Update README
This commit is contained in:
tunmx
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## HyperLPR3 高性能开源中文车牌识别框架
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## HyperLPR3 - High Performance License Plate Recognition Framework.
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#### [](https://pypi.org/project/hyperlpr3/)[](https://pypi.org/manage/project/hyperlpr3/releases/)
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#### [](https://pypi.org/project/hyperlpr3/)[](https://pypi.org/manage/project/hyperlpr3/releases/)
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[中文文档](README_CH.md)
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### 一键安装
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### Fast Install
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`python -m pip install hyperlpr3`
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###### 支持python3, 支持Windows Mac Linux 树莓派等。
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###### support：python3, Windows, Mac, Linux, Raspberry Pi。
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###### 720p cpu real-time (st on MBP r15 2.2GHz haswell).
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#### 快速体验
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#### Fast Test
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```bash
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# 使用命令行测试 - 图像url地址
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# image url
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lpr3 sample -src https://koss.iyong.com/swift/v1/iyong_public/iyong_2596631159095872/image/20190221/1550713902741045679.jpg
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# 使用命令行测试 - 本地图像路径
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# image path
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lpr3 sample -src images/test_img.jpg -det high
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```
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#### 快速上手
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#### Fast Use
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```python
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# 导入opencv库
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# import opencv
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import cv2
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# 导入依赖包
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# import hyperlpr3
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import hyperlpr3 as lpr3
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# 实例化识别对象
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# Instantiate object
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catcher = lpr3.LicensePlateCatcher()
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# 读取图片
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# load image
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image = cv2.imread("images/test_img.jpg")
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# 识别结果
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# print result
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print(catcher(image))
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```
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#### 启动WebApi服务
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#### Start the WebAPI service
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```bash
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# 启动服务
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# start server
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lpr3 rest --port 8715 --host 0.0.0.0
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```
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启动后可打开SwaggerUI的路径：[http://localhost:8715/api/v1/docs](http://localhost:8715/api/v1/docs) 查看和测试在线识别API服务:
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Path to open SwaggerUI after startup：[http://localhost:8715/api/v1/docs](http://localhost:8715/api/v1/docs) View and test the online Identification API service:
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#### Q&A
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Q：Android识别率没有所传demo apk的识别率高？
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Q：Whether the accuracy of android in the project is consistent with that of apk-demo？
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A：请自行编译或从release中下载安卓动态库放置于Prj-Android中进行测试。
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A：Please compile or download the Android shared library from the release and copy it to Prj-Android for testing。
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Q：车牌的训练数据来源？
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Q：Source of training data for license plates？
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A：由于用于训练车牌数据涉及到法律隐私等问题，本项目无法提供。开放较为大的数据集有[CCPD](https://github.com/detectRecog/CCPD)车牌数据集。
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A：Since the license plate data used for training involves legal privacy and other issues, it cannot be provided in this project. Open more big data sets [CCPD](https://github.com/detectRecog/CCPD) registration dataset。
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Q：训练代码的提供？
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Q：Provision of training code？
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A：相关资源中有提供老版的训练代码，HyperLPR3的训练方法会陆续整理并给出。
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Q：关于项目的来源？
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A：此项目来源于作者早期的研究和调试代码，代码缺少一定的规范，同时也欢迎PR。
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A：The resources provide the old training code, and the training methods for HyperLPR3 will be sorted out and presented later。
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#### 相关资源
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#### Resources
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- [五分钟搞定: 中文车牌识别光速部署与使用](https://blog.csdn.net/weixin_40193776/article/details/129258107)
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- [Linux/MacOS使用：C/C++库编译](https://blog.csdn.net/weixin_40193776/article/details/129295679)
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- 待补充...欢迎投稿
