Python posterior pole sliding crack codes

Python posterior pole sliding crack codes

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1. Posterior pole sliding codes

2. achieve

   • Basics displacement movement needed

   • Comparing two pictures, identify gaps in

   • Get the picture

   • In accordance with the displacement of the mobile

   • Detailed code

 

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Posterior pole sliding codes

 

The above picture is the most typical slide to belong to the very experience of certification, inspection pole official website: http: //www.geetest.com/.

Now Fantastic experience verification code has been updated to version 3.0, as of July 2017 has been 16 million businesses worldwide are using extreme experience, more than four hundred million times a day service response, widely used in live video, financial services, e-commerce, games entertainment, government enterprises and other major types of sites

For this type of validation, if we direct analog form request, cumbersome certification and certification process parameters will make you eggs broken in one place, we can drive the browser with selenium to solve this problem, roughly divided into the following steps

1, enter the user name and password

2. Click the button to verify, there is no gap in the pop chart

3, get no gap pictures

4. Click on the slide button to bring up a notch in Fig.

5, obtain gapped picture

6, compared to two pictures, identify gaps in that slippage displacement

7, according to human behavior habits, the total displacement into small displacement section after section

8. The displacement of the mobile

9 and complete the registration

 

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achieve

 

Basics displacement movement needed

It corresponds to displacement of the mobile gear shift uniform linear motion, similar to the car from the starting point to the end point during the (first for uniformly accelerated, then decelerated uniform).

Wherein a is the acceleration, and is a constant (i.e., the acceleration per unit time is constant), a time T

 

 

 

Displacement of the mobile code implements

def get_track(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+(1/2)at²
    ③v²-v0²=2as

    :param distance: 需要移动的距离
    :return: 存放每0.2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=0.1
    # 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

    distance += 10  # 先滑过一点，最后再反着滑动回来

    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  # 加速运动
        else:
            a = -3 # 减速运动

        # 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

    # 反着滑动到大概准确位置
    for i in range(3):
       tracks.append(-2)
    for i in range(4):
       tracks.append(-1)
    return tracks

 

对比两张图片，找出缺口

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
      '''
    # 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]) # 计算RGB差
            res_G = abs(pixel1[1] - pixel2[1])  # 计算RGB差
            res_B = abs(pixel1[2] - pixel2[2])  # 计算RGB差
            if res_R > threshold and res_G > threshold and res_B > threshold:
                return i  # 需要移动的距离

 

获得图片

def merge_image(image_file,location_list):
    """
     拼接图片
    :param image_file:
    :param location_list:
    :return:
    """
    im = Image.open(image_file)
    im.save('code.jpg')
    new_im = Image.new('RGB',(260,116))
    # 把无序的图片 切成52张小图片
    im_list_upper = []
    im_list_down = []
    # print(location_list)
    for location in location_list:
        # print(location['y'])
        if location['y'] == -58: # 上半边
            im_list_upper.append(im.crop((abs(location['x']),58,abs(location['x'])+10,116)))
        if location['y'] == 0:  # 下半边
            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))  # 把小图片放到 新的空白图片上
        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):
    '''
    下载无序的图片  然后进行拼接 获得完整的图片
    :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')
    # '替换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) # 是一张无序的图片
    image = merge_image(image_file,location_list)

    return image

 

按照位移移动

 print('第一步,点击滑动按钮')
    ActionChains(driver).click_and_hold(on_element=element).perform()  # 点击鼠标左键，按住不放
    time.sleep(1)
    print('第二步,拖动元素')
    for track in track_list:
         ActionChains(driver).move_by_offset(xoffset=track, yoffset=0).perform() # 鼠标移动到距离当前位置（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,释放鼠标')
    ActionChains(driver).release(on_element=element).perform()

 

详细代码

from selenium import webdriver
from selenium.webdriver.support.ui import WebDriverWait # 等待元素加载的
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):
    """
     拼接图片
    :param image_file:
    :param location_list:
    :return:
    """
    im = Image.open(image_file)
    im.save('code.jpg')
    new_im = Image.new('RGB',(260,116))
    # 把无序的图片 切成52张小图片
    im_list_upper = []
    im_list_down = []
    # print(location_list)
    for location in location_list:
        # print(location['y'])
        if location['y'] == -58: # 上半边
            im_list_upper.append(im.crop((abs(location['x']),58,abs(location['x'])+10,116)))
        if location['y'] == 0:  # 下半边
            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))  # 把小图片放到 新的空白图片上
        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):
    '''
    下载无序的图片  然后进行拼接 获得完整的图片
    :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')
    # '替换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) # 是一张无序的图片
    image = merge_image(image_file,location_list)

    return image

def get_track(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+(1/2)at²
    ③v²-v0²=2as

    :param distance: 需要移动的距离
    :return: 存放每0.2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=0.2
    # 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 * 7/8

    distance += 10  # 先滑过一点，最后再反着滑动回来
    # a = random.randint(1,3)
    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 = random.randint(2,4)  # 加速运动
        else:
            a = -random.randint(3,5) # 减速运动

        # 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

    # 反着滑动到大概准确位置
    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
      '''
    # 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]) # 计算RGB差
            res_G = abs(pixel1[1] - pixel2[1])  # 计算RGB差
            res_B = abs(pixel1[2] - pixel2[2])  # 计算RGB差
            if res_R > threshold and res_G > threshold and res_B > threshold:
                return i  # 需要移动的距离



def main_check_code(driver, element):
    """
     拖动识别验证码
    :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')
    # 图片上 缺口的位置的x坐标

    # 2 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
    l = get_distance(image1, image2)
    print('l=',l)
    # 3 获得移动轨迹
    track_list = get_track(l)
    print('第一步,点击滑动按钮')
    ActionChains(driver).click_and_hold(on_element=element).perform()  # 点击鼠标左键，按住不放
    time.sleep(1)
    print('第二步,拖动元素')
    for track in track_list:
         ActionChains(driver).move_by_offset(xoffset=track, yoffset=0).perform()  # 鼠标移动到距离当前位置（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,释放鼠标')
    ActionChains(driver).release(on_element=element).perform()
    time.sleep(5)


def main_check_slider(driver):
    """
    检查滑动按钮是否加载
    :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('超时错误，继续')
            time.sleep(5)


if __name__ == '__main__':
    try:
        count = 6  # 最多识别6次
        driver = webdriver.Chrome()
        # 等待滑动按钮加载完成
        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')
                # 得到成功标志
                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('成功识别！！！！！！')
                    count = 0
                    break
            except NoSuchElementException as e:
                print('识别错误，继续')
                count -= 1
                time.sleep(2)
        else:
            print('too many attempt check code ')
            exit('退出程序')
    finally:
        driver.close()

 成功识别标志css

 

本文来源作者：一只小小寄居蟹

链接：https://www.cnblogs.com/xiao-apple36/p/8878960.html

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