How to find the junction points or segments in a skeletonized image Python OpenCV?-CodePudding

I am trying to convert the result of a skeletonization into a set of line segments, where the vertices correspond to the junction points of the skeleton. The shape is not a closed polygon and it may be somewhat noisy (the segments are not as straight as they should be).

Here is an example input image:

And here are the points I want to retrieve:

I have tried using the harris corner detector, but it has trouble in some areas even after trying to tweak the algorithm's parameters (such as the angled section on the bottom of the image). Here are the results:

Do you know of any method capable of doing this? I am using python with mostly OpenCV and Numpy but I am not bound to any library. Thanks in advance.

Edit: I've gotten some good responses regarding the junction points, I am really grateful. I would also appreciate any solutions regarding extracting line segments from the junction points. I think

Code

import cv2

# Load image, grayscale, Gaussian blur, Otsus threshold
image = cv2.imread('1.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY   cv2.THRESH_OTSU)[1]

# Find horizonal lines
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,1))
horizontal = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=1)

# Find vertical lines
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,5))
vertical = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, vertical_kernel, iterations=1)

# Find joint intersections then the centroid of each joint
joints = cv2.bitwise_and(horizontal, vertical)
cnts = cv2.findContours(joints, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    # Find centroid and draw center point
    x,y,w,h = cv2.boundingRect(c)
    centroid, coord, area = cv2.minAreaRect(c)
    cx, cy = int(centroid[0]), int(centroid[1])
    cv2.circle(image, (cx, cy), 5, (36,255,12), -1)

cv2.imshow('thresh', thresh)
cv2.imshow('joints', joints)
cv2.imshow('horizontal', horizontal)
cv2.imshow('vertical', vertical)
cv2.imshow('image', image)
cv2.waitKey()