How to make cv2.HoughLinesP detect only vertical lines?

I'm trying to make it so my programme only detects an overhead wire on a train/tram but when the wire holders come into frame it detects the horizontal line of them which I don't want. I didn't know if anyone knew how to make it so it will only detect vertical lines. I tried using cv2.erode along with np.ones to only show vertical lines but I couldn't seem to get anywhere with that. Someone did mention that HoughLines can be made so there just vertical but I don't know if that's true or not. Here's my code:

import cv2
import numpy as np
import window_names
import track_bars
vid = 'blackpool_tram_result.mp4'
cap = cv2.VideoCapture(vid)
frame_counter = 0
while (True):
ret, frame = cap.read()
frame_counter += 1
if frame_counter == cap.get(cv2.CAP_PROP_FRAME_COUNT):
frame_counter = 0
cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
blank = np.zeros(frame.shape[:2], dtype='uint8')
grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
output = np.empty(grey.shape, dtype=np.uint8)
cv2.normalize(
grey,
output,
alpha=0,
beta=255,
norm_type=cv2.NORM_MINMAX)
hist = cv2.equalizeHist(output)
track_bars.lower_threshold = cv2.getTrackbarPos("lower", window_names.window_canny)
track_bars.upper_threshold = cv2.getTrackbarPos("upper", window_names.window_canny)
track_bars.smoothing_neighbourhood = cv2.getTrackbarPos("smoothing", window_names.window_canny)
track_bars.sobel_size = cv2.getTrackbarPos("sobel size", window_names.window_canny)
track_bars.smoothing_neighbourhood = max(3, track_bars.smoothing_neighbourhood)
if not (track_bars.smoothing_neighbourhood % 2):
track_bars.smoothing_neighbourhood = track_bars.smoothing_neighbourhood + 1
track_bars.sobel_size = max(3, track_bars.sobel_size)
if not (track_bars.sobel_size % 2):
track_bars.sobel_size = track_bars.sobel_size + 1
smoothed = cv2.GaussianBlur(
hist, (track_bars.smoothing_neighbourhood, track_bars.smoothing_neighbourhood), 0)
edges = cv2.Canny(
smoothed,
track_bars.lower_threshold,
track_bars.upper_threshold,
apertureSize=track_bars.sobel_size)
rho = 1  # distance resolution in pixels of the Hough grid
theta = np.pi / 180  # angular resolution in radians of the Hough grid
threshold = 15  # minimum number of votes (intersections in Hough grid cell)
minLineLength = 50  # minimum number of pixels making up a line
maxLineGap = 20  
line_image = np.copy(frame) * 0
mask = cv2.rectangle(blank, (edges.shape[1]//2 + 150, edges.shape[0]//2 - 150), (edges.shape[1]//2 - 150, edges.shape[0]//2 - 300), 255, -1)
masked = cv2.bitwise_and(edges,edges,mask=mask)
lines = cv2.HoughLinesP(masked, rho, theta, threshold, np.array([]), minLineLength, maxLineGap)
if lines is not None:
for x1, y1, x2, y2 in lines[0]:
cv2.line(frame,(x1,y1),(x2,y2),(255,0,0),5)
lines_edges = cv2.addWeighted(frame, 0.8, line_image, 1, 0)
cv2.imshow(window_names.window_hough, frame)
cv2.imshow(window_names.window_canny, edges)
cv2.imshow(window_names.window_mask, mask)
cv2.imshow(window_names.window_masked_image, masked)
key = cv2.waitKey(27)
if (key == ord('x')) & 0xFF:
break
cv2.destroyAllWindows()

HoughLines() gives you the ability to configure minimum and maximun line angles to detect. You can check here for details.

However, HoughLinesP doesn't have this option. What you can do is that filtering lines which HoughLinesP gives as output. According to the documentation:

> Output vector of lines. Each line is represented by a 4-element vector
> (x1,y1,x2,y2) , where (x1,y1) and (x2,y2) are the ending points of
> each detected line segment.

So just get the starting(x1,y1) and ending(x2,y2) points and calculate the angles with a simple math.

By getting the results you can filter each line according to the desired angle value.