76 lines
2.6 KiB
Python
76 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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We fill holes (i.e. isolated, dark spots) in an image using morphological
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reconstruction by erosion. Erosion expands the minimal values of the seed image
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until it encounters a mask image. Thus, the seed image and mask image represent
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the maximum and minimum possible values of the reconstructed 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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######################################################################
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# Now we need to create the seed image, where the minima represent the
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# starting points for erosion. To fill holes, we initialize the seed image
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# to the maximum value of the original image. Along the borders, however, we
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# use the original values of the image. These border pixels will be the
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# starting points for the erosion process. We then limit the erosion by
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# setting the mask to the values of the original image.
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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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######################################################################
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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
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# can isolate the dark regions by subtracting the reconstructed image from
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# the original image.
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#
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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
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# the *maximal* values of the seed image until it encounters a mask image.
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# Since this is an inverse operation, we initialize the seed image to the
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# minimum image intensity instead of the maximum. The remainder of the
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# process is the same.
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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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fig, ax = plt.subplots(2, 2, figsize=(5, 4), sharex=True, sharey=True)
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ax = ax.ravel()
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ax[0].imshow(image, cmap='gray')
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ax[0].set_title('Original image')
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ax[0].axis('off')
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ax[1].imshow(filled, cmap='gray')
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ax[1].set_title('after filling holes')
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ax[1].axis('off')
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ax[2].imshow(image-filled, cmap='gray')
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ax[2].set_title('holes')
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ax[2].axis('off')
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ax[3].imshow(image-rec, cmap='gray')
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ax[3].set_title('peaks')
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ax[3].axis('off')
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plt.show()
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