232 lines
10 KiB
Python
232 lines
10 KiB
Python
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import numpy as np
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from scipy import ndimage as ndi
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from ..morphology import dilation, erosion, square
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from ..util import img_as_float, view_as_windows
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from ..color import gray2rgb
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def _find_boundaries_subpixel(label_img):
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"""See ``find_boundaries(..., mode='subpixel')``.
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Notes
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-----
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This function puts in an empty row and column between each *actual*
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row and column of the image, for a corresponding shape of ``2s - 1``
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for every image dimension of size ``s``. These "interstitial" rows
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and columns are filled as ``True`` if they separate two labels in
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`label_img`, ``False`` otherwise.
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I used ``view_as_windows`` to get the neighborhood of each pixel.
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Then I check whether there are two labels or more in that
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neighborhood.
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"""
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ndim = label_img.ndim
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max_label = np.iinfo(label_img.dtype).max
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label_img_expanded = np.zeros([(2 * s - 1) for s in label_img.shape],
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label_img.dtype)
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pixels = (slice(None, None, 2), ) * ndim
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label_img_expanded[pixels] = label_img
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edges = np.ones(label_img_expanded.shape, dtype=bool)
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edges[pixels] = False
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label_img_expanded[edges] = max_label
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windows = view_as_windows(np.pad(label_img_expanded, 1,
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mode='constant', constant_values=0),
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(3,) * ndim)
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boundaries = np.zeros_like(edges)
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for index in np.ndindex(label_img_expanded.shape):
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if edges[index]:
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values = np.unique(windows[index].ravel())
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if len(values) > 2: # single value and max_label
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boundaries[index] = True
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return boundaries
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def find_boundaries(label_img, connectivity=1, mode='thick', background=0):
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"""Return bool array where boundaries between labeled regions are True.
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Parameters
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----------
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label_img : array of int or bool
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An array in which different regions are labeled with either different
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integers or boolean values.
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connectivity : int in {1, ..., `label_img.ndim`}, optional
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A pixel is considered a boundary pixel if any of its neighbors
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has a different label. `connectivity` controls which pixels are
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considered neighbors. A connectivity of 1 (default) means
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pixels sharing an edge (in 2D) or a face (in 3D) will be
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considered neighbors. A connectivity of `label_img.ndim` means
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pixels sharing a corner will be considered neighbors.
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mode : string in {'thick', 'inner', 'outer', 'subpixel'}
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How to mark the boundaries:
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- thick: any pixel not completely surrounded by pixels of the
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same label (defined by `connectivity`) is marked as a boundary.
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This results in boundaries that are 2 pixels thick.
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- inner: outline the pixels *just inside* of objects, leaving
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background pixels untouched.
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- outer: outline pixels in the background around object
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boundaries. When two objects touch, their boundary is also
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marked.
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- subpixel: return a doubled image, with pixels *between* the
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original pixels marked as boundary where appropriate.
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background : int, optional
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For modes 'inner' and 'outer', a definition of a background
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label is required. See `mode` for descriptions of these two.
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Returns
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-------
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boundaries : array of bool, same shape as `label_img`
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A bool image where ``True`` represents a boundary pixel. For
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`mode` equal to 'subpixel', ``boundaries.shape[i]`` is equal
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to ``2 * label_img.shape[i] - 1`` for all ``i`` (a pixel is
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inserted in between all other pairs of pixels).
