这篇文章主要介绍如何使用Tensorflow2识别验证码,文中介绍的非常详细,具有一定的参考价值,感兴趣的小伙伴们一定要看完!验证码是根据随机字符生成一幅图片,然后在图片中加入干扰象素,用户必须手动填入,防止有人利用机器人自动批量注册、灌水
这篇文章主要介绍如何使用Tensorflow2识别验证码,文中介绍的非常详细,具有一定的参考价值,感兴趣的小伙伴们一定要看完!
验证码是根据随机字符生成一幅图片,然后在图片中加入干扰象素,用户必须手动填入,防止有人利用机器人自动批量注册、灌水、发垃圾广告等等 。
图片是5个字母的单词,可以包含数字。这些图像应用了噪声(模糊和一条线)。它们是200 x 50 PNG。我们的任务是尝试制作光学字符识别算法的模型。
在数据集中存在的验证码png图片,对应的标签就是图片的名字。
import osimport numpy as npimport pandas as pdimport cv2import matplotlib.pyplot as pltimport seaborn as sns# imgaug 图片数据增强import imgaug.augmenters as iaaimport tensorflow as tf# Conv2D MaxPooling2D Dropout Flatten Dense BN GAPfrom tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Layer, BatchNORMalization, GlobalAveragePooling2D from tensorflow.keras.optimizers import Adamfrom tensorflow.keras import Model, Input from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau# 图片处理器from tensorflow.keras.preprocessing.image import ImageDataGeneratorimport plotly.express as pximport plotly.graph_objects as Goimport plotly.offline as pyopyo.init_notebook_mode()
对数据进行一个简单的分析,统计图像中大约出现了什么样的符号。
# 数据路径DIR = '../input/captcha-version-2-images/samples/samples'# 存储验证码的标签captcha_list = []characters = {}for captcha in os.listdir(DIR): captcha_list.append(captcha) # 每张验证码的captcha_code captcha_code = captcha.split(".")[0] for i in captcha_code: # 遍历captcha_code characters[i] = characters.get(i, 0) +1symbols = list(characters.keys())len_symbols = len(symbols)print(f'图像中只使用了{len_symbols}符号')plt.bar(*zip(*characters.items()))plt.title('Frequency of symbols')plt.show()
如何提取图像的数据建立X,y??
# 如何提取图像 建立 model X 的shape 1070 * 50 * 200 * 1 # y的shape 5 * 1070 * 19 for i, captcha in enumerate(captcha_list): captcha_code = captcha.split('.')[0] # cv2.IMREAD_GRAYSCALE 灰度图 captcha_cv2 = cv2.imread(os.path.join(DIR, captcha),cv2.IMREAD_GRAYSCALE) # 缩放 captcha_cv2 = captcha_cv2 / 255.0 # print(captcha_cv2.shape) (50, 200) # 将captcha_cv2的(50, 200) 切换成(50, 200, 1) captcha_cv2 = np.reshape(captcha_cv2, img_shape) # (5,19) targs = np.zeros((len_captcha, len_symbols)) for a, b in enumerate(captcha_code): targs[a, symbols.index(b)] = 1 X[i] = captcha_cv2 y[:, i] = targsprint("shape of X:", X.shape)print("shape of y:", y.shape)
输出如下
print("shape of X:", X.shape)
print("shape of y:", y.shape)
通过Numpy中random 随机选择数据,划分训练集和测试集
# 生成随机数from numpy.random import default_rngrng = default_rng(seed=1)test_numbers = rng.choice(1070, size=int(1070*0.3), replace=False)X_test = X[test_numbers]X_full = np.delete(X, test_numbers,0)y_test = y[:,test_numbers]y_full = np.delete(y, test_numbers,1)val_numbers = rng.choice(int(1070*0.7), size=int(1070*0.3), replace=False)X_val = X_full[val_numbers]X_train = np.delete(X_full, val_numbers,0)y_val = y_full[:,val_numbers]y_train = np.delete(y_full, val_numbers,1)
在此验证码数据中,容易出现过拟合的现象,你可能会想到添加更多的新数据、 添加正则项等, 但这里使用数据增强的方法,特别是对于机器视觉的任务,数据增强技术尤为重要。
常用的数据增强操作:imgaug
