作为经典数据收集的一项任务，它转移到一个简单的MNIST问题的解决方案。或我如何解析CEC网站

一个工作日的晚上，我老板遇到了一个有趣的问题。一个链接到达与文本：“我想从这里得到的一切，但有细微差别。” 2小时后，请告诉我您对解决问题有什么想法。时间是16:00。



本文将介绍这一细微差别。



我像往常一样开始硒，然后首先单击带有table斯坦共和国选举结果的所需表格所在的链接，然后崩溃





如您所知，细微差别在于，每次单击链接后，都会显示一个验证码。



通过分析站点的结构，发现链接数达到了约3万个。



我别无选择，只能在互联网上找到识别验证码的方法。找到一个服务



+验证码会像一个人一样被100％

识别-平均识别时间为9秒，这很长，因为我们有大约3万个不同的链接，我们需要关注并识别验证码。



我立即放弃了这个想法。经过几次尝试获取验证码后，我注意到它并没有太大变化，绿色背景上的所有黑色数字均相同。



而且，由于我一直想用手触摸“视觉计算机”，因此我决定有机会亲自尝试每个人最喜欢的MNIST问题。



已经是17:00了，我开始寻找经过预训练的模型以识别数字。在此验证码上检查了它们之后，准确性令我不满意-好的，是时候收集图片并训练您的神经网络了。



首先，您需要收集培训样本。



我打开Chrome网络驱动程序，并在文件夹中显示1000个验证码。

from selenium import webdriver
i = 1000
driver = webdriver.Chrome('/Users/aleksejkudrasov/Downloads/chromedriver')
while i>0:
    driver.get('http://www.vybory.izbirkom.ru/region/izbirkom?action=show&vrn=4274007421995&region=27&prver=0&pronetvd=0')
    time.sleep(0.5)
    with open(str(i)+'.png', 'wb') as file:
        file.write(driver.find_element_by_xpath('//*[@id="captchaImg"]').screenshot_as_png)
    i = i - 1

由于我们只有两种颜色，因此我将验证码转换为bw：

from operator import itemgetter, attrgetter
from PIL import Image
import glob
list_img = glob.glob('path/*.png')

for img in list_img:
    im = Image.open(img)
    im = im.convert("P")
    im2 = Image.new("P",im.size,255)

    im = im.convert("P")

    temp = {}
#        
    for x in range(im.size[1]):
        for y in range(im.size[0]):
            pix = im.getpixel((y,x))
            temp[pix] = pix
            if pix != 0: 
                im2.putpixel((y,x),0)

    im2.save(img)

现在我们需要将验证码切成数字并将其转换为10 * 10的单个大小。

首先，我们将验证码切成数字，然后，由于验证码沿OY轴移动，因此我们需要裁剪所有不必要的内容并将图像旋转90°。

def crop(im2):
    inletter = False
    foundletter = False
    start = 0
    end = 0
    count = 0
    letters = []
    name_slise=0
    for y in range(im2.size[0]): 
        for x in range(im2.size[1]): 
            pix = im2.getpixel((y,x))
            if pix != 255:
                inletter = True
#       OX
        if foundletter == False and inletter == True: 
            foundletter = True
            start = y
#       OX 
        if foundletter == True and inletter == False: 
            foundletter = False
            end = y
            letters.append((start,end))

        inletter = False

    for letter in letters:
#   
        im3 = im2.crop(( letter[0] , 0, letter[1],im2.size[1] )) 
#  90°
        im3 = im3.transpose(Image.ROTATE_90) 

        letters1 = []
#  
        for y in range(im3.size[0]): # slice across
            for x in range(im3.size[1]): # slice down
                pix = im3.getpixel((y,x))
                if pix != 255:
                    inletter = True
            if foundletter == False and inletter == True:
                foundletter = True
                start = y

            if foundletter == True and inletter == False:
                foundletter = False
                end = y
                letters1.append((start,end))

            inletter=False

        for letter in letters1:
#  
            im4 = im3.crop(( letter[0] , 0, letter[1],im3.size[1] )) 
#     
        im4 = im4.transpose(Image.ROTATE_270) 
        resized_img = im4.resize((10, 10), Image.ANTIALIAS)
        resized_img.save(path+name_slise+'.png')
        name_slise+=1

我想：“已经到了18：00，是时候解决这个问题了。”我想着，把文件夹中的数字和数字一路散开了。



我们声明一个简单的模型，该模型接受我们图片的扩展矩阵作为输入。



为此，请创建100个神经元的输入层，因为图片的大小为10 * 10。作为输出层，有10个神经元，每个神经元对应于0到9的数字。

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, Activation, BatchNormalization, AveragePooling2D
from tensorflow.keras.optimizers import SGD, RMSprop, Adam
def mnist_make_model(image_w: int, image_h: int):
    # Neural network model
    model = Sequential()
    model.add(Dense(image_w*image_h, activation='relu', input_shape=(image_h*image_h)))
    model.add(Dense(10, activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer=RMSprop(), metrics=['accuracy'])
    return model

