车牌识别程序python代码_Python+Tensorflow+CNN实现车牌识别的示例代码

摘要：这篇Python开发技术栏目下的“Python+Tensorflow+CNN实现车牌识别的示例代码”，介绍的技术点是“TensorFlow、Python、示例代码、车牌识别、CNN、代码”，希望对大家开发技术学习和问题解决有帮助。这篇文章主要介绍了Python+Tensorflow+CNN实现车牌识别的示例代码，文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值，需要的朋友们下面随着小编来一起学习学习吧

一、项目概述

本次项目目标是实现对自动生成的带有各种噪声的车牌识别。在噪声干扰情况下，车牌字符分割较困难，此次车牌识别是将车牌7个字符同时训练，字符包括31个省份简称、10个阿拉伯数字、24个英文字母('O'和'I'除外)，共有65个类别，7个字符使用单独的loss函数进行训练。

(运行环境：tensorflow1.14.0-GPU版)

二、生成车牌数据集

import os

import cv2 as cv

import numpy as np

from math import *

from PIL import ImageFont

from PIL import Image

from PIL import ImageDraw

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):

"""

模糊处理

:param img: 输入图像

:param Smu: 模糊图像

:return: 添加模糊后的图像

"""

rows = r(Smu.shape[0] - 50)

cols = r(Smu.shape[1] - 50)

adder = Smu[rows:rows + 50, cols:cols + 50]

adder = cv.resize(adder, (50, 50))

img = cv.resize(img,(50,50))

img = cv.bitwise_not(img)

img = cv.bitwise_and(adder, img)

img = cv.bitwise_not(img)

return img

def rot(img, angel, shape, max_angel):

"""

添加透视畸变

"""

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 = cv.getPerspectiveTransform(pts1, pts2)

dst = cv.warpPerspective(img, M, size)

return dst

def rotRandrom(img, factor, size):

"""

添加放射畸变

:param img: 输入图像

:param factor: 畸变的参数

:param size: 图片目标尺寸

:return: 放射畸变后的图像

"""

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 = cv.getPerspectiveTransform(pts1, pts2)

dst = cv.warpPerspective(img, M, size)

return dst

def tfactor(img):

"""

添加饱和度光照的噪声

"""

hsv = cv.cvtColor(img,cv.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 = cv.cvtColor(hsv, cv.COLOR_HSV2BGR)

return img

def random_envirment(img, noplate_bg):

"""

添加自然环境的噪声, noplate_bg为不含车牌的背景图

"""

bg_index = r(len(noplate_bg))

env = cv.imread(noplate_bg[bg_index])

env = cv.resize(env, (img.shape[1], img.shape[0]))

bak = (img == 0)

bak = bak.astype(np.uint8) * 255

inv = cv.bitwise_and(bak, env)

img = cv.bitwise_or(inv, img)

return img

def GenCh(f, val):

"""

生成中文字符

"""

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):

"""

生成英文字符

"""

img =Image.new("RGB", (23, 70), (255, 255, 255))

draw = ImageDraw.Draw(img)

draw.text((0, 2), val, (0, 0, 0), font=f) # val.decode('utf-8')

A = np.array(img)

return A

def AddGauss(img, level):

"""

添加高斯模糊

"""

return cv.blur(img, (level * 2 + 1, level * 2 + 1))

def r(val):

return int(np.random.random() * val)

def AddNoiseSingleChannel(single):

"""

添加高斯噪声

"""

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.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 = cv.resize(cv.imread("data\\images\\template.bmp"), (226, 70)) # template.bmp:车牌背景图

self.smu = cv.imread("data\\images\\smu2.jpg") # smu2.jpg:模糊图像

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) # decode(encoding="utf-8")

fg = cv.bitwise_not(fg)

com = cv.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 = tfactor(com)

com = random_envirment(com, self.noplates_path)

com = AddGauss(com, 1+r(4))

com = addNoise(com)

return com

@staticmethod

def genPlateString(pos, val):

"""

生成车牌string，存为图片

生成车牌list，存为label

"""

plateStr = ""

plateList=[]

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

plateList.append(val)

else:

if cpos == 0:

plateStr += chars[r(31)]

plateList.append(plateStr)

elif cpos == 1:

plateStr += chars[41 + r(24)]

plateList.append(plateStr)

else:

plateStr += chars[31 + r(34)]

plateList.append(plateStr)

plate = [plateList[0]]

b = [plateList[i][-1] for i in range(len(plateList))]

plate.extend(b[1:7])

return plateStr, plate

@staticmethod

def genBatch(batchsize, outputPath, size):

"""

