def create_training_dataset(self, event):
"""
"""
num_shuffles = self.shuffle.GetValue()
config_file = auxiliaryfunctions.read_config(self.config)
trainindex = self.trainingindex.GetValue()
if self.userfeedback.GetStringSelection() == "Yes":
userfeedback = True
else:
userfeedback = False
if config_file.get("multianimalproject", False):
if self.cropandlabel.GetStringSelection() == "Yes":
n_crops, height, width = [
int(text.GetValue()) for _, text in self.crop_widgets
]
deeplabcut.cropimagesandlabels(
self.config, n_crops, (height, width), userfeedback
)
else:
random = False
deeplabcut.create_multianimaltraining_dataset(
self.config,
num_shuffles,
Shuffles=[self.shuffle.GetValue()],
net_type=self.net_choice.GetValue(),
)
else:
if self.model_comparison_choice.GetStringSelection() == "No":
deeplabcut.create_training_dataset(
self.config,
num_shuffles,
Shuffles=[self.shuffle.GetValue()],
userfeedback=userfeedback,
net_type=self.net_choice.GetValue(),
augmenter_type=self.aug_choice.GetValue(),
)
if self.model_comparison_choice.GetStringSelection() == "Yes":
deeplabcut.create_training_model_comparison(
self.config,
trainindex=trainindex,
num_shuffles=num_shuffles,
userfeedback=userfeedback,
net_types=self.net_type,
augmenter_types=self.aug_type,
)
for image in auxiliaryfunctions.grab_files_in_folder(
image_folder, "png")
]
fake_data = np.tile(np.repeat(50 * np.arange(len(animals_id)) + 100, 2),
(len(index), 1))
df = pd.DataFrame(fake_data, index=index, columns=columns)
output_path = os.path.join(image_folder, f"CollectedData_{SCORER}.csv")
df.to_csv(output_path)
df.to_hdf(output_path.replace("csv", "h5"),
"df_with_missing",
format="table",
mode="w")
print("Artificial data created.")
print("Cropping and exchanging")
deeplabcut.cropimagesandlabels(config_path, userfeedback=False)
print("Checking labels...")
deeplabcut.check_labels(config_path, draw_skeleton=False)
print("Labels checked.")
print("Creating train dataset...")
deeplabcut.create_multianimaltraining_dataset(config_path, net_type=NET)
print("Train dataset created.")
print("Editing pose config...")
model_folder = auxiliaryfunctions.GetModelFolder(TRAIN_SIZE, 1, cfg,
cfg["project_path"])
pose_config_path = os.path.join(model_folder, "train/pose_cfg.yaml")
edits = {
"global_scale": 0.5,
