Fixed database typo and removed unnecessary class identifier.

venv/Lib/site-packages/skimage/segmentation/_clear_border.py

@@ -0,0 +1,106 @@
+import numpy as np
+from ..measure import label
+
+
+def clear_border(labels, buffer_size=0, bgval=0, in_place=False, mask=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.
+    in_place : bool, optional
+        Whether or not to manipulate the labels array in-place.
+    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.
+
+    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(np.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]])
+
+    """
+    image = labels
+
+    if any((buffer_size >= s for s in image.shape)) and mask is None:
+        # ignore buffer_size if mask
+        raise ValueError("buffer size may not be greater than image size")
+
+    if mask is not None:
+        err_msg = "image and mask should have the same shape but are {} and {}"
+        assert image.shape == mask.shape, \
+               err_msg.format(image.shape, mask.shape)
+        if mask.dtype != np.bool_:
+            raise TypeError("mask should be of type bool.")
+        borders = ~mask
+    else:
+        # create borders with buffer_size
+        borders = np.zeros_like(image, dtype=np.bool_)
+        ext = buffer_size + 1
+        slstart = slice(ext)
+        slend = slice(-ext, None)
+        slices = [slice(s) for s in image.shape]
+        for d in range(image.ndim):
+            slicedim = list(slices)
+            slicedim[d] = slstart
+            borders[tuple(slicedim)] = True
+            slicedim[d] = slend
+            borders[tuple(slicedim)] = True
+    # Re-label, in case we are dealing with a binary image
+    # and to get consistent labeling
+    labels = label(image, background=0)
+    number = np.max(labels) + 1
+
+    # 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.in1d(indices, borders_indices)
+    # create mask for pixels to clear
+    mask = label_mask[labels.ravel()].reshape(labels.shape)
+
+    if not in_place:
+        image = image.copy()
+
+    # clear border pixels
+    image[mask] = bgval
+
+    return image