/usr/lib/python3/dist-packages/photutils/segmentation/core.py is in python3-photutils 0.3-3.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import (absolute_import, division, print_function,
unicode_literals)
from copy import deepcopy
from distutils.version import LooseVersion
import numpy as np
from astropy.utils import lazyproperty
__all__ = ['SegmentationImage']
# outline_segments requires scikit-image >= 0.11
__doctest_skip__ = {'SegmentationImage.outline_segments'}
__doctest_requires__ = {('SegmentationImage', 'SegmentationImage.*'):
['scipy', 'skimage']}
class SegmentationImage(object):
"""
Class for a segmentation image.
Parameters
----------
data : array_like (int)
A 2D segmentation image where sources are labeled by different
positive integer values. A value of zero is reserved for the
background.
"""
def __init__(self, data):
self.data = np.asanyarray(data, dtype=np.int)
@property
def data(self):
"""
The 2D segmentation image.
"""
return self._data
@data.setter
def data(self, value):
if np.min(value) < 0:
raise ValueError('The segmentation image cannot contain '
'negative integers.')
self._data = value
# be sure to delete any lazy properties to reset their values.
del (self.data_masked, self.shape, self.labels, self.nlabels,
self.max, self.slices, self.areas, self.is_sequential)
@property
def array(self):
"""
The 2D segmentation image.
"""
return self._data
def __array__(self):
"""
Array representation of the segmentation image (e.g., for
matplotlib).
"""
return self._data
@lazyproperty
def data_masked(self):
"""
A `~numpy.ma.MaskedArray` version of the segmentation image
where the background (label = 0) has been masked.
"""
return np.ma.masked_where(self.data == 0, self.data)
@staticmethod
def _labels(data):
"""
Return a sorted array of the non-zero labels in the segmentation
image.
Parameters
----------
data : array_like (int)
A 2D segmentation image where sources are labeled by
different positive integer values. A value of zero is
reserved for the background.
Returns
-------
result : `~numpy.ndarray`
An array of non-zero label numbers.
Notes
-----
This is a separate static method so it can be used on masked
versions of the segmentation image (cf.
``~photutils.SegmentationImage.remove_masked_labels``.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm._labels(segm.data)
array([1, 3, 4, 5, 7])
"""
return np.unique(data[data != 0])
@lazyproperty
def shape(self):
"""
The shape of the 2D segmentation image.
"""
return self._data.shape
@lazyproperty
def labels(self):
"""The sorted non-zero labels in the segmentation image."""
return self._labels(self.data)
@lazyproperty
def nlabels(self):
"""The number of non-zero labels in the segmentation image."""
return len(self.labels)
@lazyproperty
def max(self):
"""The maximum non-zero label in the segmentation image."""
return np.max(self.data)
@lazyproperty
def slices(self):
"""The minimal bounding box slices for each labeled region."""
from scipy.ndimage import find_objects
return find_objects(self._data)
@lazyproperty
def areas(self):
"""The areas (in pixel**2) of all labeled regions."""
return np.bincount(self.data.ravel())
def area(self, labels):
"""
The areas (in pixel**2) of the regions for the input labels.
Parameters
----------
labels : int, array-like (1D, int)
The label(s) for which to return areas.
Returns
-------
areas : `~numpy.ndarray`
The areas of the labeled regions.
"""
labels = np.atleast_1d(labels)
for label in labels:
self.check_label(label, allow_zero=True)
return self.areas[labels]
@lazyproperty
def is_sequential(self):
"""
Determine whether or not the non-zero labels in the segmenation
image are sequential (with no missing values).
"""
if (self.labels[-1] - self.labels[0] + 1) == self.nlabels:
return True
else:
return False
def copy(self):
"""
Return a deep copy of this class instance.
Deep copy is used so that all attributes and values are copied.
"""
return deepcopy(self)
def check_label(self, label, allow_zero=False):
"""
Check for a valid label label number within the segmentation
image.
