mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-27 21:08:24 +08:00
Juan Nunez-Iglesias
0d134987f9
Only two forms remain in use: - `from scipy import ndimage as ndi` - `from scipy.ndimage import function`
48 lines
1.4 KiB
Python
48 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. A maximum filter is used for finding local maxima. This operation
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dilates the original image and merges neighboring local maxima closer than the
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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, ax = plt.subplots(1, 3, figsize=(8, 3))
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ax1, ax2, ax3 = ax.ravel()
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ax1.imshow(im, cmap=plt.cm.gray)
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ax1.axis('off')
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ax1.set_title('Original')
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ax2.imshow(image_max, cmap=plt.cm.gray)
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ax2.axis('off')
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ax2.set_title('Maximum filter')
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ax3.imshow(im, cmap=plt.cm.gray)
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ax3.autoscale(False)
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ax3.plot(coordinates[:, 1], coordinates[:, 0], 'r.')
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ax3.axis('off')
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ax3.set_title('Peak local max')
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fig.subplots_adjust(wspace=0.02, hspace=0.02, top=0.9,
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bottom=0.02, left=0.02, right=0.98)
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plt.show()
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