图像验证码识别，字母数字汉子均可cnn+lstm+ctc

图形验证码如下：

 

训练两轮时的准确率：上边显示的是未识别的 

 

 config_demo.yaml

System:GpuMemoryFraction: 0.7TrainSetPath: 'train/'TestSetPath: 'test/'ValSetPath: 'dev/'LabelRegex: '([\u4E00-\u9FA5]{4,8}).jpg'MaxTextLenth: 8IMG_W: 200IMG_H: 100ModelName: 'captcha2.h5'Alphabet: 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789'NeuralNet:RNNSize: 256Dropout: 0.25TrainParam:EarlyStoping:monitor: 'val_acc'patience: 10mode: 'auto'baseline: 0.02Epochs: 10BatchSize: 100TestBatchSize: 10

　　train.py

# coding=utf-8
"""
将三通道的图片转为灰度图进行训练
"""
import itertools
import os
import re
import random
import string
from collections import Counter
from os.path import join
import yaml
import cv2
import numpy as np
import tensorflow as tf
from keras import backend as K
from keras.callbacks import ModelCheckpoint, EarlyStopping, Callback
from keras.layers import Input, Dense, Activation, Dropout, BatchNormalization, Reshape, Lambda
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.layers.merge import add, concatenate
from keras.layers.recurrent import GRU
from keras.models import Model, load_modelf = open('./config/config_demo.yaml', 'r', encoding='utf-8')
cfg = f.read()
cfg_dict = yaml.load(cfg)config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
K.set_session(session)TRAIN_SET_PTAH = cfg_dict['System']['TrainSetPath']
VALID_SET_PATH = cfg_dict['System']['TrainSetPath']
TEST_SET_PATH = cfg_dict['System']['TestSetPath']
IMG_W = cfg_dict['System']['IMG_W']
IMG_H = cfg_dict['System']['IMG_H']
MODEL_NAME = cfg_dict['System']['ModelName']
LABEL_REGEX = cfg_dict['System']['LabelRegex']RNN_SIZE = cfg_dict['NeuralNet']['RNNSize']
DROPOUT = cfg_dict['NeuralNet']['Dropout']MONITOR = cfg_dict['TrainParam']['EarlyStoping']['monitor']
PATIENCE = cfg_dict['TrainParam']['EarlyStoping']['patience']
MODE = cfg_dict['TrainParam']['EarlyStoping']['mode']
BASELINE = cfg_dict['TrainParam']['EarlyStoping']['baseline']
EPOCHS = cfg_dict['TrainParam']['Epochs']
BATCH_SIZE = cfg_dict['TrainParam']['BatchSize']
TEST_BATCH_SIZE = cfg_dict['TrainParam']['TestBatchSize']letters_dict = {}
MAX_LEN = 0def get_maxlen():global MAX_LENmaxlen = 0lines = open("train.csv", "r", encoding="utf-8").readlines()for line in lines:name,label = line.strip().split(",")if len(label)>maxlen:maxlen = len(label)MAX_LEN = maxlenreturn maxlendef get_letters():global letters_dictletters = ""lines = open("train.csv","r",encoding="utf-8").readlines()maxlen = get_maxlen()for line in lines:name,label = line.strip().split(",")letters = letters+labelif len(label) < maxlen:label = label + '_' * (maxlen - len(label))letters_dict[name] = labelif os.path.exists("letters.txt"):letters = open("letters.txt","r",encoding="utf-8").read()return lettersreturn "".join(set(letters))letters = get_letters()
f_W = open("letters.txt","w",encoding="utf-8")
f_W.write("".join(letters))
class_num = len(letters) + 1   # plus 1 for blank
print('Letters:', ''.join(letters))
print("letters_num:",class_num)def labels_to_text(labels):return ''.join([letters[int(x)] if int(x) != len(letters) else '' for x in labels])def text_to_labels(text):return [letters.find(x) if letters.find(x) > -1 else len(letters) for x in text]def is_valid_str(s):for ch in s:if not ch in letters:return Falsereturn Trueclass TextImageGenerator:def __init__(self,dirpath,tag,img_w, img_h,batch_size,downsample_factor,):global letters_dictself.img_h = img_hself.img_w = img_wself.batch_size = batch_sizeself.downsample_factor = downsample_factorself.letters_dict = letters_dictself.n = len(self.letters_dict)self.indexes = list(range(self.n))self.cur_index = 0self.imgs = np.zeros((self.n, self.img_h, self.img_w))self.texts = []for i, (img_filepath, text) in enumerate(self.letters_dict.items()):img_filepath = dirpath+img_filepathif i == 0:img_filepath = "train/0.jpg"img = cv2.imread(img_filepath)img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)     # cv2默认是BGR模式img = cv2.resize(img, (self.img_w, self.img_h))img = img.astype(np.float32)img /= 255self.imgs[i, :, :] = imgself.texts.append(text)print(len(self.texts),len(self.imgs),self.n)@staticmethoddef get_output_size():return len(letters) + 1def next_sample(self):   #每次返回一个数据和对应标签self.cur_index += 1if self.cur_index >= self.n:self.cur_index = 0random.shuffle(self.indexes)return self.imgs[self.indexes[self.cur_index]], self.texts[self.indexes[self.cur_index]]def next_batch(self):   #while True:# width and height are backwards from typical Keras convention# because width is the time dimension when it gets fed into the RNNif K.image_data_format() == 'channels_first':X_data = np.ones([self.batch_size, 1, self.img_w, self.img_h])else:X_data = np.ones([self.batch_size, self.img_w, self.img_h, 1])Y_data = np.ones([self.batch_size, MAX_LEN])input_length = np.ones((self.batch_size, 1)) * (self.img_w // self.downsample_factor - 2)label_length = np.zeros((self.batch_size, 1))source_str = []for i in range(self.batch_size):img, text = self.next_sample()img = img.Tif K.image_data_format() == 'channels_first':img = np.expand_dims(img, 0)     #增加一个维度else:img = np.expand_dims(img, -1)X_data[i] = imgY_data[i] = text_to_labels(text)source_str.append(text)text = text.replace("_", "")  # important steplabel_length[i] = len(text)inputs = {'the_input': X_data,'the_labels': Y_data,'input_length': input_length,'label_length': label_length,# 'source_str': source_str}outputs = {'ctc': np.zeros([self.batch_size])}yield (inputs, outputs)# # Loss and train functions, network architecture
