车牌识别系统三：python+tensorflow实现车牌字符的识别
字符识别
在前⾯两篇博客中分别介绍了车牌定位以及字符分割的代码实现，那么在这篇⽂章中，我主要想介绍⼀下车牌识别系统的最后⼀个模块：字符识别。字符识别可以说是整个系统的核⼼部分了，这⼀部分可以有很多中实现⽅法：模板匹配、SVM以及⼈⼯神经⽹络等⽅法。本系统采⽤的是卷积神经⽹络算法（LeNet-5模型）来进⾏字符的识别。部分代码参考⾃下⾯这位博主（⾮常感谢博主的分享）：
链接: .
车牌字符数据集
在进⾏CNN⽹络模型训练前，需要收集⼤量的数据集，并对其进⾏⼀些预处理。在对⽹上进⾏了⼤量的收刮之后，总共收集到了约4万张数据样本，车牌数据集的下载链接：
链接: .（提取码4b2a）
如果链接有问题，请在下⽅留⾔，以便及时修改。
模型的训练
本系统主要采⽤两个模型分别进⾏省份和数字/字母的识别，⾸先是省份的训练代码：
# coding=gbk
"""
汉字训练代码模块（LeNet-5）
__author__ = 'kuang'
2019.5.4 7号宿舍楼
"""
#载⼊模块
import sys
import os
import time
import random
import numpy as np
import tensorflow as tf
import cv2 as cv
#设置基本参数
SIZE =1024
WIDTH =32
HEIGHT =32
NUM_CLASSES =31#总共是31个省份
iterations =1000
#设置存储模型的地址
SAVER_DIR ="XXX"#⾃⼰的路径
PROVINCES =("川","鄂","赣","⽢","贵","桂","⿊","沪","冀","津","京","吉","辽","鲁","蒙","闽","宁","青","琼","陕","苏","晋","皖","湘","新","豫","渝","粤","云","藏","浙") nProvinceIndex =0
time_begin = time.time()
#定义输⼊节点，对应于图像像素值矩阵集合和图像标签（即所代表的数字）
x = tf.placeholder(tf.float32,shape=[None,SIZE])#None表⽰batch size的⼤⼩
y_ = tf.placeholder(tf.float32,shape=[None,NUM_CLASSES])#输出标签的占位
x_image = tf.reshape(x,[-1,WIDTH,HEIGHT,1])#⽣成⼀个四维的数组
#定义卷积函数
def conv_layer(inputs,W,b,conv_strides,kernel_size,pool_strides,padding):
L1_conv = v2d(inputs,W,strides=conv_strides,padding=padding)#卷积操作
L1_relu = lu(L1_conv + b)#激活函数RELU
ax_pool(L1_relu,ksize=kernel_size,strides=pool_strides,padding='SAME')
#定义全连接函数
def full_connect(inputs,W,b):
lu(tf.matmul(inputs,W)+b)
def average(seq):
return float(sum(seq))/len(seq)
#训练模型
if __name__ =="__main__":
#第⼀次遍历图⽚⽬录是为了获取图⽚总数
input_count =0
for i in range(0,31):
dir='XXX\\train\\%s\\'% i #⾃⼰的路径
for root,dirs,files in os.walk(dir):
for filename in files:
input_count = input_count +1
#定义对应维数和各维长度的数组
input_images = np.array([[0]*SIZE for i in range(input_count)])#⽣成⼀个input_count⾏，SIZE列的全零⼆维数组
input_labels = np.array([[0]*NUM_CLASSES for i in range(input_count)])#⽣成⼀个input_count⾏，NUM_CLASSES列的全零⼆维数组#第⼆次遍历图⽚⽬录是为了⽣成图⽚数据和标签
index =0
for i in range(0,31):
dir='XXX\\train\\%s\\'% i
a =0
for root,dirs,files in os.walk(dir):
for filename in files:
filename =dir+ filename
img = cv.imread(filename,0)
print(filename)
print(a)
#cv.imshow('threshold',img)
#cv.waitKey(0)
height = img.shape[0]#⾏数
width = img.shape[1]#列数
a = a +1
for h in range(0,height):
for w in range(0,width):
m = img[h][w]
if m >150:
input_images[index][w+h*width]=1
else:
input_images[index][w+h*width]=0
input_labels[index][i]=1
index = index +1
#第⼀次遍历图⽚⽬录是为了获得图⽚总数
val_count =0
for i in range(0,31):
dir='XXX\\train\\%s\\'% i
for root,dirs,files in os.walk(dir):
for filename in files:
val_count = val_count +1
#定义对应维数和各维长度的数组
val_images = np.array([[0]*SIZE for i in range(val_count)])#⽣成⼀个input_count⾏，SIZE列的全零⼆维数组
val_labels = np.array([[0]*NUM_CLASSES for i in range(val_count)])#⽣成⼀个input_count⾏，NUM_CLASSES列的全零⼆维数组
#第⼆次遍历图⽚⽬录是为了⽣成图⽚数据和标签
index =0
for i in range(0,31):
dir='XXX\\train\\%s\\'% i
for root,dirs,files in os.walk(dir):
for filename in files:
filename =dir+ filename
img = cv.imread(filename,0)
height = img.shape[0]#⾏数
width = img.shape[1]#列数
for h in range(0,height):
for w in range(0,width):
m = img[h][w]
if m >150:
val_images[index][w+h*width]=1
val_images[index][w+h*width]=1
else:
val_images[index][w+h*width]=0
val_labels[index][i]=1
index = index +1
with tf.Session()as sess:
#第⼀个卷积层
W_conv1 = tf.uncated_normal([5,5,1,12],stddev=0.1),name="W_conv1")
b_conv1 = tf.stant(0.1,shape=[12]),name="b_conv1")#⽣成偏置项，并初始化
conv_strides =[1,1,1,1]#⾏，列的卷积步长均为1
kernel_size =[1,2,2,1]#池化层卷积核的尺⼨为2*2
pool_strides =[1,2,2,1]#池化⾏，列步长为2
L1_pool = conv_layer(x_image,W_conv1,b_conv1,conv_strides,kernel_size,pool_strides,padding='SAME')#第⼀层卷积池化的输出 ,x_image为输⼊（后⽂代码中输⼊）
#第⼆个卷积层
W_conv2 = tf.uncated_normal([5,5,12,24],stddev=0.1),name="W_conv2")
b_conv2 = tf.stant(0.1,shape=[24]),name="b_conv2")
conv_strides =[1,1,1,1]
kernel_size =[1,2,2,1]
pool_strides =[1,2,2,1]
L2_pool = conv_layer(L1_pool,W_conv2,b_conv2,conv_strides,kernel_size,pool_strides,padding="SAME")
#全连接层
W_fc1 = tf.uncated_normal([8*8*24,512],stddev=0.1),name="W_fc1")
b_fc1 = tf.stant(0.1,shape=[512]),name="b_fc1")
h_pool2_flat = tf.reshape(L2_pool,[-1,8*8*24])#将第⼆次池化的⼆维特征图排列成⼀维的⼀个数组全连接相当于⼀维的数组
h_fc1 = full_connect(h_pool2_flat,W_fc1,b_fc1)#进⾏全连接操作
#dropout
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob)
#readout层
W_fc2 = tf.uncated_normal([512,NUM_CLASSES],stddev=0.1),name="W_fc2")
b_fc2 = tf.stant(0.1,shape=[NUM_CLASSES]),name="b_fc2")
#定义优化器和训练OP
y_conv = tf.matmul(h_fc1_drop,W_fc2)+ b_fc2 #最后的输出层，因为是全连接，相当于每个神经元与权重相乘再加偏移
cross_entropy = tf.reduce_softmax_cross_entropy_with_logits(labels=y_,logits=y_conv))#交叉熵损失函数
train_step = tf.train.AdamOptimizer((1e-5)).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1),tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
#初始化saver
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())#初始化所有变量
time_elapsed = time.time()- time_begin #运⾏时间
print("读取图⽚⽂件耗费时间：%d秒"% time_elapsed)
time_begin = time.time()
print("⼀共读取了%s个训练图像，%s个标签"%(input_count,input_count))
#设置每次训练操作的输⼊个数和迭代次数，这⾥为了⽀持任意图⽚总数，定义了⼀个余数remainder，譬如，如果每次训练训练操作的输⼊个数为60，图⽚总数为150张，则前⾯两次各输⼊60张，最后⼀次输⼊30张（余数30）
batch_size =64#每次训练的图⽚数
iterations = iterations #迭代次数
batches_count =int(input_count/batch_size)
remainder = input_count % batch_size
print("训练数据集分成%s批，前⾯每批%s个数据，最后⼀批%s个数据"%(batches_count+1,batch_size,remainder))
#执⾏训练迭代
for it in range(iterations):
#这⾥的关键是要把输⼊数组转为np.array
sum_loss =[]
for n in range(batches_count):
loss, out = sess.run([cross_entropy, train_step], feed_dict ={x:input_images[n*batch_size:(n+1)*batch_size],y_:input_labels[n*batch_size:(n+1)*b
loss, out = sess.run([cross_entropy, train_step], feed_dict ={x:input_images[n*batch_size:(n+1)*batch_size],y_:input_labels[n*batch_size:(n+1)*b atch_size],keep_prob:0.5})#feed_dict相当于⼀次喂进去的数据，x表⽰输⼊，前⾯已经将输⼊的图⽚转化为input_image数组形式了
sum_loss.append(loss)
if remainder >0:
start_index = batches_count * batch_size
loss, out =sess.run([cross_entropy, train_step], feed_dict ={x:input_images[start_index:input_count-1],y_:input_labels[start_index:input_count-1 ],keep_prob:0.5})
sum_loss.append(loss)
avg_loss = average(sum_loss)
#每完成5次迭代，判断准确度是否已达到100%，达到则退出迭代循环
iterate_accuracy =0
if it %5==0:
loss1 , iterate_accuracy = sess.run([cross_entropy,accuracy],feed_dict ={x : val_images,y_ : val_labels,keep_prob :1.0}) print('第%d次训练迭代：准确率 %0.5f%% '%(it,iterate_accuracy*100)+' 损失值为：%s'% loss +' 测试损失值：%s'% loss1)
if iterate_accuracy >=0.9999999:
break
#完成训练，并输出训练时间
print('完成训练')
time_elapsed = time.time()- time_begin
print("训练耗费时间：%d秒"% time_elapsed)
time_begin = time.time()
#保存训练结果
if not ists(SAVER_DIR):
print('不存在训练数据保存⽬录，现在创建保存⽬录')
os.makedirs(SAVER_DIR)
saver_path = saver.save(sess,"%smodel.ckpt"%(SAVER_DIR))
print("保存路径为：",saver_path)
然后是数字/字母的训练代码：
# coding=gbk
"""
数字/字母训练代码模块（LeNet-5）
__author__ = 'kuang'
2019.5.4 7号宿舍楼
"""
#载⼊模块
import sys
import os
import time
import random
import numpy as np
import tensorflow as tf
import cv2 as cv
#设置基本参数
SIZE =1024
WIDTH =32
HEIGHT =32
NUM_CLASSES =34#总共是34个数字字母
iterations =1000
#设置保存的路径
SAVER_DIR = XXX\\train_saver\\numbers\\"
LETTERS_DIGITS =("A","B","C","D","E","F","G","H","J","K","L","M","N","P","Q","R","S","T","U","V","W","X","Y","Z","0","1","2","3","4","5","6","7","8","9")
#license_num = []
time_begin = time.time()
#定义输⼊节点，对应于图像像素值矩阵集合和图像标签（即所代表的数字）
x = tf.placeholder(tf.float32,shape=[None,SIZE])#None表⽰batch size的⼤⼩
y_ = tf.placeholder(tf.float32,shape=[None,NUM_CLASSES])#输出标签的占位
x_image = tf.reshape(x,[-1,WIDTH,HEIGHT,1])#对图像重新定义尺⼨
x_image = tf.reshape(x,[-1,WIDTH,HEIGHT,1])#对图像重新定义尺⼨
#定义卷积函数
def conv_layer(inputs,W,b,conv_strides,kernel_size,pool_strides,padding):
L1_conv = v2d(inputs,W,strides=conv_strides,padding=padding)#卷积操作
L1_relu = lu(L1_conv + b)#激活函数RELU
ax_pool(L1_relu,ksize=kernel_size,strides=pool_strides,padding='SAME')
#定义全连接函数
def full_connect(inputs,W,b):
lu(tf.matmul(inputs,W)+b)
session如何设置和读取def average(seq):
return float(sum(seq))/len(seq)
#训练模型
if __name__ =="__main__":
#第⼀次遍历图⽚⽬录是为了获取图⽚总数
input_count =0
for i in range(31,65):
dir='XXX\\train\\%s\\'% i
for root,dirs,files in os.walk(dir):
for filename in files:
input_count = input_count +1
#定义对应维数和各维长度的数组
input_images = np.array([[0]*SIZE for i in range(input_count)])#⽣成⼀个input_count⾏，SIZE列的全零⼆维数组
input_labels = np.array([[0]*NUM_CLASSES for i in range(input_count)])#⽣成⼀个input_count⾏，NUM_CLASSES列的全零⼆维数组#第⼆次遍历图⽚⽬录是为了⽣成图⽚数据和标签
index =0
for i in range(31,65):
dir='XXX\\train\\%s\\'% i
a =0
for root,dirs,files in os.walk(dir):
for filename in files:
filename =dir+ filename
img = cv.imread(filename,0)
print(filename)
print(a)
#cv.imshow('threshold',img)
#cv.waitKey(0)
height = img.shape[0]#⾏数
width = img.shape[1]#列数
a = a +1
for h in range(0,height):
for w in range(0,width):
m = img[h][w]
if m >150:
input_images[index][w+h*width]=1
else:
input_images[index][w+h*width]=0
input_labels[index][i-31]=1
index = index +1
#第⼀次遍历图⽚⽬录是为了获得图⽚总数
val_count =0
for i in range(31,65):
dir='XXX\\train\\%s\\'% i
for root,dirs,files in os.walk(dir):
for filename in files:
val_count = val_count +1
#定义对应维数和各维长度的数组
val_images = np.array([[0]*SIZE for i in range(val_count)])#⽣成⼀个input_count⾏，SIZE列的全零⼆维数组
val_labels = np.array([[0]*NUM_CLASSES for i in range(val_count)])#⽣成⼀个input_count⾏，NUM_CLASSES列的全零⼆维数组
#第⼆次遍历图⽚⽬录是为了⽣成图⽚数据和标签
index =0
for i in range(31,65):
dir='XXX\\train\\%s\\'% i
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