Detecting and isolating lines in an image

Question

I'm trying to write a piece of code that can detect and isolate straight lines from an image. I'm using the opencv library, together with Canny edge detection and Hough transformation to achieve this. So far I've come up with the following:

import numpy as np
import cv2

# Reading the image
img = cv2.imread('sudoku-original.jpg')
# Convert the image to grayscale
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Edge detection
edges = cv2.Canny(gray,50,150,apertureSize = 3)
# Line detection
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('linesDetected.jpg',img)

In theory this code snippet should do the job, but unfortunately it doesn't. The resulting picture clearly shows only one line being found. I'm not quite sure what I'm doing wrong here and why it's only detecting one specific line. Could someone possibly figure out the problem here?

Answer 1

Despite opencv's Hough Transform tutorial is using just one loop, the shape of lines is actual [None,1,2], thus when you use lines[0] you will only get one item for rho and one item for theta, which will only get you one line. therefore, my suggestion is to use a double loop (below) or some numpy slice magic to maintain using just 1 loop. To get all the grid detected, as Dan Masek mentioned, you will need to play with the edge detection logic. Maybe see the solution that uses HoughLinesP.

for item in lines:
    for rho,theta in item:
        ...