PaddlePaddle basic learning 2

The general process of deep learning:

Collecting data, especially labeled, high-quality data is an expensive task.
The process of crawling is the process of imitating the behavior of the browser, sending a request to the target site, receiving the response data from the server, extracting the required information, and saving it.
Python provides tools for the implementation of crawlers: requests module, BeautifulSoup library

The whole process of surfing the Internet:

general user:

Open the browser --> send a request to the target site --> receive the response data --> render to the page.

Crawler:

Simulate browser --> send request to target site --> receive response data --> extract useful data --> save to local/database.

The crawling process:

1. Send a request (requests module)

2. Get the response data (returned by the server)

3. Parse and extract data (BeautifulSoup search or re regular)

4. Save the data

request module:

requests is a simple and easy-to-use HTTP library implemented by python, the official website address: http://cn.python-requests.org/zh_CN/latest/

requests.get(url) can send an http get request and return the server response content.

BeautifulSoup library:

BeautifulSoup
is a Python library that can extract data from HTML or XML files. Website: https://beautifulsoup.readthedocs.io/zh_CN/v4.4.0/

BeautifulSoup supports the HTML parser in the Python standard library, and also supports some third-party parsers, one of which is lxml.

For BeautifulSoup(markup, “html.parser”) or BeautifulSoup(markup,
“lxml”), lxml is recommended as the parser because it is more efficient.

1. Crawl the information of all contestants in "Youth with You 2" in Baidu Encyclopedia, and return to the page data

import json
import re
import requests
import datetime
from bs4 import BeautifulSoup
import os

#获取当天的日期,并进行格式化,用于后面文件命名，格式:20200420
today = datetime.date.today().strftime('%Y%m%d')    

def crawl_wiki_data():
    """
    爬取百度百科中《青春有你2》中参赛选手信息，返回html
    """
    headers = {
    
     
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.99 Safari/537.36'
    }
    url='https://baike.baidu.com/item/青春有你第二季'                         

    try:
        response = requests.get(url,headers=headers)
        print(response.status_code)

        #将一段文档传入BeautifulSoup的构造方法,就能得到一个文档的对象, 可以传入一段字符串
        soup = BeautifulSoup(response.text,'lxml')
        
        #返回的是class为table-view log-set-param的<table>所有标签
        tables = soup.find_all('table',{
    
    'class':'table-view log-set-param'})
        # print(tables)
        crawl_table_title = "参赛学员"

        for table in  tables:           
            #对当前节点前面的标签和字符串进行查找
            table_titles = table.find_previous('div').find_all('h3')
            for title in table_titles:
                if(crawl_table_title in title):
                    return table       
    except Exception as e:
        print(e)

2. Analyze the crawled page data and save it as a JSON file

def parse_wiki_data(table_html):
    '''
    从百度百科返回的html中解析得到选手信息，以当前日期作为文件名，存JSON文件,保存到work目录下
    '''
    bs = BeautifulSoup(str(table_html),'lxml')
    all_trs = bs.find_all('tr')

    error_list = ['\'','\"']

    stars = []

    for tr in all_trs[1:]:
         all_tds = tr.find_all('td')

         star = {
    
    }

         #姓名
         star["name"]=all_tds[0].text
         #个人百度百科链接
         star["link"]= 'https://baike.baidu.com' + all_tds[0].find('a').get('href')
         #籍贯
         star["zone"]=all_tds[1].text
         #星座
         star["constellation"]=all_tds[2].text
         #身高
         star["height"]=all_tds[3].text
         #体重
         star["weight"]= all_tds[4].text

         #花语,去除掉花语中的单引号或双引号
         flower_word = all_tds[5].text
         for c in flower_word:
             if  c in error_list:
                 flower_word=flower_word.replace(c,'')
         star["flower_word"]=flower_word 
         
         #公司
         if not all_tds[6].find('a') is  None:
             star["company"]= all_tds[6].find('a').text
         else:
             star["company"]= all_tds[6].text  

         stars.append(star)

    json_data = json.loads(str(stars).replace("\'","\""))   
    with open('work/' + today + '.json', 'w', encoding='UTF-8') as f:
        json.dump(json_data, f, ensure_ascii=False)

3. Crawl the Baidu Encyclopedia picture of each contestant and save it

def crawl_pic_urls():
    '''
    爬取每个选手的百度百科图片，并保存
    ''' 
    with open('work/'+ today + '.json', 'r', encoding='UTF-8') as file:
         json_array = json.loads(file.read())

    headers = {
    
     
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.99 Safari/537.36' 
     }

    for star in json_array:

        name = star['name']
        link = star['link']

        #！！！请在以下完成对每个选手图片的爬取，将所有图片url存储在一个列表pic_urls中！！！
        pic_urls = []
        response = requests.get(link,headers=headers)

        bs = BeautifulSoup(response.text,'lxml')

        pic_list_url = 'https://baike.baidu.com' + bs.select('.summary-pic a')[0].get('href')

        pic_list_response = requests.get(pic_list_url, headers=headers)

        bs = BeautifulSoup(pic_list_response.text, 'lxml')
        pic_list_html = bs.select('.pic-list img')

        for pic_html in pic_list_html:
            pic_urls.append(pic_html.get('src'))

        #！！！根据图片链接列表pic_urls, 下载所有图片，保存在以name命名的文件夹中！！！
        down_pic(name,pic_urls)

     

def down_pic(name,pic_urls):
    '''
    根据图片链接列表pic_urls, 下载所有图片，保存在以name命名的文件夹中,
    '''
    path = 'work/'+'pics/'+name+'/'

    if not os.path.exists(path):
      os.makedirs(path)

    for i, pic_url in enumerate(pic_urls):
        print("pic_url : ", pic_url)
        try:
            pic = requests.get(pic_url, timeout=15)
            string = str(i + 1) + '.jpg'
            with open(path+string, 'wb') as f:
                f.write(pic.content)
                print('成功下载第%s张图片: %s' % (str(i + 1), str(pic_url)))
        except Exception as e:
            print('下载第%s张图片时失败: %s' % (str(i + 1), str(pic_url)))
            print(e)
            continue

Four, print the path of all pictures crawled

def show_pic_path(path):
    '''
    遍历所爬取的每张图片，并打印所有图片的绝对路径
    '''
    pic_num = 0
    for (dirpath,dirnames,filenames) in os.walk(path):
        for filename in filenames:
           pic_num += 1
           print("第%d张照片：%s" % (pic_num,os.path.join(dirpath,filename)))           
    print("共爬取《青春有你2》选手的%d照片" % pic_num)

if __name__ == '__main__':

     #爬取百度百科中《青春有你2》中参赛选手信息，返回html
     html = crawl_wiki_data()

     #解析html,得到选手信息，保存为json文件
     parse_wiki_data(html)

     #从每个选手的百度百科页面上爬取图片,并保存
     crawl_pic_urls()

     #打印所爬取的选手图片路径
     show_pic_path('/home/aistudio/work/pics/')

     print("所有信息爬取完成！")