我们将数据分为训练集和测试集：

list_folder = ['0','1','2','3','4','5','6','7','8','9']
X_Digit = []
y_digit = []
for folder in list_folder:
    for name in glob.glob('path'+folder+'/*.png'):
        im2 = Image.open(name)
        X_Digit.append(np.array(im2))
        y_digit.append(folder)

我们将其分为训练和测试集：

from sklearn.model_selection import train_test_split

X_Digit = np.array(X_Digit) 
y_digit = np.array(y_digit)
X_train, X_test, y_train, y_test = train_test_split(X_Digit, y_digit, test_size=0.15, random_state=42)
train_data = X_train.reshape(X_train.shape[0], 10*10) #    100
test_data = X_test.reshape(X_test.shape[0], 10*10) #    100
#      10
num_classes = 10
train_labels_cat = keras.utils.to_categorical(y_train, num_classes)
test_labels_cat = keras.utils.to_categorical(y_test, num_classes)

我们训练模型。



凭经验选择时期数和“批量”大小的参数：

model = mnist_make_model(10,10)
model.fit(train_data, train_labels_cat, epochs=20, batch_size=32, verbose=1, validation_data=(test_data, test_labels_cat))

我们保存权重：

model.save_weights("model.h5")

在第11个时代，精度非常好：精度= 1.0000。满意的是，我将于19:00回家休息，明天我仍然需要编写一个解析器来从CEC网站上收集信息。



第二天早上。



问题仍然很小，它仍然需要浏览CEC网站上的所有页面并获取数据：



加载经过训练的模型的权重：

model = mnist_make_model(10,10)
model.load_weights('model.h5')

我们编写一个函数来保存验证码：

def get_captcha(driver):
    with open('snt.png', 'wb') as file:
        file.write(driver.find_element_by_xpath('//*[@id="captchaImg"]').screenshot_as_png)
    im2 = Image.open('path/snt.png')
    return im2

让我们编写一个验证码预测功能：

def crop_predict(im):
    list_cap = []
    im = im.convert("P")
    im2 = Image.new("P",im.size,255)

    im = im.convert("P")

    temp = {}

    for x in range(im.size[1]):
        for y in range(im.size[0]):
            pix = im.getpixel((y,x))
            temp[pix] = pix
            if pix != 0:
                im2.putpixel((y,x),0)
    

    inletter = False
    foundletter=False
    start = 0
    end = 0
    count = 0
    letters = []
    for y in range(im2.size[0]): 
        for x in range(im2.size[1]): 
            pix = im2.getpixel((y,x))
            if pix != 255:
                inletter = True
        if foundletter == False and inletter == True:
            foundletter = True
            start = y

        if foundletter == True and inletter == False:
            foundletter = False
            end = y
            letters.append((start,end))

        inletter=False

    for letter in letters:
        im3 = im2.crop(( letter[0] , 0, letter[1],im2.size[1] ))
        im3 = im3.transpose(Image.ROTATE_90)

        letters1 = []

        for y in range(im3.size[0]):
            for x in range(im3.size[1]):
                pix = im3.getpixel((y,x))
                if pix != 255:
                    inletter = True
            if foundletter == False and inletter == True:
                foundletter = True
                start = y

            if foundletter == True and inletter == False:
                foundletter = False
                end = y
                letters1.append((start,end))

            inletter=False

        for letter in letters1:
            im4 = im3.crop(( letter[0] , 0, letter[1],im3.size[1] ))
        im4 = im4.transpose(Image.ROTATE_270)
        resized_img = im4.resize((10, 10), Image.ANTIALIAS)
        img_arr = np.array(resized_img)/255
        img_arr = img_arr.reshape((1, 10*10))
        list_cap.append(model.predict_classes([img_arr])[0])
    return ''.join([str(elem) for elem in list_cap])

添加一个下载表格的功能：

def get_table(driver):
    html = driver.page_source #   
    soup = BeautifulSoup(html, 'html.parser') #  " "
    table_result = [] #       
    tbody = soup.find_all('tbody') #   
    list_tr = tbody[1].find_all('tr') #   
    ful_name = list_tr[0].text #  
    for table in list_tr[3].find_all('table'): #   
        if len(table.find_all('tr'))>5: #  
            for tr in table.find_all('tr'): #   
                snt_tr = []#  
                for td in tr.find_all('td'):
                    snt_tr.append(td.text.strip())#    
                table_result.append(snt_tr)# 
    return (ful_name, pd.DataFrame(table_result, columns = ['index', 'name','count']))

