How to detect edge if edges of an object has same colour as background

Question

I am tasked to build a license plate detection system and my code does not work if the plate has the same colour of the paint of car (background).

Take a look at this picture below.

I have tried a variety of edge detection technique and my findings are they hardly work.

Here is my image processing pipeline:

Extract the gray channel from the image.
Reduce noise with Iterative Bilaterial Filtering
Detect edges with Adaptive Thresholding
Dilate the edges slightly
Locate contours based on some heuristics.

The edge detection part performed miserably around the plate region.

The pipeline works good and I am able to detect license plates if the car is has a different paint colour than the plate.

Code

def rectangleness(hull):
    rect = cv2.boundingRect(hull)
    rectPoints = np.array([[rect[0], rect[1]], 
                           [rect[0] + rect[2], rect[1]],
                           [rect[0] + rect[2], rect[1] + rect[3]],
                           [rect[0], rect[1] + rect[3]]])
    intersection_area = cv2.intersectConvexConvex(np.array(rectPoints), hull)[0] 
    rect_area = cv2.contourArea(rectPoints)
    rectangleness = intersection_area/rect_area
    return rectangleness


def preprocess(image):
    image = imutils.resize(image, 1000)

    # Attenuate shadows by using H channel instead of converting to gray directly
    imgHSV = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    _, _, gray = cv2.split(imgHSV)

    # Reduce noise while preserve edge with Iterative Bilaterial Filtering
    blur = cv2.bilateralFilter(gray, 11, 6, 6)

    # Detect edges by thresholding
    edge = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 5)

    # Dilate edges, kernel size cannot be too big as some fonts are very closed to the edge of the plates
    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2, 2))
    dilated = cv2.dilate(edge, kernel)

    # Detect contours
    edge, contours, _ = cv2.findContours(dilated, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)

    # Loop through contours and select the most probable ones
    contours = sorted(contours, key = cv2.contourArea, reverse=True)[:10]

    for contour in contours:
        perimeter = cv2.arcLength(contour, closed=True)
        approximate = cv2.approxPolyDP(contour, 0.02*perimeter, closed=True)

        if len(approximate) == 4:
            (x, y, w, h) = cv2.boundingRect(approximate)
            whRatio = w / h

            # Heuristics:
            # 1. Width of plate should at least be 2x greater than height
            # 2. Width of contour should be more than 5 (eliminate false positive)
            # 3. Height must not be too small
            # 4. Polygon must resemble a rectangle
            if (2.0 < whRatio < 6.0) and (w > 5.0) and (h > 20):
                hull = cv2.convexHull(approximate, returnPoints=True)
                if rectangleness(hull) > 0.75:
                    print("X Y {} {}".format(x, y))
                    print("Height: {}".format(h))
                    print("Width : {}".format(w))
                    print("Ratio : {}\n".format(w/h))
                    cv2.drawContours(image, [approximate], -1, (0, 255, 0), 2)

    cv2.imshow("Edge", edge)
    cv2.imshow("Frame", image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

You need to detect the text on the license plate, not the plate itself. — Cris Luengo
You must accept that in some cases the plate cannot be detected and work on the characters alone. — Yves Daoust

Ha Bom Ha Bom · Accepted Answer · 2018-11-14T09:26:53

You can use cv2.morphologyEx for making the plate region become more visible. Next step is to find contours and set reasonable conditions to extract the contour that contains the plate. If you want, you can have a look at this github repository where my friend and I show detailed steps about license plate detection and recognition.

import cv2
import numpy as np

img = cv2.imread("a.png")

imgBlurred = cv2.GaussianBlur(img, (7, 7), 0)
gray = cv2.cvtColor(imgBlurred, cv2.COLOR_BGR2GRAY) # convert to gray
sobelx = cv2.Sobel(gray, cv2.CV_8U, 1, 0, ksize=3) # sobelX to get the vertical edges

ret,threshold_img = cv2.threshold(sobelx, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)

morph_img_threshold = threshold_img.copy()
element = cv2.getStructuringElement(shape=cv2.MORPH_RECT, ksize=(22, 3))
cv2.morphologyEx(src=threshold_img, op=cv2.MORPH_CLOSE, kernel=element, 
dst=morph_img_threshold)

cv2.imshow("img", img)
cv2.imshow("sobelx", sobelx)
cv2.imshow("morph_img_threshold", morph_img_threshold)

cv2.waitKey()
cv2.destroyAllWindows()

How to detect edge if edges of an object has same colour as background

1 Answers