def main():
current_dir = os.path.dirname(os.path.abspath(__file__))
"""Prepares data for the person recognition demo"""
parser = argparse.ArgumentParser(description='Multi camera multi person \
tracking live demo script')
parser.add_argument('-i',
type=str,
nargs='+',
help='Input sources (indexes \
of cameras or paths to video files)',
required=True)
parser.add_argument('--config',
type=str,
default=os.path.join(current_dir, 'config.py'),
required=False,
help='Configuration file')
parser.add_argument('--detections',
type=str,
help='JSON file with bounding boxes')
parser.add_argument('-m',
'--m_detector',
type=str,
required=False,
help='Path to the person detection model')
parser.add_argument('--t_detector',
type=float,
default=0.6,
help='Threshold for the person detection model')
parser.add_argument('--m_segmentation',
type=str,
required=False,
help='Path to the person instance segmentation model')
parser.add_argument(
'--t_segmentation',
type=float,
default=0.6,
help='Threshold for person instance segmentation model')
parser.add_argument('--m_reid',
type=str,
required=True,
help='Path to the person re-identification model')
parser.add_argument('--output_video',
type=str,
default='',
required=False,
help='Optional. Path to output video')
parser.add_argument(
'--history_file',
type=str,
default='',
required=False,
help='Optional. Path to file in JSON format to save results of the demo'
)
parser.add_argument(
'--save_detections',
type=str,
default='',
required=False,
help='Optional. Path to file in JSON format to save bounding boxes')
parser.add_argument("--no_show",
help="Optional. Don't show output",
action='store_true')
parser.add_argument('-d', '--device', type=str, default='CPU')
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('-u',
'--utilization_monitors',
default='',
type=str,
help='Optional. List of monitors to show initially.')
args = parser.parse_args()
if check_detectors(args) != 1:
sys.exit(1)
if len(args.config):
log.info('Reading configuration file {}'.format(args.config))
config = read_py_config(args.config)
else:
log.error(
'No configuration file specified. Please specify parameter \'--config\''
)
sys.exit(1)
random.seed(config['random_seed'])
capture = MulticamCapture(args.i)
log.info("Creating Inference Engine")
ie = IECore()
if args.detections:
person_detector = DetectionsFromFileReader(args.detections,
args.t_detector)
elif args.m_segmentation:
person_detector = MaskRCNN(ie, args.m_segmentation,
args.t_segmentation,
args.device, args.cpu_extension,
capture.get_num_sources())
else:
person_detector = Detector(ie, args.m_detector, args.t_detector,
args.device, args.cpu_extension,
capture.get_num_sources())
if args.m_reid:
person_recognizer = VectorCNN(ie, args.m_reid, args.device,
args.cpu_extension)
else:
person_recognizer = None
run(args, config, capture, person_detector, person_recognizer)
log.info('Demo finished successfully')
def main():
current_dir = os.path.dirname(os.path.abspath(__file__))
"""Prepares data for the object tracking demo"""
parser = argparse.ArgumentParser(description='Multi camera multi object \
tracking live demo script')
parser.add_argument(
'-i',
'--input',
required=True,
nargs='+',
help=
'Required. Input sources (indexes of cameras or paths to video files)')
parser.add_argument('--loop',
default=False,
action='store_true',
help='Optional. Enable reading the input in a loop')
parser.add_argument('--config',
type=str,
default=os.path.join(current_dir, 'configs/person.py'),
required=False,
help='Configuration file')
parser.add_argument('--detections',
type=str,
help='JSON file with bounding boxes')
parser.add_argument('-m',
'--m_detector',
type=str,
required=False,
help='Path to the object detection model')
parser.add_argument('--t_detector',
type=float,
default=0.6,
help='Threshold for the object detection model')
parser.add_argument('--m_segmentation',
type=str,
required=False,
help='Path to the object instance segmentation model')
parser.add_argument(
'--t_segmentation',
type=float,
default=0.6,
help='Threshold for object instance segmentation model')
parser.add_argument(
'--m_reid',
type=str,
required=True,
help='Required. Path to the object re-identification model')
parser.add_argument('--output_video',
type=str,
default='',
required=False,
help='Optional. Path to output video')
parser.add_argument(
'--history_file',
type=str,
default='',
required=False,
help='Optional. Path to file in JSON format to save results of the demo'
)
parser.add_argument(
'--save_detections',
type=str,
default='',
required=False,
help='Optional. Path to file in JSON format to save bounding boxes')
parser.add_argument("--no_show",
help="Optional. Don't show output",
action='store_true')
parser.add_argument('-d', '--device', type=str, default='CPU')
parser.add_argument('-u',
'--utilization_monitors',
default='',
type=str,
help='Optional. List of monitors to show initially.')
args = parser.parse_args()
if check_detectors(args) != 1:
sys.exit(1)
if len(args.config):
log.debug('Reading config from {}'.format(args.config))
config = read_py_config(args.config)
else:
log.error(
'No configuration file specified. Please specify parameter \'--config\''
)
sys.exit(1)
random.seed(config.random_seed)
capture = MulticamCapture(args.input, args.loop)
log.info('OpenVINO Runtime')
log.info('\tbuild: {}'.format(get_version()))
core = Core()
if args.detections:
object_detector = DetectionsFromFileReader(args.detections,
args.t_detector)
elif args.m_segmentation:
object_detector = MaskRCNN(core, args.m_segmentation,
config.obj_segm.trg_classes,
args.t_segmentation, args.device,
capture.get_num_sources())
else:
object_detector = Detector(core, args.m_detector,
config.obj_det.trg_classes, args.t_detector,
args.device, capture.get_num_sources())
if args.m_reid:
object_recognizer = VectorCNN(core, args.m_reid, args.device)
else:
object_recognizer = None
run(args, config, capture, object_detector, object_recognizer)