mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 03:51:05 +08:00
Johannes Schönberger
70 lines
2.1 KiB
Python
70 lines
2.1 KiB
Python
"""
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==========================================
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Find the intersection of two segmentations
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==========================================
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When segmenting an image, you may want to combine multiple alternative
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segmentations. The `skimage.segmentation.join_segmentations` function
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computes the join of two segmentations, in which a pixel is placed in
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the same segment if and only if it is in the same segment in _both_
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segmentations.
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"""
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import numpy as np
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from scipy import ndimage as nd
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import matplotlib.pyplot as plt
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import matplotlib as mpl
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from skimage.filter import sobel
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from skimage.segmentation import slic, join_segmentations
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from skimage.morphology import watershed
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from skimage import data
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coins = data.coins()
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# make segmentation using edge-detection and watershed
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edges = sobel(coins)
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markers = np.zeros_like(coins)
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foreground, background = 1, 2
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markers[coins < 30] = background
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markers[coins > 150] = foreground
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ws = watershed(edges, markers)
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seg1 = nd.label(ws == foreground)[0]
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# make segmentation using SLIC superpixels
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# make the RGB equivalent of `coins`
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coins_colour = np.tile(coins[..., np.newaxis], (1, 1, 3))
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seg2 = slic(coins_colour, n_segments=30, max_iter=160, sigma=1, ratio=9,
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convert2lab=False)
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# combine the two
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segj = join_segmentations(seg1, seg2)
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### Display the result ###
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# make a random colormap for a set number of values
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def random_cmap(im):
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np.random.seed(9)
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cmap_array = np.concatenate(
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(np.zeros((1, 3)), np.random.rand(np.ceil(im.max()), 3)))
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return mpl.colors.ListedColormap(cmap_array)
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# show the segmentations
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fig, axes = plt.subplots(ncols=4, figsize=(9, 2.5))
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axes[0].imshow(coins, cmap=plt.cm.gray, interpolation='nearest')
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axes[0].set_title('Image')
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axes[1].imshow(seg1, cmap=random_cmap(seg1), interpolation='nearest')
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axes[1].set_title('Sobel+Watershed')
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axes[2].imshow(seg2, cmap=random_cmap(seg2), interpolation='nearest')
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axes[2].set_title('SLIC superpixels')
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axes[3].imshow(segj, cmap=random_cmap(segj), interpolation='nearest')
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axes[3].set_title('Join')
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for ax in axes:
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ax.axis('off')
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plt.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1)
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plt.show()
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