papyri
This is a pre-release version (2.0.0.dev0+git20260907.41eeb59).Go to latest (1.18.0)
{ } Raw JSON

bundles / scipy 2.0.0.dev0+git20260907.41eeb59 / scipy / ndimage / _morphology / black_tophat

function

scipy.ndimage._morphology:black_tophat

source: build-install/usr/lib/python3.14/site-packages/scipy/ndimage/_morphology.py:1902

Signature

def black_tophat(input, size = None, footprint = None, structure = None, output = None, mode = reflect, cval = 0.0, origin = 0, *, axes = None)

Summary

Multidimensional black tophat filter.

Parameters

input : array_like

Input.

size : tuple of ints, optional

Shape of a flat and full structuring element used for the filter. Optional if footprint or structure is provided.

footprint : array of ints, optional

Positions of non-infinite elements of a flat structuring element used for the black tophat filter.

structure : array 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)

output : array, 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'

cval : scalar, optional

Value to fill past edges of input if mode is 'constant'. Default is 0.0.

origin : scalar, optional

The origin parameter controls the placement of the filter. Default 0

axes : tuple 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_tophat : ndarray

Result of the filter of input with structure.

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]])

See also

grey_closing
grey_opening
white_tophat

Aliases

  • scipy.ndimage.black_tophat