2.6.8.22. Watershed segmentationΒΆ

This example shows how to do segmentation with watershed.

plot watershed segmentation
import numpy as np
from skimage.segmentation import watershed
from skimage.feature import peak_local_max
import matplotlib.pyplot as plt
import scipy as sp
# 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 = sp.ndimage.distance_transform_edt(image)
peak_idx = peak_local_max(distance, footprint=np.ones((3, 3)), labels=image)
peak_mask = np.zeros_like(distance, dtype=bool)
peak_mask[tuple(peak_idx.T)] = True
markers = sp.ndimage.label(peak_mask)[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="nipy_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 0.057 seconds)

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