将生成的车牌图片写入文件夹，对应的label写入label.txt

:param batchsize: 批次大小

:param outputPath: 输出图像的保存路径

:param size: 输出图像的尺寸

:return: None

"""

if not os.path.exists(outputPath):

os.mkdir(outputPath)

outfile = open('data\\plate\\label.txt', 'w', encoding='utf-8')

for i in range(batchsize):

plateStr, plate = G.genPlateString(-1, -1)

# print(plateStr, plate)

img = G.generate(plateStr)

img = cv.resize(img, size)

cv.imwrite(outputPath + "\\" + str(i).zfill(2) + ".jpg", img)

outfile.write(str(plate) + "\n")

if __name__ == '__main__':

G = GenPlate("data\\font\\platech.ttf", 'data\\font\\platechar.ttf', "data\\NoPlates")

G.genBatch(101, 'data\\plate', (272, 72))

生成的车牌图像尺寸尽量不要超过300，本次尺寸选取：272 * 72

生成车牌所需文件：

字体文件：中文‘platech.ttf'，英文及数字‘platechar.ttf'

背景图：来源于不含车牌的车辆裁剪图片

车牌(蓝底)：template.bmp

噪声图像：smu2.jpg

车牌生成后保存至plate文件夹，示例如下：

三、数据导入

from genplate import *

import matplotlib.pyplot as plt

# 产生用于训练的数据

class OCRIter:

def __init__(self, batch_size, width, height):

super(OCRIter, self).__init__()

self.genplate = GenPlate("data\\font\\platech.ttf", 'data\\font\\platechar.ttf', "data\\NoPlates")

self.batch_size = batch_size

self.height = height

self.width = width

def iter(self):

data = []

label = []

for i in range(self.batch_size):

img, num = self.gen_sample(self.genplate, self.width, self.height)

data.append(img)

label.append(num)

return np.array(data), np.array(label)

@staticmethod

def rand_range(lo, hi):

return lo + r(hi - lo)

def gen_rand(self):

name = ""

label = list([])

label.append(self.rand_range(0, 31)) #产生车牌开头32个省的标签

label.append(self.rand_range(41, 65)) #产生车牌第二个字母的标签

for i in range(5):

label.append(self.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(self, genplate, width, height):

num, label = self.gen_rand()

img = genplate.generate(num)

img = cv.resize(img, (height, width))

img = np.multiply(img, 1/255.0)

return img, label #返回的label为标签，img为车牌图像

'''

# 测试代码

O = OCRIter(2, 272, 72)

img, lbl = O.iter()

for im in img:

plt.imshow(im, cmap='gray')

plt.show()

print(img.shape)

print(lbl)

'''

四、CNN模型构建

import tensorflow as tf

def cnn_inference(images, keep_prob):

