Detecting near-horizontal lines using Image processing

Lines detection an filter by line degree of orientation

import cv2 import numpy as np import math path='images/lines.png' image = cv2.imread(path) dst = cv2.Canny(image, 50, 200, None, 3) linesP = cv2.HoughLinesP(dst, 1, np.pi / 180, 50, None, 50, 10) if linesP is not None: for i in range(0, len(linesP)): l = linesP[i][0] #here l contains x1,y1,x2,y2 of your line #so you can compute the orientation of the line p1 = np.array([l[0],l[1]]) p2 = np.array([l[2],l[3]]) p0 = np.subtract( p1,p1 ) #not used p3 = np.subtract( p2,p1 ) #translate p2 by p1 angle_radiants = math.atan2(p3[1],p3[0]) angle_degree = angle_radiants * 180 / math.pi print("line degree", angle_degree) if 0 < angle_degree < 15 or 0 > angle_degree > -15 : cv2.line(image, (l[0], l[1]), (l[2], l[3]), (0,0,255), 1, cv2.LINE_AA) cv2.imshow("Source", image) print("Press any key to close") cv2.waitKey(0) cv2.destroyAllWindows() 

I had an attempt at this using a variation on Fred's (@fmw42) suggestion. I first tried to locate all the white pixels along the image border, by converting to HSV colourspace then finding pixels that are both unsaturated and bright.

I then rotated the resulting image through -5 to +5 degrees in 0.1 degree increments. At each angle of rotation, I ran a SobelY filter looking for horizontal edges. Then I counted the white pixels in each row. Any time I find an orientation that results in a longer horizontal line, I update my best estimate and remember the rotation.

There are many variations possible but this should get you started:

#!/usr/bin/env python3 import cv2 import numpy as np # Load image im = cv2.imread('a4e.jpg') # Find white pixels, i.e. unsaturated and bright HSV = cv2.cvtColor(im, cv2.COLOR_BGR2HSV) unsat = HSV[:,:,1] < 50 bright= HSV[:,:,2] > 240 white = ((unsat & bright)*255).astype(np.uint8) cv2.imwrite('DEBUG-white.png', white) 

That looks like this:

# Pad with border so it isn't cropped when rotated, get new dimensions bw = 100 white = cv2.copyMakeBorder(white, bw, bw, bw, bw, borderType= cv2.BORDER_CONSTANT) w, h = white.shape[:2] # Find rotation that results in horizontal row with largest number of white pixels maxOverall = 0 # SobelY horizontal edge kernel kernel = np.array(( [-1, -2, -1], [0, 0, 0], [1, 2, 1]), dtype="int") # Rotate image -5 to +5 degrees in 0.1 degree increments for angle in [x * 0.1 for x in range(-50, 50)]: M = cv2.getRotationMatrix2D((h/2,w/2),angle,1) rotated = cv2.warpAffine(white,M,(h,w)) # Output image for debug purposes cv2.imwrite(f'DEBUG rotated {angle}.jpg',rotated) # Filter for horizontal edges filtered = cv2.filter2D(rotated, -1, kernel) cv2.imwrite(f'DEBUG rotated {angle} filtered.jpg',filtered) # Check for maximum white pixels in any row maxThis = np.amax(np.sum(rotated, axis=1)) if maxThis > maxOverall: print(f'Angle:{angle}: New longest horizontal row has {maxThis} white pixels') maxOverall = maxThis 

The overall process looks like this:

The output looks like this, which means the detected angle is 0.6 degrees:

