qurator-spk
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sbb_pixelwise_segmentation
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updating augmentations

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pull/18/head
vahidrezanezhad 4 months ago
parent 7be326d689
commit 95bbdf8040
2 changed files with 46 additions and 3 deletions
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8
train.py
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@ -53,6 +53,7 @@ def config_params():
degrading = False # If true, degrading will be applied to the image. The amount of degrading is defined with "degrade_scales" in config_params.json.
brightening = False # If true, brightening will be applied to the image. The amount of brightening is defined with "brightness" in config_params.json.
binarization = False # If true, Otsu thresholding will be applied to augment the input with binarized images.
rgb_background = False
dir_train = None # Directory of training dataset with subdirectories having the names "images" and "labels".
dir_eval = None # Directory of validation dataset with subdirectories having the names "images" and "labels".
dir_output = None # Directory where the output model will be saved.
@ -95,7 +96,7 @@ def run(_config, n_classes, n_epochs, input_height,
index_start, dir_of_start_model, is_loss_soft_dice,
n_batch, patches, augmentation, flip_aug,
blur_aug, padding_white, padding_black, scaling, degrading,
brightening, binarization, blur_k, scales, degrade_scales,
brightening, binarization, rgb_background, blur_k, scales, degrade_scales,
brightness, dir_train, data_is_provided, scaling_bluring,
scaling_brightness, scaling_binarization, rotation, rotation_not_90,
thetha, scaling_flip, continue_training, transformer_projection_dim,
@ -108,6 +109,7 @@ def run(_config, n_classes, n_epochs, input_height,
if data_is_provided:
dir_train_flowing = os.path.join(dir_output, 'train')
dir_eval_flowing = os.path.join(dir_output, 'eval')
dir_flow_train_imgs = os.path.join(dir_train_flowing, 'images')
dir_flow_train_labels = os.path.join(dir_train_flowing, 'labels')
@ -161,7 +163,7 @@ def run(_config, n_classes, n_epochs, input_height,
# writing patches into a sub-folder in order to be flowed from directory.
provide_patches(imgs_list, segs_list, dir_img, dir_seg, dir_flow_train_imgs,
dir_flow_train_labels, input_height, input_width, blur_k,
blur_aug, padding_white, padding_black, flip_aug, binarization,
blur_aug, padding_white, padding_black, flip_aug, binarization, rgb_background,
scaling, degrading, brightening, scales, degrade_scales, brightness,
flip_index, scaling_bluring, scaling_brightness, scaling_binarization,
rotation, rotation_not_90, thetha, scaling_flip, task, augmentation=augmentation,
@ -169,7 +171,7 @@ def run(_config, n_classes, n_epochs, input_height,
provide_patches(imgs_list_test, segs_list_test, dir_img_val, dir_seg_val,
dir_flow_eval_imgs, dir_flow_eval_labels, input_height, input_width,
blur_k, blur_aug, padding_white, padding_black, flip_aug, binarization,
blur_k, blur_aug, padding_white, padding_black, flip_aug, binarization, rgb_background,
scaling, degrading, brightening, scales, degrade_scales, brightness,
flip_index, scaling_bluring, scaling_brightness, scaling_binarization,
rotation, rotation_not_90, thetha, scaling_flip, task, augmentation=False, patches=patches)

41
utils.py
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@ -695,6 +695,47 @@ def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow
cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
resize_image(cv2.imread(dir_of_label_file), input_height, input_width))
indexer += 1
if rotation_not_90:
for thetha_i in thetha:
img_max_rotated, label_max_rotated = rotation_not_90_func(cv2.imread(dir_img + '/'+im),
cv2.imread(dir_of_label_file), thetha_i)
cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png', resize_image(img_max_rotated, input_height, input_width))
cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png', resize_image(label_max_rotated, input_height, input_width))
indexer += 1
if channels_shuffling:
for shuffle_index in shuffle_indexes:
cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png',
(resize_image(return_shuffled_channels(cv2.imread(dir_img + '/' + im), shuffle_index), input_height, input_width)))
cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
resize_image(cv2.imread(dir_of_label_file), input_height, input_width))
indexer += 1
if scaling:
for sc_ind in scales:
img_scaled, label_scaled = scale_image_for_no_patch(cv2.imread(dir_img + '/'+im),
cv2.imread(dir_of_label_file), sc_ind)
cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png', resize_image(img_scaled, input_height, input_width))
cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png', resize_image(label_scaled, input_height, input_width))
indexer += 1
if rgb_color_background:
img_bin_corr = cv2.imread(dir_img_bin + '/' + img_name+'.png')
for i_n in range(number_of_backgrounds_per_image):
background_image_chosen_name = random.choice(list_all_possible_background_images)
img_rgb_background_chosen = cv2.imread(dir_rgb_backgrounds + '/' + background_image_chosen_name)
img_with_overlayed_background = return_binary_image_with_given_rgb_background(img_bin_corr, img_rgb_background)
cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png', resize_image(img_with_overlayed_background, input_height, input_width))
cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
resize_image(cv2.imread(dir_of_label_file), input_height, input_width))
if patches:

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