mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 03:19:12 +08:00
Siva Prasad Varma
67 lines
2.3 KiB
Python
67 lines
2.3 KiB
Python
"""
|
|
======================
|
|
Watershed segmentation
|
|
======================
|
|
|
|
The watershed is a classical algorithm used for **segmentation**, that
|
|
is, for separating different objects in an image.
|
|
|
|
Starting from user-defined markers, the watershed algorithm treats
|
|
pixels values as a local topography (elevation). The algorithm floods
|
|
basins from the markers, until basins attributed to different markers
|
|
meet on watershed lines. In many cases, markers are chosen as local
|
|
minima of the image, from which basins are flooded.
|
|
|
|
In the example below, two overlapping circles are to be separated. To
|
|
do so, one computes an image that is the distance to the
|
|
background. The maxima of this distance (i.e., the minima of the
|
|
opposite of the distance) are chosen as markers, and the flooding of
|
|
basins from such markers separates the two circles along a watershed
|
|
line.
|
|
|
|
See Wikipedia_ for more details on the algorithm.
|
|
|
|
.. _Wikipedia: http://en.wikipedia.org/wiki/Watershed_(image_processing)
|
|
|
|
"""
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
from scipy import ndimage
|
|
|
|
from skimage.morphology import watershed
|
|
from skimage.feature import peak_local_max
|
|
|
|
|
|
# Generate an initial image with two overlapping circles
|
|
x, y = np.indices((80, 80))
|
|
x1, y1, x2, y2 = 28, 28, 44, 52
|
|
r1, r2 = 16, 20
|
|
mask_circle1 = (x - x1)**2 + (y - y1)**2 < r1**2
|
|
mask_circle2 = (x - x2)**2 + (y - y2)**2 < r2**2
|
|
image = np.logical_or(mask_circle1, mask_circle2)
|
|
|
|
# Now we want to separate the two objects in image
|
|
# Generate the markers as local maxima of the distance to the background
|
|
distance = ndimage.distance_transform_edt(image)
|
|
local_maxi = peak_local_max(distance, indices=False, footprint=np.ones((3, 3)),
|
|
labels=image)
|
|
markers = ndimage.label(local_maxi)[0]
|
|
labels = watershed(-distance, markers, mask=image)
|
|
|
|
fig, axes = plt.subplots(ncols=3, figsize=(8, 2.7))
|
|
ax0, ax1, ax2 = axes
|
|
|
|
ax0.imshow(image, cmap=plt.cm.gray, interpolation='nearest')
|
|
ax0.set_title('Overlapping objects')
|
|
ax1.imshow(-distance, cmap=plt.cm.jet, interpolation='nearest')
|
|
ax1.set_title('Distances')
|
|
ax2.imshow(labels, cmap=plt.cm.spectral, interpolation='nearest')
|
|
ax2.set_title('Separated objects')
|
|
|
|
for ax in axes:
|
|
ax.axis('off')
|
|
|
|
fig.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0,
|
|
right=1)
|
|
plt.show()
|