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- To be added... Contributions welcome
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#### 其他版本
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#### Other versions
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- [HyperLPRv1版](https://github.com/szad670401/HyperLPR/tree/v1)
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- [HyperLPRv1](https://github.com/szad670401/HyperLPR/tree/v1)
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- [HyperLPRv2版](https://github.com/szad670401/HyperLPR/tree/v2)
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- [HyperLPRv2](https://github.com/szad670401/HyperLPR/tree/v2)
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### TODO
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- 支持快速部署WebApi服务
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- 支持多种车牌以及双层
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- 支持大角度车牌
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- 轻量级识别模型
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- Support for rapid deployment of WebApi services
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- Support multiple license plates and double layers
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- Support large Angle license plate
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- Lightweight recognition model
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### 特性
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### Specialty
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- 速度快 720p,单核 Intel 2.2G CPU (MaBook Pro 2015)平均识别时间低于100ms
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- 基于端到端的车牌识别无需进行字符分割
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- 识别率高,卡口场景准确率在95%-97%左右
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- 支持跨平台编译和快速部署
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- 720p faster, single core Intel 2.2G CPU (MaBook Pro 2015) average recognition time is less than 100ms
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- End-to-end license plate recognition does not require character segmentation
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- The recognition rate is high, and the accuracy of the entrance and exit scene is about 95%-97%
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- Support cross-platform compilation and rapid deployment
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### 注意事项:
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### Matters Need Attention:
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- 本项目的C++实现和Python实现无任何关联，都为单独实现
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- 在编译C++工程的时候必须要使用OpenCV 4.0和MNN 2.0以上版本，否则无法编译
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- 安卓工程编译ndk尽量采用21版本
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- The C++ and Python implementations of this project are separate
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- When compiling C++ projects, OpenCV 4.0 and MNN 2.0 must be used, otherwise it will not compile
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- Android project compilation ndk as far as possible to use version 21
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### Python 依赖
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### Python Dependency
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- opencv-python (>3.3)
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- onnxruntime (>1.8.1)
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- fastapi (0.92.0)
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@@ -114,99 +111,99 @@ A：此项目来源于作者早期的研究和调试代码，代码缺少一定
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- tqdm
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- requests
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### 跨平台支持
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### Cross-platform support
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#### 平台
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#### Platform
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- Linux: x86、Armv7、Armv8
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- MacOS: x86
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- Android: arm64-v8a、armeabi-v7a
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#### 开发板
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#### Embedded Development Board
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- Rockchip: rv1109rv1126(RKNPU)
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### CPP 依赖
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### CPP Dependency
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- Opencv 4.0 以上版本
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- MNN 2.0 以上版本
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- Opencv 4.0 above
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- MNN 2.0 above
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### C/C++编译依赖库
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### C/C++ Compiling dependencies
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编译C/C++工程需要使用第三方依赖库，将库下载后解压，并将其通过拷贝或软链接放入根目录(与CMakeLists.txt同级)即可，依赖的库下载地址：[百度网盘](https://pan.baidu.com/s/138O2bSlPN0H81OYP6zc3yQ) code: 5duf
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Compiling C/C++ projects requires the use of third-party dependency libraries. After downloading the library, unzip it, and put it into the root directory (the same level as CMakeLists.txt) by copying or soft linking.[baidu drive](https://pan.baidu.com/s/138O2bSlPN0H81OYP6zc3yQ) code: 5duf
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### Linux/Mac动态链接库编译
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### Linux/Mac Shared Library Compilation
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- 需要将依赖库放置或链接在项目根目录下(与CMakeLists.txt同级)
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- Need to place or link dependencies in the project root (same level as CMakeLists.txt)
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```bash
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# 执行编译脚本
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# execute the script
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sh command/build_release_linux_share.sh
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```
