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https://github.com/qurator-spk/sbb_pixelwise_segmentation.git
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adding foreground rgb to augmentation
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parent
4f0e3efa2b
commit
c502e67c14
3 changed files with 57 additions and 12 deletions
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@ -13,13 +13,14 @@
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"augmentation" : true,
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"augmentation" : true,
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"flip_aug" : false,
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"flip_aug" : false,
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"blur_aug" : false,
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"blur_aug" : false,
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"scaling" : true,
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"scaling" : false,
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"adding_rgb_background": true,
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"adding_rgb_background": true,
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"add_red_textlines": true,
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"adding_rgb_foreground": true,
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"channels_shuffling": true,
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"add_red_textlines": false,
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"channels_shuffling": false,
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"degrading": false,
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"degrading": false,
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"brightening": false,
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"brightening": false,
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"binarization" : false,
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"binarization" : true,
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"scaling_bluring" : false,
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"scaling_bluring" : false,
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"scaling_binarization" : false,
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"scaling_binarization" : false,
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"scaling_flip" : false,
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"scaling_flip" : false,
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@ -51,6 +52,7 @@
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"dir_eval": "/home/vahid/Documents/test/sbb_pixelwise_segmentation/test_label/pageextractor_test/eval_new",
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"dir_eval": "/home/vahid/Documents/test/sbb_pixelwise_segmentation/test_label/pageextractor_test/eval_new",
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"dir_output": "/home/vahid/Documents/test/sbb_pixelwise_segmentation/test_label/pageextractor_test/output_new",
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"dir_output": "/home/vahid/Documents/test/sbb_pixelwise_segmentation/test_label/pageextractor_test/output_new",
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"dir_rgb_backgrounds": "/home/vahid/Documents/1_2_test_eynollah/set_rgb_background",
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"dir_rgb_backgrounds": "/home/vahid/Documents/1_2_test_eynollah/set_rgb_background",
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"dir_rgb_foregrounds": "/home/vahid/Documents/1_2_test_eynollah/out_set_rgb_foreground",
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"dir_img_bin": "/home/vahid/Documents/test/sbb_pixelwise_segmentation/test_label/pageextractor_test/train_new/images_bin"
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"dir_img_bin": "/home/vahid/Documents/test/sbb_pixelwise_segmentation/test_label/pageextractor_test/train_new/images_bin"
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}
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}
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19
train.py
19
train.py
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@ -54,6 +54,7 @@ def config_params():
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brightening = False # If true, brightening will be applied to the image. The amount of brightening is defined with "brightness" in config_params.json.
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brightening = False # If true, brightening will be applied to the image. The amount of brightening is defined with "brightness" in config_params.json.
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binarization = False # If true, Otsu thresholding will be applied to augment the input with binarized images.
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binarization = False # If true, Otsu thresholding will be applied to augment the input with binarized images.
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adding_rgb_background = False
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adding_rgb_background = False
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adding_rgb_foreground = False
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add_red_textlines = False
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add_red_textlines = False
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channels_shuffling = False
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channels_shuffling = False
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dir_train = None # Directory of training dataset with subdirectories having the names "images" and "labels".
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dir_train = None # Directory of training dataset with subdirectories having the names "images" and "labels".
