端到端车牌识别（1）
一 、简介
本项目主要通过tensorflow实现端到端车牌识别，无需车牌字符分割。目前，仅关注识别问题，未考虑车辆检测、车牌定位等问题，本项目尝试了采用两种方法：（1）CNN ; （2）LSTM ;
二、CNN方法实现
1. 数据集
数据集采用了一个开源的车牌生成器，该项目作者采用Mxnet实现车牌识别。生成的车牌基本都有倾斜、加噪处理。
代码 genplate.py 如下：
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 4 09:19:43 2017
@author: llc
"""
#coding=utf-8
import PIL
from PIL import ImageFont
from PIL import Image
from PIL import ImageDraw
import cv2;
import numpy as np;
import os;
from math import *
# font = ImageFont.truetype("Arial-Bold.ttf",14)
index = {"京": 0, "沪": 1, "津": 2, "渝": 3, "冀": 4, "晋": 5, "蒙": 6, "辽": 7, "吉": 8, "黑": 9, "苏": 10, "浙": 11, "皖": 12,
"闽": 13, "赣": 14, "鲁": 15, "豫": 16, "鄂": 17, "湘": 18, "粤": 19, "桂": 20, "琼": 21, "川": 22, "贵": 23, "云": 24,
"藏": 25, "陕": 26, "甘": 27, "青": 28, "宁": 29, "新": 30, "0": 31, "1": 32, "2": 33, "3": 34, "4": 35, "5": 36,
"6": 37, "7": 38, "8": 39, "9": 40, "A": 41, "B": 42, "C": 43, "D": 44, "E": 45, "F": 46, "G": 47, "H": 48,
"J": 49, "K": 50, "L": 51, "M": 52, "N": 53, "P": 54, "Q": 55, "R": 56, "S": 57, "T": 58, "U": 59, "V": 60,
"W": 61, "X": 62, "Y": 63, "Z": 64};
chars = ["京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂",
"琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A",
"B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "U", "V", "W", "X",
"Y", "Z"
];
def AddSmudginess(img, Smu):
rows = r(Smu.shape[0] - 50)
cols = r(Smu.shape[1] - 50)
adder = Smu[rows:rows + 50, cols:cols + 50];
adder = cv2.resize(adder, (50, 50));
# adder = cv2.bitwise_not(adder)
img = cv2.resize(img,(50,50))
img = cv2.bitwise_not(img)
img = cv2.bitwise_and(adder, img)
img = cv2.bitwise_not(img)
return img
def rot(img,angel,shape,max_angel):
""" 使图像轻微的畸变
img 输入图像
factor 畸变的参数
size 为图片的目标尺寸
"""
size_o = [shape[1],shape[0]]
size = (shape[1]+ int(shape[0]*cos((float(max_angel )/180) * 3.14)),shape[0])
interval = abs( int( sin((float(angel) /180) * 3.14)* shape[0]));
pts1 = np.float32([[0,0],[0,size_o[1]],[size_o[0],0],[size_o[0],size_o[1]]])
if(angel>0):
pts2 = np.float32([[interval,0],[0,size[1] ],[size[0],0 ],[size[0]-interval,size_o[1]]])
else:
pts2 = np.float32([[0,0],[interval,size[1] ],[size[0]-interval,0 ],[size[0],size_o[1]]])
M = cv2.getPerspectiveTransform(pts1,pts2);
dst = cv2.warpPerspective(img,M,size);
return dst;
def rotRandrom(img, factor, size):
shape = size;
pts1 = np.float32([[0, 0], [0, shape[0]], [shape[1], 0], [shape[1], shape[0]]])
pts2 = np.float32([[r(factor), r(factor)], [ r(factor), shape[0] - r(factor)], [shape[1] - r(factor), r(factor)],
[shape[1] - r(factor), shape[0] - r(factor)]])
M = cv2.getPerspectiveTransform(pts1, pts2);
dst = cv2.warpPerspective(img, M, size);
return dst;
def tfactor(img):
hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV);
hsv[:,:,0] = hsv[:,:,0]*(0.8+ np.random.random()*0.2);
hsv[:,:,1] = hsv[:,:,1]*(0.3+ np.random.random()*0.7);
hsv[:,:,2] = hsv[:,:,2]*(0.2+ np.random.random()*0.8);
img = cv2.cvtColor(hsv,cv2.COLOR_HSV2BGR);
return img
def random_envirment(img,data_set):
index=r(len(data_set))
env = cv2.imread(data_set[index])
env = cv2.resize(env,(img.shape[1],img.shape[0]))
bak = (img==0);
bak = bak.astype(np.uint8)*255;
inv = cv2.bitwise_and(bak,env)
img = cv2.bitwise_or(inv,img)
return img
def GenCh(f,val):
img=Image.new("RGB", (45,70),(255,255,255))
draw = ImageDraw.Draw(img)
draw.text((0, 3),val,(0,0,0),font=f)
img = img.resize((23,70))
A = np.array(img)
return A
def GenCh1(f,val):
img=Image.new("RGB", (23,70),(255,255,255))
draw = ImageDraw.Draw(img)
