最近很火的百度莱茨狗想必大家都有接触。不知道大家都有几只史诗狗了？
0x01 领狗
莱茨狗4只狗免费领取地址分享
领取地址1：https://pet-chain.baidu.com/chain/splash
领取地址2：https://pet-chain.baidu.com/chain/splash?appId=2&tpl=wallet
领取地址3：https://pet-chain.baidu.com/chain/splash?appId=3&tpl=wallet
领取地址4：https://pet-chain.baidu.com/chain/splash?appId=4&tpl=wallet
不过我昨天只领到了两只。
0x02 刷市场买便宜狗
# -*- coding: utf-8 -*-
import requests
import json
import time
headers = {
'User-Agent': 'Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Mobile Safari/537.36',
'Content-Type': 'application/json',
'Cookie': '', # Cookie替换成自己的.
'Origin': 'https://pet-chain.baidu.com',
'Referer': 'https://pet-chain.baidu.com/chain/dogMarket'
}
class pet_chain:
def getInfo(petId): #获取宠物信息
url = "https://pet-chain.baidu.com/data/pet/queryPetByIdWithAuth"
data = json.dumps({"petId":petId,"requestId":1517813967221,"appId":1,"tpl":""})
req = requests.post(url, headers=headers, data=data)
text = json.loads(req.content)
#print text['data']['userName']
#print text['data']['id']
#print text['data']['rareDegree']
#print text['data']['amount']
#print text['data']['ethAddr']
return {'userName':text['data']['userName'],'id':text['data']['id'],'rareDegree':text['data']['rareDegree'],'amount':text['data']['amount'],'ethAddr':text['data']['ethAddr'] }
def buy(self,petId): #购买宠物
url = 'https://pet-chain.baidu.com/data/txn/create'
data = json.dumps({"petId":petId,"requestId":1517818584013,"appId":1,"tpl":""})
req = requests.post(url, headers=headers, data=data)
print req.content
def getMarket(self): #搜索市场
url = 'https://pet-chain.baidu.com/data/market/queryPetsOnSale'
data = json.dumps({"pageNo":2,"pageSize":2000,"querySortType":"AMOUNT_ASC","petIds":[]}) #按价钱从低到高排序。每页2000只狗。
req = requests.post(url, headers=headers, data=data)
text = json.loads(req.content)
for pet in text['data']['petsOnSale']:
if(pet['rareDegree'] > 1): #只要卓越以上的
#print pet
print '品种:' , pet['rareDegree'] #0 - 5 分别为 普通，优秀，卓越， 史诗 后面的没见过。
print '价钱：' , pet['amount']
print 'id:' , pet['id']
print 'petId:' , pet['petId']
#self.buy(pet['petId']) #是否自动购买。
if __name__ == '__main__':
w = pet_chain()
#w.buy('1858371085567322557') #调用购买
w.getMarket() # 市场查询
通过这个脚本可以买到便宜的狗。但是后来百度对市场进行了限制。
1、每页只能显示20只狗
2、买狗需要验证码
0x03 基于SVM的机器学习识别验证码
1、批量获取验证码
# -*- coding: utf-8 -*-
import requests
import json
import base64
import time
headers = {
'User-Agent': 'Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Mobile Safari/537.36',
'Content-Type': 'application/json',
'Cookie':XXX,
'Origin': 'https://pet-chain.baidu.com',
'Referer': 'https://pet-chain.baidu.com/chain/dogMarket'
}
class captcha:
def __init__(self):
return
def getCaptcha(self):
url = 'https://pet-chain.baidu.com/data/captcha/gen'
data = json.dumps({"requestId":1517879979928,"appId":1,"tpl":""})
req = requests.post(url, headers=headers, data=data, timeout=2)
return json.loads(req.content)['data']['img']
if __name__=='__main__':
w = captcha()
# 批量抓取样本
while 1:
imgdata = ''
try:
img = w.getCaptcha()
except:
pass
imgdata = base64.b64decode(img)
file = open('img/' + str(time.time()) + '.jpg','wb')
file.write(imgdata)
file.close()
2、打码
我手动打的，根据验证码内容把图片改成 ADTF.jpg
3、机器学习
# -*- coding: utf-8 -*-
import os
import cv2
import time
import joblib
import numpy as np
from PIL import Image
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
from sklearn.svm import SVC
class learn:
def __init__(self):
self.goodCaseDir = './img/good/' #这里放打完码的图片
self.learnJobDir = './img/job/'
self.noneDir = './img/none/' #这里是没打码的图片
return
def makeCase(self):
files = os.listdir(self.goodCaseDir)
for name in files:
im = Image.open(self.goodCaseDir + name)
im1 = im.crop((50,10,97,80))
im2 = im.crop((95,10,144,80))
im3 = im.crop((142,10,188,80))
im4 = im.crop((185,10,235,80))
txt = name.split('.')[0]
