def main():
"""Prepares data for the antispoofing recognition demo"""
parser = argparse.ArgumentParser(description='antispoofing recognition live demo script')
parser.add_argument('--video', type=str, default=None, help='Input video')
parser.add_argument('--cam_id', type=int, default=-1, help='Input cam')
parser.add_argument('--config', type=str, default=None, required=False,
help='Configuration file')
parser.add_argument('--fd_model', type=str, required=True)
parser.add_argument('--fd_thresh', type=float, default=0.6, help='Threshold for FD')
parser.add_argument('--spoof_thresh', type=float, default=0.4,
help='Threshold for predicting spoof/real. The lower the more model oriented on spoofs')
parser.add_argument('--spf_model', type=str, default=None,
help='path to .pth checkpoint of model or .xml IR OpenVINO model', required=True)
parser.add_argument('--device', type=str, default='CPU')
parser.add_argument('--GPU', type=int, default=0, help='specify which GPU to use')
parser.add_argument('-l', '--cpu_extension',
help='MKLDNN (CPU)-targeted custom layers.Absolute path to a shared library with the kernels '
'impl.', type=str, default=None)
parser.add_argument('--write_video', type=bool, default=False,
help='if you set this arg to True, the video of the demo will be recoreded')
args = parser.parse_args()
device = args.device + f':{args.GPU}' if args.device == 'cuda' else 'cpu'
write_video = args.write_video
if args.cam_id >= 0:
log.info('Reading from cam {}'.format(args.cam_id))
cap = cv.VideoCapture(args.cam_id)
cap.set(cv.CAP_PROP_FRAME_WIDTH, 1280)
cap.set(cv.CAP_PROP_FRAME_HEIGHT, 720)
cap.set(cv.CAP_PROP_FOURCC, cv.VideoWriter_fourcc(*'MJPG'))
else:
assert args.video
log.info('Reading from {}'.format(args.video))
cap = cv.VideoCapture(args.video)
cap.set(cv.CAP_PROP_FOURCC, cv.VideoWriter_fourcc(*'MJPG'))
assert cap.isOpened()
face_detector = FaceDetector(args.fd_model, args.fd_thresh, args.device, args.cpu_extension)
if args.spf_model.endswith('pth.tar'):
if not args.config:
raise ValueError('You should pass config file to work with a Pytorch model')
config = utils.read_py_config(args.config)
spoof_model = utils.build_model(config, args, strict=True, mode='eval')
spoof_model = TorchCNN(spoof_model, args.spf_model, config, device=device)
else:
assert args.spf_model.endswith('.xml')
spoof_model = VectorCNN(args.spf_model)
# running demo
run(args, cap, face_detector, spoof_model, write_video)
def main():
"""Prepares data for the accuracy convertation checker"""
parser = argparse.ArgumentParser(description='antispoofing recognition live demo script')
parser.add_argument('--config', type=str, default=None, required=True,
help='Configuration file')
parser.add_argument('--spf_model_openvino', type=str, default=None,
help='path to .xml IR OpenVINO model', required=True)
parser.add_argument('--spf_model_torch', type=str, default=None,
help='path to .pth.tar checkpoint', required=True)
parser.add_argument('--device', type=str, default='CPU')
args = parser.parse_args()
config = utils.read_py_config(args.config)
assert args.spf_model_openvino.endswith('.xml') and args.spf_model_torch.endswith('.pth.tar')
spoof_model_torch = utils.build_model(config, args.device.lower(), strict=True, mode='eval')
spoof_model_torch = TorchCNN(spoof_model_torch, args.spf_model_torch, config, device=args.device.lower())
spoof_model_openvino = VectorCNN(args.spf_model_openvino)
# running checker
avg_diff = run(spoof_model_torch, spoof_model_openvino)
print((f'mean difference on the first predicted class : {avg_diff[0]}\n'
+ f'mean difference on the second predicted class : {avg_diff[1]}'))