CODEWITHSUNDEEP
pythonopencvrectangleshough-transform
Nasim
Nasim

Reputation: 169

detecting lines of a rectangle image using hough transform

i wanna detect this rectangles lines using hough transform, enter image description here

here is the opencv code :

import cv2
import numpy as np

img = cv2.imread('C:/Users/hp/rectangles.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray,50,150,apertureSize = 3)

lines = cv2.HoughLines(edges,1,np.pi/180,200)
for rho,theta in lines[0]:
    a = np.cos(theta)
    b = np.sin(theta)
    x0 = a*rho
    y0 = b*rho
    x1 = int(x0 + 1000*(-b))
    y1 = int(y0 + 1000*(a))
    x2 = int(x0 - 1000*(-b))
    y2 = int(y0 - 1000*(a))

    cv2.line(img,(x1,y1),(x2,y2),(0,0,255),2)

cv2.imwrite('houghlines3.jpg',img)

but it only detects one line, how can i detect all lines ?

after testing this code and choosing these parameters :

    import cv2
    import numpy as np


        img = cv2.imread('dave.jpg')
        gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
        edges = cv2.Canny(gray,50,150,apertureSize = 3)
        minLineLength = 100
        maxLineGap = 10
        lines = cv2.HoughLinesP(edges,1,np.pi/180,100,minLineLength,maxLineGap)
        for x1,y1,x2,y2 in lines[0]:
            cv2.line(img,(x1,y1),(x2,y2),(0,255,0),2)

        cv2.imwrite('houghlines5.jpg',img)

i got this result :

enter image description here

i changed parameters but that one line isnt detected, how should i change parameters to make that line detected?

Upvotes: 4

Views: 11902

Answers (1)

nathancy
nathancy

Reputation: 46590

enter image description here

I believe a better approach to detecting the lines of the rectangles is to use cv2.findContours() and Canny edge detection.

import cv2

image = cv2.imread('1.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
canny = cv2.Canny(gray, 130, 255, 1)

cnts = cv2.findContours(canny, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]

for c in cnts:
    cv2.drawContours(image,[c], 0, (0,255,0), 3)

cv2.imshow("result", image)
cv2.waitKey(0)

Upvotes: 4

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