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,
)
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):
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,
)
)
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("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, crop_size=(200, 200)
)
print("Train dataset created.")
# Check the training image paths are correctly stored as arrays of strings
trainingsetfolder = auxiliaryfunctions.GetTrainingSetFolder(cfg)
datafile, _ = auxiliaryfunctions.GetDataandMetaDataFilenames(
trainingsetfolder,
0.8,
1,
cfg,
)
datafile = datafile.split(".mat")[0] + ".pickle"
with open(os.path.join(cfg["project_path"], datafile), "rb") as f:
pickledata = pickle.load(f)
num_images = len(pickledata)
)
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("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, crop_size=(200, 200)
)
print("Train dataset created.")
# Check the training image paths are correctly stored as arrays of strings
trainingsetfolder = auxiliaryfunctions.GetTrainingSetFolder(cfg)
datafile, _ = auxiliaryfunctions.GetDataandMetaDataFilenames(
trainingsetfolder, 0.8, 1, cfg,
)
datafile = datafile.split(".mat")[0] + ".pickle"
with open(os.path.join(cfg["project_path"], datafile), "rb") as f:
pickledata = pickle.load(f)
num_images = len(pickledata)
assert all(len(pickledata[i]["joints"]) == 3 for i in range(num_images))
print("Editing pose config...")
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,
"batch_size": 1,
"save_iters": N_ITER,
"display_iters": N_ITER // 2,
# "multi_step": [[0.001, N_ITER]],
}
deeplabcut.auxiliaryfunctions.edit_config(pose_config_path, edits)
print("Pose config edited.")
