爬虫爬取桌面壁纸

import requests
from bs4 import BeautifulSoup
import os
import time
import random

import UserAgent

index = 'http://www.netbian.com'  # 网站根地址
interval = 3  # 爬取图片的间隔时间
firstDir = 'D:/netbian'  # 总路径
classificationDict = {}  # 存放网站分类子页面的信息


# 获取页面筛选后的内容列表
def screen(url, select):
    # 随机获取一个headers
    headers = {'User-Agent' : UserAgent.get_headers()}
    html = requests.get(url=url, headers=headers)
    html.encoding = 'gbk'  # 网站的编码
    html = html.text
    soup = BeautifulSoup(html, 'lxml')
    return soup.select(select)


# 获取页码
def screenPage(url, select):
    headers = {'User-Agent': UserAgent.get_headers()}
    html = requests.get(url=url, headers=headers)
    html.encoding = 'gbk'
    html = html.text
    soup = BeautifulSoup(html, 'lxml')
    return soup.select(select)[0].next_sibling.text


# 下载操作
def download(src, name, path):
    if (isinstance(src, str)):
        response = requests.get(src)
        path = path + '/' + name + '.jpg'
        while (os.path.exists(path)):  # 若文件名重复
            path = path.split(".")[0] + str(random.randint(2, 17)) + '.' + path.split(".") [1]
        with open(path, 'wb') as pic:
            for chunk in response.iter_content(128):
                pic.write(chunk)


# 定位到 1920 1080 分辨率图片
def handleImgs(links, path):
    for link in links:
        href = link.get('href')
        if (href == 'http://pic.netbian.com/'):  # 过滤图片广告
            continue

        # 第一次跳转
        if ('http://' in href):  # 有极个别图片不提供正确的相对地址
            url = href
        else:
            url = index + href
        select = 'div#main div.endpage div.pic div.pic-down a'
        link = screen(url, select)
        if (link == []):
            print(url + ' 无此图片，爬取失败')
            continue
        href = link [0].get('href')

        # 第二次跳转
        url = index + href

        # 获取到图片了
        select = 'div#main table a img'
        link = screen(url, select)
        if (link == []):
            print(url + " 该图片需要登录才能爬取，爬取失败")
            continue
        name = link [0].get('alt').replace('\t', '').replace('|', '').replace(':', '').replace('\\', '').replace('/',
                                                                                                                 '').replace(
            '*', '').replace('?', '').replace('"', '').replace('<', '').replace('>', '')
        print(name)  # 输出下载图片的文件名
        src = link [0].get('src')
        if (requests.get(src).status_code == 404):
            print(url + ' 该图片下载链接404，爬取失败')
            print()
            continue
        print()
        download(src, name, path)
        time.sleep(interval)


# 选择下载分类子页面
def select_classification(choice):
    print('---------------------------')
    print('--------------' + choice + '-------------')
    print('---------------------------')
    secondUrl = classificationDict [choice] ['url']
    secondDir = classificationDict [choice] ['path']

    if (not os.path.exists(secondDir)):
        os.mkdir(secondDir)  # 创建分类目录

    select = '#main > div.page > span.slh'
    pageIndex = screenPage(secondUrl, select)
    lastPagenum = int(pageIndex)  # 获取最后一页的页码
    for i in range(0, lastPagenum):
        if i == 0:
            url = secondUrl
        else:
            url = secondUrl + 'index_%d.htm' % (i + 1)

        print('--------------' + choice + ': ' + str(i + 1) + '-------------')
        path = secondDir

        select = 'div#main div.list ul li a'
        links = screen(url, select)
        handleImgs(links, path)


# ui界面，用户选择下载分类
def ui():
    print('--------------netbian-------------')
    print('全部', end=' ')
    for c in classificationDict.keys():
        print(c, end=' ')
    print()
    choice = input('请输入分类名：')
    if (choice == '全部'):
        for c in classificationDict.keys():
            select_classification(c)
    elif (choice not in classificationDict.keys()):
        print("输入错误，请重新输入！")
        print('----')
        ui()
    else:
        select_classification(choice)


# 将分类子页面信息存放在字典中
def init_classification():
    url = index
    select = '#header > div.head > ul > li:nth-child(1) > div > a'
    #header相当于 id = "header"
    #div.head相当于 div class="head"
    #li:nth-child(1) 相当于父元素下第一个子分类
    classifications = screen(url, select)
    for c in classifications:
        href = c.get('href')  # 获取的是相对地址
        text = c.string  # 获取分类名
        if (text == '4k壁纸'):  # 4k壁纸，因权限问题无法爬取，直接跳过
            continue
        secondDir = firstDir + '/' + text  # 分类目录
        url = index + href  # 分类子页面url
        global classificationDict
        classificationDict[text] = {
            'path': secondDir,
            'url': url
        }



def main():
    if not os.path.exists(firstDir):
        os.mkdir(firstDir)  # 创建总目录
    init_classification()
    ui()


if __name__ == '__main__':
    main()