def __init__(self,
cfg,
opt,
save_video=False,
video_save_opt=DEFAULT_VIDEO_SAVE_OPT,
queueSize=1024):
self.cfg = cfg
self.opt = opt
self.video_save_opt = video_save_opt
self.eval_joints = EVAL_JOINTS
self.save_video = save_video
self.heatmap_to_coord = get_func_heatmap_to_coord(cfg)
# initialize the queue used to store frames read from
# the video file
if opt.sp:
self.result_queue = Queue(maxsize=queueSize)
else:
self.result_queue = mp.Queue(maxsize=queueSize)
if opt.save_img:
if not os.path.exists(opt.outputpath + '/vis'):
os.mkdir(opt.outputpath + '/vis')
if opt.pose_flow:
from trackers.PoseFlow.poseflow_infer import PoseFlowWrapper
self.pose_flow_wrapper = PoseFlowWrapper(
save_path=os.path.join(opt.outputpath, 'poseflow'))
def __init__(self, cfg, opt, save_video=False,
video_save_opt=DEFAULT_VIDEO_SAVE_OPT,
queueSize=1024):
self.cfg = cfg
self.opt = opt
self.video_save_opt = video_save_opt
self.eval_joints = EVAL_JOINTS
self.save_video = save_video
self.heatmap_to_coord = get_func_heatmap_to_coord(cfg)
# initialize the queue used to store frames read from
# the video file
if opt.sp:
self.result_queue = Queue(maxsize=queueSize)
else:
self.result_queue = mp.Queue(maxsize=queueSize)
if opt.save_img:
if not os.path.exists(opt.outputpath + '/vis'):
os.mkdir(opt.outputpath + '/vis')
if opt.pose_flow:
from trackers.PoseFlow.poseflow_infer import PoseFlowWrapper
self.pose_flow_wrapper = PoseFlowWrapper(save_path=os.path.join(opt.outputpath, 'poseflow'))
if self.opt.save_img or self.save_video or self.opt.vis:
loss_type = self.cfg.DATA_PRESET.get('LOSS_TYPE', 'MSELoss')
num_joints = self.cfg.DATA_PRESET.NUM_JOINTS
if loss_type == 'MSELoss':
self.vis_thres = [0.4] * num_joints
elif 'JointRegression' in loss_type:
self.vis_thres = [0.05] * num_joints
elif loss_type == 'Combined':
if num_joints == 68:
hand_face_num = 42
else:
hand_face_num = 110
self.vis_thres = [0.4] * (num_joints - hand_face_num) + [0.05] * hand_face_num
self.use_heatmap_loss = (self.cfg.DATA_PRESET.get('LOSS_TYPE', 'MSELoss') == 'MSELoss')
def __init__(self,
cfg,
opt,
save_video=False,
video_save_opt=DEFAULT_VIDEO_SAVE_OPT,
queueSize=1024):
self.cfg = cfg
self.opt = opt
self.video_save_opt = video_save_opt
self.head_pose = opt.head_pose # 是否开启头部姿态相关内容
self.eval_joints = EVAL_JOINTS
self.save_video = save_video
self.heatmap_to_coord = get_func_heatmap_to_coord(cfg)
# initialize the queue used to store frames read from
# the video file
if opt.sp:
self.result_queue = Queue(maxsize=queueSize)
else:
self.result_queue = mp.Queue(maxsize=queueSize)
if opt.save_img:
if not os.path.exists(opt.outputpath + '/vis'):
os.mkdir(opt.outputpath + '/vis')
if opt.pose_flow:
from trackers.PoseFlow.poseflow_infer import PoseFlowWrapper
self.pose_flow_wrapper = PoseFlowWrapper(
save_path=os.path.join(opt.outputpath, 'poseflow'))
if opt.tracking: # 实例状态
self.reid_states = ReIDStates()
# 是否使用场景蒙版
self.scene_mask = SceneMasker(
opt.scene_mask) if opt.scene_mask else None
if self.opt.analyse_cheating and self.opt.save_cheaters:
try:
os.mkdir(os.path.join(self.opt.outputpath, 'cheating'))
except FileExistsError:
pass