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【Python】爬虫：图形验证码识别（一）

一， tesseract 安装，以及相关库安装

对于安装的过程，百度一下就会有很多，这里就记录下自己安装时觉得还有注意的几点

二， demo

使用的验证码是从中国知网下载的验证码，先下载了100张验证码。

import requestsfrom lxml import etreeimport timeimport osrootPath = 'e:\\yanzheng'URL = 'http://my.cnki.net/elibregister/commonRegister.aspx#'headers = {        'User-Agent':'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.103 Safari/537.36'    }def download_img(num):    filename = os.path.join(rootPath,str(num)+'.png')    response = requests.get(URL,headers = headers)    if response.status_code == 200:        content = response.text        html = etree.HTML(content)        img_url = 'http://my.cnki.net/elibregister/' + html.xpath('//*[@id="checkcode"]/@src')[0]        html_img = requests.get(img_url)        with open(filename, 'wb') as fd:            fd.write(html_img.content) if __name__ == '__main__':    for i in range(1,101):        download_img(i)        time.sleep(3)

下载后，人工对每张验证码进行标注，将结果保存为文件名。

def binarizing(img,threshold): #二值化。i参数 灰度图，阈值    pixdata = img.load()    w, h = img.size    for y in range(h):        for x in range(w):            if pixdata[x, y] < threshold:                pixdata[x, y] = 0            else:                pixdata[x, y] = 255    return img    img_path = 'E:\\yanzheng\\3gA2.png'image = Image.open(img_path)  # 打开图片文件imgry = image.convert('L')  # 转化为灰度图img = binarizing(imgry,130)       #二值化# img = cut_noise(img)text = tesserocr.image_to_text(img)      # tesserocr.识别图片内容result = re.findall(r'[\w]+',text)         #正则匹配验证码。img.show()print(result)

接下来一口气识别100张，看下正确率能达到多少？验证码一般可以忽略大小写，这里也设置忽略大小写。

def binarizing(img,threshold): # #二值化。i参数 灰度图，阈值    pixdata = img.load()    w, h = img.size    for y in range(h):        for x in range(w):            if pixdata[x, y] < threshold:                pixdata[x, y] = 0            else:                pixdata[x, y] = 255    return imgdef Compute_Correct_Rate(Correct,Recognition):  #计算正确率，打印识别错误的    Correct_num = 0    for i in range(len(Correct)):        if Correct[i].lower() == Recognition[i].lower():            Correct_num+=1        else:            print('{} is Wrong,result is {}'.format(Recognition[i],Correct[i]))    print('the Correct Rate is ',Correct_num/len(Correct))def process_img():    Correct_result = []    Recognition_result = []    for root,dirs,files in os.walk(rootPath):    #遍历整个文件夹，识别所有图片        for file in  files:            filepath = os.path.join(rootPath, file)            image = Image.open(filepath)  # 打开图片文件            imgry = image.convert('L')  # 转化为灰度图            img = binarizing(imgry, 130)            text = tesserocr.image_to_text(img)            match =  re.findall(r'[\w]+', text)            if len(match) != 0:                r_result = ''.join(match)            else:                r_result = ''            c_result = os.path.splitext(file)[0]            Correct_result.append(c_result)            Recognition_result.append(r_result)    Compute_Correct_Rate(Correct_result, Recognition_result)if __name__ == '__main__':    process_img()

识别正确率有41%

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