Skip to content

Latest commit

 

History

History
187 lines (139 loc) · 5.64 KB

README.md

File metadata and controls

187 lines (139 loc) · 5.64 KB

LPRNet: License Plate Recognition

Setup

Prerequisites

  • Ubuntu 16.04
  • Python 3.6
  • TensorFlow 1.13.1
  • OpenVINO 2019 R1 with Python API

Installation

  1. Create virtual environment
virtualenv venv -p python3 --prompt="(lpr)"
  1. Activate virtual environment and setup OpenVINO variables
. venv/bin/activate
. /opt/intel/openvino/bin/setupvars.sh

NOTE Good practice is adding . /opt/intel/openvino/bin/setupvars.sh to the end of the venv/bin/activate.

echo ". /opt/intel/openvino/bin/setupvars.sh" >> venv/bin/activate
  1. Install the modules
pip3 install -e .
pip3 install -e ../utils

Train LPRNet model

To train a LPRNet, jump to training_toolbox/lpr directory. You'll see the folder with sample code demonstrating how to train a LPRNet model.

We provide predefined configuration for:

As training dataset for this model Synthetic Chinese License Plates dataset was used.

To train a model, go through the following steps:

Prepare dataset

  1. Download training data and extract it in data/synthetic_chinese_license_plates folder. After extracting it will consist from folder with training images named crops and text file with annotations named annotation.

  2. After extracting training data archive run python script from data/synthetic_chinese_license_plates/make_train_val_split.py to make split of the whole annotations into train and val feeding him path to data/synthetic_chinese_license_plates/annotation file from archive as an input. As a result you'll find data/synthetic_chinese_license_plates/train, data/synthetic_chinese_license_plates/val annotation files with full path to images and labels in the folder with extracted data.

    python3 make_train_val_split.py data/synthetic_chinese_license_plates/annotation

    The result structure of the folder should be:

    ./data/synthetic_chinese_license_plates/
├── make_train_val_split.py
└── Synthetic_Chinese_License_Plates/
    ├── annotation
    ├── crops/
    │   ├── 000000.png
    |   ...
    ├── LICENSE
    ├── README
    ├── train
    └── val

  3. Then edit training_toolbox/lpr/chinese_lp/config.py by pointing out train.file_list_path and eval.file_list_path parameters in train section to paths of obtained train and val annotation files accordingly.

Train and evaluation

  1. To start training process type in command line:

    python3 tools/train.py chinese_lp/config.py

    To start from pretrained checkpoint type in command line:

    python3 tools/train.py chinese_lp/config.py \
  --init_checkpoint <data_path>/license-plate-recognition-barrier-0007/model.ckpt

  2. To start evaluation process type in command line:

    python3 tools/eval.py chinese_lp/config.py

    NOTE Before doing step 4, make sure that parameter eval.file_list_path in lpr/chinese_lp/config.py pointing out to file with annotations to test on. Do step 4 in another terminal, so training and evaluation are performed simultaneously.

  3. Training and evaluation artifacts will be stored by default in lpr/chinese_lp/model. To visualize training and evaluation, run vinotensorboard with:

    tensorboard --logdir=./model

    And view results in a browser: http://localhost:6006.

Export to OpenVINO

To run the model via OpenVINO one has to freeze TensorFlow graph and then convert it to OpenVINO Internal Representation (IR) using Model Optimizer:

python3 tools/export.py --data_type FP32 --output_dir <export_path> chinese_lp/config.py

default export path: lpr/model/export_<step>/frozen_graph - path to frozen graph lpr/model/export_<step>/IR/<data_type> - path to converted model in IR format

Demo

For the latest checkpoint

NOTE Input data for infer should be set via parameter infer.file_list_path in training_toolbox/lpr/chinese_lp/config.py and must be look like text file with list of path to license plates images in format:

path_to_lp_image1
path_to_lp_image2
...

When training is complete, model from the checkpoint could be infered on input data by running training_toolbox/lpr/chinese_lp/infer.py:

python3 tools/infer_checkpoint.py chinese_lp/config.py

For frozen graph

python3 tools/infer.py --model model/export_<step>/frozen_graph/graph.pb.frozen \
    --config chinese_lp/config.py \
    <image_path>

For Intermediate Representation (IR)

python3 tools/infer_ie.py --model model/export_<step>/IR/FP32/lpr.xml \
  --device=CPU \
  --cpu_extension="${INTEL_OPENVINO_DIR}/deployment_tools/inference_engine/lib/intel64/libcpu_extension_avx2.so" \
  --config chinese_lp/config.py \
  <image_path>

Citation

If you find LPRNet useful in your research, please, consider to cite the following paper:

@article{icv2018lprnet,
title={LPRNet: License Plate Recognition via Deep Neural Networks},
author={Sergey Zherzdev and Alexey Gruzdev},
journal={arXiv:1806.10447},
year={2018}
}