我们收集了9月13日的所有链接：

df_table = []
driver.get('http://www.vybory.izbirkom.ru')
driver.find_element_by_xpath('/html/body/table[2]/tbody/tr[2]/td/center/table/tbody/tr[2]/td/div/table/tbody/tr[3]/td[3]').click()
html = driver.page_source
soup = BeautifulSoup(html, 'html.parser')
list_a = soup.find_all('table')[1].find_all('a')
for a in list_a:
    name = a.text
    link = a['href']
    df_table.append([name,link])
df_table = pd.DataFrame(df_table, columns = ['name','link'])

在13:00之前，我将遍历所有页面完成编写代码：

result_df = []
for index, line in df_table.iterrows():#     
    driver.get(line['link'])# 
    time.sleep(0.6)
    try:#    
        captcha = crop(get_captcha(driver))
        driver.find_element_by_xpath('//*[@id="captcha"]').send_keys(captcha)
        driver.find_element_by_xpath('//*[@id="send"]').click()
        time.sleep(0.6)
        true_cap(driver)
    except NoSuchElementException:#     
        pass
    html = driver.page_source
    soup = BeautifulSoup(html, 'html.parser')
    if soup.find('select') is None:#      
        time.sleep(0.6)
        html = driver.page_source
        soup = BeautifulSoup(html, 'html.parser')          
        for i in range(len(soup.find_all('tr'))):#    
            if '\n \n' == soup.find_all('tr')[i].text:# ,          
                rez_link = soup.find_all('tr')[i+1].find('a')['href']
        driver.get(rez_link)
        time.sleep(0.6)
        try:
            captcha = crop(get_captcha(driver))
            driver.find_element_by_xpath('//*[@id="captcha"]').send_keys(captcha)
            driver.find_element_by_xpath('//*[@id="send"]').click()
            time.sleep(0.6)
            true_cap(driver)
        except NoSuchElementException:
            pass
        ful_name , table = get_table(driver)# 
        head_name = line['name']
        child_name = ''
        result_df.append([line['name'],line['link'],rez_link,head_name,child_name,ful_name,table])
    else:#   ,   
        options = soup.find('select').find_all('option')
        for option in options:
            if option.text == '---':#     
                continue
            else:
                link = option['value']
                head_name = option.text
                driver.get(link)
                try:
                    time.sleep(0.6)
                    captcha = crop(get_captcha(driver))
                    driver.find_element_by_xpath('//*[@id="captcha"]').send_keys(captcha)
                    driver.find_element_by_xpath('//*[@id="send"]').click()
                    time.sleep(0.6)
                    true_cap(driver)
                except NoSuchElementException:
                    pass
                html2 = driver.page_source
                second_soup = BeautifulSoup(html2, 'html.parser')
                for i in range(len(second_soup.find_all('tr'))):
                    if '\n \n' == second_soup.find_all('tr')[i].text:
                        rez_link = second_soup.find_all('tr')[i+1].find('a')['href']
                driver.get(rez_link)
                try:
                    time.sleep(0.6)
                    captcha = crop(get_captcha(driver))
                    driver.find_element_by_xpath('//*[@id="captcha"]').send_keys(captcha)
                    driver.find_element_by_xpath('//*[@id="send"]').click()
                    time.sleep(0.6)
                    true_cap(driver)
                except NoSuchElementException:
                    pass
                ful_name , table = get_table(driver)
                child_name = ''
                result_df.append([line['name'],line['link'],rez_link,head_name,child_name,ful_name,table])
                if second_soup.find('select') is None:
                    continue
                else:
                    options_2 = second_soup.find('select').find_all('option')
                    for option_2 in options_2:
                        if option_2.text == '---':
                            continue
                        else:
                            link_2 = option_2['value']
                            child_name = option_2.text
                            driver.get(link_2)
                            try:
                                time.sleep(0.6)
                                captcha = crop(get_captcha(driver))
                                driver.find_element_by_xpath('//*[@id="captcha"]').send_keys(captcha)
                                driver.find_element_by_xpath('//*[@id="send"]').click()
                                time.sleep(0.6)
                                true_cap(driver)
                            except NoSuchElementException:
                                pass
                            html3 = driver.page_source
                            thrid_soup = BeautifulSoup(html3, 'html.parser')
                            for i in range(len(thrid_soup.find_all('tr'))):
                                if '\n \n' == thrid_soup.find_all('tr')[i].text:
                                    rez_link = thrid_soup.find_all('tr')[i+1].find('a')['href']
                            driver.get(rez_link)
                            try:
                                time.sleep(0.6)
                                captcha = crop(get_captcha(driver))
                                driver.find_element_by_xpath('//*[@id="captcha"]').send_keys(captcha)
                                driver.find_element_by_xpath('//*[@id="send"]').click()
                                time.sleep(0.6)
                                true_cap(driver)
                            except NoSuchElementException:
                                pass
                            ful_name , table = get_table(driver)
                            result_df.append([line['name'],line['link'],rez_link,head_name,child_name,ful_name,table])

然后是改变我生活的推文