滑動驗(yàn)證碼破解思路
對于這類驗(yàn)證，如果我們直接模擬表單請求，繁瑣的認(rèn)證參數(shù)與認(rèn)證流程會讓你蛋碎一地，我們可以用selenium驅(qū)動瀏覽器來解決這個問題，大致分為以下幾個步驟
1、輸入用戶名，密碼
2、點(diǎn)擊按鈕驗(yàn)證，彈出沒有缺口的圖
3、獲得沒有缺口的圖片
4、點(diǎn)擊滑動按鈕，彈出有缺口的圖
5、獲得有缺口的圖片
6、對比兩張圖片，找出缺口，即滑動的位移
7、按照人的行為行為習(xí)慣，把總位移切成一段段小的位移
8、按照位移移動
9、完成登錄
實(shí)現(xiàn)
位移移動需要的基礎(chǔ)知識
位移移動相當(dāng)于勻變速直線運(yùn)動，類似于小汽車從起點(diǎn)開始運(yùn)行到終點(diǎn)的過程（首先為勻加速，然后再勻減速）。
其中a為加速度，且為恒量（即單位時(shí)間內(nèi)的加速度是不變的），t為時(shí)間
位移移動的代碼實(shí)現(xiàn)
def get_track(distance):
'''
拿到移動軌跡，模仿人的滑動行為，先勻加速后勻減速
勻變速運(yùn)動基本公式：
①v=v0+at
②s=v0t+(1/2)at2
③v2-v02=2as
:param distance: 需要移動的距離
:return: 存放每0.2秒移動的距離
'''
# 初速度
v=0
# 單位時(shí)間為0.2s來統(tǒng)計(jì)軌跡，軌跡即0.2內(nèi)的位移
t=0.1
# 位移/軌跡列表，列表內(nèi)的一個元素代表0.2s的位移
tracks=[]
# 當(dāng)前的位移
current=0
# 到達(dá)mid值開始減速
mid=distance * 4/5
distance += 10 # 先滑過一點(diǎn)，最后再反著滑動回來
while current < distance:
if current < mid:
# 加速度越小，單位時(shí)間的位移越小,模擬的軌跡就越多越詳細(xì)
a = 2 # 加速運(yùn)動
else:
a = -3 # 減速運(yùn)動
# 初速度
v0 = v
# 0.2秒時(shí)間內(nèi)的位移
s = v0*t+0.5*a*(t**2)
# 當(dāng)前的位置
current += s
# 添加到軌跡列表
tracks.append(round(s))
# 速度已經(jīng)達(dá)到v,該速度作為下次的初速度
v= v0+a*t
# 反著滑動到大概準(zhǔn)確位置
for i in range(3):
tracks.append(-2)
for i in range(4):
tracks.append(-1)
return tracks對比兩張圖片，找出缺口
def get_distance(image1,image2):
'''
拿到滑動驗(yàn)證碼需要移動的距離
:param image1:沒有缺口的圖片對象
:param image2:帶缺口的圖片對象
:return:需要移動的距離
'''
# print('size', image1.size)
threshold = 50
for i in range(0,image1.size[0]): # 260
for j in range(0,image1.size[1]): # 160
pixel1 = image1.getpixel((i,j))
pixel2 = image2.getpixel((i,j))
res_R = abs(pixel1[0]-pixel2[0]) # 計(jì)算RGB差
res_G = abs(pixel1[1] - pixel2[1]) # 計(jì)算RGB差
res_B = abs(pixel1[2] - pixel2[2]) # 計(jì)算RGB差
if res_R > threshold and res_G > threshold and res_B > threshold:
return i # 需要移動的距離獲得圖片
def merge_image(image_file,location_list):
"""
拼接圖片
:param image_file:
:param location_list:
:return:
"""
im = Image.open(image_file)
im.save('code.jpg')
new_im = Image.new('RGB',(260,116))
# 把無序的圖片 切成52張小圖片
im_list_upper = []
im_list_down = []
# print(location_list)
for location in location_list:
# print(location['y'])
if location['y'] == -58: # 上半邊
im_list_upper.append(im.crop((abs(location['x']),58,abs(location['x'])+10,116)))
if location['y'] == 0: # 下半邊
im_list_down.append(im.crop((abs(location['x']),0,abs(location['x'])+10,58)))
x_offset = 0
for im in im_list_upper:
new_im.paste(im,(x_offset,0)) # 把小圖片放到 新的空白圖片上
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.show()
return new_im
def get_image(driver,div_path):
'''
下載無序的圖片 然后進(jìn)行拼接 獲得完整的圖片
:param driver:
:param div_path:
:return:
'''
time.sleep(2)
background_images = driver.find_elements_by_xpath(div_path)
location_list = []
for background_image in background_images:
location = {}
result = re.findall('background-image: url("(.*?)"); background-position: (.*?)px (.*?)px;',background_image.get_attribute('style'))
# print(result)
location['x'] = int(result[0][1])
location['y'] = int(result[0][2])
image_url = result[0][0]
location_list.append(location)
print('==================================')
image_url = image_url.replace('webp','jpg')
