lpd_data.py

# -*- coding: utf-8 -*-
"""LPD-Data.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1wK8tj-WzhG_Zx-F3LZ9ro8ZG94QhdsJP
"""

from google.colab import drive
drive.mount('/content/drive')

!pip3 install opencv-python
!pip3 install pytesseract
!pip install easyocr 
import easyocr
! apt install tesseract-ocr
! apt install libtesseract-dev

import numpy as np
import cv2
from PIL import Image
from google.colab.patches import cv2_imshow
import imutils
import glob
import tensorflow as tf
import matplotlib.pylab as plt
tf.test.gpu_device_name()

def ratioCheck(area, width, height):
    ratio = float(width) / float(height)
    if ratio < 1:
        ratio = 1 / ratio
    if (area < 1063.62 or area > 73862.5) or (ratio < 3 or ratio > 6):
        return False
    return True

def isMaxWhite(plate):
    avg = np.mean(plate)
    if(avg>=115):
        return True
    else:
         return False

def ratio_and_rotation(rect):
    (x, y), (width, height),rect_angle = rect
    if(width>height):
        angle = -rect_angle
    else:
        angle = 90 + rect_angle
    if angle>15:
         return False
    if height == 0 or width == 0:
        return False
    area = height*width
    if not ratioCheck(area,width,height):
        return False
    else:
        return True

def clean2_plate(plate):
    gray_img = cv2.cvtColor(plate, cv2.COLOR_BGR2GRAY)
    _, thresh = cv2.threshold(gray_img, 110, 255, cv2.THRESH_BINARY)
    if cv2.waitKey(0) & 0xff == ord('q'):
        pass
    num_contours,hierarchy = cv2.findContours(thresh.copy(),cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    if num_contours:
        contour_area = [cv2.contourArea(c) for c in num_contours]
        max_cntr_index = np.argmax(contour_area)
        max_cnt = num_contours[max_cntr_index]
        max_cntArea = contour_area[max_cntr_index]
        x,y,w,h = cv2.boundingRect(max_cnt)
        if not ratioCheck(max_cntArea,w,h):
            return plate,None
        final_img = thresh[y:y+h, x:x+w]
        return final_img,[x,y,w,h]
    else:
        return plate, None

#img = cv2.imread("/content/drive/MyDrive/Colab Notebooks/car_15.jpg")
path=glob.glob("/content/drive/MyDrive/Colab Notebooks/data/*.jpg")
for file in path:
  img=cv2.imread(file)
  print("Number  input image...",)
  plt.imshow(img)
  plt.show()
  if cv2.waitKey(0) & 0xff == ord('q'):
    pass
  img2 = cv2.GaussianBlur(img, (3,3), 0)
  img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
  img2 = cv2.Sobel(img2,cv2.CV_8U,1,0,ksize=3)    
  _,img2 = cv2.threshold(img2,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
  element = cv2.getStructuringElement(shape=cv2.MORPH_RECT, ksize=(17, 3))
  morph_img_threshold = img2.copy()
  cv2.morphologyEx(src=img2, op=cv2.MORPH_CLOSE, kernel=element, dst=morph_img_threshold)
  num_contours, hierarchy= cv2.findContours(morph_img_threshold,mode=cv2.RETR_EXTERNAL,method=cv2.CHAIN_APPROX_NONE)
  cv2.drawContours(img2, num_contours, -1, (0,255,0), 1)
  print("Number after masking the whole and visibling the number plate only...")
  cv2_imshow(img2)
  for i,cnt in enumerate(num_contours):
    min_rect = cv2.minAreaRect(cnt)
    if ratio_and_rotation(min_rect):
       x,y,w,h = cv2.boundingRect(cnt)
       plate_img = img[y:y+h,x:x+w]
       print("Number  identified number plate...")
       cv2_imshow(plate_img)
       if cv2.waitKey(0) & 0xff == ord('q'):
         pass
       if(isMaxWhite(plate_img)):
         clean_plate, rect = clean2_plate(plate_img)
         if rect:
           fg=0
           x1,y1,w1,h1 = rect
           x,y,w,h = x+x1,y+y1,w1,h1
           # cv2.imwrite("clena.png",clean_plate)
           plate_im =clean_plate 
           reader=easyocr.Reader(['en'])
           result=reader.readtext(plate_im)
           print("Number  Detected Plate Text : ",result[0][1])
           with open('/content/drive/MyDrive/Colab Notebooks/abc.txt', 'a') as writefile:
             writefile.write(result[0][1])
             writefile.write("\n")