Parameters
----------
label : int
The label number to check.
allow_zero : bool
If `True` then a label of 0 is valid, otherwise 0 is
invalid.
Raises
------
ValueError
If the input ``label`` is invalid.
"""
if label == 0:
if allow_zero:
return
else:
raise ValueError('label "0" is reserved for the background')
if label < 0:
raise ValueError('label must be a positive integer, got '
'"{0}"'.format(label))
if label not in self.labels:
raise ValueError('label "{0}" is not in the segmentation '
'image'.format(label))
def outline_segments(self, mask_background=False):
"""
Outline the labeled segments.
The "outlines" represent the pixels *just inside* the segments,
leaving the background pixels unmodified. This corresponds to
the ``mode='inner'`` in `skimage.segmentation.find_boundaries`.
Parameters
----------
mask_background : bool, optional
Set to `True` to mask the background pixels (labels = 0) in
the returned image. This is useful for overplotting the
segment outlines on an image. The default is `False`.
Returns
-------
boundaries : 2D `~numpy.ndarray` or `~numpy.ma.MaskedArray`
An image with the same shape of the segmenation image
containing only the outlines of the labeled segments. The
pixel values in the outlines correspond to the labels in the
segmentation image. If ``mask_background`` is `True`, then
a `~numpy.ma.MaskedArray` is returned.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[0, 0, 0, 0, 0, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 2, 2, 2, 2, 0],
... [0, 0, 0, 0, 0, 0]])
>>> segm.outline_segments()
array([[0, 0, 0, 0, 0, 0],
[0, 2, 2, 2, 2, 0],
[0, 2, 0, 0, 2, 0],
[0, 2, 0, 0, 2, 0],
[0, 2, 2, 2, 2, 0],
[0, 0, 0, 0, 0, 0]])
"""
import skimage
if LooseVersion(skimage.__version__) < LooseVersion('0.11'):
raise ImportError('The outline_segments() function requires '
'scikit-image >= 0.11')
from skimage.segmentation import find_boundaries
outlines = self.data * find_boundaries(self.data, mode='inner')
if mask_background:
outlines = np.ma.masked_where(outlines == 0, outlines)
return outlines
def relabel(self, labels, new_label):
"""
Relabel one or more label numbers.
The input ``labels`` will all be relabeled to ``new_label``.
Parameters
----------
labels : int, array-like (1D, int)
The label numbers(s) to relabel.
new_label : int
The relabeled label number.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.relabel(labels=[1, 7], new_label=2)
>>> segm.data
array([[2, 2, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[2, 0, 0, 0, 0, 5],
[2, 2, 0, 5, 5, 5],
[2, 2, 0, 0, 5, 5]])
"""
labels = np.atleast_1d(labels)
for label in labels:
data = self.data
data[np.where(data == label)] = new_label
self.data = data # needed to call the data setter
def relabel_sequential(self, start_label=1):
"""
Relabel the label numbers sequentially, such that there are no
missing label numbers (up to the maximum label number).
Parameters
----------
start_label : int, optional
The starting label number, which should be a positive
integer. The default is 1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.relabel_sequential()
>>> segm.data
array([[1, 1, 0, 0, 3, 3],
[0, 0, 0, 0, 0, 3],
[0, 0, 2, 2, 0, 0],
[5, 0, 0, 0, 0, 4],
[5, 5, 0, 4, 4, 4],
[5, 5, 0, 0, 4, 4]])
"""
if start_label <= 0:
raise ValueError('start_label must be > 0.')
if self.is_sequential and (self.labels[0] == start_label):
return
forward_map = np.zeros(self.max + 1, dtype=np.int)
forward_map[self.labels] = np.arange(self.nlabels) + start_label
self.data = forward_map[self.data]
def keep_labels(self, labels, relabel=False):
"""
Keep only the specified label numbers.
