scipy.ndimage.

white_tophat#

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

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]])