Keras TensorFlow 验证码识别（附数据集）

川长思鸟来 2022-11-27 07:03 182阅读 0赞

#### 效果 ####

先看效果图，左边是 两张测试数据，右边是 预测结果

标注数据集下载地址已更新  
![tensor验证码识别][tensor]

实现

#### 1. 训练模型 ####

bathcsize 为 700 轮次 50

#!/usr/bin/env python
    # coding: utf-8
    
    # # 训练模型
    # 
    # ## 引入第三方包
    
    # In[1]:
    
    
    from PIL import Image
    from keras import backend as K
    from keras.utils.vis_utils import plot_model
    from keras.models import *
    from keras.layers import *
    
    import glob
    import pickle
    
    import numpy as np
    import tensorflow.gfile as gfile
    import matplotlib.pyplot as plt
    
    # ## 定义超参数和字符集
    
    # In[2]:
    
    
    NUMBER = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
    
    
    CAPTCHA_CHARSET = NUMBER  # 验证码字符集
    CAPTCHA_LEN = 5  # 验证码长度
    CAPTCHA_HEIGHT = 50  # 验证码高度
    CAPTCHA_WIDTH = 200  # 验证码宽度
    
    TRAIN_DATA_DIR = './train-data/'  # 验证码数据集目录
    TEST_DATA_DIR = './test-data/'
    
    BATCH_SIZE = 700
    EPOCHS = 50
    OPT = 'adam'  # adam
    LOSS = 'binary_crossentropy'
    
    MODEL_DIR = './model/train_demo/'
    MODEL_FORMAT = '.h5'
    HISTORY_DIR = './history/train_demo/'
    HISTORY_FORMAT = '.history'
    
    filename_str = "{}captcha_{}_{}_bs_{}_epochs_{}{}"
    
    # 模型网络结构文件
    MODEL_VIS_FILE = 'captcha_classfication' + '.png'
    # 模型文件
    MODEL_FILE = filename_str.format(MODEL_DIR, OPT, LOSS, str(BATCH_SIZE), str(EPOCHS), MODEL_FORMAT)
    # 训练记录文件
    HISTORY_FILE = filename_str.format(HISTORY_DIR, OPT, LOSS, str(BATCH_SIZE), str(EPOCHS), HISTORY_FORMAT)
    
    
    # ## 将 RGB 验证码图像转为灰度图
    
    # In[3]:
    
    
    def rgb2gray(img):
        # Y' = 0.299 R + 0.587 G + 0.114 B 
        # https://en.wikipedia.org/wiki/Grayscale#Converting_color_to_grayscale
        return np.dot(img[..., :3], [0.299, 0.587, 0.114])
    
    
    # ## 对验证码中每个字符进行 one-hot 编码
    
    # In[4]:
    
    
    def text2vec(text, length=CAPTCHA_LEN, charset=CAPTCHA_CHARSET):
        text_len = len(text)
        # 验证码长度校验
        if text_len != length:
            raise ValueError('Error: length of captcha should be {}, but got {}'.format(length, text_len))
    
        # 生成一个形如（CAPTCHA_LEN*CAPTHA_CHARSET,) 的一维向量
        # 例如，4个纯数字的验证码生成形如(4*10,)的一维向量
        vec = np.zeros(length * len(charset))
        for i in range(length):
            # One-hot 编码验证码中的每个数字
            # 每个字符的热码 = 索引 + 偏移量
            vec[charset.index(text[i]) + i * len(charset)] = 1
        return vec
    
    
    # ## 将验证码向量解码为对应字符
    
    # In[5]:
    
    
    def vec2text(vector):
        if not isinstance(vector, np.ndarray):
            vector = np.asarray(vector)
        vector = np.reshape(vector, [CAPTCHA_LEN, -1])
        text = ''
        for item in vector:
            text += CAPTCHA_CHARSET[np.argmax(item)]
        return text
    
    
    # ## 适配 Keras 图像数据格式
    
    # In[6]:
    
    
    def fit_keras_channels(batch, rows=CAPTCHA_HEIGHT, cols=CAPTCHA_WIDTH):
        if K.image_data_format() == 'channels_first':
            batch = batch.reshape(batch.shape[0], 1, rows, cols)
            input_shape = (1, rows, cols)
        else:
            batch = batch.reshape(batch.shape[0], rows, cols, 1)
            input_shape = (rows, cols, 1)
    
        return batch, input_shape
    
    
    # ## 读取训练集
    
    # In[7]:
    
    
    X_train = []
    Y_train = []
    for filename in glob.glob(TRAIN_DATA_DIR + '*.jpg'):
        X_train.append(np.array(Image.open(filename)))
        Y_train.append(filename.lstrip(TRAIN_DATA_DIR).rstrip('.jpg'))
    
