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b62898a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 | import numpy as np
from scipy import ndimage as ndi
from .._shared.utils import _supported_float_type
from ..morphology import dilation, erosion, footprint_rectangle
from ..util import img_as_float, view_as_windows
from ..color import gray2rgb
def _find_boundaries_subpixel(label_img):
"""See ``find_boundaries(..., mode='subpixel')``.
Notes
-----
This function puts in an empty row and column between each *actual*
row and column of the image, for a corresponding shape of ``2s - 1``
for every image dimension of size ``s``. These "interstitial" rows
and columns are filled as ``True`` if they separate two labels in
`label_img`, ``False`` otherwise.
I used ``view_as_windows`` to get the neighborhood of each pixel.
Then I check whether there are two labels or more in that
neighborhood.
"""
ndim = label_img.ndim
max_label = np.iinfo(label_img.dtype).max
label_img_expanded = np.zeros(
[(2 * s - 1) for s in label_img.shape], label_img.dtype
)
pixels = (slice(None, None, 2),) * ndim
label_img_expanded[pixels] = label_img
edges = np.ones(label_img_expanded.shape, dtype=bool)
edges[pixels] = False
label_img_expanded[edges] = max_label
windows = view_as_windows(np.pad(label_img_expanded, 1, mode='edge'), (3,) * ndim)
boundaries = np.zeros_like(edges)
for index in np.ndindex(label_img_expanded.shape):
if edges[index]:
values = np.unique(windows[index].ravel())
if len(values) > 2: # single value and max_label
boundaries[index] = True
return boundaries
def find_boundaries(label_img, connectivity=1, mode='thick', background=0):
"""Return bool array where boundaries between labeled regions are True.
Parameters
----------
label_img : array of int or bool
An array in which different regions are labeled with either different
integers or boolean values.
connectivity : int in {1, ..., `label_img.ndim`}, optional
A pixel is considered a boundary pixel if any of its neighbors
has a different label. `connectivity` controls which pixels are
considered neighbors. A connectivity of 1 (default) means
pixels sharing an edge (in 2D) or a face (in 3D) will be
considered neighbors. A connectivity of `label_img.ndim` means
pixels sharing a corner will be considered neighbors.
mode : string in {'thick', 'inner', 'outer', 'subpixel'}
How to mark the boundaries:
- thick: any pixel not completely surrounded by pixels of the
same label (defined by `connectivity`) is marked as a boundary.
This results in boundaries that are 2 pixels thick.
- inner: outline the pixels *just inside* of objects, leaving
background pixels untouched.
- outer: outline pixels in the background around object
boundaries. When two objects touch, their boundary is also
marked.
- subpixel: return a doubled image, with pixels *between* the
original pixels marked as boundary where appropriate.
background : int, optional
For modes 'inner' and 'outer', a definition of a background
label is required. See `mode` for descriptions of these two.
Returns
-------
boundaries : array of bool, same shape as `label_img`
A bool image where ``True`` represents a boundary pixel. For
`mode` equal to 'subpixel', ``boundaries.shape[i]`` is equal
to ``2 * label_img.shape[i] - 1`` for all ``i`` (a pixel is
inserted in between all other pairs of pixels).