W_conv = {

'conv1': tf.Variable(tf.random.truncated_normal([3, 3, 3, 32],

stddev=0.1)),

'conv2': tf.Variable(tf.random.truncated_normal([3, 3, 32, 32],

stddev=0.1)),

'conv3': tf.Variable(tf.random.truncated_normal([3, 3, 32, 64],

stddev=0.1)),

'conv4': tf.Variable(tf.random.truncated_normal([3, 3, 64, 64],

stddev=0.1)),

'conv5': tf.Variable(tf.random.truncated_normal([3, 3, 64, 128],

stddev=0.1)),

'conv6': tf.Variable(tf.random.truncated_normal([3, 3, 128, 128],

stddev=0.1)),

'fc1_1': tf.Variable(tf.random.truncated_normal([5*30*128, 65],

stddev=0.01)),

'fc1_2': tf.Variable(tf.random.truncated_normal([5*30*128, 65],

stddev=0.01)),

'fc1_3': tf.Variable(tf.random.truncated_normal([5*30*128, 65],

stddev=0.01)),

'fc1_4': tf.Variable(tf.random.truncated_normal([5*30*128, 65],

stddev=0.01)),

'fc1_5': tf.Variable(tf.random.truncated_normal([5*30*128, 65],

stddev=0.01)),

'fc1_6': tf.Variable(tf.random.truncated_normal([5*30*128, 65],

stddev=0.01)),

'fc1_7': tf.Variable(tf.random.truncated_normal([5*30*128, 65],

stddev=0.01)),

}

b_conv = {

'conv1': tf.Variable(tf.constant(0.1, dtype=tf.float32,

shape=[32])),

'conv2': tf.Variable(tf.constant(0.1, dtype=tf.float32,

shape=[32])),

'conv3': tf.Variable(tf.constant(0.1, dtype=tf.float32,

shape=[64])),

'conv4': tf.Variable(tf.constant(0.1, dtype=tf.float32,

shape=[64])),

'conv5': tf.Variable(tf.constant(0.1, dtype=tf.float32,

shape=[128])),

'conv6': tf.Variable(tf.constant(0.1, dtype=tf.float32,

shape=[128])),

'fc1_1': tf.Variable(tf.constant(0.1, dtype=tf.float32,

shape=[65])),

'fc1_2': tf.Variable(tf.constant(0.1, dtype=tf.float32,

shape=[65])),

'fc1_3': tf.Variable(tf.constant(0.1, dtype=tf.float32,

shape=[65])),

'fc1_4': tf.Variable(tf.constant(0.1, dtype=tf.float32,

shape=[65])),

'fc1_5': tf.Variable(tf.constant(0.1, dtype=tf.float32,

shape=[65])),

'fc1_6': tf.Variable(tf.constant(0.1, dtype=tf.float32,

shape=[65])),

'fc1_7': tf.Variable(tf.constant(0.1, dtype=tf.float32,

shape=[65])),

}

# 第1层卷积层

conv1 = tf.nn.conv2d(images, W_conv['conv1'], strides=[1,1,1,1], padding='VALID')

conv1 = tf.nn.bias_add(conv1, b_conv['conv1'])

conv1 = tf.nn.relu(conv1)

# 第2层卷积层

conv2 = tf.nn.conv2d(conv1, W_conv['conv2'], strides=[1,1,1,1], padding='VALID')

conv2 = tf.nn.bias_add(conv2, b_conv['conv2'])

conv2 = tf.nn.relu(conv2)

# 第1层池化层

pool1 = tf.nn.max_pool2d(conv2, ksize=[1,2,2,1], strides=[1,2,2,1], padding='VALID')

# 第3层卷积层

conv3 = tf.nn.conv2d(pool1, W_conv['conv3'], strides=[1,1,1,1], padding='VALID')

conv3 = tf.nn.bias_add(conv3, b_conv['conv3'])

conv3 = tf.nn.relu(conv3)

# 第4层卷积层

conv4 = tf.nn.conv2d(conv3, W_conv['conv4'], strides=[1,1,1,1], padding='VALID')

conv4 = tf.nn.bias_add(conv4, b_conv['conv4'])

conv4 = tf.nn.relu(conv4)

# 第2层池化层

pool2 = tf.nn.max_pool2d(conv4, ksize=[1,2,2,1], strides=[1,2,2,1], padding='VALID')

# 第5层卷积层

conv5 = tf.nn.conv2d(pool2, W_conv['conv5'], strides=[1,1,1,1], padding='VALID')

conv5 = tf.nn.bias_add(conv5, b_conv['conv5'])

conv5 = tf.nn.relu(conv5)

# 第4层卷积层

conv6 = tf.nn.conv2d(conv5, W_conv['conv6'], strides=[1,1,1,1], padding='VALID')

conv6 = tf.nn.bias_add(conv6, b_conv['conv6'])

conv6 = tf.nn.relu(conv6)

# 第3层池化层

pool3 = tf.nn.max_pool2d(conv6, ksize=[1,2,2,1], strides=[1,2,2,1], padding='VALID')

#第1_1层全连接层

# print(pool3.shape)

reshape = tf.reshape(pool3, [-1, 5 * 30 * 128])

fc1 = tf.nn.dropout(reshape, keep_prob)

fc1_1 = tf.add(tf.matmul(fc1, W_conv['fc1_1']), b_conv['fc1_1'])

#第1_2层全连接层

fc1_2 = tf.add(tf.matmul(fc1, W_conv['fc1_2']), b_conv['fc1_2'])

#第1_3层全连接层

fc1_3 = tf.add(tf.matmul(fc1, W_conv['fc1_3']), b_conv['fc1_3'])

#第1_4层全连接层

fc1_4 = tf.add(tf.matmul(fc1, W_conv['fc1_4']), b_conv['fc1_4'])

#第1_5层全连接层

fc1_5 = tf.add(tf.matmul(fc1, W_conv['fc1_5']), b_conv['fc1_5'])

#第1_6层全连接层

fc1_6 = tf.add(tf.matmul(fc1, W_conv['fc1_6']), b_conv['fc1_6'])

