Fastest 2D convolution or image filter in Python

Question

Several users have asked about the speed or memory consumption of image convolutions in numpy or scipy [1, 2, 3, 4]. From the responses and my experience using Numpy, I bel

Answer 1

Scipy has a function fftconvolve, that can be used for 1D and 2D signals.

from scipy import signal
from scipy import misc
import numpy as np
import matplotlib.pyplot as plt

face = misc.face(gray=True)
kernel = np.outer(signal.gaussian(70, 8), signal.gaussian(70, 8))
blurred = signal.fftconvolve(face, kernel, mode='same')

fig, (ax_orig, ax_kernel, ax_blurred) = plt.subplots(3, 1, figsize=(6, 15))
ax_orig.imshow(face, cmap='gray')
ax_orig.set_title('Original')
ax_orig.set_axis_off()
ax_kernel.imshow(kernel, cmap='gray')
ax_kernel.set_title('Gaussian kernel')
ax_kernel.set_axis_off()
ax_blurred.imshow(blurred, cmap='gray')
ax_blurred.set_title('Blurred')
ax_blurred.set_axis_off()
fig.show()