def test_download_and_extract_cvc_muscima_SR_dataset_expect_folder_to_be_created(
self):
# Arrange
destination_directory = "CvcMuscimaStaffRemovalData"
downloader = CvcMuscimaDatasetDownloader(
destination_directory, CvcMuscimaDataset.StaffRemoval)
zip_file = downloader.get_dataset_filename()
number_of_samples_in_the_dataset = 1
target_file_extension = "data"
def test_download_and_extract_cvc_muscima_WI_dataset_expect_folder_to_be_created(
self):
# Arrange
destination_directory = "CvcMuscimaWriterIdentificationData"
downloader = CvcMuscimaDatasetDownloader(
destination_directory, CvcMuscimaDataset.WriterIdentification)
zip_file = downloader.get_dataset_filename()
number_of_samples_in_the_dataset = 1
target_file_extension = "png"
def test_download_and_extract_cvc_muscima_MCA_dataset_expect_folder_to_be_created(
self):
destination_directory = "CvcMuscimaMultiConditionAlignedData"
downloader = CvcMuscimaDatasetDownloader(
CvcMuscimaDataset.MultiConditionAligned)
zip_file = downloader.get_dataset_filename()
number_of_samples_in_the_dataset = 10000
target_file_extension = "*.png"
self.download_dataset_and_verify_correct_extraction(
destination_directory, number_of_samples_in_the_dataset,
target_file_extension, zip_file, downloader)
def test_download_and_extract_cvc_muscima_SR_dataset_expect_folder_to_be_created(
self):
destination_directory = "CvcMuscimaStaffRemovalData"
downloader = CvcMuscimaDatasetDownloader(
CvcMuscimaDataset.StaffRemoval)
zip_file = downloader.get_dataset_filename()
number_of_samples_in_the_dataset = 36000
target_file_extension = "*.png"
self.download_dataset_and_verify_correct_extraction(
destination_directory, number_of_samples_in_the_dataset,
target_file_extension, zip_file, downloader)
def download_all_datasets(dataset_directory):
muscima_pp_directory = os.path.join(dataset_directory, "muscima_pp")
downloader = MuscimaPlusPlusDatasetDownloader()
downloader.download_and_extract_dataset(muscima_pp_directory)
cvc_muscima_directory = os.path.join(dataset_directory, "cvc_muscima")
downloader = CvcMuscimaDatasetDownloader(CvcMuscimaDataset.StaffRemoval)
downloader.download_and_extract_dataset(cvc_muscima_directory)
mensural_directory = os.path.join(dataset_directory, "mensural")
downloader = MensuralDatasetDownloader()
downloader.download_and_extract_dataset(mensural_directory)
deepscores_directory = os.path.join(dataset_directory, "deepscores")
downloader = DeepScoresDatasetDownloader()
downloader.download_and_extract_dataset(deepscores_directory)
import os
import shutil
from omrdatasettools.converters.ImageInverter import ImageInverter
from omrdatasettools.downloaders.CvcMuscimaDatasetDownloader import CvcMuscimaDatasetDownloader, CvcMuscimaDataset
from omrdatasettools.downloaders.MuscimaPlusPlusDatasetDownloader import MuscimaPlusPlusDatasetDownloader
if __name__ == "__main__":
dataset_directory = "data"
muscima_pp_raw_dataset_directory = os.path.join(dataset_directory,
"muscima_pp_raw")
muscima_image_directory = os.path.join(dataset_directory,
"cvcmuscima_staff_removal")
print("Deleting dataset directory {0}".format(dataset_directory))
if os.path.exists(dataset_directory):
shutil.rmtree(dataset_directory, ignore_errors=True)
downloader = MuscimaPlusPlusDatasetDownloader()
downloader.download_and_extract_dataset(muscima_pp_raw_dataset_directory)
downloader = CvcMuscimaDatasetDownloader(CvcMuscimaDataset.StaffRemoval)
downloader.download_and_extract_dataset(muscima_image_directory)
inverter = ImageInverter()
# We would like to work with black-on-white images instead of white-on-black images
inverter.invert_images(muscima_image_directory, "*.png")
def train_model(dataset_directory: str,
model_name: str,
delete_and_recreate_dataset_directory: bool,
configuration_name: str,
output_weight_path: str,
configuration_filename: str,
number_of_epochs: int,
early_stopping: int,
learning_rate_reduction_patience: int,
learning_rate_reduction_factor: float,
non_max_suppression_overlap_threshold: float,
non_max_suppression_max_boxes: int,
input_weight_path: str = None):
muscima_pp_raw_dataset_directory = os.path.join(dataset_directory,
"muscima_pp_raw")
muscima_image_directory = os.path.join(dataset_directory,
"cvcmuscima_staff_removal")
muscima_cropped_directory = os.path.join(dataset_directory,
"muscima_pp_cropped_images")
if not dataset_directory: # if filename is not given
parser.error(
'Error: path to training data must be specified. Pass --path to command line'
)
network = NetworkFactory.get_network_by_name(model_name)
try:
all_images, classes_count, class_mapping = get_data(
muscima_cropped_directory)
data_loaded = True
except:
print(
"Could not load dataset. Automatically downloading and recreating dataset."