def ctc_lambda_func(args):    #ctc损失是时间序列损失函数y_pred, labels, input_length, label_length = args# the 2 is critical here since the first couple outputs of the RNN# tend to be garbage:y_pred = y_pred[:, 2:, :]return K.ctc_batch_cost(labels, y_pred, input_length, label_length)downsample_factor = 4def train(img_w=IMG_W, img_h=IMG_H, dropout=DROPOUT, batch_size=BATCH_SIZE, rnn_size=RNN_SIZE):# Input Parameters# Network parametersconv_filters = 16kernel_size = (3, 3)pool_size = 2time_dense_size = 32if K.image_data_format() == 'channels_first':input_shape = (1, img_w, img_h)else:input_shape = (img_w, img_h, 1)global downsample_factordownsample_factor = pool_size ** 2tiger_train = TextImageGenerator(TRAIN_SET_PTAH, 'train', img_w, img_h, batch_size, downsample_factor)tiger_val = TextImageGenerator(VALID_SET_PATH, 'val', img_w, img_h, batch_size, downsample_factor)act = 'relu'input_data = Input(name='the_input', shape=input_shape, dtype='float32')inner = Conv2D(conv_filters, kernel_size, padding='same',activation=None, kernel_initializer='he_normal',name='conv1')(input_data)inner = BatchNormalization()(inner)  # add BNinner = Activation(act)(inner)inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max1')(inner)inner = Conv2D(conv_filters, kernel_size, padding='same',activation=None, kernel_initializer='he_normal',name='conv2')(inner)inner = BatchNormalization()(inner)  # add BNinner = Activation(act)(inner)inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max2')(inner)conv_to_rnn_dims = (img_w // (pool_size ** 2), (img_h // (pool_size ** 2)) * conv_filters)inner = Reshape(target_shape=conv_to_rnn_dims, name='reshape')(inner)# cuts down input size going into RNN:inner = Dense(time_dense_size, activation=None, name='dense1')(inner)inner = BatchNormalization()(inner)  # add BNinner = Activation(act)(inner)if dropout:inner = Dropout(dropout)(inner)  # 防止过拟合# Two layers of bidirecitonal GRUs# GRU seems to work as well, if not better than LSTM:gru_1 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru1')(inner)gru_1b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru1_b')(inner)gru1_merged = add([gru_1, gru_1b])gru_2 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru2')(gru1_merged)gru_2b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru2_b')(gru1_merged)inner = concatenate([gru_2, gru_2b])if dropout:inner = Dropout(dropout)(inner)  # 防止过拟合# transforms RNN output to character activations:inner = Dense(tiger_train.get_output_size(), kernel_initializer='he_normal',name='dense2')(inner)y_pred = Activation('softmax', name='softmax')(inner)base_model = Model(inputs=input_data, outputs=y_pred)base_model.summary()labels = Input(name='the_labels', shape=[MAX_LEN], dtype='float32')input_length = Input(name='input_length', shape=[1], dtype='int64')label_length = Input(name='label_length', shape=[1], dtype='int64')# Keras doesn't currently support loss funcs with extra parameters# so CTC loss is implemented in a lambda layerloss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length])model = Model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out)# the loss calc occurs elsewhere, so use a dummy lambda func for the lossmodel.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer='adadelta')earlystoping = EarlyStopping(monitor=MONITOR, patience=PATIENCE, verbose=1, mode=MODE, baseline=BASELINE)train_model_path = './tmp/train_' + MODEL_NAMEcheckpointer = ModelCheckpoint(filepath=train_model_path,verbose=1,save_best_only=True)if os.path.exists(train_model_path):model.load_weights(train_model_path)print('load model weights:%s' % train_model_path)evaluator = Evaluate(model)model.fit_generator(generator=tiger_train.next_batch(),steps_per_epoch=tiger_train.n,epochs=EPOCHS,initial_epoch=1,validation_data=tiger_val.next_batch(),validation_steps=tiger_val.n,callbacks=[checkpointer, earlystoping, evaluator])print('----train end----')# For a real OCR application, this should be beam search with a dictionary
# and language model.  For this example, best path is sufficient.
def decode_batch(out):ret = []for j in range(out.shape[0]):out_best = list(np.argmax(out[j, 2:], 1))out_best = [k for k, g in itertools.groupby(out_best)]outstr = ''for c in out_best:if c < len(letters):outstr += letters[c]ret.append(outstr)return retclass Evaluate(Callback):def __init__(self, model):self.accs = []self.model = modeldef on_epoch_end(self, epoch, logs=None):acc = evaluate(self.model)self.accs.append(acc)# Test on validation images
def evaluate(model):global downsample_factortiger_test = TextImageGenerator(VALID_SET_PATH, 'test', IMG_W, IMG_H, TEST_BATCH_SIZE, downsample_factor)net_inp = model.get_layer(name='the_input').inputnet_out = model.get_layer(name='softmax').outputpredict_model = Model(inputs=net_inp, outputs=net_out)equalsIgnoreCaseNum = 0.00equalsNum = 0.00totalNum = 0.00for inp_value, _ in tiger_test.next_batch():batch_size = inp_value['the_input'].shape[0]X_data = inp_value['the_input']net_out_value = predict_model.predict(X_data)pred_texts = decode_batch(net_out_value)labels = inp_value['the_labels']texts = []for label in labels:text = labels_to_text(label)texts.append(text)for i in range(batch_size):totalNum += 1if pred_texts[i] == texts[i]:equalsNum += 1if pred_texts[i].lower() == texts[i].lower():equalsIgnoreCaseNum += 1else:print('Predict: %s ---> Label: %s' % (pred_texts[i], texts[i]))if totalNum >= 10000:breakprint('---Result---')print('Test num: %d, accuracy: %.5f, ignoreCase accuracy: %.5f' % (totalNum, equalsNum / totalNum, equalsIgnoreCaseNum / totalNum))return equalsIgnoreCaseNum / totalNumif __name__ == '__main__':train()test = Trueif test:model_path = './tmp/train_' + MODEL_NAMEmodel = load_model(model_path, compile=False)evaluate(model)print('----End----')

