目前藝賽旗RPA已經更新到8.0版本，可以讓所有用戶免費下載試用http://www.i-search.com.cn/index.html?from=line1 （復制鏈接下載）
一，介紹
現在極驗驗證碼已經更新到了3.0版本，截至2017年7月全球已有十六萬家企業正在使用極驗，每天服務響應超過四億次，廣泛應用于直播視頻，金融服務，電子商務，游戲娛樂，政府企業等各大類型網站
對于這類驗證，如果我們直接模擬表單請求，繁瑣的認證參數與認證流程會讓你蛋碎一地，我們可以用selenium驅動瀏覽器來解決這個問題，大致分為以下幾個步驟
＃1，輸入賬號，密碼，然后點擊登陸
二，實現
這里用的是Chrome
C:\Users\Administrator>pip3 install pillow
from PIL import Image
代碼如下（增加部分注釋）
def get_snap():
def get_image():
top=localtion['y']
bottom=localtion['y']+size['height']
left=localtion['x']
right=localtion['x']+size['width']page_snap_obj=get_snap()
crop_imag_obj=page_snap_obj.crop((left,top,right,bottom))
return crop_imag_obj
def get_distance(image1,image2):
:param distance: 需要移動的距離
:return: 存放每0.3秒移動的距離
'''
#初速度
v=0
#單位時間為0.2s來統計軌跡，軌跡即0.2內的位移
t=0.3
#位移/軌跡列表，列表內的一個元素代表0.2s的位移
tracks=[]
#當前的位移
current=0
#到達mid值開始減速
mid=distance*4/5while current < distance:if current < mid:# 加速度越小，單位時間的位移越小,模擬的軌跡就越多越詳細a= 2else:a=-3#初速度v0=v#0.2秒時間內的位移s=v0*t+0.5*a*(t**2)#當前的位置current+=s#添加到軌跡列表tracks.append(round(s))#速度已經達到v,該速度作為下次的初速度v=v0+a*t
return tracks
try:
#步驟一：先點擊按鈕，彈出沒有缺口的圖片
button=wait.until(EC.presence_of_element_located((By.CLASS_NAME,'geetest_radar_tip')))
button.click()#步驟二：拿到沒有缺口的圖片
image1=get_image()#步驟三：點擊拖動按鈕，彈出有缺口的圖片
button=wait.until(EC.presence_of_element_located((By.CLASS_NAME,'geetest_slider_button')))
button.click()#步驟四：拿到有缺口的圖片
image2=get_image()# print(image1,image1.size)
# print(image2,image2.size)#步驟五：對比兩張圖片的所有RBG像素點，得到不一樣像素點的x值，即要移動的距離
distance=get_distance(image1,image2)#步驟六：模擬人的行為習慣（先勻加速拖動后勻減速拖動），把需要拖動的總距離分成一段一段小的軌跡
tracks=get_tracks(distance)
print(tracks)
print(image1.size)
print(distance,sum(tracks))#步驟七：按照軌跡拖動，完全驗證
button=wait.until(EC.presence_of_element_located((By.CLASS_NAME,'geetest_slider_button')))
ActionChains(driver).click_and_hold(button).perform()
for track in tracks:ActionChains(driver).move_by_offset(xoffset=track,yoffset=0).perform()
else:ActionChains(driver).move_by_offset(xoffset=3,yoffset=0).perform() #先移過一點ActionChains(driver).move_by_offset(xoffset=-3,yoffset=0).perform() #再退回來，是不是更像人了time.sleep(0.5) #0.5秒后釋放鼠標
ActionChains(driver).release().perform()#步驟八：完成登錄
input_email=driver.find_element_by_id('email')
input_password=driver.find_element_by_id('password')
button=wait.until(EC.element_to_be_clickable((By.CLASS_NAME,'login-btn')))input_email.send_keys('18611453110@163.com')
input_password.send_keys('linhaifeng123')
# button.send_keys(Keys.ENTER)
button.click()import time
time.sleep(200)
finally:
def get_snap():
def get_image():
left=location['x']
top=location['y']
right=left+size['width']
bottom=top+size['height']page_snap_obj=get_snap()
image_obj=page_snap_obj.crop((left,top,right,bottom))
# image_obj.show()
return image_obj
def get_distance(image1,image2):
for i in range(start,image1.size[0]):for j in range(image1.size[1]):rgb1=image1.load()[i,j]rgb2=image2.load()[i,j]res1=abs(rgb1[0]-rgb2[0])res2=abs(rgb1[1]-rgb2[1])res3=abs(rgb1[2]-rgb2[2])# print(res1,res2,res3)if not (res1 < threhold and res2 < threhold and res3 < threhold):return i-7
return i-7
def get_tracks(distance):
current=0
mid=distance*3/5
while current < distance:if current < mid:a=2else:a=-3s=v*t+0.5*a*(t**2)v=v+a*tcurrent+=sforward_tracks.append(round(s))#反著滑動到準確位置
back_tracks=[-3,-3,-2,-2,-2,-2,-2,-1,-1,-1] #總共等于-20return {'forward_tracks':forward_tracks,'back_tracks':back_tracks}
try:
username = driver.find_element_by_id('input1')
