module3.py

## -*- coding: utf-8 -*-

#import os,sys,string
#import sys
#import logging
#import multiprocessing
#import time
#import json
#import cv2
#import numpy as np

#import keras
#import keras.backend as K
#from keras.datasets import mnist
#from keras.models import *
#from keras.layers import *
#from keras.optimizers import *
#from keras.callbacks import *
#from keras import backend as K

##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 0-9
#label_count=len(char_ocr)+1

#def 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 lab

#def gen_image_data(dir=r'data', 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 == '.jpg':  # 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 function

#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:, :] 测试感觉没影响
#    y_pred = y_pred[:, 2:, :]
#    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])
#    #e=loss_out.get_shape()
#    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.compile(optimizer='rmsprop',
#    #          loss='mse')
#    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_epoch_end(self, epoch, logs=None):
#            model.save_weights('model_1018.w')
#            base_model.save_weights('base_model_1018.w')
#            #test(base_model)


#    # 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()
#    u=np.array([[10],[10],[10],[12],[10],[10]])
#    v=np.array([[5],[5],[5],[5],[5],[5]])
#    maxin=1
#    subseq_size = 1
#    batch_size=1
#    #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=1,
#    #                 epochs=5,
#    #                 callbacks=[history, 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:]),
#    #                 )

#    model.fit([X, Y, u,v],v,batch_size=4,epochs=10,          
#                     callbacks=[history, 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()