6-爬虫-scrapy图片数据(二进制数据)爬取、深度爬取、核心组件、中间件、网易新闻爬虫

scrapy图片数据(二进制数据)爬取

1、在爬虫文件中解析出图片地址+图片名称封装到item对象提交给管道
2、在管道文件中:
  - from scrapy.pipelines.images import ImagesPipeline
  - 封装一个管道类,继承与ImagesPipeline
  - 重写父类的三个方法:
    - get_media_requests
    - file_path:只需要返回图片名称
    - item_completed
3、在配置文件中添加如下配置:
  - IMAGES_STORE = '文件夹路径'

xiaohua.py

# -*- coding: utf-8 -*-
import scrapy
from xiaohuaPro.items import XiaohuaproItem

class XiaohuaSpider(scrapy.Spider):
    name = 'xiaohua'
    # allowed_domains = ['www.xxx.com']
    start_urls = ['http://www.521609.com/daxuemeinv/']

    def parse(self, response):
        #图片地址+名称
        li_list = response.xpath('//*[@id="content"]/div[2]/div[2]/ul/li')
        for li in li_list:
            img_src = 'http://www.521609.com'+li.xpath('./a[1]/img/@src').extract_first()
            img_name = li.xpath('./a[1]/img/@alt').extract_first()+'.jpg'
            item = XiaohuaproItem()
            item['img_name'] = img_name
            item['img_src'] = img_src

            yield item

pipelines.py

import scrapy
from scrapy.pipelines.images import ImagesPipeline


class XiaohuaproPipeline(ImagesPipeline):
    # 对图片数据进行请求发送
    # 该方法参数item就是接受爬虫文件提交过来的item
    def get_media_requests(self, item, info):
        # meta可以将字典传递给file_path方法
        yield scrapy.Request(item['img_src'], meta={'item': item})

    # 指定图片存储的路径
    def file_path(self, request, response=None, info=None):
        # 如何获取图片名称
        item = request.meta['item']
        img_name = item['img_name']
        return img_name

    # 可以将item 传递给下一个即将被执行的管道类
    def item_completed(self, results, item, info):
        return item

scrapy深度爬取:请求传参

什么叫深度爬取:
  - 爬取的数据没有存在于同一张页面
请求传参:
  - 在手动请求发送的时候可以通过meta参数将meta表示的字典传递给callback
  - 在callback通过response.meta的形式接收传递过来的meta就可以了
    - 可以实现让不同的解析方法共享同一个item对象

# -*- coding: utf-8 -*-
import scrapy
from moviePro.items import MovieproItem


class MovieSpider(scrapy.Spider):
    name = 'movie'
    # allowed_domains = ['www.xxx.com']
    start_urls = ['https://www.4567kan.com/index.php/vod/show/id/1.html']
    url_model = 'https://www.4567kan.com/index.php/vod/show/id/%d.html'
    page = 2

    # 解析首页数据
    def parse(self, response):
        li_list = response.xpath('/html/body/div[1]/div/div/div/div[2]/ul/li')
        for li in li_list:
            title = li.xpath('./div/a/@title').extract_first()
            item = MovieproItem()
            item['title'] = title

            detail_url = 'https://www.4567kan.com' + li.xpath('./div/a/@href').extract_first()
            # 对详情页发起请求
            # 请求传参:meta字典可以传递给callback
            yield scrapy.Request(url=detail_url, callback=self.parse_detail, meta={'item': item})

        if self.page < 6:
            new_url = format(self.url_model % self.page)
            self.page += 1
            yield scrapy.Request(url=new_url, callback=self.parse)

