mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 22:21:06 +08:00
emmanuelle
a5a771a8e4
background=0 by default. Modified label function so that background pixels are labeled with 0, and background=0 by default. All tests of _ccomp.pyx pass Modified a couple of files to be consistent with the new behavior of measure.label Modified doctring of label to pass doctest Modified TODO.txt as well as release notes to mention the new behavior of label. Typo in docstring Typo in docstring Changed default value of kw argument background in measure.label Removed unnecessary and outdated comment
80 lines
2.4 KiB
Python
80 lines
2.4 KiB
Python
import numpy as np
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from ..measure import label
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def clear_border(labels, buffer_size=0, bgval=0, in_place=False):
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"""Clear objects connected to the label image border.
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The changes will be applied directly to the input.
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Parameters
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----------
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labels : (N, M) array of int
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Label or binary image.
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buffer_size : int, optional
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The width of the border examined. By default, only objects
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that touch the outside of the image are removed.
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bgval : float or int, optional
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Cleared objects are set to this value.
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in_place : bool, optional
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Whether or not to manipulate the labels array in-place.
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Returns
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-------
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labels : (N, M) array
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Cleared binary image.
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Examples
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--------
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>>> import numpy as np
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>>> from skimage.segmentation import clear_border
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>>> labels = np.array([[0, 0, 0, 0, 0, 0, 0, 1, 0],
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... [0, 0, 0, 0, 1, 0, 0, 0, 0],
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... [1, 0, 0, 1, 0, 1, 0, 0, 0],
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... [0, 0, 1, 1, 1, 1, 1, 0, 0],
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... [0, 1, 1, 1, 1, 1, 1, 1, 0],
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... [0, 0, 0, 0, 0, 0, 0, 0, 0]])
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>>> clear_border(labels)
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array([[0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 1, 0, 0, 0, 0],
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[0, 0, 0, 1, 0, 1, 0, 0, 0],
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[0, 0, 1, 1, 1, 1, 1, 0, 0],
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[0, 1, 1, 1, 1, 1, 1, 1, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0]])
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"""
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image = labels
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rows, cols = image.shape
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if buffer_size >= rows or buffer_size >= cols:
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raise ValueError("buffer size may not be greater than image size")
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# create borders with buffer_size
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borders = np.zeros_like(image, dtype=np.bool_)
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ext = buffer_size + 1
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borders[:ext] = True
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borders[- ext:] = True
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borders[:, :ext] = True
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borders[:, - ext:] = True
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# Re-label, in case we are dealing with a binary image
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# and to get consistent labeling
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labels = label(image, background=0)
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number = np.max(labels) + 1
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# determine all objects that are connected to borders
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borders_indices = np.unique(labels[borders])
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indices = np.arange(number + 1)
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# mask all label indices that are connected to borders
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label_mask = np.in1d(indices, borders_indices)
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# create mask for pixels to clear
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mask = label_mask[labels.ravel()].reshape(labels.shape)
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if not in_place:
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image = image.copy()
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# clear border pixels
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image[mask] = bgval
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return image
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