# '替換url http://static.geetest.com/pictures/gt/579066de6/579066de6.webp'
image_result = requests.get(image_url).content
# with open('1.jpg','wb') as f:
# f.write(image_result)
image_file = BytesIO(image_result) # 是一張無序的圖片
image = merge_image(image_file,location_list)
return image按照位移移動
print('第一步,點(diǎn)擊滑動按鈕')
ActionChains(driver).click_and_hold(on_element=element).perform() # 點(diǎn)擊鼠標(biāo)左鍵，按住不放
time.sleep(1)
print('第二步,拖動元素')
for track in track_list:
ActionChains(driver).move_by_offset(xoffset=track, yoffset=0).perform() # 鼠標(biāo)移動到距離當(dāng)前位置（x,y）
if l<100:
ActionChains(driver).move_by_offset(xoffset=-2, yoffset=0).perform()
else:
ActionChains(driver).move_by_offset(xoffset=-5, yoffset=0).perform()
time.sleep(1)
print('第三步,釋放鼠標(biāo)')
ActionChains(driver).release(on_element=element).perform()詳細(xì)代碼
from selenium import webdriver
from selenium.webdriver.support.ui import WebDriverWait # 等待元素加載的
from selenium.webdriver.common.action_chains import ActionChains #拖拽
from selenium.webdriver.support import expected_conditions as EC
from selenium.common.exceptions import TimeoutException, NoSuchElementException
from selenium.webdriver.common.by import By
from PIL import Image
import requests
import time
import re
import random
from io import BytesIO
def merge_image(image_file,location_list):
"""
拼接圖片
:param image_file:
:param location_list:
:return:
"""
im = Image.open(image_file)
im.save('code.jpg')
new_im = Image.new('RGB',(260,116))
# 把無序的圖片 切成52張小圖片
im_list_upper = []
im_list_down = []
# print(location_list)
for location in location_list:
# print(location['y'])
if location['y'] == -58: # 上半邊
im_list_upper.append(im.crop((abs(location['x']),58,abs(location['x'])+10,116)))
if location['y'] == 0: # 下半邊
im_list_down.append(im.crop((abs(location['x']),0,abs(location['x'])+10,58)))
x_offset = 0
for im in im_list_upper:
new_im.paste(im,(x_offset,0)) # 把小圖片放到 新的空白圖片上
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.show()
return new_im
def get_image(driver,div_path):
'''
下載無序的圖片 然后進(jìn)行拼接 獲得完整的圖片
:param driver:
:param div_path:
:return:
'''
time.sleep(2)
background_images = driver.find_elements_by_xpath(div_path)
location_list = []
for background_image in background_images:
location = {}
result = re.findall('background-image: url("(.*?)"); background-position: (.*?)px (.*?)px;',background_image.get_attribute('style'))
# print(result)
location['x'] = int(result[0][1])
location['y'] = int(result[0][2])
image_url = result[0][0]
location_list.append(location)
print('==================================')
image_url = image_url.replace('webp','jpg')
# '替換url http://static.geetest.com/pictures/gt/579066de6/579066de6.webp'
image_result = requests.get(image_url).content
# with open('1.jpg','wb') as f:
# f.write(image_result)
image_file = BytesIO(image_result) # 是一張無序的圖片
image = merge_image(image_file,location_list)
return image
def get_track(distance):
'''
拿到移動軌跡，模仿人的滑動行為，先勻加速后勻減速
勻變速運(yùn)動基本公式：
①v=v0+at
②s=v0t+(1/2)at2
③v2-v02=2as
:param distance: 需要移動的距離
:return: 存放每0.2秒移動的距離
'''
# 初速度
v=0
# 單位時(shí)間為0.2s來統(tǒng)計(jì)軌跡，軌跡即0.2內(nèi)的位移
t=0.2
# 位移/軌跡列表，列表內(nèi)的一個元素代表0.2s的位移
tracks=[]
# 當(dāng)前的位移
current=0
# 到達(dá)mid值開始減速
mid=distance * 7/8
