用Python批量下载图片并改名

确保Python已安装依赖项：pandas和openpyxl
把需要下载的图片链接和修改后的名字放在Excel文件：Down.xlsx

pip install pandas openpyxl

把以下代码保存为download_images.py文件：

import pandas as pd
import os
import requests
from datetime import datetime
import urllib.parse
from pathlib import Path
import time
import random
from concurrent.futures import ThreadPoolExecutor, wait
from fake_useragent import UserAgent
import logging
from tenacity import retry, stop_after_attempt, wait_exponential

# 设置日志
logging.basicConfig(
    filename='download_errors.log',
    level=logging.ERROR,
    format='%(asctime)s - %(levelname)s - %(message)s'
)

# 设置下载目录
base_dir = r"D:\Down"
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
download_dir = os.path.join(base_dir, f"Task_{timestamp}")
os.makedirs(download_dir, exist_ok=True)

# 写死 Excel 文件路径
excel_file = r"C:\Users\XXX\Downloads\Down.xlsx"

# 初始化 User-Agent
ua = UserAgent()

# 可选代理设置（如果需要使用代理，取消注释并配置）
# proxies = {
#     'http': 'http://your_proxy:port',
#     'https': 'https://your_proxy:port'
# }

# 下载单张图片
@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
def download_image(row, index, session):
    image_url = row['Image Src']
    new_name = row['New Name']
    
    if not image_url or not new_name:
        return index, '', 'Missing URL or Name'
    
    try:
        # 获取文件扩展名
        parsed_url = urllib.parse.urlparse(image_url)
        ext = os.path.splitext(parsed_url.path)[1]
        if not ext:
            ext = '.jpg'
        new_filename = f"{new_name}{ext}"
        full_path = os.path.join(download_dir, new_filename)
        
        # 设置请求头
        headers = {'User-Agent': ua.random}
        
        # 下载图片
        response = session.get(image_url, timeout=10, headers=headers)  # proxies=proxies if needed
        
        # 检查响应状态
        if response.status_code == 200:
            with open(full_path, 'wb') as f:
                f.write(response.content)
            return index, full_path, 'Success'
        else:
            return index, '', f"HTTP {response.status_code}"
            
    except requests.exceptions.RequestException as e:
        logging.error(f"Failed to download {image_url}: {str(e)}")
        return index, '', f"Error: {str(e)}"
    except Exception as e:
        logging.error(f"Unexpected error for {image_url}: {str(e)}")
        return index, '', f"Error: {str(e)}"

# 主处理函数
def process_downloads():
    # 读取 Excel 文件
    try:
        df = pd.read_excel(excel_file)
    except FileNotFoundError:
        print(f"Error: Excel file {excel_file} not found.")
        return
    
    # 确保所需列存在
    required_columns = ['Image Src', 'New Name', 'Full Path', 'Status']
    for col in required_columns:
        if col not in df.columns:
            df[col] = ''
    
    # 使用 Session 保持连接
    with requests.Session() as session:
        # 使用线程池，最大10个并行任务
        with ThreadPoolExecutor(max_workers=10) as executor:
            futures = []  # 明确使用列表
            print(f"Initial type of futures: {type(futures)}")  # 调试：初始类型
            for index, row in df.iterrows():
                # 调试：每次 append 前检查类型
                if not isinstance(futures, list):
                    print(f"Error: futures is not a list, type is {type(futures)}")
                    return
                futures.append(executor.submit(download_image, row, index, session))
                print(f"Appended future for index {index}, futures length: {len(futures)}")  # 调试
                
                # 如果达到10个并行任务，等待任意一个完成
                if len(futures) >= 10:
                    completed, uncompleted = wait(
                        futures, return_when='FIRST_COMPLETED'
                    )
                    futures = list(uncompleted)  # 转换回列表
                    print(f"Completed {len(completed)} tasks, futures length: {len(futures)}")  # 调试
                    # 随机延迟1-3秒
                    time.sleep(random.uniform(1, 3))
            
            # 等待所有剩余任务完成
            print("Waiting for remaining tasks...")
            for future in wait(futures)[0]:
                index, full_path, status = future.result()
                df.at[index, 'Full Path'] = full_path
                df.at[index, 'Status'] = status
    
    # 保存更新后的 Excel
    output_file = os.path.join(download_dir, "updated_excel.xlsx")
    df.to_excel(output_file, index=False)
    print(f"处理完成，更新后的 Excel 已保存至: {output_file}")

if __name__ == "__main__":
    process_downloads()