Auto Number Plate Recognition for EU countries
🦋 Available Countries: (We are adding more countries.)
🇦🇱 Albania 🇨🇿 Czechia 🇦🇩 Andorra 🇩🇰 Denmark 🇦🇹 Austria 🇫🇮 Finland
🇧🇪 Belgium 🇫🇷 France 🇧🇦 Bosnia and Herzegovina
🇩🇪 Germany 🇧🇬 Bulgaria 🇬🇷 Greece 🇭🇷 Croatia 🇭🇺 Hungary 🇨🇾 Cyprus 🇮🇪 Ireland
🦋 Recognisable Characters:
char_list = [
"-", ".", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9",
"A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N",
"O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z", "a", "d", "i",
"m", "o", "Ö", "Ü", "Ć", "Č", "Đ", "Š", "Ž", "П"
]
한국 자동차 번호판 인식 솔루션
인식 가능 문자:
char_list = [
'0', '1', '2', '3', '4', '5', '6', '7', '8', '9',
'가', '강', '거', '경', '고', '광', '구', '기',
'나', '남', '너', '노', '누',
'다', '대', '더', '도', '동', '두',
'라', '러', '로', '루',
'마', '머', '모', '무', '문',
'바', '배', '버', '보', '부', '북',
'사', '산', '서', '세', '소', '수',
'아', '어', '오', '우', '울', '원', '육', '인',
'자', '저', '전', '제', '조', '종', '주',
'천', '충',
'하', '허', '호'
]
To install the MareArts ANPR package, use the following pip command:
pip install marearts-anpr
If you want to use CUDA version inference, please install the following additional packages:
pip install onnxruntime-gpu==1.18.1
For private keys, please visit MareArts ANPR Solution. For inquiries about private keys, contact us at hello@marearts.com.
Check here to see the license plate recognition results in YouTube videos.
Model | File Size | Precision | Recall | Speed |
---|---|---|---|---|
middle | 103mb | legacy | legacy | 0.23 sec |
v10_small | 29mb | 0.9713 | 0.9669 | 0.0623 sec |
v10_middle | 61mb | 0.9731 | 0.9617 | 0.1262 sec |
v10_large | 93mb | 0.9735 | 0.9687 | 0.1764 sec |
- speed test is based on i7-9800X 3.8GHz
Model | File Size | Accuracy | Speed |
---|---|---|---|
eu | - | - | - |
kr | - | - | - |
Here's an example of how to use the updated SDK:
# pip install marearts-anpr
import cv2
from PIL import Image
from marearts_anpr import ma_anpr_detector
from marearts_anpr import ma_anpr_ocr
from marearts_anpr import marearts_anpr_from_pil
from marearts_anpr import marearts_anpr_from_image_file
from marearts_anpr import marearts_anpr_from_cv2
if __name__ == '__main__':
#################################
## Initiate MareArts ANPR
print("EU ANPR")
user_name = "your_email"
serial_key = "your_serial_key"
detector_model_version = "middle" # Options: middle, v10_small, v10_middle, v10_large
ocr_model_version = "eu" # Options: eu, kr
# MareArts ANPR Detector Inference
anpr_d = ma_anpr_detector(detector_model_version, user_name, serial_key, conf_thres=0.3, iou_thres=0.5)
# MareArts ANPR OCR Inference
anpr_r = ma_anpr_ocr(ocr_model_version, user_name, serial_key)
#################################
#################################
# Routine Task 1 - Predict from File
image_path = './sample_images/eu_test1.jpg'
output = marearts_anpr_from_image_file(anpr_d, anpr_r, image_path)
print(output)
# Routine Task 2 - Predict from cv2
img = cv2.imread(image_path)
output = marearts_anpr_from_cv2(anpr_d, anpr_r, img)
print(output)
# Routine Task 3 - Predict from Pillow
pil_img = Image.open(image_path)
output = marearts_anpr_from_pil(anpr_d, anpr_r, pil_img)
print(output)
#################################
#################################
## Initiate MareArts ANPR for Korea
print("ANPR Korean")
# user_name, serial_key are already defined
# anpr_d is also already initiated before
ocr_model_version = "kr"
# MareArts ANPR OCR Inference
anpr_r = ma_anpr_ocr(ocr_model_version, user_name, serial_key)
#################################
# Routine Task 1 - Predict from File
image_path = './sample_images/kr_test2.jpg'
output = marearts_anpr_from_image_file(anpr_d, anpr_r, image_path)
print(output)
# Routine Task 2 - Predict from cv2
img = cv2.imread(image_path)
output = marearts_anpr_from_cv2(anpr_d, anpr_r, img)
print(output)
# Routine Task 3 - Predict from Pillow
pil_img = Image.open(image_path)
output = marearts_anpr_from_pil(anpr_d, anpr_r, pil_img)
print(output)
#################################
Please refer to the advanced.py code in the ./example_code folder. This file demonstrates how to implement the ma_anpr_detector and ma_anpr_ocr separately.
