MareArts Computer Vision Study.: ANPR

Showing posts with label ANPR. Show all posts

11/03/2024

Auto Number Plate Recognition (ANPR), SDK source code

# install

pip install marearts-anpr

# code

# 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: refer to detector model table

    ocr_model_version = "eu" # Options: refer to ocr model table

    # 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)

    #################################

# Ask license is here: https://study.marearts.com/p/anpr-lpr-solution.html

# Live Test is here: https://live.marearts.com

1/26/2015

Real-time yard trailer identification by detection of vehicle ID numbers

Real-time yard trailer identification by detection of vehicle ID numbers

Project period : (2013.09~2013.11)

*Introduction

• Y/T(Yard Trailer) Y/T(Yard Trailer) number identification solution using image processing.
• The solution using camera is easy to installation and maintance compare to the RFID. And It is more free from distance constraint.
• Machine learning methods - SVM (Support Vector Machine), MLP (Multi Layers Perceptron) are used to recognize the number ID
• A high-speed image processing through the GPU parallel programming

*Real-time pre-processing for features extraction

• The process of preprocessing for ID number extraction
-In the first step, we apply different filters, morphological operations, contour algorithms, and validations to retrieve those parts of the image that could have targeted region.
-Especially, we targeted to detect a ventilating opening instead of number ID, because that target is less shape change than the 3 characters of number ID.

*Vehicle Identification

• HOG(Histogram of gradient) feature extraction and SVM machine learning to detect a ventilating opening

• Each segmented character is to extract the features for training and classifying the MLP algorithm
• The feature is horizontal, vertical histogram values from 5x5 low resolution image.

*Experiment

• Recognition rate over the 95%
• Detection speed about 0.05 sec/frame (Image size : 1280x720, Intel® core™ i5-3570 cpu 3.40GHz, NVIDIA Geforce GTX 650)
• The trailer enter speed about 20~30 km/h

#include < stdio.h>

#include "ShinPortOCR.h"


void main()
{

 ShinPortOCR cShinPortOCR;

 //printf("연속으로 읽을 이미지 파일 갯수? (ex:10 -> ./data/1.jpg, ./data/2.jpg ... ./data/10.jpg) \n");
 printf("How many images do you want to test? (ex:100, 500,  1630\n");
 int num;
 scanf_s("%d", &num);

 int p = 0, n = 0;
 char str[100];
 for (int i = 0; i< num; ++i)
 {
  printf("%d/%d\n", i, num);

  sprintf_s(str, "./data/%d.jpg", i + 1);
  Mat inImg = imread(str, 1);//, CV_LOAD_IMAGE_GRAYSCALE);
  Mat OutImg; 
  if (cShinPortOCR.GoGoXing(inImg, OutImg, 1) == -111) //1 is debug print, 0 is no dubug out
  {
   sprintf_s(str, ".\\Log\\fail\\%d.jpg", i + 1);
   imwrite(str, inImg);
  }
  else{
   sprintf_s(str, ".\\Log\\success\\%d.jpg", i + 1);
   imwrite(str, inImg);
  }


  sprintf_s(str, ".\\Log\\processing\\%d.jpg", i + 1);
  imwrite(str, OutImg);


  imshow("result", OutImg);
  waitKey(10);

 }
}

///

Source code is here
https://github.com/MareArts/Container-Yard-Trailer-ID-number-recognition

you can down opencv dll/lib/header files on here
opencv 249 64bit cuda 60
https://www.amazon.com/clouddrive/share/7bPR5HgbCbNZJHwG0ldq1gwHtydLXRxtQVYc5JYPlSF?ref_=cd_ph_share_link_copy

Pages

11/03/2024

Auto Number Plate Recognition (ANPR), SDK source code

1/26/2015

Real-time yard trailer identification by detection of vehicle ID numbers