recognizer

• Building
  • Windows
  • Generic GCC
  • Raspberry Pi (Raspbian OS)
• Testing
  • Usage
  • Examples

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.

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 and the project is at recognizer.vcxproj.

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 Android ARM64 they would be equal to android and jniLibs/arm64-v8a respectively.
• If you're cross compiling then, you'll have to change g++ with the correct triplet. For example, on Android ARM64 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>] \
      [--tokenfile <path-to-license-token-file>] \
      [--tokendata <base64-license-token-data>]

Options surrounded with [] are optional.

• --image Path to the image(JPEG/PNG/BMP) to process. You can use default image at ../../../assets/images/lic_us_1280x720.jpg.
• --assets Path to the assets folder containing the configuration files and models. Default value is the current folder.
• --parallel Whether to enabled the parallel mode. More info about the parallel mode at https://www.doubango.org/SDKs/anpr/docs/Parallel_versus_sequential_processing.html. Default: false.
• --rectify Whether to enable the rectification layer. More info about the rectification layer at https://www.doubango.org/SDKs/anpr/docs/Rectification_layer.html. Default: false.
• --tokenfile Path to the file containing the base64 license token if you have one. If not provided then, the application will act like a trial version. Default: null.
• --tokendata Base64 license token if you have one. If not provided then, the application will act like a trial version. Default: null.

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 \
    --parallel false \
    --rectify false

On Android ARM64 you may use the next command:

LD_LIBRARY_PATH=../../../binaries/android/jniLibs/arm64-v8a:$LD_LIBRARY_PATH ./recognizer \
    --image ../../../assets/images/lic_us_1280x720.jpg \
    --assets ../../../assets \
    --parallel false \
    --rectify 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.