    # ## 处理训练集图像
    
    # In[8]:
    
    
    # list -> rgb(numpy)
    X_train = np.array(X_train, dtype=np.float32)
    # rgb -> gray
    X_train = rgb2gray(X_train)
    # normalize
    X_train = X_train / 255
    # Fit keras channels
    X_train, input_shape = fit_keras_channels(X_train)
    
    print(X_train.shape, type(X_train))
    print(input_shape)
    
    # ## 处理训练集标签
    
    # In[9]:
    
    
    Y_train = list(Y_train)
    
    for i in range(len(Y_train)):
        Y_train[i] = text2vec(Y_train[i])
    
    Y_train = np.asarray(Y_train)
    
    print(Y_train.shape, type(Y_train))
    
    # ## 读取测试集，处理对应图像和标签
    
    # In[10]:
    
    
    X_test = []
    Y_test = []
    for filename in glob.glob(TEST_DATA_DIR + '*.jpg'):
        X_test.append(np.array(Image.open(filename)))
        Y_test.append(filename.lstrip(TEST_DATA_DIR).rstrip('.jpg'))
    
    # list -> rgb -> gray -> normalization -> fit keras 
    X_test = np.array(X_test, dtype=np.float32)
    X_test = rgb2gray(X_test)
    X_test = X_test / 255
    X_test, _ = fit_keras_channels(X_test)
    
    Y_test = list(Y_test)
    for i in range(len(Y_test)):
        Y_test[i] = text2vec(Y_test[i])
    
    Y_test = np.asarray(Y_test)
    
    print(X_test.shape, type(X_test))
    print(Y_test.shape, type(Y_test))
    
    # ## 创建验证码识别模型
    
    # In[11]:
    
    
    # 输入层
    inputs = Input(shape=input_shape, name="inputs")
    
    # 第1层卷积
    conv1 = Conv2D(32, (3, 3), name="conv1")(inputs)
    relu1 = Activation('relu', name="relu1")(conv1)
    
    # 第2层卷积
    conv2 = Conv2D(32, (3, 3), name="conv2")(relu1)
    relu2 = Activation('relu', name="relu2")(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2), padding='same', name="pool2")(relu2)
    
    # 第3层卷积
    conv3 = Conv2D(64, (3, 3), name="conv3")(pool2)
    relu3 = Activation('relu', name="relu3")(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2), padding='same', name="pool3")(relu3)
    
    # 将 Pooled feature map 摊平后输入全连接网络
    x = Flatten()(pool3)
    
    # Dropout
    x = Dropout(0.25)(x)
    
    # 4个全连接层分别做10分类，分别对应4个字符。
    x = [Dense(10, activation='softmax', name='fc%d' % (i + 1))(x) for i in range(5)]
    
    # 4个字符向量拼接在一起，与标签向量形式一致，作为模型输出。
    outs = Concatenate()(x)
    
    # 定义模型的输入与输出
    model = Model(inputs=inputs, outputs=outs)
    model.compile(optimizer=OPT, loss=LOSS, metrics=['accuracy'])
    
    # ## 查看模型摘要
    
    # In[12]:
    
    
    model.summary()
    
    # ## 模型可视化
    
    # In[13]:
    
    
    plot_model(model, to_file=MODEL_VIS_FILE, show_shapes=True)
    
    # ## 训练模型
    
    # In[14]:
    
    
    history = model.fit(X_train,
                        Y_train,
                        batch_size=BATCH_SIZE,
                        epochs=EPOCHS,
                        verbose=2,
                        validation_data=(X_test, Y_test))
    
    # ## 预测样例
    
    # In[47]:
    
    
    print(vec2text(Y_test[9]))
    
    # In[48]:
    
    
    yy = model.predict(X_test[9].reshape(1, 50, 200, 1))
    
    # In[49]:
    
    
    print(vec2text(yy))
    
