Note

Click here to download the full example code or to run this example in your browser via Binder

# Find the intersection of two segmentationsΒΆ

When segmenting an image, you may want to combine multiple alternative
segmentations. The `skimage.segmentation.join_segmentations()`

function computes the join of two segmentations, in which a pixel is
placed in the same segment if and only if it is in the same segment in
*both* segmentations.

```
import numpy as np
import matplotlib.pyplot as plt
from skimage.filters import sobel
from skimage.measure import label
from skimage.segmentation import slic, join_segmentations, watershed
from skimage.color import label2rgb
from skimage import data
coins = data.coins()
# Make segmentation using edge-detection and watershed.
edges = sobel(coins)
# Identify some background and foreground pixels from the intensity values.
# These pixels are used as seeds for watershed.
markers = np.zeros_like(coins)
foreground, background = 1, 2
markers[coins < 30.0] = background
markers[coins > 150.0] = foreground
ws = watershed(edges, markers)
seg1 = label(ws == foreground)
# Make segmentation using SLIC superpixels.
seg2 = slic(coins, n_segments=117, max_iter=160, sigma=1, compactness=0.75,
multichannel=False, start_label=0)
# Combine the two.
segj = join_segmentations(seg1, seg2)
# Show the segmentations.
fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(9, 5),
sharex=True, sharey=True)
ax = axes.ravel()
ax[0].imshow(coins, cmap='gray')
ax[0].set_title('Image')
color1 = label2rgb(seg1, image=coins, bg_label=0)
ax[1].imshow(color1)
ax[1].set_title('Sobel+Watershed')
color2 = label2rgb(seg2, image=coins, image_alpha=0.5, bg_label=-1)
ax[2].imshow(color2)
ax[2].set_title('SLIC superpixels')
color3 = label2rgb(segj, image=coins, image_alpha=0.5, bg_label=-1)
ax[3].imshow(color3)
ax[3].set_title('Join')
for a in ax:
a.axis('off')
fig.tight_layout()
plt.show()
```

**Total running time of the script:** ( 0 minutes 1.126 seconds)