#draw.text((0, 2),val.decode('utf-8'),(0,0,0),font=f)
draw.text((0, 2),val,(0,0,0),font=f)
A = np.array(img)
return A
def AddGauss(img, level):
return cv2.blur(img, (level * 2 + 1, level * 2 + 1));
def r(val):
return int(np.random.random() * val)
def AddNoiseSingleChannel(single):
diff = 255-single.max();
noise = np.random.normal(0,1+r(6),single.shape);
noise = (noise - noise.min())/(noise.max()-noise.min())
noise= diff*noise;
noise= noise.astype(np.uint8)
dst = single + noise
return dst
def addNoise(img,sdev = 0.5,avg=10):
img[:,:,0] = AddNoiseSingleChannel(img[:,:,0]);
img[:,:,1] = AddNoiseSingleChannel(img[:,:,1]);
img[:,:,2] = AddNoiseSingleChannel(img[:,:,2]);
return img;
class GenPlate:
def __init__(self,fontCh,fontEng,NoPlates):
self.fontC = ImageFont.truetype(fontCh,43,0);
self.fontE = ImageFont.truetype(fontEng,60,0);
self.img=np.array(Image.new("RGB", (226,70),(255,255,255)))
self.bg = cv2.resize(cv2.imread("./images/template.bmp"),(226,70));
self.smu = cv2.imread("./images/smu2.jpg");
self.noplates_path = [];
for parent,parent_folder,filenames in os.walk(NoPlates):
for filename in filenames:
path = parent+"/"+filename;
self.noplates_path.append(path);
def draw(self,val):
offset= 2 ;
self.img[0:70,offset+8:offset+8+23]= GenCh(self.fontC,val[0]);
self.img[0:70,offset+8+23+6:offset+8+23+6+23]= GenCh1(self.fontE,val[1]);
for i in range(5):
base = offset+8+23+6+23+17 +i*23 + i*6 ;
self.img[0:70, base : base+23]= GenCh1(self.fontE,val[i+2]);
return self.img
def generate(self,text):
if len(text) == 7:
fg = self.draw(text.encode('utf-8').decode(encoding="utf-8"));
fg = cv2.bitwise_not(fg);
com = cv2.bitwise_or(fg,self.bg);
com = rot(com,r(60)-30,com.shape,30);
com = rotRandrom(com,10,(com.shape[1],com.shape[0]));
#com = AddSmudginess(com,self.smu)
com = tfactor(com)
com = random_envirment(com,self.noplates_path);
com = AddGauss(com, 1+r(4));
com = addNoise(com);
return com
def genPlateString(self,pos,val):
plateStr = "";
box = [0,0,0,0,0,0,0];
if(pos!=-1):
box[pos]=1;
for unit,cpos in zip(box,range(len(box))):
if unit == 1:
plateStr += val
else:
if cpos == 0:
plateStr += chars[r(31)]
elif cpos == 1:
plateStr += chars[41+r(24)]
else:
plateStr += chars[31 + r(34)]
return plateStr;
def genBatch(self, batchSize,pos,charRange, outputPath,size):
if (not os.path.exists(outputPath)):
os.mkdir(outputPath)
for i in range(batchSize):
plateStr = G.genPlateString(-1,-1)
img = G.generate(plateStr);
img = cv2.resize(img,size);
cv2.imwrite(outputPath + "/" + str(i).zfill(2) + ".jpg", img);
# return img
G = GenPlate("./font/platech.ttf",'./font/platechar.ttf',"./NoPlates")
#G.genBatch(15,2,range(31,65),"./plate",(272,72)) #注释原因为每次其他模块运行，若导入该库，都会刷性该函数
# cv2.imshow("a",com)
# cv2.waitKey(0);
2. 数据读入
网络训练读入的数据并不是利用上述1中生成的车牌图片保存到本地，然后再读取，加载进网络。而是，直接边生成车牌数据边读入数据训练。
数据读入代码input_data.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 5 15:44:33 2017
@author: llc
"""
#%%
import numpy as np
import cv2
from genplate import *
#产生用于训练的数据
class OCRIter():
def __init__(self,batch_size,height,width):
super(OCRIter, self).__init__()
self.genplate = GenPlate("./font/platech.ttf",'./font/platechar.ttf','./NoPlates')
self.batch_size = batch_size
self.height = height
self.width = width
#print("make plate data")
# def iter(self):
# for k in range((int)(self.count / self.batch_size)):
# data = []
# label = []
# for i in range(self.batch_size):
# num, img = gen_sample(self.genplate, self.width, self.height)
# data.append(img)
# label.append(num)
# data_all = data
# label_all = label
# return data_all,label_all
def iter(self):
