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  • **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:
    

    知乎高颜值图片抓取到本地(Python3 爬虫.人脸检测.颜值检测)
    知乎高颜值图片抓取到本地(Python3 爬虫.人脸检测.颜值检测)

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    浪杉博客 » 知乎高颜值图片抓取到本地(Python3 爬虫.人脸检测.颜值检测)