def __init__(self,
input_size=416,
config_file='Models/yolo-tiny-onecls/yolov3-tiny-onecls.cfg',
weight_file='Models/yolo-tiny-onecls/best-model.pth',
nms=0.2,
conf_thres=0.45,
device='cuda'):
self.input_size = input_size
self.model = Darknet(config_file).to(device)
self.model.load_state_dict(torch.load(weight_file))
self.model.eval()
self.device = device
self.nms = nms
self.conf_thres = conf_thres
self.resize_fn = ResizePadding(input_size, input_size)
self.transf_fn = transforms.ToTensor()
def __init__(self, master: tk.Tk):
self.master = master
self.master.title('Human Falling Detection')
self.master.protocol('WM_DELETE_WINDOW', self._on_closing)
self.main_screen = get_monitor_from_coord(master.winfo_x(),
master.winfo_y())
self.width = int(self.main_screen.width * .85)
self.height = int(self.main_screen.height * .85)
self.master.geometry('{}x{}'.format(self.width, self.height + 15))
self.cam = None
self.canvas = tk.Canvas(master,
width=int(self.width * .65),
height=self.height)
self.canvas.grid(row=0, column=0, padx=5, pady=5, sticky=tk.NSEW)
fig = plt.Figure(figsize=(6, 8), dpi=100)
fig.suptitle('Actions')
self.ax = fig.add_subplot(111)
self.fig_canvas = FigureCanvasTkAgg(fig, self.master)
self.fig_canvas.get_tk_widget().grid(row=0,
column=1,
padx=5,
pady=5,
sticky=tk.NSEW)
# Load Models
self.resize_fn = ResizePadding(416, 416)
self.models = Models()
self.actions_graph()
self.delay = 15
self.load_cam('../Data/falldata/Home/Videos/video (1).avi')
self.update()
# POSE MODEL.
inp_pose = args.pose_input_size.split('x')
inp_pose = (int(inp_pose[0]), int(inp_pose[1]))
pose_model = SPPE_FastPose(args.pose_backbone,
inp_pose[0],
inp_pose[1],
device=device)
# Tracker.
max_age = 30
tracker = Tracker(max_age=max_age, n_init=3)
# Actions Estimate.
action_model = TSSTG()
resize_fn = ResizePadding(inp_dets, inp_dets)
cam_source = args.camera
if type(cam_source) is str and os.path.isfile(cam_source):
# Use loader thread with Q for video file.
cam = CamLoader_Q(cam_source, queue_size=1000,
preprocess=preproc).start()
else:
# Use normal thread loader for webcam.
cam = CamLoader(
int(cam_source) if cam_source.isdigit() else cam_source,
preprocess=preproc).start()
#frame_size = cam.frame_size
#scf = torch.min(inp_size / torch.FloatTensor([frame_size]), 1)[0]