mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 05:43:17 +08:00
Juan Nunez-Iglesias
0d134987f9
Only two forms remain in use: - `from scipy import ndimage as ndi` - `from scipy.ndimage import function`
56 lines
1.5 KiB
Python
56 lines
1.5 KiB
Python
"""
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===============================
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Markers for watershed transform
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===============================
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The watershed is a classical algorithm used for **segmentation**, that
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is, for separating different objects in an image.
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Here a marker image is build from the region of low gradient inside the image.
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See Wikipedia_ for more details on the algorithm.
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.. _Wikipedia: http://en.wikipedia.org/wiki/Watershed_(image_processing)
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"""
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from scipy import ndimage as ndi
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import matplotlib.pyplot as plt
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from skimage.morphology import watershed, disk
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from skimage import data
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from skimage.filters import rank
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from skimage.util import img_as_ubyte
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image = img_as_ubyte(data.camera())
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# denoise image
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denoised = rank.median(image, disk(2))
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# find continuous region (low gradient) --> markers
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markers = rank.gradient(denoised, disk(5)) < 10
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markers = ndi.label(markers)[0]
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#local gradient
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gradient = rank.gradient(denoised, disk(2))
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# process the watershed
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labels = watershed(gradient, markers)
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# display results
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fig, axes = plt.subplots(ncols=4, figsize=(8, 2.7))
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ax0, ax1, ax2, ax3 = axes
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ax0.imshow(image, cmap=plt.cm.gray, interpolation='nearest')
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ax1.imshow(gradient, cmap=plt.cm.spectral, interpolation='nearest')
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ax2.imshow(markers, cmap=plt.cm.spectral, interpolation='nearest')
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ax3.imshow(image, cmap=plt.cm.gray, interpolation='nearest')
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ax3.imshow(labels, cmap=plt.cm.spectral, interpolation='nearest', alpha=.7)
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for ax in axes:
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ax.axis('off')
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fig.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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