Parameters
----------
labels : int, array-like (1D, int)
The label number(s) to keep. Labels of zero and those not
in the segmentation image will be ignored.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in sequential order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.keep_labels(labels=3)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.keep_labels(labels=[5, 3])
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[0, 0, 0, 0, 0, 5],
[0, 0, 0, 5, 5, 5],
[0, 0, 0, 0, 5, 5]])
"""
labels = np.atleast_1d(labels)
labels_tmp = list(set(self.labels) - set(labels))
self.remove_labels(labels_tmp, relabel=relabel)
def remove_labels(self, labels, relabel=False):
"""
Remove one or more label numbers.
Parameters
----------
labels : int, array-like (1D, int)
The label number(s) to remove. Labels of zero and those not
in the segmentation image will be ignored.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in sequential order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_labels(labels=5)
>>> segm.data
array([[1, 1, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 0],
[7, 7, 0, 0, 0, 0],
[7, 7, 0, 0, 0, 0]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_labels(labels=[5, 3])
>>> segm.data
array([[1, 1, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 0, 0, 0, 0],
[7, 0, 0, 0, 0, 0],
[7, 7, 0, 0, 0, 0],
[7, 7, 0, 0, 0, 0]])
"""
self.relabel(labels, new_label=0)
if relabel:
self.relabel_sequential()
def remove_border_labels(self, border_width, partial_overlap=True,
relabel=False):
"""
Remove labeled segments near the image border.
Labels within the defined border region will be removed.
Parameters
----------
border_width : int
The width of the border region in pixels.
partial_overlap : bool, optional
If this is set to `True` (the default), a segment that
partially extends into the border region will be removed.
Segments that are completely within the border region are
always removed.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in sequential order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_border_labels(border_width=1)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_border_labels(border_width=1,
... partial_overlap=False)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 5],
[7, 7, 0, 5, 5, 5],
[7, 7, 0, 0, 5, 5]])
"""
if border_width >= min(self.shape) / 2:
raise ValueError('border_width must be smaller than half the '
'image size in either dimension')
border = np.zeros(self.shape, dtype=np.bool)
border[:border_width, :] = True
border[-border_width:, :] = True
border[:, :border_width] = True
border[:, -border_width:] = True
self.remove_masked_labels(border, partial_overlap=partial_overlap,
relabel=relabel)
def remove_masked_labels(self, mask, partial_overlap=True,
relabel=False):
"""
Remove labeled segments located within a masked region.
Parameters
----------
mask : array_like (bool)
A boolean mask, with the same shape as the segmentation
image (``.data``), where `True` values indicate masked
pixels.
partial_overlap : bool, optional
If this is set to `True` (the default), a segment that
partially extends into a masked region will also be removed.
Segments that are completely within a masked region are
always removed.
relabel : bool, optional
If `True`, then the segmentation image will be relabeled
such that the labels are in sequential order starting from
1.
Examples
--------
>>> from photutils import SegmentationImage
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> mask = np.zeros_like(segm.data, dtype=np.bool)
>>> mask[0, :] = True # mask the first row
>>> segm.remove_masked_labels(mask)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 5],
[7, 7, 0, 5, 5, 5],
[7, 7, 0, 0, 5, 5]])
>>> segm = SegmentationImage([[1, 1, 0, 0, 4, 4],
... [0, 0, 0, 0, 0, 4],
... [0, 0, 3, 3, 0, 0],
... [7, 0, 0, 0, 0, 5],
... [7, 7, 0, 5, 5, 5],
... [7, 7, 0, 0, 5, 5]])
>>> segm.remove_masked_labels(mask, partial_overlap=False)
>>> segm.data
array([[0, 0, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[7, 0, 0, 0, 0, 5],
[7, 7, 0, 5, 5, 5],
[7, 7, 0, 0, 5, 5]])
"""
if mask.shape != self.shape:
raise ValueError('mask must have the same shape as the '
'segmentation image')
remove_labels = self._labels(self.data[mask])
if not partial_overlap:
interior_labels = self._labels(self.data[~mask])
remove_labels = list(set(remove_labels) - set(interior_labels))
self.remove_labels(remove_labels, relabel=relabel)
|