Nomeroff Net. Automatic numberplate recognition system. Version 1.0.0
Nomeroff Net is an opensource python license plate recognition framework based on the application of a segmentation neural network and cusomized OCR-module powered by GRU architecture.
The project is now at the initial stage of development, write to us if you are interested in helping us in the formation of a dataset for your country.
Version 1.0 2.5x faster Nomeroff Net 0.4.x! This improvement was achieved by replacing Mask RCNN with a CenterMask2 (more modern and high-speed implementation of the instance segmaentation task).
Nomeroff Net requires Python >= 3.6 and opencv 3.4 or latest
Clone Project and clone related projects
git clone https://github.com/ria-com/nomeroff-net.git
cd nomeroff-net
git clone https://github.com/youngwanLEE/centermask2.git# for Opencv
yum install libSM
# for pycocotools install
yum install python3-devel
# ensure that you have installed gcc compiler
yum install gcc# ensure that you have installed gcc compiler
apt-get install gcc
# for opencv install
apt-get install -y libglib2.0
apt-get install -y libsm6
apt-get install -y libfontconfig1 libxrender1
apt-get install -y libxtst6
# for pycocotools install (Check the name of the dev-package for your python3)
apt-get install python3.6-devpip3 install torch==1.7
pip3 install PyYAML==5.3
pip3 install 'git+https://github.com/facebookresearch/detectron2.git'
pip3 install torchvision==0.8
pip3 install Cython
pip3 install numpy
pip3 install -r requirements.txtOn Windows, you must have the Visual C++ 2015 build tools on your path. If you don't, make sure to install them from here:
Then, run visualcppbuildtools_full.exe and select default options:
# Specify device
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
os.environ["TF_FORCE_GPU_ALLOW_GROWTH"]="true"
# Import all necessary libraries.
import numpy as np
import sys
import matplotlib.image as mpimg
# NomeroffNet path
NOMEROFF_NET_DIR = os.path.abspath('../')
sys.path.append(NOMEROFF_NET_DIR)
# Import license plate recognition tools.
from NomeroffNet import Detector
from NomeroffNet import filters
from NomeroffNet import RectDetector
from NomeroffNet import OptionsDetector
from NomeroffNet import TextDetector
from NomeroffNet import textPostprocessing
# load models
rectDetector = RectDetector()
optionsDetector = OptionsDetector()
optionsDetector.load("latest")
textDetector = TextDetector.get_static_module("eu")()
textDetector.load("latest")
nnet = Detector()
nnet.loadModel(NOMEROFF_NET_DIR)
# Detect numberplate
img_path = 'images/example2.jpeg'
img = mpimg.imread(img_path)
# Generate image mask.
cv_imgs_masks = nnet.detect_mask([img])
for cv_img_masks in cv_imgs_masks:
# Detect points.
arrPoints = rectDetector.detect(cv_img_masks)
# cut zones
zones = rectDetector.get_cv_zonesBGR(img, arrPoints, 64, 295)
# find standart
regionIds, stateIds, countLines = optionsDetector.predict(zones)
regionNames = optionsDetector.getRegionLabels(regionIds)
# find text with postprocessing by standart
textArr = textDetector.predict(zones)
textArr = textPostprocessing(textArr, regionNames)
print(textArr)
# ['JJF509', 'RP70012']
In order to evaluate the quality of work of Nomeroff Net without spending time on setting up and installing, we made an online form in which you can upload your photo and get the recognition result online
All data on the basis of which the training was conducted is provided by RIA.com. In the following, we will call this data the AUTO.RIA Numberplate Dataset.
We will be grateful for your help in the formation and layout of the dataset with the image of the license plates of your country. For markup, we recommend using VGG Image Annotator (VIA)
Nomeroff-Net Mask-RCNN Example:
Mask detection example
Key points detection example
The system uses several neural networks. One of them is the classifier of numbers at the post-processing stage. It uses dataset AUTO.RIA Numberplate Options Dataset.
The categorizer accurately (99%) determines the country and the type of license plate. Please note that now the classifier is configured mainly for the definition of Ukrainian numbers, for other countries it will be necessary to train the classifier with new data.
As OCR, we use a specialized implementation of a neural network with GRU layers, for which we have created several datasets:
- AUTO.RIA Numberplate OCR UA Dataset (Ukrainian)
- AUTO.RIA Numberplate OCR UA Dataset (Ukrainian) with old design Dataset
- AUTO.RIA Numberplate OCR EU Dataset (European)
- AUTO.RIA Numberplate OCR RU Dataset (Russian)
- AUTO.RIA Numberplate OCR KZ Dataset (Kazakh)
- AUTO.RIA Numberplate OCR GE Dataset (Georgian)
- AUTO.RIA Numberplate OCR BY Dataset (Belarus)
- AUTO.RIA Numberplate OCR SU Dataset (exUSSR)
- AUTO.RIA Numberplate OCR KG Dataset (Kyrgyzstan)
If we did not manage to update the link on dataset you can find the latest version here
This gives you the opportunity to get 98% accuracy on photos that are uploaded to AUTO.RIA project
Number plate recognition example
Contributions to this repository are welcome. Examples of things you can contribute:
- Training on other datasets.
- Accuracy Improvements.
- Dmytro Probachay <dmytro.probachay@ria.com>
- Oleg Cherniy <oleg.cherniy@ria.com>