)
data_loaded = False
delete_and_recreate_dataset_directory = True
if delete_and_recreate_dataset_directory:
print("Deleting dataset directory {0}".format(dataset_directory))
if os.path.exists(dataset_directory):
shutil.rmtree(dataset_directory)
downloader = MuscimaPlusPlusDatasetDownloader(
muscima_pp_raw_dataset_directory)
downloader.download_and_extract_dataset()
downloader = CvcMuscimaDatasetDownloader(
muscima_image_directory, CvcMuscimaDataset.StaffRemoval)
downloader.download_and_extract_dataset()
delete_unused_images(muscima_image_directory)
inverter = ImageInverter()
# We would like to work with black-on-white images instead of white-on-black images
inverter.invert_images(muscima_image_directory, "*.png")
shutil.copy("Staff-Vertical-Positions.txt", dataset_directory)
cut_images(
muscima_image_directory,
os.path.join(dataset_directory, "Staff-Vertical-Positions.txt"),
muscima_cropped_directory, muscima_pp_raw_dataset_directory)
# pass the settings from the command line, and persist them in the config object
C = ConfigurationFactory.get_configuration_by_name(configuration_name)
C.model_path = output_weight_path
start_time = time.time()
if not data_loaded:
all_images, classes_count, class_mapping = get_data(
muscima_cropped_directory)
if 'bg' not in classes_count:
classes_count['bg'] = 0
class_mapping['bg'] = len(class_mapping)
C.class_mapping = class_mapping
# inv_map = {v: k for k, v in class_mapping.items()}
print('Training images per class:')
pprint.pprint(classes_count)
print('Num classes (including bg) = {}'.format(len(classes_count)))
print(
'Hyperparameters: {0} RoIs generated per run with {1} boxes remaining from non-max suppression and using '
'non-max suppression threshold of {2:.2f}'.format(
C.num_rois, non_max_suppression_max_boxes,
non_max_suppression_overlap_threshold))
config_output_filename = configuration_filename
with open(config_output_filename, 'wb') as config_f:
pickle.dump(C, config_f)
print(
'Config has been written to {}, and can be loaded when testing to ensure correct results'
.format(config_output_filename))
random.seed(1)
random.shuffle(all_images)
# num_imgs = len(all_images)
train_imgs = [s for s in all_images if s['imageset'] == 'train']
val_imgs = [s for s in all_images if s['imageset'] == 'val']
print('Num train samples {}'.format(len(train_imgs)))
print('Num val samples {}'.format(len(val_imgs)))
if not use_fast_data_generators:
print("Using standard data_generator")
data_gen_train = data_generators.get_anchor_gt(
train_imgs,
classes_count,
C,
network.get_img_output_length,
mode='train')
data_gen_val = data_generators.get_anchor_gt(
val_imgs,
classes_count,
C,
network.get_img_output_length,
mode='val')
else:
print("Using fast data_generator")
data_gen_train = data_generators_fast.get_anchor_gt(
train_imgs,
classes_count,
C,
network.get_img_output_length,
mode='train')
data_gen_val = data_generators_fast.get_anchor_gt(
val_imgs,
classes_count,
C,
network.get_img_output_length,
mode='val')
input_shape_img = (None, None, 3)
img_input = Input(shape=input_shape_img)
roi_input = Input(shape=(None, 4))
