pythonselenium验证码识别 selenium 识别验证码

一、简介

初次开始使用Selenium，于是开始用登录作为联手项目，是一个真实的登录界面，包含验证码（验证码是难点，获取与识别），以下会讲的很详细，我怕我下次又忘记了
我采用Selenium + python + 百度云OCR，安装等教程自行百度，我就不多说了

二、selenium打开浏览器获取相应el

from selenium import webdriver
from selenium.webdriver.common.by import By
import base64
#注册Chrome驱动,打开浏览器网址
driver = webdriver.Chrome()
url = "************"
driver.get(url)
#获取相应el
driver.find_element(By.NAME,"***").send_keys('***')
driver.find_element(By.NAME, "***").send_keys('***')
#获取图片
png = driver.find_element(By.CSS_SELECTOR,"****")
img_url = png.get_attribute("src")  # 获得图片的url，本次测试的是bs64生成，采用解码获得图片
head, context = img_url.split(",")  # 截取后字段的bas64编码
img_data = base64.b64decode(context)
image = Image.open(BytesIO(img_data));
image.save("capt.png")

这段代码主要是利用selenium打开网站，获取到相应的dom，find_element_by_id等写法已经统一换为find_element(By.ID, “***”)等了，先将图片保存下来考研方便后续的识别

三、验证码预处理

验证码一般无法直接识别，我们需要通过灰度、二值、降噪等手段，使得图片更加清晰，提高图片的识别准确率
下列代码需要提前引入以下包

from PIL import Image

1.灰度化

img = img.convert('L')  # P模式转换为L模式(灰度模式默认阈值127)

2.二值化

count = 230  # 设定阈值，阈值的大小由图片的清晰度等自行调节
table = []
for i in range(256):
    if i < count:
        table.append(0)
    else:
        table.append(1)
img = img.point(table, '1')
img.save('captcha.png')

3.降噪

pixdata = img.load()
w,h = img.size
for y in range(1,h-1):
   for x in range(1,w-1):
        count = 0
        if pixdata[x,y-1] > 245:
            count = count + 1
        if pixdata[x,y+1] > 245:
            count = count + 1
        if pixdata[x-1,y] > 245:
            count = count + 1
        if pixdata[x+1,y] > 245:
            count = count + 1
        if count > 2:
            pixdata[x,y] = 255
return img

四、百度云识别

需要提前注册

from aip import AipOcr
# 识别码
APP_ID = '****'
API_KEY = '****'
SECRET_KEY = '****'
# 初始化对象
client = AipOcr(APP_ID, API_KEY, SECRET_KEY)
# 读取图片
def get_file_content(file_path):
    with open(file_path, 'rb') as f:
        return f.read()

image = get_file_content('end.png') # 传入图片
# 定义参数变量
options = {'language_type': 'ENG', }  # 识别语言类型，默认为'CHN_ENG'中英文混合
#  调用通用文字识别
result = client.basicGeneral(image, options)  # 高精度接口 basicAccurate
for word in result['words_result']:
    captcha = (word['words'])

print('识别结果：' + captcha)
return captcha

五、完整代码

import base64
from io import BytesIO
from selenium import webdriver
from selenium.webdriver.common.by import By
from PIL import Image
from time import sleep
from aip import AipOcr

# 对二值化的图像进行降噪处理，使得识别更加准确
def depoint(img):
    pixdata = img.load()
    w,h = img.size
    for y in range(1,h-1):
        for x in range(1,w-1):
            count = 0
            if pixdata[x,y-1] > 245:
                count = count + 1
            if pixdata[x,y+1] > 245:
                count = count + 1
            if pixdata[x-1,y] > 245:
                count = count + 1
            if pixdata[x+1,y] > 245:
                count = count + 1
            if count > 2:
                pixdata[x,y] = 255
    return img

# 获得验证码，并进行处理，二值化
def deal_image():
    png = driver.find_element(By.CSS_SELECTOR,".*******")
    img_url = png.get_attribute("src")  # 获得图片的url，本次测试的是bs64生成，采用解码获得图片
    head, context = img_url.split(",")  # 截取后字段的bas64编码
    img_data = base64.b64decode(context)
    image = Image.open(BytesIO(img_data));
    image.save("capt.png")

    img = Image.open('capt.png')
    img = img.convert('L')  # P模式转换为L模式(灰度模式默认阈值127)
    count = 230  # 设定阈值，阈值的大小由图片的清晰度等自行调节
    table = []
    for i in range(256):
        if i < count:
            table.append(0)
        else:
            table.append(1)

    # print(table)

    img = img.point(table, '1')
    img.save('captcha.png')

# 调用百度云OCR API 进行识别
def discern_captcha():
    # 识别码
    APP_ID = '****'
    API_KEY = '****'
    SECRET_KEY = '****'
    # 初始化对象
    client = AipOcr(APP_ID, API_KEY, SECRET_KEY)
    # 读取图片
    def get_file_content(file_path):
        with open(file_path, 'rb') as f:
            return f.read()

    image = get_file_content('end.png') # 传入图片
    # 定义参数变量
    options = {'language_type': 'ENG', }  # 识别语言类型，默认为'CHN_ENG'中英文混合
    #  调用通用文字识别
    result = client.basicGeneral(image, options)  # 高精度接口 basicAccurate
    for word in result['words_result']:
        captcha = (word['words'])

    print('识别结果：' + captcha)
    return captcha

# 登录事件，若识别不准确需点击第二次重复
def ClickRandomCode():
    deal_image()
    image = depoint(Image.open('captcha.png'))
    image.save('end.png')
    image = depoint(Image.open('end.png'))
    image.save("end.png")
    randomCode = discern_captcha()
    driver.find_element(By.NAME, "randomCode").send_keys(randomCode)
    driver.find_element(By.CSS_SELECTOR, ".card-z .el-button").click()
    sleep(2) # 错误或成功信息有延迟
    try:
        text = driver.find_element(By.CSS_SELECTOR, ".el-message__content").text
        if (text == "验证码错误"):
            driver.find_element(By.CSS_SELECTOR, ".verification-code > img").click()
            driver.find_element(By.CSS_SELECTOR, ".verification-code > img").clear()
            ClickRandomCode() # 递归调用直至正确
            print("验证码错误")
        else:
            print("登录成功")
    except:
        print("ok")

def login(***,***):
    driver.find_element(By.NAME,"****").send_keys(***)
    driver.find_element(By.NAME, "***").send_keys(***)
    ClickRandomCode()

if __name__ == '__main__':
    driver = webdriver.Chrome()
    url = "*****"
    driver.get(url)
    login("****","****")