def prepare_models(root_dir: str = '/models'):
backend_name = os.getenv('INFERENCE_BACKEND', 'trt')
rec_name = os.getenv("REC_NAME", "arcface_r100_v1")
rec_batch_size = int(os.getenv("REC_BATCH_SIZE", 1))
det_name = os.getenv("DET_NAME", "retinaface_mnet025_v2")
ga_name = os.getenv("GA_NAME", "genderage_v1")
force_fp16 = tobool(os.getenv('FORCE_FP16', True)) #False
max_size = parse_size(os.getenv('MAX_SIZE'))
if max_size is None:
max_size = [640, 640]
config = Configs(models_dir=root_dir)
for model in [rec_name, det_name, ga_name]:
batch_size = 1
if model in [
'arcface_r100_v1', 'r50-arcface-msfdrop75',
'r100-arcface-msfdrop75', 'glint360k_r100FC_1.0',
'glint360k_r100FC_0.1'
]:
batch_size = rec_batch_size
logging.info(f"Preparing '{model}' model...")
prepare_backend(model_name=model,
backend_name=backend_name,
im_size=max_size,
force_fp16=force_fp16,
max_batch_size=batch_size,
config=config)
def __init__(self):
# Global parameters
self.threshold = float(os.getenv('DET_THRESH', 0.6))
self.max_size = parse_size(os.getenv('MAX_SIZE'))
self.return_face_data = tobool(os.getenv('DEF_RETURN_FACE_DATA',
False))
self.extract_embedding = tobool(
os.getenv('DEF_EXTRACT_EMBEDDING', True))
self.extract_ga = tobool(os.getenv('DEF_EXTRACT_GA', False))
self.api_ver = os.getenv('DEF_API_VER', "1")
device = os.environ.get("DEVICE", 'cuda')
rec_name = os.environ.get("REC_NAME", "arcface_r100_v1")
det_name = os.environ.get("DET_NAME", "retinaface_mnet025_v2")
ga_name = os.environ.get("GA_NAME", "genderage_v1")
ga_ignore = tobool(os.environ.get('GA_IGNORE', False))
rec_ignore = tobool(os.environ.get('REC_IGNORE', False))
if rec_ignore:
rec_name = None
if ga_ignore:
ga_name = None
# Global parameters
max_size = parse_size(os.environ.get('MAX_SIZE'))
return_face_data = tobool(os.environ.get('DEF_RETURN_FACE_DATA', False))
extract_embedding = tobool(os.environ.get('DEF_EXTRACT_EMBEDDING', True))
extract_ga = tobool(os.environ.get('DEF_EXTRACT_GA', False))
api_ver = os.environ.get('DEF_API_VER', "1")
# MTCNN parameters
select_largest = tobool(os.environ.get("SELECT_LARGEST", True))
keep_all = tobool(os.environ.get("KEEP_ALL", True))
min_face_size = int(os.environ.get("MIN_FACE_SIZE", 20))
mtcnn_factor = float(os.environ.get("MTCNN_FACTOR", 0.709))
processing = Processing(det_name=det_name, rec_name=rec_name, ga_name=ga_name, device=device,
max_size=max_size,select_largest=select_largest, keep_all=keep_all, min_face_size=min_face_size,
backend_name = os.getenv('INFERENCE_BACKEND', 'trt')
rec_name = os.getenv("REC_NAME", "arcface_r100_v1")
det_name = os.getenv("DET_NAME", "retinaface_mnet025_v2")
ga_name = os.getenv("GA_NAME", "genderage_v1")
ga_ignore = tobool(os.getenv('GA_IGNORE', False))
rec_ignore = tobool(os.getenv('REC_IGNORE', False))
if rec_ignore:
rec_name = None
if ga_ignore:
ga_name = None
# Global parameters
max_size = parse_size(os.getenv('MAX_SIZE'))
return_face_data = tobool(os.getenv('DEF_RETURN_FACE_DATA', False))
extract_embedding = tobool(os.getenv('DEF_EXTRACT_EMBEDDING', True))
extract_ga = tobool(os.getenv('DEF_EXTRACT_GA', False))
api_ver = os.getenv('DEF_API_VER', "1")
# MTCNN parameters
select_largest = tobool(os.getenv("SELECT_LARGEST", True))
keep_all = tobool(os.getenv("KEEP_ALL", True))
min_face_size = int(os.getenv("MIN_FACE_SIZE", 20))
mtcnn_factor = float(os.getenv("MTCNN_FACTOR", 0.709))
logging.basicConfig(
level=log_level,
format='%(asctime)s %(levelname)s - %(message)s',
datefmt='[%H:%M:%S]',