data = []
label = []
for i in range(self.batch_size):
num, img = gen_sample(self.genplate, self.width, self.height)
data.append(img)
label.append(num)
data_all = data
label_all = label
return data_all,label_all
def rand_range(lo,hi):
return lo+r(hi-lo);
def gen_rand():
name = ""
label=[]
label.append(rand_range(0,31)) #产生车牌开头32个省的标签
label.append(rand_range(41,65)) #产生车牌第二个字母的标签
for i in range(5):
label.append(rand_range(31,65)) #产生车牌后续5个字母的标签
name+=chars[label[0]]
name+=chars[label[1]]
for i in range(5):
name+=chars[label[i+2]]
return name,label
def gen_sample(genplate, width, height):
num,label =gen_rand()
img = genplate.generate(num)
img = cv2.resize(img,(width,height))
img = np.multiply(img,1/255.0) #[height,width,channel]
#img = img.transpose(2,0,1)
#img = img.transpose(1,0,2)
return label,img #返回的label为标签，img为深度为3的图像像素
3. 网络模型(tensorflow)
先附上代码model.py:
"""
Created on Tue Sep 5 14:04:19 2017
@author: llc
"""
import tensorflow as tf
import numpy as np
def inference (images,keep_prob):
'''
Build the model
Args:
image: image batch,4D tensor,tf.float32,[batch_size,height,width,channels]
Returns:
output tensor with the computed logits,float,[batch_size,65]
'''
with tf.variable_scope('conv1') as scope:
weights = tf.get_variable('weights',
shape = [3,3,3,32],
dtype = tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32))
conv = tf.nn.conv2d(images,weights,strides=[1,1,1,1],padding='VALID')
biases = tf.get_variable('biases',
shape=[32],
dtype=tf.float32,
initializer=tf.constant_initializer(0.1))
pre_activation = tf.nn.bias_add(conv,biases)
conv1 = tf.nn.relu(pre_activation,name= scope.name)
# conv2
with tf.variable_scope('conv2') as scope:
weights = tf.get_variable('weights',shape=[3,3,32,32],dtype=tf.float32,initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32))
conv = tf.nn.conv2d(conv1,weights,strides=[1,1,1,1],padding='VALID')
biases = tf.get_variable('biases',
shape=[32],
dtype=tf.float32,
initializer=tf.constant_initializer(0.1))
pre_activation = tf.nn.bias_add(conv,biases)
conv2 = tf.nn.relu(pre_activation,name= scope.name)
with tf.variable_scope('max_pooling1') as scope:
pool1 = tf.nn.max_pool(conv2,ksize = [1,2,2,1],strides= [1,2,2,1],padding='VALID',name='pooling1')
#%%
#conv3
with tf.variable_scope('conv3') as scope:
weights = tf.get_variable('weights',shape=[3,3,32,64],dtype=tf.float32,initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32))
conv = tf.nn.conv2d(pool1,weights,strides=[1,1,1,1],padding='VALID')
biases = tf.get_variable('biases',shape=[64],dtype = tf.float32,initializer= tf.constant_initializer(0.1))
pre_activation = tf.nn.bias_add(conv,biases)
conv3 = tf.nn.relu(pre_activation,name=scope.name)
#conv4
with tf.variable_scope('conv4') as scope:
weights = tf.get_variable('weights',shape=[3,3,64,64],dtype=tf.float32,initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32))
conv =tf.nn.conv2d(conv3,weights,strides=[1,1,1,1],padding='VALID')
biases = tf.get_variable('biases',shape=[64],dtype=tf.float32,initializer=tf.constant_initializer(0.1))
pre_activation = tf.nn.bias_add(conv,biases)
conv4 = tf.nn.relu(pre_activation,name=scope.name)
with tf.variable_scope('max_pooling2') as scope:
pool2 = tf.nn.max_pool(conv4,ksize=[1,2,2,1],strides=[1,2,2,1],padding='VALID',name='pooling2')
#conv5
with tf.variable_scope('conv5') as scope:
weights = tf.get_variable('weights',shape=[3,3,64,128],dtype=tf.float32,initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32))
