爬虫之极验滑动验证码

#安装：selenium+chrome/phantomjs

#安装：Pillow
Pillow:基于PIL，处理python 3.x的图形图像库.因为PIL只能处理到python 2.x，而这个模块能处理Python3.x，目前用它做图形的很多.
http://www.cnblogs.com/apexchu/p/4231041.html

C:\Users\Administrator>pip3 install pillow
C:\Users\Administrator>python3
Python 3.6.1 (v3.6.1:69c0db5, Mar 21 2017, 18:41:36) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> from PIL import Image
>>>

1.破解欢动验证码

2.

from selenium import webdriver
from selenium.webdriver import ActionChains
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.support.wait import WebDriverWait
from PIL import Image
import time

def get_snap():
    \'\'\'
    对整个网页截图，保存成图片，然后用PIL.Image拿到图片对象
    :return: 图片对象
    \'\'\'
    driver.save_screenshot(\'snap.png\')
    page_snap_obj=Image.open(\'snap.png\')
    return page_snap_obj

def get_image():
    \'\'\'
    从网页的网站截图中，截取验证码图片
    :return: 验证码图片
    \'\'\'
    img=wait.until(EC.presence_of_element_located((By.CLASS_NAME,\'geetest_canvas_img\')))
    time.sleep(2) #保证图片刷新出来
    localtion=img.location
    size=img.size

    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):
    \'\'\'
    拿到滑动验证码需要移动的距离
    :param image1:没有缺口的图片对象
    :param image2:带缺口的图片对象
    :return:需要移动的距离
    \'\'\'
    threshold=60
    left=57
    for i in range(left,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])
            if not (res1 < threshold and res2 < threshold and res3 < threshold):
                return i-7 #经过测试，误差为大概为7
    return i-7 #经过测试，误差为大概为7


def get_tracks(distance):
    \'\'\'
    拿到移动轨迹，模仿人的滑动行为，先匀加速后匀减速
    匀变速运动基本公式：
    ①v=v0+at
    ②s=v0t+½at²
    ③v²-v0²=2as

    :param distance: 需要移动的距离
    :return: 存放每0.3秒移动的距离
    \'\'\'
    #初速度
    v=0
    #单位时间为0.2s来统计轨迹，轨迹即0.2内的位移
    t=0.3
    #位移/轨迹列表，列表内的一个元素代表0.2s的位移
    tracks=[]
    #当前的位移
    current=0
    #到达mid值开始减速
    mid=distance*4/5

    while current < distance:
        if current < mid:
            # 加速度越小，单位时间的位移越小,模拟的轨迹就越多越详细
            a= 2
        else:
            a=-3

        #初速度
        v0=v
        #0.2秒时间内的位移
        s=v0*t+0.5*a*(t**2)
        #当前的位置
        current+=s
        #添加到轨迹列表
        tracks.append(round(s))

        #速度已经达到v,该速度作为下次的初速度
        v=v0+a*t
    return tracks


try:
    driver=webdriver.Chrome()
    driver.get(\'https://account.geetest.com/login\')
    wait=WebDriverWait(driver,10)

    #步骤一：先点击按钮，弹出没有缺口的图片
    button=wait.until(EC.presence_of_element_located((By.CLASS_NAME,\'geetest_radar_tip\')))
    button.click()

    #步骤二：拿到没有缺口的图片
    image1=get_image()

    #步骤三：点击拖动按钮，弹出有缺口的图片
    button=wait.until(EC.presence_of_element_located((By.CLASS_NAME,\'geetest_slider_button\')))
    button.click()

    #步骤四：拿到有缺口的图片
    image2=get_image()

    # print(image1,image1.size)
    # print(image2,image2.size)

    #步骤五：对比两张图片的所有RBG像素点，得到不一样像素点的x值，即要移动的距离
    distance=get_distance(image1,image2)

    #步骤六：模拟人的行为习惯（先匀加速拖动后匀减速拖动），把需要拖动的总距离分成一段一段小的轨迹
    tracks=get_tracks(distance)
    print(tracks)
    print(image1.size)
    print(distance,sum(tracks))


    #步骤七：按照轨迹拖动，完全验证
    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() #先移过一点
        ActionChains(driver).move_by_offset(xoffset=-3,yoffset=0).perform() #再退回来，是不是更像人了

    time.sleep(0.5) #0.5秒后释放鼠标
    ActionChains(driver).release().perform()


    #步骤八：完成登录
    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:
    driver.close()

破解滑动发验证码

from selenium import webdriver
from selenium.webdriver import ActionChains
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.support.wait import WebDriverWait
from PIL import Image
import time

def get_snap():
    driver.save_screenshot(\'full_snap.png\')
    page_snap_obj=Image.open(\'full_snap.png\')
    return page_snap_obj

def get_image():
    img=driver.find_element_by_class_name(\'geetest_canvas_img\')
    time.sleep(2)
    location=img.location
    size=img.size

    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):
    start=57
    threhold=60

    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):
    distance+=20 #先滑过一点，最后再反着滑动回来
    v=0
    t=0.2
    forward_tracks=[]

    current=0
    mid=distance*3/5
    while current < distance:
        if current < mid:
            a=2
        else:
            a=-3

        s=v*t+0.5*a*(t**2)
        v=v+a*t
        current+=s
        forward_tracks.append(round(s))

    #反着滑动到准确位置
    back_tracks=[-3,-3,-2,-2,-2,-2,-2,-1,-1,-1] #总共等于-20

    return {\'forward_tracks\':forward_tracks,\'back_tracks\':back_tracks}

try:
    # 1、输入账号密码回车
    driver = webdriver.Chrome()
    driver.implicitly_wait(3)
    driver.get(\'https://passport.cnblogs.com/user/signin\')

    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、点击按钮，得到没有缺口的图片
    button = driver.find_element_by_class_name(\'geetest_radar_tip\')
    button.click()

    # 3、获取没有缺口的图片
    image1 = get_image()

    # 4、点击滑动按钮，得到有缺口的图片
    button = driver.find_element_by_class_name(\'geetest_slider_button\')
    button.click()

    # 5、获取有缺口的图片
    image2 = get_image()

    # 6、对比两种图片的像素点，找出位移
    distance = get_distance(image1, image2)

    # 7、模拟人的行为习惯，根据总位移得到行为轨迹
    tracks = get_tracks(distance)
    print(tracks)

    # 8、按照行动轨迹先正向滑动，后反滑动
    button = driver.find_element_by_class_name(\'geetest_slider_button\')
    ActionChains(driver).click_and_hold(button).perform()

    # 正常人类总是自信满满地开始正向滑动，自信地表现是疯狂加速
    for track in tracks[\'forward_tracks\']:
        ActionChains(driver).move_by_offset(xoffset=track, yoffset=0).perform()

    # 正常的人类停顿了一下，回过神来发现，卧槽，滑过了,然后开始反向滑动
    time.sleep(0.5)
    for back_track in tracks[\'back_tracks\']:
        ActionChains(driver).move_by_offset(xoffset=back_track, yoffset=0).perform()

    # 小范围震荡一下，进一步迷惑极验后台，这一步可以极大地提高成功率
    ActionChains(driver).move_by_offset(xoffset=-3, yoffset=0).perform()
    ActionChains(driver).move_by_offset(xoffset=3, yoffset=0).perform()

    # 成功后，骚包人类总喜欢默默地欣赏一下自己拼图的成果，然后恋恋不舍地松开那只脏手
    time.sleep(0.5)
    ActionChains(driver).release().perform()

    time.sleep(10)  # 睡时间长一点，确定登录成功
finally:
    driver.close()

破解博客园后台登录

from selenium import webdriver
from selenium.webdriver import ActionChains
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.support.wait import WebDriverWait
from PIL import Image
import time

def get_snap(driver):
    driver.save_screenshot(\'full_snap.png\')
    page_snap_obj=Image.open(\'full_snap.png\')
    return page_snap_obj

def get_image(driver):
    img=driver.find_element_by_class_name(\'geetest_canvas_img\')
    time.sleep(2)
    location=img.location
    size=img.size

    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):
    start=57
    threhold=60

    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):
    distance+=20 #先滑过一点，最后再反着滑动回来
    v=0
    t=0.2
    forward_tracks=[]

    current=0
    mid=distance*3/5
    while current < distance:
        if current < mid:
            a=2
        else:
            a=-3

        s=v*t+0.5*a*(t**2)
        v=v+a*t
        current+=s
        forward_tracks.append(round(s))

    #反着滑动到准确位置
    back_tracks=[-3,-3,-2,-2,-2,-2,-2,-1,-1,-1] #总共等于-20

    return {\'forward_tracks\':forward_tracks,\'back_tracks\':back_tracks}

def crack(driver): #破解滑动认证
    # 1、点击按钮，得到没有缺口的图片
    button = driver.find_element_by_class_name(\'geetest_radar_tip\')
    button.click()

    # 2、获取没有缺口的图片
    image1 = get_image(driver)

    # 3、点击滑动按钮，得到有缺口的图片
    button = driver.find_element_by_class_name(\'geetest_slider_button\')
    button.click()

    # 4、获取有缺口的图片
    image2 = get_image(driver)

    # 5、对比两种图片的像素点，找出位移
    distance = get_distance(image1, image2)

    # 6、模拟人的行为习惯，根据总位移得到行为轨迹
    tracks = get_tracks(distance)
    print(tracks)

    # 7、按照行动轨迹先正向滑动，后反滑动
    button = driver.find_element_by_class_name(\'geetest_slider_button\')
    ActionChains(driver).click_and_hold(button).perform()

    # 正常人类总是自信满满地开始正向滑动，自信地表现是疯狂加速
    for track in tracks[\'forward_tracks\']:
        ActionChains(driver).move_by_offset(xoffset=track, yoffset=0).perform()

    # 结果傻逼了，正常的人类停顿了一下，回过神来发现，卧槽，滑过了,然后开始反向滑动
    time.sleep(0.5)
    for back_track in tracks[\'back_tracks\']:
        ActionChains(driver).move_by_offset(xoffset=back_track, yoffset=0).perform()

    # 小范围震荡一下，进一步迷惑极验后台，这一步可以极大地提高成功率
    ActionChains(driver).move_by_offset(xoffset=-3, yoffset=0).perform()
    ActionChains(driver).move_by_offset(xoffset=3, yoffset=0).perform()

    # 成功后，骚包人类总喜欢默默地欣赏一下自己拼图的成果，然后恋恋不舍地松开那只脏手
    time.sleep(0.5)
    ActionChains(driver).release().perform()

def login_cnblogs(username,password):
    driver = webdriver.Chrome()
    try:
        # 1、输入账号密码回车
        driver.implicitly_wait(3)
        driver.get(\'https://passport.cnblogs.com/user/signin\')

        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、破解滑动认证
        crack(driver)

        time.sleep(10)  # 睡时间长一点，确定登录成功
    finally:
        driver.close()

if __name__ == \'__main__\':
    login_cnblogs(username=\'linhaifeng\',password=\'xxxx\')

修订版

修订版