前段时间偶然看到一个抓取知乎上照片的文章，出于兴趣就动手实践了一下。并没有去改他的代码，只是跑了一下，去学习一下相关东西。他用到了百度云的人脸评分的API，学习一哈。闲话不多说，直接上代码。
# coding: utf-8
import time
import os
import re
import requests
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 = "11325632"
API_KEY = "W1un6zCLOGfGgu3l4d7m6nQR"
SECRET_KEY = "MoznMmFTU6ZxCmrg4oiutwdINxsqVYQ5"
# 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 = 70
# 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
# 后期又找了若干可能出现人像的话题
# 旅行攻略19571906 女装搭配19868718 摄影19551388
SOURCE = "20013528"
# 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(0.5)
结果如下：
后期又在微信小程序看到了百度云的一些测试功能，效果还挺好。
建了一个QQ群，群成员就差一个你了。
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