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()