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This application is used as reference code for developers to show how to use the C++ API and could be used to easily check the accuracy. The application accepts path to a JPEG/PNG/BMP file as input. This is not the recommended way to use the API. We recommend reading the data directly from the camera and feeding the SDK with the uncompressed YUV data without saving it to a file or converting it to RGB.

If you don't want to build this sample and is looking for a quick way to check the accuracy then, try our cloud-based solution at https://www.doubango.org/webapps/alpr/.

This sample is open source and doesn't require registration or license key.

Dependencies

The SDK is developed in C++11 and you'll need glibc 2.27+ on Linux and Visual C++ Redistributable for Visual Studio 2015 (any later version is ok) on Windows. You most likely already have these dependencies on you machine as almost every program require it.

If you're planning to use OpenVINO, then you'll need Intel C++ Compiler Redistributable (choose newest). Please note that OpenVINO is packaged in the SDK as plugin and loaded (dlopen) at runtime. The engine will fail to load the plugin if Intel C++ Compiler Redistributable is missing on your machine but the program will work as expected with Tensorflow as fallback. We highly recommend using OpenVINO to speedup the inference time. See benchmark numbers with/without OpenVINO at https://www.doubango.org/SDKs/anpr/docs/Benchmark.html#core-i7-windows.

Debugging missing dependencies

To check if all dependencies are present:

GPGPU acceleration

By default GPGPU acceleration is disabled. Check here for more information on how to enable it.

Pre-built binaries

If you don't want to build this sample by yourself then, use the pre-built C++ versions:

On Windows, the easiest way to try this sample is to navigate to binaries/windows/x86_64 and run binaries/windows/x86_64/recognizer.bat. You can edit these files to use your own images and configuration options.

Building

This sample contains a single C++ source file and is easy to build. The documentation about the C++ API is at https://www.doubango.org/SDKs/anpr/docs/cpp-api.html.

Windows

You'll need Visual Studio to build the code. The VS project is at recognizer.vcxproj. Open it.

  1. You will need to change the "Command Arguments" like the below image. Default value: --image $(ProjectDir)..\..\..\assets\images\lic_us_1280x720.jpg --charset latin --assets $(ProjectDir)..\..\..\assets
  2. You will need to change the "Environment" variable like the below image. Default value: PATH=$(VCRedistPaths)%PATH%;$(ProjectDir)..\..\..\binaries\windows\x86_64

VC++ config

You're now ready to build and run the sample.

Generic GCC

Next command is a generic GCC command:

cd ultimateALPR-SDK/samples/c++/recognizer

g++ recognizer.cxx -O3 -I../../../c++ -L../../../binaries/<yourOS>/<yourArch> -lultimate_alpr-sdk -o recognizer
  • You've to change yourOS and yourArch with the correct values. For example, on Linux x86_64 they would be equal to linux and x86_64 respectively.
  • If you're cross compiling then, you'll have to change g++ with the correct triplet. For example, on Linux host for Android ARM64 target the triplet would be equal to aarch64-linux-android-g++.

Raspberry Pi (Raspbian OS)

To build the sample for Raspberry Pi you can either do it on the device itself or cross compile it on Windows, Linux or OSX machines. For more information on how to install the toolchain for cross compilation please check here.

cd ultimateALPR-SDK/samples/c++/recognizer

arm-linux-gnueabihf-g++ recognizer.cxx -O3 -I../../../c++ -L../../../binaries/raspbian/armv7l -lultimate_alpr-sdk -o recognizer
  • On Windows: replace arm-linux-gnueabihf-g++ with arm-linux-gnueabihf-g++.exe
  • If you're building on the device itself: replace arm-linux-gnueabihf-g++ with g++ to use the default GCC

Testing

After building the application you can test it on your local machine.

Usage

recognizer is a command line application with the following usage:

recognizer \
      --image <path-to-image-with-to-process> \
      [--assets <path-to-assets-folder>] \
      [--parallel <whether-to-enable-parallel-mode:true/false>] \
      [--rectify <whether-to-enable-rectification-layer:true/false>] \
      [--charset <recognition-charset:latin/korean/chinese>] \
      [--openvino_enabled <whether-to-enable-OpenVINO:true/false>] \
      [--openvino_device <openvino-device-to-use>] \
      [--klass_lpci_enabled <whether-to-enable-LPCI:true/false>] \
      [--klass_vcr_enabled <whether-to-enable-VCR:true/false>] \
      [--klass_vmmr_enabled <whether-to-enable-VMMR:true/false>] \
      [--tokenfile <path-to-license-token-file>] \
      [--tokendata <base64-license-token-data>]

Options surrounded with [] are optional.

Examples

For example, on Raspberry Pi you may call the recognizer application using the following command:

LD_LIBRARY_PATH=../../../binaries/raspbian/armv7l:$LD_LIBRARY_PATH ./recognizer \
    --image ../../../assets/images/lic_us_1280x720.jpg \
    --assets ../../../assets \
    --charset latin \
    --parallel false \
    --rectify false

On Linux x86_64, you may use the next command:

LD_LIBRARY_PATH=../../../binaries/linux/x86_64:$LD_LIBRARY_PATH ./recognizer \
    --image ../../../assets/images/lic_us_1280x720.jpg \
    --assets ../../../assets \
    --charset latin \
    --parallel false

On Linux aarch64, you may use the next command:

LD_LIBRARY_PATH=../../../binaries/linux/aarch64:$LD_LIBRARY_PATH ./recognizer \
    --image ../../../assets/images/lic_us_1280x720.jpg \
    --assets ../../../assets \
    --charset latin \
    --parallel false

On Windows x86_64, you may use the next command:

recognizer.exe ^
    --image ../../../assets/images/lic_us_1280x720.jpg ^
    --assets ../../../assets ^
    --charset latin ^
    --parallel false

Please note that if you're cross compiling the application then you've to make sure to copy the application and both the assets and binaries folders to the target device.