Python How to detect vertical and horizontal lines in an image with HoughLines with OpenCV?

Question

I m trying to obtain a threshold of the calibration chessboard. I cant detect directly the chessboard corners as there is some dust as i observe a micro chessboard. I try several methods and HoughLinesP seems to be the easiest approach. But the results are not good, how to improve my results?

import numpy as np
import cv2

img = cv2.imread('lines.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray,50,150,apertureSize = 3)
print img.shape[1]
print img.shape
minLineLength=100
lines = cv2.HoughLinesP(image=edges,rho=0.02,theta=np.pi/500, threshold=10,lines=np.array([]), minLineLength=minLineLength,maxLineGap=100)

a,b,c = lines.shape
for i in range(a):
    cv2.line(img, (lines[i][0][0], lines[i][0][1]), (lines[i][0][2], lines[i][0][3]), (0, 0, 255), 3, cv2.LINE_AA)
    cv2.imwrite('houghlines5.jpg',img)

As you can see on figure below, i cant obtain my chessboard, the lines are plotted in a lot of directions... (the original picture : https://s22.postimg.org/iq2b91xq9/droite_Image_00000.jpg )

Answer 1

You are using too small value for rho.

Try the below code:-

import numpy as np
import cv2

gray = cv2.imread('lines.jpg')
edges = cv2.Canny(gray,50,150,apertureSize = 3)
cv2.imwrite('edges-50-150.jpg',edges)
minLineLength=100
lines = cv2.HoughLinesP(image=edges,rho=1,theta=np.pi/180, threshold=100,lines=np.array([]), minLineLength=minLineLength,maxLineGap=80)

a,b,c = lines.shape
for i in range(a):
    cv2.line(gray, (lines[i][0][0], lines[i][0][1]), (lines[i][0][2], lines[i][0][3]), (0, 0, 255), 3, cv2.LINE_AA)
    cv2.imwrite('houghlines5.jpg',gray)

Note, the change in rho value, pi value and maxLineGap to reduce outliers.

Input Image

Edges Image

Output Image

Miscellaneous - Tips for Beginners

1. A lot of Computer Vision algorithms assume certain assumptions, well, in how the input should be. When building Proof-of-Concept, always try to view intermediate inputs you generate before applying such algorithms.

2. For quick hack, if an algorithm accepts some parameters, use a for loop on possible values of these parameters and see how the results varies. Link to an answer on how to quickly generate these possible values.

3. To really understand the algorithm, read on wiki or even better sources where if necessary. And then again/still do the above hack(point 2). It will further clear your understanding.

Answer 2

I would rather write this as a comment but unfortunately I can't. You should change the minLineLength and minLineGap. Or what if its just sqaures that you have to find, I would get all the lines and check the angles between them to get lines only along squares. I have worked with HoughLineP before and it is pretty much based on the above two arguments. Additionally, try using Bilateral filtering. I really helps when the sharpening using median filter doesn't help.

Bilateral Filter

Answer 3

在图像处理中，它们是您在进行边缘检测之前必须经历的一些角色，例如过滤器，在您的情况下，灰尘只是您必须通过过滤器去除的噪音，在使用阈值后使用gausse或blure然后使用对于边缘和在opencv中它们是可以使用的角膜检测，或者如果我没有错误你可以在threshholding之后找到关键点...尝试做那些步骤并看到结果