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import numpy as np |
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import torch |
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import shutil |
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import os |
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import matplotlib.pyplot as plt |
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import cv2 |
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import json |
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from PIL import Image |
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import pickle |
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from skimage.transform import resize |
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from utils.dataset_prepare import split_data, save_fileLabel |
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def FGADR_split(): |
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pkl_path = './files_split/fgadr_pkl_file.pkl' |
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path = "./dataset_demo/FGADR" |
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f = open(pkl_path, 'rb') |
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a = pickle.load(f) |
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a_key = a.keys() |
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B = ["train", "test"] |
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C = ["Training", "Testing"] |
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for index, i in enumerate(B): |
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print(i) |
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print(len(a[i])) |
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folder_type = os.path.join(path, i) |
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if os.path.exists(folder_type.replace(i, C[index])): |
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shutil.rmtree(os.path.join(path, C[index])) |
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os.mkdir(os.path.join(path, C[index])) |
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for j in a[i]: |
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folder_class = os.path.join(folder_type, str(j[1])) |
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if not os.path.exists(folder_class.replace(i, C[index])): |
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os.mkdir(folder_class.replace(i, C[index])) |
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file = j[0].replace("/mnt/sda/haal02-data/FGADR-Seg-Set", "./dataset_demo/FGADR") |
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img = cv2.imread(file) |
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img = resize(img, (512, 512), order=0, preserve_range=True, anti_aliasing=False).astype('uint8') |
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name_img = file.split("/")[-1] |
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cv2.imwrite(os.path.join(folder_class.replace(i, C[index]), name_img), img) |