pwd = driver.find_element_by_id('input2')
signin = driver.find_element_by_id('signin')username.send_keys('linhaifeng')
pwd.send_keys('xxxxx')
signin.click()# 2、點擊按鈕，得到沒有缺口的圖片
button = driver.find_element_by_class_name('geetest_radar_tip')
button.click()# 3、獲取沒有缺口的圖片
image1 = get_image()# 4、點擊滑動按鈕，得到有缺口的圖片
button = driver.find_element_by_class_name('geetest_slider_button')
button.click()# 5、獲取有缺口的圖片
image2 = get_image()# 6、對比兩種圖片的像素點，找出位移
distance = get_distance(image1, image2)# 7、模擬人的行為習慣，根據總位移得到行為軌跡
tracks = get_tracks(distance)
print(tracks)# 8、按照行動軌跡先正向滑動，后反滑動
button = driver.find_element_by_class_name('geetest_slider_button')
ActionChains(driver).click_and_hold(button).perform()# 正常人類總是自信滿滿地開始正向滑動，自信地表現是瘋狂加速
for track in tracks['forward_tracks']:ActionChains(driver).move_by_offset(xoffset=track, yoffset=0).perform()# 結果傻逼了，正常的人類停頓了一下，回過神來發現，臥槽，滑過了,然后開始反向滑動
time.sleep(0.5)
for back_track in tracks['back_tracks']:ActionChains(driver).move_by_offset(xoffset=back_track, yoffset=0).perform()# 小范圍震蕩一下，進一步迷惑極驗后臺，這一步可以極大地提高成功率
ActionChains(driver).move_by_offset(xoffset=-3, yoffset=0).perform()
ActionChains(driver).move_by_offset(xoffset=3, yoffset=0).perform()# 成功后，騷包人類總喜歡默默地欣賞一下自己拼圖的成果，然后戀戀不舍地松開那只臟手
time.sleep(0.5)
ActionChains(driver).release().perform()time.sleep(10) # 睡時間長一點，確定登錄成功
finally:
def get_snap(driver):
def get_image(driver):
left=location['x']
top=location['y']
right=left+size['width']
bottom=top+size['height']page_snap_obj=get_snap(driver)
image_obj=page_snap_obj.crop((left,top,right,bottom))
# image_obj.show()
return image_obj
def get_distance(image1,image2):
for i in range(start,image1.size[0]):for j in range(image1.size[1]):rgb1=image1.load()[i,j]rgb2=image2.load()[i,j]res1=abs(rgb1[0]-rgb2[0])res2=abs(rgb1[1]-rgb2[1])res3=abs(rgb1[2]-rgb2[2])# print(res1,res2,res3)if not (res1 < threhold and res2 < threhold and res3 < threhold):return i-7
return i-7
def get_tracks(distance):
current=0
mid=distance*3/5
while current < distance:if current < mid:a=2else:a=-3s=v*t+0.5*a*(t**2)v=v+a*tcurrent+=sforward_tracks.append(round(s))#反著滑動到準確位置
back_tracks=[-3,-3,-2,-2,-2,-2,-2,-1,-1,-1] #總共等于-20return {'forward_tracks':forward_tracks,'back_tracks':back_tracks}
def crack(driver): #破解滑動認證
# 2、獲取沒有缺口的圖片
image1 = get_image(driver)# 3、點擊滑動按鈕，得到有缺口的圖片
button = driver.find_element_by_class_name('geetest_slider_button')
button.click()# 4、獲取有缺口的圖片
image2 = get_image(driver)# 5、對比兩種圖片的像素點，找出位移
distance = get_distance(image1, image2)# 6、模擬人的行為習慣，根據總位移得到行為軌跡
tracks = get_tracks(distance)
print(tracks)# 7、按照行動軌跡先正向滑動，后反滑動
button = driver.find_element_by_class_name('geetest_slider_button')
ActionChains(driver).click_and_hold(button).perform()# 正常人類總是自信滿滿地開始正向滑動，自信地表現是瘋狂加速
for track in tracks['forward_tracks']:ActionChains(driver).move_by_offset(xoffset=track, yoffset=0).perform()# 結果傻逼了，正常的人類停頓了一下，回過神來發現，臥槽，滑過了,然后開始反向滑動
time.sleep(0.5)
for back_track in tracks['back_tracks']:ActionChains(driver).move_by_offset(xoffset=back_track, yoffset=0).perform()# 小范圍震蕩一下，進一步迷惑極驗后臺，這一步可以極大地提高成功率
ActionChains(driver).move_by_offset(xoffset=-3, yoffset=0).perform()
ActionChains(driver).move_by_offset(xoffset=3, yoffset=0).perform()# 成功后，騷包人類總喜歡默默地欣賞一下自己拼圖的成果，然后戀戀不舍地松開那只臟手
time.sleep(0.5)
ActionChains(driver).release().perform()
def login_cnblogs(username,password):
