skimage2.morphology.flood#

skimage2.morphology.flood(image, seed_point, *, footprint=None, connectivity=None, tolerance=None)[source]#

Mask corresponding to a flood fill.

Starting at a specific seed_point, connected points equal or within tolerance of the seed value are found.

Parameters:

imagendarray: An n-dimensional array.
seed_pointtuple or int: The point in image used as the starting point for the flood fill. If the image is 1D, this point may be given as an integer.
footprintndarray, optional: The footprint (structuring element) used to determine the neighborhood of each evaluated pixel. It must contain only 1’s and 0’s, have the same number of dimensions as image. If not given, all adjacent pixels are considered as part of the neighborhood (fully connected).
connectivityint, optional: A number used to determine the neighborhood of each evaluated pixel. Adjacent pixels whose squared distance from the center is less than or equal to connectivity are considered neighbors. Ignored if footprint is not None.
tolerancefloat or int, optional: If None (default), adjacent values must be strictly equal to the initial value of image at seed_point. This is fastest. If a value is given, a comparison will be done at every point and if within tolerance of the initial value will also be filled (inclusive).

Returns:

maskndarray: A Boolean array with the same shape as image is returned, with True values for areas connected to and equal (or within tolerance of) the seed point. All other values are False.

Notes

The conceptual analogy of this operation is the ‘paint bucket’ tool in many raster graphics programs. This function returns just the mask representing the fill.

If indices are desired rather than masks for memory reasons, the user can simply run numpy.nonzero on the result, save the indices, and discard this mask.

Examples

>>> from _skimage2.morphology import flood
>>> image = np.zeros((4, 7), dtype=int)
>>> image[1:3, 1:3] = 1
>>> image[3, 0] = 1
>>> image[1:3, 4:6] = 2
>>> image[3, 6] = 3
>>> image
array([[0, 0, 0, 0, 0, 0, 0],
       [0, 1, 1, 0, 2, 2, 0],
       [0, 1, 1, 0, 2, 2, 0],
       [1, 0, 0, 0, 0, 0, 3]])

Fill connected ones with 5, with full connectivity (diagonals included):

>>> mask = flood(image, (1, 1))
>>> image_flooded = image.copy()
>>> image_flooded[mask] = 5
>>> image_flooded
array([[0, 0, 0, 0, 0, 0, 0],
       [0, 5, 5, 0, 2, 2, 0],
       [0, 5, 5, 0, 2, 2, 0],
       [5, 0, 0, 0, 0, 0, 3]])

Fill connected ones with 5, excluding diagonal points (connectivity 1):

>>> mask = flood(image, (1, 1), connectivity=1)
>>> image_flooded = image.copy()
>>> image_flooded[mask] = 5
>>> image_flooded
array([[0, 0, 0, 0, 0, 0, 0],
       [0, 5, 5, 0, 2, 2, 0],
       [0, 5, 5, 0, 2, 2, 0],
       [1, 0, 0, 0, 0, 0, 3]])

Fill with a tolerance:

>>> mask = flood(image, (0, 0), tolerance=1)
>>> image_flooded = image.copy()
>>> image_flooded[mask] = 5
>>> image_flooded
array([[5, 5, 5, 5, 5, 5, 5],
       [5, 5, 5, 5, 2, 2, 5],
       [5, 5, 5, 5, 2, 2, 5],
       [5, 5, 5, 5, 5, 5, 3]])