#第1_7层全连接层

fc1_7 = tf.add(tf.matmul(fc1, W_conv['fc1_7']), b_conv['fc1_7'])

return fc1_1, fc1_2, fc1_3, fc1_4, fc1_5, fc1_6, fc1_7

def calc_loss(logit1, logit2, logit3, logit4, logit5, logit6, logit7, labels):

labels = tf.convert_to_tensor(labels, tf.int32)

loss1 = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(

logits=logit1, labels=labels[:, 0]))

tf.compat.v1.summary.scalar('loss1', loss1)

loss2 = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(

logits=logit2, labels=labels[:, 1]))

tf.compat.v1.summary.scalar('loss2', loss2)

loss3 = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(

logits=logit3, labels=labels[:, 2]))

tf.compat.v1.summary.scalar('loss3', loss3)

loss4 = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(

logits=logit4, labels=labels[:, 3]))

tf.compat.v1.summary.scalar('loss4', loss4)

loss5 = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(

logits=logit5, labels=labels[:, 4]))

tf.compat.v1.summary.scalar('loss5', loss5)

loss6 = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(

logits=logit6, labels=labels[:, 5]))

tf.compat.v1.summary.scalar('loss6', loss6)

loss7 = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(

logits=logit7, labels=labels[:, 6]))

tf.compat.v1.summary.scalar('loss7', loss7)

return loss1, loss2, loss3, loss4, loss5, loss6, loss7

def train_step(loss1, loss2, loss3, loss4, loss5, loss6, loss7, learning_rate):

optimizer1 = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)

train_op1 = optimizer1.minimize(loss1)

optimizer2 = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)

train_op2 = optimizer2.minimize(loss2)

optimizer3 = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)

train_op3 = optimizer3.minimize(loss3)

optimizer4 = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)

train_op4 = optimizer4.minimize(loss4)

optimizer5 = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)

train_op5 = optimizer5.minimize(loss5)

optimizer6 = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)

train_op6 = optimizer6.minimize(loss6)

optimizer7 = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)

train_op7 = optimizer7.minimize(loss7)

return train_op1, train_op2, train_op3, train_op4, train_op5, train_op6, train_op7

def pred_model(logit1, logit2, logit3, logit4, logit5, logit6, logit7, labels):

labels = tf.convert_to_tensor(labels, tf.int32)

labels = tf.reshape(tf.transpose(labels), [-1])

logits = tf.concat([logit1, logit2, logit3, logit4, logit5, logit6, logit7], 0)

prediction = tf.nn.in_top_k(logits, labels, 1)

accuracy = tf.reduce_mean(tf.cast(prediction, tf.float32))

tf.compat.v1.summary.scalar('accuracy', accuracy)

return accuracy

五、模型训练

import os

import time

import datetime

import numpy as np

import tensorflow as tf

from input_data import OCRIter

import model

os.environ["TF_CPP_MIN_LOG_LEVEL"] = '3'

img_h = 72

img_w = 272

num_label = 7

batch_size = 32

epoch = 10000

learning_rate = 0.0001

logs_path = 'logs\\1005'

model_path = 'saved_model\\1005'

image_holder = tf.compat.v1.placeholder(tf.float32, [batch_size, img_h, img_w, 3])

label_holder = tf.compat.v1.placeholder(tf.int32, [batch_size, 7])

keep_prob = tf.compat.v1.placeholder(tf.float32)

def get_batch():

data_batch = OCRIter(batch_size, img_h, img_w)

image_batch, label_batch = data_batch.iter()

return np.array(image_batch), np.array(label_batch)

logit1, logit2, logit3, logit4, logit5, logit6, logit7 = model.cnn_inference(

image_holder, keep_prob)

loss1, loss2, loss3, loss4, loss5, loss6, loss7 = model.calc_loss(

logit1, logit2, logit3, logit4, logit5, logit6, logit7, label_holder)

train_op1, train_op2, train_op3, train_op4, train_op5, train_op6, train_op7 = model.train_step(

loss1, loss2, loss3, loss4, loss5, loss6, loss7, learning_rate)

accuracy = model.pred_model(logit1, logit2, logit3, logit4, logit5, logit6, logit7, label_holder)

input_image=tf.compat.v1.summary.image('input', image_holder)

summary_op = tf.compat.v1.summary.merge(tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.SUMMARIES))

init_op = tf.compat.v1.global_variables_initializer()

with tf.compat.v1.Session() as sess:

sess.run(init_op)

train_writer = tf.compat.v1.summary.FileWriter(logs_path, sess.graph)

saver = tf.compat.v1.train.Saver()

start_time1 = time.time()

for step in range(epoch):

