`skimage.morphology._flood_fill:flood_fill`

source: /dev/scikit-image/src/skimage/morphology/_flood_fill.py :19

Signature

   def     flood_fill (    image    ,    seed_point    ,    new_value    ,  * ,     footprint    =  None   ,    connectivity    =  None   ,    tolerance    =  None   ,    in_place    =  False     )

Summary

Perform flood filling on an image.

Extended Summary

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

Parameters

image : ndarray: An n-dimensional array.
seed_point : tuple 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.
new_value : `image` type: New value to set the entire fill. This must be chosen in agreement with the dtype of image.
footprint : ndarray, 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).
connectivity : int, 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.
tolerance : float or int, optional: If None (default), adjacent values must be strictly equal to the value of image at seed_point to be filled. This is fastest. If a tolerance is provided, adjacent points with values within plus or minus tolerance from the seed point are filled (inclusive).
in_place : bool, optional: If True, flood filling is applied to image in place. If False, the flood filled result is returned without modifying the input image (default).

Returns

filled : ndarray: An array with the same shape as image is returned, with values in areas connected to and equal (or within tolerance of) the seed point replaced with new_value.

Notes

The conceptual analogy of this operation is the 'paint bucket' tool in many raster graphics programs.

Examples

from skimage.morphology import flood_fill
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

✓

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

flood_fill(image, (1, 1), 5)

✓

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

flood_fill(image, (1, 1), 5, connectivity=1)

✓

Fill with a tolerance:

flood_fill(image, (0, 0), 5, tolerance=1)

✓

Aliases

skimage.morphology.flood_fill