PS:浏览器和电脑的缩放应该调为100%(Windows默认为125%),否则可能会导致获取局部图片时出现误差!
from selenium import webdriver
from selenium.webdriver.common.action_chains import ActionChains
from PIL import Image
import time
def get_snapshot():
"获取整个页面的快照,并返回图片对象"
driver.save_screenshot('snapshot.png')
pic_obj = Image.open('snapshot.png')
return pic_obj
def get_image():
# 获取滑块验证码图片所在div的元素对象
element = driver.find_element_by_xpath('//div[@class="geetest_slicebg geetest_absolute"]')
# 计算滑块验证码部分截图的坐标
left = element.location['x']
top = element.location['y']
right = left + element.size['width']
bottom = top + element.size['height']
# 先获取整个界面截图的对象
pic_obj = get_snapshot()
# 获取滑块验证码部分截图对象
part_pic_obj = pic_obj.crop((left, top, right, bottom))
return part_pic_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
'''
# 从图像的左侧60像素开始匹配,把填充的那部分去掉,不比较,避免干扰。
left = 60
for i in range(left, image1.size[0]): # 图像的宽
for j in range(image1.size[1]): # 图像的高
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])
# 自定义容差为60定义为缺口
if res1 >= 60 and res2 >= 60 and res3 >= 60:
return i - 7 # 误差大概是7
return left
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0.2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 * 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
if __name__ == '__main__':
try:
driver = webdriver.Chrome()
driver.get('https://account.ch.com/NonRegistrations-Regist')
driver.maximize_window()
driver.find_element_by_name('phoneNumberInput').send_keys('15216122411')
# 单击,让滑块验证码的图片显示
getDynamicPwd = driver.find_element_by_xpath('//a[@id="getDynamicPwd"]')
ActionChains(driver).move_to_element(getDynamicPwd).move_by_offset(5, 0).click().perform()
# driver.find_element_by_xpath('//a[@id="getDynamicPwd"]').click()
# driver.execute_script('document.getElementById("getDynamicPwd").click()')
# driver.find_element_by_id('getDynamicPwd').click()
time.sleep(2) # 沉睡两秒,加载滑块验证码
# 获取有缺口的滑块验证局部截图
image1 = get_image()
image1.save('imag1.png') # 获取局部截图
# 获取没有缺口的图片
driver.execute_script('document.getElementsByClassName("geetest_canvas_fullbg")[0].style="display:block"')
image2 = get_image()
image2.save('imag2.png') # 获取局部截图
distance = get_distance(image1, image2)
# 获取滑块
huakuai = driver.find_element_by_xpath('//div[@class="geetest_slider_button"]')
# 拉动滑块开始执行
ActionChains(driver).click_and_hold(on_element=huakuai).perform()
ActionChains(driver).move_by_offset(xoffset=distance * 0.5, yoffset=0).perform() # 闪现50%
# 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 * 0.5) # 剩下的50%在模拟移动
for x in tracks:
ActionChains(driver).move_by_offset(xoffset=x, 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,看上去更像人为
# 0.5s释放鼠标
time.sleep(0.5)
ActionChains(driver).release().perform()
except Exception as error:
print(error)
driver.close()