自然语言处理使用python

import os from shutil import copy, rmtree import random def mk_file(file_path: str): if os.path.exists(file_path): # 如果文件夹存在，则先删除原文件夹在重新创建 rmtree(file_path) os.makedirs(file_path) def main(): # 保证随机可复现 random.seed(0) # 将数据集中10%的数据划分到验证集中 split_rate = 0.1 # 指向你解压后的flower_photos文件夹 cwd = os.getcwd() data_root = os.path.join(cwd, "flower_data") origin_flower_path = os.path.join(data_root, "flower_photos") assert os.path.exists(origin_flower_path), "path '{}' does not exist.".format(origin_flower_path) flower_class = [cla for cla in os.listdir(origin_flower_path) if os.path.isdir(os.path.join(origin_flower_path, cla))] # 建立保存训练集的文件夹 train_root = os.path.join(data_root, "train") mk_file(train_root) for cla in flower_class: # 建立每个类别对应的文件夹 mk_file(os.path.join(train_root, cla)) # 建立保存验证集的文件夹 val_root = os.path.join(data_root, "val") mk_file(val_root) for cla in flower_class: # 建立每个类别对应的文件夹 mk_file(os.path.join(val_root, cla)) for cla in flower_class: cla_path = os.path.join(origin_flower_path, cla) images = os.listdir(cla_path) num = len(images) # 随机采样验证集的索引 eval_index = random.sample(images, k=int(num*split_rate)) for index, image in enumerate(images): if image in eval_index: # 将分配至验证集中的文件复制到相应目录 image_path = os.path.join(cla_path, image) new_path = os.path.join(val_root, cla) copy(image_path, new_path) else: # 将分配至训练集中的文件复制到相应目录 image_path = os.path.join(cla_path, image) new_path = os.path.join(train_root, cla) copy(image_path, new_path) print("\r[{}] processing [{}/{}]".format(cla, index+1, num), end="") # processing bar print() print("processing done!") if __name__ == '__main__': main()