def main():
if not mlboard:
print_fun("Skipped: no mlboard detected")
return
parser = argparse.ArgumentParser()
add_common_args(parser)
parser.add_argument(
'model_name',
help='Exported model name.',
type=str,
)
parser.add_argument(
'model_version',
help='Exported model version.',
type=str,
)
args = parser.parse_args()
utils.print_fun('Uploading model...')
mlboard.model_upload(args.model_name, args.model_version,
args.classifiers_dir)
update_task_info({
'model_reference':
catalog_ref(args.model_name, 'mlmodel', args.model_version)
})
utils.print_fun("New model uploaded as '%s', version '%s'." %
(args.model_name, args.model_version))
def main():
parser = argparse.ArgumentParser()
add_common_args(parser)
add_bg_remove_args(parser)
add_detector_args(parser)
add_image_args(parser)
args = parser.parse_args()
detector = detector_args(args)
image = image_args(detector, args)
image.process()
def main():
parser = argparse.ArgumentParser()
utils.configure_logging()
add_common_args(parser)
add_bg_remove_args(parser)
add_detector_args(parser)
add_listener_args(parser)
add_video_args(parser)
add_video_notify_args(parser)
add_notify_args(parser)
args = parser.parse_args()
detector = detector_args(args)
listener = listener_args(args)
camera = video_args(detector, listener, args)
init_notifier_args(args)
init_in_video_detected(args)
camera.start()
def main():
parser = argparse.ArgumentParser()
add_aligner_args(parser)
add_common_args(parser)
add_classifier_args(parser)
add_bg_remove_args(parser)
utils.add_normalization_args(parser)
parser.add_argument(
'--skip_align',
help=
'Skip alignment for source images from input dir, only calculate embeddings and train classifiers.',
action='store_true',
)
parser.add_argument(
'--skip_train',
help=
'Skip calculating embeddings and training, only alignment for source images from input dir.',
action='store_true',
)
parser.add_argument(
'--align_images_limit',
type=int,
help='Set limit for processed images in alignment.',
default=None,
)
parser.add_argument(
'--model_name',
help='Exported model name.',
type=str,
default=None,
)
parser.add_argument(
'--model_version',
help='Exported model version.',
type=str,
default=None,
)
parser.add_argument(
'--complementary',
help='Complementary align and training.',
action='store_true',
)
parser.add_argument(
'--best_threshold',
help='Find best threshold.',
action='store_true',
)
args = parser.parse_args()
if not args.skip_align:
al = aligner_args(args)
al.align(args.align_images_limit)
if not args.skip_train:
clf = classifiers_args(args)
clf.train()
from app.control.client import Client
cl = Client()
cl.call('reload_classifiers')
if args.model_name is not None and args.model_version is not None:
from app.mlboard import mlboard, update_task_info, catalog_ref
utils.print_fun('Uploading model...')
model_version = args.model_version
if args.device not in args.model_version:
model_version = args.model_version + '-%s' % args.device
mlboard.model_upload(args.model_name, model_version,
args.classifiers_dir)
update_task_info({
'model_reference':
catalog_ref(args.model_name, 'mlmodel', args.model_version)
})
utils.print_fun("New model uploaded as '%s', version '%s'." %
(args.model_name, args.model_version))
import argparse
from app.recognize import add_common_args
from app.tools import downloader
parser = argparse.ArgumentParser()
add_common_args(parser)
parser.add_argument(
'classifiers_url',
type=str,
help='URL for the pretrained classifiers',
)
args = parser.parse_args()
err = downloader.Downloader(args.classifiers_url, args.classifiers_dir).extract()
if err is not None:
raise ValueError(err)