import pandas as pd# 读取Excel文件file_path = 'D:/1.xlsx'  # 替换为你的文件路径sheet_name = '1'  # 替换为你的工作表名称table_name = 'your_table_name'  # 替换为你的数据库表名称# 使用pandas读取Excel数据df = pd.read_excel(file_path, sheet_name=sheet_name)# 获取表的列名columns = ', '.join(df.columns)# 将每行数据转换为SQL INSERT语句insert_statements = []for index, row in df.iterrows():    # 将每行数据转换为元组格式    values = ', '.join([f"'{str(value)}'" for value in row.values])    sql = f"INSERT INTO {table_name} ({columns}) VALUES ({values});"    insert_statements.append(sql)# 输出SQL INSERT语句for statement in insert_statements:    print(statement)

yum install -y libjpeg-develyum install -y zlib-develyum install -y libjpeg libtiff freetype 

pip3 install tinifypip3 install Pillow

import osfrom PIL import Imageimport tinifyfrom concurrent.futures import ThreadPoolExecutor# 配置部分input_folder = '/img'  # 要压缩的图片文件夹whitelist = {'example1.jpg', 'important_image.png'}  # 白名单中的文件quality = 75  # 压缩质量def compress_image(img_path, quality):    """    压缩单张图片并覆盖原文件。        :param img_path: 图片路径    :param quality: 压缩质量    """    try:        img = Image.open(img_path)        img.save(img_path, optimize=True, quality=quality)        print(f"Compressed and saved in place: {img_path}")    except Exception as e:        print(f"Failed to compress {img_path}: {e}")def compress_images_in_place(input_folder, whitelist=None, quality=75):    """    使用 Pillow 压缩图片并覆盖原文件，支持白名单功能。使用多线程加速处理。        :param input_folder: 原始图片文件夹路径    :param whitelist: 白名单列表，包含不希望被压缩的图片文件名（可选）    :param quality: 压缩质量，默认75    """    if whitelist is None:        whitelist = set()    # 创建一个 ThreadPoolExecutor 来处理压缩任务    with ThreadPoolExecutor() as executor:        futures = []        for root, dirs, files in os.walk(input_folder):            for file in files:                if file in whitelist:                    print(f"Skipping (whitelisted): {file}")                    continue                if file.lower().endswith(('.jpg', '.jpeg', '.png')):                    img_path = os.path.join(root, file)                    futures.append(executor.submit(compress_image, img_path, quality))        # 等待所有任务完成        for future in futures:            future.result()# 调用压缩函数compress_images_in_place(input_folder, whitelist, quality)