scipy.ndimage.black_tophat#
- scipy.ndimage.black_tophat(input, size=None, footprint=None, structure=None, output=None, mode='reflect', cval=0.0, origin=0)[source]#
Multidimensional black tophat filter.
- Parameters
- inputarray_like
Input.
- sizetuple of ints, optional
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 non-infinite elements of a flat structuring element used for the black 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 0
- Returns
- black_tophatndarray
Result of the filter of input with structure.
See also
Examples
Change dark peak to bright peak and subtract background.
>>> from scipy.ndimage import generate_binary_structure, black_tophat >>> square = generate_binary_structure(rank=2, connectivity=3) >>> dark_on_gray = np.array([[7, 6, 6, 6, 7], ... [6, 5, 4, 5, 6], ... [6, 4, 0, 4, 6], ... [6, 5, 4, 5, 6], ... [7, 6, 6, 6, 7]]) >>> black_tophat(input=dark_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]])