python|Keras TensorFlow 验证码识别（附数据集） tensorflow|深度学习|验证码识别

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

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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 pickleimport 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=https://www.it610.com/article/(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

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3. 建立预测服务

import base64import numpy as np import tensorflow as tffrom io import BytesIO from flask import Flask, request, jsonify from keras.models import load_model from PIL import ImageNUMBER = ['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 rmspropadam 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 textdef 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 【python|Keras TensorFlow 验证码识别（附数据集）】train-data.zip