def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('--load', help='load model')
parser.add_argument('--sample',
action='store_true',
help='view generated examples')
parser.add_argument(
'--data',
help='a jpeg directory',
default='/media/kaicao/Data/Data/Rolled/MSP/Image_Aligned')
parser.add_argument('--load-size',
help='size to load the original images',
type=int)
parser.add_argument('--crop-size',
help='crop the original images',
type=int)
parser.add_argument(
'--log_dir',
help='directory to save checkout point',
type=str,
default=
'/media/kaicao/Data/checkpoint/FingerprintSynthesis/tensorpack/AutoEncoder/'
)
args = parser.parse_args()
opt.use_argument(args)
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
return args
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.', default='0')
parser.add_argument('--model', help='model for minutiae extraction.', type=str, default='AEC_Model')
parser.add_argument('--load', help='load model',
default='/AutomatedLatentRecognition/models/Minutiae/AEC_net/minutiae_AEC_64_fcn_2/model-224000.index')
parser.add_argument('--inference', action='store_true', help='extract minutiae on input images')
parser.add_argument('--image_dir', help='a jpeg directory',
default='/AutomatedLatentRecognition/Data/minutiae_cylinder_uint8')
parser.add_argument('--sample_dir', help='a jpeg directory',
default='/AutomatedLatentRecognition/pred_minutiae_cylinder_aug_texture/')
parser.add_argument('--data', help='a jpeg directory',
default='/AutomatedLatentRecognition/Data/minutiae_cylinder_uint8_MSPLatents_STFT/')
parser.add_argument('--load-size', help='size to load the original images', type=int)
parser.add_argument('--batch_size', help='batch size', type=int, default=128)
parser.add_argument('--crop-size', help='crop the original images', type=int)
parser.add_argument('--log_dir', help='directory to save checkout point', type=str,
default='/AutomatedLatentRecognition/models/Minutiae/AEC_net/minutiae_AEC_64_fcn_2_Latent_STFT/')
args = parser.parse_args()
opt.use_argument(args)
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.batch_size:
opt.BATCH = args.batch_size
return args
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('--load', help='load model')
parser.add_argument('--sample',
action='store_true',
help='view generated examples')
parser.add_argument(
'--data',
help='a jpeg directory',
default='/home/kaicao/Research/AutomatedLatentRecognition/Patches')
parser.add_argument('--load-size',
help='size to load the original images',
type=int)
parser.add_argument('--crop-size',
help='crop the original images',
type=int)
parser.add_argument(
'--log_dir',
help='directory to save checkout point',
type=str,
default=
'/home/kaicao/Research/AutomatedLatentRecognition/log_AutoEncoder/')
args = parser.parse_args()
opt.use_argument(args)
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
return args
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('--load', help='load model')
parser.add_argument('--sample', type=int, default=0,
help='the number of samples in the synthetic output.')
parser.add_argument('--data', help='a npz file')
parser.add_argument('--output', type=str)
parser.add_argument('--exp_name', type=str, default=None)
