# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Classes for a segmentation image and a single segment within a
segmentation image.
"""
import inspect
import warnings
from collections import defaultdict
from copy import copy, deepcopy
import numpy as np
from astropy.utils import lazyproperty
from astropy.utils.exceptions import AstropyUserWarning
from scipy.ndimage import find_objects, grey_dilation
from scipy.signal import fftconvolve
from photutils.aperture import BoundingBox
from photutils.aperture.converters import _shapely_polygon_to_region
from photutils.utils._deprecation import (deprecated_getattr,
deprecated_positional_kwargs)
from photutils.utils._optional_deps import HAS_RASTERIO, HAS_SHAPELY
from photutils.utils._parameters import as_pair
from photutils.utils.colormaps import make_random_cmap
__all__ = ['Segment', 'SegmentationImage']
# Remove in 4.0
_SEGM_DEPRECATED_ATTRIBUTES = {
'nlabels': 'n_labels',
'data_ma': 'data_masked',
'deblended_labels_map': 'deblended_label_to_parent',
'deblended_labels_inverse_map': 'parent_to_deblended_labels',
}
# Remove in 4.0
_SEGMENT_DEPRECATED_ATTRIBUTES = {
'data_ma': 'data_masked',
}
[docs]
class SegmentationImage:
"""
Class for a segmentation image.
Parameters
----------
data : 2D int `~numpy.ndarray`
A 2D segmentation array where source regions are labeled by
different positive integer values. A value of zero is reserved
for the background. The segmentation image must have integer
type.
Notes
-----
The `SegmentationImage` instance may be sliced, but note that the
sliced `SegmentationImage` data array will be a view into the
original `SegmentationImage` array (this is the same behavior as
`~numpy.ndarray`). Explicitly use the :meth:`SegmentationImage.copy`
method to create a copy of the sliced `SegmentationImage`.
"""
def __init__(self, data):
if not isinstance(data, np.ndarray):
msg = 'Input data must be a numpy array'
raise TypeError(msg)
self.data = data
self._deblend_label_map = {} # set by source deblender
def __str__(self):
cls_name = f'<{self.__class__.__module__}.{self.__class__.__name__}>'
params = ['shape', 'n_labels']
cls_info = [(param, getattr(self, param)) for param in params]
cls_info.append(('labels', self.labels))
with np.printoptions(threshold=25, edgeitems=5):
fmt = [f'{key}: {val}' for key, val in cls_info]
return f'{cls_name}\n' + '\n'.join(fmt)
def __repr__(self):
return self.__str__()
# Remove in 4.0
def __getattr__(self, name):
return deprecated_getattr(self, name, _SEGM_DEPRECATED_ATTRIBUTES,
since='3.0', until='4.0')
def __getitem__(self, key):
"""
Slice the segmentation image, returning a new SegmentationImage
object.
"""
if (isinstance(key, tuple) and len(key) == 2
and all(isinstance(key[i], slice) for i in (0, 1))):
result = self.data[key]
if result.size == 0:
msg = ('The sliced result is empty; cannot create '
'a SegmentationImage with zero size')
raise ValueError(msg)
return SegmentationImage(result)
msg = f'{key!r} is not a valid 2D slice object'
raise TypeError(msg)
def __array__(self):
"""
Array representation of the segmentation array (e.g., for
matplotlib).
"""
return self._data
@staticmethod
def _get_labels(data):
"""
Return a sorted array of the non-zero labels in the segmentation
image.
Parameters
----------
data : array_like (int)
A segmentation array where source regions 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 static method so it can be used in
:meth:`remove_masked_labels` on a masked version of the
segmentation array.
"""
# np.unique preserves dtype and also sorts elements
return np.unique(data[data != 0])
@lazyproperty
def segments(self):
"""
A list of `Segment` objects.
The list starts with the *non-zero* label. The returned list has
a length equal to the number of labels and matches the order of
the ``labels`` attribute.