Examples
--------
>>> labels = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
... [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
... [0, 0, 0, 0, 0, 5, 5, 5, 0, 0],
... [0, 0, 1, 1, 1, 5, 5, 5, 0, 0],
... [0, 0, 1, 1, 1, 5, 5, 5, 0, 0],
... [0, 0, 1, 1, 1, 5, 5, 5, 0, 0],
... [0, 0, 0, 0, 0, 5, 5, 5, 0, 0],
... [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
... [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8)
>>> find_boundaries(labels, mode='thick').astype(np.uint8)
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 0, 1, 1, 0],
[0, 1, 1, 0, 1, 1, 0, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 0, 1, 1, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
>>> find_boundaries(labels, mode='inner').astype(np.uint8)
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 0, 1, 0, 0],
[0, 0, 1, 0, 1, 1, 0, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
>>> find_boundaries(labels, mode='outer').astype(np.uint8)
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 0, 0, 1, 0],
[0, 1, 0, 0, 1, 1, 0, 0, 1, 0],
[0, 1, 0, 0, 1, 1, 0, 0, 1, 0],
[0, 1, 0, 0, 1, 1, 0, 0, 1, 0],
[0, 0, 1, 1, 1, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
>>> labels_small = labels[::2, ::3]
>>> labels_small
array([[0, 0, 0, 0],
[0, 0, 5, 0],
[0, 1, 5, 0],
[0, 0, 5, 0],
[0, 0, 0, 0]], dtype=uint8)
>>> find_boundaries(labels_small, mode='subpixel').astype(np.uint8)
array([[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 0],
[0, 0, 0, 1, 0, 1, 0],
[0, 1, 1, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0],
[0, 1, 1, 1, 0, 1, 0],
[0, 0, 0, 1, 0, 1, 0],
[0, 0, 0, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
>>> bool_image = np.array([[False, False, False, False, False],
... [False, False, False, False, False],
... [False, False, True, True, True],
... [False, False, True, True, True],
... [False, False, True, True, True]],
... dtype=bool)
>>> find_boundaries(bool_image)
array([[False, False, False, False, False],
[False, False, True, True, True],
[False, True, True, True, True],
[False, True, True, False, False],
[False, True, True, False, False]])
"""
if label_img.dtype == 'bool':
label_img = label_img.astype(np.uint8)
ndim = label_img.ndim
footprint = ndi.generate_binary_structure(ndim, connectivity)
if mode != 'subpixel':
boundaries = dilation(label_img, footprint) != erosion(label_img, footprint)
if mode == 'inner':
foreground_image = label_img != background
boundaries &= foreground_image
elif mode == 'outer':
max_label = np.iinfo(label_img.dtype).max
background_image = label_img == background
footprint = ndi.generate_binary_structure(ndim, ndim)
inverted_background = np.array(label_img, copy=True)
inverted_background[background_image] = max_label
adjacent_objects = (
dilation(label_img, footprint)
!= erosion(inverted_background, footprint)
) & ~background_image
boundaries &= background_image | adjacent_objects
return boundaries
else:
boundaries = _find_boundaries_subpixel(label_img)
return boundaries
def mark_boundaries(
image,
label_img,
color=(1, 1, 0),
outline_color=None,
mode='outer',
background_label=0,
):
"""Return image with boundaries between labeled regions highlighted.
Parameters
----------
image : (M, N[, 3]) array
Grayscale or RGB image.
label_img : (M, N) array of int
Label array where regions are marked by different integer values.
color : length-3 sequence, optional
RGB color of boundaries in the output image.
outline_color : length-3 sequence, optional
RGB color surrounding boundaries in the output image. If None, no
outline is drawn.
mode : string in {'thick', 'inner', 'outer', 'subpixel'}, optional
The mode for finding boundaries.
background_label : int, optional
Which label to consider background (this is only useful for
modes ``inner`` and ``outer``).
Returns
-------
marked : (M, N, 3) array of float
An image in which the boundaries between labels are
superimposed on the original image.
See Also
--------
find_boundaries
"""
float_dtype = _supported_float_type(image.dtype)
marked = img_as_float(image, force_copy=True)
marked = marked.astype(float_dtype, copy=False)
if marked.ndim == 2:
marked = gray2rgb(marked)
if mode == 'subpixel':
# Here, we want to interpose an extra line of pixels between
# each original line - except for the last axis which holds
# the RGB information. ``ndi.zoom`` then performs the (cubic)
# interpolation, filling in the values of the interposed pixels
marked = ndi.zoom(
marked, [2 - 1 / s for s in marked.shape[:-1]] + [1], mode='mirror'
)
boundaries = find_boundaries(label_img, mode=mode, background=background_label)
if outline_color is not None:
outlines = dilation(boundaries, footprint_rectangle((3, 3)))
marked[outlines] = outline_color
marked[boundaries] = color
return marked
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