Increase accuracy of detecting lines using OpenCV

Question

I am implementing a program to detect lines in images from a camera. The problem is that when the photo is blurry, my line detection algorithm misses a few lines. Is there a way to increase the accuracy of the cv.HoughLines() function without editing the parameters?

Example input image:

Desired image:

My current implementation:

def find_lines(img):
    gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
    edges = cv.dilate(gray,np.ones((3,3), np.uint8),iterations=5)
    edges = cv.Canny(gray, 50, 150, apertureSize=3)
    lines = cv.HoughLines(edges, 1, np.pi/180, 350)

Answer 1

It would be a good idea to preprocess the image before giving it to cv2.HoughLines(). Also I think cv2.HoughLinesP() would be better. Here's a simple approach

• Convert image to grayscale

• Apply a sharpening kernel

• Threshold image

• Perform morphological operations to smooth/filter image

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We apply a sharpening kernel using cv2.filter2D() which gives us the general shape of the line and removes the blurred sections. Other filters can be found here.

Now we threshold the image to get solid lines

There are small imperfections so we can use morphological operations with a cv2.MORPH_ELLIPSE kernel to get clean diamond shapes

Finally to get the desired result, we dilate using the same kernel. Depending on the number of iterations, we can obtain thinner or wider lines

Left (iterations=2), Right (iterations=3)

import cv2
import numpy as np

image = cv2.imread('1.png', 0)
sharpen_kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
sharpen = cv2.filter2D(image, -1, sharpen_kernel)
thresh = cv2.threshold(sharpen,220, 255,cv2.THRESH_BINARY)[1]
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3,3))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=3)
result = cv2.dilate(opening, kernel, iterations=3)

cv2.imshow('thresh', thresh)
cv2.imshow('sharpen', sharpen)
cv2.imshow('opening', opening)
cv2.imshow('result', result)
cv2.waitKey()