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https://github.com/szad670401/hyperlpr

基于深度学习高性能中文车牌识别 High Performance Chinese License Plate Recognition Framework.
https://github.com/szad670401/hyperlpr

android cpp deep-learning plate-recognition tensorflow

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基于深度学习高性能中文车牌识别 High Performance Chinese License Plate Recognition Framework.

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README 

        
![logo_t](./images/hyperlpr_logo.png)



## HyperLPR3 - High Performance License Plate Recognition Framework.



#### [![1](https://badge.fury.io/py/hyperlpr3.svg "title")](https://pypi.org/project/hyperlpr3/)[![1](https://img.shields.io/pypi/pyversions/hyperlpr3.svg "title")](https://pypi.org/manage/project/hyperlpr3/releases/)[![](https://jitpack.io/v/HyperInspire/hyperlpr3-android-sdk.svg)](https://jitpack.io/#HyperInspire/hyperlpr3-android-sdk)

[中文文档](README_CH.md)



### Demo APP Installation



- Android APP：[Scan QR Code](http://fir.tunm.top/hyperlpr)



### Quick Installation



`python -m pip install hyperlpr3`



###### support：python3, Windows, Mac, Linux, Raspberry Pi。



###### 720p cpu real-time (st on MBP r15 2.2GHz haswell).



#### Quick Test



```bash

# image url

lpr3 sample -src https://koss.iyong.com/swift/v1/iyong_public/iyong_2596631159095872/image/20190221/1550713902741045679.jpg



# image path

lpr3 sample -src images/test_img.jpg -det high

```



#### How to Use



```python

# import opencv

import cv2

# import hyperlpr3

import hyperlpr3 as lpr3



# Instantiate object

catcher = lpr3.LicensePlateCatcher()

# load image

image = cv2.imread("images/test_img.jpg")

# print result

print(catcher(image))



``` 

#### Start the WebAPI service



```bash

# start server

lpr3 rest --port 8715 --host 0.0.0.0

```

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:



![swagger_ui](./images/swagger-ui.png)



#### Q&A



Q：Whether the accuracy of android in the project is consistent with that of apk-demo？



A：Please compile or download the Android shared library from the release and copy it to Prj-Android for testing。



Q：Source of training data for license plates？



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。



Q：Provision of training code？



A：The resources provide the old training code, and the training methods for HyperLPR3 will be sorted out and presented later。



#### Resources



- [HyperLPR3车牌识别-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)



- [HyperLPR3车牌识别-Android-SDK光速部署与使用](https://blog.csdn.net/weixin_40193776/article/details/129394240)



- [HyperLPR3车牌识别-Linux/MacOS使用：C/C++库编译](https://blog.csdn.net/weixin_40193776/article/details/129295679)



- [HyperLPR3车牌识别-Android使用：SDK编译与部署](https://blog.csdn.net/weixin_40193776/article/details/129354938)



- To be added... Contributions welcome



#### Other Versions



- [HyperLPRv1](https://github.com/szad670401/HyperLPR/tree/v1)



- [HyperLPRv2](https://github.com/szad670401/HyperLPR/tree/v2)



### TODO



- Support for rapid deployment of WebApi services

- Support multiple license plates and double layers

- Support large Angle license plate

- Lightweight recognition model



### Specialty



- 720p faster, single core Intel 2.2G CPU (MaBook Pro 2015) average recognition time is less than 100ms

- End-to-end license plate recognition does not require character segmentation

- The recognition rate is high, and the accuracy of the entrance and exit scene is about 95%-97%

- Support cross-platform compilation and rapid deployment



### Matters Need Attention:



- The C++ and Python implementations of this project are separate

- When compiling C++ projects, OpenCV 4.0 and MNN 2.0 must be used, otherwise it will not compile

- Android project compilation ndk as far as possible to use version 21



### Python Dependency

- opencv-python (>3.3)

- onnxruntime (>1.8.1)

- fastapi (0.92.0)

- uvicorn (0.20.0)

- loguru (0.6.0)

- python-multipart

- tqdm

- requests 



### Cross-platform support



#### Platform

- Linux: x86、Armv7、Armv8

- MacOS: x86

- Android: arm64-v8a、armeabi-v7a

  

#### Embedded Development Board

- Rockchip: rv1109rv1126(RKNPU)



### CPP Dependency



- Opencv 4.0 above

- MNN 2.0 above



### C/C++ Compiling Dependencies



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/1zfP2MSsG1jgxB_MjvpwZJQ) code: eu31



### Linux/Mac Shared Library Compilation



- Need to place or link dependencies in the project root (same level as CMakeLists.txt)

- We recommend you to compile OpenCV yourself and install it into the system. This can help reduce compilation errors caused by version mismatches and compiler issues with system dependencies. However, you can also try using the pre-compiled OpenCV static library we provide for compilation. To do this, you need to enable the **LINUX_USE_3RDPARTY_OPENCV** switch.



```bash

# execute the script

sh command/build_release_linux_share.sh



```

Compiled to the **build/linux/install/hyperlpr3** dir，Which contains：

- include - header file

- lib - shared dir

- resource - test-images and models dir



Copy the files you need into your project



### Linux/Mac Compiling the Demo



- You need to complete the previous compilation step and ensure it's successful. The compiled files will be located in the root directory: **build/linux/install/hyperlpr3**. You will need to manually copy them to the current directory.

