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')
Exemple #2
0
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)