From 7be326d6897c81e516a181113ce0a01557140ad7 Mon Sep 17 00:00:00 2001 From: vahidrezanezhad Date: Wed, 21 Aug 2024 00:48:30 +0200 Subject: [PATCH] augmentation function for red textlines, rgb background and scaling for no patch case --- utils.py | 70 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 70 insertions(+) diff --git a/utils.py b/utils.py index 891ee15..2278849 100644 --- a/utils.py +++ b/utils.py @@ -12,6 +12,76 @@ from tensorflow.keras.utils import to_categorical from PIL import Image, ImageEnhance +def return_shuffled_channels(img, channels_order): + """ + channels order in ordinary case is like this [0, 1, 2]. In the case of shuffling the order should be provided. + """ + img_sh = np.copy(img) + + img_sh[:,:,0]= img[:,:,channels_order[0]] + img_sh[:,:,1]= img[:,:,channels_order[1]] + img_sh[:,:,2]= img[:,:,channels_order[2]] + return img_sh + +def return_binary_image_with_red_textlines(img_bin): + img_red = np.copy(img_bin) + + img_red[:,:,0][img_bin[:,:,0] == 0] = 255 + return img_red + +def return_binary_image_with_given_rgb_background(img_bin, img_rgb_background): + img_rgb_background = resize_image(img_rgb_background ,img_bin.shape[0], img_bin.shape[1]) + + img_final = np.copy(img_bin) + + 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] + + return img_final + +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_final = np.copy(img_color) + + 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] + + return img_final + +def scale_image_for_no_patch(img, label, scale): + h_n = int(img.shape[0]*scale) + w_n = int(img.shape[1]*scale) + + channel0_avg = int( np.mean(img[:,:,0]) ) + channel1_avg = int( np.mean(img[:,:,1]) ) + channel2_avg = int( np.mean(img[:,:,2]) ) + + h_diff = img.shape[0] - h_n + w_diff = img.shape[1] - w_n + + h_start = int(h_diff / 2.) + w_start = int(w_diff / 2.) + + img_res = resize_image(img, h_n, w_n) + label_res = resize_image(label, h_n, w_n) + + img_scaled_padded = np.copy(img) + + label_scaled_padded = np.zeros(label.shape) + + 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+h_n, w_start:w_start+w_n,:] = img_res[:,:,:] + label_scaled_padded[h_start:h_start+h_n, w_start:w_start+w_n,:] = label_res[:,:,:] + + return img_scaled_padded, label_scaled_padded + + def return_number_of_total_training_data(path_classes): sub_classes = os.listdir(path_classes) n_tot = 0