mirror of https://github.com/injetlee/Python.git
203 lines
6.8 KiB
Python
203 lines
6.8 KiB
Python
# -*- coding:UTF-8 -*-
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import requests , time ,random
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import hmac ,json ,base64
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from bs4 import BeautifulSoup
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from hashlib import sha1
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import TencentYoutuyun
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from PIL import Image
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import uuid
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def recognition_captcha(data):
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''' 识别验证码 '''
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file_id = str(uuid.uuid1())
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filename = 'captcha_'+ file_id +'.gif'
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filename_png = 'captcha_'+ file_id +'.png'
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if(data is None):
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return
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data = base64.b64decode(data.encode('utf-8'))
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with open( filename ,'wb') as fb:
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fb.write( data )
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appid = 'appid' # 接入优图服务，注册账号获取
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secret_id = 'secret_id'
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secret_key = 'secret_key'
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userid= 'userid'
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end_point = TencentYoutuyun.conf.API_YOUTU_END_POINT
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youtu = TencentYoutuyun.YouTu(appid, secret_id, secret_key, userid, end_point) # 初始化
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# 拿到的是gif格式，而优图只支持 JPG PNG BMP 其中之一，这时我们需要 pip install Pillow 来转换格式
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im = Image.open( filename)
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im.save( filename_png ,"png")
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im.close()
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result = youtu.generalocr( filename_png , data_type = 0 , seq = '') # 0代表本地路径，1代表url
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return result
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def get_captcha(sessiona,headers):
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''' 获取验证码 '''
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need_cap = False
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while( need_cap is not True):
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try:
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sessiona.get('https://www.zhihu.com/signin',headers=headers) # 拿cookie:_xsrf
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resp2 = sessiona.get('https://www.zhihu.com/api/v3/oauth/captcha?lang=cn',headers=headers) # 拿cookie:capsion_ticket
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need_cap = json.loads(resp2.text)["show_captcha"] # {"show_captcha":false} 表示不用验证码
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time.sleep( 0.5 + random.randint(1,9)/10 )
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except Exception:
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continue
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try:
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resp3 = sessiona.put('https://www.zhihu.com/api/v3/oauth/captcha?lang=cn',headers=headers) # 拿到验证码数据，注意是put
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img_data = json.loads(resp3.text)["img_base64"]
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except Exception:
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return
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return img_data
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def create_point( point_data, confidence ):
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''' 获得点阵 '''
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# 实际操作下，套路不深，x间隔25，y相同，共7个点 ，先模拟意思一下
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points = {1:[ 20.5,25.1875],2:[ 45.5,25.1875],3:[ 70.5,25.1875],4:[ 95.5,25.1875],5:[120.5,25.1875],6:[145.5,25.1875],7:[170.5,25.1875]}
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wi = 0
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input_points = []
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for word in ( point_data['items'][0]['words'] ):
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wi = wi+1
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if( word['confidence'] < confidence ):
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try:
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input_points.append(points[wi]) # 倒置的中文，优图识别不出来，置信度会低于0.5
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except KeyError:
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continue
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if( len(input_points) > 2 or len(input_points) == 0 ):
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return [] # 7个字中只有2个倒置中文的成功率高
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result = {}
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result['img_size']=[200,44]
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result['input_points']=input_points
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result = json.dumps(result)
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print(result)
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return result
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def bolting(k_low,k_hi,k3_confidence):
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''' 筛选把握大的进行验证 '''
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start = time.time()
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is_success = False
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while(is_success is not True):
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points_len = 1
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angle = -20
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img_ko = []
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while(points_len != 21 or angle < k_low or angle > k_hi ):
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img_data = get_captcha(sessiona,headers)
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img_ko = recognition_captcha(img_data)
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## json.dumps 序列化时对中文默认使用的ascii编码.想输出真正的中文需要指定ensure_ascii=False
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# img_ko_json = json.dumps(img_ko , indent =2 ,ensure_ascii=False )
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# img_ko_json = img_ko_json.encode('raw_unicode_escape') ## 因为python3的原因，也因为优图自身的原因，此处要特殊处理
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# with open( "json.txt" ,'wb') as fb:
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# fb.write( img_ko_json )
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try:
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points_len = len(img_ko['items'][0]['itemstring'])
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angle = img_ko['angle']
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except Exception:
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points_len = 1
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angle = -20
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continue
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# print(img_ko_json.decode('utf8')) ## stdout用的是utf8，需转码才能正常显示
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# print('-'*50)
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input_text = create_point( img_ko ,k3_confidence )
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if(type(input_text) == type([])):
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continue
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data = {
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"input_text":input_text
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}
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# 提交过快会被拒绝，{"code":120005,"name":"ERR_VERIFY_CAPTCHA_TOO_QUICK"} ，假装思考5秒钟
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time.sleep( 4 + random.randint(1,9)/10 )
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try:
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resp5 = sessiona.post('https://www.zhihu.com/api/v3/oauth/captcha?lang=cn',data,headers=headers)
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except Exception:
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continue
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print("angle: "+ str(angle) )
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print(BeautifulSoup(resp5.content ,'html.parser')) # 如果验证成功，会回应{"success":true}，开心
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print('-'*50)
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try:
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is_success = json.loads(resp5.text)["success"]
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except KeyError:
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continue
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end = time.time()
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return end-start
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if __name__ == "__main__":
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sessiona = requests.Session()
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headers = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:47.0) Gecko/20100101 Firefox/47.0','authorization':'oauth c3cef7c66a1843f8b3a9e6a1e3160e20'}
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k3_confidence = 0.71
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'''
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# 可视化数据会被保存在云端供浏览
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# https://plot.ly/~weldon2010/4
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# 纯属学习，并未看出"角度"范围扩大对图像识别的影响，大部分时候60s内能搞定，说明优图还是很强悍的，识别速度也非常快
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'''
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runtime_list_x = []
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runtime_list_y = []
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nn = range(1,11) # 愿意的话搞多线程，1百万次更有意思
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# 成功尝试100次，形成2维数据以热力图的方式展示
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for y in nn :
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for x in nn :
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runtime_list_x.append( bolting(-3,3,k3_confidence) )
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print( "y: " + str(runtime_list_y) )
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print( "x: " + str(runtime_list_x) )
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runtime_list_y.append(runtime_list_x.copy())
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runtime_list_x = []
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print ("-"*30)
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print( runtime_list_y )
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print ("-"*30)
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# pip install plotly 数据可视化
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import plotly
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import plotly.graph_objs as go
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plotly.tools.set_credentials_file(username='username', api_key='username') # 设置账号，去官网注册
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trace = go.Heatmap(z = runtime_list_y , x = [n for n in nn ] ,y =[n for n in nn ])
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data=[trace]
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plotly.plotly.plot(data, filename='weldon-time2-heatmap')
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# 尝试后发现一个特点，基本都是1~2个倒置中文，这样我们可以借此提速
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# 角度范围放大，仅当识别出倒置中文为1~2个时才提交验证否则放弃继续寻找
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### chcp 65001 (win下改变cmd字符集)
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### python c:\python34\image_recognition_zhihu.py
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