　　interface_testset.py

import itertools
import string
import yaml
from tqdm import tqdm
import cv2
import numpy as np
import os
import tensorflow as tf
from keras import backend as K
from keras.models import Model, load_modelf = open('./config/config_demo.yaml', 'r', encoding='utf-8')
cfg = f.read()
cfg_dict = yaml.load(cfg)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)
K.set_session(session)MODEL_NAME = cfg_dict['System']['ModelName']letters = string.ascii_uppercase + string.ascii_lowercase+string.digitsdef decode_batch(out):ret = []for j in range(out.shape[0]):out_best = list(np.argmax(out[j, 2:], 1))out_best = [k for k, g in itertools.groupby(out_best)]outstr = ''for c in out_best:if c < len(letters):outstr += letters[c]ret.append(outstr)return retdef get_x_data(img_data, img_w, img_h):img = cv2.cvtColor(img_data, cv2.COLOR_RGB2GRAY)img = cv2.resize(img, (img_w, img_h))img = img.astype(np.float32)img /= 255batch_size = 1if K.image_data_format() == 'channels_first':X_data = np.ones([batch_size, 1, img_w, img_h])else:X_data = np.ones([batch_size, img_w, img_h, 1])img = img.Tif K.image_data_format() == 'channels_first':img = np.expand_dims(img, 0)else:img = np.expand_dims(img, -1)X_data[0] = imgreturn X_data# Test on validation images
def interface(datapath ="./testset" ,img_w = 200,img_h = 100):save_file = open("answer.csv","a",encoding="utf-8")save_file.truncate()model_path = './tmp/train_' + MODEL_NAMEmodel = load_model(model_path, compile=False)net_inp = model.get_layer(name='the_input').inputnet_out = model.get_layer(name='softmax').outputpredict_model = Model(inputs=net_inp, outputs=net_out)print("开始预测，预测结果：")listdir = os.listdir(datapath)bar = tqdm(range(len(listdir)),total=len(listdir))for idx in bar:img_data = cv2.imread(datapath+"/" + str(idx) + ".jpg")X_data = get_x_data(img_data, img_w, img_h)net_out_value = predict_model.predict(X_data)pred_texts = decode_batch(net_out_value)#print(str(idx) + ".jpg" + "\t", pred_texts[0])save_file.write(str(idx)+","+pred_texts[0]+"\r\n")if __name__ == '__main__':interface(datapath="./testset")