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sbb_pixelwise_segmentation
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addinh shifting augmentation

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unifying-training-models
vahidrezanezhad 3 months ago
parent 4150675621
commit 3ef865e0b5
2 changed files with 58 additions and 4 deletions
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7
train.py
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@ -50,6 +50,7 @@ def config_params():
padding_white = False # If true, white padding will be applied to the image.
padding_black = False # If true, black padding will be applied to the image.
scaling = False # If true, scaling will be applied to the image. The amount of scaling is defined with "scales" in config_params.json.
shifting = False
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.
@ -104,7 +105,7 @@ 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,channels_shuffling,
blur_aug, padding_white, padding_black, scaling, shifting, degrading,channels_shuffling,
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,
scaling_brightness, scaling_binarization, rotation, rotation_not_90,
@ -183,7 +184,7 @@ def run(_config, n_classes, n_epochs, input_height,
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, adding_rgb_background,adding_rgb_foreground, add_red_textlines, channels_shuffling,
scaling, degrading, brightening, scales, degrade_scales, brightness,
scaling, shifting, degrading, brightening, scales, degrade_scales, brightness,
flip_index,shuffle_indexes, scaling_bluring, scaling_brightness, scaling_binarization,
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, dir_rgb_foregrounds=dir_rgb_foregrounds,list_all_possible_foreground_rgbs=list_all_possible_foreground_rgbs)
@ -191,7 +192,7 @@ 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, adding_rgb_background, adding_rgb_foreground, add_red_textlines, channels_shuffling,
scaling, degrading, brightening, scales, degrade_scales, brightness,
scaling, shifting, degrading, brightening, scales, degrade_scales, brightness,
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,dir_rgb_foregrounds=dir_rgb_foregrounds,list_all_possible_foreground_rgbs=list_all_possible_foreground_rgbs )

55
utils.py
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@ -78,7 +78,50 @@ def return_image_with_red_elements(img, img_bin):
img_final[:,:,2][img_bin[:,:,0]==0] = 255
return img_final
def shift_image_and_label(img, label, type_shift):
h_n = int(img.shape[0]*1.06)
w_n = int(img.shape[1]*1.06)
channel0_avg = int( np.mean(img[:,:,0]) )
channel1_avg = int( np.mean(img[:,:,1]) )
channel2_avg = int( np.mean(img[:,:,2]) )
h_diff = abs( img.shape[0] - h_n )
w_diff = abs( img.shape[1] - w_n )
h_start = int(h_diff / 2.)
w_start = int(w_diff / 2.)
img_scaled_padded = np.zeros((h_n, w_n, 3))
label_scaled_padded = np.zeros((h_n, w_n, 3))
img_scaled_padded[:,:,0] = channel0_avg
img_scaled_padded[:,:,1] = channel1_avg
img_scaled_padded[:,:,2] = channel2_avg
img_scaled_padded[h_start:h_start+img.shape[0], w_start:w_start+img.shape[1],:] = img[:,:,:]
label_scaled_padded[h_start:h_start+img.shape[0], w_start:w_start+img.shape[1],:] = label[:,:,:]
if type_shift=="xpos":
img_dis = img_scaled_padded[h_start:h_start+img.shape[0],2*w_start:2*w_start+img.shape[1],:]
label_dis = label_scaled_padded[h_start:h_start+img.shape[0],2*w_start:2*w_start+img.shape[1],:]
elif type_shift=="xmin":
img_dis = img_scaled_padded[h_start:h_start+img.shape[0],:img.shape[1],:]
label_dis = label_scaled_padded[h_start:h_start+img.shape[0],:img.shape[1],:]
elif type_shift=="ypos":
img_dis = img_scaled_padded[2*h_start:2*h_start+img.shape[0],w_start:w_start+img.shape[1],:]
label_dis = label_scaled_padded[2*h_start:2*h_start+img.shape[0],w_start:w_start+img.shape[1],:]
elif type_shift=="ymin":
img_dis = img_scaled_padded[:img.shape[0],w_start:w_start+img.shape[1],:]
label_dis = label_scaled_padded[:img.shape[0],w_start:w_start+img.shape[1],:]
elif type_shift=="xypos":
img_dis = img_scaled_padded[2*h_start:2*h_start+img.shape[0],2*w_start:2*w_start+img.shape[1],:]
label_dis = label_scaled_padded[2*h_start:2*h_start+img.shape[0],2*w_start:2*w_start+img.shape[1],:]
elif type_shift=="xymin":
img_dis = img_scaled_padded[:img.shape[0],:img.shape[1],:]
label_dis = label_scaled_padded[:img.shape[0],:img.shape[1],:]
return img_dis, label_dis
def scale_image_for_no_patch(img, label, scale):
h_n = int(img.shape[0]*scale)
@ -660,7 +703,7 @@ 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,
dir_flow_train_labels, input_height, input_width, blur_k, blur_aug,
padding_white, padding_black, flip_aug, binarization, adding_rgb_background, adding_rgb_foreground, 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, shifting, degrading,
brightening, scales, degrade_scales, brightness, flip_index, shuffle_indexes,
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, dir_rgb_foregrounds=None, list_all_possible_foreground_rgbs=None):
@ -759,6 +802,16 @@ def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow
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 shifting:
shift_types = ['xpos', 'xmin', 'ypos', 'ymin', 'xypos', 'xymin']
for st_ind in shift_types:
img_shifted, label_shifted = shift_image_and_label(cv2.imread(dir_img + '/'+im),
cv2.imread(dir_of_label_file), st_ind)
cv2.imwrite(dir_flow_train_imgs + '/img_' + str(indexer) + '.png', resize_image(img_shifted, input_height, input_width))
cv2.imwrite(dir_flow_train_labels + '/img_' + str(indexer) + '.png', resize_image(label_shifted, input_height, input_width))
indexer += 1
if adding_rgb_background:
img_bin_corr = cv2.imread(dir_img_bin + '/' + img_name+'.png')

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