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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 BUID_split(): |
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dataset_name = "BUID" |
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file = "./dataset_demo/BUID" |
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if os.path.exists(os.path.join(file, "normal")): |
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shutil.rmtree(os.path.join(file, "normal")) |
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dir_data = "./dataset_demo/BUID/images" |
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dir_label = "./dataset_demo/BUID/labels" |
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if os.path.exists(dir_data): |
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shutil.rmtree(dir_data) |
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os.mkdir(dir_data) |
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if os.path.exists(dir_label): |
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shutil.rmtree(dir_label) |
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os.mkdir(dir_label) |
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for i in os.listdir(file): |
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if i == "labels" or i == "images" or "label" in i: |
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continue |
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file_label = os.path.join(file, i) |
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for img in os.listdir(file_label): |
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img_file = os.path.join(file_label, img) |
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if "mask" in img: |
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shutil.copy(img_file, os.path.join(dir_label, img)) |
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else: |
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shutil.copy(img_file, os.path.join(dir_data, img)) |
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file = os.listdir(dir_label) |
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label_uni = -1 |
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check = False |
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b = 0 |
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for i in os.listdir(dir_data): |
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for k in range(10): |
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if k == 0: |
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mask = "_mask" |
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else: |
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mask = "_mask_" + str(k) |
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a = i.replace(".png", mask+".png") |
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if a in file: |
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b = k |
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if not check: |
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label_uni = cv2.imread(os.path.join(dir_label, a)) |
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check = True |
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else: |
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img = cv2.imread(os.path.join(dir_label, a)) |
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label_uni = label_uni + img |
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os.remove(os.path.join(dir_label, a)) |
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else: |
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check = False |
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break |
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cv2.imwrite(os.path.join(dir_label, i), label_uni) |
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label_uni = -1 |
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check = False |
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b = 0 |
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split_data(dataset_name) |
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save_fileLabel(dataset_name) |