conv =tf.nn.conv2d(pool2,weights,strides=[1,1,1,1],padding='VALID')
biases = tf.get_variable('biases',shape=[128],dtype=tf.float32,initializer=tf.constant_initializer(0.1))
pre_activation = tf.nn.bias_add(conv,biases)
conv5 = tf.nn.relu(pre_activation,name=scope.name)
#conv6
with tf.variable_scope('conv6') as scope:
weights = tf.get_variable('weights',shape=[3,3,128,128],dtype=tf.float32,initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32))
conv =tf.nn.conv2d(conv5,weights,strides=[1,1,1,1],padding='VALID')
biases = tf.get_variable('biases',shape=[128],dtype=tf.float32,initializer=tf.constant_initializer(0.1))
pre_activation = tf.nn.bias_add(conv,biases)
conv6 = tf.nn.relu(pre_activation,name=scope.name)
#pool3
with tf.variable_scope('max_pool3') as scope:
pool3 = tf.nn.max_pool(conv6,ksize=[1,2,2,1],strides=[1,2,2,1],padding='VALID',name='pool3')
#%%
#fc1_flatten
with tf.variable_scope('fc1') as scope:
shp = pool3.get_shape()
flattened_shape =shp[1].value*shp[2].value*shp[3].value
reshape = tf.reshape(pool3,[-1,flattened_shape])
# dim = reshape.get_shape()[-1].value
# weights = tf.get_variable('weights',shape=[dim,256],dtype = tf.float32,
# initializer = tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32))
# biases = tf.get_variable('biases',
# shape=[256],
# dtype=tf.float32,
# initializer = tf.truncated_normal_initializer(0.1)
# )
# fc1 = tf.matmul(reshape,weights)+biases
# fc1 = tf.nn.relu(fc1,name=scope.name)
fc1 = tf.nn.dropout(reshape,keep_prob,name='fc1_dropdot')
with tf.variable_scope('fc21') as scope:
weights = tf.get_variable('weights',
shape=[flattened_shape,65],
dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32))
biases = tf.get_variable('biases',
shape=[65],
dtype=tf.float32,
initializer = tf.truncated_normal_initializer(0.1)
)
fc21 = tf.matmul(fc1,weights)+biases
with tf.variable_scope('fc22') as scope:
weights = tf.get_variable('weights',
shape=[flattened_shape,65],
dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32))
biases = tf.get_variable('biases',
shape=[65],
dtype=tf.float32,
initializer = tf.truncated_normal_initializer(0.1)
)
fc22 = tf.matmul(fc1,weights)+biases
with tf.variable_scope('fc23') as scope:
weights = tf.get_variable('weights',
shape=[flattened_shape,65],
dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32))
biases = tf.get_variable('biases',
shape=[65],
dtype=tf.float32,
initializer = tf.truncated_normal_initializer(0.1)
)
fc23= tf.matmul(fc1,weights)+biases
with tf.variable_scope('fc24') as scope:
weights = tf.get_variable('weights',
shape=[flattened_shape,65],
dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32))
biases = tf.get_variable('biases',
shape=[65],
dtype=tf.float32,
initializer = tf.truncated_normal_initializer(0.1)
)
fc24 = tf.matmul(fc1,weights)+biases
with tf.variable_scope('fc25') as scope:
weights = tf.get_variable('weights',
shape=[flattened_shape,65],
dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32))
biases = tf.get_variable('biases',
shape=[65],
dtype=tf.float32,
initializer = tf.truncated_normal_initializer(0.1)
)
fc25 = tf.matmul(fc1,weights)+biases
with tf.variable_scope('fc26') as scope:
weights = tf.get_variable('weights',
shape=[flattened_shape,65],
dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32))
biases = tf.get_variable('biases',
shape=[65],
dtype=tf.float32,
initializer = tf.truncated_normal_initializer(0.1)
)
fc26 = tf.matmul(fc1,weights)+biases