# parameters for model tuning.
parser.add_argument('--batch_size', type=int, default=200)
parser.add_argument('--z_dim', type=int, default=100)
parser.add_argument('--max_epoch', type=int, default=100)
parser.add_argument('--steps_per_epoch', type=int, default=1000)
parser.add_argument('--num_gen_rnn', type=int, default=400)
parser.add_argument('--num_gen_feature', type=int, default=100)
parser.add_argument('--num_dis_layers', type=int, default=2)
parser.add_argument('--num_dis_hidden', type=int, default=200)
parser.add_argument('--noise', type=float, default=0.2)
parser.add_argument('--optimizer', type=str, default='AdamOptimizer',
choices=['GradientDescentOptimizer', 'AdamOptimizer', 'AdadeltaOptimizer'])
parser.add_argument('--learning_rate', type=float, default=0.001)
parser.add_argument('--l2norm', type=float, default=0.00001)
args = parser.parse_args()
opt.use_argument(args)
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
return args
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('--load', help='load model')
parser.add_argument('--sample', action='store_true', help='view generated examples')
parser.add_argument('--data', help='a jpeg directory')
parser.add_argument('--load-size', help='size to load the original images', type=int)
parser.add_argument('--crop-size', help='crop the original images', type=int)
args = parser.parse_args()
opt.use_argument(args)
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
return args
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('--load', help='load model')
parser.add_argument('--sample', action='store_true', help='view generated examples')
parser.add_argument('--data', help='a jpeg directory')
parser.add_argument('--load-size', help='size to load the original images', type=int)
parser.add_argument('--crop-size', help='crop the original images', type=int)
args = parser.parse_args()
opt.use_argument(args)
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
return args
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('--model',
help='model for minutiae extraction.',
type=str,
default='Cao_Model')
parser.add_argument('--load', help='load model')
parser.add_argument('--inference',
action='store_true',
help='extract minutiae on input images')
parser.add_argument(
'--image_dir',
help='a jpeg directory',
default=
'/home/kaicao/Dropbox/Research/AutomatedLatentRecognition/Data/minutiae_cylinder_uint8'
)
parser.add_argument(
'--sample_dir',
help='a jpeg directory',
default=
'/home/kaicao/Dropbox/Research/AutomatedLatentRecognition/Data/minutiae_cylinder_uint8'
)
parser.add_argument(
'--data',
help='a jpeg directory',
default=
'/home/kaicao/Dropbox/Research/AutomatedLatentRecognition/Data/minutiae_cylinder_uint8'
)
parser.add_argument('--load-size',
help='size to load the original images',
type=int)
parser.add_argument('--batch_size', help='batch size', type=int)
parser.add_argument('--crop-size',
help='crop the original images',
type=int)
parser.add_argument(
'--log_dir',
help='directory to save checkout point',
type=str,
default=
'/home/kaicao/Research/AutomatedLatentRecognition/log_AutoEncoder/Minutiae_Cao'
)
args = parser.parse_args()
opt.use_argument(args)
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.batch_size:
opt.BATCH = args.batch_size
return args
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu',
help='comma separated list of GPU(s) to use.',
default='1')
parser.add_argument('--load', help='load model')
parser.add_argument('--enhance',
action='store_true',
help='enhance examples')
parser.add_argument(
'--test_data',
help='a jpeg directory',
default='/future/Data/Rolled/selected_rolled_prints/MI0479144T_07/')
parser.add_argument(
'--sample_dir',
help='directory for generated examples',
type=str,
default=
'/home/kaicao/Research/AutomatedLatentRecognition/Enhancement_test')
parser.add_argument(
'--data',
help='a jpeg directory',
default=
'/media/kaicao/data2/AutomatedLatentRecognition/Data/enhancement_training/'
) #'/home/kaicao/Research/AutomatedLatentRecognition/Patches'
parser.add_argument('--load-size',
help='size to load the original images',
type=int)
parser.add_argument('--batch_size', help='batch size', type=int)
parser.add_argument('--crop-size',
help='crop the original images',
type=int)
parser.add_argument(
'--log_dir',
help='directory to save checkout point',
type=str,
default=
'/media/kaicao/data2/AutomatedLatentRecognition/models/Enhancement/AEC_net/Enhancement_AEC_128_depth_4_STFT_2/'
)
args = parser.parse_args()
opt.use_argument(args)
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.batch_size:
opt.BATCH = args.batch_size
return args
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('--load',
help='load model',
default='model/I-WGAN_CAE/model-620000.index')
parser.add_argument(
'--sample_dir',
help='directory for generated examples',
type=str,
default=
'/media/kaicao/Data/Data/FingerprintSynthesis/tensorpack/I-WGAN_CAE_10M_JPEG/'
)
parser.add_argument('--num_images',
help='number of fingerprint images ',
type=int,
default=250)
args = parser.parse_args()
opt.use_argument(args)
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
return args
