| import numpy as np |
| import torch |
| import shutil |
| import os |
| import matplotlib.pyplot as plt |
| import cv2 |
| import json |
| from PIL import Image |
| import pickle |
| from skimage.transform import resize |
| from utils.dataset_prepare import split_data, save_fileLabel |
|
|
| def BUID_split(): |
| dataset_name = "BUID" |
| file = "./dataset_demo/BUID" |
| if os.path.exists(os.path.join(file, "normal")): |
| shutil.rmtree(os.path.join(file, "normal")) |
|
|
| dir_data = "./dataset_demo/BUID/images" |
| dir_label = "./dataset_demo/BUID/labels" |
|
|
| if os.path.exists(dir_data): |
| shutil.rmtree(dir_data) |
| os.mkdir(dir_data) |
|
|
| if os.path.exists(dir_label): |
| shutil.rmtree(dir_label) |
| os.mkdir(dir_label) |
|
|
| for i in os.listdir(file): |
| if i == "labels" or i == "images" or "label" in i: |
| continue |
| file_label = os.path.join(file, i) |
| for img in os.listdir(file_label): |
| img_file = os.path.join(file_label, img) |
| if "mask" in img: |
| shutil.copy(img_file, os.path.join(dir_label, img)) |
| else: |
| shutil.copy(img_file, os.path.join(dir_data, img)) |
|
|
| file = os.listdir(dir_label) |
| label_uni = -1 |
| check = False |
| b = 0 |
| for i in os.listdir(dir_data): |
| for k in range(10): |
| if k == 0: |
| mask = "_mask" |
| else: |
| mask = "_mask_" + str(k) |
| a = i.replace(".png", mask+".png") |
|
|
| if a in file: |
| b = k |
| if not check: |
| label_uni = cv2.imread(os.path.join(dir_label, a)) |
| check = True |
| else: |
| img = cv2.imread(os.path.join(dir_label, a)) |
| label_uni = label_uni + img |
| os.remove(os.path.join(dir_label, a)) |
| else: |
| check = False |
| break |
|
|
| |
| cv2.imwrite(os.path.join(dir_label, i), label_uni) |
| label_uni = -1 |
| check = False |
| b = 0 |
|
|
| split_data(dataset_name) |
| save_fileLabel(dataset_name) |