"""
segments = []
if HAS_RASTERIO and HAS_SHAPELY:
for label, slc, bbox, area, polygon in zip(self.labels,
self.slices,
self.bbox,
self.areas,
self.polygons,
strict=True):
segments.append(Segment(self.data, label, slc, bbox, area,
polygon=polygon))
else:
for label, slc, bbox, area in zip(self.labels, self.slices,
self.bbox, self.areas,
strict=True):
segments.append(Segment(self.data, label, slc, bbox, area))
return segments
@lazyproperty
def deblended_labels(self):
"""
A sorted 1D array of deblended label numbers.
The list will be empty if deblending has not been performed or
if no sources were deblended.
"""
if len(self._deblend_label_map) == 0:
return np.array([], dtype=self._data.dtype)
return np.sort(np.concatenate(list(self._deblend_label_map.values())))
@lazyproperty
def deblended_label_to_parent(self):
"""
A dictionary mapping deblended label numbers to the original
parent label numbers.
The keys are the deblended label numbers and the values are the
original parent label numbers. Only deblended sources are
included in the dictionary.
The dictionary will be empty if deblending has not been
performed or if no sources were deblended.
"""
inverse_map = {}
for key, values in self._deblend_label_map.items():
for value in values:
inverse_map[value] = key
return inverse_map
@lazyproperty
def parent_to_deblended_labels(self):
"""
A dictionary mapping the original parent label numbers to the
deblended label numbers.
The keys are the original parent label numbers and the values
are the deblended label numbers. Only deblended sources are
included in the dictionary.
The dictionary will be empty if deblending has not been
performed or if no sources were deblended.
"""
return self._deblend_label_map
@property
def data(self):
"""
The segmentation array.
"""
return self._data
@property
def _lazyproperties(self):
"""
A list of all class lazyproperties (even in superclasses).
The result is cached on the class to avoid repeated
introspection via `inspect.getmembers`.
"""
cls = self.__class__
attr = '_cached_lazyproperties'
# Subclasses get their own lazyproperty list
if attr not in cls.__dict__:
def islazyproperty(obj):
return isinstance(obj, lazyproperty)
setattr(cls, attr,
[i[0] for i in inspect.getmembers(
cls, predicate=islazyproperty)])
return getattr(cls, attr)
def _reset_lazyproperties(self):
for key in self._lazyproperties:
self.__dict__.pop(key, None)
@data.setter
def data(self, value):
if not np.issubdtype(value.dtype, np.integer):
msg = 'data must have integer type'
raise TypeError(msg)
labels = self._get_labels(value) # array([]) if value all zeros
if labels.shape != (0,) and np.min(labels) < 0:
msg = 'The segmentation image cannot contain negative integers.'
raise ValueError(msg)
if '_data' in self.__dict__:
# Reset cached properties when data is reassigned, but not on init
self._reset_lazyproperties()
self._data = value # pylint: disable=attribute-defined-outside-init
self.__dict__['labels'] = labels
# Reset deblended labels explicitly since _deblend_label_map
# is a regular attribute, not a lazyproperty cleared by
# _reset_lazyproperties above.
self.__dict__['_deblend_label_map'] = {}
@lazyproperty
def data_masked(self):
"""
A `~numpy.ma.MaskedArray` version of the segmentation array
where the background (label = 0) has been masked.
"""
return np.ma.masked_where(self.data == 0, self.data)
@lazyproperty
def shape(self):
"""
The shape of the segmentation array.
"""
return self._data.shape
@lazyproperty
def _ndim(self):
"""
The number of array dimensions of the segmentation array.
"""
return self._data.ndim
@lazyproperty
def labels(self):
"""
The sorted non-zero labels in the segmentation array.
"""
if '_raw_slices' in self.__dict__:
labels_all = np.arange(len(self._raw_slices)) + 1
labels = []
# If a label is missing, raw_slices will be None instead of a slice
for label, slc in zip(labels_all, self._raw_slices, strict=True):
if slc is not None:
labels.append(label)
return np.array(labels, dtype=self._data.dtype)
return self._get_labels(self.data)
@lazyproperty
def n_labels(self):
"""
The number of non-zero labels in the segmentation array.
"""
return len(self.labels)
@lazyproperty
def max_label(self):
"""
The maximum label in the segmentation array.
"""
if self.n_labels == 0:
return 0
return np.max(self.labels)
[docs]
def get_index(self, label):
"""
Find the index of the input ``label``.
Parameters
----------
label : int
The label number to find.
Returns
-------
index : int
The array index.
Raises
------
ValueError
If ``label`` is invalid.
"""
self.check_labels(label)
# self.labels is always sorted
return np.searchsorted(self.labels, label)
[docs]
def get_indices(self, labels):
"""
Find the indices of the input ``labels``.
Parameters
----------
labels : int, array_like (1D, int)
The label numbers(s) to find.
Returns
-------
indices : int `~numpy.ndarray`
An integer array of indices with the same shape as
``labels``. If ``labels`` is a scalar, then the returned
index will also be a scalar.
Raises
------
ValueError
If any input ``labels`` are invalid.
"""
self.check_labels(labels)
# self.labels is always sorted
return np.searchsorted(self.labels, labels)
@lazyproperty
def _raw_slices(self):
"""
A list of tuples, where each tuple contains two slices representing
the minimal box that contains the labeled region.
The list starts with the *non-zero* label. The returned list has
a length equal to the maximum label number and is indexed by
(label - 1). If a label is missing, then the corresponding list
element will be `None` instead of a slice.
"""
return find_objects(self.data)
@lazyproperty
def slices(self):
"""
A list of tuples, where each tuple contains two slices
representing the minimal box that contains the labeled region.
The list starts with the *non-zero* label. The returned list has
a length equal to the number of labels and matches the order of
the ``labels`` attribute.
"""
return [slc for slc in self._raw_slices if slc is not None]
@lazyproperty
def bbox(self):
"""
A list of `~photutils.aperture.BoundingBox` of the minimal
bounding boxes containing the labeled regions.
"""
if self._ndim != 2:
msg = "The 'bbox' attribute requires a 2D segmentation image."
raise ValueError(msg)
return [BoundingBox(ixmin=slc[1].start, ixmax=slc[1].stop,
iymin=slc[0].start, iymax=slc[0].stop)
for slc in self.slices]
@lazyproperty
def background_area(self):
"""
The area (in pixel**2) of the background (label=0) region.
"""
return self._data.size - np.count_nonzero(self._data)
@lazyproperty
def areas(self):
"""
A 1D array of areas (in pixel**2) of the non-zero labeled
regions.
The `~numpy.ndarray` starts with the *non-zero* label. The
returned array has a length equal to the number of labels and
matches the order of the ``labels`` attribute.
"""
# NOTE: np.bincount was benchmarked but is slower for typical
# large images because its cost is O(total_pixels) whereas the
# per-bbox loop below is O(sum_of_bbox_areas), which is much
# smaller when segments occupy a small fraction of the image.
areas = []
for label, slices in zip(self.labels, self.slices, strict=True):
areas.append(np.count_nonzero(self._data[slices] == label))
return np.array(areas)
[docs]
def get_area(self, label):
"""
The area (in pixel**2) of the region for the input label.
Parameters
----------
label : int
The label whose area to return. Label must be non-zero.
Returns
-------
area : float
The area of the labeled region.
"""
return self.get_areas(label)[0]
[docs]
def get_areas(self, labels):
"""
The areas (in pixel**2) of the regions for the input labels.
Parameters
----------
labels : int, 1D array_like (int)
The label(s) for which to return areas. Label must be
non-zero.
Returns
-------
areas : `~numpy.ndarray`
The areas of the labeled regions.
"""
idx = self.get_indices(np.atleast_1d(labels))
return self.areas[idx]
def _make_polygon(self, label, slc):
"""
Create a Shapely polygon for a single label using only its
bounding-box cutout.
Parameters
----------
label : int
The label number.
slc : tuple of slices
The slice for the bounding box of the label.
Returns
-------
polygon : `shapely.Polygon` or `shapely.MultiPolygon` or `None`
A Shapely Polygon or MultiPolygon, or `None` if rasterio and
shapely are not available.
