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sbb_pixelwise_segmentation
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scaling, channels shuffling, rgb background and red content added to no patch augmentation

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pull/18/head
vahidrezanezhad 4 months ago
parent 95bbdf8040
commit f31219b1c9
3 changed files with 68 additions and 26 deletions
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30
config_params.json
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@ -1,19 +1,22 @@
{ {
"backbone_type" : "transformer", "backbone_type" : "transformer",
"task": "binarization", "task": "segmentation",
"n_classes" : 2, "n_classes" : 2,
"n_epochs" : 2, "n_epochs" : 0,
"input_height" : 224, "input_height" : 448,
"input_width" : 224, "input_width" : 448,
"weight_decay" : 1e-6, "weight_decay" : 1e-6,
"n_batch" : 1, "n_batch" : 1,
"learning_rate": 1e-4, "learning_rate": 1e-4,
"patches" : true, "patches" : false,
"pretraining" : true, "pretraining" : true,
"augmentation" : false, "augmentation" : true,
"flip_aug" : false, "flip_aug" : false,
"blur_aug" : false, "blur_aug" : false,
"scaling" : true, "scaling" : true,
"adding_rgb_background": true,
"add_red_textlines": true,
"channels_shuffling": true,
"degrading": false, "degrading": false,
"brightening": false, "brightening": false,
"binarization" : false, "binarization" : false,
@ -31,18 +34,23 @@
"transformer_num_heads": 1, "transformer_num_heads": 1,
"transformer_cnn_first": false, "transformer_cnn_first": false,
"blur_k" : ["blur","guass","median"], "blur_k" : ["blur","guass","median"],
"scales" : [0.6, 0.7, 0.8, 0.9, 1.1, 1.2, 1.4], "scales" : [0.6, 0.7, 0.8, 0.9],
"brightness" : [1.3, 1.5, 1.7, 2], "brightness" : [1.3, 1.5, 1.7, 2],
"degrade_scales" : [0.2, 0.4], "degrade_scales" : [0.2, 0.4],
"flip_index" : [0, 1, -1], "flip_index" : [0, 1, -1],
"thetha" : [10, -10], "shuffle_indexes" : [ [0,2,1], [1,2,0], [1,0,2] , [2,1,0]],
"thetha" : [5, -5],
"number_of_backgrounds_per_image": 2,
"continue_training": false, "continue_training": false,
"index_start" : 0, "index_start" : 0,
"dir_of_start_model" : " ", "dir_of_start_model" : " ",
"weighted_loss": false, "weighted_loss": false,
"is_loss_soft_dice": false, "is_loss_soft_dice": false,
"data_is_provided": false, "data_is_provided": false,
"dir_train": "./train", "dir_train": "/home/vahid/Documents/test/sbb_pixelwise_segmentation/test_label/pageextractor_test/train_new",
"dir_eval": "./eval", "dir_eval": "/home/vahid/Documents/test/sbb_pixelwise_segmentation/test_label/pageextractor_test/eval_new",
"dir_output": "./output" "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_img_bin": "/home/vahid/Documents/test/sbb_pixelwise_segmentation/test_label/pageextractor_test/train_new/images_bin"
} }

