2.6.8.22. Watershed segmentationΒΆ

This example shows how to do segmentation with watershed.

../../../_images/sphx_glr_plot_watershed_segmentation_001.png
import numpy as np
from skimage.morphology import watershed
from skimage.feature import peak_local_max
import matplotlib.pyplot as plt
from scipy import ndimage
# 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)
plt.figure(figsize=(9, 3.5))
plt.subplot(131)
plt.imshow(image, cmap='gray', interpolation='nearest')
plt.axis('off')
plt.subplot(132)
plt.imshow(-distance, interpolation='nearest')
plt.axis('off')
plt.subplot(133)
plt.imshow(labels, cmap='spectral', interpolation='nearest')
plt.axis('off')
plt.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0,
right=1)
plt.show()

Total running time of the script: ( 0 minutes 0.110 seconds)

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