Python Selenium、PIL、pytesser 识别验证码

思路：

1. 使用Selenium库把带有验证码的页面截取下来
2. 利用验证码的xpath截取该页面的验证码
3. 对验证码图片进行降噪、二值化、灰度化处理后再使用pytesser识别
4. 使用固定的账户密码对比验证码正确或错误的关键字判断识别率

1. 截取验证码

def cutcode(url,brower,vcodeimgxpath):  #裁剪验证码
 
    picName = url.replace(url,"capture.png")   #改为.png后缀保存图片
    brower.get(url)
    brower.maximize_window()                     #放大
    brower.save_screenshot(picName)              #截取网页
 
    imgelement = brower.find_element_by_xpath(vcodeimgxpath)  # 通过xpath定位验证码
    location = imgelement.location  # 获取验证码的x,y轴
    size = imgelement.size  # 获取验证码的长宽
    rangle = (int(location['x']), \
              int(location['y']), \
              int(location['x'] + size['width']), \
              int(location['y'] + size['height']))  # 写成我们需要截取的位置坐标
    i = Image.open(os.getcwd()+r'\capture.png')  # 打开截图
    verifycodeimage = i.crop(rangle)  # 使用Image的crop函数，从截图中再次截取我们需要的区域
    verifycodeimage.save(os.getcwd()+r'\verifycodeimage.png')
    return brower

2.  对验证码图片进行降噪、二值化、灰度化处理并识别

def initTable(threshold=140):   #降噪，图片二值化
    table = []
    for i in range(256):
        if i < threshold:
            table.append(0)
        else:
            table.append(1)
 
    return table
 
def recode():
    image=Image.open(os.getcwd()+r'\verifycodeimage.png')
    image = image.convert('L')  #彩色图转换为灰度图
 
    binaryImage = image.point(initTable(), '')  #将灰度图二值化
 
    time.sleep(1)  
 
    vcode=image_to_string(binaryImage) #使用image_to_string识别验证码
    vcode = vcode.strip()
    return vcode

3. 通过点击登录按钮返回的信息判断验证码是否识别正确

def login(vcode,brower,usernamexpath,passwordxpath,vcodexpath,submitxpath,username,password):
 
    brower.find_element_by_xpath(usernamexpath).send_keys(username)
 
    brower.find_element_by_xpath(passwordxpath).send_keys(password)
 
    # 对文本框输入验证码值
    brower.find_element_by_xpath(vcodexpath).send_keys(vcode)
 
    time.sleep(1)
    # 点击登录，sleep防止没输入就点击了登录
    brower.find_element_by_xpath(submitxpath).click()
 
    # 等待页面加载出来
    time.sleep(1)
 
    result = brower.page_source  #获取页面的html
    return result

4. 接收识别验证码需要的参数，循环识别验证码

def main():
    file_path = raw_input("param.txt path:")
    username = raw_input("username(default 'admin'):")
    password = raw_input("password(default '123456'):")
    codeerror = raw_input("vcode error key word in html(default '验证码错误'):")
    passerror = raw_input("vcode pass key word in html(default '密码错误'):")
    frequency = raw_input("How many time(default '100'):")
    vcodelen = raw_input("How many characters(default '4'):")
    remod = raw_input("choose remod(default:en+num，1:num，2:en):")
 
    starttime = datetime.datetime.now()
    txt = open(file_path)    #txt中需要的参数：url usernamexpath passwordxpath vcode_input_xpath vcode_image_xpath submit_xpath
    lines = txt.readlines()
    url = lines[0].split("=",1)[1]
    usernamexpath = lines[1].split("=",1)[1]
    passwordxpath = lines[2].split("=",1)[1]
    vcodexpath = lines[3].split("=",1)[1]
    vcodeimgxpath = lines[4].split("=",1)[1]
    submitxpath = lines[5].split("=",1)[1]
 
    brower = webdriver.PhantomJS(executable_path=r'D:\Python27\PY\phantomjs-2.1.1-windows\bin\phantomjs.exe') #打开phantomjs.exe
    if username == '':
        username = "admin"
    if password == '':
        password = ''
    if codeerror == '':
        codeerror = u"验证码错误"  #验证码错误时的关键字
    else:
        codeerror = codeerror.decode(sys.stdin.encoding)  #识别为Unicode自动转换
    if passerror == '':
        passerror = u"密码错误"  #验证码正确时的关键字
    else:
        passerror = passerror.decode(sys.stdin.encoding)  #识别为Unicode自动转换
    if vcodelen == '':
        vcodelen = 4
    else:
        vcodelen = int(vcodelen)
    if remod == '':
        remod = '^[0-9]+$'
    elif remod == '':
        remod = '^[A-Za-z]+$'
    else:
        remod = '^[A-Za-z0-9]+$'
 
    counterror = 0
    countture = 0
    if frequency == '':
        frequency = 100
    else:
        frequency = int(frequency)
    a = 0
    while a < frequency:
        brower = cutcode(url,brower,vcodeimgxpath)
        vcode = recode()
        if len(vcode) != vcodelen:  #识别到的验证码长度不为4直接重新循环
            continue
        if re.match(remod,vcode):   #判断识别到的验证码是否只有字母加数字
            result = login(vcode,brower,usernamexpath,passwordxpath,vcodexpath,submitxpath,username,password)
            if codeerror in result:
                print "[-]验证码错误"+vcode
                counterror += 1
            elif passerror in result:
                print "[+]验证码正确"+vcode
                countture += 1
            else:
                continue
        else:
            continue
        a += 1
 
    os.remove(os.getcwd()+r'\verifycodeimage.png')
    os.remove(os.getcwd()+r'\capture.png')
    brower.close()         #关闭浏览器
 
    #把数字转换为str再print
    rat = str('%.3f%%' % (countture/frequency*100))
    countture = bytes(countture)
    counterror = bytes(counterror)
    endtime = datetime.datetime.now()
    runtime = str((endtime-starttime).seconds/3600*60)
    print "[+]验证码正确次数："+countture
    print "[-]验证码错误次数："+counterror
    print "[+]识别率："+rat
    print "运行时间："+runtime+"min"
 
if __name__ == '__main__':
    main()

　　这种方法识别验证码的效率比较低，但是因为写这个代码要识别的网站的验证码url打开时空白、空白的！然后想到这种方法虽然是效率比较低，但是适用性还是较广的，毕竟可以模拟人为操作浏览器。

　　然后有个缺点就是识别全数字的验证码正确率奇低==因为处理完验证码图片后数字就会变得有缺失==

　　如果说运行的过程中xpath的value出现问题了，有可能是网页还没加载出来就已经被截图了（xpath直接在网页上右键检查元素，然后再那个html代码里右键复制xpath就好了）

param.txt的demo（=与路径中间不要有空格！！）：

url =
username_xpath =//*[@id="txtUserName"]
password_xpath =//*[@id="txtPassword"]
vcode_input_xpath =//*[@id="txtValCode"]
vcode_image_xpath =//*[@id="imgVerify"]
submit_xpath =//*[@id="Button1"]

一开始写这个打算识别的目标站，只有57识别率==然后效率很低==毕竟不用自己写算法识别什么的。代码的排布什么的也挺烂的，不要介意啦==：