mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 17:51:07 +08:00
Siva Prasad Varma
86 lines
2.6 KiB
Python
86 lines
2.6 KiB
Python
"""
|
|
===============================
|
|
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
|
|
"""
|