python实现知乎高颜值图片爬取

导入相关包

import time
import pydash
import base64
import requests
from lxml import etree
from aip import AipFace
from pathlib import Path

百度云 人脸检测 申请信息

#唯一必须填的信息就这三行
APP_ID = "xxxxxxxx"
API_KEY = "xxxxxxxxxxxxxxxx"
SECRET_KEY = "xxxxxxxxxxxxxxxx"
# 过滤颜值阈值，存储空间大的请随意
BEAUTY_THRESHOLD = 55
AUTHORIZATION = "oauth c3cef7c66a1843f8b3a9e6a1e3160e20"
# 如果权限错误，浏览器中打开知乎，在开发者工具复制一个，无需登录
# 建议最好换一个，因为不知道知乎的反爬虫策略，如果太多人用同一个，可能会影响程序运行

以下皆无需改动

# 每次请求知乎的讨论列表长度，不建议设定太长，注意节操
LIMIT = 5
# 这是话题『美女』的 ID，其是『颜值』（20013528）的父话题
SOURCE = "19552207"

爬虫假装下正常浏览器请求

USER_AGENT = "Mozilla/5.0 (Windows NT 5.1) AppleWebKit/534.55.3 (KHTML, like Gecko) Version/5.1.5 Safari/534.55.3"
REFERER = "https://www.zhihu.com/topic/％s/newest" ％ SOURCE
# 某话题下讨论列表请求 url
BASE_URL = "https://www.zhihu.com/api/v4/topics/％s/feeds/timeline_activity"
# 初始请求 url 附带的请求参数
URL_QUERY = "?include=data％5B％3F％28target.type％3Dtopic_sticky_module％29％5D.target.data％5B％3F％28target.type％3Danswer％29％5D.target.content％2Crelationship.is_authorized％2Cis_author％2Cvoting％2Cis_thanked％2Cis_nothelp％3Bdata％5B％3F％28target.type％3Dtopic_sticky_module％29％5D.target.data％5B％3F％28target.type％3Danswer％29％5D.target.is_normal％2Ccomment_count％2Cvoteup_count％2Ccontent％2Crelevant_info％2Cexcerpt.author.badge％5B％3F％28type％3Dbest_answerer％29％5D.topics％3Bdata％5B％3F％28target.type％3Dtopic_sticky_module％29％5D.target.data％5B％3F％28target.type％3Darticle％29％5D.target.content％2Cvoteup_count％2Ccomment_count％2Cvoting％2Cauthor.badge％5B％3F％28type％3Dbest_answerer％29％5D.topics％3Bdata％5B％3F％28target.type％3Dtopic_sticky_module％29％5D.target.data％5B％3F％28target.type％3Dpeople％29％5D.target.answer_count％2Carticles_count％2Cgender％2Cfollower_count％2Cis_followed％2Cis_following％2Cbadge％5B％3F％28type％3Dbest_answerer％29％5D.topics％3Bdata％5B％3F％28target.type％3Danswer％29％5D.target.content％2Crelationship.is_authorized％2Cis_author％2Cvoting％2Cis_thanked％2Cis_nothelp％3Bdata％5B％3F％28target.type％3Danswer％29％5D.target.author.badge％5B％3F％28type％3Dbest_answerer％29％5D.topics％3Bdata％5B％3F％28target.type％3Darticle％29％5D.target.content％2Cauthor.badge％5B％3F％28type％3Dbest_answerer％29％5D.topics％3Bdata％5B％3F％28target.type％3Dquestion％29％5D.target.comment_count&limit=" + str(
 LIMIT)

HEADERS = {
 "User-Agent": USER_AGENT,
 "Referer": REFERER,
 "authorization": AUTHORIZATION

指定 url，获取对应原始内容 / 图片

def fetch_image(url):
 try:
   response = requests.get(url, headers=HEADERS)
 except Exception as e:
   raise e
 return response.content

指定 url，获取对应 JSON 返回 / 话题列表

def fetch_activities(url):
 try:
   response = requests.get(url, headers=HEADERS)
 except Exception as e:
   raise e
 return response.json()

处理返回的话题列表

def parser_activities(datums, face_detective):
 for data in datums["data"]:
   target = data["target"]
   if "content" not in target or "question" not in target or "author" not in target:
     continue
   html = etree.HTML(target["content"])
   seq = 0
   title = target["question"]["title"]
   author = target["author"]["name"]
   images = html.xpath("//img/@src")
   for image in images:
     if not image.startswith("http"):
       continue
     image_data = fetch_image(image)
     score = face_detective(image_data)
     if not score:
       continue
     name = "{}--{}--{}--{}.jpg".format(score, author, title, seq)
     seq = seq + 1
     path = Path(__file__).parent.joinpath("image").joinpath(name)
     try:
       f = open(path, "wb")
       f.write(image_data)
       f.flush()
       f.close()
       print(path)
       time.sleep(2)
     except Exception as e:
       continue
 if not datums["paging"]["is_end"]:
   return datums["paging"]["next"]
 else:
   return None

初始化颜值检测工具

def init_detective(app_id, api_key, secret_key):
 client = AipFace(app_id, api_key, secret_key)
 options = {"face_field": "age,gender,beauty,qualities"}
 def detective(image):
   image = str(base64.b64encode(image), "utf-8")
   response = client.detect(str(image), "BASE64", options)
   response = response.get("result")
   if not response:
     return
   if (not response) or (response["face_num"] == 0):
     return
   face_list = response["face_list"]
   if pydash.get(face_list, "0.face_probability") < 0.6:
     return
   if pydash.get(face_list, "0.beauty") < BEAUTY_THRESHOLD:
     return
   if pydash.get(face_list, "0.gender.type") != "female":
     return
   score = pydash.get(face_list, "0.beauty")
   return score
 return detective

程序入口

def main():
 face_detective = init_detective(APP_ID, API_KEY, SECRET_KEY)
 url = BASE_URL ％ SOURCE + URL_QUERY
 while url is not None:
   datums = fetch_activities(url)
   url = parser_activities(datums, face_detective)
   time.sleep(5)
if __name__ == '__main__':
 main()

来源：https://www.cnblogs.com/li1992/p/10599398.html