# define the base network (resnet here, can be VGG, Inception, etc)
shared_layers = network.nn_base(img_input, trainable=True)
# define the RPN, built on the base layers
num_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios)
rpn = network.rpn(shared_layers, num_anchors)
classifier = network.classifier(shared_layers,
roi_input,
C.num_rois,
nb_classes=len(classes_count),
trainable=True)
model_rpn = Model(img_input, rpn[:2])
model_classifier = Model([img_input, roi_input], classifier)
# this is a model that holds both the RPN and the classifier, used to load/save weights for the models
model_all = Model([img_input, roi_input], rpn[:2] + classifier)
start_of_training = datetime.date.today()
tensorboard_callback = TensorBoard(log_dir="./logs/{0}_{1}/".format(
start_of_training, configuration_name))
tensorboard_callback.set_model(model_all)
try:
print('Loading weights from {0}'.format(input_weight_path))
model_rpn.load_weights(input_weight_path, by_name=True)
model_classifier.load_weights(input_weight_path, by_name=True)
except:
print(
'Could not load pretrained model weights. Weights can be found in the keras application folder \
https://github.com/fchollet/keras/tree/master/keras/applications')
optimizer = Adadelta()
optimizer_classifier = Adadelta()
model_rpn.compile(optimizer=optimizer,
loss=[
faster_rcnn_losses.rpn_loss_cls(num_anchors),
faster_rcnn_losses.rpn_loss_regr(num_anchors)
])
model_classifier.compile(
optimizer=optimizer_classifier,
loss=[
faster_rcnn_losses.class_loss_cls,
faster_rcnn_losses.class_loss_regr(len(classes_count) - 1)
],
metrics={'dense_class_{}'.format(len(classes_count)): 'accuracy'})
model_all.compile(optimizer=Adadelta(), loss='mae')
epoch_length = 1000
validation_epoch_length = len(val_imgs)
validation_interval = 1
losses = np.zeros((epoch_length, 5))
losses_val = np.zeros((validation_epoch_length, 5))
rpn_accuracy_rpn_monitor = []
rpn_accuracy_for_epoch = []
best_loss_training = np.inf
best_loss_epoch = 0
best_total_loss_validation = np.Inf
best_loss_rpn_cls = np.inf
best_loss_rpn_regr = np.inf
best_loss_class_cls = np.inf
best_loss_class_regr = np.inf
best_class_acc = 0.0
model_classifier.summary()
print(C.summary())
print('Starting training')
train_names = [
'train_loss_rpn_cls', 'train_loss_rpn_reg', 'train_loss_class_cls',
'train_loss_class_reg', 'train_total_loss', 'train_acc'
]
val_names = [
'val_loss_rpn_cls', 'val_loss_rpn_reg', 'val_loss_class_cls',
'val_loss_class_reg', 'val_total_loss', 'val_acc'
]
epochs_without_improvement = 0
for epoch_num in range(number_of_epochs):
progbar = generic_utils.Progbar(epoch_length)
print('Epoch {}/{}'.format(epoch_num + 1, number_of_epochs))
for iter_num in range(epoch_length):
try:
if len(rpn_accuracy_rpn_monitor) == epoch_length and C.verbose:
mean_overlapping_bboxes = float(
sum(rpn_accuracy_rpn_monitor)) / len(
rpn_accuracy_rpn_monitor)
rpn_accuracy_rpn_monitor = []
print(
'\nAverage number of overlapping bounding boxes from RPN = {} for {} previous iterations'
.format(mean_overlapping_bboxes, epoch_length))
if mean_overlapping_bboxes == 0:
print(
'RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.'
