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Gallery: Use Horse to illustrate Convex Hull #2431

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Gallery: Use Horse to illustrate Convex Hull #2431

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Use Horse to illustrate Convex Hull
sciunto Jul 17, 2016
d868ea2
Add a plot to show the difference
sciunto Jan 7, 2017
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45 changes: 19 additions & 26 deletions doc/examples/edges/plot_convex_hull.py
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Expand Up @@ -6,51 +6,44 @@
The convex hull of a binary image is the set of pixels included in the
smallest convex polygon that surround all white pixels in the input.

In this example, we show how the input pixels (white) get filled in by the
convex hull (white and grey).

A good overview of the algorithm is given on `Steve Eddin's blog
<http://blogs.mathworks.com/steve/2011/10/04/binary-image-convex-hull-algorithm-notes/>`__.

"""

import numpy as np
import matplotlib.pyplot as plt

from skimage.morphology import convex_hull_image
from skimage import data, img_as_float
from skimage.util import invert


image = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 1, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 1, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=float)

original_image = np.copy(image)
# The original image is inverted as the object must be white.
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-> "Invert the image so to make horse silhouette have higher intensity values"?

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But Convex-hull works only on binary images. Do you think that "higher" is appropriated?

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I just think that it is worthwhile to mention the correspondence between intensities and colors here.
From the first read this sentence was very unclear to me: object be white -> it was black? -> what does it mean to be white? -> what does invert actually do?. I think if you could clarify this a bit, it wil be perfect!

image = invert(data.horse())

chull = convex_hull_image(image)
image[chull] += 1

# image is now:
# [[ 0. 0. 0. 0. 0. 0. 0. 0. 0.]
# [ 0. 0. 0. 0. 2. 0. 0. 0. 0.]
# [ 0. 0. 0. 2. 1. 2. 0. 0. 0.]
# [ 0. 0. 2. 1. 1. 1. 2. 0. 0.]
# [ 0. 2. 1. 1. 1. 1. 1. 2. 0.]
# [ 0. 0. 0. 0. 0. 0. 0. 0. 0.]]

fig, axes = plt.subplots(1, 2, figsize=(9, 3))
fig, axes = plt.subplots(1, 2, figsize=(8, 4))
ax = axes.ravel()

ax[0].set_title('Original picture')
ax[0].imshow(original_image, cmap=plt.cm.gray, interpolation='nearest')
ax[0].imshow(image, cmap=plt.cm.gray, interpolation='nearest')
ax[0].set_axis_off()

ax[1].set_title('Transformed picture')
ax[1].imshow(image, cmap=plt.cm.gray, interpolation='nearest')
ax[1].imshow(chull, cmap=plt.cm.gray, interpolation='nearest')
ax[1].set_axis_off()
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I really like the way the current example looks.
@sciunto Have you tried to add an 1-pixel contour of the horse to the second plot? For the horse image I think it should be less 'puzzling'. 🤔

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OK. I think I'll try to make a second plot to better explain that gray = added pixels.

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done


plt.tight_layout()
plt.show()

######################################################################
# We prepare a second plot to show the difference.
#

chull_diff = img_as_float(chull.copy())
chull_diff[image] = 2

fig, ax = plt.subplots()
ax.imshow(chull_diff, cmap=plt.cm.gray, interpolation='nearest')
ax.set_title('Difference')
plt.show()