47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
"""
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====================
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Finding local maxima
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====================
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The ``peak_local_max`` function returns the coordinates of local peaks (maxima)
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in an image. Internally, a maximum filter is used for finding local maxima. This
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operation dilates the original image and merges neighboring local maxima closer
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than the size of the dilation. Locations where the original image is equal to the
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dilated image are returned as local maxima.
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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.feature import peak_local_max
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from skimage import data, img_as_float
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im = img_as_float(data.coins())
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# image_max is the dilation of im with a 20*20 structuring element
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# It is used within peak_local_max function
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image_max = ndi.maximum_filter(im, size=20, mode='constant')
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# Comparison between image_max and im to find the coordinates of local maxima
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coordinates = peak_local_max(im, min_distance=20)
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# display results
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fig, axes = plt.subplots(1, 3, figsize=(8, 3), sharex=True, sharey=True)
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ax = axes.ravel()
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ax[0].imshow(im, cmap=plt.cm.gray)
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ax[0].axis('off')
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ax[0].set_title('Original')
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ax[1].imshow(image_max, cmap=plt.cm.gray)
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ax[1].axis('off')
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ax[1].set_title('Maximum filter')
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ax[2].imshow(im, cmap=plt.cm.gray)
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ax[2].autoscale(False)
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ax[2].plot(coordinates[:, 1], coordinates[:, 0], 'r.')
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ax[2].axis('off')
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ax[2].set_title('Peak local max')
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fig.tight_layout()
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plt.show()
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