distance += 10 # 先滑過一點(diǎn)，最后再反著滑動回來
# a = random.randint(1,3)
while current < distance:
if current < mid:
# 加速度越小，單位時(shí)間的位移越小,模擬的軌跡就越多越詳細(xì)
a = random.randint(2,4) # 加速運(yùn)動
else:
a = -random.randint(3,5) # 減速運(yùn)動
# 初速度
v0 = v
# 0.2秒時(shí)間內(nèi)的位移
s = v0*t+0.5*a*(t**2)
# 當(dāng)前的位置
current += s
# 添加到軌跡列表
tracks.append(round(s))
# 速度已經(jīng)達(dá)到v,該速度作為下次的初速度
v= v0+a*t
# 反著滑動到大概準(zhǔn)確位置
for i in range(4):
tracks.append(-random.randint(2,3))
for i in range(4):
tracks.append(-random.randint(1,3))
return tracks
def get_distance(image1,image2):
'''
拿到滑動驗(yàn)證碼需要移動的距離
:param image1:沒有缺口的圖片對象
:param image2:帶缺口的圖片對象
:return:需要移動的距離
'''
# print('size', image1.size)
threshold = 50
for i in range(0,image1.size[0]): # 260
for j in range(0,image1.size[1]): # 160
pixel1 = image1.getpixel((i,j))
pixel2 = image2.getpixel((i,j))
res_R = abs(pixel1[0]-pixel2[0]) # 計(jì)算RGB差
res_G = abs(pixel1[1] - pixel2[1]) # 計(jì)算RGB差
res_B = abs(pixel1[2] - pixel2[2]) # 計(jì)算RGB差
if res_R > threshold and res_G > threshold and res_B > threshold:
return i # 需要移動的距離
def main_check_code(driver, element):
"""
拖動識別驗(yàn)證碼
:param driver:
:param element:
:return:
"""
image1 = get_image(driver, '//div[@class="gt_cut_bg gt_show"]/div')
image2 = get_image(driver, '//div[@class="gt_cut_fullbg gt_show"]/div')
# 圖片上 缺口的位置的x坐標(biāo)
# 2 對比兩張圖片的所有RBG像素點(diǎn)，得到不一樣像素點(diǎn)的x值，即要移動的距離
l = get_distance(image1, image2)
print('l=',l)
# 3 獲得移動軌跡
track_list = get_track(l)
print('第一步,點(diǎn)擊滑動按鈕')
ActionChains(driver).click_and_hold(on_element=element).perform() # 點(diǎn)擊鼠標(biāo)左鍵，按住不放
time.sleep(1)
print('第二步,拖動元素')
for track in track_list:
ActionChains(driver).move_by_offset(xoffset=track, yoffset=0).perform() # 鼠標(biāo)移動到距離當(dāng)前位置（x,y） time.sleep(0.002)
# if l>100:
ActionChains(driver).move_by_offset(xoffset=-random.randint(2,5), yoffset=0).perform()
time.sleep(1)
print('第三步,釋放鼠標(biāo)')
ActionChains(driver).release(on_element=element).perform()
time.sleep(5)
def main_check_slider(driver):
"""
檢查滑動按鈕是否加載
:param driver:
:return:
"""
while True:
try :
driver.get('http://www.cnbaowen.net/api/geetest/')
element = WebDriverWait(driver, 30, 0.5).until(EC.element_to_be_clickable((By.CLASS_NAME, 'gt_slider_knob')))
if element:
return element
except TimeoutException as e:
print('超時(shí)錯誤，繼續(xù)')
time.sleep(5)
if __name__ == '__main__':
try:
count = 6 # 最多識別6次
driver = webdriver.Chrome()
# 等待滑動按鈕加載完成
element = main_check_slider(driver)
while count > 0:
main_check_code(driver,element)
time.sleep(2)
try:
success_element = (By.CSS_SELECTOR, '.gt_holder .gt_ajax_tip.gt_success')
# 得到成功標(biāo)志
print('suc=',driver.find_element_by_css_selector('.gt_holder .gt_ajax_tip.gt_success'))
success_images = WebDriverWait(driver, 20).until(EC.presence_of_element_located(success_element))
if success_images:
print('成功識別！?。。。。?#39;)
count = 0
break
except NoSuchElementException as e:
print('識別錯誤，繼續(xù)')
count -= 1
time.sleep(2)
else:
print('too many attempt check code ')
exit('退出程序')
finally:
driver.close()成功識別標(biāo)志css
以上就是Python破解滑動驗(yàn)證碼的詳細(xì)內(nèi)容，更多Python自動化測試的學(xué)習(xí)內(nèi)容請關(guān)注W3Cschool其它相關(guān)文章。
