predict.argmax(1)[0]]
result_queue.put((ip, img, dt, prob, name))
async def embedding_loop(preload):
# =================== FR MODEL ====================
embedding = face_embedding.EmbeddingModel(preload)
while True:
result = embedding.arcface_deal(suspicion_face_queue.get())
result_queue.put(result)
import imageGenerator
from sklearn import metrics
from helper import read_pkl_model, start_up_init, get_dataset, get_image_paths_and_labels
import face_embedding
import face_detector
# =================== ARGS ====================
os.environ["MXNET_CUDNN_AUTOTUNE_DEFAULT"] = "0"
args = start_up_init()
args.retina_model = './model/M25'
args.scales = [0.5]
# =================== MODEL CLASS ====================
detector = face_detector.DetectorModel(args)
arcface = face_embedding.EmbeddingModel(args)
# =================== LOAD DATASET ====================.
dir_train = './Temp/train.npy'
data_train = './Temp/train_data'
dataset_train = get_dataset(data_train)
paths_train, labels_train = get_image_paths_and_labels(dataset_train)
try:
train_emb_array = np.load(dir_train)
train_emb_array = imageGenerator.generator()
except OSError:
if not os.path.exists('./Temp/raw/'):
os.makedirs('./Temp/raw/')
detector.get_all_boxes_from_path(paths_train, save_img=True)
dataset_train = get_dataset(data_train)
paths_train, labels_train = get_image_paths_and_labels(dataset_train)