def augment_photos_folder(self, batch_size=64, new_size=None):
"""
Augment photos in data/photos/
Args:
batch_size: Size of each augmentation batch.
new_size: tuple, new image size.
Returns:
None
"""
LOGGER.info(f'Started augmentation with {self.workers} workers')
LOGGER.info(f'Total images to augment: {self.total_images}')
LOGGER.info(f'Session assigned id: {self.session_id}')
with ThreadPoolExecutor(max_workers=self.workers) as executor:
while self.image_paths_copy:
current_batch, current_paths = self.load_batch(
new_size, batch_size)
future_augmentations = {
executor.submit(self.augment_image, image, path): path
for image, path in zip(current_batch, current_paths)
}
for future_augmented in as_completed(future_augmentations):
future_augmented.result()
LOGGER.info(f'Augmentation completed')
augmentation_frame = pd.DataFrame(self.augmentation_data,
columns=self.mapping.columns)
saving_path = get_abs_path('output',
'data',
f'augmented_data_plus_original.csv',
create_parents=True)
combined = pd.concat([self.mapping, augmentation_frame])
for item in ['bx', 'by', 'bw', 'bh']:
combined = combined.drop(combined[combined[item] > 1].index)
combined.to_csv(saving_path, index=False)
LOGGER.info(f'Saved old + augmented labels to {saving_path}')
adjusted_combined = adjust_non_voc_csv(saving_path, self.image_folder,
self.image_width,
self.image_height)
adjusted_saving_path = saving_path.replace('augmented', 'adjusted_aug')
adjusted_combined.to_csv(adjusted_saving_path, index=False)
LOGGER.info(
f'Saved old + augmented (adjusted) labels to {adjusted_saving_path}'
)
return adjusted_combined
def get_adjusted_labels(self, configuration):
"""
Adjust labels according to given configuration.
Args:
configuration: A dictionary containing any of the following keys:
- relative_labels
- xml_labels_folder
- voc_conf (required if xml_labels_folder)
- coordinate_labels
Returns:
pandas DataFrame with adjusted labels.
"""
labels_frame = None
check = 0
if configuration.get('relative_labels'):
labels_frame = adjust_non_voc_csv(
configuration['relative_labels'],
self.image_folder,
self.image_width,
self.image_height,
)
check += 1
if xml_folder := configuration.get('xml_labels_folder'):
if check:
raise ValueError(f'Got more than one configuration')
voc_conf = configuration.get('voc_conf')
assert voc_conf, f'Missing VOC configuration json file.'
labels_frame = parse_voc_folder(
xml_folder,
get_abs_path(voc_conf, verify=True),
)
labels_frame.to_csv(
get_abs_path('output',
'data',
'parsed_from_xml.csv',
create_parents=True),
index=False,
)
check += 1
def get_adjusted_labels(self, configuration):
"""
Adjust labels according to given configuration.
Args:
configuration: A dictionary containing any of the following keys:
- relative_labels
- from_xml
- coordinate_labels
Returns:
pandas DataFrame with adjusted labels.
"""
labels_frame = None
check = 0
if configuration.get('relative_labels'):
labels_frame = adjust_non_voc_csv(
configuration['relative_labels'],
self.image_folder,
self.image_width,
self.image_height,
)
check += 1
if configuration.get('from_xml'):
if check:
raise ValueError(f'Got more than one configuration')
labels_frame = parse_voc_folder(
self.xml_folder,
os.path.join('config', 'voc_conf.json'),
)
labels_frame.to_csv(
os.path.join('output', 'data', 'parsed_from_xml.csv'),
index=False,
)
check += 1
if configuration.get('coordinate_labels'):
if check:
raise ValueError(f'Got more than one configuration')
labels_frame = pd.read_csv(configuration['coordinate_labels'])
check += 1
return labels_frame