qurator-spk/sbb_pixelwise_segmentation
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adding foreground rgb to augmentation

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vahidrezanezhad 2024-08-28 02:09:27 +02:00
parent 4f0e3efa2b
commit c502e67c14
3 changed files with 57 additions and 12 deletions
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10
config_params.json
View file

@ -13,13 +13,14 @@
"augmentation" : true, "augmentation" : true,
"flip_aug" : false, "flip_aug" : false,
"blur_aug" : false, "blur_aug" : false,
"scaling" : true, "scaling" : false,
"adding_rgb_background": true, "adding_rgb_background": true,
"add_red_textlines": true, "adding_rgb_foreground": true,
"channels_shuffling": true, "add_red_textlines": false,
"channels_shuffling": false,
"degrading": false, "degrading": false,
"brightening": false, "brightening": false,
"binarization" : false, "binarization" : true,
"scaling_bluring" : false, "scaling_bluring" : false,
"scaling_binarization" : false, "scaling_binarization" : false,
"scaling_flip" : false, "scaling_flip" : false,
@ -51,6 +52,7 @@
"dir_eval": "/home/vahid/Documents/test/sbb_pixelwise_segmentation/test_label/pageextractor_test/eval_new", "dir_eval": "/home/vahid/Documents/test/sbb_pixelwise_segmentation/test_label/pageextractor_test/eval_new",
"dir_output": "/home/vahid/Documents/test/sbb_pixelwise_segmentation/test_label/pageextractor_test/output_new", "dir_output": "/home/vahid/Documents/test/sbb_pixelwise_segmentation/test_label/pageextractor_test/output_new",
"dir_rgb_backgrounds": "/home/vahid/Documents/1_2_test_eynollah/set_rgb_background", "dir_rgb_backgrounds": "/home/vahid/Documents/1_2_test_eynollah/set_rgb_background",
"dir_rgb_foregrounds": "/home/vahid/Documents/1_2_test_eynollah/out_set_rgb_foreground",
"dir_img_bin": "/home/vahid/Documents/test/sbb_pixelwise_segmentation/test_label/pageextractor_test/train_new/images_bin" "dir_img_bin": "/home/vahid/Documents/test/sbb_pixelwise_segmentation/test_label/pageextractor_test/train_new/images_bin"
} }

19
train.py
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@ -54,6 +54,7 @@ def config_params():
brightening = False # If true, brightening will be applied to the image. The amount of brightening is defined with "brightness" 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. binarization = False # If true, Otsu thresholding will be applied to augment the input with binarized images.
adding_rgb_background = False adding_rgb_background = False
adding_rgb_foreground = False
add_red_textlines = False add_red_textlines = False
channels_shuffling = False channels_shuffling = False
dir_train = None # Directory of training dataset with subdirectories having the names "images" and "labels". dir_train = None # Directory of training dataset with subdirectories having the names "images" and "labels".
@ -95,6 +96,7 @@ def config_params():
dir_img_bin = None dir_img_bin = None
number_of_backgrounds_per_image = 1 number_of_backgrounds_per_image = 1
dir_rgb_backgrounds = None dir_rgb_backgrounds = None
dir_rgb_foregrounds = None
@ex.automain @ex.automain
@ -103,20 +105,25 @@ def run(_config, n_classes, n_epochs, input_height,
index_start, dir_of_start_model, is_loss_soft_dice, index_start, dir_of_start_model, is_loss_soft_dice,
n_batch, patches, augmentation, flip_aug, n_batch, patches, augmentation, flip_aug,
blur_aug, padding_white, padding_black, scaling, degrading,channels_shuffling, blur_aug, padding_white, padding_black, scaling, degrading,channels_shuffling,
brightening, binarization, adding_rgb_background, add_red_textlines, blur_k, scales, degrade_scales,shuffle_indexes, brightening, binarization, adding_rgb_background, adding_rgb_foreground, add_red_textlines, blur_k, scales, degrade_scales,shuffle_indexes,
brightness, dir_train, data_is_provided, scaling_bluring, brightness, dir_train, data_is_provided, scaling_bluring,
scaling_brightness, scaling_binarization, rotation, rotation_not_90, scaling_brightness, scaling_binarization, rotation, rotation_not_90,
thetha, scaling_flip, continue_training, transformer_projection_dim, thetha, scaling_flip, continue_training, transformer_projection_dim,
transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_cnn_first, transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_cnn_first,
transformer_patchsize_x, transformer_patchsize_y, transformer_patchsize_x, transformer_patchsize_y,
transformer_num_patches_xy, backbone_type, flip_index, dir_eval, dir_output, transformer_num_patches_xy, backbone_type, flip_index, dir_eval, dir_output,
pretraining, learning_rate, task, f1_threshold_classification, classification_classes_name, dir_img_bin, number_of_backgrounds_per_image,dir_rgb_backgrounds): pretraining, learning_rate, task, f1_threshold_classification, classification_classes_name, dir_img_bin, number_of_backgrounds_per_image,dir_rgb_backgrounds, dir_rgb_foregrounds):
if dir_rgb_backgrounds: if dir_rgb_backgrounds:
list_all_possible_background_images = os.listdir(dir_rgb_backgrounds) list_all_possible_background_images = os.listdir(dir_rgb_backgrounds)
else: else:
list_all_possible_background_images = None list_all_possible_background_images = None
if dir_rgb_foregrounds:
list_all_possible_foreground_rgbs = os.listdir(dir_rgb_foregrounds)
else:
list_all_possible_foreground_rgbs = None
if task == "segmentation" or task == "enhancement" or task == "binarization": if task == "segmentation" or task == "enhancement" or task == "binarization":
if data_is_provided: if data_is_provided:
dir_train_flowing = os.path.join(dir_output, 'train') dir_train_flowing = os.path.join(dir_output, 'train')
@ -175,18 +182,18 @@ def run(_config, n_classes, n_epochs, input_height,
# writing patches into a sub-folder in order to be flowed from directory. # 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, provide_patches(imgs_list, segs_list, dir_img, dir_seg, dir_flow_train_imgs,
dir_flow_train_labels, input_height, input_width, blur_k, dir_flow_train_labels, input_height, input_width, blur_k,
blur_aug, padding_white, padding_black, flip_aug, binarization, adding_rgb_background,add_red_textlines, channels_shuffling, blur_aug, padding_white, padding_black, flip_aug, binarization, adding_rgb_background,adding_rgb_foreground, add_red_textlines, channels_shuffling,
scaling, degrading, brightening, scales, degrade_scales, brightness, scaling, degrading, brightening, scales, degrade_scales, brightness,
flip_index,shuffle_indexes, scaling_bluring, scaling_brightness, scaling_binarization, flip_index,shuffle_indexes, scaling_bluring, scaling_brightness, scaling_binarization,
rotation, rotation_not_90, thetha, scaling_flip, task, augmentation=augmentation, rotation, rotation_not_90, thetha, scaling_flip, task, augmentation=augmentation,
patches=patches, dir_img_bin=dir_img_bin,number_of_backgrounds_per_image=number_of_backgrounds_per_image,list_all_possible_background_images=list_all_possible_background_images, dir_rgb_backgrounds=dir_rgb_backgrounds) patches=patches, dir_img_bin=dir_img_bin,number_of_backgrounds_per_image=number_of_backgrounds_per_image,list_all_possible_background_images=list_all_possible_background_images, dir_rgb_backgrounds=dir_rgb_backgrounds, dir_rgb_foregrounds=dir_rgb_foregrounds,list_all_possible_foreground_rgbs=list_all_possible_foreground_rgbs)
provide_patches(imgs_list_test, segs_list_test, dir_img_val, dir_seg_val, 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, dir_flow_eval_imgs, dir_flow_eval_labels, input_height, input_width,
blur_k, blur_aug, padding_white, padding_black, flip_aug, binarization, adding_rgb_background, add_red_textlines, channels_shuffling, blur_k, blur_aug, padding_white, padding_black, flip_aug, binarization, adding_rgb_background, adding_rgb_foreground, add_red_textlines, channels_shuffling,
scaling, degrading, brightening, scales, degrade_scales, brightness, scaling, degrading, brightening, scales, degrade_scales, brightness,
flip_index, shuffle_indexes, scaling_bluring, scaling_brightness, scaling_binarization, flip_index, shuffle_indexes, scaling_bluring, scaling_brightness, scaling_binarization,
rotation, rotation_not_90, thetha, scaling_flip, task, augmentation=False, patches=patches,dir_img_bin=dir_img_bin,number_of_backgrounds_per_image=number_of_backgrounds_per_image,list_all_possible_background_images=list_all_possible_background_images, dir_rgb_backgrounds=dir_rgb_backgrounds) rotation, rotation_not_90, thetha, scaling_flip, task, augmentation=False, patches=patches,dir_img_bin=dir_img_bin,number_of_backgrounds_per_image=number_of_backgrounds_per_image,list_all_possible_background_images=list_all_possible_background_images, dir_rgb_backgrounds=dir_rgb_backgrounds,dir_rgb_foregrounds=dir_rgb_foregrounds,list_all_possible_foreground_rgbs=list_all_possible_foreground_rgbs )
if weighted_loss: if weighted_loss:
weights = np.zeros(n_classes) weights = np.zeros(n_classes)

