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.
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.
You'll need Visual Studio and the project is at recognizer.vcxproj.
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
yourOSandyourArchwith the correct values. For example, on Android ARM64 they would be equal toandroidandjniLibs/arm64-v8arespectively. - 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 toaarch64-linux-android-g++.
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++witharm-linux-gnueabihf-g++.exe - If you're building on the device itself: replace
arm-linux-gnueabihf-g++withg++to use the default GCC
After building the application you can test it on your local machine.
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.
--imagePath to the image(JPEG/PNG/BMP) to process. You can use default image at ../../../assets/images/lic_us_1280x720.jpg.--assetsPath to the assets folder containing the configuration files and models. Default value is the current folder.--parallelWhether 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.--rectifyWhether to enable the rectification layer. More info about the rectification layer at https://www.doubango.org/SDKs/anpr/docs/Rectification_layer.html. Default: false.--tokenfilePath 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.--tokendataBase64 license token if you have one. If not provided then, the application will act like a trial version. Default: null.
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.