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import cv2
import numpy as np
import imutils
import easyocr


# Loading image
img = cv2.imread('images/7.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
bfilter = cv2.bilateralFilter(gray, 11, 17, 17)
edged = cv2.Canny(bfilter, 30, 200)

# Get location of contours and also array of sorted width
keypoints = cv2.findContours(
    edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours = imutils.grab_contours(keypoints)
contours = sorted(contours, key=cv2.contourArea, reverse=True)[:10]

# Grab location based on shape that has 4 points (Plate)
location = None
for contour in contours:
    approx = cv2.approxPolyDP(contour, 10, True)
    if len(approx) == 4:
        location = approx
        break

# Drawing Black Screen except the plate number
mask = np.zeros(gray.shape, np.uint8)
new_image = cv2.drawContours(mask, [location], 0, 255, -1)
new_image = cv2.bitwise_and(img, img, mask=mask)


# # Cropped Image
(x, y) = np.where(mask == 255)
(x1, y1) = (np.min(x), np.min(y))
(x2, y2) = (np.max(x), np.max(y))
cropped_image = gray[x1:x2+1, y1:y2+1]

# Using OCR
reader = easyocr.Reader(['en'])
result = reader.readtext(cropped_image)
print(result)


# Drawing the image again
text = result[0][-2]
res = cv2.putText(img, text, (approx[0][0][0], approx[1]
                  [0][1]+60), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
res = cv2.rectangle(img, tuple(approx[0][0]), tuple(
    approx[2][0]), (0, 255, 0), 3)

cv2.imshow("Image", cv2.cvtColor(res, cv2.COLOR_BGR2RGB))
cv2.waitKey(0)