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编译后的相关物料放置于根目录下**build/linux/install/hyperlpr3**中，其中包含：
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- include 头文件
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- lib 动态库路径
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- resource 包含测试图片与模型等静态资源
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Compiled to the **build/linux/install/hyperlpr3** dir，Which contains：
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- include - header file
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- lib - shared dir
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- resource - test-images and models dir
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按需取走需要的文件即可
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Copy the files you need into your project
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### Linux/Mac编译Demo
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### Linux/Mac Compiling the Demo
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- 需要完成上一步的编译动作，并保证编译成功且编译完成后的物料放置于根目录下的**build/linux/install/hyperlpr3**路径中
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- 需要从根目录中进入到子工程**Prj-Linux**文件夹中进行操作
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- You need to complete the compilation action of the previous step，And ensure that the compilation is successful and the compiled file is placed in the root directory: **build/linux/install/hyperlpr3**
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- Go to the **Prj-Linux** folder
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```bash
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# 进入到子工程demo
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# go to Prj-linux
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cd Prj-Linux
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# 创建build文件夹并进入
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# make build and enter
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mkdir build && cd build
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# 开始编译
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# Start compiling
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cmake .. && make -j
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```
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编译完成后生成可执行程序**PlateRecDemo**，执行运行测试
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The executable program is generated after compilation: **PlateRecDemo**，and Run the program
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```bash
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# 传入模型文件夹路径和需要预测的图像执行程序
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# first param models dir, second param image path
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./PlateRecDemo ../hyperlpr3/resource/models/r2_mobile ../hyperlpr3/resource/images/test_img.jpg
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```
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### Linux/Mac快速使用SDK代码示例
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### Linux/Mac Quick use SDK code example
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```C
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// 读取图像
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// Load image
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cv::Mat image = cv::imread(image_path);
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// 创建ImageData
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// Create a ImageData
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HLPR_ImageData data = {0};
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data.data = image.ptr<uint8_t>(0); // 设置图像数据流
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data.width = image.cols; // 设置图像宽
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data.height = image.rows; // 设置图像高
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data.format = STREAM_BGR; // 设置当前图像编码格式
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data.rotation = CAMERA_ROTATION_0; // 设置当前图像转角
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// 创建数据Buffer
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data.data = image.ptr<uint8_t>(0); // Setting the image data flow
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data.width = image.cols; // Setting the image width
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data.height = image.rows; // Setting the image height
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data.format = STREAM_BGR; // Setting the current image encoding format
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data.rotation = CAMERA_ROTATION_0; // Setting the current image corner
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// Create a Buffer
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P_HLPR_DataBuffer buffer = HLPR_CreateDataBuffer(&data);
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// 配置车牌识别参数
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// Configure license plate recognition parameters
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HLPR_ContextConfiguration configuration = {0};
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configuration.models_path = model_path; // 模型文件夹路径
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configuration.max_num = 5; // 最大识别车牌数量
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configuration.det_level = DETECT_LEVEL_LOW; // 检测器等级
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configuration.models_path = model_path; // Model folder path
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configuration.max_num = 5; // Maximum number of license plates
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configuration.det_level = DETECT_LEVEL_LOW; // Level of detector
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configuration.use_half = false;
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configuration.nms_threshold = 0.5f; // 非极大值抑制置信度阈值
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configuration.rec_confidence_threshold = 0.5f; // 车牌号文本阈值
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configuration.box_conf_threshold = 0.30f; // 检测器阈值
