How to apply the proper threshold to remove edges on binary plates

Question

I am facing a contour problem, so that you can read the license plate in a correct way, it is best to take out the contours, and thus perhaps apply some OCR.

For example if I want to use this photo, you can see that on the axis it has a white outline, how could I eliminate those white outlines in a generic way? for can be used on more license plates

I am thinking of applying a threshold in the 2 axis (horizontally and vertically) again to clean possible white borders, any ideas?

A little what I'm have made:

# Creating copies of the original images
output_cp = output.copy()
img_cp = straightened.copy()

# threshold
ret,thresh = cv2.threshold(output_cp, 215, 255, cv2.THRESH_BINARY_INV)
imshow(thresh)

But then when It suposed to work, doesn't work because it is a binary image, pseudo-code, when xxx I don't know what contour apply:

mask = np.zeros(image.shape, np.uint8)
mask_cnt = cv2.drawContours(mask.copy(), [xxx], 0, (255,255,255,255), -1)
removed = cv2.subtract(mask_cnt, image)
plt.figure()
plt.imshow(removed, cmap='gray')

Any help is welcome!

Do you mean the last image i attached? the black lines represent the white outline of the image (horizontally), a mask was used so that the white of the original image is black as the background — Sebastián

antoine antoine · Accepted Answer · 2020-06-03T13:03:21

To remove the white margin around the plate, you can :

find the external contours of the image with findCountours
take its rotated bounding box with minAreaRect
compute a transformation to correct the orientation with getPerspectiveTransforme
then apply it with warpPerspective

This remove the margin and also correct the orientation of the plate which should make it much easier to read by any OCR.

Here is a python implementation of my solution:

#!/usr/bin/env python3
import numpy as np
import cv2

img = cv2.imread("plate.png")
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

# invert image and detect contours
inverted = cv2.bitwise_not(gray)
contours, hierarchy = cv2.findContours(inverted,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

# get the biggest contour
biggest_index = -1
biggest_area = -1
i = 0
for c in contours:
    area = cv2.contourArea(c)
    if area > biggest_area:
        biggest_area = area
        biggest_index = i
    i = i+1

print("biggest area: " + str(biggest_area) + " index: " + str(biggest_index))

cv2.drawContours(img, contours, biggest_index, [0,0,255])
center, size, angle = cv2.minAreaRect(contours[biggest_index])

rot_mat = cv2.getRotationMatrix2D(center, angle, 1.)

#cv2.warpPerspective()
print(size)
dst = cv2.warpAffine(gray, rot_mat, (int(size[0]), int(size[1])))

mask = dst * 0
x1 = max([int(center[0] - size[0] / 2)+1, 0])
y1 = max([int(center[1] - size[1] / 2)+1, 0])
x2 = int(center[0] + size[0] / 2)-1
y2 = int(center[1] + size[1] / 2)-1

point1 = (x1, y1)
point2 = (x2, y2)
print(point1)
print(point2)

cv2.rectangle(dst, point1, point2, [0,0,0])
cv2.rectangle(mask, point1, point2, [255,255,255], cv2.FILLED)

masked = cv2.bitwise_and(dst, mask)

cv2.imshow("img", img)
cv2.imshow("dst", dst)
cv2.imshow("masked", masked)
cv2.imshow("mask", mask)
key = -1;
while key != 27:
    key = cv2.waitKey(1)

And the resulting image :

This is not perfect, but a good start I think, slightly different approche than thresholding.

You might also try to apply some morphological operator to close some gap or remove dirty parts.

How to apply the proper threshold to remove edges on binary plates

1 Answers