def load_demo_data(config, createtrainingset=True):
"""
Loads the demo data. Make sure that you are in the same directory where you have downloaded or cloned the deeplabcutcore.
Parameter
----------
config : string
Full path of the config.yaml file of the provided demo dataset as a string.
Example
--------
>>> deeplabcutcore.load_demo_data('config.yaml')
--------
"""
config = Path(config).resolve()
config = str(config)
transform_data(config)
if createtrainingset:
print("Loaded, now creating training data...")
deeplabcutcore.create_training_dataset(config, num_shuffles=1)
dataFrame.to_csv(
os.path.join(cfg['project_path'], 'labeled-data', videoname,
"CollectedData_" + scorer + ".csv"))
dataFrame.to_hdf(os.path.join(cfg['project_path'], 'labeled-data', videoname,
"CollectedData_" + scorer + '.h5'),
'df_with_missing',
format='table',
mode='w')
print("Plot labels...")
dlc.check_labels(path_config_file)
print("CREATING TRAININGSET")
dlc.create_training_dataset(path_config_file,
net_type=net_type,
augmenter_type=augmenter_type)
posefile = os.path.join(
cfg['project_path'], 'dlc-models/iteration-' + str(cfg['iteration']) +
'/' + cfg['Task'] + cfg['date'] + '-trainset' +
str(int(cfg['TrainingFraction'][0] * 100)) + 'shuffle' + str(1),
'train/pose_cfg.yaml')
DLC_config = dlc.auxiliaryfunctions.read_plainconfig(posefile)
DLC_config['save_iters'] = numiter
DLC_config['display_iters'] = 2
DLC_config['multi_step'] = [[0.001, numiter]]
print("CHANGING training parameters to end quickly!")
dlc.auxiliaryfunctions.write_plainconfig(posefile, DLC_config)
dataFrame.to_csv(
os.path.join(cfg['project_path'], 'labeled-data', videoname,
"CollectedData_" + scorer + ".csv"))
dataFrame.to_hdf(os.path.join(cfg['project_path'], 'labeled-data', videoname,
"CollectedData_" + scorer + '.h5'),
'df_with_missing',
format='table',
mode='w')
print("Plot labels...")
dlc.check_labels(path_config_file)
print("CREATING TRAININGSET")
dlc.create_training_dataset(path_config_file,
net_type=net_type,
augmenter_type=augmenter_type)
posefile = os.path.join(
cfg['project_path'], 'dlc-models/iteration-' + str(cfg['iteration']) +
'/' + cfg['Task'] + cfg['date'] + '-trainset' +
str(int(cfg['TrainingFraction'][0] * 100)) + 'shuffle' + str(1),
'train/pose_cfg.yaml')
DLC_config = dlc.auxiliaryfunctions.read_plainconfig(posefile)
DLC_config['save_iters'] = numiter
DLC_config['display_iters'] = 2
DLC_config['multi_step'] = [[0.001, numiter]]
print("CHANGING training parameters to end quickly!")
dlc.auxiliaryfunctions.write_plainconfig(posefile, DLC_config)