    # 解析详情页的数据
    def parse_detail(self, response):
        item = response.meta['item']
        desc = response.xpath('/html/body/div[1]/div/div/div/div[2]/p[5]/span[2]/text()').extract_first()
        item['desc'] = desc

        yield item

scrapy五大核心组件

引擎(Scrapy)
  用来处理整个系统的数据流处理, 触发事务(框架核心)
调度器(Scheduler)
  用来接受引擎发过来的请求, 压入队列中, 并在引擎再次请求的时候返回. 可以想像成一个URL(抓取网页的网址或者说是链接)的优先队列, 由它来决定下一个要抓取的网址是什么, 同时去除重复的网址
下载器(Downloader)
  用于下载网页内容, 并将网页内容返回给蜘蛛(Scrapy下载器是建立在twisted这个高效的异步模型上的)
爬虫(Spiders)
  爬虫是主要干活的, 用于从特定的网页中提取自己需要的信息, 即所谓的实体(Item)。用户也可以从中提取出链接,让Scrapy继续抓取下一个页面
项目管道(Pipeline)
  负责处理爬虫从网页中抽取的实体,主要的功能是持久化实体、验证实体的有效性、清除不需要的信息。当页面被爬虫解析后,将被发送到项目管道,并经过几个特定的次序处理数据。

 6-爬虫-scrapy图片数据(二进制数据)爬取、深度爬取、核心组件、中间件、网易新闻爬虫_第1张图片

scrapy的中间件

爬虫中间件
下载中间件(*)
  - 作用:可以批量拦截框架中发起的所有请求和响应
  - 拦截请求干什么
    - 请求头的篡改
      - UA
    - 给请求设置代理
      - 写在process_exception:
        - request.meta['proxy'] = 'http://ip:port'
  - 拦截响应干什么
    - 为了篡改响应数据
      - 我们请求到的一组不符合需求响应数据,我们就需要将响应进行拦截,将不满足
        需求的响应数据篡改为满足需求响应数据。

 配置中需要解开对应中间件代码的注释

DOWNLOADER_MIDDLEWARES = {
'middlePro.middlewares.MiddleproDownloaderMiddleware': 543,
}

middleware.py

from scrapy import signals


import  random
user_agent_list = [
        "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 "
        "(KHTML, like Gecko) Chrome/22.0.1207.1 Safari/537.1",
        "Mozilla/5.0 (X11; CrOS i686 2268.111.0) AppleWebKit/536.11 "
        "(KHTML, like Gecko) Chrome/20.0.1132.57 Safari/536.11",
        "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.6 "
        "(KHTML, like Gecko) Chrome/20.0.1092.0 Safari/536.6",
        "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.6 "
        "(KHTML, like Gecko) Chrome/20.0.1090.0 Safari/536.6",
        "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.1 "
        "(KHTML, like Gecko) Chrome/19.77.34.5 Safari/537.1",
        "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/536.5 "
        "(KHTML, like Gecko) Chrome/19.0.1084.9 Safari/536.5",
        "Mozilla/5.0 (Windows NT 6.0) AppleWebKit/536.5 "
        "(KHTML, like Gecko) Chrome/19.0.1084.36 Safari/536.5",
        "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 "
        "(KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3",
        "Mozilla/5.0 (Windows NT 5.1) AppleWebKit/536.3 "
        "(KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3",
        "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0) AppleWebKit/536.3 "
        "(KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3",
        "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 "
        "(KHTML, like Gecko) Chrome/19.0.1062.0 Safari/536.3",
        "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 "
        "(KHTML, like Gecko) Chrome/19.0.1062.0 Safari/536.3",
        "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 "
        "(KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3",
        "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 "
        "(KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3",
        "Mozilla/5.0 (Windows NT 6.1) AppleWebKit/536.3 "
        "(KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3",
        "Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 "
        "(KHTML, like Gecko) Chrome/19.0.1061.0 Safari/536.3",
        "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/535.24 "
        "(KHTML, like Gecko) Chrome/19.0.1055.1 Safari/535.24",
        "Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/535.24 "
        "(KHTML, like Gecko) Chrome/19.0.1055.1 Safari/535.24"
]

