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README
Apache-2.0

EasyPR

EasyPR是一个中文的开源车牌识别系统,其目标是成为一个简单、高效、准确的车牌识别引擎。

相比于其他的车牌识别系统,EasyPR有如下特点:

  • 它基于openCV这个开源库。这意味着你可以获取全部源代码,并且移植到java等平台。
  • 它能够识别中文。例如车牌为苏EUK722的图片,它可以准确地输出std:string类型的"苏EUK722"的结果。
  • 它的识别率较高。图片清晰情况下,车牌检测与字符识别可以达到90%以上的精度。

版本

EasyPR最开始是发布在GitHub上的,然后在国内的oschina上也部署了一份镜像。 相关的issue欢迎在GitHub上统一提交。目前除了windows版本以外,还有以下其他平台的版本:

版本 开发者 地址
android goldriver linuxxx/EasyPR_Android
linux Micooz Micooz/EasyPR/tree/linux-dev
ios zhoushiwei zhoushiwei/EasyPR-iOS
mac zhoushiwei zhoushiwei/EasyPR
c# 招聘中

感谢以上所有开发者的努力!

更新

目前EasyPR的版本是1.1,相比上一个版本,有以下更新。可以在ChangeLog中找到更多信息。 目前版本的EasyPR在车牌定位上还不够鲁棒,在下个版本中计划对定位做个完善与升级。

  • 新的SVM模型。新模型使用rbf核替代了liner核,在车牌判断的准确率提升了8个百分点。
  • 新增两个特征提取方法。并提供了相关的回调函数接口。
  • 新增Debug模式。可以在image/tmp文件夹下看到所有输出的中间图片。
  • 新增LifeMode模式。相比默认模式,更适合在生活场景下定位车牌。
  • 新增批量测试功能。这个功能可供测试EasyPR在多幅图片上的整体表现效果。
  • 引入GDTS(General Data Test Set。通用数据测试集)概念,作为EasyPR准确率的评测数据集。
  • 引入GDSL协议。此协议是为了确保GDTS中的数据不受到任何商业性与恶性目的行为的滥用。
  • 完善SVM训练功能。提供了一个方便的训练操作窗口。这些功能是为了配合即将发布的SVM开发详解这篇文章。
  • 强化SVM模型验证。使用了三个新的数据集概念,即learn data,train data,test data。
  • 新增评价指标。引入Precise,Recall,FSocre三个指标这三个指标作为SVM模型准确率评判的参数与改善的依据。
  • 新增整体指标。引入levenshtein距离作为EasyPR整体识别准确率误差的评判参数与改善依据。
  • 大幅增加训练数据。SVM训练数据中增加了近千张新数据(未经直方图均衡化的车牌图片和非车牌图片)。
  • 新增命令行窗口,作为测试与训练的辅助工具。

注意:上一个版本中image文件下的test.jpg请删除。它的格式已经不符合新的GDSL协议的约定。 如果想测试,可以使用本版本中替换的test.jpg。

兼容性

EasyPR是基于opencv2.4.8版本开发的,2.4.8以上的版本应该可以兼容,以前的版本可能会存在不兼容的现象。opencv3.0的版本还没有经过测试。

例子

假设我们有如下的原始图片,需要识别出中间的车牌字符与颜色:

EasyPR 原始图片

经过EasyPR的第一步处理车牌检测(PlateDetect)以后,我们获得了原始图片中仅包含车牌的图块:

EasyPR 车牌

接着,我们对图块进行OCR过程,在EasyPR中,叫做字符识别(CharsRecognize)。我们得到了一个包含车牌颜色与字符的字符串:

“蓝牌:苏EUK722”

安装

EasyPR不需要安装,开发者直接在其上做改动。如果想使用DLL形式引用或者使用其他语言调用,则可以在EasyPR_DLL_src中找到。

详细的开发与教程请见介绍与开发教程

使用

使用Git克隆一份拷贝到你本机或者直接下载zip压缩吧。使用vs2010或以上版本的IDE选择“从现有代码创建项目”,引用EasyPR的目录。

以下表格是本工程中所有目录的解释:

目录 解释
src 所有源文件
model 机器学习的模型
train 训练数据与说明
image 测试用的图片
doc 相关文档

以下表格是image目录中子目录的解释:

目录 解释
general_test GDTS(通用数据测试集)
natvie_test NDTS(本地数据测试集)
baidu_image 从百度下载的图片
tmp Debug模式下EasyPR输出中间图片的目录

以下表格是src目录中子目录的解释:

目录 解释
core 核心功能
include 相关头文件
test 测试目录,包括单图测试与批量测试
train 训练目录,存放模型训练的代码
util 辅助功能

以下表格是src目录下一些核心文件的解释与关系:

文件 解释
plate_locate 车牌定位
plate_judge 车牌判断
plate_detect 车牌检测,是车牌定位与车牌判断功能的组合
chars_segment 字符分割
chars_identify 字符鉴别
chars_recognise 字符识别,是字符分割与字符鉴别功能的组合
plate_recognize 车牌识别,是车牌检测与字符识别的共有子类
features 特征提取回调函数
prep.h 预包含头文件

以下表格是src目录下一些辅助文件的解释与关系:

文件 解释
util.h 辅助功能头文件
main.cpp 主命令行窗口
test.cpp 单例测试
accuracy_test.cpp 批量测试
svm_train.cpp svm训练函数
generate_gdts.cpp GDTS生成函数

问题

如果有任何问题或者建议请在issues里直接提交,或者发email:easypr_dev@163.com。 建议与问题一经采纳即会将您的贡献大名列入EasyPR的感谢名单( Credits )中。

鸣谢

taotao1233,唐大侠,jsxyhelu,如果有一天(zhoushiwei),学习奋斗,袁承志,圣城小石匠,goldriver,Micooz,梦里时光,Rain Wang

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简介

EasyPR是一个中文的开源车牌识别系统,其目标是成为一个简单、高效、准确的车牌识别引擎。 相比于其他的车牌识别系统,EasyPR有如下特点: * 它基于openCV这个开源库。这意味着你可以获取全部源代码,并且移植到java等平台。 * 它能够识别中文。例如车牌为苏EUK722的图片,它可以准确地输出std:string类型的"苏EUK722"的结果。 * 它的识别率较高。图片清晰情况下,车牌检测与字符识别可以达到90%以上的精度。 展开 收起
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