The peak_local_max function returns the coordinates of local peaks (maxima)
in an image. A maximum filter is used for finding local maxima. This operation
dilates the original image and merges neighboring local maxima closer than the
size of the dilation. Locations where the original image is equal to the
dilated image are returned as local maxima.
Sourcefrom scipy import ndimage as ndi
import matplotlib.pyplot as plt
from skimage.feature import peak_local_max
from skimage import data, img_as_float
im = img_as_float(data.coins())
# image_max is the dilation of im with a 20*20 structuring element
# It is used within peak_local_max function
image_max = ndi.maximum_filter(im, size=20, mode='constant')
# Comparison between image_max and im to find the coordinates of local maxima
coordinates = peak_local_max(im, min_distance=20)
# display results
fig, ax = plt.subplots(1, 3, figsize=(8, 3), sharex=True, sharey=True,
subplot_kw={'adjustable': 'box-forced'})
ax1, ax2, ax3 = ax.ravel()
ax1.imshow(im, cmap=plt.cm.gray)
ax1.axis('off')
ax1.set_title('Original')
ax2.imshow(image_max, cmap=plt.cm.gray)
ax2.axis('off')
ax2.set_title('Maximum filter')
ax3.imshow(im, cmap=plt.cm.gray)
ax3.autoscale(False)
ax3.plot(coordinates[:, 1], coordinates[:, 0], 'r.')
ax3.axis('off')
ax3.set_title('Peak local max')
fig.tight_layout()
plt.show()
Python source code: download
(generated using skimage 0.12.3)
IPython Notebook: download
(generated using skimage 0.12.3)