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@ -95,6 +96,7 @@ def config_params():
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dir_img_bin = None
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dir_img_bin = None
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number_of_backgrounds_per_image = 1
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number_of_backgrounds_per_image = 1
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dir_rgb_backgrounds = None
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dir_rgb_backgrounds = None
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dir_rgb_foregrounds = None
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@ex.automain
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@ex.automain
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@ -103,20 +105,25 @@ def run(_config, n_classes, n_epochs, input_height,
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index_start, dir_of_start_model, is_loss_soft_dice,
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index_start, dir_of_start_model, is_loss_soft_dice,
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n_batch, patches, augmentation, flip_aug,
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n_batch, patches, augmentation, flip_aug,
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blur_aug, padding_white, padding_black, scaling, degrading,channels_shuffling,
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blur_aug, padding_white, padding_black, scaling, degrading,channels_shuffling,
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brightening, binarization, adding_rgb_background, add_red_textlines, blur_k, scales, degrade_scales,shuffle_indexes,
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brightening, binarization, adding_rgb_background, adding_rgb_foreground, add_red_textlines, blur_k, scales, degrade_scales,shuffle_indexes,
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brightness, dir_train, data_is_provided, scaling_bluring,
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brightness, dir_train, data_is_provided, scaling_bluring,
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scaling_brightness, scaling_binarization, rotation, rotation_not_90,
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scaling_brightness, scaling_binarization, rotation, rotation_not_90,
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thetha, scaling_flip, continue_training, transformer_projection_dim,
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thetha, scaling_flip, continue_training, transformer_projection_dim,
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transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_cnn_first,
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transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_cnn_first,
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transformer_patchsize_x, transformer_patchsize_y,
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transformer_patchsize_x, transformer_patchsize_y,
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transformer_num_patches_xy, backbone_type, flip_index, dir_eval, dir_output,
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transformer_num_patches_xy, backbone_type, flip_index, dir_eval, dir_output,
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pretraining, learning_rate, task, f1_threshold_classification, classification_classes_name, dir_img_bin, number_of_backgrounds_per_image,dir_rgb_backgrounds):
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pretraining, learning_rate, task, f1_threshold_classification, classification_classes_name, dir_img_bin, number_of_backgrounds_per_image,dir_rgb_backgrounds, dir_rgb_foregrounds):
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if dir_rgb_backgrounds:
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if dir_rgb_backgrounds:
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list_all_possible_background_images = os.listdir(dir_rgb_backgrounds)
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list_all_possible_background_images = os.listdir(dir_rgb_backgrounds)
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else:
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else:
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list_all_possible_background_images = None
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list_all_possible_background_images = None
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if dir_rgb_foregrounds:
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list_all_possible_foreground_rgbs = os.listdir(dir_rgb_foregrounds)
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else:
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list_all_possible_foreground_rgbs = None
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if task == "segmentation" or task == "enhancement" or task == "binarization":
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if task == "segmentation" or task == "enhancement" or task == "binarization":
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if data_is_provided:
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if data_is_provided:
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dir_train_flowing = os.path.join(dir_output, 'train')
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dir_train_flowing = os.path.join(dir_output, 'train')
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@ -175,18 +182,18 @@ def run(_config, n_classes, n_epochs, input_height,
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# writing patches into a sub-folder in order to be flowed from directory.
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# writing patches into a sub-folder in order to be flowed from directory.
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provide_patches(imgs_list, segs_list, dir_img, dir_seg, dir_flow_train_imgs,
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provide_patches(imgs_list, segs_list, dir_img, dir_seg, dir_flow_train_imgs,
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dir_flow_train_labels, input_height, input_width, blur_k,
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dir_flow_train_labels, input_height, input_width, blur_k,
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blur_aug, padding_white, padding_black, flip_aug, binarization, adding_rgb_background,add_red_textlines, channels_shuffling,
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blur_aug, padding_white, padding_black, flip_aug, binarization, adding_rgb_background,adding_rgb_foreground, add_red_textlines, channels_shuffling,
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scaling, degrading, brightening, scales, degrade_scales, brightness,
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scaling, degrading, brightening, scales, degrade_scales, brightness,
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flip_index,shuffle_indexes, scaling_bluring, scaling_brightness, scaling_binarization,
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flip_index,shuffle_indexes, scaling_bluring, scaling_brightness, scaling_binarization,
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rotation, rotation_not_90, thetha, scaling_flip, task, augmentation=augmentation,
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rotation, rotation_not_90, thetha, scaling_flip, task, augmentation=augmentation,
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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)
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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)
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provide_patches(imgs_list_test, segs_list_test, dir_img_val, dir_seg_val,
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provide_patches(imgs_list_test, segs_list_test, dir_img_val, dir_seg_val,
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dir_flow_eval_imgs, dir_flow_eval_labels, input_height, input_width,
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dir_flow_eval_imgs, dir_flow_eval_labels, input_height, input_width,
