scipy.ndimage.

white_tophat#

scipy.ndimage.white_tophat(input, size=None, footprint=None, structure=None, output=None, mode='reflect', cval=0.0, origin=0, *, axes=None)[source]#

Multidimensional white tophat filter.

Parameters:

inputarray_like: Input.
sizetuple of ints: Shape of a flat and full structuring element used for the filter. Optional if footprint or structure is provided.
footprintarray of ints, optional: Positions of elements of a flat structuring element used for the white tophat filter.
structurearray of ints, optional: Structuring element used for the filter. structure may be a non-flat structuring element. The structure array applies offsets to the pixels in a neighborhood (the offset is additive during dilation and subtractive during erosion)
outputarray, optional: An array used for storing the output of the filter may be provided.
mode{‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’}, optional: The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’
cvalscalar, optional: Value to fill past edges of input if mode is ‘constant’. Default is 0.0.
originscalar, optional: The origin parameter controls the placement of the filter. Default is 0.
axestuple of int or None: The axes over which to apply the filter. If None, input is filtered along all axes. If an origin tuple is provided, its length must match the number of axes.

Returns:

outputndarray: Result of the filter of input with structure.

See also

black_tophat

Examples

Subtract gray background from a bright peak.

>>> from scipy.ndimage import generate_binary_structure, white_tophat
>>> import numpy as np
>>> square = generate_binary_structure(rank=2, connectivity=3)
>>> bright_on_gray = np.array([[2, 3, 3, 3, 2],
...                            [3, 4, 5, 4, 3],
...                            [3, 5, 9, 5, 3],
...                            [3, 4, 5, 4, 3],
...                            [2, 3, 3, 3, 2]])
>>> white_tophat(input=bright_on_gray, structure=square)
array([[0, 0, 0, 0, 0],
       [0, 0, 1, 0, 0],
       [0, 1, 5, 1, 0],
       [0, 0, 1, 0, 0],
       [0, 0, 0, 0, 0]])