    # ## 保存模型
    
    # In[50]:
    
    
    if not gfile.Exists(MODEL_DIR):
        gfile.MakeDirs(MODEL_DIR)
    
    model.save(MODEL_FILE)
    print('Saved trained model at %s ' % MODEL_FILE)
    
    # ## 保存训练过程记录
    
    # In[51]:
    
    print(history.history['acc'])
    
    # In[52]:
    
    
    history.history.keys()
    
    # In[53]:
    
    
    if gfile.Exists(HISTORY_DIR) == False:
        gfile.MakeDirs(HISTORY_DIR)
    
    # with open(HISTORY_FILE, 'wb') as f:
    # pickle.dump(history.history, f)
    
    
    # In[54]:
    
    
    # print(HISTORY_FILE)
    
    
    # In[ ]:

#### 2 模型结构设计 ####

![模型结构设计][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzI4MDE4Mjgz_size_16_color_FFFFFF_t_70_pic_center]

#### 3. 建立预测服务 ####

import base64
    
    import numpy as np
    import tensorflow as tf
    
    from io import BytesIO
    from flask import Flask, request, jsonify
    from keras.models import load_model
    from PIL import Image
    
    NUMBER = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
    LOWERCASE = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u',
                'v', 'w', 'x', 'y', 'z']
    UPPERCASE = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U',
               'V', 'W', 'X', 'Y', 'Z']
    
    CAPTCHA_CHARSET = NUMBER   # 验证码字符集
    CAPTCHA_LEN = 5            # 验证码长度
    CAPTCHA_HEIGHT = 50        # 验证码高度
    CAPTCHA_WIDTH = 200        # 验证码宽度
    
    # 10 个 Epochs 训练的模型 rmsprop adam
    MODEL_FILE = './model/train_demo/captcha_adam_binary_crossentropy_bs_700_epochs_50.h5'
    
    def vec2text(vector):
        if not isinstance(vector, np.ndarray):
            vector = np.asarray(vector)
        vector = np.reshape(vector, [CAPTCHA_LEN, -1])
        text = ''
        for item in vector:
            text += CAPTCHA_CHARSET[np.argmax(item)]
        return text
    
    def rgb2gray(img):
        # Y' = 0.299 R + 0.587 G + 0.114 B 
        # https://en.wikipedia.org/wiki/Grayscale#Converting_color_to_grayscale
        return np.dot(img[...,:3], [0.299, 0.587, 0.114])
    
    app = Flask(__name__) # 创建 Flask 实例
    
    # 测试 URL
    @app.route('/ping', methods=['GET', 'POST'])
    def hello_world():
        return 'pong'
    
    # 验证码识别 URL
    @app.route('/predict', methods=['POST'])
    def predict():
        response = { 'success': False, 'prediction': '', 'debug': 'error'}
        received_image= False
        if request.method == 'POST':
            if request.files.get('image'): # 图像文件
                image = request.files['image'].read()
                # print("image is ")
                # print(image)
                received_image = True
                response['debug'] = 'get image'
            elif request.get_json(): # base64 编码的图像文件
                encoded_image = request.get_json()['image']
                image = base64.b64decode(encoded_image)
                received_image = True
                response['debug'] = 'get json'
            if received_image:
                image = np.array(Image.open(BytesIO(image)))
                image = rgb2gray(image).reshape(1, 50, 200, 1).astype('float32') / 255
                with graph.as_default():
                    pred = model.predict(image)
                response['prediction'] = response['prediction'] + vec2text(pred)
                response['success'] = True
                response['debug'] = 'predicted'
        else:
            response['debug'] = 'No Post'
        return jsonify(response)
    
    model = load_model(MODEL_FILE) # 加载模型
    graph = tf.get_default_graph() # 获取 TensorFlow 默认数据流图
    
    # 启动命令
    # export FLASK_ENV=development && flask run --host=0.0.0.0
    # curl 127.0.0.1:5000/ping
    # curl -X POST -F image=@/root/Workspace/leon/test-data/56497.jpg 'http://localhost:5000/predict'

#### 4 数据集下载 ####

[train-data.zip][]

[tensor]: /images/20221124/8423304c33a84c8cbe02b8d00755c5a1.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzI4MDE4Mjgz_size_16_color_FFFFFF_t_70_pic_center]: /images/20221124/3cafb5e3e36e4492909fc08346d6dc1e.png
[train-data.zip]: https://download.csdn.net/download/qq_28018283/12711858