二值图像的凸壳指的是包围输入二值图像白色区域的最小的凸多边形的像素集合。

skimage中的函数

from skimage.morphology import convex_hull_image
chull = convex_hull_image(image)

完整代码：

"""
===========
Convex Hull
===========

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.

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 matplotlib.pyplot as plt

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

# The original image is inverted as the object must be white.
image = invert(data.horse())

chull = convex_hull_image(image)

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

ax[0].set_title(‘Original picture‘)
ax[0].imshow(image, cmap=plt.cm.gray)
ax[0].set_axis_off()

ax[1].set_title(‘Transformed picture‘)
ax[1].imshow(chull, cmap=plt.cm.gray)
ax[1].set_axis_off()

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)
ax.set_title(‘Difference‘)
plt.show()

实验输出