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blur_k, blur_aug, padding_white, padding_black, flip_aug, binarization, adding_rgb_background, add_red_textlines, channels_shuffling,
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blur_k, blur_aug, padding_white, padding_black, flip_aug, binarization, adding_rgb_background, adding_rgb_foreground, add_red_textlines, channels_shuffling,
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scaling, degrading, brightening, scales, degrade_scales, brightness,
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scaling, degrading, brightening, scales, degrade_scales, brightness,
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flip_index, shuffle_indexes, scaling_bluring, scaling_brightness, scaling_binarization,
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flip_index, shuffle_indexes, scaling_bluring, scaling_brightness, scaling_binarization,
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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)
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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 )
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if weighted_loss:
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if weighted_loss:
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weights = np.zeros(n_classes)
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weights = np.zeros(n_classes)
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40
utils.py
40
utils.py
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@ -40,6 +40,25 @@ def return_binary_image_with_given_rgb_background(img_bin, img_rgb_background):
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return img_final
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return img_final
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def return_binary_image_with_given_rgb_background_and_given_foreground_rgb(img_bin, img_rgb_background, rgb_foreground):
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img_rgb_background = resize_image(img_rgb_background ,img_bin.shape[0], img_bin.shape[1])
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img_final = np.copy(img_bin)
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img_foreground = np.zeros(img_bin.shape)
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img_foreground[:,:,0][img_bin[:,:,0] == 0] = rgb_foreground[0]
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img_foreground[:,:,1][img_bin[:,:,0] == 0] = rgb_foreground[1]
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img_foreground[:,:,2][img_bin[:,:,0] == 0] = rgb_foreground[2]
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img_final[:,:,0][img_bin[:,:,0] != 0] = img_rgb_background[:,:,0][img_bin[:,:,0] != 0]
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img_final[:,:,1][img_bin[:,:,1] != 0] = img_rgb_background[:,:,1][img_bin[:,:,1] != 0]
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img_final[:,:,2][img_bin[:,:,2] != 0] = img_rgb_background[:,:,2][img_bin[:,:,2] != 0]
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img_final = img_final + img_foreground
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return img_final
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def return_binary_image_with_given_rgb_background_red_textlines(img_bin, img_rgb_background, img_color):
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def return_binary_image_with_given_rgb_background_red_textlines(img_bin, img_rgb_background, img_color):
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img_rgb_background = resize_image(img_rgb_background ,img_bin.shape[0], img_bin.shape[1])
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img_rgb_background = resize_image(img_rgb_background ,img_bin.shape[0], img_bin.shape[1])
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@ -641,10 +660,10 @@ def get_patches_num_scale_new(dir_img_f, dir_seg_f, img, label, height, width, i
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def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow_train_imgs,
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def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow_train_imgs,
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dir_flow_train_labels, input_height, input_width, blur_k, blur_aug,
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dir_flow_train_labels, input_height, input_width, blur_k, blur_aug,
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padding_white, padding_black, flip_aug, binarization, adding_rgb_background, add_red_textlines, channels_shuffling, scaling, degrading,
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padding_white, padding_black, flip_aug, binarization, adding_rgb_background, adding_rgb_foreground, add_red_textlines, channels_shuffling, scaling, degrading,
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brightening, scales, degrade_scales, brightness, flip_index, shuffle_indexes,
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brightening, scales, degrade_scales, brightness, flip_index, shuffle_indexes,
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scaling_bluring, scaling_brightness, scaling_binarization, rotation,
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scaling_bluring, scaling_brightness, scaling_binarization, rotation,
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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):
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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):
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indexer = 0
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indexer = 0
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for im, seg_i in tqdm(zip(imgs_list_train, segs_list_train)):
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for im, seg_i in tqdm(zip(imgs_list_train, segs_list_train)):
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@ -754,6 +773,23 @@ def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow
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indexer += 1
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indexer += 1
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if adding_rgb_foreground:
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img_bin_corr = cv2.imread(dir_img_bin + '/' + img_name+'.png')
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for i_n in range(number_of_backgrounds_per_image):
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background_image_chosen_name = random.choice(list_all_possible_background_images)
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foreground_rgb_chosen_name = random.choice(list_all_possible_foreground_rgbs)
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img_rgb_background_chosen = cv2.imread(dir_rgb_backgrounds + '/' + background_image_chosen_name)
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foreground_rgb_chosen = np.load(dir_rgb_foregrounds + '/' + foreground_rgb_chosen_name)
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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)
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cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png', resize_image(img_with_overlayed_background, input_height, input_width))
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cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png',
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resize_image(cv2.imread(dir_of_label_file), input_height, input_width))
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indexer += 1
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if add_red_textlines:
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if add_red_textlines:
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img_bin_corr = cv2.imread(dir_img_bin + '/' + img_name+'.png')
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img_bin_corr = cv2.imread(dir_img_bin + '/' + img_name+'.png')
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img_red_context = return_image_with_red_elements(cv2.imread(dir_img + '/'+im), img_bin_corr)
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img_red_context = return_image_with_red_elements(cv2.imread(dir_img + '/'+im), img_bin_corr)
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