**1.代码在vscode和centos下均可成功执行
2.安装好python3和pip3
3.安装好依赖库(pip3 install requests lxml baidu-aip requests)
4.在百度云注册登录账号.开通人脸检查服务(https://cloud.baidu.com/product/face).在代码中填写appid和ak信息
5.image目录必须和代码文件在同一个目录下
**
#!/usr/bin/python3
#coding: utf-8
import time
import os
import re
import requests
# shell pip install requests lxml baidu-aip
from lxml import etree
from aip import AipFace
#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 = \\\"\\\"
API_KEY = \\\"\\\"
SECRET_KEY = \\\"\\\"
# 文件存放目录名，相对于当前目录
DIR = \\\"image\\\"
# 过滤颜值阈值，存储空间大的请随意
BEAUTY_THRESHOLD = 45
#浏览器中打开知乎，在开发者工具复制一个，无需登录
#如何替换该值下文有讲述
AUTHORIZATION = \\\"oauth c3cef7c66a1843f8b3a9e6a1e3160e20\\\"
#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\\\"
#爬虫假装下正常浏览器请求
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)
#指定 url，获取对应原始内容 / 图片
def fetch_image(url):
try:
headers = {
\\\"User-Agent\\\": USER_AGENT,
\\\"Referer\\\": REFERER,
\\\"authorization\\\": AUTHORIZATION
}
s = requests.get(url, headers=headers)
except Exception as e:
print(\\\"fetch last activities fail. \\\" + url)
raise e
return s.content
#指定 url，获取对应 JSON 返回 / 话题列表
def fetch_activities(url):
try:
headers = {
\\\"User-Agent\\\": USER_AGENT,
\\\"Referer\\\": REFERER,
\\\"authorization\\\": AUTHORIZATION
}
s = requests.get(url, headers=headers)
except Exception as e:
print(\\\"fetch last activities fail. \\\" + url)
raise e
return s.json()
#处理返回的话题列表
def process_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
#解析列表中每一个元素的内容
html = etree.HTML(target[\\\"content\\\"])
seq = 0
#question_url = target[\\\"question\\\"][\\\"url\\\"]
question_title = target[\\\"question\\\"][\\\"title\\\"]
author_name = target[\\\"author\\\"][\\\"name\\\"]
#author_id = target[\\\"author\\\"][\\\"url_token\\\"]
print(\\\"current answer: \\\" + question_title + \\\" author: \\\" + author_name)
#获取所有图片地址
images = html.xpath(\\\"//img/@src\\\")
for image in images:
if not image.startswith(\\\"http\\\"):
continue
s = fetch_image(image)
#请求人脸检测服务
scores = face_detective(s)
for score in scores:
filename = (\\\"%d--\\\" % score) + author_name + \\\"--\\\" + question_title + (\\\"--%d\\\" % seq) + \\\".jpg\\\"
filename = re.sub(r\\\'(?u)[^-\\\\w.]\\\', \\\'_\\\', filename)
#注意文件名的处理，不同平台的非法字符不一样，这里只做了简单处理，特别是 author_name / question_title 中的内容
seq = seq + 1
with open(os.path.join(DIR, filename), \\\"wb\\\") as fd:
fd.write(s)
#人脸检测 免费，但有 QPS 限制
time.sleep(2)
if not datums[\\\"paging\\\"][\\\"is_end\\\"]:
#获取后续讨论列表的请求 url
return datums[\\\"paging\\\"][\\\"next\\\"]
else:
return None
def get_valid_filename(s):
s = str(s).strip().replace(\\\' \\\', \\\'_\\\')
return re.sub(r\\\'(?u)[^-\\\\w.]\\\', \\\'_\\\', s)
import base64
def detect_face(image, token):
try:
URL = \\\"https://aip.baidubce.com/rest/2.0/face/v3/detect\\\"
params = {
\\\"access_token\\\": token
}
data = {
\\\"face_field\\\": \\\"age,gender,beauty,qualities\\\",
\\\"image_type\\\": \\\"BASE64\\\",
\\\"image\\\": base64.b64encode(image)
}
s = requests.post(URL, params=params, data=data)
return s.json()[\\\"result\\\"]
except Exception as e:
print(\\\"detect face fail. \\\" + url)
raise e
def fetch_auth_token(api_key, secret_key):
try:
URL = \\\"https://aip.baidubce.com/oauth/2.0/token\\\"
params = {
\\\"grant_type\\\": \\\"client_credentials\\\",
\\\"client_id\\\": api_key,
\\\"client_secret\\\": secret_key
}
s = requests.post(URL, params=params)
return s.json()[\\\"access_token\\\"]
except Exception as e:
print(\\\"fetch baidu auth token fail. \\\" + url)
raise e
def init_face_detective(app_id, api_key, secret_key):
# client = AipFace(app_id, api_key, secret_key)
# 百度云 V3 版本接口，需要先获取 access token
token = fetch_auth_token(api_key, secret_key)
def detective(image):
#r = client.detect(image, options)
# 直接使用 HTTP 请求
r = detect_face(image, token)
#如果没有检测到人脸
if r is None or r[\\\"face_num\\\"] == 0:
return []
scores = []
for face in r[\\\"face_list\\\"]:
#人脸置信度太低
if face[\\\"face_probability\\\"] < 0.6:
continue
#颜值低于阈值
if face[\\\"beauty\\\"] < BEAUTY_THRESHOLD:
continue
#性别非女性
if face[\\\"gender\\\"][\\\"type\\\"] != \\\"female\\\":
continue
scores.append(face[\\\"beauty\\\"])
return scores
return detective
def init_env():
if not os.path.exists(DIR):
os.makedirs(DIR)
init_env()
face_detective = init_face_detective(APP_ID, API_KEY, SECRET_KEY)
url = BASE_URL % SOURCE + URL_QUERY
while url is not None:
print(\\\"current url: \\\" + url)
datums = fetch_activities(url)
url = process_activities(datums, face_detective)
#注意节操，爬虫休息间隔不要调小
time.sleep(5)
# vim: set ts=4 sw=4 sts=4 tw=100 et:
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源码资源库 » 知乎高颜值图片抓取到本地(Python3 爬虫.人脸检测.颜值检测)
2. 分享目的仅供大家学习和交流，您必须在下载后24小时内删除！
3. 不得使用于非法商业用途，不得违反国家法律。否则后果自负！
4. 本站提供的源码、模板、插件等等其他资源，都不包含技术服务请大家谅解！
5. 如有链接无法下载、失效或广告，请联系管理员处理！
6. 本站资源售价只是赞助，收取费用仅维持本站的日常运营所需！
源码资源库 » 知乎高颜值图片抓取到本地(Python3 爬虫.人脸检测.颜值检测)