"""
if not (HAS_RASTERIO and HAS_SHAPELY):
return None
if slc is None:
return None
from rasterio.features import shapes
from rasterio.transform import Affine
from shapely import MultiPolygon
from shapely.geometry import shape
cutout = self._data[slc]
# Create a mask for only this label within the cutout
label_mask = (cutout == label)
# Shift the vertices so that the (0, 0) origin is at the
# center of the lower-left pixel, offset by the slice origin
y0 = slc[0].start
x0 = slc[1].start
transform = Affine(1.0, 0.0, x0 - 0.5, 0.0, 1.0, y0 - 0.5)
# Create a single-label array for the cutout
label_data = np.where(label_mask, label, 0).astype(np.int32)
raw_polys = list(shapes(label_data, connectivity=8,
mask=label_mask, transform=transform))
geo_polys = [poly for poly, val in raw_polys if int(val) == label]
if len(geo_polys) == 0:
return None
if len(geo_polys) == 1:
return shape(geo_polys[0])
return MultiPolygon([shape(poly) for poly in geo_polys])
def _make_segment(self, label):
"""
Create a single `Segment` object for the given label.
Parameters
----------
label : int
The label number.
Returns
-------
segment : `Segment`
The segment object.
"""
# _raw_slices is indexed by (label - 1) since it includes all
# labels up to max_label, even if some are missing
label = self._data.dtype.type(label)
slc = self._raw_slices[label - 1]
bbox = BoundingBox(ixmin=slc[1].start, ixmax=slc[1].stop,
iymin=slc[0].start, iymax=slc[0].stop)
area = np.count_nonzero(self._data[slc] == label)
polygon = self._make_polygon(label, slc)
return Segment(self.data, label, slc, bbox, area, polygon=polygon)
[docs]
def get_segment(self, label):
"""
Return a `Segment` object for the given label.
This is significantly faster than ``segments[index]`` for
segmentation images with many labels because it constructs only
the requested `Segment` without building the full list.
Parameters
----------
label : int
The segment label number.
Returns
-------
segment : `Segment`
The segment object for the input label.
Raises
------
TypeError
If ``label`` is not a scalar.
ValueError
If ``label`` is invalid.
"""
if np.ndim(label) != 0:
msg = 'label must be a scalar value'
raise TypeError(msg)
self.check_labels(label)
return self._make_segment(label)
[docs]
def get_segments(self, labels):
"""
Return a list of `Segment` objects for the given labels.
This is significantly faster than indexing into ``segments``
when only a subset of labels is needed because it constructs
only the requested `Segment` objects without building the full
list.
Parameters
----------
labels : int, array_like (1D, int)
The label number(s) for which to return `Segment` objects.
Returns
-------
segments : list of `Segment`
A list of `Segment` objects in the same order as the input
``labels``.
Raises
------
ValueError
If any input ``labels`` are invalid.
"""
labels = np.atleast_1d(labels)
self.check_labels(labels)
return [self._make_segment(label) for label in labels]
@lazyproperty
def is_consecutive(self):
"""
Boolean value indicating whether the non-zero labels in the
segmentation array are consecutive and start from 1.
"""
if self.n_labels == 0:
return False
return ((self.labels[-1] - self.labels[0] + 1) == self.n_labels
and self.labels[0] == 1)
@lazyproperty
def missing_labels(self):
"""
A 1D `~numpy.ndarray` of the sorted non-zero labels that are
missing in the consecutive sequence from one to the maximum
label number.
"""
if self.n_labels == 0:
return np.array([], dtype=self._data.dtype)
present = np.zeros(self.max_label + 1, dtype=bool)
present[self.labels] = True
present[0] = True # exclude 0 from missing
return np.where(~present)[0].astype(self._data.dtype)
[docs]
def copy(self):
"""
Return a deep copy of this object.
Returns
-------
result : `SegmentationImage`
A deep copy of this object.
"""
return deepcopy(self)
[docs]
def check_label(self, label):
"""
Check that the input label is a valid label number within the
segmentation array.
Parameters
----------
label : int
The label number to check.
Raises
------
ValueError
If the input ``label`` is invalid.
"""
self.check_labels(label)
[docs]
def check_labels(self, labels):
"""
Check that the input label(s) are valid label numbers within the
segmentation array.
Parameters
----------
labels : int, 1D array_like (int)
The label(s) to check.
Raises
------
ValueError
If any input ``labels`` are invalid.
