def refine_labels(config):
"""
Refines the labels of the outlier frames extracted from the analyzed videos.\n Helps in augmenting the training dataset.
Use the function ``analyze_video`` to analyze a video and extracts the outlier frames using the function
``extract_outlier_frames`` before refining the labels.
Parameters
----------
config : string
Full path of the config.yaml file as a string.
Screens : int value of the number of Screens in landscape mode, i.e. if you have 2 screens, enter 2. Default is 1.
scale_h & scale_w : you can modify how much of the screen the GUI should occupy. The default is .9 and .8, respectively.
img_scale : if you want to make the plot of the frame larger, consider changing this to .008 or more. Be careful though, too large and you will not see the buttons fully!
Examples
--------
>>> deeplabcut.refine_labels('/analysis/project/reaching-task/config.yaml', Screens=2, imag_scale=.0075)
--------
"""
wd = Path(config).resolve().parents[0]
os.chdir(str(wd))
from deeplabcut.refine_training_dataset import refinement
refinement.show(config)
def refine_labels(config):
"""
Refines the labels of the outlier frames extracted from the analyzed videos.\n Helps in augmenting the training dataset.
Use the function ``analyze_video`` to analyze a video and extracts the outlier frames using the function
``extract_outlier_frames`` before refining the labels.
Parameters
----------
config : string
Full path of the config.yaml file as a string.
Examples
--------
>>> deeplabcut.refine_labels('/analysis/project/reaching-task/config.yaml')
--------
"""
wd = Path(config).resolve().parents[0]
os.chdir(str(wd))
from deeplabcut.refine_training_dataset import refinement
refinement.show(config)
