An illustration of IPython's interactive capabilities by building an image comparison tool, inspired by Github's image diff.
Notes:
Since this notebook contains interactive widgets, if you read it on IPython's nbviewer you will not be able to see the interactive effects. You'll need to download it and run it locally.
This notebook's original location is this repository, it was copied to this tutorial for convenience.
Fernando Perez, @fperez_org, May 2014.
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from __future__ import division
from __future__ import print_function
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%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
Let's consider the problem of comparing two related images. While working interactively with code, most of us probably simply will plot both next to each other and look at them side by side...
Since this example was written as an illustration for a recent talk at Neil Lawrence's Data Science and Machine Learning initiative at U. Sheffield, we'll use for our illustration the old and new version of the coat of arms for the University of Sheffield:
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a = plt.imread("images/University_of_Sheffield_coat_of_arms_old.png")
b = plt.imread("images/University_of_Sheffield_coat_of_arms_new.png")
plt.rcParams['figure.figsize'] = (8,6)
fig, (ax_a, ax_b) = plt.subplots(1, 2)
ax_a.imshow(a)
ax_b.imshow(b);
It would be great to be able to visualize the changes in various forms: as an image difference, blending the two, superimposing them... Let's build a set of comparison functions that will return a new image $c = f(a, b)$, so we can display the result. This is precisely the funcitonality offered by Github's image diff tools:
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def c_blend(a, b, alpha):
"""Blend images a and b.
"""
return (1.0-alpha)*a + alpha*b
def c_diff(a, b, alpha):
"""Return the difference between a and b.
"""
return a-b
def c_horizontal_shade(a, b, alpha):
"""Form c from top rows of a and bottom rows of b.
"""
nrows = a.shape[0]
r_index = int(alpha*nrows)
c = np.empty_like(a)
c[:r_index] = a[:r_index]
c[r_index:] = b[r_index:]
return c
def c_vertical_shade(a, b, alpha):
"""Form c from left columns of a and right columns of b.
"""
ncols = a.shape[1]
c_index = int(alpha*ncols)
c = np.empty_like(a)
c[:, :c_index] = a[:, :c_index]
c[:, c_index:] = b[:, c_index:]
return c
def compare(a, b, method=c_vertical_shade, alpha=0.5):
"""Show the comparison between images a and b using the
selected comparison method.
"""
c = method(a, b, alpha)
cmin = min(a.min(), b.min())
cmax = max(a.max(), b.max())
plt.imshow(c, clim=(cmin, cmax))
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compare(a, b, alpha=0.50)
Now, using the IPython interact facility, we can easily provide interactive controls that let us much more easily see these differences:
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from IPython.html.widgets import interact, fixed
interact(compare, a=fixed(a), b=fixed(b), alpha=(0.0, 1.0),
method=dict(blend=c_blend, diff=c_diff,
horizontal_shade=c_horizontal_shade,
vertical_shade=c_vertical_shade));
The above illustrates the basic idea and is already useful. With only a small amount more work, we can turn this into a richer tool that can come in handy any time we want to compare two images.
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def ax3_show(axes, images):
"""Show three images on three axes with a common color range.
"""
a, b = images[:2]
cmin = min(a.min(), b.min())
cmax = max(a.max(), b.max())
for ax, im in zip(axes, images):
ax.imshow(im, clim=(cmin, cmax))
ax.set_xticks([])
ax.set_yticks([])
plt.subplots_adjust(0, 0, 1, 1, 0, 0)
def show3_square(a, b, c, fig_width = 8):
"""Show three images for which a roughly square aspect ratio layout works.
"""
# Compute the figure size to accomodate comparison image and originals
nr, nc = c.shape[:2]
ar = nr/nc
fig_height = (2/3)*fig_width*ar
f = plt.figure(None, (fig_width, fig_height) )
# For some reason, I'm not getting the images to line up at the bottom. Puzzled.
ax_c = f.add_axes((0, 0, 2/3, 1))
ax_a = f.add_axes((2/3, 0, 1/3, 1/2))
ax_b = f.add_axes((2/3, 1/2, 1/3, 1/2))
# Put each image into its own axis
ax3_show([ax_a, ax_b, ax_c], [a, b, c])
def show3_stack(a, b, c, mode='tall'):
"""Show three images stacked in the tall or wide direction.
"""
shape = (1, 3) if mode=='tall' else (3, 1)
f, ax = plt.subplots(*shape, sharex=True, sharey=True, figsize=(7, 5))
ax3_show(ax, [c, a, b])
def show3(a, b, c, layout='auto'):
"""Show three images.
The layout can be specified or left for automatic selection. If `layout`
is `auto`, the aspect ratio of the images will be used to select the
best layout (tall, wide or square).
"""
funcs = dict(square = show3_square,
tall = lambda a,b,c: show3_stack(a,b,c, 'tall'),
wide = lambda a,b,c: show3_stack(a,b,c, 'wide'))
if layout == 'auto':
nr, nc = c.shape[:2]
ar = nr/nc
if ar > 3:
layout = 'tall'
elif ar < 1/3:
layout = 'wide'
else:
layout = 'square'
show = funcs[layout]
show(a, b, c)
Let's see what these three layout options look like:
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c = a-b
for layout in ['square', 'tall', 'wide']:
show3(a, b, c, layout)
Now, we have all the pieces we need to build a better image comparison tool.
First, we build a pure function that takes the arguments we will want to control interactively, including the option to easily swap $a$ and $b$:
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def compare2(a, b, method=c_vertical_shade, alpha=0, layout='auto', swap=False):
"""Utility function to compare two images.
"""
if swap:
a, b = b, a
c = method(a, b, alpha)
show3(a, b, c, layout=layout)
And now, it's easy to build our interactive widget:
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def compare22(*a, **kw):
print(kw['alpha'])
def imcompare(a, b):
"""Compare images a and b with interactive controls.
"""
# Exercise: add support for cropping images to a common size if
# they have a size mismatch.
if a.shape != b.shape:
raise ValueError("Both images must have the exact same shape")
# Now, call compare2 above with an interactive widget
interact(compare2, a=fixed(a), b=fixed(b),
method=dict(blend=c_blend, diff=c_diff,
horizontal_shade=c_horizontal_shade,
vertical_shade=c_vertical_shade),
alpha=(0.0, 1.0, 0.05),
swap=False,
layout=['auto', 'tall', 'wide'])
With this widget, it's now simply a matter of making a function call any time we want to compare two images:
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imcompare(a, b)
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