前期条件：已经使用labelme做好json文件。可以看以下博客。

https://blog.csdn.net/Wu_GuiMing/article/details/130625940?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522171280483116800213081813%2522%252C%2522scm%2522%253A%252220140713.130102334..%2522%257D&request_id=171280483116800213081813&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~top_positive~default-1-130625940-null-null.nonecase&utm_term=labelme%E5%AE%89%E8%A3%85&spm=1018.2226.3001.4450

转化过程：1、将json文件转化成txt文件

                  2、将文件分成训练集、验证集、测试集

以下代码将json文件转化成txt文件

import json
import os
import argparse
from tqdm import tqdm


def convert_label_json(json_dir, save_dir, classes):
    json_paths = os.listdir(json_dir)
    classes = classes.split(',')

    for json_path in tqdm(json_paths):
        # for json_path in json_paths:
        path = os.path.join(json_dir, json_path)
        try:
            with open(path, 'r', encoding='utf-8') as load_f:
                json_dict = json.loads(load_f.read())
        except json.JSONDecodeError:
            print(f"Error decoding file {path}. Skipping.")
            continue

        # with open(path, 'r', encoding='utf-8') as load_f:
        #     json_dict = json.load(load_f)
        h, w = json_dict['imageHeight'], json_dict['imageWidth']

        # save txt path
        txt_path = os.path.join(save_dir, json_path.replace('json', 'txt'))
        txt_file = open(txt_path, 'w')

        for shape_dict in json_dict['shapes']:
            label = shape_dict['label']
            label_index = classes.index(label)
            points = shape_dict['points']

            points_nor_list = []

            for point in points:
                points_nor_list.append(point[0] / w)
                points_nor_list.append(point[1] / h)

            points_nor_list = list(map(lambda x: str(x), points_nor_list))
            points_nor_str = ' '.join(points_nor_list)

            label_str = str(label_index) + ' ' + points_nor_str + '\n'
            txt_file.writelines(label_str)


if __name__ == "__main__":
    """
    python json2txt_nomalize.py --json-dir my_datasets/color_rings/jsons --save-dir my_datasets/color_rings/txts --classes "duck"
    """
    parser = argparse.ArgumentParser(description='json convert to txt params')
    parser.add_argument('--json-dir', type=str, default='H:/labelImg-master/labelImg-master/datas/data/eggbox1json/',
                        help='json path dir')
    parser.add_argument('--save-dir', type=str, default='H:/labelImg-master/labelImg-master/datas/data/eggbox1txt/',
                        help='txt save dir')
    parser.add_argument('--classes', type=str, default='eggbox', help='classes')
    args = parser.parse_args()
    json_dir = args.json_dir
    save_dir = args.save_dir
    classes = args.classes
    convert_label_json(json_dir, save_dir, classes)复制

以下路径是自己json文件的路径

    parser = argparse.ArgumentParser(description='json convert to txt params')
    parser.add_argument('--json-dir', type=str, default='H:/labelImg-master/labelImg-master/datas/data/eggbox1json/',
                        help='json path dir')
    parser.add_argument('--save-dir', type=str, default='H:/labelImg-master/labelImg-master/datas/data/eggbox1txt/',
                        help='txt save dir')
    parser.add_argument('--classes', type=str, default='eggbox', help='classes')复制

--json-dir：json的文件夹；--save-dir：生成txt的文件夹；--classes：类别

生成文件夹如下：

以下代码将文件分成训练集、验证集、测试集

# 将图片和标注数据按比例切分为 训练集和测试集
import shutil
import random
import os
import argparse


# 检查文件夹是否存在
def mkdir(path):
    if not os.path.exists(path):
        os.makedirs(path)


def main(image_dir, txt_dir, save_dir):
    # 创建文件夹
    mkdir(save_dir)
    images_dir = os.path.join(save_dir, 'images')
    labels_dir = os.path.join(save_dir, 'labels')

    img_train_path = os.path.join(images_dir, 'train')
    img_test_path = os.path.join(images_dir, 'test')
    img_val_path = os.path.join(images_dir, 'val')

    label_train_path = os.path.join(labels_dir, 'train')
    label_test_path = os.path.join(labels_dir, 'test')
    label_val_path = os.path.join(labels_dir, 'val')

    mkdir(images_dir);
    mkdir(labels_dir);
    mkdir(img_train_path);
    mkdir(img_test_path);
    mkdir(img_val_path);
    mkdir(label_train_path);
    mkdir(label_test_path);
    mkdir(label_val_path);