"""
labels = np.atleast_1d(labels)
bad_labels = set()
# Check if label is in the segmentation array
valid_mask = np.isin(labels, self.labels)
bad_labels.update(labels[~valid_mask])
if bad_labels:
bad_labels = sorted(bad_labels)
label_str = 'label'
conj_str = 'is'
if len(bad_labels) > 1:
label_str = 'labels'
conj_str = 'are'
msg = f'{label_str} {bad_labels} {conj_str} invalid'
raise ValueError(msg)
def _make_cmap(self, n_colors, *, background_color='#000000ff',
seed=None):
"""
Define a matplotlib colormap consisting of (random) muted
colors.
This is useful for plotting the segmentation array.
Parameters
----------
n_colors : int
The number of the colors in the colormap.
background_color : Matplotlib color, optional
The color of the first color in the colormap.
The color may be specified using any of
the `Matplotlib color formats
<https://matplotlib.org/stable/tutorials/colors/colors.html>`_.
This color will be used as the background color (label = 0)
when plotting the segmentation image. The default color is
black with alpha=1.0 ('#000000ff').
seed : int, optional
A seed to initialize the `numpy.random.BitGenerator`. If
`None`, then fresh, unpredictable entropy will be pulled
from the OS. Separate function calls with the same ``seed``
will generate the same colormap.
Returns
-------
cmap : `matplotlib.colors.ListedColormap`
The matplotlib colormap with colors in RGBA format.
"""
if self.n_labels == 0:
return None
from matplotlib import colors
cmap = make_random_cmap(n_colors=n_colors, seed=seed)
if background_color is not None:
cmap.colors[0] = colors.to_rgba(background_color)
return cmap
[docs]
@deprecated_positional_kwargs(since='3.0', until='4.0')
def make_cmap(self, background_color='#000000ff', seed=None):
"""
Define a matplotlib colormap consisting of (random) muted
colors.
This is useful for plotting the segmentation array.
Parameters
----------
background_color : Matplotlib color, optional
The color of the first color in the colormap.
The color may be specified using any of
the `Matplotlib color formats
<https://matplotlib.org/stable/tutorials/colors/colors.html>`_.
This color will be used as the background color (label = 0)
when plotting the segmentation image. The default color is
black with alpha=1.0 ('#000000ff').
seed : int, optional
A seed to initialize the `numpy.random.BitGenerator`. If
`None`, then fresh, unpredictable entropy will be pulled
from the OS. Separate function calls with the same ``seed``
will generate the same colormap.
Returns
-------
cmap : `matplotlib.colors.ListedColormap`
The matplotlib colormap with colors in RGBA format.
"""
return self._make_cmap(self.max_label + 1,
background_color=background_color,
seed=seed)
[docs]
@deprecated_positional_kwargs(since='3.0', until='4.0')
def reset_cmap(self, seed=None):
"""
Reset the colormap (`cmap` attribute) to a new random colormap.
Parameters
----------
seed : int, optional
A seed to initialize the `numpy.random.BitGenerator`. If
`None`, then fresh, unpredictable entropy will be pulled
from the OS. Separate function calls with the same ``seed``
will generate the same colormap.
"""
self.cmap = self.make_cmap(background_color='#000000ff', seed=seed)
@lazyproperty
def cmap(self):
"""
A matplotlib colormap consisting of (random) muted colors.
This is useful for plotting the segmentation array.
"""
return self.make_cmap(background_color='#000000ff', seed=0)
def _update_deblend_label_map(self, relabel_map):
"""
Update the deblended label map based on the input
``relabel_map``.
Parameters
----------
relabel_map : `~numpy.ndarray`
An array mapping the original label numbers to the new label
numbers.
"""
# child_labels are the deblended labels
for parent_label, child_labels in self._deblend_label_map.items():
self._deblend_label_map[parent_label] = relabel_map[child_labels]
[docs]
@deprecated_positional_kwargs(since='3.0', until='4.0')
def reassign_label(self, label, new_label, relabel=False):
"""
Reassign a label number to a new number.
If ``new_label`` is already present in the segmentation array,
then it will be combined with the input ``label`` number.
Note that this can result in a label that is no longer pixel
connected.
