划分数据集
众所周知，将一个数据集只区分为训练集和验证集是不行的，还需要有测试集，本博文针对上一篇没有分出测试集的不足，重新划分数据集
直接上代码：
#split_data.py #划分数据集flower_data，数据集划分到flower_datas中，训练集：验证集：测试集比例为6：2：2 import os import random from shutil import copy2 # 源文件路径 file_path = r"D:/other/ClassicalModel/other/flower_data" # 新文件路径 new_file_path = r"D:/other/ClassicalModel/other/flower_datas" # 划分数据比例为6:2:2 split_rate = [0.6, 0.2, 0.2] print("Starting...") print("Ratio= {}:{}:{}".format(int(split_rate[0] * 10), int(split_rate[1] * 10), int(split_rate[2] * 10))) class_names = os.listdir(file_path) # 在目标目录下创建文件夹 split_names = ['train', 'val', 'test'] # 判断是否存在木匾文件夹 if os.path.isdir(new_file_path): pass else: os.mkdir(new_file_path) for split_name in split_names: # split_path = os.path.join(new_file_path, split_name) split_path = new_file_path + "/" + split_name if os.path.isdir(split_path): pass else: os.mkdir(split_path) # 然后在split_path的目录下创建类别文件夹 for class_name in class_names: class_split_path = os.path.join(split_path, class_name) if os.path.isdir(class_split_path): pass else: os.mkdir(class_split_path) # 按照比例划分数据集，并进行数据图片的复制 # 首先进行分类遍历 for class_name in class_names: current_class_data_path = os.path.join(file_path, class_name) current_all_data = os.listdir(current_class_data_path) current_data_length = len(current_all_data) current_data_index_list = list(range(current_data_length)) random.shuffle(current_data_index_list) train_path = os.path.join(os.path.join(new_file_path, 'train'), class_name) val_path = os.path.join(os.path.join(new_file_path, 'val'), class_name) test_path = os.path.join(os.path.join(new_file_path, 'test'), class_name) train_stop_flag = current_data_length * split_rate[0] val_stop_flag = current_data_length * (split_rate[0] + split_rate[1]) current_idx = 0 train_num = 0 val_num = 0 test_num = 0 for i in current_data_index_list: src_img_path = os.path.join(current_class_data_path, current_all_data[i]) if current_idx <= train_stop_flag: copy2(src_img_path, train_path train_num = train_num + 1 elif (current_idx > train_stop_flag) and (current_idx <= val_stop_flag): copy2(src_img_path, val_path) val_num = val_num + 1 else: copy2(src_img_path, test_path test_num = test_num + 1 current_idx = current_idx + 1 print("<{}> has {} pictures,train:val:test={}:{}:{}".format(class_name, current_data_length, train_num, val_num, test_num)) print("Done")
输出结果：
注意：
只需要修改file_path（源文件夹）和new_file_path（新生成的文件夹）
其次是修改split_rate
