mirror of
https://github.com/wassname/scikit-image.git
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Tony S Yu
70 lines
1.9 KiB
Python
70 lines
1.9 KiB
Python
"""
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==========
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Find spots
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==========
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In this example, we find bright spots in an image using morphological
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reconstruction by dilation. Dilation expands the maximal values of the seed
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image until it encounters a mask image. Thus, the seed image and mask image
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represent the minimum and maximum 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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# convenience function for plotting images
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def imshow(image, **kwargs):
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plt.figure(figsize=(5, 4))
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plt.imshow(image, **kwargs)
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plt.axis('off')
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imshow(image)
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plt.title('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 maxima represent the starting
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points for dilation. To find spots, we initialize the seed image to the minimum
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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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dilation process. We then limit the dilation 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.min()
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mask = image
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rec = reconstruction(seed, mask, method='dilation')
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imshow(rec, vmin=image.min(), vmax=image.max())
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plt.title('')
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"""
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.. image:: PLOT2RST.current_figure
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As shown above, dilating inward from the edges removes peaks, since (by
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definition) peaks are surrounded by pixels of darker value. Finally, we can
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isolate the bright spots by subtracting the reconstructed image from the
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original image.
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"""
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imshow(image - rec)
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plt.title('"holes"')
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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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