python自动化操作之动态验证码、滑动验证码的降噪和识别

from selenium import webdriver
from PIL import Image
 
# 实例化浏览器
driver = webdriver.Chrome()
 
# 最大化窗口
driver.maximize_window()
 
# 打开登陆页面
driver.get(# 你的url地址)
 
# 保存页面截图
driver.get_screenshot_as_file('./screen.png')
 
# 定位验证码的位置
location = driver.find_element_by_id('login_yzm_img').location
size = driver.find_element_by_id('login_yzm_img').size
left = location['x']
top =  location['y']
right = location['x'] + size['width']
bottom = location['y'] + size['height']
 
# 裁剪保存
img = Image.open('./screen.png').crop((left,top,right,bottom))
img.save('./code.png')
 
driver.quit()

# 下载两个滑块
bg = self.driver.find_element_by_xpath('//*[@id="captcha_container"]/div/div[2]/img[1]').get_attribute('src')
slider = self.driver.find_element_by_xpath('//*[@id="captcha_container"]/div/div[2]/img[2]').get_attribute('src')
 
request.urlretrieve(bg, os.getcwd() + '/bg.png')
request.urlretrieve(slider, os.getcwd() + '/slider.png')
 
 
# 获取两个滑块偏移量方法
def getGap(self, sliderImage, bgImage):
 '''
 Get the gap distance
 :param sliderImage: the image of slider
 :param bgImage: the image of background
 :return: int
 '''
 bgImageInfo = cv2.imread(bgImage, 0)
 bgWidth, bgHeight = bgImageInfo.shape[::-1]
 bgRgb = cv2.imread(bgImage)
 bgGray = cv2.cvtColor(bgRgb, cv2.COLOR_BGR2GRAY)
 
 slider = cv2.imread(sliderImage, 0)
 sliderWidth, sliderHeight = slider.shape[::-1]
 
 res = cv2.matchTemplate(bgGray, slider, cv2.TM_CCOEFF)
 a, b, c, d = cv2.minMaxLoc(res)
 # print(a,b,c,d)
 # 正常如下即可
 # return c[0] if abs(a) >= abs(b) else d[0]
 # 但是头条显示验证码的框跟验证码本身的像素不一致，所以需要根据比例计算
 if abs(a) >= abs(b):
  return c[0] * bgWidth / (bgWidth - sliderWidth)
 else:
  return d[0] * bgWidth / (bgWidth - sliderWidth)
 
# 移动路径方法
def getTrack(self, distance):
 '''
 Get the track by the distance
 :param distance: the distance of gap
 :return: list
 '''
 # 移动轨迹
 track = []
 # 当前位移
 current = 0
 # 减速阈值
 mid = distance * 4 / 5
 # 计算间隔
 t = 0.2
 # 初速度
 v = 0
 
 while current < distance:
  if current < mid:
# 加速度为正2
a = 2
  else:
# 加速度为负3
a = -3
  # 初速度v0
  v0 = v
  # 当前速度v = v0 + at
  v = v0 + a * t
  # 移动距离x = v0t + 1/2 * a * t^2
  move = v0 * t + 1 / 2 * a * t * t
  # 当前位移
  current += move
  # 加入轨迹
  track.append(round(move))
 return track
 
 
# 滑动到缺口位置
def moveToGap(self, track):
 '''
 Drag the mouse to gap
 :param track: the track of mouse
 :return: None
 '''
 ActionChains(self.driver).click_and_hold(self.driver.find_element_by_xpath('//*[@id="captcha_container"]/div/div[3]/div[2]/div[2]/div')).perform()
 while track:
  x = random.choice(track)
  ActionChains(self.driver).move_by_offset(xoffset=x, yoffset=0).perform()
  track.remove(x)
 time.sleep(0.5)
 ActionChains(self.driver).release().perform()

img = cv2.imread('yzm.png')
# 将图片灰度化处理,降维,加权进行灰度化c
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cv2.imshow('min_gray',gray)
 
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
 
img = cv2.imread('yzm.png')
# 将图片灰度化处理,降维,加权进行灰度化c
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
 
t,gray2 = cv2.threshold(gray,220,255,cv2.THRESH_BINARY)
 
cv2.imshow('threshold',gray2)
 
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2
 
img = cv2.imread('yzm.png')
# 将图片灰度化处理,降维,加权进行灰度化c
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
 
t,gray2 = cv2.threshold(gray,220,255,cv2.THRESH_BINARY)
 
def remove_noise(img, k=4):
 img2 = img.copy()
 
 #  img处理数据，k过滤条件
 w, h = img2.shape
 
 def get_neighbors(img3, r, c):
  count = 0
  for i in [r - 1, r, r + 1]:
for j in [c - 1, c, c + 1]:
 if img3[i, j] > 10:  # 纯白色
  count += 1
  return count
 
 #  两层for循环判断所有的点
 for x in range(w):
  for y in range(h):
if x == 0 or y == 0 or x == w - 1 or y == h - 1:
 img2[x, y] = 255
else:
 n = get_neighbors(img2, x, y)  # 获取邻居数量，纯白色的邻居
 if n > k:
  img2[x, y] = 255
 return img2
 
 
result = remove_noise(gray2)
cv2.imshow('8neighbors', result)
 
cv2.waitKey(0)
cv2.destroyAllWindows()

#识别降噪后的图片
code = tesserocr.image_to_text(nrImg)
 
#消除空白字符
code.strip()
 
#打印
print(code)