# 生成车牌图像以及标签数据

img_batch, lbl_batch = get_batch()

start_time2 = time.time()

time_str = datetime.datetime.now().isoformat()

feed_dict = {image_holder:img_batch, label_holder:lbl_batch, keep_prob:0.6}

_1, _2, _3, _4, _5, _6, _7, ls1, ls2, ls3, ls4, ls5, ls6, ls7, acc = sess.run(

[train_op1, train_op2, train_op3, train_op4, train_op5, train_op6, train_op7,

loss1, loss2, loss3, loss4, loss5, loss6, loss7, accuracy], feed_dict)

summary_str = sess.run(summary_op, feed_dict)

train_writer.add_summary(summary_str,step)

duration = time.time() - start_time2

loss_total = ls1 + ls2 + ls3 + ls4 + ls5 + ls6 + ls7

if step % 10 == 0:

sec_per_batch = float(duration)

print('%s: Step %d, loss_total = %.2f, acc = %.2f%%, sec/batch = %.2f' %

(time_str, step, loss_total, acc * 100, sec_per_batch))

if step % 5000 == 0 or (step + 1) == epoch:

checkpoint_path = os.path.join(model_path,'model.ckpt')

saver.save(sess, checkpoint_path, global_step=step)

end_time = time.time()

print("Training over. It costs {:.2f} minutes".format((end_time - start_time1) / 60))

六、训练结果展示

训练参数：

batch_size = 32

epoch = 10000

learning_rate = 0.0001

在tensorboard中查看训练过程

accuracy :

accuracy

曲线在epoch = 10000左右时达到收敛，最终精确度在94%左右

loss :

以上三张分别是loss1，loss2， loss7的曲线图像，一号位字符是省份简称，识别相对字母数字较难，loss1=0.08左右，二号位字符是字母，loss2稳定在0.001左右，但是随着字符往后，loss值也将越来越大，7号位字符loss7稳定在0.6左右。

七、预测单张车牌

import os

import cv2 as cv

import numpy as np

import tensorflow as tf

import matplotlib.pyplot as plt

from PIL import Image

import model

os.environ["TF_CPP_MIN_LOG_LEVEL"] = '3' # 只显示 Error

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 get_one_image(test):

""" 随机获取单张车牌图像 """

n = len(test)

rand_num =np.random.randint(0,n)

img_dir = test[rand_num]

image_show = Image.open(img_dir)

plt.imshow(image_show) # 显示车牌图片

image = cv.imread(img_dir)

image = image.reshape(72, 272, 3)

image = np.multiply(image, 1 / 255.0)

return image

batch_size = 1

x = tf.compat.v1.placeholder(tf.float32, [batch_size, 72, 272, 3])

keep_prob = tf.compat.v1.placeholder(tf.float32)

test_dir = 'data\\plate\\'

test_image = []

for file in os.listdir(test_dir):

test_image.append(test_dir + file)

test_image = list(test_image)

image_array = get_one_image(test_image)

logit1, logit2, logit3, logit4, logit5, logit6, logit7 = model.cnn_inference(x, keep_prob)

model_path = 'saved_model\\1005'

saver = tf.compat.v1.train.Saver()

with tf.compat.v1.Session() as sess:

print ("Reading checkpoint...")

ckpt = tf.train.get_checkpoint_state(model_path)

if ckpt and ckpt.model_checkpoint_path:

global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]

saver.restore(sess, ckpt.model_checkpoint_path)

print('Loading success, global_step is %s' % global_step)

else:

print('No checkpoint file found')

pre1, pre2, pre3, pre4, pre5, pre6, pre7 = sess.run(

[logit1, logit2, logit3, logit4, logit5, logit6, logit7],

feed_dict={x:image_array, keep_prob:1.0})

prediction = np.reshape(np.array([pre1, pre2, pre3, pre4, pre5, pre6, pre7]), [-1, 65])

max_index = np.argmax(prediction, axis=1)

print(max_index)

line = ''

result = np.array([])

for i in range(prediction.shape[0]):

if i == 0:

result = np.argmax(prediction[i][0:31])

if i == 1:

result = np.argmax(prediction[i][41:65]) + 41

if i > 1:

result = np.argmax(prediction[i][31:65]) + 31

line += chars[result]+" "

print ('predicted: ' + line)

plt.show()

随机测试20张车牌，18张预测正确，2张预测错误，从最后两幅预测错误的图片可以看出，模型对相似字符以及遮挡字符识别成功率仍有待提高。测试结果部分展示如下：

八、总结

本次构建的CNN模型较为简单，只有6卷积层+3池化层+1全连接层，可以通过增加模型深度以及每层之间的神经元数量来优化模型，提高识别的准确率。此次训练数据集来源于自动生成的车牌，由于真实的车牌图像与生成的车牌图像在噪声干扰上有所区分，所以识别率上会有所出入。如果使用真实的车牌数据集，需要对车牌进行滤波、均衡化、腐蚀、矢量量化等预处理方法。

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持Java大数据社区。