batch_size=batch_size_value,
shape=(img_height, img_width, img_channels),
shuffle=True,
da=True,
rotation90=rotation90,
rotation_range=rotation_range,
vflip=vflips,
hflip=hflips,
elastic=elastic,
g_blur=g_blur,
median_blur=median_blur,
gamma_contrast=gamma_contrast,
zoom=zoom,
random_crops_in_DA=random_crops_in_DA)
extra_generator = ImageDataGenerator(**extra_gen_args)
extra_x, extra_y = extra_generator.get_transformed_samples(
extra_train_data, force_full_images=True)
X_train = np.vstack((X_train, extra_x))
Y_train = np.vstack((Y_train, extra_y))
print("{} extra train data generated, the new shape of the train now is {}"\
.format(extra_train_data, X_train.shape))
print("#######################\n"
"# DATA AUGMENTATION #\n"
"#######################\n")
if custom_da == False:
print("Keras DA selected")
zoom=zoom,
random_crops_in_DA=random_crops_in_DA,
prob_map=probability_map,
train_prob=train_prob,
n_classes=n_classes,
extra_data_factor=replicate_train)
data_gen_val_args = dict(X=X_val,
Y=Y_val,
batch_size=batch_size_value,
shape=(img_height, img_width, img_channels),
shuffle=shuffle_val_data_each_epoch,
da=False,
random_crops_in_DA=random_crops_in_DA,
val=True,
n_classes=n_classes)
train_generator = ImageDataGenerator(**data_gen_args)
val_generator = ImageDataGenerator(**data_gen_val_args)
# Generate examples of data augmentation
if aug_examples:
train_generator.get_transformed_samples(10,
save_to_dir=True,
train=False,
out_dir=da_samples_dir)
print("#################################\n"
"# BUILD AND TRAIN THE NETWORK #\n"
"#################################\n")
print("Creating the network . . .")
model = U_Net_2D([img_height, img_width, img_channels],
