1，cookie登录
利用cookie的特性：cookie会保持较长的时间，来避免用户频繁登录
2OCR库里的tesseract（光学文字识别）可以解决大多数的传统验证码
软件tesserract-ocr先安装，然后安装pytesserract类库
注意：1Windows需要下载软件安装包，再配置环境变量 2linux 直接在命令窗口输入：sudo apt-get tesseract-ocr
模拟浏览器，selenium和PIL库的截屏功能，来识别验证码（save_screenshot截图）
3打码平台
打码兔和QQ超人打码，有提供Python的接入方式，人工打码平台需要收费。
以QQ超人打码平台，先要注册开发者账号，在识别程序中需要填写个人账号进行认证计费，登录之后接入，开始计费（一个码六分钱）
4selenium 来模拟拉动来破解滑动验证码
import random
import time
from selenium.webdriver import ActionChains
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.common.by import By
from urllib.request import urlretrieve
from selenium import webdriver
from bs4 import BeautifulSoup
import PIL.Image as image
import re
import random
class Crack():
def __init__(self, username, passwd):
self.url = 'https://passport.bilibili.com/login'
self.browser = webdriver.Chrome()
self.wait = WebDriverWait(self.browser, 100)
self.BORDER = 6
self.passwd = passwd
self.username = username
def open(self):
"""
打开浏览器,并输入查询内容
"""
self.browser.get(self.url)
keyword = self.wait.until(EC.presence_of_element_located((By.ID, 'login-username')))
keyword.send_keys(self.username)
keyword = self.wait.until(EC.presence_of_element_located((By.ID, 'login-passwd')))
keyword.send_keys(self.passwd)
# bowton.click()
def get_images(self, bg_filename='bg.jpg', fullbg_filename='fullbg.jpg'):
"""
获取验证码图片
:return: 图片的location信息
"""
bg = []
fullgb = []
while bg == [] and fullgb == []:
bf = BeautifulSoup(self.browser.page_source, 'lxml')
bg = bf.find_all('div', class_='gt_cut_bg_slice')
fullgb = bf.find_all('div', class_='gt_cut_fullbg_slice')
bg_url = re.findall('url\(\"(.*)\"\);', bg[0].get('style'))[0].replace('webp', 'jpg')
fullgb_url = re.findall('url\(\"(.*)\"\);', fullgb[0].get('style'))[0].replace('webp', 'jpg')
bg_location_list = []
fullbg_location_list = []
for each_bg in bg:
location = {}
location['x'] = int(re.findall('background-position: (.*)px (.*)px;', each_bg.get('style'))[0][0])
location['y'] = int(re.findall('background-position: (.*)px (.*)px;', each_bg.get('style'))[0][1])
bg_location_list.append(location)
for each_fullgb in fullgb:
location = {}
location['x'] = int(re.findall('background-position: (.*)px (.*)px;', each_fullgb.get('style'))[0][0])
location['y'] = int(re.findall('background-position: (.*)px (.*)px;', each_fullgb.get('style'))[0][1])
fullbg_location_list.append(location)
# 把资源下载到临时目录
urlretrieve(url=bg_url, filename=bg_filename)
print('缺口图片下载完成')
urlretrieve(url=fullgb_url, filename=fullbg_filename)
print('背景图片下载完成')
return bg_location_list, fullbg_location_list
def get_merge_image(self, filename, location_list):
"""
根据位置对图片进行合并还原
:filename:图片
:location_list:图片位置
"""
im = image.open(filename)
new_im = image.new('RGB', (260, 116))
im_list_upper = []
im_list_down = []
for location in location_list:
if location['y'] == -58:
im_list_upper.append(im.crop((abs(location['x']), 58, abs(location['x']) + 10, 166)))
if location['y'] == 0:
im_list_down.append(im.crop((abs(location['x']), 0, abs(location['x']) + 10, 58)))
new_im = image.new('RGB', (260, 116))
x_offset = 0
for im in im_list_upper:
new_im.paste(im, (x_offset, 0))
x_offset += im.size[0]
x_offset = 0
for im in im_list_down:
new_im.paste(im, (x_offset, 58))
x_offset += im.size[0]
new_im.save(filename)
return new_im
def is_pixel_equal(self, img1, img2, x, y):
"""
判断两个像素是否相同
:param image1: 图片1
:param image2: 图片2
:param x: 位置x
:param y: 位置y
:return: 像素是否相同
"""
# 取两个图片的像素点
pix1 = img1.load()[x, y]
pix2 = img2.load()[x, y]
threshold = 60
if (abs(pix1[0] - pix2[0] < threshold) and abs(pix1[1] - pix2[1] < threshold) and abs(
pix1[2] - pix2[2] < threshold)):
return True
else:
return False
def get_gap(self, img1, img2):
"""
获取缺口偏移量
:param img1: 不带缺口图片
:param img2: 带缺口图片
:return:
"""
left = 43
for i in range(left, img1.size[0]):
for j in range(img1.size[1]):
if not self.is_pixel_equal(img1, img2, i, j):
left = i
return left
return left
def get_track(self, distance):
"""
根据偏移量获取移动轨迹
:param distance: 偏移量
:return: 移动轨迹
"""
# 移动轨迹
track = []
# 当前位移
current = 0
# 减速阈值
mid = distance * 3 / 5
# 计算间隔
t = 0.2
# 初速度
v = 0
while current < distance:
if current < mid:
# 加速度为正2
a = 2
else:
# 加速度为负3
a = -3 # 初速度v0
v0 = v
# 当前速度v = v0 + at
v = v0 + a * t
# 移动距离x = v0t + 1/2 * a * t^2
move = v0 * t + 1 / 2 * a * t * t
# 当前位移
current += move
# 加入轨迹
track.append(round(move))
# track.append(round(move))
time.sleep(random.random()*6)
# print('111')
return track
def get_slider(self):
"""
获取滑块
:return: 滑块对象
"""
while True:
try:
slider = self.browser.find_element_by_xpath("//div[@class='gt_slider_knob gt_show']")
break
except:
time.sleep(0.5)
return slider
def move_to_gap(self, slider, track):
"""
拖动滑块到缺口处
:param slider: 滑块
:param track: 轨迹
:return:
"""
ActionChains(self.browser).click_and_hold(slider).perform()
while track:
x = random.choice(track)
ActionChains(self.browser).move_by_offset(xoffset=x, yoffset=0).perform()
track.remove(x)
time.sleep(0.8)
ActionChains(self.browser).release().perform()
time.sleep(2)
self.browser.quit()
def crack(self):
# 打开浏览器
self.open()
# 保存的图片名字
bg_filename = './images/bg.jpg'
fullbg_filename = './images/fullbg.jpg'
# 获取图片
bg_location_list, fullbg_location_list = self.get_images(bg_filename, fullbg_filename)
# 根据位置对图片进行合并还原
bg_img = self.get_merge_image(bg_filename, bg_location_list)
fullbg_img = self.get_merge_image(fullbg_filename, fullbg_location_list)
# 获取缺口位置
gap = self.get_gap(fullbg_img, bg_img)
print('缺口位置', gap)
track = self.get_track(gap - self.BORDER)
print('滑动滑块')
print(track)
# 点按呼出缺口
slider = self.get_slider()
# 拖动滑块到缺口处
self.move_to_gap(slider, track)
if __name__ == '__main__':
crack = Crack('username', 'passwd')
crack.crack()
print('验证成功')