I have detected a lane boundary line which is not straight using hough transform and then extracted that line separately. Then blended with another image that has a straight line. Now I need to calculate the angle between those two lines, but I do not know the coordinates of those lines. So I tried with code that gives the coordinates of vertical lines, but it can not specifically identify those coordinates. Is there a way to measure the angle between those lines? Here is my coordinate calculation code and blended image with two lines

import cv2 as cv
import numpy as npsrc = cv.imread("blended2.png", cv.IMREAD_COLOR)if len(src.shape) != 2:gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
else:gray = srcgray = cv.bitwise_not(gray)
bw = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY, 15, -2)horizontal = np.copy(bw)
vertical = np.copy(bw)cols = horizontal.shape[1]
horizontal_size = int(cols / 30)horizontalStructure = cv.getStructuringElement(cv.MORPH_RECT, (horizontal_size, 1))
horizontal = cv.erode(horizontal, horizontalStructure)
horizontal = cv.dilate(horizontal, horizontalStructure)cv.imwrite("img_horizontal8.png", horizontal)h_transpose = np.transpose(np.nonzero(horizontal))
print("h_transpose")
print(h_transpose[:100])rows = vertical.shape[0]
verticalsize = int(rows / 30)
verticalStructure = cv.getStructuringElement(cv.MORPH_RECT, (1, verticalsize))
vertical = cv.erode(vertical, verticalStructure)
vertical = cv.dilate(vertical, verticalStructure)cv.imwrite("img_vertical8.png", vertical)v_transpose = np.transpose(np.nonzero(vertical))print("v_transpose")
print(v_transpose[:100])img = src.copy()# edges = cv.Canny(vertical,50,150,apertureSize = 3)
minLineLength = 100
maxLineGap = 200
lines = cv.HoughLinesP(vertical,1,np.pi/180,100,minLineLength,maxLineGap)
for line in lines:for x1,y1,x2,y2 in line:cv.line(img,(x1,y1),(x2,y2),(0,255,0),2)cv.imshow('houghlinesP_vert', img)
cv.waitKey(0)

One approach is to use the Hough Transform to detect the lines and obtain the angle of each line. The angle between the two lines can then be found by subtracting the difference between the two lines.

We begin by performing an arithmetic average using np.mean to essentially threshold the image which results in this.

image = cv2.imread('2.png')# Compute arithmetic mean
image = np.mean(image, axis=2)

Now we perform skimage.transform.hough_line to detect lines

# Perform Hough Transformation to detect lines
hspace, angles, distances = hough_line(image)# Find angle
angle=[]
for _, a , distances in zip(*hough_line_peaks(hspace, angles, distances)):angle.append(a)

Next we obtain the angle for each line and find the difference to obtain our result

# Obtain angle for each line
angles = [a*180/np.pi for a in angle]# Compute difference between the two lines
angle_difference = np.max(angles) - np.min(angles)
print(angle_difference)

16.08938547486033

Full code

from skimage.transform import (hough_line, hough_line_peaks)
import numpy as np
import cv2image = cv2.imread('2.png')# Compute arithmetic mean
image = np.mean(image, axis=2)# Perform Hough Transformation to detect lines
hspace, angles, distances = hough_line(image)# Find angle
angle=[]
for _, a , distances in zip(*hough_line_peaks(hspace, angles, distances)):angle.append(a)# Obtain angle for each line
angles = [a*180/np.pi for a in angle]# Compute difference between the two lines
angle_difference = np.max(angles) - np.min(angles)
print(angle_difference)