im1.save(self.learnJobDir + txt[0]+str(time.time())+'.jpg')
im2.save(self.learnJobDir + txt[1]+str(time.time())+'.jpg')
im3.save(self.learnJobDir + txt[2]+str(time.time())+'.jpg')
im4.save(self.learnJobDir + txt[3]+str(time.time())+'.jpg')
def extractLetters(self,path):
x = []
y = []
files = os.listdir(self.learnJobDir)
for name in files:
x.append(self.getletter(self.learnJobDir + name))
y.append(name[0].lower())
return x, y
def getletter(self,fn):
fnimg = cv2.imread(fn) # 读取图像
img = cv2.resize(fnimg, (100, 100)) # 将图像大小调整
alltz = []
for now_h in range(0, 100):
xtz = []
for now_w in range(0, 100):
b = img[now_h, now_w, 0]
g = img[now_h, now_w, 1]
r = img[now_h, now_w, 2]
btz = 255 - b
gtz = 255 - g
rtz = 255 - r
if btz > 0 or gtz > 0 or rtz > 0:
nowtz = 1
else:
nowtz = 0
xtz.append(nowtz)
alltz += xtz
return alltz
def trainSVM(self):
array = self.extractLetters(self.learnJobDir)
#print array
letterSVM = SVC(kernel="linear",C=1).fit(array[0], array[1])
joblib.dump(letterSVM, 'letter.pkl')
def ocrImg(self,fileName):
clf = joblib.load('letter.pkl')
img = Image.open(fileName)
data = self.getletter(fileName)
data = np.array([data])
#print(data)
oneLetter = clf.predict(data)[0]
return oneLetter
def run(self):
files = os.listdir(self.noneDir)
for name in files:
im = Image.open(self.noneDir + name)
im.show()
im1 = im.crop((50,10,97,80))
im2 = im.crop((95,10,144,80))
im3 = im.crop((142,10,188,80))
im4 = im.crop((185,10,235,80))
txt = name.split('.')[0]
im1.save('./img/tmp/1.jpg')
im2.save('./img/tmp/2.jpg')
im3.save('./img/tmp/3.jpg')
im4.save('./img/tmp/4.jpg')
tmpfiles = os.listdir('./img/tmp/')
for tmpfile in tmpfiles:
print self.ocrImg('./img/tmp/' + tmpfile)
raw_input()
def main(self,im):
im1 = im.crop((50,10,97,80))
im2 = im.crop((95,10,144,80))
im3 = im.crop((142,10,188,80))
im4 = im.crop((185,10,235,80))
im1.save('./img/tmp/1.jpg')
im2.save('./img/tmp/2.jpg')
im3.save('./img/tmp/3.jpg')
im4.save('./img/tmp/4.jpg')
tmpfiles = os.listdir('./img/tmp/')
vcode = []
for tmpfile in tmpfiles:
vcode.append(self.ocrImg('./img/tmp/' + tmpfile))
return ''.join(vcode)
if __name__ == '__main__':
w = learn()
print '开始对正样本切图.....'
#w.makeCase()
print '样本切图完成，开始进行学习建模.....'
#w.trainSVM()
print '建模完成'
#w.ocrImg('./img/3.jpg')
print '开始识别验证码'
#w.run()
im = Image.open('code.jpg')
print w.main(im)
0x04 再次刷狗
整合一下，这次只发部分代码
def buy(self,petId,validCode):
cap_url = 'https://pet-chain.baidu.com/data/captcha/gen'
data = json.dumps({"requestId":1517889037133,"appId":1,"tpl":""})
req = requests.post(cap_url, headers=headers, data=data, timeout=2)
img = json.loads(req.content)['data']['img']
image_string = cStringIO.StringIO(base64.b64decode(img))
im = Image.open(image_string)
captcha = q.main(im)
print captcha
url = 'https://pet-chain.baidu.com/data/txn/create'
data = json.dumps({"petId":petId,"captcha":captcha,"validCode":validCode,"requestId":1517888890852,"appId":1,"tpl":""})
req = requests.post(url, headers=headers, data=data, timeout=2)
print json.loads(req.content)['errorMsg']
print req.content
def getMarket(self,page):
url = 'https://pet-chain.baidu.com/data/market/queryPetsOnSale'
i = 1
while i <= page:
print i
try:
data = json.dumps({"pageNo":page,"pageSize":1,"querySortType":"AMOUNT_ASC","petIds":[],"requestId":1517887362977,"appId":1,"tpl":""})
req = requests.post(url, headers=headers, data=data, timeout=5)
#print req.content
text = json.loads(req.content)
for pet in text['data']['petsOnSale']:
if(pet['rareDegree'] >= 0):# and float(pet['amount']) <= 1800) :
print pet
print '品种:' , pet['rareDegree']
print '价钱：' , pet['amount']
print 'id:' , pet['id']
print 'petId:' , pet['petId']
i += 1
except:
pass
time.sleep(2)
return {'petId': pet['petId'],'validCode': pet['validCode']}
最后我刷到了两只史诗狗、三只卓越狗。希望大家也能有所收获。【技术上的！】