LPRNet

Environment Setup

info

Follow RKNN Installation to set up the environment.

Follow RKNN Model Zoo to download the example files.

Model Download

Download the ONNX model file.

X64 Linux PC

cd rknn_model_zoo/examples/LPRNet/model/
bash download_model.sh

Model Conversion

Select the target platform.

rk3588

X64 Linux PC

export TARGET_PLATFORM=rk3588

rk356x

X64 Linux PC

export TARGET_PLATFORM=rk356x

rk3576

X64 Linux PC

export TARGET_PLATFORM=rk3576

Convert the ONNX model to an RKNN model.

X64 Linux PC

cd ../python/
python convert.py ../model/lprnet.onnx ${TARGET_PLATFORM}

C API

Build the Example

Go to the rknn_model_zoo directory and run build-linux.sh to build.

X64 Linux PC

cd ../../..
bash build-linux.sh -t ${TARGET_PLATFORM} -a aarch64 -d LPRNet

Sync Files to the Device

Copy the built demo directory under the install folder to the device.

X64 Linux PC

cd install/${TARGET_PLATFORM}_linux_aarch64/
scp -r rknn_lprnet_demo/ user@your_device_ip:target_directory

Run the Example

Export the runtime libraries to the environment variable.

Device

cd rknn_lprnet_demo/
export LD_LIBRARY_PATH=./lib

Run the example.

Device

./rknn_lprnet_demo ./model/lprnet.rknn ./model/test.jpg

$ ./rknn_lprnet_demo ./model/lprnet.rknn ./model/test.jpg
model input num: 1, output num: 1
input tensors:
  index=0, name=input, n_dims=4, dims=[1, 24, 94, 3], n_elems=6768, size=6768, fmt=NHWC, type=INT8, qnt_type=AFFINE, zp=0, scale=0.007843
output tensors:
  index=0, name=output, n_dims=3, dims=[1, 68, 18], n_elems=1224, size=1224, fmt=UNDEFINED, type=INT8, qnt_type=AFFINE, zp=50, scale=0.643529
model is NHWC input fmt
model input height=24, width=94, channel=3
origin size=94x24 crop size=80x16
input image: 94 x 24, subsampling: 4:2:0, colorspace: YCbCr, orientation: 1
rknn_run
Plate recognition result: 湘F6CL03

Test Image

Python API

Activate the virtual environment

Device

conda activate rknn

Run the Example

Copy the related files to the device and run the following commands.

Device

python lprnet.py --model_path ../model/lprnet.rknn --target ${TARGET_PLATFORM}

$ python lprnet.py --model_path ../model/lprnet.rknn --target rk3588
/home/radxa/miniforge3/envs/rknn/lib/python3.12/site-packages/rknn/api/rknn.py:51: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
  self.rknn_base = RKNNBase(cur_path, verbose)
I rknn-toolkit2 version: 2.3.2
done
rk3588
--> Init runtime environment
I target set by user is: rk3588
done
--> Running model
W inference: The 'data_format' is not set, and its default value is 'nhwc'!
--> PostProcess
Plate recognition result: 湘F6CL03