https_proxy = [
    '1.1.1.1:8888',
    '2.2.2.2:8880',
]
http_proxy = [
    '11.11.11.11:8888',
    '21.21.21.21:8880',
]
class MiddleproDownloaderMiddleware:
# 拦截所有的请求 # 所有的请求:正常的请求,异常的请求 # 参数:request就是拦截到的请求 def process_request(self, request, spider): # 尽可能给每一个请求对象赋值一个不同的请求载体身份标识 request.headers['User-Agent'] = random.choice(user_agent_list) print('i am process_request:'+request.url) return None
# 拦截所有的响应 # response:拦截到的响应 # request:拦截到响应对应的请求对象 def process_response(self, request, response, spider): print('i am process_response:',response) return response
# 拦截发生异常的请求对象 # 拦截到异常的请求是为什么? # 可以将异常的请求进行修正,然后让其进行重新发送 def process_exception(self, request, exception, spider): print('i am process_exception:',request.url) # print('异常请求的异常信息::',exception) # 如何对请求进行重新发送 # return request即可 # 设置代理 if request.url.split(':')[0] == 'https': request.meta['proxy'] = random.choice(https_proxy) else: request.meta['proxy'] = random.choice(http_proxy) return request

 网易新闻爬虫(中间件使用案例)

 wangyi.py

# -*- coding: utf-8 -*-
import scrapy
from selenium import webdriver
from wangyiPro.items import WangyiproItem


class WangyiSpider(scrapy.Spider):
    name = 'wangyi'
    # allowed_domains = ['www.xxx.com']
    start_urls = ['https://news.163.com/']
    model_urls = []

    # 实例化浏览器对象
    bro = webdriver.Chrome('/Users/bobo/Desktop/29期授课相关/day06/chromedriver')

    # 用来解析五个板块对应的url
    def parse(self, response):
        li_list = response.xpath('//*[@id="index2016_wrap"]/div[1]/div[2]/div[2]/div[2]/div[2]/div/ul/li')
        model_index = [3, 4, 6, 7, 8]
        for index in model_index:
            li = li_list[index]
            model_url = li.xpath('./a/@href').extract_first()
            self.model_urls.append(model_url)
        # 对每一个板块对应的url进行请求发送
        for url in self.model_urls:
            yield scrapy.Request(url=url, callback=self.parse_model)

    # 用来解析每一个板块对应页面中的数据
    # 每一个板块对应的新闻数据是动态加载
    def parse_model(self, response):
        # 解析新闻的标题和新闻详情页的url
        # 遇到了不满足需求的响应对象
        div_list = response.xpath('/html/body/div/div[3]/div[4]/div[1]/div/div/ul/li/div/div')
        for div in div_list:
            title = div.xpath('./div/div[1]/h3/a/text()').extract_first()
            new_detail_url = div.xpath('./div/div[1]/h3/a/@href').extract_first()
            if new_detail_url:
                item = WangyiproItem()
                item['title'] = title
                yield scrapy.Request(url=new_detail_url, callback=self.parse_new, meta={'item': item})

    # 解析每一条新闻详情页的新闻内容
    def parse_new(self, response):
        item = response.meta['item']
        content = response.xpath('//*[@id="endText"]//text()').extract()
        content = ''.join(content)
        item['content'] = content

        yield item

    # 重写父类方法
    def closed(self, spider):
        print('整个操作结束!!!')
        self.bro.quit()

middlewares.py

from time import sleep
from scrapy.http import HtmlResponse


class WangyiproDownloaderMiddleware:

    def process_request(self, request, spider):

        return None

    def process_response(self, request, response, spider):
        # 拦截指定的响应对象,将其进行篡改
        bro = spider.bro
        if request.url in spider.model_urls:
            # response就是五个板块对应响应对象
            # 获取满足需求的响应数据(selenium)
            bro.get(request.url)
            sleep(2)
            bro.execute_script('window.scrollTo(0,document.body.scrollHeight)')
            sleep(1)
            # 满足需求的响应数据
            page_text = bro.page_source
            # 实例化一个新的响应对象,将page_text作为新响应对象的响应数据
            new_response = HtmlResponse(url=request.url, body=page_text, request=request, encoding='utf-8')
            return new_response
        else:
            return response

    def process_exception(self, request, exception, spider):
        pass

 

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