)
X, Y, img_data = next(data_gen_train)
loss_rpn = model_rpn.train_on_batch(X, Y)
P_rpn = model_rpn.predict_on_batch(X)
R = roi_helpers.rpn_to_roi(
P_rpn[0],
P_rpn[1],
C,
use_regr=True,
overlap_thresh=non_max_suppression_overlap_threshold,
max_boxes=non_max_suppression_max_boxes)
# note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
X2, Y1, Y2, IouS = roi_helpers.calc_iou(
R, img_data, C, class_mapping)
if X2 is None:
rpn_accuracy_rpn_monitor.append(0)
rpn_accuracy_for_epoch.append(0)
continue
neg_samples = np.where(Y1[0, :, -1] == 1)
pos_samples = np.where(Y1[0, :, -1] == 0)
if len(neg_samples) > 0:
neg_samples = neg_samples[0]
else:
neg_samples = []
if len(pos_samples) > 0:
pos_samples = pos_samples[0]
else:
pos_samples = []
rpn_accuracy_rpn_monitor.append(len(pos_samples))
rpn_accuracy_for_epoch.append((len(pos_samples)))
if C.num_rois > 1:
if len(pos_samples) < C.num_rois // 2:
selected_pos_samples = pos_samples.tolist()
else:
selected_pos_samples = np.random.choice(
pos_samples, C.num_rois // 2,
replace=False).tolist()
try:
selected_neg_samples = np.random.choice(
neg_samples,
C.num_rois - len(selected_pos_samples),
replace=False).tolist()
except:
selected_neg_samples = np.random.choice(
neg_samples,
C.num_rois - len(selected_pos_samples),
replace=True).tolist()
sel_samples = selected_pos_samples + selected_neg_samples
else:
# in the extreme case where num_rois = 1, we pick a random pos or neg sample
if np.random.randint(0, 2):
sel_samples = random.choice(neg_samples)
else:
sel_samples = random.choice(pos_samples)
loss_class = model_classifier.train_on_batch(
[X, X2[:, sel_samples, :]],
[Y1[:, sel_samples, :], Y2[:, sel_samples, :]])
losses[iter_num, 0] = loss_rpn[1]
losses[iter_num, 1] = loss_rpn[2]
losses[iter_num, 2] = loss_class[1]
losses[iter_num, 3] = loss_class[2]
losses[iter_num, 4] = loss_class[3]
progbar.update(
iter_num + 1,
[('rpn_cls', np.mean(losses[:iter_num + 1, 0])),
('rpn_regr', np.mean(losses[:iter_num + 1, 1])),
('detector_cls', np.mean(losses[:iter_num + 1, 2])),
('detector_regr', np.mean(losses[:iter_num + 1, 3]))])
except Exception as e:
print('Exception during training: {}'.format(e))
continue
# Calculate losses after the specified number of iterations
loss_rpn_cls = np.mean(losses[:, 0])
loss_rpn_regr = np.mean(losses[:, 1])
loss_class_cls = np.mean(losses[:, 2])
loss_class_regr = np.mean(losses[:, 3])
class_acc = np.mean(losses[:, 4])
mean_overlapping_bboxes = float(
sum(rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch)
rpn_accuracy_for_epoch = []
if C.verbose:
print('[INFO TRAINING]')
print(
'Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'
.format(mean_overlapping_bboxes))
print('Classifier accuracy for bounding boxes from RPN: {}'.format(
class_acc))
print('Loss RPN classifier: {}'.format(loss_rpn_cls))
print('Loss RPN regression: {}'.format(loss_rpn_regr))
print('Loss Detector classifier: {}'.format(loss_class_cls))
print('Loss Detector regression: {}'.format(loss_class_regr))
print('Elapsed time: {}'.format(time.time() - start_time))
print("Best loss for training: {0:.3f}".format(best_loss_training))
curr_total_loss = loss_rpn_cls + loss_rpn_regr + loss_class_cls + loss_class_regr
val_start_time = time.time()
write_log(tensorboard_callback, train_names, [
loss_rpn_cls, loss_rpn_regr, loss_class_cls, loss_class_regr,
curr_total_loss, class_acc
], epoch_num)
if curr_total_loss < best_loss_training:
model_path = C.model_path[:-5] + "_training.hdf5"
if C.verbose:
print(
'Total training loss decreased from {0:.3f} to {1:.3f}, saving weights to {2}'
.format(best_loss_training, curr_total_loss, model_path))
best_loss_training = curr_total_loss
model_all.save_weights(model_path)
#############
