1.用于目标检测的数据集划分

目标检测数据集文件结构如下所示：

 而yolo数据集标注完后往往如下图所示

所以现在 需要进行数据划分，将数据集里的图片和标签按照比例进行划分，代码如下所示：

import os
import random
from shutil import copy2


# 判断是否存在目标文件夹，不存在则创建---->创建train\val\test文件夹
def create_folder(new_file_path, split_names):
    if os.path.isdir(new_file_path):
        pass
    else:
        os.makedirs(new_file_path)
    for split_name in split_names:
        split_path = new_file_path + "/" + split_name

        if os.path.isdir(split_path):
            pass
        else:
            os.makedirs(split_path)
            split_images_path = split_path + "/images"
            split_labels_path = split_path + "/labels"
            os.makedirs(split_images_path)
            os.makedirs(split_labels_path)
            print(split_path + "创建成功")
    print("文件夹创建成功")

def data_split(old_path, new_file_path, labels_path, split_rate):
    current_data_path = old_path
    current_all_data = os.listdir(current_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_images_path = os.path.join(new_file_path, 'train/images/')
    val_images_path = os.path.join(new_file_path, 'val/images/')
    test_images_path = os.path.join(new_file_path, 'test/images/')

    train_labels_path = os.path.join(new_file_path, 'train/labels/')
    val_labels_path = os.path.join(new_file_path, 'val/labels/')
    test_labels_path = os.path.join(new_file_path, 'test/labels/')

    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
    # 图片复制到文件夹中
    for i in current_data_index_list:
        src_img_path = os.path.join(current_data_path, current_all_data[i])
        txt_name = current_all_data[i].split(".")[0]+'.txt'
        src_label_path =os.path.join(labels_path, txt_name)

        if current_idx < train_stop_flag:
            copy2(src_img_path, train_images_path)
            copy2(src_label_path, train_labels_path)
            train_num += 1
        elif current_idx < val_stop_flag:
            copy2(src_img_path, val_images_path)
            copy2(src_label_path, val_labels_path)
            val_num += 1
        else:
            copy2(src_img_path, test_images_path)
            copy2(src_label_path, test_labels_path)
            test_num += 1
        current_idx += 1
        print(f'第{current_idx}条数据划分完成')
    print("=======================划分完成======================")
    print("数据集:", current_idx)
    print("训练集:", train_num)
    print("验证集:", val_num)
    print("测试集:", test_num)


if __name__ == '__main__':
    # 图片文件夹路径
    file_path = "./images"
    # yolo标签路径
    labels_path = "./labels"
    # 新文件路径
    new_file_path = "./data"
    # 划分数据比例7:2:1
    split_rate = [0.7, 0.2, 0.1]
    # 目标文件夹下创建文件夹
    split_names = ['train', 'val', 'test']
    # 创建文件夹
    create_folder(new_file_path, split_names)
    # 数据划分
    data_split(file_path, new_file_path, labels_path, split_rate)

代码运行结果如图所示：

 

 2.用于分类模型的数据集划分

运行前

运行后

# 工具类
import os
import random
from shutil import copy2


def data_set_split(src_data_folder, target_data_folder, split_rate):
    train_scale = split_rate[0]
    val_scale = split_rate[1]
    test_scale = split_rate[2]

    print("开始数据集划分")
    class_names = os.listdir(src_data_folder)
    # 在目标目录下创建文件夹
    split_names = ['train', 'val', 'test']
    for split_name in split_names:
        split_path = os.path.join(target_data_folder, split_name)
        if os.path.isdir(split_path):
            pass
        else:
            os.makedirs(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.makedirs(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(src_data_folder, 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_folder = os.path.join(os.path.join(target_data_folder, 'train'), class_name)
        val_folder = os.path.join(os.path.join(target_data_folder, 'val'), class_name)
        test_folder = os.path.join(os.path.join(target_data_folder, 'test'), class_name)

        train_stop_flag = current_data_length * train_scale
        val_stop_flag = current_data_length * val_scale + train_stop_flag

        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_folder)
                train_num = train_num + 1
            elif current_idx < val_stop_flag:
                copy2(src_img_path, val_folder)
                val_num = val_num + 1
            else:
                copy2(src_img_path, test_folder)
                test_num = test_num + 1
            current_idx = current_idx + 1

        print("*********************************{}*************************************".format(class_name))
        print(
            "{}类按照{}：{}：{}的比例划分完成，一共{}张图片".format(class_name, train_scale, val_scale, test_scale, current_data_length))
        print("训练集{}：{}张".format(train_folder, train_num))
        print("验证集{}：{}张".format(val_folder, val_num))
        print("测试集{}：{}张".format(test_folder, test_num))


if __name__ == '__main__':
    src_data_folder = "images"
    target_data_folder = "dataset"
    split_rate = [0.7, 0.2, 0.1]
    data_set_split(src_data_folder, target_data_folder, split_rate)

运行结果