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configuration.nms_threshold = 0.5f; // Non-maxima suppress the confidence threshold
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configuration.rec_confidence_threshold = 0.5f; // License plate number text threshold
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configuration.box_conf_threshold = 0.30f; // Detector threshold
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configuration.threads = 1;
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// 实例化车牌识别算法Context
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// Instantiating a Context
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P_HLPR_Context ctx = HLPR_CreateContext(&configuration);
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// 查询实例化状态
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// Query the Context state
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HREESULT ret = HLPR_ContextQueryStatus(ctx);
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if (ret != HResultCode::Ok) {
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printf("create error.\n");
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return -1;
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}
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HLPR_PlateResultList results = {0};
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// 执行车牌识别算法
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// Execute LPR
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HLPR_ContextUpdateStream(ctx, buffer, &results);
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for (int i = 0; i < results.plate_size; ++i) {
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// 解析识别后的数据
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// Getting results
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std::string type;
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if (results.plates[i].type == HLPR_PlateType::PLATE_TYPE_UNKNOWN) {
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type = "未知";
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type = “Unknown";
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} else {
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type = TYPES[results.plates[i].type];
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}
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@@ -215,27 +212,27 @@ for (int i = 0; i < results.plate_size; ++i) {
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results.plates[i].code, results.plates[i].text_confidence);
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}
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// 销毁Buffer
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// Release Buffer
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HLPR_ReleaseDataBuffer(buffer);
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// 销毁Context
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// Release Context
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HLPR_ReleaseContext(ctx);
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```
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### Android编译动态链接库
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- 需要完成上一步的编译动作，并保证编译成功且编译完成后的物料放置于根目录下的**build/linux/install/hyperlpr3**路径中
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### Android: Compile the Shared Library
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- The first step is to install third-party dependencies
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```bash
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# 执行编译脚本
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# execute the script
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sh command/build_release_android_share.sh
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```
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编译完成后android的动态库会放置于**build/release_android/**，其中包含：
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- arm64-v8a 64位的动态库
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- armeabi-v7a 32位的动态库
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Compiled to the: **build/release_android/**，Which contains：
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- arm64-v8a - 64bit shard library
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- armeabi-v7a - 32bit shard library
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完成Android的动态库编译后，将**arm64-v8a**和**armeabi-v7a**文件夹放置于子项目路径**Prj-Android/hyperlpr3/libs**中，再编译android项目即可使用。**Prj-Android**项目中已内置hyperlpr3的SDK与使用demo。
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After compiling，Copy**arm64-v8a**and**armeabi-v7a** dirs to **Prj-Android/hyperlpr3/libs**，And compile the **Prj-Android** project to use.
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###
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### 可识别和待支持的车牌的类型
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### License plate type is supported(Chinese)
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#### 支持
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- [x] 单行蓝牌
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@@ -253,15 +250,16 @@ sh command/build_release_android_share.sh
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- [ ] 双层军牌
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- [ ] 双层农用车牌
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- [ ] 双层个性化车牌
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- [ ] License plates from more countries
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###### Note:由于训练的时候样本存在一些不均衡的问题,一些特殊车牌存在一定识别率低下的问题，如(使馆/港澳车牌)，会在后续的版本进行改进。
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###### Note:Due to some imbalanced samples during training, some special license plates have low recognition rates, such as (Embassy/Hong Kong and Macao license plates), which will be improved in the subsequent versions.
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### 识别测试APP
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### Demo APP Install
|
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|
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- 体验 Android APP：[扫码下载](http://fir.tunm.top/hyperlpr)
|
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- Android APP：[Scan Code](http://fir.tunm.top/hyperlpr)
