main.py

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


def preprocess(img):
    cv2.imshow("Input", img)
    imgBlurred = cv2.GaussianBlur(img, (5, 5), 0)
    gray = cv2.cvtColor(imgBlurred, cv2.COLOR_BGR2GRAY)
    sobelx = cv2.Sobel(gray, cv2.CV_8U, 1, 0, ksize=3)
    ret2, threshold_img = cv2.threshold(sobelx, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    return threshold_img


def extract_contours(threshold_img):
    element = cv2.getStructuringElement(shape=cv2.MORPH_RECT, ksize=(17, 3))
    morph_img_threshold = threshold_img.copy()
    cv2.morphologyEx(src=threshold_img, op=cv2.MORPH_CLOSE, kernel=element, dst=morph_img_threshold)
    cv2.imshow("Morphed", morph_img_threshold)
    cv2.waitKey(0)
    im2, contours, hierarchy = cv2.findContours(morph_img_threshold, mode=cv2.RETR_EXTERNAL,method=cv2.CHAIN_APPROX_NONE)
    return contours


def clean(img,contours):
    for i, cnt in enumerate(contours):
        min_rect = cv2.minAreaRect(cnt)
        if validate_rotation_and_ratio(min_rect):
            x, y, w, h = cv2.boundingRect(cnt)
            plate_img = img[y:y + h, x:x + w]
            if (is_max_white(plate_img)):
                clean_plate, rect = cleanPlate(plate_img)
                if rect:
                    x1, y1, w1, h1 = rect
                    x, y, w, h = x + x1, y + y1, w1, h1
                    cv2.imshow("Cleaned Plate", clean_plate)
                    cv2.waitKey(0)
                    plate_im = Image.fromarray(clean_plate)
                    text = tess.image_to_string(plate_im, lang='eng')
                    print("Detected Text : ", text)
                    img = cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
                    cv2.imshow("Detected Plate", img)
                    cv2.waitKey(0)



def validate_rotation_and_ratio(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 ratio_check(area, width, height):
        return False
    else:
        return True


def ratio_check(area, width, height):
    ratio = float(width) / float(height)
    if ratio < 1:
        ratio = 1 / ratio
    aspect = 4.7272
    min = 15 * aspect * 15
    max = 125 * aspect * 125
    rmin = 3
    rmax = 6
    if (area < min or area > max) or (ratio < rmin or ratio > rmax):
        return False
    return True


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

def cleanPlate(plate):
    gray = cv2.cvtColor(plate, cv2.COLOR_BGR2GRAY)
    _, thresh = cv2.threshold(gray, 150, 255, cv2.THRESH_BINARY)
    im1, contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    if contours:
        areas = [cv2.contourArea(c) for c in contours]
        max_index = np.argmax(areas)
        max_cnt = contours[max_index]
        max_cntArea = areas[max_index]
        x, y, w, h = cv2.boundingRect(max_cnt)
        if not ratio_check(max_cntArea, w, h):
            return plate, None
        cleaned_final = thresh[y:y + h, x:x + w]
        return cleaned_final, [x, y, w, h]
    else:
        return plate, None


img = cv2.imread("testData/car6.jpg")
threshold_img = preprocess(img)
contours= extract_contours(threshold_img)
cleanedPlate  = clean(img,contours)