法1，划分为images/train-test-val,labels/train-test-val

目录结构

import os
import shutil
import random

random.seed(0)

def split_data(img_dir, label_dir, output_dir, ratios=(0.7, 0.1, 0.2)):
    """
    划分数据集并保持图像-标签对应关系
    
    参数:
    img_dir: 原始图像目录
    label_dir: 原始标签目录
    output_dir: 输出根目录
    ratios: 训练/验证/测试集划分比例 (train, val, test)
    """
    # 创建输出目录结构
    img_output = {
        'train': os.path.join(output_dir, 'images', 'train'),
        'val': os.path.join(output_dir, 'images', 'val'),
        'test': os.path.join(output_dir, 'images', 'test')
    }
    label_output = {
        'train': os.path.join(output_dir, 'labels', 'train'),
        'val': os.path.join(output_dir, 'labels', 'val'),
        'test': os.path.join(output_dir, 'labels', 'test')
    }
    
    # 确保输出目录存在
    for d in [*img_output.values(), *label_output.values()]:
        os.makedirs(d, exist_ok=True)

    # 获取图像和标签的配对关系
    img_files = sorted(os.listdir(img_dir))
    label_files = sorted(os.listdir(label_dir))
    
    # 验证基础文件名是否匹配（不含扩展名）
    paired_data = []
    for img in img_files:
        base_name = os.path.splitext(img)[0]
        matching_labels = [l for l in label_files if os.path.splitext(l)[0] == base_name]
        
        if not matching_labels:
            print(f"警告: 图像 {img} 没有对应的标签文件")
            continue
        
        # 使用找到的第一个匹配标签
        paired_data.append((img, matching_labels[0]))
    
    # 随机打乱数据对
    random.shuffle(paired_data)
    total = len(paired_data)
    print(f"找到 {total} 对有效图像-标签数据")
    
    # 计算划分点
    train_end = int(ratios[0] * total)
    val_end = train_end + int(ratios[1] * total)
    
    # 划分数据集
    sets = {
        'train': paired_data[:train_end],
        'val': paired_data[train_end:val_end],
        'test': paired_data[val_end:]
    }
    
    # 复制文件到对应目录
    for set_name, pairs in sets.items():
        print(f"正在处理 {set_name} 集 ({len(pairs)} 个样本)")
        
        for img_file, label_file in pairs:
            # 复制图像
            shutil.copy2(
                os.path.join(img_dir, img_file),
                os.path.join(img_output[set_name], img_file)
            )
            # 复制标签
            shutil.copy2(
                os.path.join(label_dir, label_file),
                os.path.join(label_output[set_name], label_file)
            )


if __name__ == '__main__':
    # 配置路径参数
    IMAGE_DIR = "xx"
    LABEL_DIR = "xx"
    OUTPUT_DIR = "xx"
    
    # 执行划分 (70% 训练集, 10% 验证集, 20% 测试集)
    split_data(IMAGE_DIR, LABEL_DIR, OUTPUT_DIR)

 法2. val/images、labels等等

import os
import random
import shutil

def copy_files(src_dir, dst_dir, filenames, extension):
    os.makedirs(dst_dir, exist_ok=True)
    missing_files = 0
    for filename in filenames:
        src_path = os.path.join(src_dir, filename + extension)
        dst_path = os.path.join(dst_dir, filename + extension)
        
        # Check if the file exists before copying
        if os.path.exists(src_path):
            shutil.copy(src_path, dst_path)
        else:
            print(f"Warning: File not found for {filename}")
            missing_files += 1

    return missing_files

def split_and_copy_dataset(image_dir, label_dir, output_dir, train_ratio=0.7, valid_ratio=0.1, test_ratio=0.2):
    # 获取所有图像文件的文件名（不包括文件扩展名）
    image_filenames = [os.path.splitext(f)[0] for f in os.listdir(image_dir)]

    # 随机打乱文件名列表
    random.shuffle(image_filenames)

    # 计算训练集、验证集和测试集的数量
    total_count = len(image_filenames)
    train_count = int(total_count * train_ratio)
    valid_count = int(total_count * valid_ratio)
    test_count = total_count - train_count - valid_count

    # 定义输出文件夹路径
    train_image_dir = os.path.join(output_dir, 'train', 'images')
    train_label_dir = os.path.join(output_dir, 'train', 'labels')
    valid_image_dir = os.path.join(output_dir, 'valid', 'images')
    valid_label_dir = os.path.join(output_dir, 'valid', 'labels')
    test_image_dir = os.path.join(output_dir, 'test', 'images')
    test_label_dir = os.path.join(output_dir, 'test', 'labels')

    # 复制图像和标签文件到对应的文件夹
    train_missing_files = copy_files(image_dir, train_image_dir, image_filenames[:train_count], '.jpg')
    train_missing_files += copy_files(label_dir, train_label_dir, image_filenames[:train_count], '.txt')

    valid_missing_files = copy_files(image_dir, valid_image_dir, image_filenames[train_count:train_count + valid_count], '.jpg')
    valid_missing_files += copy_files(label_dir, valid_label_dir, image_filenames[train_count:train_count + valid_count], '.txt')

    test_missing_files = copy_files(image_dir, test_image_dir, image_filenames[train_count + valid_count:], '.jpg')
    test_missing_files += copy_files(label_dir, test_label_dir, image_filenames[train_count + valid_count:], '.txt')

    # Print the count of each dataset
    print(f"Train dataset count: {train_count}, Missing files: {train_missing_files}")
    print(f"Validation dataset count: {valid_count}, Missing files: {valid_missing_files}")
    print(f"Test dataset count: {test_count}, Missing files: {test_missing_files}")

# 使用例子
image_dir = 'xx'
label_dir = 'xx'
output_dir = 'xx'



split_and_copy_dataset(image_dir, label_dir, output_dir)