Applying the Sobel filter using scipy

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 is as follows:

import scipy
from scipy import ndimage

im = scipy.misc.imread('bike.jpg')
processed = ndimage.sobel(im, 0)
scipy.misc.imsave('sobel.jpg', processed)

I don't know what I'm doing wrong, but the processed image does not look anything like what it should. The image, 'bike.jpg' is a greyscale (mode 'L' not 'RGB') image so each pixel has only one value associated with it.

Unfortunately I can't post the images here yet (don't have enough reputation) but I've provided links below:

Original Image (bike.jpg): http:开发者_运维知识库//s2.postimage.org/64q8w613j/bike.jpg

Scipy Filtered (sobel.jpg): http://s2.postimage.org/64qajpdlb/sobel.jpg

Expected Output: http://s1.postimage.org/5vexz7kdr/normal_sobel.jpg

I'm obviously going wrong somewhere! Can someone please tell me where. Thanks.

---

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)

---

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)

---

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

for more see here