|
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#### 获取帮助
|
||||
#### Help
|
||||
|
||||
- HyperAI讨论QQ群: 529385694，加前请备注HyperLPR交流
|
||||
- HyperAI QQ Group: 529385694
|
||||
|
||||
|
||||
+268
@@ -0,0 +1,268 @@
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||||
|
||||
|
||||
## HyperLPR3 高性能开源中文车牌识别框架
|
||||
|
||||
#### [](https://pypi.org/project/hyperlpr3/)[](https://pypi.org/manage/project/hyperlpr3/releases/)
|
||||
|
||||
### 一键安装
|
||||
|
||||
`python -m pip install hyperlpr3`
|
||||
|
||||
###### 支持python3, 支持Windows Mac Linux 树莓派等。
|
||||
|
||||
|
||||
###### 720p cpu real-time (st on MBP r15 2.2GHz haswell).
|
||||
|
||||
#### 快速体验
|
||||
|
||||
```bash
|
||||
# 使用命令行测试 - 图像url地址
|
||||
lpr3 sample -src https://koss.iyong.com/swift/v1/iyong_public/iyong_2596631159095872/image/20190221/1550713902741045679.jpg
|
||||
|
||||
# 使用命令行测试 - 本地图像路径
|
||||
lpr3 sample -src images/test_img.jpg -det high
|
||||
```
|
||||
|
||||
#### 快速上手
|
||||
|
||||
```python
|
||||
# 导入opencv库
|
||||
import cv2
|
||||
# 导入依赖包
|
||||
import hyperlpr3 as lpr3
|
||||
|
||||
# 实例化识别对象
|
||||
catcher = lpr3.LicensePlateCatcher()
|
||||
# 读取图片
|
||||
image = cv2.imread("images/test_img.jpg")
|
||||
# 识别结果
|
||||
print(catcher(image))
|
||||
|
||||
```
|
||||
#### 启动WebApi服务
|
||||
|
||||
```bash
|
||||
# 启动服务
|
||||
lpr3 rest --port 8715 --host 0.0.0.0
|
||||
```
|
||||
启动后可打开SwaggerUI的路径：[http://localhost:8715/api/v1/docs](http://localhost:8715/api/v1/docs) 查看和测试在线识别API服务:
|
||||
|
||||
|
||||
|
||||
|
||||
#### Q&A
|
||||
|
||||
Q：Android识别率没有所传demo apk的识别率高？
|
||||
|
||||
A：请自行编译或从release中下载安卓动态库放置于Prj-Android中进行测试。
|
||||
|
||||
Q：车牌的训练数据来源？
|
||||
|
||||
A：由于用于训练车牌数据涉及到法律隐私等问题，本项目无法提供。开放较为大的数据集有[CCPD](https://github.com/detectRecog/CCPD)车牌数据集。
|
||||
|
||||
Q：训练代码的提供？
|
||||
|
||||
A：相关资源中有提供老版的训练代码，HyperLPR3的训练方法会陆续整理并给出。
|
||||
|
||||
Q：关于项目的来源？
|
||||
|
||||
A：此项目来源于作者早期的研究和调试代码，代码缺少一定的规范，同时也欢迎PR。
|
||||
|
||||
|
||||
#### 相关资源
|
||||
|
||||
- [五分钟搞定: 中文车牌识别光速部署与使用](https://blog.csdn.net/weixin_40193776/article/details/129258107)
|
||||
|
||||
- [Linux/MacOS使用：C/C++库编译](https://blog.csdn.net/weixin_40193776/article/details/129295679)
|
||||
|
||||
- 待补充...欢迎投稿
|
||||
|
||||
#### 其他版本
|
||||
|
||||
- [HyperLPRv1版](https://github.com/szad670401/HyperLPR/tree/v1)
|
||||
|
||||
- [HyperLPRv2版](https://github.com/szad670401/HyperLPR/tree/v2)
|
||||
|
||||
### TODO
|
||||
|
||||
- 支持快速部署WebApi服务
|
||||
- 支持多种车牌以及双层
|
||||
- 支持大角度车牌
|
