black_tophat#
- scipy.ndimage.black_tophat(input, size=None, footprint=None, structure=None, output=None, mode='reflect', cval=0.0, origin=0, *, axes=None)[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. 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 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:
- 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 >>> import numpy as np >>> 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]])