40
utils.py
View file

@ -40,6 +40,25 @@ def return_binary_image_with_given_rgb_background(img_bin, img_rgb_background):
return img_final return img_final
def return_binary_image_with_given_rgb_background_and_given_foreground_rgb(img_bin, img_rgb_background, rgb_foreground):
img_rgb_background = resize_image(img_rgb_background ,img_bin.shape[0], img_bin.shape[1])
img_final = np.copy(img_bin)
img_foreground = np.zeros(img_bin.shape)
img_foreground[:,:,0][img_bin[:,:,0] == 0] = rgb_foreground[0]
img_foreground[:,:,1][img_bin[:,:,0] == 0] = rgb_foreground[1]
img_foreground[:,:,2][img_bin[:,:,0] == 0] = rgb_foreground[2]
img_final[:,:,0][img_bin[:,:,0] != 0] = img_rgb_background[:,:,0][img_bin[:,:,0] != 0]
img_final[:,:,1][img_bin[:,:,1] != 0] = img_rgb_background[:,:,1][img_bin[:,:,1] != 0]
img_final[:,:,2][img_bin[:,:,2] != 0] = img_rgb_background[:,:,2][img_bin[:,:,2] != 0]
img_final = img_final + img_foreground
return img_final
def return_binary_image_with_given_rgb_background_red_textlines(img_bin, img_rgb_background, img_color): def return_binary_image_with_given_rgb_background_red_textlines(img_bin, img_rgb_background, img_color):
img_rgb_background = resize_image(img_rgb_background ,img_bin.shape[0], img_bin.shape[1]) img_rgb_background = resize_image(img_rgb_background ,img_bin.shape[0], img_bin.shape[1])
@ -641,10 +660,10 @@ def get_patches_num_scale_new(dir_img_f, dir_seg_f, img, label, height, width, i
def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow_train_imgs, def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow_train_imgs,
dir_flow_train_labels, input_height, input_width, blur_k, blur_aug, dir_flow_train_labels, input_height, input_width, blur_k, blur_aug,
padding_white, padding_black, flip_aug, binarization, adding_rgb_background, add_red_textlines, channels_shuffling, scaling, degrading, padding_white, padding_black, flip_aug, binarization, adding_rgb_background, adding_rgb_foreground, add_red_textlines, channels_shuffling, scaling, degrading,
brightening, scales, degrade_scales, brightness, flip_index, shuffle_indexes, brightening, scales, degrade_scales, brightness, flip_index, shuffle_indexes,
scaling_bluring, scaling_brightness, scaling_binarization, rotation, scaling_bluring, scaling_brightness, scaling_binarization, rotation,
rotation_not_90, thetha, scaling_flip, task, augmentation=False, patches=False, dir_img_bin=None,number_of_backgrounds_per_image=None,list_all_possible_background_images=None, dir_rgb_backgrounds=None): rotation_not_90, thetha, scaling_flip, task, augmentation=False, patches=False, dir_img_bin=None,number_of_backgrounds_per_image=None,list_all_possible_background_images=None, dir_rgb_backgrounds=None, dir_rgb_foregrounds=None, list_all_possible_foreground_rgbs=None):
indexer = 0 indexer = 0
for im, seg_i in tqdm(zip(imgs_list_train, segs_list_train)): for im, seg_i in tqdm(zip(imgs_list_train, segs_list_train)):
@ -754,6 +773,23 @@ def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow
indexer += 1 indexer += 1
if adding_rgb_foreground:
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)
foreground_rgb_chosen_name = random.choice(list_all_possible_foreground_rgbs)
img_rgb_background_chosen = cv2.imread(dir_rgb_backgrounds + '/' + background_image_chosen_name)
foreground_rgb_chosen = np.load(dir_rgb_foregrounds + '/' + foreground_rgb_chosen_name)
img_with_overlayed_background = return_binary_image_with_given_rgb_background_and_given_foreground_rgb(img_bin_corr, img_rgb_background_chosen, foreground_rgb_chosen)
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))
indexer += 1
if add_red_textlines: if add_red_textlines:
img_bin_corr = cv2.imread(dir_img_bin + '/' + img_name+'.png') img_bin_corr = cv2.imread(dir_img_bin + '/' + img_name+'.png')
img_red_context = return_image_with_red_elements(cv2.imread(dir_img + '/'+im), img_bin_corr) img_red_context = return_image_with_red_elements(cv2.imread(dir_img + '/'+im), img_bin_corr)