Flood Fill

Flood fill is an algorithm to identify and/or change adjacent values in an image based on their similarity to an initial seed point 1. The conceptual analogy is the ‘paint bucket’ tool in many graphic editors.



Basic example

First, a basic example where we will change a checkerboard square from white to mid-gray.

import numpy as np
import matplotlib.pyplot as plt
from skimage import data, filters
from skimage.segmentation import flood, flood_fill

checkers = data.checkerboard()

# Fill a square near the middle with value 127, starting at index (76, 76)
filled_checkers = flood_fill(checkers, (76, 76), 127)

fig, ax = plt.subplots(ncols=2, figsize=(10, 5))

ax[0].imshow(checkers, cmap=plt.cm.gray, interpolation='none')

ax[1].imshow(filled_checkers, cmap=plt.cm.gray, interpolation='none')
ax[1].plot(76, 76, 'wo')  # seed point
ax[1].set_title('After flood fill')


Advanced example

Because standard flood filling requires the neighbors to be strictly equal, its use is limited on real-world images with color gradients and noise. The tolerance keyword argument widens the permitted range about the initial value, allowing use on real-world images.

Here we will experiment a bit on the cameraman. First, turning his coat from dark to light.

cameraman = data.camera()

# Change the cameraman's coat from dark to light (255).  The seed point is
# chosen as (200, 100),
light_coat = flood_fill(cameraman, (200, 100), 255, tolerance=10)
fig, ax = plt.subplots(ncols=2, figsize=(10, 5))

ax[0].imshow(cameraman, cmap=plt.cm.gray)

ax[1].imshow(light_coat, cmap=plt.cm.gray)
ax[1].plot(100, 200, 'wo')  # seed point
ax[1].set_title('After flood fill')


Because the cameraman is dark haired it also changed his hair, as well as parts of the tripod.

Experimentation with tolerance

To get a better intuitive understanding of how the tolerance parameter works, here is a set of images progressively increasing the parameter with seed point in the upper left corner.

output = []

for i in range(8):
    tol = 5 + 20*i
    output.append(flood_fill(cameraman, (0, 0), 255, tolerance=tol))

# Initialize plot and place original image
fig, ax = plt.subplots(nrows=3, ncols=3, figsize=(12, 12))
ax[0, 0].imshow(cameraman, cmap=plt.cm.gray)
ax[0, 0].set_title('Original')
ax[0, 0].axis('off')

# Plot all eight different tolerances for comparison.
for i in range(8):
    m, n = np.unravel_index(i+1, (3, 3))
    ax[m, n].imshow(output[i], cmap=plt.cm.gray)
    ax[m, n].set_title('Tolerance {0}'.format(str(5 + 20*i)))
    ax[m, n].axis('off')
    ax[m, n].plot(0, 0, 'bo')  # seed point


Flood as mask

A sister function, flood, is available which returns a mask identifying the flood rather than modifying the image itself. This is useful for segmentation purposes and more advanced analysis pipelines.

Here we segment the nose of a cat. However, multi-channel images are not supported by flood[_fill]. Instead we Sobel filter the red channel to enhance edges, then flood the nose with a tolerance.

cat = data.chelsea()
cat_sobel = filters.sobel(cat[..., 0])
cat_nose = flood(cat_sobel, (240, 265), tolerance=0.03)

fig, ax = plt.subplots(nrows=3, figsize=(10, 20))


ax[1].set_title('Sobel filtered')

ax[2].imshow(cat_nose, cmap=plt.cm.gray, alpha=0.3)
ax[2].plot(265, 240, 'wo')  # seed point
ax[2].set_title('Nose segmented with `flood`')


Total running time of the script: ( 0 minutes 1.783 seconds)

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