import cv2 import ffmpeg import imageio import argparse from model_init import model_initializer from config.video_config_setting_bg_thread_final_test import video_config from video_read.video_read_func import get_video_info, read_frame_as_jpeg from run_det_bing import detection_tracking_process from back_ground_model.train_bg_subtracktor import train_bg_subtractor from deep_sort.track_frame_run import MOT from evaluate_code.count_num_concat_tracks import count_main # Model Initialize print('Load Models...') Model = model_initializer() exitFlag = 0 queueLock = threading.Lock() def pipe_line(video_path, video_name, cfg, vid, frame_nums): # while True: global exitFlag global Model # global frame_ind exitFlag = 0 print('{} images wait to put in queue.'.format(frame_nums)) work_queue = Queue(frame_nums) thread_num = 2
def main(self): p = platform() arg_parser = ArgumentParser( description='Launchpad for Parameterized Docker' ' builds') arg_parser.add_argument('-b', "--batch-size", help="Specify the batch size. If this " \ "parameter is not specified or is -1, the " \ "largest ideal batch size for the model will " \ "be used.", dest="batch_size", type=int, default=-1) arg_parser.add_argument('-n', "--num-cores", help='Specify the number of cores to use. ' \ 'If the parameter is not specified ' \ 'or is -1, all cores will be used.', dest="num_cores", type=int, default=-1) #This adds support for a --single-socket param with a default value of False. #Only if '--single-socket' is on the command-line will the value be true. arg_parser.add_argument('-s','--single-socket', help='Indicates that only one socket should ' \ 'be used. If used in conjunction with ' \ '--num-cores, all cores will be allocated ' \ 'on the single socket.', dest="single_socket", action='store_true') #This adds support for a --inference-only param with a default value of False. #Only if '--inference-only' is on the command-line will the value be true. arg_parser.add_argument('-f', "--inference-only", help='Only do inference.', dest='inference_only', action='store_true') arg_parser.add_argument('-c', "--checkpoint", help='Specify the location of checkpoint/training model. ' \ 'If --forward-only is not specified, training model/weights will be ' \ 'written to this location. If --forward-only is specified, ' \ 'assumes that the location ' \ 'points to a model that has already been trained. ', dest="checkpoint", default=None) arg_parser.add_argument("-d", "--data-location", help="Specify the location of the data. " \ "If this parameter is not specified, " \ "the benchmark will use random/dummy data.", dest="data_location", default=None) arg_parser.add_argument( '-a', "--num_intra_threads", type=int, help="Specify the number of threads within the layer", dest="num_intra_threads", default=(p.num_cores_per_socket() * p.num_cpu_sockets())) arg_parser.add_argument( '-e', "--num_inter_threads", type=int, help='Specify the number threads between layers', dest="num_inter_threads", default=DEFAULT_INTEROP_VALUE_) arg_parser.add_argument('-v', "--verbose", help='Print verbose information.', dest='verbose', action='store_true') args, unknown = arg_parser.parse_known_args() mi = model_initializer(args, unknown) mi.run()