"""
===============================
Filling holes and finding peaks
===============================

We fill holes (i.e. isolated, dark spots) in an image using morphological
reconstruction by erosion. Erosion expands the minimal values of the seed image
until it encounters a mask image. Thus, the seed image and mask image represent
the maximum and minimum possible values of the reconstructed image.

We start with an image containing both peaks and holes:

"""
import matplotlib.pyplot as plt

from skimage import data
from skimage.exposure import rescale_intensity

image = data.moon()
# Rescale image intensity so that we can see dim features.
image = rescale_intensity(image, in_range=(50, 200))

######################################################################
# Now we need to create the seed image, where the minima represent the
# starting points for erosion.  To fill holes, we initialize the seed image
# to the maximum value of the original image. Along the borders, however, we
# use the original values of the image. These border pixels will be the
# starting points for the erosion process. We then limit the erosion by
# setting the mask to the values of the original image.

import numpy as np
from skimage.morphology import reconstruction

seed = np.copy(image)
seed[1:-1, 1:-1] = image.max()
mask = image

filled = reconstruction(seed, mask, method='erosion')

######################################################################
# As shown above, eroding inward from the edges removes holes, since (by
# definition) holes are surrounded by pixels of brighter value. Finally, we
# can isolate the dark regions by subtracting the reconstructed image from
# the original image.
#
# Alternatively, we can find bright spots in an image using morphological
# reconstruction by dilation. Dilation is the inverse of erosion and expands
# the *maximal* values of the seed image until it encounters a mask image.
# Since this is an inverse operation, we initialize the seed image to the
# minimum image intensity instead of the maximum. The remainder of the
# process is the same.

seed = np.copy(image)
seed[1:-1, 1:-1] = image.min()
rec = reconstruction(seed, mask, method='dilation')

fig, ax = plt.subplots(2, 2, figsize=(5, 4), sharex=True, sharey=True,
                       subplot_kw={'adjustable': 'box-forced'})
ax = ax.ravel()

ax[0].imshow(image, cmap='gray')
ax[0].set_title('Original image')
ax[0].axis('off')

ax[1].imshow(filled, cmap='gray')
ax[1].set_title('after filling holes')
ax[1].axis('off')

ax[2].imshow(image-filled, cmap='gray')
ax[2].set_title('holes')
ax[2].axis('off')

ax[3].imshow(image-rec, cmap='gray')
ax[3].set_title('peaks')
ax[3].axis('off')
plt.show()