import numpy as np
import scipy
import scipy.signal as sig
# With mode="L", we force the image to be parsed in the grayscale, so it is
# actually unnecessary to convert the photo color beforehand.
img = scipy.misc.imread("manu-2004.jpg", mode="L")

# Define the Sobel operator kernels.
kernel_x = np.array([[-1, 0, 1],[-2, 0, 2],[-1, 0, 1]])
kernel_y = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]])

G_x = sig.convolve2d(img, kernel_x, mode='same') 
G_y = sig.convolve2d(img, kernel_y, mode='same') 

# Plot them!
fig = plt.figure()
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)

# Actually plt.imshow() can handle the value scale well even if I don't do 
# the transformation (G_x + 255) / 2.
ax1.imshow((G_x + 255) / 2, cmap='gray'); ax1.set_xlabel("Gx")
ax2.imshow((G_y + 255) / 2, cmap='gray'); ax2.set_xlabel("Gy")
plt.show()