Python如何破解滑动验证码

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滑动验证码破解思路

对于这类验证，如果我们直接模拟表单请求，繁琐的认证参数与认证流程会让你蛋碎一地，我们可以用selenium驱动浏览器来解决这个问题，大致分为以下几个步骤

1、输入用户名，密码

2、点击按钮验证，弹出没有缺口的图

3、获得没有缺口的图片

4、点击滑动按钮，弹出有缺口的图

5、获得有缺口的图片

6、对比两张图片，找出缺口，即滑动的位移

7、按照人的行为行为习惯，把总位移切成一段段小的位移

8、按照位移移动

9、完成登录

实现

位移移动的代码实现

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_imdef 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 webdriverfrom 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 ECfrom selenium.common.exceptions import TimeoutException, NoSuchElementExceptionfrom selenium.webdriver.common.by import Byfrom PIL import Imageimport requestsimport timeimport reimport randomfrom io import BytesIOdef 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_imdef 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 imagedef 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 tracksdef 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()

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