table of Contents
A: posterior pole slide Code Description
In recent years there have been some new code, do not want the old code unfriendly to humans, but this code is the code for identifying the difficulty up a few levels. Hence the need for other means of treatment.
Identifying needs python libraries: selenium and ChromeDriver drivers, different browser to download the driver library.
CAPTCHA Get Website: http: //www.geetest.com/
pole test slide code has come to version 3.0, related to greater difficulty CAPTCHA recognition, the principle is drag the image to notch, then flatten the image for verification, generates three encryption parameters, by a form submitted to the background, the background then verified.
Pole test code also adds machine learning to identify whether it is a malicious program to identify, anti-simulation, anti-counterfeiting, anti-violent way, just 0.4 seconds, and the protection of resources are not misused and stolen.
Our procedures are generally not malicious as long as crawling and comply with reptiles agreement, you can. Do not burden the server.
II: posterior pole sliding identification codes ideas
Here simulate movements of the browser we can use to complete verification, use Selenium to fully simulate human behavior verified.
Is divided into three steps
(1) to simulate a click verification button
(2) identifying the location of the sliding gap
(3) simulated drag the slider
(1) relatively to say step, (2) identify the position of the operation key interface comparison step, need to use image processing see the position of the interface, and a picture contrast and detection methods to identify the location of the notch. At the same time get two pictures, setting a contrast threshold, then traversed two pictures to find the same pixel RGB pixel gap exceeds this threshold, then the pixel position is the position of the notch.
(3) step is difficult, due to human movement trajectory after the first acceleration and deceleration, constant speed movement and random movement or the like can not be verified to be a good simulation of the process.
III: posterior pole identification codes
1. The test electrode codes official website: https: //auth.geetest.com/login/
Official website Photo:
2. Initial Configuration
# 注册的用户名和密码
email = ''
password = ''
class CrackGeetest():
def __init__(self):
self.url = 'https://account.geetest.com/login'
self.browser = webdriver.Chrome()
self.wait = WebDriverWait(self.browser, 20)
self.email = email
self.password = password3. Click simulation
The first step is the analog identification codes initial verification button click, a method for obtaining explicit wait.
def get_geetest_button(self):
"""
获取初始验证按钮
返回值:按钮对象
"""
button = self.wait.until(EC.element_to_be_clickable((By.CLASS_NAME, 'geetest_radar_tip')))
return buttonIn the call position to simulate a click:
# 点击验证按钮
button = self.get_geetest_button()
button.click()4. Identify the gap
Next, identify the gap position, first obtain two pictures, comparison, the gap is not the same position.
Take a picture with no gaps. Select a picture element selenium get the whole page with screenshots can then crop, as follows:
def get_screenshot(self):
"""
获取网页截图
:return: 截图对象
"""
screenshot = self.browser.get_screenshot_as_png()
screenshot = Image.open(BytesIO(screenshot))
return screenshot
def get_position(self):
"""
获取验证码位置
:return: 验证码位置元组
"""
img = self.wait.until(EC.presence_of_element_located((By.CLASS_NAME, 'geetest_canvas_img')))
time.sleep(2)
location = img.location
size = img.size
top, bottom, left, right = location['y'], location['y'] + size['height'], location['x'], location['x'] + size[
'width']
return (top, bottom, left, right)
def get_geetest_image(self, name='captcha.png'):
"""
获取验证码图片
:return: 图片对象
"""
top, bottom, left, right = self.get_position()
print('验证码位置', top, bottom, left, right)
screenshot = self.get_screenshot()
captcha = screenshot.crop((left, top, right, bottom))
captcha.save(name)
return captchaNext you need to get the second picture, that picture with a gap, just click on the slider below the gap can appear, as follows:
def get_slider(self):
"""
获取滑块
:return: 滑块对象
"""
slider = self.wait.until(EC.element_to_be_clickable((By.CLASS_NAME, 'geetest_slider_button')))
return sliderWith a click () to trigger the click, as follows:
# 点按呼出缺口
slider = self.get_slider()
slider.click()The next step is to obtain the gap by comparing pictures, by traversing each coordinate point on the picture to get two pictures of the corresponding pixel RGB data. If within a certain range, it is representative of two identical pixels continue contrast of a pixel. If the difference exceeds a certain range, pixel represents a different point, the current position is that the gap position. Threshold by setting a threshold value, determination is performed, as follows:
def is_pixel_equal(self, image1, image2, 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
pixel1 = image1.load()[x, y]
pixel2 = image2.load()[x, y]
threshold = 60
if abs(pixel1[0] - pixel2[0]) < threshold and abs(pixel1[1] - pixel2[1]) < threshold and abs(
pixel1[2] - pixel2[2]) < threshold:
return True
else:
return False
def get_gap(self, image1, image2):
"""
获取缺口偏移量
:param image1: 不带缺口图片
:param image2: 带缺口图片
:return:
"""
left = 60
for i in range(left, image1.size[0]):
for j in range(image1.size[1]):
if not self.is_pixel_equal(image1, image2, i, j):
left = i
return left
return left5. Analog Drag
Drag the simulation is not complicated, but there are more details. Associated with the function corresponding to the slider position. But if the uniform drag, will inevitably identify procedural, non-human operation because humans can not be completely uniform drag, it recognizes the machine operation so that the code fails.