input_username = driver.find_element_by_id('input1')input_pwd = driver.find_element_by_id('input2')signin = driver.find_element_by_id('signin')input_username.send_keys(username)input_pwd.send_keys(password)signin.click()# 2、破解滑動認證crack(driver)time.sleep(10) # 睡時間長一點，確定登錄成功
finally:driver.close()
if name == ‘main ’:
import time
from selenium.webdriver import ActionChains
def simulate_reaction(func):
“”“模擬人類的反應時間”""
from functools import wraps
@wraps
def inner(self, *args, **kwargs):
time.sleep(random.uniform(0.2, 1))
ret = func(self, *args, **kwargs)
return ret
return inner
class SVCR:
def __init__(self, driver):self.driver = driverself.get_full_img = True# @simulate_reaction
def run(self):"""執行識別流程"""# 1. 點擊按鈕開始驗證self.click_start_btn()# 2. 根據驗證類型驗證return self.judge_and_auth()def judge_and_auth(self):"""判斷驗證類型并執行相應的驗證方法"""if True:return self.auth_slide()else:passdef auth_slide(self):def get_distance(img1, img2):"""計算滑動距離"""threshold = 60# 忽略可動滑塊部分start_x = 57for i in range(start_x, img1.size[0]):for j in range(img1.size[1]):rgb1 = img1.load()[i, j]rgb2 = img2.load()[i, j]res1 = abs(rgb1[0] - rgb2[0])res2 = abs(rgb1[1] - rgb2[1])res3 = abs(rgb1[2] - rgb2[2])if not (res1 < threshold and res2 < threshold and res3 < threshold):return i - 7 # 經過測試，誤差為大概為7def get_tracks(distance):"""制造滑動軌跡策略：勻加速再勻減速，超過一些，再回調，左右小幅度震蕩"""v = 0current = 0t = 0.2tracks = []# 正向滑動while current < distance+10:if current < distance*2/3:a = 2else:a = -3s = v*t + 0.5*a*(t**2)current += stracks.append(round(s))v = v + a*t# 往回滑動current = 0while current < 13:if current < distance*2/3:a = 2else:a = -3s = v*t + 0.5*a*(t**2)current += stracks.append(-round(s))v = v + a*t# 最后修正tracks.extend([2, 2, -3, 2])return tracks# 1. 截取完整圖片if self.get_full_img:time.sleep(2) # 等待圖片加載完畢img_before = self.get_img()else:img_before = self._img_before# 2. 點擊出現缺口圖片slider_btn = self.driver.find_element_by_class_name("geetest_slider_button")slider_btn.click()# 3. 截取缺口圖片time.sleep(2) # 等待圖片加載完畢img_after = self.get_img()# 4. 生成移動軌跡tracks = get_tracks(get_distance(img_before, img_after))# 5. 模擬滑動slider_btn = self.driver.find_element_by_class_name("geetest_slider_button")ActionChains(self.driver).click_and_hold(slider_btn).perform()for track in tracks:ActionChains(self.driver).move_by_offset(xoffset=track, yoffset=0).perform()# 6. 釋放鼠標time.sleep(0.5) # 0.5秒后釋放鼠標ActionChains(self.driver).release().perform()# 7. 驗證是否成功time.sleep(2)div_tag = self.driver.find_element_by_class_name("geetest_fullpage_click")if "display: block" in div_tag.get_attribute("style"):'''判斷模塊對話框是否存在，如果存在就說明沒有驗證成功，"display: block"，重新去驗證'''self.get_full_img = Falsesetattr(self, "_img_before", img_before)return self.auth_slide()else:#如果驗證成功"display: none"time.sleep(1000)return True# @simulate_reaction
def click_start_btn(self, search_style="CLASS_NAME", search_content="geetest_radar_tip"):"""找到開始按鈕并點擊"""btn = getattr(self.driver, "find_element")(getattr(By, search_style), search_content)btn.click()def get_img(self):"""截取圖片"""div_tag = self.driver.find_element_by_class_name("geetest_slicebg")# 計算截取圖片大小img_pt = div_tag.location # {'x': 296, 'y': 15}img_size = div_tag.size # {'height': 159, 'width': 258}img_box = (img_pt["x"], img_pt["y"], img_pt["x"] + img_size["width"], img_pt["y"] + img_size["height"])# 保存當前瀏覽頁面self.driver.save_screenshot("snap.png")# 截取目標圖片img = Image.open("snap.png")return img.crop(img_box)
使用類
from svcr import SVCR
def auth():
def main():
if name == ‘main ’:
總結
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