number_plate_detector.py

import cv2
import imutils
import numpy as np
import pytesseract
from PIL import Image

def wait():
    cv2.waitKey(0)
    cv2.destroyAllWindows()

def main():
    filename = 'images/car4.jpg'

    img = cv2.imread(filename, cv2.IMREAD_COLOR)

    img = cv2.resize(img, (620,480))

    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #convert to grey scale
    gray = cv2.bilateralFilter(gray, 11, 17, 17) #Blur to reduce noise
    edged = cv2.Canny(gray, 30, 200) #Perform Edge detection

    # find contours in the edged image, keep only the largest
    # ones, and initialize our screen contour
    cnts = cv2.findContours(edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    cnts = imutils.grab_contours(cnts)
    cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:10]
    screenCnt = None

    # loop over our contours
    for c in cnts:
        # approximate the contour
        peri = cv2.arcLength(c, True)
        approx = cv2.approxPolyDP(c, 0.018 * peri, True)
        
        # if our approximated contour has four points, then
        # we can assume that we have found our screen
        if len(approx) == 4:
            screenCnt = approx
            break

        

    if screenCnt is None:
        detected = 0
        print("No contour detected")
    else:
        detected = 1

    if detected == 1:
        cv2.drawContours(img, [screenCnt], -1, (0, 255, 0), 3)

    # Masking the part other than the number plate
    mask = np.zeros(gray.shape,np.uint8)
    new_image = cv2.drawContours(mask, [screenCnt], 0, 255, -1,)
    new_image = cv2.bitwise_and(img, img, mask=mask)

    # Now crop
    (x, y) = np.where(mask == 255)
    (topx, topy) = (np.min(x), np.min(y))
    (bottomx, bottomy) = (np.max(x), np.max(y))
    Cropped = gray[topx:bottomx + 1, topy:bottomy + 1]

    #Read the number plate
    text = pytesseract.image_to_string(Cropped, config='--psm 11')
    print("Detected Number is:", text)

    cv2.imshow('image', img)
    cv2.imshow('Cropped', Cropped)

    wait()

main()