Let's first detect edges from a grayscale cameraman.jpg image using convolution with the Laplace kernel and also blur an image using the box kernel:

im = rgb2gray(imread('../image s/cameraman.jpg')).astype(float)
print(np.max(im))
# 1.0
print(im.shape)
# (225, 225)
blur_box_kernel = np.ones((3,3)) / 9
edge_laplace_kernel = np.array([[0,1,0],[1,-4,1],[0,1,0]])
im_blurred = signal.convolve2d(im, blur_box_kernel)
im_edges = np.clip(signal.convolve2d(im, edge_laplace_kernel), 0, 1)
fig, axes = pylab.subplots(ncols=3, sharex=True, sharey=True, figsize=(18, 6))
axes[0].imshow(im, cmap=pylab.cm.gray)
axes[0].set_title('Original Image', size=20)
axes[1].imshow(im_blurred, cmap=pylab.cm.gray)
axes[1].set_title('Box Blur', size=20)
axes[2].imshow(im_edges, cmap=pylab.cm.gray)
axes[2].set_title('Laplace Edge Detection', size=20)
for ax in axes:
 ax.axis('off')
pylab.show()

Here is the output—the original cameraman image along with the ones created after convolving with the box blur and Laplace kernel, which we obtained using the scipy.signal module's convolve2d() function: