Straight lines are everywhere in the world and detecting them using Computer Vision is an important skill to learn. Straight lines are found throughout the world ranging from roads to the tiny bacteria under the microscope. As a Computer Vision developer, you may be required to detect the straight lines.

Its important to learn how to perform Canny Edge Detection and perform Hough Transform to detect straight lines in OpenCV.

This post teaches you how to detect horizontal lines in a given image. Lets quickly set the imports and get the image in OpenCV

import os
import math
import cv2
from matplotlib import pyplot as plt # To show on the screen

# Read the image
img = cv2.imread("img/birds-power-lines-animals-sky.jpg")

# Make it Black & White
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

Now that we have the image, lets find the edges in the image and create a mask using these edges. We will be using Canny to detect those edges. The parameters of Canny function are:

cv2.canny(Image, Threshold lower, Threshold Uppper)

The thresholds are called Hysteresis counters. The value of point on a line is accepted, rejected on the basis of the following information:

Point on line is above upper Threshold	Point is immediately accepted and is visible in contour.
Point on line is below lower Threshold	Point is immediately rejected and is not visible in contour.
Point is between the lower and upper thresholds	Point is accepted if it has an immediate connection with another point whose intensity is above the Upper Threshold.

But which of those lines is actually Horizontal? Time to introduce Hough Line Transform

Hough Line Transform

Hough Line Transform is a transform algorithm which is used to detect straight lines. both horizontal as well as vertical. It is desirable for this algorithm that edges are detected prior to using this algorithm to make our lives easier. Example:

cv.HoughLinesP(	image, rho, theta, threshold[, lines[, minLineLength[, maxLineGap]]])

The Hough Algorithm here requires these parameters:

Parameter Name	Value assigned in example	Description
Input OpenCV binary Image <array> variable	edges	We provide the image of the Edges after performing Canny edge detection.
rho	1	Distance resolution of the accumulator in pixels.
theta	math.pi/2	Angle resolution of the accumulator in radians.
threshold	2	Accumulator threshold parameter. Only those lines are returned that get enough votes ( >threshold ).
minLineLength	30	Minimum line length. Line segments shorter than that are rejected.
maxLineGap	1	Maximum allowed gap between points on the same line to link them.

We use this algorithm as

lines = cv2.HoughLinesP(edges, 1, math.pi/2, 2, None, 30, 1);

Now in the lines variable, we have got a multidimensional array containing every found straight horizontal line. Parse these lines

for line in lines[0]:
    pt1 = (line[0],line[1])
    pt2 = (line[2],line[3])
    cv2.line(img, pt1, pt2, (0,0,255), 3)

Finally, display on Matplotlib

plt.imshow(img,  cmap='Greys_r') # cmap prevents yellow image
plt.title('my picture')
plt.show()