Parameters
----------
label : int
The label number to reassign.
new_label : int
The newly assigned label number.
relabel : bool, optional
If `True`, then the segmentation array will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils.segmentation import SegmentationImage
>>> data = np.array([[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 = SegmentationImage(data)
>>> segm.reassign_label(label=1, new_label=2)
>>> segm.data
array([[2, 2, 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]])
>>> data = np.array([[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 = SegmentationImage(data)
>>> segm.reassign_label(label=1, new_label=4)
>>> segm.data
array([[4, 4, 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]])
>>> data = np.array([[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 = SegmentationImage(data)
>>> segm.reassign_label(label=1, new_label=4, relabel=True)
>>> segm.data
array([[2, 2, 0, 0, 2, 2],
[0, 0, 0, 0, 0, 2],
[0, 0, 1, 1, 0, 0],
[4, 0, 0, 0, 0, 3],
[4, 4, 0, 3, 3, 3],
[4, 4, 0, 0, 3, 3]])
"""
self.reassign_labels(label, new_label, relabel=relabel)
[docs]
@deprecated_positional_kwargs(since='3.0', until='4.0')
def reassign_labels(self, labels, new_label, relabel=False):
"""
Reassign one or more label numbers.
Multiple input ``labels`` will all be reassigned to the same
``new_label`` number. If ``new_label`` is already present in
the segmentation array, then it will be combined with the input
``labels``. Note that both of these can result in a label that
is no longer pixel connected.
Parameters
----------
labels : int, array_like (1D, int)
The label numbers(s) to reassign.
new_label : int
The reassigned label number.
relabel : bool, optional
If `True`, then the segmentation array will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils.segmentation import SegmentationImage
>>> data = np.array([[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 = SegmentationImage(data)
>>> segm.reassign_labels(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]])
>>> data = np.array([[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 = SegmentationImage(data)
>>> segm.reassign_labels(labels=[1, 7], new_label=4)
>>> segm.data
array([[4, 4, 0, 0, 4, 4],
[0, 0, 0, 0, 0, 4],
[0, 0, 3, 3, 0, 0],
[4, 0, 0, 0, 0, 5],
[4, 4, 0, 5, 5, 5],
[4, 4, 0, 0, 5, 5]])
>>> data = np.array([[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 = SegmentationImage(data)
>>> segm.reassign_labels(labels=[1, 7], new_label=2, relabel=True)
>>> segm.data
array([[1, 1, 0, 0, 3, 3],
[0, 0, 0, 0, 0, 3],
[0, 0, 2, 2, 0, 0],
[1, 0, 0, 0, 0, 4],
[1, 1, 0, 4, 4, 4],
[1, 1, 0, 0, 4, 4]])
"""
self.check_labels(labels)
labels = np.atleast_1d(labels)
if labels.size == 0:
return
dtype = self.data.dtype # keep the original dtype
relabel_map = np.zeros(self.max_label + 1, dtype=dtype)
relabel_map[self.labels] = self.labels
relabel_map[labels] = new_label # reassign labels
if relabel:
labels = np.unique(relabel_map[relabel_map != 0])
if len(labels) != 0:
map2 = np.zeros(max(labels) + 1, dtype=dtype)
map2[labels] = np.arange(len(labels), dtype=dtype) + 1
relabel_map = map2[relabel_map]
data_new = relabel_map[self.data]
self._reset_lazyproperties() # reset all cached properties
self._data = data_new # use _data to avoid validation
self._update_deblend_label_map(relabel_map)
[docs]
@deprecated_positional_kwargs(since='3.0', until='4.0')
def relabel_consecutive(self, start_label=1):
"""
Reassign the label numbers consecutively starting from a given
label number.
Parameters
----------
start_label : int, optional
The starting label number, which should be a strictly
positive integer. The default is 1.