with tf.variable_scope('fc27') as scope:
weights = tf.get_variable('weights',
shape=[flattened_shape,65],
dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32))
biases = tf.get_variable('biases',
shape=[65],
dtype=tf.float32,
initializer = tf.truncated_normal_initializer(0.1)
)
fc27 = tf.matmul(fc1,weights)+biases
# with tf.variable_scope('fc2') as scope:
# fc2 = tf.concat([fc21,fc22,fc23,fc24,fc25,fc26,fc27],0) #shape = [7xbatch_size,65]
#
# return fc2
return fc21,fc22,fc23,fc24,fc25,fc26,fc27 #shape = [7,batch_size,65]
def losses(logits1,logits2,logits3,logits4,logits5,logits6,logits7,labels):
'''Compute loss from logits and labels
Args:
logits: logits tensor, float, [7*batch_size, 65]
labels: label tensor, tf.int32, [7*batch_size]
Returns:
loss tensor of float type
'''
labels = tf.convert_to_tensor(labels,tf.int32)
with tf.variable_scope('loss1') as scope:
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits1, labels=labels[:,0], name='xentropy_per_example')
#cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=labels,name='xentropy_per_example')
loss1 = tf.reduce_mean(cross_entropy, name='loss1')
tf.summary.scalar(scope.name+'/loss1', loss1)
with tf.variable_scope('loss2') as scope:
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits2, labels=labels[:,1], name='xentropy_per_example')
#cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=labels,name='xentropy_per_example')
loss2 = tf.reduce_mean(cross_entropy, name='loss2')
tf.summary.scalar(scope.name+'/loss2', loss2)
with tf.variable_scope('loss3') as scope:
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits3, labels=labels[:,2], name='xentropy_per_example')
#cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=labels,name='xentropy_per_example')
loss3 = tf.reduce_mean(cross_entropy, name='loss3')
tf.summary.scalar(scope.name+'/loss3', loss3)
with tf.variable_scope('loss4') as scope:
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits4, labels=labels[:,3], name='xentropy_per_example')
#cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=labels,name='xentropy_per_example')
loss4 = tf.reduce_mean(cross_entropy, name='loss4')
tf.summary.scalar(scope.name+'/loss4', loss4)
with tf.variable_scope('loss5') as scope:
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits5, labels=labels[:,4], name='xentropy_per_example')
#cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=labels,name='xentropy_per_example')
loss5 = tf.reduce_mean(cross_entropy, name='loss5')
tf.summary.scalar(scope.name+'/loss5', loss5)
with tf.variable_scope('loss6') as scope:
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits6, labels=labels[:,5], name='xentropy_per_example')
#cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=labels,name='xentropy_per_example')
loss6 = tf.reduce_mean(cross_entropy, name='loss6')
tf.summary.scalar(scope.name+'/loss6', loss6)
with tf.variable_scope('loss7') as scope:
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits7, labels=labels[:,6], name='xentropy_per_example')
#cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=labels,name='xentropy_per_example')
loss7 = tf.reduce_mean(cross_entropy, name='loss7')
tf.summary.scalar(scope.name+'/loss7', loss7)
return loss1,loss2,loss3,loss4,loss5,loss6,loss7
def trainning( loss1,loss2,loss3,loss4,loss5,loss6,loss7, learning_rate):
'''Training ops, the Op returned by this function is what must be passed to
'sess.run()' call to cause the model to train.
