Hough Lines detecting too many lines

Question

I am trying to extract the vertical lines from the fabric image using hough lines in opencv. I applied contrast enhancement to enhance the lines and bilateral filtering to try and remove the other fabric textures. However, on applying the houghlines, the code detects lines all over the image. I tried playing around with the parameters for hough but the results were the same.

Input image after applying histogram equalization and bilateral filter:

Here is the image after applying the hough line, red representing the detected lines. Output showing hough detections:

What is another approach I can try so that the hough does not start detecting the minute fabric patterns as lines as well?

Here is the code I have:

`

    img1= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    img2 = cv2.equalizeHist(img1)
    img3 = cv2.equalizeHist(img2)
    img4 = cv2.equalizeHist(img3)
    img5 = cv2.bilateralFilter(img4, 9, 75,75)
    cv2.imshow("threshold",img5)
    edges = cv2.Canny(img4,50,127,apertureSize = 3)
    lines= cv2.HoughLines(edges, 1, math.pi/180.0, 200, np.array([]), 0, 0)
    a,b,c = lines.shape
    for i in range(a):
        rho = lines[i][0][0]
        theta = lines[i][0][1]
        a = math.cos(theta)
        b = math.sin(theta)
        x0, y0 = a*rho, b*rho
        pt1 = ( int(x0+1000*(-b)), int(y0+1000*(a)) )
        pt2 = ( int(x0-1000*(-b)), int(y0-1000*(a)) )
        cv2.line(img, pt1, pt2, (0, 0, 255), 2, cv2.LINE_AA)

cv2.imshow('image1',img)
cv2.waitKey(0)
cv2.destroyAllWindows()`

Answer 1

You need to threshold your equalized image, apply morphology to clean it up before doing the canny edge detection and hough line extraction. Using Python/OpenCV to do the following processing.

Input:

import cv2
import numpy as np
import math

# read image
img = cv2.imread('fabric_equalized.png')

# convert to grayscale
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

# threshold
thresh = cv2.threshold(gray,165,255,cv2.THRESH_BINARY)[1]

# apply close to connect the white areas
kernel = np.ones((15,1), np.uint8)
morph = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
kernel = np.ones((17,3), np.uint8)
morph = cv2.morphologyEx(morph, cv2.MORPH_CLOSE, kernel)

# apply canny edge detection
edges = cv2.Canny(img, 175, 200)

# get hough lines
result = img.copy()
lines= cv2.HoughLines(edges, 1, math.pi/180.0, 165, np.array([]), 0, 0)
a,b,c = lines.shape
for i in range(a):
    rho = lines[i][0][0]
    theta = lines[i][0][1]
    a = math.cos(theta)
    b = math.sin(theta)
    x0, y0 = a*rho, b*rho
    pt1 = ( int(x0+1000*(-b)), int(y0+1000*(a)) )
    pt2 = ( int(x0-1000*(-b)), int(y0-1000*(a)) )
    cv2.line(result, pt1, pt2, (0, 0, 255), 2, cv2.LINE_AA)


# save resulting images
cv2.imwrite('fabric_equalized_thresh.jpg',thresh)
cv2.imwrite('fabric_equalized_morph.jpg',morph)
cv2.imwrite('fabric_equalized_edges.jpg',edges)
cv2.imwrite('fabric_equalized_lines.jpg',result)

# show thresh and result    
cv2.imshow("thresh", thresh)
cv2.imshow("morph", morph)
cv2.imshow("edges", edges)
cv2.imshow("result", result)
cv2.waitKey(0)
cv2.destroyAllWindows()

Thresholded image:

Morphology cleaned image:

Edge image:

Resulting Hough Lines: