qurator-spk/sbb_pixelwise_segmentation
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scaling, channels shuffling, rgb background and red content added to no patch augmentation

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vahidrezanezhad 2024-08-21 19:33:23 +02:00
parent 95bbdf8040
commit f31219b1c9
3 changed files with 68 additions and 26 deletions
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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.
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
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_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.
@ -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_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.
shuffle_indexes = None
blur_k = None # Blur image for augmentation.
scales = None # Scale patches 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.
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"
dir_img_bin = None
number_of_backgrounds_per_image = 1
dir_rgb_backgrounds = None
@ex.automain
@ -95,15 +102,20 @@ def run(_config, n_classes, n_epochs, input_height,
input_width, weight_decay, weighted_loss,
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, rgb_background, blur_k, scales, degrade_scales,
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,
brightness, dir_train, data_is_provided, scaling_bluring,
scaling_brightness, scaling_binarization, rotation, rotation_not_90,
thetha, scaling_flip, continue_training, transformer_projection_dim,
transformer_mlp_head_units, transformer_layers, transformer_num_heads, transformer_cnn_first,
transformer_patchsize_x, transformer_patchsize_y,
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 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.
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, 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,
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,
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,
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,
flip_index, scaling_bluring, scaling_brightness, scaling_binarization,
rotation, rotation_not_90, thetha, scaling_flip, task, augmentation=False, patches=patches)
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)
if weighted_loss:
weights = np.zeros(n_classes)