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README.md

LPRNet Example

Usage

Make sure you have downloaded the data files first for the examples. You only need to do this once for all examples.

cd example/
git clone --depth=1 https://github.com/swdee/go-rknnlite-data.git data

Run the LPRNet example on rk3588 or replace with your Platform model.

cd example/lprnet
go run lprnet.go -p rk3588

This will result in the output of:

Driver Version: 0.9.6, API Version: 2.3.0 (c949ad889d@2024-11-07T11:35:33)
Model Input Number: 1, Ouput Number: 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, 0], n_elems=1224, size=1224, fmt=UNDEFINED, type=INT8, qnt_type=AFFINE, zp=50, scale=0.643529
Model first run speed: inference=4.203128ms, post processing=30.916µs, total time=4.234044ms
License plate recognition result: 湘F6CL03
Benchmark time=350.625899ms, count=100, average total time=3.506258ms
done

To use your own RKNN compiled model and images.

go run lprnet.go -m <RKNN model file> -i <image file> -p <platform>

See the help for command line parameters.

$ go run lprnet.go --help

Usage of /tmp/go-build233788912/b001/exe/lprnet:
  -i string
        Image file to run inference on (default "../data/lplate.jpg")
  -m string
        RKNN compiled model file (default "../data/models/rk3588/lprnet-rk3588.rknn")
  -p string
        Rockchip CPU Model number [rk3562|rk3566|rk3568|rk3576|rk3582|rk3582|rk3588] (default "rk3588")

Docker

To run the ALPR example using the prebuilt docker image, make sure the data files have been downloaded first, then run.

# from project root directory

docker run --rm \
  --device /dev/dri:/dev/dri \
  -v "$(pwd):/go/src/app" \
  -v "$(pwd)/example/data:/go/src/data" \
  -v "/usr/include/rknn_api.h:/usr/include/rknn_api.h" \
  -v "/usr/lib/librknnrt.so:/usr/lib/librknnrt.so" \
  -w /go/src/app \
  swdee/go-rknnlite:latest \
  go run ./example/lprnet/lprnet.go -p rk3588

Proprietary Models

This example makes use of the Chinese License Plate Recognition LPRNet. You can train your own LPRNet's for other countries but need to initialize the postprocess.NewLPRNet with your specific LPRNetParams containing the maximum length of your countries number plates and character set used.

Benchmarks

The following table shows a comparison of the benchmark results across the three distinct platforms.

Platform Execution Time Average Inference Time Per Image
rk3588 0.35s 3.50ms
rk3576 0.49s 4.96ms
rk3566 1.63s 16.32ms

Note that these examples are only using a single NPU core to run inference on. The results would be different when running a Pool of models using all NPU cores available.

Background

This LPRNet example is a Go conversion of the C API Example

References