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pro-data.py
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pro-data.py
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# import random
# import os.path as osp
# from glob import glob
# import argparse
#
# def get_parser():
# parser = argparse.ArgumentParser()
# parser.add_argument('--data_dir', type=str, default='LEVIR-CD', help='data directory path')
# return parser
#
# def write_rel_paths(phase, names, out_dir, prefix=''):
# """将文件相对路径存储在txt格式文件中"""
# with open(osp.join(out_dir, phase+'.txt'), 'w') as f:
# for name in names:
# f.write(
# ' '.join([
# osp.join(prefix, 'A', name),
# osp.join(prefix, 'B', name),
# osp.join(prefix, 'label', name)
# ])
# )
# f.write('\n')
#
#
# if __name__ == "__main__":
# # 数据集路径
# parser = get_parser()
# args = parser.parse_args()
#
# DATA_DIR = args.data_dir
# # 随机数生成器种子
# RNG_SEED = 114514
# random.seed(RNG_SEED)
#
# write_rel_paths(
# 'train',
# map(osp.basename, glob(osp.join(DATA_DIR, 'train', 'label', '*.png'))),
# DATA_DIR,
# prefix='train'
# )
#
# write_rel_paths(
# 'val',
# map(osp.basename, glob(osp.join(DATA_DIR, 'val', 'label', '*.png'))),
# DATA_DIR,
# prefix='val'
# )
#
# write_rel_paths(
# 'test',
# map(osp.basename, glob(osp.join(DATA_DIR, 'test', 'label', '*.png'))),
# DATA_DIR,
# prefix='test'
# )
#
# print("数据集划分已完成。")
# import os
# paths='LEVIR-CD/train/A'
# f = open('train.txt','w')
# filenames =os.listdir(paths)
# filenames.sort()
# for filename in filenames:
# out_path= filename
# print(out_path)
# f.write(out_path +'\n')
# f.close()
import random
import os.path as osp
from glob import glob
# 随机数生成器种子
RNG_SEED = 114514
# 调节此参数控制训练集数据的占比
TRAIN_RATIO = 0.9
# 数据集路径
DATA_DIR = 'LEVIR-CD'
def write_rel_paths(phase, names, out_dir, prefix=''):
"""将文件相对路径存储在txt格式文件中"""
with open(osp.join(out_dir, phase+'.txt'), 'w') as f:
for name in names:
f.write(
' '.join([
osp.join(name),
# osp.join(prefix, 'B', name),
# osp.join(prefix, 'label', name)
])
)
f.write('\n')
random.seed(RNG_SEED)
# 随机划分训练集/验证集
names = list(map(osp.basename, glob(osp.join(DATA_DIR, 'train', 'label', '*.png'))))
# 对文件名进行排序，以确保多次运行结果一致
names.sort()
random.shuffle(names)
len_train = int(len(names)*TRAIN_RATIO) # 向下取整
write_rel_paths('train', names[:len_train], DATA_DIR, prefix='train')
write_rel_paths('val', names[len_train:], DATA_DIR, prefix='train')
# 处理测试集
test_names = map(osp.basename, glob(osp.join(DATA_DIR, 'test', 'A', '*.png')))
test_names = sorted(test_names)
write_rel_paths(
'test',
test_names,
DATA_DIR,
prefix='test'
)
print("数据集划分已完成。")