107 lines
3.9 KiB
Python
107 lines
3.9 KiB
Python
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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, mask=None):
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"""Clear objects connected to the label image border.
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Parameters
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----------
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labels : (M[, N[, ..., P]]) array of int or bool
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Imaging data labels.
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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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mask : ndarray of bool, same shape as `image`, optional.
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Image data mask. Objects in labels image overlapping with
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False pixels of mask will be removed. If defined, the
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argument buffer_size will be ignored.
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Returns
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-------
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out : (M[, N[, ..., P]]) array
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Imaging data labels with cleared borders
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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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... [1, 1, 0, 0, 1, 0, 0, 1, 0],
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... [1, 1, 0, 1, 0, 1, 0, 0, 0],
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... [0, 0, 0, 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, 0, 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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>>> mask = np.array([[0, 0, 1, 1, 1, 1, 1, 1, 1],
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... [0, 0, 1, 1, 1, 1, 1, 1, 1],
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... [1, 1, 1, 1, 1, 1, 1, 1, 1],
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... [1, 1, 1, 1, 1, 1, 1, 1, 1],
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... [1, 1, 1, 1, 1, 1, 1, 1, 1],
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... [1, 1, 1, 1, 1, 1, 1, 1, 1]]).astype(np.bool)
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>>> clear_border(labels, mask=mask)
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array([[0, 0, 0, 0, 0, 0, 0, 1, 0],
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[0, 0, 0, 0, 1, 0, 0, 1, 0],
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[0, 0, 0, 1, 0, 1, 0, 0, 0],
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[0, 0, 0, 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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if any((buffer_size >= s for s in image.shape)) and mask is None:
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# ignore buffer_size if mask
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raise ValueError("buffer size may not be greater than image size")
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if mask is not None:
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err_msg = "image and mask should have the same shape but are {} and {}"
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assert image.shape == mask.shape, \
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err_msg.format(image.shape, mask.shape)
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if mask.dtype != np.bool_:
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raise TypeError("mask should be of type bool.")
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borders = ~mask
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else:
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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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slstart = slice(ext)
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slend = slice(-ext, None)
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slices = [slice(s) for s in image.shape]
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for d in range(image.ndim):
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slicedim = list(slices)
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slicedim[d] = slstart
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borders[tuple(slicedim)] = True
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slicedim[d] = slend
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borders[tuple(slicedim)] = 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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