库。imgaug是提供了各种图像增强操作的python库 https://GitHub.com/aleju/imgaug
。
imgaug几乎包含了所有主流的数据增强的图像处理操作, 增强方法详见github
# Sequential(C, R) 尺寸增加了5倍,# 选取一系列子增强器C作用于每张图片的位置,第二个参数表示是否对每个batch的图片应用不同顺序的Augmenter list # rotate=(-8, 8) 旋转# iaa.CropAndPad 截取(crop)或者填充(pad),填充时,被填充区域为黑色。# px: 想要crop(negative values)的或者pad(positive values)的像素点。# (top, right, bottom, left)# 当pad_mode=constant的时候选择填充的值aug =iaa.Sequential([iaa.CropAndPad( px=((0, 10), (0, 35), (0, 10), (0, 35)), pad_mode=['edge'], pad_cval=1),iaa.Rotate(rotate=(-8,8))])X_aug_train = Noney_aug_train = y_trainfor i in range(40): X_aug = aug(images = X_train) if X_aug_train is not None: X_aug_train = np.concatenate([X_aug_train, X_aug], axis = 0) y_aug_train = np.concatenate([y_aug_train, y_train], axis = 1) else: X_aug_train = X_aug
让我们看看一些数据增强的训练图像。
fig, ax = plt.subplots(nrows=2, ncols =5, figsize = (16,16))for i in range(10): index = np.random.randint(X_aug_train.shape[0]) ax[i//5][i%5].imshow(X_aug_train[index],cmap='gray')
这次使用函数式api创建模型,函数式API是创建模型的另一种方式,它具有更多的灵活性,包括创建更为复杂的模型。
需要定义inputs
和outputs
#函数式API模型创建captcha = Input(shape=(50,200,channels))x = Conv2D(32, (5,5),padding='valid',activation='relu')(captcha)x = MaxPooling2D((2,2),padding='same')(x)x = Conv2D(64, (3,3),padding='same',activation='relu')(x)x = MaxPooling2D((2,2),padding='same')(x)x = Conv2D(128, (3,3),padding='same',activation='relu')(x)maxpool = MaxPooling2D((2,2),padding='same')(x)outputs = []for i in range(5): x = Conv2D(256, (3,3),padding='same',activation='relu')(maxpool) x = MaxPooling2D((2,2),padding='same')(x) x = Flatten()(x) x = Dropout(0.5)(x) x = BatchNormalization()(x) x = Dense(64, activation='relu')(x) x = Dropout(0.5)(x) x = BatchNormalization()(x) x = Dense(len_symbols , activation='softmax' , name=f'char_{i+1}')(x) outputs.append(x) model = Model(inputs = captcha , outputs=outputs)# ReduceLROnPlateau更新学习率reduce_lr = ReduceLROnPlateau(patience =3, factor = 0.5,verbose = 1)model.compile(loss='categorical_crossentropy', optimizer=Adam(learning_rate=0.0005), metrics=["accuracy"])# EarlyStopping用于提前停止训练的callbacks。具体地,可以达到当训练集上的loss不在减小earlystopping = EarlyStopping(monitor ="val_loss", mode ="min", patience = 10, min_delta = 1e-4, restore_best_weights = True) history = model.fit(X_train, [y_train[i] for i in range(5)], batch_size=32, epochs=30, verbose=1, validation_data = (X_val, [y_val[i] for i in range(5)]), callbacks =[earlystopping,reduce_lr])
下面对model进行一个测试和评估。
score = model.evaluate(X_test,[y_test[0], y_test[1], y_test[2], y_test[3], y_test[4]],verbose=1)metrics = ['loss','char_1_loss', 'char_2_loss', 'char_3_loss', 'char_4_loss', 'char_5_loss', 'char_1_acc', 'char_2_acc', 'char_3_acc', 'char_4_acc', 'char_5_acc']for i,j in zip(metrics, score): print(f'{i}: {j}')
具体输出如下:
11/11 [==============================] - 0s 11ms/step - loss: 0.7246 - char_1_loss: 0.0682 - char_2_loss: 0.1066 - char_3_loss: 0.2730 - char_4_loss: 0.2636 - char_5_loss: 0.0132 - char_1_accuracy: 0.9844 - char_2_accuracy: 0.9657 - char_3_accuracy: 0.9408 - char_4_accuracy: 0.9626 - char_5_accuracy: 0.9938
loss: 0.7246273756027222
char_1_loss: 0.06818050146102905
char_2_loss: 0.10664034634828568
char_3_loss: 0.27299806475639343
char_4_loss: 0.26359987258911133
char_5_loss: 0.013208594173192978
char_1_acc: 0.9844236969947815
char_2_acc: 0.9657320976257324
char_3_acc: 0.940809965133667
char_4_acc: 0.9626168012619019
char_5_acc: 0.9937694668769836
字母1到字母5的精确值都大于
绘制loss和score
metrics_df = pd.DataFrame(history.history)columns = [col for col in metrics_df.columns if 'loss' in col and len(col)>8]fig = px.line(metrics_df, y = columns)fig.show()
plt.figure(figsize=(15,8))plt.plot(history.history['loss'])plt.plot(history.history['val_loss'])plt.title('model loss')plt.ylabel('loss')plt.xlabel('epoch')plt.legend(['train', 'val'], loc='upper right',prop={'size': 10})plt.show()
# 预测数据def predict(captcha): captcha = np.reshape(captcha , (1, 50,200,channels)) result = model.predict(captcha) result = np.reshape(result ,(5,len_symbols)) # 取出最大预测中的输出 label = ''.join([symbols[np.argmax(i)] for i in result]) return label predict(X_test[2])# 25277
下面预测所有的数据
actual_pred = []for i in range(X_test.shape[0]): actual = ''.join([symbols[i] for i in (np.argmax(y_test[:, i],axis=1))]) pred = predict(X_test[i]) actual_pred.append((actual, pred))print(actal_pred[:10])
输出如下:
[('n4b4m', 'n4b4m'), ('42nxy', '42nxy'), ('25257', '25277'), ('cewnm', 'cewnm'), ('w46ep', 'w46ep'), ('cdcb3', 'edcb3'), ('8gf7n', '8gf7n'), ('nny5e', 'nny5e'), ('gm2c2', 'gm2c2'), ('g7fmc', 'g7fmc')]
sameCount = 0diffCount = 0letterDiff = {i:0 for i in range(5)}incorrectness = {i:0 for i in range(1,6)}for real, pred in actual_pred: # 预测和输出相同 if real == pred: sameCount += 1 else: # 失败 diffCount += 1 # 遍历 incorrectnessPoint = 0 for i in range(5): if real[i] != pred[i]: letterDiff[i] += 1 incorrectnessPoint += 1 incorrectness[incorrectnessPoint] += 1x = ['True predicted', 'False predicted']y = [sameCount, diffCount]fig = go.Figure(data=[go.Bar(x = x, y = y)])fig.show()
在预测数据中,一共有287个数据预测正确。
在这里,我们可以看到出现错误到底是哪一个index。
x1 = ["Character " + str(x) for x in range(1, 6)] fig = go.Figure(data=[go.Bar(x = x1, y = list(letterDiff.values()))])fig.show()
为了计算每个单词的错误数,绘制相关的条形图。
x2 = [str(x) + " incorrect" for x in incorrectness.keys()]y2 = list(incorrectness.values())fig = go.Figure(data=[go.Bar(x = x2, y = y2)])fig.show()
下面绘制错误的验证码图像,并标准正确和错误的区别。
fig, ax = plt.subplots(nrows = 8, ncols=4,figsize = (16,20))count = 0for i, (actual , pred) in enumerate(actual_pred): if actual != pred: img = X_test[i] try: ax[count//4][count%4].imshow(img, cmap = 'gray') ax[count//4][count%4].title.set_text(pred + ' - ' + actual) count += 1 except: pass
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