mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 16:30:54 +08:00
emmanuelle
88 lines
2.6 KiB
Python
88 lines
2.6 KiB
Python
"""
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===============================
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Filling holes and finding peaks
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===============================
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In this example, we fill holes (i.e. isolated, dark spots) in an image using
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morphological reconstruction by erosion. Erosion expands the minimal values of
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the seed image until it encounters a mask image. Thus, the seed image and mask
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image represent the maximum and minimum possible values of the reconstructed
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image.
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We start with an image containing both peaks and holes:
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"""
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import matplotlib.pyplot as plt
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from skimage import data
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from skimage.exposure import rescale_intensity
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image = data.moon()
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# Rescale image intensity so that we can see dim features.
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image = rescale_intensity(image, in_range=(50, 200))
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# convenience function for plotting images
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def imshow(image, title, **kwargs):
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fig, ax = plt.subplots(figsize=(5, 4))
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ax.imshow(image, **kwargs)
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ax.axis('off')
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ax.set_title(title)
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imshow(image, 'Original image')
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"""
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.. image:: PLOT2RST.current_figure
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Now we need to create the seed image, where the minima represent the starting
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points for erosion. To fill holes, we initialize the seed image to the maximum
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value of the original image. Along the borders, however, we use the original
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values of the image. These border pixels will be the starting points for the
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erosion process. We then limit the erosion by setting the mask to the values
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of the original image.
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"""
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import numpy as np
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from skimage.morphology import reconstruction
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seed = np.copy(image)
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seed[1:-1, 1:-1] = image.max()
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mask = image
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filled = reconstruction(seed, mask, method='erosion')
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imshow(filled, 'after filling holes', vmin=image.min(), vmax=image.max())
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"""
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.. image:: PLOT2RST.current_figure
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As shown above, eroding inward from the edges removes holes, since (by
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definition) holes are surrounded by pixels of brighter value. Finally, we can
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isolate the dark regions by subtracting the reconstructed image from the
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original image.
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"""
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imshow(image - filled, 'holes')
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# plt.title('holes')
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"""
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.. image:: PLOT2RST.current_figure
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Alternatively, we can find bright spots in an image using morphological
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reconstruction by dilation. Dilation is the inverse of erosion and expands the
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*maximal* values of the seed image until it encounters a mask image. Since this
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is an inverse operation, we initialize the seed image to the minimum image
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intensity instead of the maximum. The remainder of the process is the same.
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"""
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seed = np.copy(image)
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seed[1:-1, 1:-1] = image.min()
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rec = reconstruction(seed, mask, method='dilation')
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imshow(image - rec, 'peaks')
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plt.show()
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"""
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.. image:: PLOT2RST.current_figure
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"""
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