Args:
loss: loss tensor, from losses()
Returns:
train_op: The op for trainning
'''
with tf.name_scope('optimizer1'):
optimizer1 = tf.train.AdamOptimizer(learning_rate= learning_rate)
global_step = tf.Variable(0, name='global_step', trainable=False)
train_op1 = optimizer1.minimize(loss1, global_step= global_step)
with tf.name_scope('optimizer2'):
optimizer2 = tf.train.AdamOptimizer(learning_rate= learning_rate)
global_step = tf.Variable(0, name='global_step', trainable=False)
train_op2 = optimizer2.minimize(loss2, global_step= global_step)
with tf.name_scope('optimizer3'):
optimizer3 = tf.train.AdamOptimizer(learning_rate= learning_rate)
global_step = tf.Variable(0, name='global_step', trainable=False)
train_op3 = optimizer3.minimize(loss3, global_step= global_step)
with tf.name_scope('optimizer4'):
optimizer4 = tf.train.AdamOptimizer(learning_rate= learning_rate)
global_step = tf.Variable(0, name='global_step', trainable=False)
train_op4 = optimizer4.minimize(loss4, global_step= global_step)
with tf.name_scope('optimizer5'):
optimizer5 = tf.train.AdamOptimizer(learning_rate= learning_rate)
global_step = tf.Variable(0, name='global_step', trainable=False)
train_op5 = optimizer5.minimize(loss5, global_step= global_step)
with tf.name_scope('optimizer6'):
optimizer6 = tf.train.AdamOptimizer(learning_rate= learning_rate)
global_step = tf.Variable(0, name='global_step', trainable=False)
train_op6 = optimizer6.minimize(loss6, global_step= global_step)
with tf.name_scope('optimizer7'):
optimizer7 = tf.train.AdamOptimizer(learning_rate= learning_rate)
global_step = tf.Variable(0, name='global_step', trainable=False)
train_op7 = optimizer7.minimize(loss7, global_step= global_step)
return train_op1,train_op2,train_op3,train_op4,train_op5,train_op6,train_op7
def evaluation(logits1,logits2,logits3,logits4,logits5,logits6,logits7,labels):
"""Evaluate the quality of the logits at predicting the label.
Args:
logits: Logits tensor, float - [batch_size, NUM_CLASSES].
labels: Labels tensor, int32 - [batch_size], with values in the
range [0, NUM_CLASSES).
Returns:
A scalar int32 tensor with the number of examples (out of batch_size)
that were predicted correctly.
"""
logits_all = tf.concat([logits1,logits2,logits3,logits4,logits5,logits6,logits7],0)
labels = tf.convert_to_tensor(labels,tf.int32)
labels_all = tf.reshape(tf.transpose(labels),[-1])
with tf.variable_scope('accuracy') as scope:
correct = tf.nn.in_top_k(logits_all, labels_all, 1)
correct = tf.cast(correct, tf.float16)
accuracy = tf.reduce_mean(correct)
tf.summary.scalar(scope.name+'/accuracy', accuracy)
return accuracy
#代码可以简化，用keras写更简单，网上有keras版的教程keras版车牌识别。由于车牌为7个字符，每个字符对应65（数字/英文字母/省份）个标签，当然，前面两个字符仅分别为：省份、字母。因此，神经网络的输出要求有7个单元（全连接层），每个单元对应65个字符标签（验证码等识别可根据需求设计）。
网络设计中的几个关键点：
（1）全连接层Fc1：该层将卷积层及池化层提取出的特征全部flatten为1维张量，便于后续7个输出单元。
（2）Dropout:对Fc1层训练阶段进行dropout，防止过拟合，测试时无需dropout。
（3）7个输出单元：fc21,fc22,fc23,fc24,fc25,fc26,fc27。
（4）Loss:刚开始设计时，本人将7个输出单元tf.concat在一起，即合成为一个logits，计算其loss。发现loss始终在24左右，无法下降。后来，将7个输出单元分别计算loss,每个loss都基本正常。
即使loss7也还行： 
（以上结果并不是最优的训练版本。）
（5）训练精确度：直接将7个输出单元concat,利用tf.nn.in_top_k计算。
（未完，见下一篇）
端到端车牌识别（2）