scipy.ndimage.white_tophat

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

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.

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
>>> 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]])

scipy.ndimage.morphological_laplace Orthogonal distance regression (scipy.odr)