Applying the Sobel filter using scipy

Question

I\'m trying to apply the Sobel filter on an image to detect edges using scipy. I\'m using Python 3.2 (64 bit) and scipy 0.9.0 on Windows 7 Ultimate (64 bit). Currently my code i

Answer 1

1) Use a higher precision. 2) You are only calculating the approximation of the derivative along the zero axis. The 2D Sobel operator is explained on Wikipedia. Try this code:

import numpy
import scipy
from scipy import ndimage

im = scipy.misc.imread('bike.jpg')
im = im.astype('int32')
dx = ndimage.sobel(im, 0)  # horizontal derivative
dy = ndimage.sobel(im, 1)  # vertical derivative
mag = numpy.hypot(dx, dy)  # magnitude
mag *= 255.0 / numpy.max(mag)  # normalize (Q&D)
scipy.misc.imsave('sobel.jpg', mag)

Answer 2

I couldn't comment on cgohlke's answer so I repeated his answer with a corrction. Parameter 0 is used for vertical derivative and 1 for horizontal derivative (first axis of an image array is y/vertical direction - rows, and second axis is x/horizontal direction - columns). Just wanted to warn other users, because I lost 1 hour searching for mistake in the wrong places.

import numpy
import scipy
from scipy import ndimage

im = scipy.misc.imread('bike.jpg')
im = im.astype('int32')
dx = ndimage.sobel(im, 1)  # horizontal derivative
dy = ndimage.sobel(im, 0)  # vertical derivative
mag = numpy.hypot(dx, dy)  # magnitude
mag *= 255.0 / numpy.max(mag)  # normalize (Q&D)
scipy.misc.imsave('sobel.jpg', mag)

Answer 3

or you can use :

def sobel_filter(im, k_size):

    im = im.astype(np.float)
    width, height, c = im.shape
    if c > 1:
        img = 0.2126 * im[:,:,0] + 0.7152 * im[:,:,1] + 0.0722 * im[:,:,2]
    else:
        img = im

    assert(k_size == 3 or k_size == 5);

    if k_size == 3:
        kh = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], dtype = np.float)
        kv = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]], dtype = np.float)
    else:
        kh = np.array([[-1, -2, 0, 2, 1], 
                   [-4, -8, 0, 8, 4], 
                   [-6, -12, 0, 12, 6],
                   [-4, -8, 0, 8, 4],
                   [-1, -2, 0, 2, 1]], dtype = np.float)
        kv = np.array([[1, 4, 6, 4, 1], 
                   [2, 8, 12, 8, 2],
                   [0, 0, 0, 0, 0], 
                   [-2, -8, -12, -8, -2],
                   [-1, -4, -6, -4, -1]], dtype = np.float)

    gx = signal.convolve2d(img, kh, mode='same', boundary = 'symm', fillvalue=0)
    gy = signal.convolve2d(img, kv, mode='same', boundary = 'symm', fillvalue=0)

    g = np.sqrt(gx * gx + gy * gy)
    g *= 255.0 / np.max(g)

    #plt.figure()
    #plt.imshow(g, cmap=plt.cm.gray)      

    return g

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