教你使用TensorFlow2识别验证码

import os
import numpy as np
import pandas as pd
import cv2
import matplotlib.pyplot as plt
import seaborn as sns
# imgaug 图片数据增强
import imgaug.augmenters as iaa
import tensorflow as tf
# Conv2D MaxPooling2D Dropout Flatten Dense BN  GAP
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Layer, BatchNormalization, GlobalAveragePooling2D 
from tensorflow.keras.optimizers import Adam
from tensorflow.keras import Model, Input 
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
# 图片处理器
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import plotly.express as px
import plotly.graph_objects as go
import plotly.offline as pyo
pyo.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) +1
symbols = 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] = targs

print("shape of X:", X.shape)
print("shape of y:", y.shape)

# 生成随机数
from numpy.random import default_rng

rng = 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 = None
y_aug_train = y_train
for 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 = 0
diffCount = 0
letterDiff = {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] += 1


x = ['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 = 0
for 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