Datasets:

Modalities:
Image
Text
ArXiv:
License:
ObjEarth-Data / WTBD /generate_split.py
ZhanYang-nwpu's picture
Upload 3203 files
d483542 verified
Raw
History Blame Contribute Delete
1.54 kB
import random
import os
def generate_split_file(total_images=1065, seed=42):
# 1. 准备文件名列表 (0.jpg 到 1064.jpg)
filenames = [f"{i}.jpg" for i in range(total_images)]
# 2. 设置随机种子以保证可复现性 (Reviewer 重点关注点)
random.seed(seed)
# 3. 打乱列表
shuffled_files = filenames.copy()
random.shuffle(shuffled_files)
# 4. 计算划分数量 (70% / 15% / 15%)
n_train = int(total_images * 0.70)
n_val = int(total_images * 0.15)
# 剩余归为测试集
train_files = set(shuffled_files[:n_train])
val_files = set(shuffled_files[n_train:n_train + n_val])
test_files = set(shuffled_files[n_train + n_val:])
# 5. 写入文件
# 为了方便用户查阅,我们按文件名顺序(0->1064)写入,并在后面标注它属于哪个集
output_filename = "train_val_test_split.txt"
with open(output_filename, "w") as f:
# 写入表头
f.write("ImageID,Subset\n")
for i in range(total_images):
fname = f"{i}.jpg"
if fname in train_files:
subset = "train"
elif fname in val_files:
subset = "val"
else:
subset = "test"
f.write(f"{fname},{subset}\n")
print(f"Successfully generated {output_filename}")
print(f"Stats: Train={len(train_files)}, Val={len(val_files)}, Test={len(test_files)}")
if __name__ == "__main__":
generate_split_file()