||||
- 轻量级识别模型
|
||||
|
||||
|
||||
### 特性
|
||||
|
||||
- 速度快 720p,单核 Intel 2.2G CPU (MaBook Pro 2015)平均识别时间低于100ms
|
||||
- 基于端到端的车牌识别无需进行字符分割
|
||||
- 识别率高,卡口场景准确率在95%-97%左右
|
||||
- 支持跨平台编译和快速部署
|
||||
|
||||
### 注意事项:
|
||||
|
||||
- 本项目的C++实现和Python实现无任何关联，都为单独实现
|
||||
- 在编译C++工程的时候必须要使用OpenCV 4.0和MNN 2.0以上版本，否则无法编译
|
||||
- 安卓工程编译ndk尽量采用21版本
|
||||
|
||||
### Python 依赖
|
||||
- opencv-python (>3.3)
|
||||
- onnxruntime (>1.8.1)
|
||||
- fastapi (0.92.0)
|
||||
- uvicorn (0.20.0)
|
||||
- loguru (0.6.0)
|
||||
- python-multipart
|
||||
- tqdm
|
||||
- requests
|
||||
|
||||
### 跨平台支持
|
||||
|
||||
#### 平台
|
||||
- Linux: x86、Armv7、Armv8
|
||||
- MacOS: x86
|
||||
- Android: arm64-v8a、armeabi-v7a
|
||||
|
||||
#### 开发板
|
||||
- Rockchip: rv1109rv1126(RKNPU)
|
||||
|
||||
### CPP 依赖
|
||||
|
||||
- Opencv 4.0 以上版本
|
||||
- MNN 2.0 以上版本
|
||||
|
||||
### C/C++编译依赖库
|
||||
|
||||
编译C/C++工程需要使用第三方依赖库，将库下载后解压，并将其通过拷贝或软链接放入根目录(与CMakeLists.txt同级)即可，依赖的库下载地址：[百度网盘](https://pan.baidu.com/s/138O2bSlPN0H81OYP6zc3yQ) code: 5duf
|
||||
|
||||
### Linux/Mac动态链接库编译
|
||||
|
||||
- 需要将依赖库放置或链接在项目根目录下(与CMakeLists.txt同级)
|
||||
|
||||
```bash
|
||||
# 执行编译脚本
|
||||
sh command/build_release_linux_share.sh
|
||||
|
||||
```
|
||||
编译后的相关物料放置于根目录下**build/linux/install/hyperlpr3**中，其中包含：
|
||||
- include 头文件
|
||||
- lib 动态库路径
|
||||
- resource 包含测试图片与模型等静态资源
|
||||
|
||||
按需取走需要的文件即可
|
||||
|
||||
### Linux/Mac编译Demo
|
||||
|
||||
- 需要完成上一步的编译动作，并保证编译成功且编译完成后的物料放置于根目录下的**build/linux/install/hyperlpr3**路径中
|
||||
- 需要从根目录中进入到子工程**Prj-Linux**文件夹中进行操作
|
||||
```bash
|
||||
# 进入到子工程demo
|
||||
cd Prj-Linux
|
||||
# 创建build文件夹并进入
|
||||
mkdir build && cd build
|
||||
# 开始编译
|
||||
cmake .. && make -j
|
||||
```
|
||||
编译完成后生成可执行程序**PlateRecDemo**，执行运行测试
|
||||
```bash
|
||||
# 传入模型文件夹路径和需要预测的图像执行程序
|
||||
./PlateRecDemo ../hyperlpr3/resource/models/r2_mobile ../hyperlpr3/resource/images/test_img.jpg
|
||||
```
|
||||
### Linux/Mac快速使用SDK代码示例
|
||||
```C
|
||||
// 读取图像
|
||||
cv::Mat image = cv::imread(image_path);
|
||||
// 创建ImageData
|
||||
HLPR_ImageData data = {0};
|
||||
data.data = image.ptr<uint8_t>(0); // 设置图像数据流
|
||||
data.width = image.cols; // 设置图像宽
|
||||
data.height = image.rows; // 设置图像高
|
||||
data.format = STREAM_BGR; // 设置当前图像编码格式
|
||||
data.rotation = CAMERA_ROTATION_0; // 设置当前图像转角
|
||||
// 创建数据Buffer
|
||||
P_HLPR_DataBuffer buffer = HLPR_CreateDataBuffer(&data);
|
||||
|
||||
// 配置车牌识别参数
|
||||
HLPR_ContextConfiguration configuration = {0};
