A comparison between different methods to obtain out of the focus bokeh with Python (scikit image)

digital_bokeh.py

import numpy as np

from skimage.morphology import disk
from skimage.filters.rank import mean
from skimage.filters import gaussian

import matplotlib.pyplot as plt

n = 20
l = 256
im = np.zeros((l, l))
points = l * np.random.random((2, n ** 2))

# Original
im[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1

# Gaussian filter (as in iPhone's)
im_gauss = gaussian(im, sigma=3) #gauss with a 3 pixels as std

# Mean filter (more realistic bokeh!)
selection_element = disk(5) # matrix of n pixels with a disk shape
im_mean = mean(im, selection_element)

# Plotting code
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(10, 6))

axes[0].imshow(im, cmap=plt.cm.viridis)
axes[0].set_title('Original synthetic image', fontsize=10)
axes[0].set_axis_off()

axes[1].imshow(im_gauss, cmap=plt.cm.viridis)
axes[1].set_title('Gaussian blur filter \n(iPhone\'s 7 plus)', fontsize=10)
axes[1].set_axis_off()

axes[2].imshow(im_mean, cmap=plt.cm.viridis)
axes[2].set_title('Simulated bokeh \n(convultion by an uniform disk)', fontsize=10)
axes[2].set_axis_off()

plt.axis('off')
plt.show()