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image_recognition_zhihu.py
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image_recognition_zhihu.py
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# -*- coding:UTF-8 -*-
import requests , time ,random
import hmac ,json ,base64
from bs4 import BeautifulSoup
from hashlib import sha1
import TencentYoutuyun
from PIL import Image
import uuid
def recognition_captcha(data):
''' 识别验证码 '''
file_id = str(uuid.uuid1())
filename = 'captcha_'+ file_id +'.gif'
filename_png = 'captcha_'+ file_id +'.png'
if(data is None):
return
data = base64.b64decode(data.encode('utf-8'))
with open( filename ,'wb') as fb:
fb.write( data )
appid = 'appid' # 接入优图服务，注册账号获取
secret_id = 'secret_id'
secret_key = 'secret_key'
userid= 'userid'
end_point = TencentYoutuyun.conf.API_YOUTU_END_POINT
youtu = TencentYoutuyun.YouTu(appid, secret_id, secret_key, userid, end_point) # 初始化
# 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
im = Image.open( filename)
im.save( filename_png ,"png")
im.close()
result = youtu.generalocr( filename_png , data_type = 0 , seq = '') # 0代表本地路径，1代表url
return result
def get_captcha(sessiona,headers):
''' 获取验证码 '''
need_cap = False
while( need_cap is not True):
try:
sessiona.get('https://www.zhihu.com/signin',headers=headers) # 拿cookie:_xsrf
resp2 = sessiona.get('https://www.zhihu.com/api/v3/oauth/captcha?lang=cn',headers=headers) # 拿cookie:capsion_ticket
need_cap = json.loads(resp2.text)["show_captcha"] # {"show_captcha":false} 表示不用验证码
time.sleep( 0.5 + random.randint(1,9)/10 )
except Exception:
continue
try:
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
img_data = json.loads(resp3.text)["img_base64"]
except Exception:
return
return img_data
def create_point( point_data, confidence ):
''' 获得点阵 '''
# 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
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]}
wi = 0
input_points = []
for word in ( point_data['items'][0]['words'] ):
wi = wi+1
if( word['confidence'] < confidence ):
try:
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
except KeyError:
continue
if( len(input_points) > 2 or len(input_points) == 0 ):
return [] # 7个字中只有2个倒置中文的成功率高
result = {}
result['img_size']=[200,44]
result['input_points']=input_points
result = json.dumps(result)
print(result)
return result
def bolting(k_low,k_hi,k3_confidence):
''' 筛选把握大的进行验证 '''
start = time.time()
is_success = False
while(is_success is not True):
points_len = 1
angle = -20
img_ko = []
while(points_len != 21 or angle < k_low or angle > k_hi ):
img_data = get_captcha(sessiona,headers)
img_ko = recognition_captcha(img_data)
## 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
# img_ko_json = json.dumps(img_ko , indent =2 ,ensure_ascii=False )
# 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
# with open( "json.txt" ,'wb') as fb:
# fb.write( img_ko_json )
try:
points_len = len(img_ko['items'][0]['itemstring'])
angle = img_ko['angle']
except Exception:
points_len = 1
angle = -20
continue
# print(img_ko_json.decode('utf8')) ## stdout用的是utf8，需转码才能正常显示
# print('-'*50)
input_text = create_point( img_ko ,k3_confidence )
if(type(input_text) == type([])):
continue
data = {
"input_text":input_text
}
# 提交过快会被拒绝，{"code":120005,"name":"ERR_VERIFY_CAPTCHA_TOO_QUICK"} ，假装思考5秒钟
time.sleep( 4 + random.randint(1,9)/10 )
try:
resp5 = sessiona.post('https://www.zhihu.com/api/v3/oauth/captcha?lang=cn',data,headers=headers)
except Exception:
continue
print("angle: "+ str(angle) )
print(BeautifulSoup(resp5.content ,'html.parser')) # 如果验证成功，会回应{"success":true}，开心
print('-'*50)
try:
is_success = json.loads(resp5.text)["success"]
except KeyError:
continue
end = time.time()
return end-start
if __name__ == "__main__":
sessiona = requests.Session()
headers = {'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:47.0) Gecko/20100101 Firefox/47.0','authorization':'oauth c3cef7c66a1843f8b3a9e6a1e3160e20'}
k3_confidence = 0.71
'''
# 可视化数据会被保存在云端供浏览
# https://plot.ly/~weldon2010/4
# 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
'''
runtime_list_x = []
runtime_list_y = []
nn = range(1,11) # 愿意的话搞多线程，1百万次更有意思
# 成功尝试100次，形成2维数据以热力图的方式展示
for y in nn :
for x in nn :
runtime_list_x.append( bolting(-3,3,k3_confidence) )
print( "y: " + str(runtime_list_y) )
print( "x: " + str(runtime_list_x) )
runtime_list_y.append(runtime_list_x.copy())
runtime_list_x = []
print ("-"*30)
print( runtime_list_y )
print ("-"*30)
# pip install plotly 数据可视化
import plotly
import plotly.graph_objs as go
plotly.tools.set_credentials_file(username='username', api_key='username') # 设置账号，去官网注册
trace = go.Heatmap(z = runtime_list_y , x = [n for n in nn ] ,y =[n for n in nn ])
data=[trace]
plotly.plotly.plot(data, filename='weldon-time2-heatmap')
# 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
# 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
### chcp 65001 (win下改变cmd字符集)
### python c:\python34\image_recognition_zhihu.py