Detected by different methods, we have found to do the pre-stage constant acceleration slider after the slider section made uniform deceleration, to complete verification.
Here the acceleration represented by a current speed represented by v, the initial velocity is represented by VO, the displacement is represented by x, it is represented by the time t.
code show as below:
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 * 4 / 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))
return track
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()
for x in track:
ActionChains(self.browser).move_by_offset(xoffset=x, yoffset=0).perform()
time.sleep(0.5)
ActionChains(self.browser).release().perform()6: all code
import time
from io import BytesIO
from PIL import Image
from selenium import webdriver
from selenium.webdriver import ActionChains
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
EMAIL = '[email protected]'
PASSWORD = ''
BORDER = 6
INIT_LEFT = 60
# 注册的用户名和密码
email = ''
password = ''
class CrackGeetest():
def __init__(self):
self.url = 'https://account.geetest.com/login'
self.browser = webdriver.Chrome()
self.wait = WebDriverWait(self.browser, 20)
self.email = email
self.password = password
def __del__(self):
self.browser.close()
def get_geetest_button(self):
"""
获取初始验证按钮
返回值:按钮对象
"""
button = self.wait.until(EC.element_to_be_clickable((By.CLASS_NAME, 'geetest_radar_tip')))
return button
def get_screenshot(self):
"""
获取网页截图
:return: 截图对象
"""
screenshot = self.browser.get_screenshot_as_png()
screenshot = Image.open(BytesIO(screenshot))
return screenshot
def get_position(self):
"""
获取验证码位置
:return: 验证码位置元组
"""
img = self.wait.until(EC.presence_of_element_located((By.CLASS_NAME, 'geetest_canvas_img')))
time.sleep(2)
location = img.location
size = img.size
top, bottom, left, right = location['y'], location['y'] + size['height'], location['x'], location['x'] + size[
'width']
return (top, bottom, left, right)
def get_geetest_image(self, name='captcha.png'):
"""
获取验证码图片
:return: 图片对象
"""
top, bottom, left, right = self.get_position()
print('验证码位置', top, bottom, left, right)
screenshot = self.get_screenshot()
captcha = screenshot.crop((left, top, right, bottom))
captcha.save(name)
return captcha
def get_slider(self):
"""
获取滑块
:return: 滑块对象
"""
slider = self.wait.until(EC.element_to_be_clickable((By.CLASS_NAME, 'geetest_slider_button')))
return slider
def open(self):
"""
打开网页输入用户名密码
:return: None
"""
self.browser.get(self.url)
email = self.wait.until(EC.presence_of_element_located((By.ID, 'email')))
password = self.wait.until(EC.presence_of_element_located((By.ID, 'password')))
email.send_keys(self.email)
password.send_keys(self.password)
def is_pixel_equal(self, image1, image2, 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
pixel1 = image1.load()[x, y]
pixel2 = image2.load()[x, y]
threshold = 60
if abs(pixel1[0] - pixel2[0]) < threshold and abs(pixel1[1] - pixel2[1]) < threshold and abs(
pixel1[2] - pixel2[2]) < threshold:
return True
else:
return False
def get_gap(self, image1, image2):
"""
获取缺口偏移量
:param image1: 不带缺口图片
:param image2: 带缺口图片
:return:
"""
left = 60
for i in range(left, image1.size[0]):
for j in range(image1.size[1]):
if not self.is_pixel_equal(image1, image2, 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 * 4 / 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))
return track
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()
for x in track:
ActionChains(self.browser).move_by_offset(xoffset=x, yoffset=0).perform()
time.sleep(0.5)
ActionChains(self.browser).release().perform()
def login(self):
"""
登录
:return: None
"""
submit = self.wait.until(EC.element_to_be_clickable((By.CLASS_NAME, 'login-btn')))
submit.click()
time.sleep(10)
print('登录成功')
def crack(self):
# 输入用户名密码
self.open()
# 点击验证按钮
button = self.get_geetest_button()
button.click()
# 获取验证码图片
image1 = self.get_geetest_image('captcha1.png')
# 点按呼出缺口
slider = self.get_slider()
slider.click()
# 获取带缺口的验证码图片
image2 = self.get_geetest_image('captcha2.png')
# 获取缺口位置
gap = self.get_gap(image1, image2)
print('缺口位置', gap)
# 减去缺口位移
gap -= BORDER
# 获取移动轨迹
track = self.get_track(gap)
print('滑动轨迹', track)
# 拖动滑块
self.move_to_gap(slider, track)
success = self.wait.until(
EC.text_to_be_present_in_element((By.CLASS_NAME, 'geetest_success_radar_tip_content'), '验证成功'))
print(success)
# 失败后重试
if not success:
self.crack()
else:
self.login()
if __name__ == '__main__':
crack = CrackGeetest()
crack.crack()This approach is very different for sliding test codes are applicable, the key idea is to identify how to identify the position of the gap, how to generate the motion trajectory. After encountering similar codes, this can be identified.