How to group nearby contours in OpenCV Python? - Zebra Crossing detection

Question

I want to detect the zebra crossing lines. I have tried to find out the co-ordinates of zebra crossing line in the image using contour but it gives the output for the distinct white boxes (only white lines in the zebra crossing). But I need the co-ordinates of the entire zebra crossing.

Please let me know the way to group the contours or suggest me another method to detect zebra crossing.

Input image

Output image obtained

Expected output

import cv2
import numpy as np
image = cv2.imread('d.jpg',-1)
paper = cv2.resize(image,(500,500))
ret, thresh_gray = cv2.threshold(cv2.cvtColor(paper, cv2.COLOR_BGR2GRAY),
                        200, 255, cv2.THRESH_BINARY)
image, contours, hier = cv2.findContours(thresh_gray, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
for c in contours:
    rect = cv2.minAreaRect(c)
    box = cv2.boxPoints(rect)
    # convert all coordinates floating point values to int
    box = np.int0(box)
    cv2.drawContours(paper, [box], 0, (0, 255, 0),1)
cv2.imshow('paper', paper)
cv2.imwrite('paper.jpg',paper)
cv2.waitKey(0)

Have you looked at [merge nearby contours](https://answers.opencv.org/question/204128/how-to-merge-nearby-rectangles/)? — coffeewin, Feb 17 '20 at 09:21
@coffeewin I have tried it, but the source image detects many contours along with the zebra crossing. So it merges the whole image and doesn't provide required output. — Thrisha Ramkumar, Feb 17 '20 at 10:37
@YunusTemurlenk I have added the source image in the Description. Please check — Thrisha Ramkumar, Feb 17 '20 at 10:40
@YunusTemurlenk I want to detect the zebra crossing lines. I have added the expected output image in the Description. Please check. Thank you — Thrisha Ramkumar, Feb 17 '20 at 10:51
An algorithm can be created but you can not expect it to work in every image. I think training seems a better solution for this. — Yunus Temurlenk, Feb 17 '20 at 11:08
Try color thresholding on white using cv2.inRange(). Then use morphology to merge the regions in the binary image. Then get your contours. — fmw42, Feb 17 '20 at 18:29

score 4 · Answer 1 · answered Feb 17 '20 at 22:15

You can closing morphological operation for closing the gaps.

I cat suggest the following stages:

Find contours in thresh_gray.
Erase contours with very small area (noise).
Erase contours with low aspect ratio (assume zebra line must be long and narrow.
Use morphologyEx to perform closing morphological operation - the closing merges close components.
Find contours again in the image after erasing and closing.
At the last stage, ignore small contours.

Here is a working code sample:

import cv2
import numpy as np

image = cv2.imread('d.jpg', -1)
paper = cv2.resize(image, (500,500))
ret, thresh_gray = cv2.threshold(cv2.cvtColor(paper, cv2.COLOR_BGR2GRAY), 200, 255, cv2.THRESH_BINARY)
image, contours, hier = cv2.findContours(thresh_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

# Erase small contours, and contours which small aspect ratio (close to a square)
for c in contours:
    area = cv2.contourArea(c)

    # Fill very small contours with zero (erase small contours).
    if area < 10:
        cv2.fillPoly(thresh_gray, pts=[c], color=0)
        continue

    # https://stackoverflow.com/questions/52247821/find-width-and-height-of-rotatedrect
    rect = cv2.minAreaRect(c)
    (x, y), (w, h), angle = rect
    aspect_ratio = max(w, h) / min(w, h)

    # Assume zebra line must be long and narrow (long part must be at lease 1.5 times the narrow part).
    if (aspect_ratio < 1.5):
        cv2.fillPoly(thresh_gray, pts=[c], color=0)
        continue


# Use "close" morphological operation to close the gaps between contours
# https://stackoverflow.com/questions/18339988/implementing-imcloseim-se-in-opencv
thresh_gray = cv2.morphologyEx(thresh_gray, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (51,51)));

# Find contours in thresh_gray after closing the gaps
image, contours, hier = cv2.findContours(thresh_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

for c in contours:
    area = cv2.contourArea(c)

    # Small contours are ignored.
    if area < 500:
        cv2.fillPoly(thresh_gray, pts=[c], color=0)
        continue

    rect = cv2.minAreaRect(c)
    box = cv2.boxPoints(rect)
    # convert all coordinates floating point values to int
    box = np.int0(box)
    cv2.drawContours(paper, [box], 0, (0, 255, 0),1)

cv2.imshow('paper', paper)
cv2.imwrite('paper.jpg', paper)
cv2.waitKey(0)
cv2.destroyAllWindows()

thresh_gray before erasing small and squared contours:

thresh_gray after erasing small and squared contours:

thresh_gray after close operation:

Final result:

Remark:
I have some doubts about the benefit of using morphological operation for closing the gaps.
It might be better using a smart logic based on geometry instead.

How to group nearby contours in OpenCV Python? - Zebra Crossing detection

1 Answers1