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
function. To use the compact form, simply pass a
compactness value greater
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 0.280 seconds)