利用keras框架cnn+ctc_loss识别不定长字符图片

# -*- coding: utf-8 -*-#keras==2.0.5#tensorflow==1.1.0import os,sys,stringimport sysimport loggingimport multiprocessingimport timeimport jsonimport cv2import numpy as npfrom sklearn.model_selection import train_test_splitimport kerasimport keras.backend as Kfrom keras.datasets import mnistfrom keras.models import *from keras.layers import *from keras.optimizers import *from keras.callbacks import *from keras import backend as K# from keras.utils.visualize_util import plotfrom visual_callbacks import AccLossPlotterplotter = AccLossPlotter(graphs=['acc', 'loss'], save_graph=True, save_graph_path=sys.path[0])#识别字符集char_ocr='0123456789' #string.digits#定义识别字符串的最大长度seq_len=8#识别结果集合个数 0-9label_count=len(char_ocr)+1def get_label(filepath):    # print(str(os.path.split(filepath)[-1]).split('.')[0].split('_')[-1])    lab=[]    for num in str(os.path.split(filepath)[-1]).split('.')[0].split('_')[-1]:        lab.append(int(char_ocr.find(num)))    if len(lab) < seq_len:        cur_seq_len = len(lab)        for i in range(seq_len - cur_seq_len):            lab.append(label_count) #    return labdef gen_image_data(dir=r'data\train', file_list=[]):    dir_path = dir    for rt, dirs, files in os.walk(dir_path):  # =pathDir        for filename in files:            # print (filename)            if filename.find('.') >= 0:                (shotname, extension) = os.path.splitext(filename)                # print shotname,extension                if extension == '.tif':  # extension == '.png' or                    file_list.append(os.path.join('%s\\%s' % (rt, filename)))                    # print (filename)    print(len(file_list))    index = 0    X = []    Y = []    for file in file_list:        index += 1        # if index>1000:        #     break        # print(file)        img = cv2.imread(file, 0)        # print(np.shape(img))        # cv2.namedWindow("the window")        # cv2.imshow("the window",img)        img = cv2.resize(img, (150, 50), interpolation=cv2.INTER_CUBIC)        img = cv2.transpose(img,(50,150))        img =cv2.flip(img,1)        # cv2.namedWindow("the window")        # cv2.imshow("the window",img)        # cv2.waitKey()        img = (255 - img) / 256  # 反色处理        X.append([img])        Y.append(get_label(file))        # print(get_label(file))        # print(np.shape(X))        # print(np.shape(X))    # print(np.shape(X))    X = np.transpose(X, (0, 2, 3, 1))    X = np.array(X)    Y = np.array(Y)    return X,Y# the actual loss calc occurs here despite it not being# an internal Keras loss functiondef 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:, :] 测试感觉没影响    y_pred = y_pred[:, :, :]    return K.ctc_batch_cost(labels, y_pred, input_length, label_length)if __name__ == '__main__':    height=150    width=50    input_tensor = Input((height, width, 1))    x = input_tensor    for i in range(3):        x = Convolution2D(32*2**i, (3, 3), activation='relu', padding='same')(x)        # x = Convolution2D(32*2**i, (3, 3), activation='relu')(x)        x = MaxPooling2D(pool_size=(2, 2))(x)    conv_shape = x.get_shape()    # print(conv_shape)    x = Reshape(target_shape=(int(conv_shape[1]), int(conv_shape[2] * conv_shape[3])))(x)    x = Dense(32, activation='relu')(x)    gru_1 = GRU(32, return_sequences=True, kernel_initializer='he_normal', name='gru1')(x)    gru_1b = GRU(32, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru1_b')(x)    gru1_merged = add([gru_1, gru_1b])  ###################    gru_2 = GRU(32, return_sequences=True, kernel_initializer='he_normal', name='gru2')(gru1_merged)    gru_2b = GRU(32, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru2_b')(        gru1_merged)    x = concatenate([gru_2, gru_2b])  ######################    x = Dropout(0.25)(x)    x = Dense(label_count, kernel_initializer='he_normal', activation='softmax')(x)    base_model = Model(inputs=input_tensor, outputs=x)    labels = Input(name='the_labels', shape=[seq_len], dtype='float32')    input_length = Input(name='input_length', shape=[1], dtype='int64')    label_length = Input(name='label_length', shape=[1], dtype='int64')    loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([x, labels, input_length, label_length])    model = Model(inputs=[input_tensor, labels, input_length, label_length], outputs=[loss_out])    model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer='adadelta')    model.summary()    def test(base_model):        file_list = []        X, Y = gen_image_data(r'data\test', file_list)        y_pred = base_model.predict(X)        shape = y_pred[:, :, :].shape  # 2:        out = K.get_value(K.ctc_decode(y_pred[:, :, :], input_length=np.ones(shape[0]) * shape[1])[0][0])[:,              :seq_len]  # 2:        print()        error_count=0        for i in range(len(X)):            print(file_list[i])            str_src = str(os.path.split(file_list[i])[-1]).split('.')[0].split('_')[-1]            print(out[i])            str_out = ''.join([str(x) for x in out[i] if x!=-1 ])            print(str_src, str_out)            if str_src!=str_out:                error_count+=1                print('################################',error_count)            # img = cv2.imread(file_list[i])            # cv2.imshow('image', img)            # cv2.waitKey()    class LossHistory(Callback):        def on_train_begin(self, logs={}):            self.losses = []        def on_epoch_end(self, epoch, logs=None):            model.save_weights('model_1018.w')            base_model.save_weights('base_model_1018.w')            test(base_model)        def on_batch_end(self, batch, logs={}):            self.losses.append(logs.get('loss'))    # checkpointer = ModelCheckpoint(filepath="keras_seq2seq_1018.hdf5", verbose=1, save_best_only=True, )    history = LossHistory()    # base_model.load_weights('base_model_1018.w')    # model.load_weights('model_1018.w')    X,Y=gen_image_data()    maxin=4900    subseq_size = 100    batch_size=10    result=model.fit([X[:maxin], Y[:maxin], np.array(np.ones(len(X))*int(conv_shape[1]))[:maxin], np.array(np.ones(len(X))*seq_len)[:maxin]], Y[:maxin],                     batch_size=20,                     epochs=1000,                     callbacks=[history, plotter, EarlyStopping(patience=10)], #checkpointer, history,                     validation_data=([X[maxin:], Y[maxin:], np.array(np.ones(len(X))*int(conv_shape[1]))[maxin:], np.array(np.ones(len(X))*seq_len)[maxin:]], Y[maxin:]),                     )    test(base_model)    K.clear_session()