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Examples
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--------
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>>> labels = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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... [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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... [0, 0, 0, 0, 0, 5, 5, 5, 0, 0],
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... [0, 0, 1, 1, 1, 5, 5, 5, 0, 0],
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... [0, 0, 1, 1, 1, 5, 5, 5, 0, 0],
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... [0, 0, 1, 1, 1, 5, 5, 5, 0, 0],
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... [0, 0, 0, 0, 0, 5, 5, 5, 0, 0],
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... [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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... [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8)
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>>> find_boundaries(labels, mode='thick').astype(np.uint8)
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array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
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[0, 1, 1, 1, 1, 1, 0, 1, 1, 0],
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[0, 1, 1, 0, 1, 1, 0, 1, 1, 0],
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[0, 1, 1, 1, 1, 1, 0, 1, 1, 0],
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[0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
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[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
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>>> find_boundaries(labels, mode='inner').astype(np.uint8)
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array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 1, 0, 1, 0, 0],
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[0, 0, 1, 0, 1, 1, 0, 1, 0, 0],
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[0, 0, 1, 1, 1, 1, 0, 1, 0, 0],
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[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
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>>> find_boundaries(labels, mode='outer').astype(np.uint8)
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array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
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[0, 0, 1, 1, 1, 1, 0, 0, 1, 0],
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[0, 1, 0, 0, 1, 1, 0, 0, 1, 0],
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[0, 1, 0, 0, 1, 1, 0, 0, 1, 0],
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[0, 1, 0, 0, 1, 1, 0, 0, 1, 0],
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[0, 0, 1, 1, 1, 1, 0, 0, 1, 0],
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[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
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>>> labels_small = labels[::2, ::3]
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>>> labels_small
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array([[0, 0, 0, 0],
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[0, 0, 5, 0],
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[0, 1, 5, 0],
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[0, 0, 5, 0],
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[0, 0, 0, 0]], dtype=uint8)
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>>> find_boundaries(labels_small, mode='subpixel').astype(np.uint8)
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array([[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 1, 1, 1, 0],
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[0, 0, 0, 1, 0, 1, 0],
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[0, 1, 1, 1, 0, 1, 0],
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[0, 1, 0, 1, 0, 1, 0],
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[0, 1, 1, 1, 0, 1, 0],
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[0, 0, 0, 1, 0, 1, 0],
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[0, 0, 0, 1, 1, 1, 0],
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[0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
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>>> bool_image = np.array([[False, False, False, False, False],
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... [False, False, False, False, False],
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... [False, False, True, True, True],
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... [False, False, True, True, True],
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... [False, False, True, True, True]],
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... dtype=bool)
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>>> find_boundaries(bool_image)
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array([[False, False, False, False, False],
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[False, False, True, True, True],
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[False, True, True, True, True],
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[False, True, True, False, False],
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[False, True, True, False, False]])
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"""
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if label_img.dtype == 'bool':
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label_img = label_img.astype(np.uint8)
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ndim = label_img.ndim
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selem = ndi.generate_binary_structure(ndim, connectivity)
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if mode != 'subpixel':
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boundaries = dilation(label_img, selem) != erosion(label_img, selem)
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if mode == 'inner':
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foreground_image = (label_img != background)
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boundaries &= foreground_image
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elif mode == 'outer':
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max_label = np.iinfo(label_img.dtype).max
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background_image = (label_img == background)
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selem = ndi.generate_binary_structure(ndim, ndim)
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inverted_background = np.array(label_img, copy=True)
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inverted_background[background_image] = max_label
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adjacent_objects = ((dilation(label_img, selem) !=
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erosion(inverted_background, selem)) &
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~background_image)
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boundaries &= (background_image | adjacent_objects)
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return boundaries
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else:
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boundaries = _find_boundaries_subpixel(label_img)
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return boundaries
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def mark_boundaries(image, label_img, color=(1, 1, 0),
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outline_color=None, mode='outer', background_label=0):
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"""Return image with boundaries between labeled regions highlighted.
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Parameters
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----------
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image : (M, N[, 3]) array
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Grayscale or RGB image.
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label_img : (M, N) array of int
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Label array where regions are marked by different integer values.
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color : length-3 sequence, optional
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RGB color of boundaries in the output image.
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outline_color : length-3 sequence, optional
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RGB color surrounding boundaries in the output image. If None, no
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outline is drawn.
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mode : string in {'thick', 'inner', 'outer', 'subpixel'}, optional
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The mode for finding boundaries.
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background_label : int, optional
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Which label to consider background (this is only useful for
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modes ``inner`` and ``outer``).
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Returns
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-------
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marked : (M, N, 3) array of float
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An image in which the boundaries between labels are
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superimposed on the original image.
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See Also
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--------
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find_boundaries
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"""
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marked = img_as_float(image, force_copy=True)
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if marked.ndim == 2:
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marked = gray2rgb(marked)
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if mode == 'subpixel':
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# Here, we want to interpose an extra line of pixels between
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# each original line - except for the last axis which holds
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# the RGB information. ``ndi.zoom`` then performs the (cubic)
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# interpolation, filling in the values of the interposed pixels
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marked = ndi.zoom(marked, [2 - 1/s for s in marked.shape[:-1]] + [1],
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mode='mirror')
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boundaries = find_boundaries(label_img, mode=mode,
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background=background_label)
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if outline_color is not None:
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outlines = dilation(boundaries, square(3))
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marked[outlines] = outline_color
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marked[boundaries] = color
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return marked
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