|
||||
configuration.models_path = model_path; // 模型文件夹路径
|
||||
configuration.max_num = 5; // 最大识别车牌数量
|
||||
configuration.det_level = DETECT_LEVEL_LOW; // 检测器等级
|
||||
configuration.use_half = false;
|
||||
configuration.nms_threshold = 0.5f; // 非极大值抑制置信度阈值
|
||||
configuration.rec_confidence_threshold = 0.5f; // 车牌号文本阈值
|
||||
configuration.box_conf_threshold = 0.30f; // 检测器阈值
|
||||
configuration.threads = 1;
|
||||
// 实例化车牌识别算法Context
|
||||
P_HLPR_Context ctx = HLPR_CreateContext(&configuration);
|
||||
// 查询实例化状态
|
||||
HREESULT ret = HLPR_ContextQueryStatus(ctx);
|
||||
if (ret != HResultCode::Ok) {
|
||||
printf("create error.\n");
|
||||
return -1;
|
||||
}
|
||||
HLPR_PlateResultList results = {0};
|
||||
// 执行车牌识别算法
|
||||
HLPR_ContextUpdateStream(ctx, buffer, &results);
|
||||
|
||||
for (int i = 0; i < results.plate_size; ++i) {
|
||||
// 解析识别后的数据
|
||||
std::string type;
|
||||
if (results.plates[i].type == HLPR_PlateType::PLATE_TYPE_UNKNOWN) {
|
||||
type = "未知";
|
||||
} else {
|
||||
type = TYPES[results.plates[i].type];
|
||||
}
|
||||
|
||||
printf("<%d> %s, %s, %f\n", i + 1, type.c_str(),
|
||||
results.plates[i].code, results.plates[i].text_confidence);
|
||||
}
|
||||
|
||||
// 销毁Buffer
|
||||
HLPR_ReleaseDataBuffer(buffer);
|
||||
// 销毁Context
|
||||
HLPR_ReleaseContext(ctx);
|
||||
```
|
||||
|
||||
### Android编译动态链接库
|
||||
- 需要完成上面的步骤：安装第三方依赖库
|
||||
```bash
|
||||
# 执行编译脚本
|
||||
sh command/build_release_android_share.sh
|
||||
```
|
||||
编译完成后android的动态库会放置于**build/release_android/**，其中包含：
|
||||
- arm64-v8a 64位的动态库
|
||||
- armeabi-v7a 32位的动态库
|
||||
|
||||
完成Android的动态库编译后，将**arm64-v8a**和**armeabi-v7a**文件夹放置于子项目路径**Prj-Android/hyperlpr3/libs**中，再编译android项目即可使用。**Prj-Android**项目中已内置hyperlpr3的SDK与使用demo。
|
||||
|
||||
###
|
||||
|
||||
### 可识别和待支持的车牌的类型(中文)
|
||||
|
||||
#### 支持
|
||||
- [x] 单行蓝牌
|
||||
- [x] 单行黄牌
|
||||
- [x] 新能源车牌
|
||||
- [x] 教练车牌
|
||||
#### 有限支持
|
||||
- [x] 白色警用车牌
|
||||
- [x] 使馆/港澳车牌
|
||||
- [x] 双层黄牌
|
||||
- [x] 武警车牌
|
||||
#### 待支持
|
||||
- [ ] 民航车牌
|
||||
- [ ] 双层武警
|
||||
- [ ] 双层军牌
|
||||
- [ ] 双层农用车牌
|
||||
- [ ] 双层个性化车牌
|
||||
- [ ] 更多国家车牌
|
||||
|
||||
###### Note:由于训练的时候样本存在一些不均衡的问题,一些特殊车牌存在一定识别率低下的问题，如(使馆/港澳车牌)，会在后续的版本进行改进。
|
||||
|
||||
|
||||
### 识别测试APP
|
||||
|
||||
- 体验 Android APP：[扫码下载](http://fir.tunm.top/hyperlpr)
|
||||
|
||||
#### 获取帮助
|
||||
|
||||
- HyperAI讨论QQ群: 529385694，加前请备注HyperLPR交流
|
||||
|
||||
Reference in New Issue
Block a user