Angle:-5.0: New longest horizontal row has 34287 white pixels Angle:-4.9: New longest horizontal row has 34517 white pixels Angle:-4.800000000000001: New longest horizontal row has 34809 white pixels Angle:-4.7: New longest horizontal row has 35191 white pixels Angle:-4.6000000000000005: New longest horizontal row has 35625 white pixels Angle:-4.5: New longest horizontal row has 36108 white pixels Angle:-4.4: New longest horizontal row has 36755 white pixels Angle:-4.3: New longest horizontal row has 37436 white pixels Angle:-4.2: New longest horizontal row has 38151 white pixels Angle:-4.1000000000000005: New longest horizontal row has 38876 white pixels Angle:-4.0: New longest horizontal row has 39634 white pixels Angle:-3.9000000000000004: New longest horizontal row has 40414 white pixels Angle:-3.8000000000000003: New longest horizontal row has 41240 white pixels Angle:-3.7: New longest horizontal row has 42074 white pixels Angle:-3.6: New longest horizontal row has 42889 white pixels Angle:-3.5: New longest horizontal row has 43570 white pixels Angle:-3.4000000000000004: New longest horizontal row has 44252 white pixels Angle:-3.3000000000000003: New longest horizontal row has 44902 white pixels Angle:-3.2: New longest horizontal row has 45776 white pixels Angle:-3.1: New longest horizontal row has 46620 white pixels Angle:-3.0: New longest horizontal row has 47414 white pixels Angle:-2.9000000000000004: New longest horizontal row has 48178 white pixels Angle:-2.8000000000000003: New longest horizontal row has 48705 white pixels Angle:-2.7: New longest horizontal row has 49225 white pixels Angle:-2.6: New longest horizontal row has 49962 white pixels Angle:-2.5: New longest horizontal row has 51501 white pixels Angle:-2.4000000000000004: New longest horizontal row has 53217 white pixels Angle:-2.3000000000000003: New longest horizontal row has 54997 white pixels Angle:-2.2: New longest horizontal row has 56926 white pixels Angle:-2.1: New longest horizontal row has 59033 white pixels Angle:-2.0: New longest horizontal row has 61017 white pixels Angle:-1.9000000000000001: New longest horizontal row has 62538 white pixels Angle:-1.8: New longest horizontal row has 63370 white pixels Angle:-1.7000000000000002: New longest horizontal row has 64144 white pixels Angle:-1.6: New longest horizontal row has 65685 white pixels Angle:-1.5: New longest horizontal row has 68510 white pixels Angle:-1.4000000000000001: New longest horizontal row has 72377 white pixels Angle:-1.3: New longest horizontal row has 76693 white pixels Angle:-1.2000000000000002: New longest horizontal row has 80932 white pixels Angle:-1.1: New longest horizontal row has 84101 white pixels Angle:-1.0: New longest horizontal row has 86557 white pixels Angle:-0.9: New longest horizontal row has 90499 white pixels Angle:-0.8: New longest horizontal row has 97179 white pixels Angle:-0.7000000000000001: New longest horizontal row has 101430 white pixels Angle:-0.6000000000000001: New longest horizontal row has 105001 white pixels Angle:-0.5: New longest horizontal row has 112976 white pixels Angle:-0.4: New longest horizontal row has 117256 white pixels Angle:-0.30000000000000004: New longest horizontal row has 131478 white pixels Angle:-0.2: New longest horizontal row has 141468 white pixels Angle:-0.1: New longest horizontal row has 164588 white pixels Angle:0.0: New longest horizontal row has 186150 white pixels Angle:0.1: New longest horizontal row has 206695 white pixels Angle:0.2: New longest horizontal row has 230821 white pixels Angle:0.30000000000000004: New longest horizontal row has 249003 white pixels Angle:0.4: New longest horizontal row has 258888 white pixels Angle:0.6000000000000001: New longest horizontal row has 264409 white pixels 

You can find the angle by which the line is parallel to ground by using tan inverse.

for x1,y1,x2,y2 in lines[0]: angle = math.degrees(math.atan((abs(y2-y1))/abs(x2-x1))) cv2.line(img2,(x1,y1),(x2,y2),(255,0,0),1) print(angle) 

Then you can filter the lines as told by @Mario. Since above uses abs() to find difference, you will have to filter the angles only using a positive range.