    # 数据集划分比例，训练集75%，验证集15%，测试集15%，按需修改
    train_percent = 0.85
    val_percent = 0.14
    test_percent = 0.01

    total_txt = os.listdir(txt_dir)
    num_txt = len(total_txt)
    list_all_txt = range(num_txt)  # 范围 range(0, num)

    num_train = int(num_txt * train_percent)
    num_val = int(num_txt * val_percent)
    num_test = num_txt - num_train - num_val

    train = random.sample(list_all_txt, num_train)
    # 在全部数据集中取出train
    val_test = [i for i in list_all_txt if not i in train]
    # 再从val_test取出num_val个元素，val_test剩下的元素就是test
    val = random.sample(val_test, num_val)

    print("训练集数目：{}, 验证集数目：{},测试集数目：{}".format(len(train), len(val), len(val_test) - len(val)))
    for i in list_all_txt:
        name = total_txt[i][:-4]

        srcImage = os.path.join(image_dir, name + '.jpg')
        srcLabel = os.path.join(txt_dir, name + '.txt')

        if i in train:
            dst_train_Image = os.path.join(img_train_path, name + '.jpg')
            dst_train_Label = os.path.join(label_train_path, name + '.txt')
            shutil.copyfile(srcImage, dst_train_Image)
            shutil.copyfile(srcLabel, dst_train_Label)
        elif i in val:
            dst_val_Image = os.path.join(img_val_path, name + '.jpg')
            dst_val_Label = os.path.join(label_val_path, name + '.txt')
            shutil.copyfile(srcImage, dst_val_Image)
            shutil.copyfile(srcLabel, dst_val_Label)
        else:
            dst_test_Image = os.path.join(img_test_path, name + '.jpg')
            dst_test_Label = os.path.join(label_test_path, name + '.txt')
            shutil.copyfile(srcImage, dst_test_Image)
            shutil.copyfile(srcLabel, dst_test_Label)


if __name__ == '__main__':
    """
    python split_datasets.py --image-dir my_datasets/color_rings/imgs --txt-dir my_datasets/color_rings/txts --save-dir my_datasets/color_rings/train_data
    """
    parser = argparse.ArgumentParser(description='split datasets to train,val,test params')
    parser.add_argument('--image-dir', type=str, default='H:/labelImg-master/labelImg-master/datas/data/eggboximage1/',
                        help='image path dir')
    parser.add_argument('--txt-dir', type=str, default='H:/labelImg-master/labelImg-master/datas/data/eggbox1txt/',
                        help='txt path dir')
    parser.add_argument('--save-dir', default='H:/labelImg-master/labelImg-master/datas/data/eggbox1split', type=str,
                        help='save dir')
    args = parser.parse_args()
    image_dir = args.image_dir
    txt_dir = args.txt_dir
    save_dir = args.save_dir

    main(image_dir, txt_dir, save_dir)复制

改变以下路径

    parser = argparse.ArgumentParser(description='split datasets to train,val,test params')
    parser.add_argument('--image-dir', type=str, default='H:/labelImg-master/labelImg-master/datas/data/eggboximage1/',
                        help='image path dir')
    parser.add_argument('--txt-dir', type=str, default='H:/labelImg-master/labelImg-master/datas/data/eggbox1txt/',
                        help='txt path dir')
    parser.add_argument('--save-dir', default='H:/labelImg-master/labelImg-master/datas/data/eggbox1split', type=str,
                        help='save dir')复制

--image-dir：图像文件夹内容如下所示（有图像就可）

生成如下文件：

参考以下博客：制作自己的数据集并训练的YOLOv8模型_yolov8 分类 训练-CSDN博客