- Go to the **Prj-Linux** folder

```bash

# go to Prj-linux

cd Prj-Linux

# exec sh

sh build.sh

```

The executable program is generated after compilation: **PlateRecDemo**，and Run the program

```bash

# go to build

cd build/

# first param models dir, second param image path

./PlateRecDemo ../hyperlpr3/resource/models/r2_mobile ../hyperlpr3/resource/images/test_img.jpg

```

### Linux/Mac Quick Use SDK Code Example

```C

// Load image

cv::Mat image = cv::imread(image_path);

// Create a ImageData

HLPR_ImageData data = {0};

data.data = image.ptr(0);         // Setting the image data flow

data.width = image.cols;                   // Setting the image width

data.height = image.rows;                  // Setting the image height

data.format = STREAM_BGR;                  // Setting the current image encoding format

data.rotation = CAMERA_ROTATION_0;         // Setting the current image corner

// Create a Buffer

P_HLPR_DataBuffer buffer = HLPR_CreateDataBuffer(&data);



// Configure license plate recognition parameters

HLPR_ContextConfiguration configuration = {0};

configuration.models_path = model_path;         // Model folder path

configuration.max_num = 5;                      // Maximum number of license plates

configuration.det_level = DETECT_LEVEL_LOW;     // Level of detector

configuration.use_half = false;

configuration.nms_threshold = 0.5f;             // Non-maxima suppress the confidence threshold

configuration.rec_confidence_threshold = 0.5f;  // License plate number text threshold

configuration.box_conf_threshold = 0.30f;       // Detector threshold

configuration.threads = 1;

// Instantiating a Context

P_HLPR_Context ctx = HLPR_CreateContext(&configuration);

// Query the Context state

HREESULT ret = HLPR_ContextQueryStatus(ctx);

if (ret != HResultCode::Ok) {

    printf("create error.\n");

    return -1;

}

HLPR_PlateResultList results = {0};

// Execute LPR

HLPR_ContextUpdateStream(ctx, buffer, &results);



for (int i = 0; i < results.plate_size; ++i) {

	// Getting results

    std::string type;

    if (results.plates[i].type == HLPR_PlateType::PLATE_TYPE_UNKNOWN) {

        type = “Unknown";

    } 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);

}



// Release Buffer

HLPR_ReleaseDataBuffer(buffer);

// Release Context

HLPR_ReleaseContext(ctx);

```



### Android: Compile the Shared Library

- The first step is to install third-party dependencies

- You need to prepare NDKS and configure environment variables: $ANDROID_NDK

- Supports cross-compilation on Linux/MacOS

```bash

# execute the script

sh command/build_release_android_share.sh

```

Compiled to the: **build/release_android/**，Which contains：

- arm64-v8a - 64bit shard library

- armeabi-v7a - 32bit shard library



After compiling，Copy**arm64-v8a**and**armeabi-v7a** dirs to **Prj-Android/hyperlpr3/libs**，And compile the **Prj-Android** project to use.



### Compile with Docker



If you need to compile with docker, we provide a few ways to compile:



#### 1. Compile the Linux-x86 Shared Library using Docker



You need to install docker and docker-compose，Build Image for **hyperlpr_build**:



```Bash

docker build -t hyperlpr_build .

```



Start compiling the shared library:



```Bash

docker-compose up build_linux_x86_shared_lib

```



Build dir: **build/linux**



### Android SDK Demo



We have provided a demo project from the Android SDK source: [hyperlpr3-android-sdk](https://github.com/HyperInspire/hyperlpr3-android-sdk.git)，You can compile the shared library and use the project as needed.



### Quick to use in Android



If you need to quickly integrate our sdk in your own Android project, then you can add the following dependency to your project's build.gradle:



- Step 1. Add the JitPack repository to your build file.Add it in your root build.gradle at the end of repositories:



```Java

allprojects {

    repositories {

        ...

        maven { url 'https://jitpack.io' }

    }

}

```



- Step 2. Add the dependency

```Java

dependencies {

    implementation 'com.github.HyperInspire:hyperlpr3-android-sdk:1.0.3'

}

```



- Step 3. Use hyperlpr in your application



```Java

// Initialization, which can be performed only once, is usually performed at program initialization

HyperLPR3.getInstance().init(this, new HyperLPRParameter());



…



// exec recognition

Plate[] plates =  HyperLPR3.getInstance().plateRecognition(bitmap, HyperLPR3.CAMERA_ROTATION_0, HyperLPR3.STREAM_BGRA);

```



Know more about: **[Prj-Android](./Prj-Android)**



### License Plate Type is Supported(Chinese)



#### 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

- [ ] License plates from more countries



###### 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.



### Example



![demo](./images/demo.png)



### Author

- Jingyu Yan ([email protected])

- Jack Yu ([email protected] / https://github.com/szad670401)

- XiaoXiao ([email protected])



#### Help



- HyperInspire QQ Group: 529385694