pycharm中文车牌识别依赖库HyperLPR

 ## HyperLPR3 - High Performance License Plate Recognition Framework. #### [](https://pypi.org/project/hyperlpr3/)[](https://pypi.org/manage/project/hyperlpr3/releases/)[](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:  #### 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) ```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**a