我就废话不多说了，大家还是直接看代码吧~
# -*- coding: utf-8 -*-
#keras==2.0.5
#tensorflow==1.1.0
import os,sys,string
import sys
import logging
import multiprocessing
import time
import json
import cv2
import numpy as np
from sklearn.model_selection import train_test_split
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
# from keras.utils.visualize_util import plot
from visual_callbacks import AccLossPlotter
plotter = 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 0-9
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\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 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[:, :, :]
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()
补充知识：日常填坑之keras.backend.ctc_batch_cost参数问题
InvalidArgumentError sequence_length(0) <=30错误
下面的代码是在网上绝大多数文章给出的关于k.ctc_batch_cost()函数的使用代码
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)
可以注意到有一句：y_pred = y_pred[:, 2:, :]，这里把y_pred 的第二维数据去掉了两列，说人话：把送进lstm序列的step减了2步。后来偶然在一篇文章中有提到说这里之所以减2是因为在将feature送入keras的lstm时自动少了2维，所以这里就写成这样了。估计是之前老版本的bug，现在的新版本已经修复了。如果依然按照上面的写法，会得到如下错误：
InvalidArgumentError sequence_length(0) <=30
'<='后面的数值 = 你cnn最后的输出维度 - 2。这个错误我找了很久，一直不明白30哪里来的，后来一行行的检查代码是发现了这里很可疑，于是改成如下形式错误解决。
def ctc_lambda_func(args): y_pred, labels, input_length, label_length = args return K.ctc_batch_cost(labels, y_pred, input_length, label_length)
训练时出现ctc_loss_calculator.cc:144] No valid path found或loss: inf错误
熟悉CTC算法的话，这个提示应该是ctc没找到有效路径。既然是没找到有效路径，那肯定是label和input之间哪个地方又出问题了！和input相关的错误已经解决了，那么肯定就是label的问题了。再看ctc_batch_cost的四个参数，labels和label_length这两个地方有可疑。对于ctc_batch_cost()的参数，labels需要one-hot编码，形状：[batch, max_labelLength]，其中max_labelLength指预测的最大字符长度；label_length就是每个label中的字符长度了，受之前tf.ctc_loss的影响把这里都设置成了最大长度，所以报错。
对于参数labels而言，max_labelLength是能预测的最大字符长度。这个值与送lstm的featue的第二维，即特征序列的max_step有关，表面上看只要max_labelLength<max_step即可，但是如果小的不多依然会出现上述错误。至于到底要小多少，还得从ctc算法里找，由于ctc算法在标签中的每个字符后都加了一个空格，所以应该把这个长度考虑进去，所以有 max_labelLength < max_step//2。没仔细研究keras里ctc_batch_cost()函数的实现细节，上面是我的猜测。如果有很明确的答案，还请麻烦告诉我一声，谢了先！
错误代码：
batch_label_length = np.ones(batch_size) * max_labelLength
正确打开方式：
batch_x, batch_y = [], [] batch_input_length = np.ones(batch_size) * (max_img_weigth//8) batch_label_length = [] for j in range(i, i + batch_size): x, y = self.get_img_data(index_all[j]) batch_x.append(x) batch_y.append(y) batch_label_length.append(self.label_length[j])
最后附一张我的crnn的模型图：
以上这篇使用keras框架cnn+ctc_loss识别不定长字符图片操作就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。