CODEWITHSUNDEEP
numpyscipyscikit-image
Eric Bal
Eric Bal

Reputation: 1185

Image Boundary Finding and Filling

I have a single-band binary image (consisting of only 0 and 1 pixel values) as shown in the figure below.

I have to convert all the black pixels inside the outer white boundaries into whites. The black pixels outside the outer white boundaries should remain black.

How would you do it?

enter image description here

Upvotes: 3

Views: 4392

Answers (1)

Stefan van der Walt
Stefan van der Walt

Reputation: 7263

The code below yields the following result:

enter image description here

I've commented the code inline to explain what I've done.

from skimage import io, img_as_bool, measure, morphology
from scipy import ndimage

import numpy as np
import matplotlib.pyplot as plt

# Read the image, convert the values to True or False;
# discard all but the red channel (since it's a black and
# white image, they're all the same)
image = img_as_bool(io.imread('borders.png'))[..., 0]

# Compute connected regions in the image; we're going to use this
# to find centroids for our watershed segmentation
labels = measure.label(image)
regions = measure.regionprops(labels)

# Marker locations for the watershed segmentation; we choose these to
# be the centroids of the different connected regions in the image
markers = np.array([r.centroid for r in regions]).astype(np.uint16)
marker_image = np.zeros_like(image, dtype=np.int64)
marker_image[markers[:, 0], markers[:, 1]]  = np.arange(len(markers)) + 1

# Compute the distance map, which provides a "landscape" for our watershed
# segmentation
distance_map = ndimage.distance_transform_edt(1 - image)

# Compute the watershed segmentation; it will over-segment the image
filled = morphology.watershed(1 - distance_map, markers=marker_image)

# In the over-segmented image, combine touching regions
filled_connected = measure.label(filled != 1, background=0) + 1

# In this optional step, filter out all regions that are < 25% the size
# of the mean region area found
filled_regions = measure.regionprops(filled_connected)
mean_area = np.mean([r.area for r in filled_regions])
filled_filtered = filled_connected.copy()
for r in filled_regions:
    if r.area < 0.25 * mean_area:
        coords = np.array(r.coords).astype(int)
        filled_filtered[coords[:, 0], coords[:, 1]] = 0

# And display!
f, (ax0, ax1, ax2) = plt.subplots(1, 3)
ax0.imshow(image, cmap='gray')
ax1.imshow(filled_filtered, cmap='spectral')
ax2.imshow(distance_map, cmap='gray')
plt.savefig('/tmp/labeled_filled_regions.png', bbox_inches='tight')

Upvotes: 5

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