def label_frames(config, multiple=False):
"""
Manually label/annotate the extracted frames. Update the list of body parts you want to localize in the config.yaml file first.
Parameter
----------
config : string
String containing the full path of the config file in the project.
multiple: bool, optional
If this is set to True, a user can label multiple individuals.
The default is ``False``; if provided it must be either ``True`` or ``False``.
Example
--------
To label multiple individuals
>>> deeplabcut.label_frames('/analysis/project/reaching-task/config.yaml',multiple=True)
--------
"""
startpath = os.getcwd()
wd = Path(config).resolve().parents[0]
os.chdir(str(wd))
if multiple == False:
from deeplabcut.generate_training_dataset import labeling_toolbox
# labeling_toolbox.show(config,Screens,scale_w,scale_h, winHack, img_scale)
labeling_toolbox.show(config)
else:
from deeplabcut.generate_training_dataset import multiple_individual_labeling_toolbox
multiple_individual_labeling_toolbox.show(config)
os.chdir(startpath)
def label_frames(config, ma=False):
"""
Manually label/annotate the extracted frames. Update the list of body parts you want to localize in the config.yaml file first.
Parameter
----------
config : string
String containing the full path of the config file in the project.
Example
--------
>>> deeplabcut.label_frames('/analysis/project/reaching-task/config.yaml')
--------
"""
startpath = os.getcwd()
wd = Path(config).resolve().parents[0]
os.chdir(str(wd))
if ma == False:
from deeplabcut.generate_training_dataset import labeling_toolbox
# labeling_toolbox.show(config,Screens,scale_w,scale_h, winHack, img_scale)
labeling_toolbox.show(config)
else:
from deeplabcut.generate_training_dataset import multiple_individual_labeling_toolbox
multiple_individual_labeling_toolbox.show(config)
os.chdir(startpath)