Nomeroff Net. Automatic numberplate recognition system. Version 0.2.3
Nomeroff Net is a opensource python license plate recognition framework based on the application of a convolutional neural network on the Mask_RCNN architecture, 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.
Nomeroff Net requires last version of Mask_RCNN,
Python 3.5, 3.6 or 3.7 (if you plan to install the latest tensorflow >=1.13.rc2) and opencv 3.4 or latest
git clone https://github.com/ria-com/nomeroff-net.git
cd ./nomeroff-net
git clone https://github.com/matterport/Mask_RCNN.git
pip3 install -r requirements.txtDownload the latest models that are required for your neural network to work and place them in the ./models folder of the nomeroff-net project
import os
import sys
import matplotlib.image as mpimg
import warnings
warnings.filterwarnings('ignore')
# change this property
NOMEROFF_NET_DIR = os.path.abspath('../')
# specify the path to Mask_RCNN if you placed it outside Nomeroff-net project
MASK_RCNN_DIR = os.path.join(NOMEROFF_NET_DIR, 'Mask_RCNN')
MASK_RCNN_LOG_DIR = os.path.join(NOMEROFF_NET_DIR, 'logs')
MASK_RCNN_MODEL_PATH = os.path.join(NOMEROFF_NET_DIR, "models/mask_rcnn_numberplate_0700.h5")
OPTIONS_MODEL_PATH = os.path.join(NOMEROFF_NET_DIR, "models/numberplate_options_2019_03_05.h5")
# If you use gpu version tensorflow please change model to gpu version named like *-gpu.pb
mode = "cpu"
OCR_NP_UKR_TEXT = os.path.join(NOMEROFF_NET_DIR, "models/anpr_ocr_ua_12-{}.h5".format(mode))
OCR_NP_EU_TEXT = os.path.join(NOMEROFF_NET_DIR, "models/anpr_ocr_eu_2-{}.h5".format(mode))
OCR_NP_RU_TEXT = os.path.join(NOMEROFF_NET_DIR, "models/anpr_ocr_ru_3-{}.h5".format(mode))
sys.path.append(NOMEROFF_NET_DIR)
# Import license plate recognition tools.
from NomeroffNet import filters, RectDetector, TextDetector, OptionsDetector, Detector, textPostprocessing
# Initialize npdetector with default configuration file.
nnet = Detector(MASK_RCNN_DIR, MASK_RCNN_LOG_DIR)
# Load weights in keras format.
nnet.loadModel(MASK_RCNN_MODEL_PATH)
# Initialize rect detector with default configuration file.
rectDetector = RectDetector()
# Initialize text detector.
# Also you may use gpu version models.
textDetector = TextDetector({
"eu_ua_2004_2015": {
"for_regions": ["eu_ua_2015", "eu_ua_2004"],
"model_path": OCR_NP_UKR_TEXT
},
"eu": {
"for_regions": ["eu", "eu_ua_1995"],
"model_path": OCR_NP_EU_TEXT
},
"ru": {
"for_regions": ["ru"],
"model_path": OCR_NP_RU_TEXT
}
})
# Initialize train detector.
optionsDetector = OptionsDetector()
optionsDetector.load(OPTIONS_MODEL_PATH)
# Detect numberplate
img_path = 'images/example2.jpeg'
img = mpimg.imread(img_path)
NP = nnet.detect([img])
# Generate image mask.
cv_img_masks = filters.cv_img_mask(NP)
# Detect points.
arrPoints = rectDetector.detect(cv_img_masks)
zones = rectDetector.get_cv_zonesBGR(img, arrPoints)
# find standart
# Added a classifier (isHiddenIds) for determining the fact of hide text of number, in order not to recognize a deliberately damaged license plate image.
regionIds, isHiddenIds = optionsDetector.predict(zones)
regionNames = optionsDetector.getRegionLabels(regionIds)
# find text with postprocessing by standart
textArr = textDetector.predict(zones, regionNames)
textArr = textPostprocessing(textArr, regionNames)
print(textArr)
#['RP70012', 'JJF509']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)
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
- AUTO.RIA Numberplate OCR EU Dataset
- AUTO.RIA Numberplate OCR RU Dataset
- AUTO.RIA Numberplate OCR KZ Dataset
This gives you the opportunity to get 97% accuracy on photos that are uploaded to AUTO.RIA project
For several months now, we have been devoting some of our time to developing new features for the Nomeroff Net project. In the near future we plan:
- Post a detailed instruction on the training of recognition models and classifier for license plates of your country.
- To expand the classification of countries of license plates by which to determine the country in which this license plate is registered.
Contributions to this repository are welcome. Examples of things you can contribute:
- Training on other datasets.
- Accuracy Improvements.
- Dmytro Probachay <[email protected]>
- Oleg Cherniy <[email protected]>
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