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import glob
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import os
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import time
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from datetime import datetime
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import pdb
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import platform
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from tensorflow.keras.preprocessing.image import load_img
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from PIL import ImageOps, Image
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from tensorflow import keras
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import numpy as np
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from tensorflow.keras.preprocessing.image import load_img
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from tensorflow.keras import layers
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from imgrender import render
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if platform.system() == "Windows":
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base_dir = "C:/data/"
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else:
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base_dir = "/home/maduschek/ssd/mine-sector-detection/"
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datasets = [base_dir + "images_trainset/",
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base_dir + "masks_trainset/",
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base_dir + "images_testset/",
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base_dir + "masks_testset/"]
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for dataset in datasets:
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for img_path in glob.glob(os.path.join(dataset, "*.png")):
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img = Image.open(img_path)
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print(img_path)
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if img.size != (256, 256):
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print("file: ", img_path, ', size: ', str(img.size))
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width, height = img.size
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right = 256 - width
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bottom = 256 - height
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padded_img = Image.new(img.mode, (width + right, height + bottom))
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padded_img.paste(img, (0, 0))
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padded_img.save(img_path)
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