'''Testing openCV line detection'''
# Found on https://stackoverflow.com/questions/45322630/how-to-detect-lines-in-opencv

import cv2
import numpy as np

# Get gray image
imgInput = 'test_grid.png'
imgOutput = 'test_grid_lines.png'
img = cv2.imread(imgInput)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Process gray image with GaussianBlur
kernal_size = 5
blur_gray = cv2.GaussianBlur(gray, (kernal_size, kernal_size), 0)

# Process edge detection using Canny
low_threshold = 50
high_threshold = 150
edges = cv2.Canny(blur_gray, low_threshold, high_threshold)

# Get lines with HoughLinesP
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)
min_line_length = 50  # minimum number of pixels making up a line
max_line_gap = 20  # maximum gap in pixels between connectable line segments
line_image = np.copy(img) * 0  # creating a blank to draw lines on

# Run Hough on edge detected image
# Output "lines" is an array containing endpoints of detected line segments
lines = cv2.HoughLinesP(edges, rho, theta, threshold, np.array([]),
                        min_line_length, max_line_gap)

for line in lines:
    for x1, y1, x2, y2 in line:
        cv2.line(line_image, (x1, y1), (x2, y2), (255, 0, 0), 5)

# Draw the lines on the  image
lines_edges = cv2.addWeighted(img, 0.8, line_image, 1, 0)

cv2.imwrite(imgOutput, line_image)

'''Testing openCV line detection'''
# Found on https://stackoverflow.com/questions/45322630/how-to-detect-lines-in-opencv

import cv2
import numpy as np

# Get gray image
imgInput = '../images/test_grid.png'
imgOutput = '../images/test_grid_lines.png'
img = cv2.imread(imgInput)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Process gray image with GaussianBlur
kernal_size = 5
blur_gray = cv2.GaussianBlur(gray, (kernal_size, kernal_size), 0)

# Process edge detection using Canny
low_threshold = 50
high_threshold = 150
edges = cv2.Canny(blur_gray, low_threshold, high_threshold)

# Get lines with HoughLinesP
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)
min_line_length = 50  # minimum number of pixels making up a line
max_line_gap = 20  # maximum gap in pixels between connectable line segments
line_image = np.copy(img) * 0  # creating a blank to draw lines on

# Run Hough on edge detected image
# Output "lines" is an array containing endpoints of detected line segments
lines = cv2.HoughLinesP(edges, rho, theta, threshold, np.array([]),
                        min_line_length, max_line_gap)

for line in lines:
    for x1, y1, x2, y2 in line:
        cv2.line(line_image, (x1, y1), (x2, y2), (255, 0, 0), 5)

# Draw the lines on the  image
lines_edges = cv2.addWeighted(img, 0.8, line_image, 1, 0)

cv2.imwrite(imgOutput, line_image)

Change path in line_detection.py