自学教程：教你使用TensorFlow2识别验证码

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()

# 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 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)

# 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)

# 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

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')

#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])

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}')

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

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])

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()

x1 = ["Character " + str(x) for x in range(1, 6)]    fig = go.Figure(data=[go.Bar(x = x1, y = list(letterDiff.values()))])fig.show()

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()

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