Line Detection - Using Hough Lines

cv2.HoughLines(binarized/thresholded image, 𝜌 accuracy, 𝜃 accuracy, threshold)

Threshold here is the minimum vote for it to be considered a line



In [13]:

    
import cv2
import numpy as np

image = cv2.imread('images/soduku.jpg')

# Grayscale and Canny Edges extracted
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 100, 170, apertureSize = 3)

# Run HoughLines using a rho accuracy of 1 pixel
# theta accuracy of np.pi / 180 which is 1 degree
# Our line threshold is set to 240 (number of points on line)
lines = cv2.HoughLines(edges, 1, np.pi / 180, 240)

# We iterate through each line and convert it to the format
# required by cv.lines (i.e. requiring end points)
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(image, (x1, y1), (x2, y2), (255, 0, 0), 2)

cv2.imshow('Hough Lines', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Probabilistic Hough Lines

**cv2.HoughLinesP(binarized image, 𝜌 accuracy, 𝜃 accuracy, threshold, minimum line length, max line gap)



In [19]:

    
import cv2
import numpy as np

# Grayscale and Canny Edges extracted
image = cv2.imread('images/soduku.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 100, 170, apertureSize = 3)

# Again we use the same rho and theta accuracies
# However, we specific a minimum vote (pts along line) of 100
# and Min line length of 5 pixels and max gap between lines of 10 pixels
lines = cv2.HoughLinesP(edges, 1, np.pi / 180, 200, 5, 10)
print lines.shape

for x1, y1, x2, y2 in lines[0]:
    cv2.line(image, (x1, y1), (x2, y2),(0, 255, 0), 3)

cv2.imshow('Probabilistic Hough Lines', image)
cv2.waitKey(0)
cv2.destroyAllWindows()









    



(1L, 167L, 4L)

http://cmp.felk.cvut.cz/~matas/papers/matas-bmvc98.pdf



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