# 2.6.8.24. Segmentation with spectral clusteringΒΆ

This example uses spectral clustering to do segmentation.

```import numpy as np
import matplotlib.pyplot as plt

from sklearn.feature_extraction import image
from sklearn.cluster import spectral_clustering
```
```l = 100
x, y = np.indices((l, l))

center1 = (28, 24)
center2 = (40, 50)
center3 = (67, 58)
center4 = (24, 70)

circle1 = (x - center1[0]) ** 2 + (y - center1[1]) ** 2 < radius1**2
circle2 = (x - center2[0]) ** 2 + (y - center2[1]) ** 2 < radius2**2
circle3 = (x - center3[0]) ** 2 + (y - center3[1]) ** 2 < radius3**2
circle4 = (x - center4[0]) ** 2 + (y - center4[1]) ** 2 < radius4**2
```

4 circles

```img = circle1 + circle2 + circle3 + circle4
img = img.astype(float)

rng = np.random.default_rng(27446968)
img += 1 + 0.2 * rng.normal(size=img.shape)

# Convert the image into a graph with the value of the gradient on the
# edges.

# Take a decreasing function of the gradient: we take it weakly
# dependent from the gradient the segmentation is close to a voronoi
graph.data = np.exp(-graph.data / graph.data.std())

# Force the solver to be arpack, since amg is numerically
# unstable on this example
labels = spectral_clustering(graph, n_clusters=4)

plt.figure(figsize=(6, 3))
plt.subplot(121)
plt.imshow(img, cmap=plt.cm.nipy_spectral, interpolation="nearest")
plt.axis("off")
plt.subplot(122)
plt.imshow(label_im, cmap=plt.cm.nipy_spectral, interpolation="nearest")
plt.axis("off")

plt.subplots_adjust(wspace=0, hspace=0.0, top=0.99, bottom=0.01, left=0.01, right=0.99)
plt.show()
```

Total running time of the script: (0 minutes 0.251 seconds)

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