Note

Click here to download the full example code

The watershed transform is commonly used as a starting point for many segmentation algorithms. However, without a judicious choice of seeds, it can produce very uneven fragment sizes, which can be difficult to deal with in downstream analyses.

The *compact* watershed transform remedies this by favoring seeds that are
close to the pixel being considered.

Both algorithms are implemented in the `skimage.morphology.watershed()`

function. To use the compact form, simply pass a `compactness`

value greater
than 0.

```
import numpy as np
from skimage import data, util, filters, color
from skimage.morphology import watershed
import matplotlib.pyplot as plt
coins = data.coins()
edges = filters.sobel(coins)
grid = util.regular_grid(coins.shape, n_points=468)
seeds = np.zeros(coins.shape, dtype=int)
seeds[grid] = np.arange(seeds[grid].size).reshape(seeds[grid].shape) + 1
w0 = watershed(edges, seeds)
w1 = watershed(edges, seeds, compactness=0.01)
fig, (ax0, ax1) = plt.subplots(1, 2)
ax0.imshow(color.label2rgb(w0, coins))
ax0.set_title('Classical watershed')
ax1.imshow(color.label2rgb(w1, coins))
ax1.set_title('Compact watershed')
plt.show()
```

**Total running time of the script:** ( 0 minutes 0.220 seconds)