爬取链家网租房图 使用ImagesPipeline保存图片

# 爬虫文件

# -*- coding: utf-8 -*-
import scrapy
import os
from urllib import request
from lianjia.items import LianjiaItem
class LianjiaspiderSpider(scrapy.Spider):
    name = 'lianjiaSpider'
    # allowed_domains = ['www.xxx.com']
    start_urls = ['https://bj.lianjia.com/zufang/l1rp5/#contentList ']

    def parse(self, response):
        div_list = response.xpath('//div[@class="content__list"]/div[@class="content__list--item"]')
        # print(len(div_list))
        for div in div_list:
            title = div.xpath('.//div[@class="content__list--item--main"]/p[1]/a/text()').get()
            title = title.strip()
            detail_url = div.xpath('.//div[@class="content__list--item--main"]/p[1]/a/@href').get()
            detail_url = "https://bj.lianjia.com" + detail_url
            # print(detail_url)
            location = div.xpath('.//div[@class="content__list--item--main"]/p[2]//text()').getall()
            location = list(map(lambda x:x.replace("\n","").replace("-","").replace("/","").strip(),location))
            location = "".join(location)
            # print(location)
            price = div.xpath('.//div[@class="content__list--item--main"]/span//text()').getall()
            price = price[0]+price[1]
            # print(price)

            yield scrapy.Request(url=detail_url, callback=self.parse_detail,meta={'info':(title,location,price,detail_url)})

        # 2-100页的url
        for i in range(2,101):
            next_url = "https://bj.lianjia.com/zufang/pg%dl1rp5/#contentList" % i
            yield scrapy.Request(url=next_url, callback=self.parse)

    def parse_detail(self,response):
        title,location,price,detail_url = response.meta.get("info")
        # pic_src = response.xpath("//div[@class='content__thumb--box']/ul/li[2]/img/@src").get()
        pic_srcs = response.xpath("//div[@class='content__thumb--box']/ul//img/@src").getall()
        # print('户型图链接:',pic_srcs)
        print('房源链接:',detail_url)

        item = LianjiaItem()
        item["title"] = title
        item["location"] = location
        item["price"] = price
        item['detail_url']=detail_url
        # item['pic_srcs'] = pic_srcs
        item['image_urls'] = pic_srcs
        yield item

# 管道文件
# 保存图片
# 普通方法保存图片

import os
from urllib import request

class LianjiaPipeline(object):
    def __init__(self):
        # 获取当前pipeline文件所在的目录路径 os.path.dirname(__file__)
        # 获取最外层bmw的路径os.path.dirname(os.path.dirname(__file__))
        # 在最外层bmw目录下创建一个文件夹 images, 获取images的路径
        self.path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'images') # 生成images文件夹
        if not os.path.exists(self.path):
            print("images文件夹不存在")
            os.mkdir(self.path)  # 创建images文件夹

    def process_item(self, item, spider):
        location = item['location']
        urls = item['pic_srcs']
        per_house_pic_path = os.path.join(self.path,location)
        # path2=self.path  # G:\Crawler and Data\21days_spider\lianjia\images

       # 处理路径拼接  打印出来的是一个斜杠的  但是系统里是两个斜杠的, 会报错
        per_house_pic_path = per_house_pic_path.replace('/','\\')
        print('每一个户型图的保存路径:',per_house_pic_path)

        if not os.path.exists(per_house_pic_path):
            os.mkdir(per_house_pic_path)
        for url in urls:
            # 每个图片的url
            url = url.replace('126x86.jpg','780x439.jpg')  # 更改保存图片的大小
            # 切割图片url  拼接图片的名称  防止图片保存被覆盖 不然最后爬下的始终只有一张图片
            pic_name = url.split('.')[2][-9:-1]  # 防止图片被覆盖

            # os.path.join 的两个参数:户型图文件夹 和 图片的名称 拼接出来图片路径
            request.urlretrieve(url=url,filename=os.path.join(per_house_pic_path,pic_name+'.png'))
        return item

# item文件
class LianjiaItem(scrapy.Item):
    # define the fields for your item here like:

    # 普通的字段
    title = scrapy.Field()
    detail_url = scrapy.Field()
    location = scrapy.Field()
    price = scrapy.Field()
    pic_srcs = scrapy.Field()

# setting中
ITEM_PIPELINES = {
   'lianjia.pipelines.LianjiaPipeline': 300,

}

# 使用scrapy中的 image pipleline方法保存图片
import os
from urllib import request
from scrapy.pipelines.images import ImagesPipeline
from lianjia import settings

class LjImagesPipeline(ImagesPipeline):
    # 这个方法是下载请求前调用的, 就是发送下载请求的时候调用
    def get_media_requests(self,item,info):
        request_objs = super(LjImagesPipeline,self).get_media_requests(item,info)
        for request_obj in request_objs:
            request_obj.item = item   # 把item绑定到request上面,为了下面的方法可以通过request获取item
        return request_objs

    def file_path(self,request,response=None,info=None):
        # 这个方法是图片被存储的时候调用,来获取这个图片存储的路径
        path = super(LjImagesPipeline,self).file_path(request,response,info)
        location = request.item.get('location')
        # 获取图片存储路径    images文件夹路径
        images_store = settings.IMAGES_STORE
        # 判断这里有没有目录   每个房源的目录(这里面存房子图片)
        per_house_pic_path = os.path.join(images_store, location)
        if not os.path.exists(per_house_pic_path):
            os.mkdir(per_house_pic_path)
        image_name = path.replace('full/','') # 加个斜杠/是把full删除
        # print('image_name:',image_name)  #c554f76249059833f3a454830ec2cc2067465968.jpg

        image_path = os.path.join(per_house_pic_path,image_name)
        return image_path

# 对应的item文件
class LianjiaItem(scrapy.Item):
    # define the fields for your item here like:

    # 普通的字段
    title = scrapy.Field()
    detail_url = scrapy.Field()
    location = scrapy.Field()
    price = scrapy.Field()
    # pic_srcs = scrapy.Field()

    # 使用Images Pipeline需要的字段
    image_urls=scrapy.Field()
    images = scrapy.Field()

#settings文件
ITEM_PIPELINES = {
   # 'lianjia.pipelines.LianjiaPipeline': 300,
   # "scrapy.pipelines.images.ImagesPipeline":1 #不执行管道文件

  'lianjia.pipelines.LjImagesPipeline': 1,   #执行管道文件里重写的两个方法
}

# 图片下载的路径 供image.pipelines使用
import os
# 图片存储路径
IMAGES_STORE = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'images') # 生成images文件夹

# 总结:
1.  def process_item()方法中 self.path 获取到的是images文件夹的路径, 要在这个文件下面保存每一个户型图的图片

2.  在window系统的路径拼接, os.path.join() 生成的路径通过print打印出来是一个斜杠/, 但是系统找路径的时候是找的双斜杠//, 这个时候就会报错.