/
_clear_border.py
109 lines (96 loc) · 3.9 KB
/
_clear_border.py
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import numpy as np
from ..measure import label
def clear_border(labels, buffer_size=0, bgval=0, mask=None, *, out=None):
"""Clear objects connected to the label image border.
Parameters
----------
labels : (M[, N[, ..., P]]) array of int or bool
Imaging data labels.
buffer_size : int, optional
The width of the border examined. By default, only objects
that touch the outside of the image are removed.
bgval : float or int, optional
Cleared objects are set to this value.
mask : ndarray of bool, same shape as `image`, optional.
Image data mask. Objects in labels image overlapping with
False pixels of mask will be removed. If defined, the
argument buffer_size will be ignored.
out : ndarray
Array of the same shape as `labels`, into which the
output is placed. By default, a new array is created.
Returns
-------
out : (M[, N[, ..., P]]) array
Imaging data labels with cleared borders
Examples
--------
>>> import numpy as np
>>> from skimage.segmentation import clear_border
>>> labels = np.array([[0, 0, 0, 0, 0, 0, 0, 1, 0],
... [1, 1, 0, 0, 1, 0, 0, 1, 0],
... [1, 1, 0, 1, 0, 1, 0, 0, 0],
... [0, 0, 0, 1, 1, 1, 1, 0, 0],
... [0, 1, 1, 1, 1, 1, 1, 1, 0],
... [0, 0, 0, 0, 0, 0, 0, 0, 0]])
>>> clear_border(labels)
array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]])
>>> mask = np.array([[0, 0, 1, 1, 1, 1, 1, 1, 1],
... [0, 0, 1, 1, 1, 1, 1, 1, 1],
... [1, 1, 1, 1, 1, 1, 1, 1, 1],
... [1, 1, 1, 1, 1, 1, 1, 1, 1],
... [1, 1, 1, 1, 1, 1, 1, 1, 1],
... [1, 1, 1, 1, 1, 1, 1, 1, 1]]).astype(bool)
>>> clear_border(labels, mask=mask)
array([[0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0, 0, 1, 0],
[0, 0, 0, 1, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]])
"""
if any(buffer_size >= s for s in labels.shape) and mask is None:
# ignore buffer_size if mask
raise ValueError("buffer size may not be greater than labels size")
if out is None:
out = labels.copy()
if mask is not None:
err_msg = (
f'labels and mask should have the same shape but '
f'are {out.shape} and {mask.shape}'
)
if out.shape != mask.shape:
raise (ValueError, err_msg)
if mask.dtype != bool:
raise TypeError("mask should be of type bool.")
borders = ~mask
else:
# create borders with buffer_size
borders = np.zeros_like(out, dtype=bool)
ext = buffer_size + 1
slstart = slice(ext)
slend = slice(-ext, None)
slices = [slice(None) for _ in out.shape]
for d in range(out.ndim):
slices[d] = slstart
borders[tuple(slices)] = True
slices[d] = slend
borders[tuple(slices)] = True
slices[d] = slice(None)
# Re-label, in case we are dealing with a binary out
# and to get consistent labeling
labels, number = label(out, background=0, return_num=True)
# determine all objects that are connected to borders
borders_indices = np.unique(labels[borders])
indices = np.arange(number + 1)
# mask all label indices that are connected to borders
label_mask = np.isin(indices, borders_indices)
# create mask for pixels to clear
mask = label_mask[labels.reshape(-1)].reshape(labels.shape)
# clear border pixels
out[mask] = bgval
return out