The output from the ANPR will be similar to:
{
'results': [
{'ocr': 'SL593LM', 'ocr_conf': 99, 'ltrb': [819, 628, 1085, 694], 'ltrb_conf': 90}
],
'ltrb_proc_sec': 0.22,
'ocr_proc_sec': 0.15
}
{
'results': [
{'ocr': '123가4568', 'ocr_conf': 99, 'ltrb': [181, 48, 789, 186], 'ltrb_conf': 83},
{'ocr': '123가4568', 'ocr_conf': 99, 'ltrb': [154, 413, 774, 557], 'ltrb_conf': 82},
{'ocr': '123가4568', 'ocr_conf': 99, 'ltrb': [154, 601, 763, 746], 'ltrb_conf': 80},
{'ocr': '123가4568', 'ocr_conf': 99, 'ltrb': [156, 217, 773, 369], 'ltrb_conf': 80}
],
'ltrb_proc_sec': 0.23,
'ocr_proc_sec': 0.6
}
- Results: Contains OCR text, probabilities, and detection coordinate(left, top, right, bottom).
- Processing Speeds: Provided for license plate detection and OCR.
API key limits: 1000 requests per day.
User ID: marearts@public
X-API-Key: J4K9L2Wory34@G7T1Y8rt-PP83uSSvkV3Z6ioSTR!
To make an API call for ANPR, use the following command:
Content-Type : image/jpeg
x-api-key: api_key
user-id: user-id
detector_model_version: Specifies which version of the detector model to use. Refer to version table
ocr_model_version: Specifies which version of the OCR model to use. Refer to version table
#!bin/bash
curl -X POST https://we303v9ck8.execute-api.eu-west-1.amazonaws.com/Prod/marearts_anpr \
-H "Content-Type: image/jpeg" \
-H "x-api-key: api_key" \
-H "user-id: user-id" \
-H "detector_model_version: detector_version" \
-H "ocr_model_version: ocr_version" \
--data-binary "@./a.jpg"
Please refer to ./API_docker_example folder.
API_docker_example
│
├── api_call.sh # Shell script to make API calls to the MareArts ANPR application.
├── app.py # Python script containing the MareArts ANPR application.
├── build_image.sh # Shell script to build the Docker image from the Dockerfile.
├── Dockerfile # Dockerfile containing instructions for building the Docker image.
├── request.py # Python script to send requests to the MareArts ANPR API server.
├── requirements.txt # Contains a list of Python packages that the app requires.
└── run_container.sh # Shell script to run the Docker container from the built image.
email : hello@marearts.com
home page : https://marearts.com
blog : http://study.marearts.com
subscription : https://study.marearts.com/p/anpr-lpr-solution.html
live test : http://live.marearts.com
🙇🏻♂️ Thank you!