Join lines to create closed contours using OpenCV

Question

I have the following image:

And I would like to join all of these "green" linear pixels to create a bounding box contour - is this something that is possible with opencv?

Thanks

A closing operator (used multiple times) will possibly do the trick. It will affect other areas as well, so it might not be what you want. I don't know OpenCV well, but I guess it can do this. — Stefan, Aug 20 '20 at 17:49
https://docs.opencv.org/trunk/d9/d61/tutorial_py_morphological_ops.html — Stefan, Aug 20 '20 at 17:51
I tried this already Stefan and didn't seem to work for me unfortunately. Thanks — user1655130, Aug 20 '20 at 19:50
Another approach is using the Hough transform to detect the line(s). Here you'll get the intercept and slope of the detected line. — Stefan, Aug 20 '20 at 21:07
[This](https://stackoverflow.com/questions/43859750/how-to-connect-broken-lines-in-a-binary-image-using-python-opencv) can help? — Yunus Temurlenk, Aug 21 '20 at 05:52
@YunusTemurlenk that suggestion is a closing operation as we have discussed above — Stefan, Aug 21 '20 at 06:09

score 1 · Answer 1 · answered Apr 23 '22 at 16:11

I cleaned up the given image and used the following as input:

Input image

Performing threshold and finding contours:

th = cv2.threshold(img, 150, 255, cv2.THRESH_BINARY)[1]
cnts = cv2.findContours(th , cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]

Now the fun part!

For each contour, find the extreme points. These are the top-most, bottom-most, right-most and left-most points of the contour.
Compare the distance between every extreme point of a contour with those of every other contour
Draw a line between points with the least Euclidean distance.

Code:

for i in range(len(cnts)):
    min_dist = max(img.shape[0], img.shape[1])
    cl = []
    
    ci = cnts[i]
    ci_left = tuple(ci[ci[:, :, 0].argmin()][0])
    ci_right = tuple(ci[ci[:, :, 0].argmax()][0])
    ci_top = tuple(ci[ci[:, :, 1].argmin()][0])
    ci_bottom = tuple(ci[ci[:, :, 1].argmax()][0])
    ci_list = [ci_bottom, ci_left, ci_right, ci_top]
    
    for j in range(i + 1, len(cnts)):
        cj = cnts[j]
        cj_left = tuple(cj[cj[:, :, 0].argmin()][0])
        cj_right = tuple(cj[cj[:, :, 0].argmax()][0])
        cj_top = tuple(cj[cj[:, :, 1].argmin()][0])
        cj_bottom = tuple(cj[cj[:, :, 1].argmax()][0])
        cj_list = [cj_bottom, cj_left, cj_right, cj_top]
        
        for pt1 in ci_list:
            for pt2 in cj_list:
                dist = int(np.linalg.norm(np.array(pt1) - np.array(pt2)))     #dist = sqrt( (x2 - x1)**2 + (y2 - y1)**2 )
                if dist < min_dist:
                    min_dist = dist             
                    cl = []
                    cl.append([pt1, pt2, min_dist])
    if len(cl) > 0:
        cv2.line(img1, cl[0][0], cl[0][1], (255, 255, 255), thickness = 5)

Output image:

Join lines to create closed contours using OpenCV

1 Answers1