32
train.py
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@ -53,7 +53,9 @@ 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. 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. 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.
rgb_background = False adding_rgb_background = False
add_red_textlines = 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".
dir_eval = None # Directory of validation 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. dir_output = None # Directory where the output model will be saved.
@ -65,6 +67,7 @@ def config_params():
scaling_brightness = False # If true, a combination of scaling and brightening will be applied to the image. scaling_brightness = False # If true, a combination of scaling and brightening will be applied to the image.
scaling_flip = False # If true, a combination of scaling and flipping will be applied to the image. scaling_flip = False # If true, a combination of scaling and flipping will be applied to the image.
thetha = None # Rotate image by these angles for augmentation. thetha = None # Rotate image by these angles for augmentation.
shuffle_indexes = None
blur_k = None # Blur image for augmentation. blur_k = None # Blur image for augmentation.
scales = None # Scale patches for augmentation. scales = None # Scale patches for augmentation.
degrade_scales = None # Degrade image for augmentation. degrade_scales = None # Degrade image for augmentation.
@ -88,6 +91,10 @@ def config_params():
f1_threshold_classification = None # This threshold is used to consider models with an evaluation f1 scores bigger than it. The selected model weights undergo a weights ensembling. And avreage ensembled model will be written to output. f1_threshold_classification = None # This threshold is used to consider models with an evaluation f1 scores bigger than it. The selected model weights undergo a weights ensembling. And avreage ensembled model will be written to output.
classification_classes_name = None # Dictionary of classification classes names. classification_classes_name = None # Dictionary of classification classes names.
backbone_type = None # As backbone we have 2 types of backbones. A vision transformer alongside a CNN and we call it "transformer" and only CNN called "nontransformer" backbone_type = None # As backbone we have 2 types of backbones. A vision transformer alongside a CNN and we call it "transformer" and only CNN called "nontransformer"
dir_img_bin = None
number_of_backgrounds_per_image = 1
dir_rgb_backgrounds = None
@ex.automain @ex.automain
@ -95,15 +102,20 @@ def run(_config, n_classes, n_epochs, input_height,
input_width, weight_decay, weighted_loss, input_width, weight_decay, weighted_loss,
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, blur_aug, padding_white, padding_black, scaling, degrading,channels_shuffling,
brightening, binarization, rgb_background, blur_k, scales, degrade_scales, brightening, binarization, adding_rgb_background, 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): pretraining, learning_rate, task, f1_threshold_classification, classification_classes_name, dir_img_bin, number_of_backgrounds_per_image,dir_rgb_backgrounds):
if dir_rgb_backgrounds:
list_all_possible_background_images = os.listdir(dir_rgb_backgrounds)
else:
list_all_possible_background_images = 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:
@ -163,18 +175,18 @@ 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, rgb_background, blur_aug, padding_white, padding_black, flip_aug, binarization, adding_rgb_background,add_red_textlines, channels_shuffling,
scaling, degrading, brightening, scales, degrade_scales, brightness, scaling, degrading, brightening, scales, degrade_scales, brightness,
flip_index, 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) 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)
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, rgb_background, blur_k, blur_aug, padding_white, padding_black, flip_aug, binarization, adding_rgb_background, add_red_textlines, channels_shuffling,
scaling, degrading, brightening, scales, degrade_scales, brightness, scaling, degrading, brightening, scales, degrade_scales, brightness,
flip_index, 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) 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)
if weighted_loss: if weighted_loss:
weights = np.zeros(n_classes) weights = np.zeros(n_classes)

32
utils.py
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@ -51,6 +51,16 @@ def return_binary_image_with_given_rgb_background_red_textlines(img_bin, img_rgb
return img_final return img_final
def return_image_with_red_elements(img, img_bin):
img_final = np.copy(img)
img_final[:,:,0][img_bin[:,:,0]==0] = 0
img_final[:,:,1][img_bin[:,:,0]==0] = 0
img_final[:,:,2][img_bin[:,:,0]==0] = 255
return img_final
def scale_image_for_no_patch(img, label, scale): def scale_image_for_no_patch(img, label, scale):
h_n = int(img.shape[0]*scale) h_n = int(img.shape[0]*scale)
w_n = int(img.shape[1]*scale) w_n = int(img.shape[1]*scale)
@ -631,10 +641,10 @@ 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, scaling, degrading, padding_white, padding_black, flip_aug, binarization, adding_rgb_background, add_red_textlines, channels_shuffling, scaling, degrading,
brightening, scales, degrade_scales, brightness, flip_index, 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): 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):
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)):
@ -724,17 +734,29 @@ 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(label_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 indexer += 1
if rgb_color_background: if adding_rgb_background:
img_bin_corr = cv2.imread(dir_img_bin + '/' + img_name+'.png') img_bin_corr = cv2.imread(dir_img_bin + '/' + img_name+'.png')
for i_n in range(number_of_backgrounds_per_image): for i_n in range(number_of_backgrounds_per_image):
background_image_chosen_name = random.choice(list_all_possible_background_images) 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_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) img_with_overlayed_background = return_binary_image_with_given_rgb_background(img_bin_corr, img_rgb_background_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_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', cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
resize_image(cv2.imread(dir_of_label_file), input_height, input_width)) resize_image(cv2.imread(dir_of_label_file), input_height, input_width))
indexer += 1
if add_red_textlines:
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)
cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png', resize_image(img_red_context, 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

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