# VALIDATION
#############
if (epoch_num + 1) % validation_interval != 0:
continue
progbar = generic_utils.Progbar(validation_epoch_length)
for iter_num in range(validation_epoch_length):
try:
X, Y, img_data = next(data_gen_val)
loss_rpn = model_rpn.test_on_batch(X, Y)
P_rpn = model_rpn.predict_on_batch(X)
R = roi_helpers.rpn_to_roi(
P_rpn[0],
P_rpn[1],
C,
use_regr=True,
overlap_thresh=non_max_suppression_overlap_threshold,
max_boxes=non_max_suppression_max_boxes)
# note: calc_iou converts from (x1,y1,x2,y2) to (x,y,w,h) format
X2, Y1, Y2, IouS = roi_helpers.calc_iou(
R, img_data, C, class_mapping)
neg_samples = np.where(Y1[0, :, -1] == 1)
pos_samples = np.where(Y1[0, :, -1] == 0)
if len(neg_samples) > 0:
neg_samples = neg_samples[0]
else:
neg_samples = []
if len(pos_samples) > 0:
pos_samples = pos_samples[0]
else:
pos_samples = []
rpn_accuracy_rpn_monitor.append(len(pos_samples))
rpn_accuracy_for_epoch.append((len(pos_samples)))
if C.num_rois > 1:
if len(pos_samples) < C.num_rois // 2:
selected_pos_samples = pos_samples.tolist()
else:
selected_pos_samples = np.random.choice(
pos_samples, C.num_rois // 2,
replace=False).tolist()
try:
selected_neg_samples = np.random.choice(
neg_samples,
C.num_rois - len(selected_pos_samples),
replace=False).tolist()
except:
selected_neg_samples = np.random.choice(
neg_samples,
C.num_rois - len(selected_pos_samples),
replace=True).tolist()
sel_samples = selected_pos_samples + selected_neg_samples
else:
# in the extreme case where num_rois = 1, we pick a random pos or neg sample
selected_pos_samples = pos_samples.tolist()
selected_neg_samples = neg_samples.tolist()
if np.random.randint(0, 2):
sel_samples = random.choice(neg_samples)
else:
sel_samples = random.choice(pos_samples)
loss_class = model_classifier.test_on_batch(
[X, X2[:, sel_samples, :]],
[Y1[:, sel_samples, :], Y2[:, sel_samples, :]])
losses_val[iter_num, 0] = loss_rpn[1]
losses_val[iter_num, 1] = loss_rpn[2]
losses_val[iter_num, 2] = loss_class[1]
losses_val[iter_num, 3] = loss_class[2]
losses_val[iter_num, 4] = loss_class[3]
progbar.update(
iter_num + 1,
[('rpn_cls', np.mean(losses_val[:iter_num + 1, 0])),
('rpn_regr', np.mean(losses_val[:iter_num + 1, 1])),
('detector_cls', np.mean(losses_val[:iter_num + 1, 2])),
('detector_regr', np.mean(losses_val[:iter_num + 1, 3]))])
except Exception as e:
print('Exception during validation: {}'.format(e))
continue
# Computer aggregated losses
loss_rpn_cls = np.mean(losses_val[:, 0])
loss_rpn_regr = np.mean(losses_val[:, 1])
loss_class_cls = np.mean(losses_val[:, 2])
loss_class_regr = np.mean(losses_val[:, 3])
class_acc = np.mean(losses[:, 4])
mean_overlapping_bboxes = float(
sum(rpn_accuracy_for_epoch)) / len(rpn_accuracy_for_epoch)
rpn_accuracy_for_epoch = []
curr_total_loss = loss_rpn_cls + loss_rpn_regr + loss_class_cls + loss_class_regr
write_log(tensorboard_callback, val_names, [
loss_rpn_cls, loss_rpn_regr, loss_class_cls, loss_class_regr,
curr_total_loss, class_acc
], epoch_num)
if C.verbose:
print('[INFO VALIDATION]')
print(
'Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'
.format(mean_overlapping_bboxes))
print('Classifier accuracy for bounding boxes from RPN: {}'.format(
class_acc))
print('Loss RPN classifier: {}'.format(loss_rpn_cls))
print('Loss RPN regression: {}'.format(loss_rpn_regr))
print('Loss Detector classifier: {}'.format(loss_class_cls))
print('Loss Detector regression: {}'.format(loss_class_regr))
print(
"Current validation loss: {0:.3f}, Best validation loss: {1:.3f} at epoch: {2}"
.format(curr_total_loss, best_total_loss_validation,
best_loss_epoch))
print('Elapsed time: {}'.format(time.time() - val_start_time))
if curr_total_loss < best_total_loss_validation:
if C.verbose:
print(
