不定长图片验证码训练

基于LSTM和CTCLoss训练不定长图片验证码
Github项目地址：https://github.com/JansonJo/captcha_ocr.git

# 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
# config.gpu_options.per_process_gpu_memory_fraction = cfg_dict['System']['GpuMemoryFraction']
session = tf.Session(config=config)
K.set_session(session)# System config
TRAIN_SET_PTAH = cfg_dict['System']['TrainSetPath']
VALID_SET_PATH = cfg_dict['System']['TestSetPath']
TEST_SET_PATH = cfg_dict['System']['TestSetPath']
MAX_TEXT_LEN = cfg_dict['System']['MaxTextLenth']
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']
ALPHABET = cfg_dict['System']['Alphabet']# NeuralNet config
RNN_SIZE = cfg_dict['NeuralNet']['RNNSize']
DROPOUT = cfg_dict['NeuralNet']['Dropout']# TrainParam config
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']
TEST_SET_NUM = cfg_dict['TrainParam']['TestSetNum']def get_counter(dirpath):letters = ''lens = []for root, dirs, files in os.walk(dirpath):for filename in files:m = re.search(LABEL_REGEX, filename, re.M | re.I)description = m.group(1)lens.append(len(description))letters += descriptionprint('Max plate length in "%s":' % dirpath, max(Counter(lens).keys()))return Counter(letters)c_val = get_counter(VALID_SET_PATH)
c_train = get_counter(TRAIN_SET_PTAH)
letters_train = set(c_train.keys())
letters_val = set(c_val.keys())
print('letters_train: %s' % ''.join(sorted(letters_train)))
print('letters_val: %s' % ''.join(sorted(letters_val)))
if letters_train == letters_val:print('Letters in train and val do match')
else:raise Exception('Letters in train and val don\'t match')
# print(len(letters_train), len(letters_val), len(letters_val | letters_train))
# letters = sorted(list(letters_train))
# letters = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
letters = ALPHABET
if len(letters) == 0:letters = string.digits + string.ascii_uppercase + string.ascii_lowercase
class_num = len(letters) + 1   # plus 1 for blank
print('Alphabet Letters:', ''.join(letters))# Input data generatordef labels_to_text(labels):# return ''.join(list(map(lambda x: letters[int(x)], labels)))return ''.join([letters[int(x)] if int(x) != len(letters) else '' for x in labels])def text_to_labels(text):# return list(map(lambda x: letters.index(x), 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,max_text_len=MAX_TEXT_LEN):self.img_h = img_hself.img_w = img_wself.batch_size = batch_sizeself.max_text_len = max_text_lenself.downsample_factor = downsample_factorimg_dirpath = dirpathself.samples = []for filename in os.listdir(img_dirpath):name, ext = os.path.splitext(filename)if ext in ['.png', '.jpg']:img_filepath = join(img_dirpath, filename)m = re.search(LABEL_REGEX, filename, re.M | re.I)description = m.group(1)if len(description) < MAX_TEXT_LEN:description = description + '_' * (MAX_TEXT_LEN - len(description))# if is_valid_str(description):#     self.samples.append([img_filepath, description])self.samples.append([img_filepath, description])self.n = len(self.samples)self.indexes = list(range(self.n))self.cur_index = 0# build data:self.imgs = np.zeros((self.n, self.img_h, self.img_w))self.texts = []for i, (img_filepath, text) in enumerate(self.samples):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 /= 255# width and height are backwards from typical Keras convention# because width is the time dimension when it gets fed into the RNNself.imgs[i, :, :] = imgself.texts.append(text)@staticmethoddef get_output_size():return len(letters) + 1def next_sample(self):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, self.max_text_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)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)tiger = TextImageGenerator(VALID_SET_PATH, 'val', IMG_W, IMG_H, 8, 4)for inp, out in tiger.next_batch():print('Text generator output (data which will be fed into the neutral network):')print('1) the_input (image)')if K.image_data_format() == 'channels_first':img = inp['the_input'][0, 0, :, :]else:img = inp['the_input'][0, :, :, 0]# plt.imshow(img.T, cmap='gray')# plt.show()print('2) the_labels (plate number): %s is encoded as %s' %(labels_to_text(inp['the_labels'][0]), list(map(int, inp['the_labels'][0]))))print('3) input_length (width of image that is fed to the loss function): %d == %d / 4 - 2' %(inp['input_length'][0], tiger.img_w))print('4) label_length (length of plate number): %d' % inp['label_length'][0])break# # Loss and train functions, network architecture
def ctc_lambda_func(args):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=[tiger_train.max_text_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')# if not load:# captures output of softmax so we can decode the output during visualization# test_func = K.function([input_data], [y_pred])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])base_model.save('./model/' + MODEL_NAME)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(TEST_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 = sess.run(net_out, feed_dict={net_inp: X_data})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):# print('Predict: %s ---> Label: %s' % (pred_texts[i], texts[i]))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 >= TEST_SET_NUM:breakprint('---Result---')print('Test num: %d, accuracy: %.5f, ignoreCase accuracy: %.5f' % (totalNum, equalsNum / totalNum, equalsIgnoreCaseNum / totalNum))return equalsIgnoreCaseNum / totalNumif __name__ == '__main__':train()