Sample For Car License Recognization

Description
Prerequisition
Download
Build Parser
Run

Description

This sample is to show how to use graded models for detection and classification with DeepStream SDK version not less than 5.0.1. The models in this sample are all TLT3.0 models. Image is written to a folder when the license plate crosses a virtual line. The line crossing is achieved by using the NVAnalytics module of Deepstream python (https://github.com/NVIDIA-AI-IOT/deepstream_python_apps).

PGIE(car detection) -> SGIE(car license plate detection) -> SGIE(car license plate recognization)

This pipeline is based on three TLT models below

Car detection model https://ngc.nvidia.com/catalog/models/nvidia:tlt_trafficcamnet
LPD (car license plate detection) model https://ngc.nvidia.com/catalog/models/nvidia:tlt_lpdnet
LPR (car license plate recognization/text extraction) model https://ngc.nvidia.com/catalog/models/nvidia:tlt_lprnet

More details for TLT3.0 LPD and LPR models and TLT training, please refer to TLT document.

Prerequisition

DeepStream SDK 5.1

Make sure deepstream-test1 sample can run successful to verify your DeepStream installation

Prepare Models and TensorRT engine

Download repo:

    git clone https://github.com/preronamajumder/deepstream-lpr-python-version

Download models:

    cd deepstream_lpr_python_app
    //for us car plate
    ./download_us.sh
    //for chinese car plate
    ./download_ch.sh

Download tlt-converter:

Download x86 or Jetson tlt-converter which is compatible to your platform from the following links inside the folder.

Platform	Compute	Link
x86 + GPU	CUDA 10.2/cuDNN 8.0/TensorRT 7.1	link
x86 + GPU	CUDA 10.2/cuDNN 8.0/TensorRT 7.2	link
x86 + GPU	CUDA 11.0/cuDNN 8.0/TensorRT 7.1	link
x86 + GPU	CUDA 11.0/cuDNN 8.0/TensorRT 7.2	link
Jetson	JetPack 4.4	link
Jetson	JetPack 4.5	link

    wget <url>
    unzip <filename>

Convert LPR Model:
DS5.0.1 gst-nvinfer cannot generate TRT engine for LPR model, so generate it with tlt-converter

    cd <foldername>
    chmod +x tlt-converter
    ./tlt-converter -k nvidia_tlt -p image_input,1x3x48x96,4x3x48x96,16x3x48x96 \
           ../models/LP/LPR/us_lprnet_baseline18_deployable.etlt -t fp16 -e ../models/LP/LPR/lpr_us_onnx_b16.engine
    cd ..

if you get the following:
[WARNING] Half2 support requested on hardware without native FP16 support, performance will be negatively affected.
Then use fp32 instead of fp16:

    ./tlt-converter -k nvidia_tlt -p image_input,1x3x48x96,4x3x48x96,16x3x48x96 \
           ../models/LP/LPR/us_lprnet_baseline18_deployable.etlt -t fp32 -e ../models/LP/LPR/lpr_us_onnx_b32.engine
    cd ..

Build Parser

    cd nvinfer_custom_lpr_parser
    make
    cd ..

Run app

Run the application: