This time I am trying another example from Solem's blog. It's a module that detects lines and circles in an image by using the Hough transform. Here is the code (houghlines.py):
import numpy as np
import cv2
"""
Script using OpenCV's Hough transforms for reading images of
simple dials.
"""
# load grayscale image
im = cv2.imread("house2.jpg")
gray_im = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY)
# create version to draw on and blurred version
draw_im = cv2.cvtColor(gray_im, cv2.COLOR_GRAY2BGR)
blur = cv2.GaussianBlur(gray_im, (0,0), 5)
m,n = gray_im.shape
# Hough transform for circles
circles = cv2.HoughCircles(gray_im, cv2.cv.CV_HOUGH_GRADIENT, 2, 10, np.array([]), 20, 60, m/10)[0]
# Hough transform for lines (regular and probabilistic)
edges = cv2.Canny(blur, 20, 60)
lines = cv2.HoughLines(edges, 2, np.pi/90, 40)[0]
plines = cv2.HoughLinesP(edges, 1, np.pi/180, 20, np.array([]), 10)[0]
# draw
for c in circles[:3]:
# green for circles (only draw the 3 strongest)
cv2.circle(draw_im, (c[0],c[1]), c[2], (0,255,0), 2)
for (rho, theta) in lines[:5]:
# blue for infinite lines (only draw the 5 strongest)
x0 = np.cos(theta)*rho
y0 = np.sin(theta)*rho
pt1 = ( int(x0 + (m+n)*(-np.sin(theta))), int(y0 + (m+n)*np.cos(theta)) )
pt2 = ( int(x0 - (m+n)*(-np.sin(theta))), int(y0 - (m+n)*np.cos(theta)) )
cv2.line(draw_im, pt1, pt2, (255,0,0), 2)
for l in plines:
# red for line segments
cv2.line(draw_im, (l[0],l[1]), (l[2],l[3]), (0,0,255), 2)
cv2.imshow("circles",draw_im)
cv2.waitKey()
# save the resulting image
cv2.imwrite("res.jpg",draw_im)
When I execute the file in python by:
execfile('houghlines.py')
the following error comes up:
File "houghlines.py", line 24, in <module>
lines = cv2.HoughLines(edges, 2, np.pi/90, 40)[0]
TypeError: 'NoneType' object has no attribute '__getitem__'
Do you guys have any idea how to solve it?
Thanks in advance.
It is because of the threshold value in lines = cv2.HoughLines(edges, 2, np.pi/90, 40)[0]
, the last argument you passed. It indicates minimum length to be considered as a line, 40 pixels in your case. Try decreasing it.