|
|
|
|
|
|
|
|
## HyperLPR3 - High Performance License Plate Recognition Framework.
|
|
|
|
|
|
#### [](https://pypi.org/project/hyperlpr3/)[](https://pypi.org/manage/project/hyperlpr3/releases/)
|
|
|
[中文文档](README_CH.md)
|
|
|
|
|
|
### 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:
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
#### 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
|
|
|
|
|
|
- [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)
|
|
|
|
|
|
- [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/138O2bSlPN0H81OYP6zc3yQ) code: 5duf
|
|
|
|
|
|
### Linux/Mac Shared Library Compilation
|
|
|
|
|
|
- Need to place or link dependencies in the project root (same level as CMakeLists.txt)
|
|
|
|
|
|
```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 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**
|
|
|
- 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<uint8_t>(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.
|
|
|
|
|
|
###
|
|
|
|
|
|
### 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.
|
|
|
|
|
|
|
|
|
### Demo APP Install
|
|
|
|
|
|
- Android APP：[Scan QR Code](http://fir.tunm.top/hyperlpr)
|
|
|
|
|
|
#### Help
|
|
|
|
|
|
- HyperInspire QQ Group: 529385694
|
|
|
|