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前面是分部讲解，完整代码在最后。
导入模块 ：
import os from shutil import copy, rmtree import random
创建文件夹 ：
def make_file(file_path: str): if os.path.exists(file_path): rmtree(file_path)
os.makedirs(file_path)
划分数据集的比例，本文是0.1：验证集的数量占总数据集的10%比如填0.1就是验证集的数量占总数据集的10%
random.seed(0) split_rate = 0.1
数据集的存放：新建一个数据文件夹，将划分的数据集存放进去
data_path = r'D:\chengxu\data\caodi' # 数据集存放的地方 data_root =
r'D:\chengxu\data\cd' # 这里是生成的训练集和验证集所处的位置，这里设置的是在当前文件夹下。 data_class = [cla for
cla in os.listdir(data_path)] print("数据的种类分别为：") print(data_class) # 输出数据种类
建立训练集文件夹：
train_data_root = os.path.join(data_root, "train") # 训练集的文件夹名称为 train
make_file(train_data_root) for num_class in data_class:
make_file(os.path.join(train_data_root, num_class))
建立测试集文件夹：
val_data_root = os.path.join(data_root, "val") # 验证集的文件夹名称为 val
make_file(val_data_root) for num_class in data_class:
make_file(os.path.join(val_data_root, num_class))
划分数据：
for num_class in data_class: num_class_path = os.path.join(data_path,
num_class) images = os.listdir(num_class_path) num = len(images) val_index =
random.sample(images, k=int(num * split_rate)) # 随机抽取图片 for index, image in
enumerate(images): if image in val_index: # 将划分到验证集中的文件复制到相应目录 data_image_path
= os.path.join(num_class_path, image) val_new_path =
os.path.join(val_data_root, num_class) copy(data_image_path, val_new_path)
else: # 将划分到训练集中的文件复制到相应目录 data_image_path = os.path.join(num_class_path,
image) train_new_path = os.path.join(train_data_root, num_class)
copy(data_image_path, train_new_path) print("\r[{}] split_rating
[{}/{}]".format(num_class, index + 1, num), end="") # processing bar print()
print(" ") print(" ") print("划分成功")
完整代码：
import os from shutil import copy, rmtree import random def
make_file(file_path: str): if os.path.exists(file_path): rmtree(file_path)
os.makedirs(file_path) random.seed(0) # 将数据集中10%的数据划分到验证集中 split_rate = 0.1
data_path = r'D:\chengxu\data\caodi' #
数据集存放的地方，建议在程序所在的文件夹下新建一个data文件夹，将需要划分的数据集存放进去 data_root =
r'D:\chengxu\data\cd' # 这里是生成的训练集和验证集所处的位置，这里设置的是在当前文件夹下。 data_class = [cla for
cla in os.listdir(data_path)] print("数据的种类分别为：") print(data_class) #
建立保存训练集的文件夹 train_data_root = os.path.join(data_root, "train") # 训练集的文件夹名称为
train make_file(train_data_root) for num_class in data_class: # 建立每个类别对应的文件夹
make_file(os.path.join(train_data_root, num_class)) # 建立保存验证集的文件夹 val_data_root
= os.path.join(data_root, "val") # 验证集的文件夹名称为 val make_file(val_data_root) for
num_class in data_class: # 建立每个类别对应的文件夹 make_file(os.path.join(val_data_root,
num_class)) for num_class in data_class: num_class_path =
os.path.join(data_path, num_class) images = os.listdir(num_class_path) num =
len(images) val_index = random.sample(images, k=int(num * split_rate)) # 随机抽取图片
for index, image in enumerate(images): if image in val_index: data_image_path =
os.path.join(num_class_path, image) val_new_path = os.path.join(val_data_root,
num_class) copy(data_image_path, val_new_path) else: data_image_path =
os.path.join(num_class_path, image) train_new_path =
os.path.join(train_data_root, num_class) copy(data_image_path, train_new_path)
print("\r[{}] split_rating [{}/{}]".format(num_class, index + 1, num), end="")
# processing bar print() print(" ") print(" ") print("划分成功")