def main():
args = vars(argument_parser())
with open(args['properties']) as f:
properties_str = json.load(f)
properties_str = json.dumps(
properties_str) if properties_str is not None else None
engine = ObjectDetector(properties_str=properties_str,
inference=build_engine(args['capsule']))
engine.run()
return 0
def main():
args = vars(argument_parser())
with open(args['properties']) as f:
properties_str = json.load(f)
properties_str = json.dumps(
properties_str) if properties_str is not None else None
if args['is_detector']:
engine = ObjectDetector(properties_str=properties_str,
inference=build_detection_engine(
args['model'], args['label']))
else:
engine = FrameClassifier(properties_str=properties_str,
inference=build_classification_engine(
args['model'], args['label']))
engine.run()
return 0
def main():
args = vars(argument_parser())
with open(args['properties']) as f:
properties_str = json.load(f)
properties_str = json.dumps(
properties_str) if properties_str is not None else None
model = detectron2_models.get(args['model'], None)
if model is None:
log.error(
f"Unrecognied model {args['model']}. Should be one of \n{detectron2_models.keys()}"
)
return 1
engine = ObjectDetector(properties_str=properties_str,
inference=build_engine(model))
engine.run()
return 0
def main():
args = vars(argument_parser())
with open(args['properties']) as f:
properties_str = json.load(f)
properties_str = json.dumps(
properties_str) if properties_str is not None else None
tv_model = tv_models[args['model']]
if tv_model.type == 'detection':
engine = ObjectDetector(properties_str=properties_str,
inference=build_detection_engine(
tv_model.model, args['label']))
elif tv_model.type == 'classification':
engine = FrameClassifier(properties_str=properties_str,
inference=build_classification_engine(
tv_model.model, args['label']))
else:
log.error('Unrecognized model type.')
return 1
engine.run()
return 0