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

In this example, 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))

# convenience function for plotting images
def imshow(image, title, **kwargs):
    fig, ax = plt.subplots(figsize=(5, 4))
    ax.imshow(image, **kwargs)
    ax.axis('off')
    ax.set_title(title)

imshow(image, 'Original image')

"""
.. image:: PLOT2RST.current_figure

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')

imshow(filled, 'after filling holes',vmin=image.min(), vmax=image.max())

"""
.. image:: PLOT2RST.current_figure

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.
"""

imshow(image - filled, 'holes')
# plt.title('holes')

"""
.. image:: PLOT2RST.current_figure

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')
imshow(image - rec, 'peaks')
plt.show()

"""
.. image:: PLOT2RST.current_figure
"""

numpy.copy