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.
- 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
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]])