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ddZdS )    N   )label)
remove_argin_placez1.0z Please use out argument instead.)changed_versionhelp_msgF)outc                   s  t  fdd| jD r|du rtd|dur"tj|| dd d}|s)|  }n|du r/| }|durTd|j d	|j }|j|jkrGt|f|jtkrPtd
| }n?tj	|td} d }t
|}	t
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dd |jD }t|jD ]}|	||< d|t|< |
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d||< qvt|ddd\} }t| | }t|d }t||}|| d | j}|||< |S )aQ	  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.
    in_place : bool, optional
        Whether or not to manipulate the labels array in-place.
        Deprecated since version 0.19. Please use `out` instead.
    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]])

    c                 3   s    | ]} |kV  qd S N ).0sbuffer_sizer
   I/tmp/pip-target-vg8gfxp4/lib/python/skimage/segmentation/_clear_border.py	<genexpr>F   s    zclear_border.<locals>.<genexpr>Nz/buffer size may not be greater than labels sizeno)castingTz3labels and mask should have the same shape but are z and zmask should be of type bool.)dtype   c                 S   s   g | ]}t d qS r	   )slice)r   _r
   r
   r   
<listcomp>a   s    z clear_border.<locals>.<listcomp>r   )
background
return_num)anyshape
ValueErrornpcopytocopyr   bool	TypeError
zeros_liker   rangendimtupler   uniquearangein1dreshape)labelsr   bgvalr   maskr   err_msgbordersextslstartslendslicesdnumberborders_indicesindices
label_maskr
   r   r   clear_border   sL    ?

r9   )r   r   FN)numpyr   measurer   _shared.utilsr   r9   r
   r
   r
   r   <module>   s    