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('class="lazy" data-src')
slider = self.driver.find_element_by_xpath('//*[@id="captcha_container"]/div/div[2]/img[2]').get_attribute('class="lazy" data-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)