'Total validation loss decreased from {0:.3f} to {1:.3f}, saving weights to {2}'
.format(best_total_loss_validation, curr_total_loss,
C.model_path))
best_total_loss_validation = curr_total_loss
best_loss_rpn_cls = loss_rpn_cls
best_loss_rpn_regr = loss_rpn_regr
best_loss_class_cls = loss_class_cls
best_loss_class_regr = loss_class_regr
best_class_acc = class_acc
best_loss_epoch = epoch_num
model_all.save_weights(C.model_path)
epochs_without_improvement = 0
else:
epochs_without_improvement += validation_interval
if epochs_without_improvement > early_stopping:
print(
"Early stopping training after {0} epochs without improvement on validation set"
.format(epochs_without_improvement))
break
if epochs_without_improvement > learning_rate_reduction_patience:
current_learning_rate = K.get_value(model_classifier.optimizer.lr)
new_learning_rate = current_learning_rate * learning_rate_reduction_factor
print(
"Not improved validation accuracy for {0} epochs. Reducing learning rate from {1} to {2}"
.format(learning_rate_reduction_patience,
current_learning_rate, new_learning_rate))
K.set_value(model_classifier.optimizer.lr, new_learning_rate)
K.set_value(model_rpn.optimizer.lr, new_learning_rate)
K.set_value(model_all.optimizer.lr, new_learning_rate)
end_time = time.time()
execution_time_in_seconds = round(end_time - start_time)
print("Execution time: {0:.1f}s".format(end_time - start_time))
notification_message = "Training on {0} dataset with model {1} and configuration {2} finished. " \
"Val. accuracy: {3:0.5f}%".format("muscima_pp",
model_name,
configuration_name,
best_class_acc * 100)
TelegramNotifier.send_message_via_telegram(notification_message)
today = "{0:02d}.{1:02d}.{2}".format(start_of_training.day,
start_of_training.month,
start_of_training.year)
GoogleSpreadsheetReporter.append_result_to_spreadsheet(
dataset_size=len(all_images),
model_name=model_name,
configuration_name=configuration_name,
data_augmentation="",
early_stopping=early_stopping,
reduction_patience=learning_rate_reduction_patience,
learning_rate_reduction_factor=learning_rate_reduction_factor,
optimizer="Adadelta",
initial_learning_rate=1.0,
non_max_suppression_overlap_threshold=
non_max_suppression_overlap_threshold,
non_max_suppression_max_boxes=non_max_suppression_max_boxes,
validation_accuracy=best_class_acc,
validation_total_loss=best_total_loss_validation,
best_loss_rpn_cls=best_loss_rpn_cls,
best_loss_rpn_regr=best_loss_rpn_regr,
best_loss_class_cls=best_loss_class_cls,
best_loss_class_regr=best_loss_class_regr,
date=today,
datasets="muscima_pp",
execution_time_in_seconds=execution_time_in_seconds)
import os
import shutil
from omrdatasettools.converters.ImageInverter import ImageInverter
from omrdatasettools.downloaders.CvcMuscimaDatasetDownloader import CvcMuscimaDatasetDownloader, CvcMuscimaDataset
from omrdatasettools.downloaders.MuscimaPlusPlusDatasetDownloader import MuscimaPlusPlusDatasetDownloader
if __name__ == "__main__":
dataset_directory = "data"
muscima_pp_raw_dataset_directory = os.path.join(dataset_directory,
"muscima_pp_raw")
muscima_image_directory = os.path.join(dataset_directory,
"cvcmuscima_staff_removal")
print("Deleting dataset directory {0}".format(dataset_directory))
if os.path.exists(dataset_directory):
shutil.rmtree(dataset_directory, ignore_errors=True)
downloader = MuscimaPlusPlusDatasetDownloader(
muscima_pp_raw_dataset_directory)
downloader.download_and_extract_dataset()
downloader = CvcMuscimaDatasetDownloader(muscima_image_directory,
CvcMuscimaDataset.StaffRemoval)
downloader.download_and_extract_dataset()
inverter = ImageInverter()
# We would like to work with black-on-white images instead of white-on-black images
inverter.invert_images(muscima_image_directory, "*.png")