Examples
--------
>>> from photutils.segmentation import SegmentationImage
>>> data = np.array([[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 = SegmentationImage(data)
>>> segm.relabel_consecutive()
>>> 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 self.n_labels == 0:
msg = 'Cannot relabel a segmentation image with no non-zero labels'
warnings.warn(msg, AstropyUserWarning)
return
if start_label <= 0:
msg = 'start_label must be > 0'
raise ValueError(msg)
if ((self.labels[0] == start_label)
and (self.labels[-1] - self.labels[0] + 1) == self.n_labels):
return
old_slices = self.__dict__.get('slices', None)
dtype = self.data.dtype # keep the original dtype
new_labels = np.arange(self.n_labels, dtype=dtype) + start_label
new_label_map = np.zeros(self.max_label + 1, dtype=dtype)
new_label_map[self.labels] = new_labels
data_new = new_label_map[self.data]
self._reset_lazyproperties() # reset all cached properties
self._data = data_new # use _data to avoid validation
self.__dict__['labels'] = new_labels
if old_slices is not None:
self.__dict__['slices'] = old_slices # slice order is unchanged
self._update_deblend_label_map(new_label_map)
[docs]
@deprecated_positional_kwargs(since='3.0', until='4.0')
def keep_label(self, label, relabel=False):
"""
Keep only the specified label.
Parameters
----------
label : int
The label number to keep.
relabel : bool, optional
If `True`, then the single segment will be assigned a label
value of 1.
Examples
--------
>>> from photutils.segmentation import SegmentationImage
>>> data = np.array([[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 = SegmentationImage(data)
>>> segm.keep_label(label=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]])
>>> data = np.array([[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 = SegmentationImage(data)
>>> segm.keep_label(label=3, relabel=True)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]])
"""
self.keep_labels(label, relabel=relabel)
[docs]
@deprecated_positional_kwargs(since='3.0', until='4.0')
def keep_labels(self, labels, relabel=False):
"""
Keep only the specified labels.
Parameters
----------
labels : int, array_like (1D, int)
The label number(s) to keep.
relabel : bool, optional
If `True`, then the segmentation array will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils.segmentation import SegmentationImage
>>> data = np.array([[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 = SegmentationImage(data)
>>> 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]])
>>> data = np.array([[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 = SegmentationImage(data)
>>> segm.keep_labels(labels=[5, 3], relabel=True)
>>> segm.data
array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 2],
[0, 0, 0, 2, 2, 2],
[0, 0, 0, 0, 2, 2]])
"""
self.check_labels(labels)
labels = np.atleast_1d(labels)
labels_tmp = np.setdiff1d(self.labels, labels)
self.remove_labels(labels_tmp, relabel=relabel)
[docs]
@deprecated_positional_kwargs(since='3.0', until='4.0')
def remove_label(self, label, relabel=False):
"""
Remove the label number.
The removed label is assigned a value of zero (i.e.,
background).
Parameters
----------
label : int
The label number to remove.
relabel : bool, optional
If `True`, then the segmentation array will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils.segmentation import SegmentationImage
>>> data = np.array([[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 = SegmentationImage(data)
>>> segm.remove_label(label=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]])
>>> data = np.array([[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 = SegmentationImage(data)
>>> segm.remove_label(label=5, relabel=True)
>>> segm.data
array([[1, 1, 0, 0, 3, 3],
[0, 0, 0, 0, 0, 3],
[0, 0, 2, 2, 0, 0],
[4, 0, 0, 0, 0, 0],
[4, 4, 0, 0, 0, 0],
[4, 4, 0, 0, 0, 0]])
"""
self.remove_labels(label, relabel=relabel)
[docs]
@deprecated_positional_kwargs(since='3.0', until='4.0')
def remove_labels(self, labels, relabel=False):
"""
Remove one or more labels.
Removed labels are assigned a value of zero (i.e., background).
Parameters
----------
labels : int, array_like (1D, int)
The label number(s) to remove.
relabel : bool, optional
If `True`, then the segmentation array will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils.segmentation import SegmentationImage
>>> data = np.array([[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 = SegmentationImage(data)
>>> 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]])
>>> data = np.array([[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 = SegmentationImage(data)
>>> segm.remove_labels(labels=[5, 3], relabel=True)
>>> segm.data
array([[1, 1, 0, 0, 2, 2],
[0, 0, 0, 0, 0, 2],
[0, 0, 0, 0, 0, 0],
[3, 0, 0, 0, 0, 0],
[3, 3, 0, 0, 0, 0],
[3, 3, 0, 0, 0, 0]])
"""
self.check_labels(labels)
self.reassign_labels(labels, new_label=0, relabel=relabel)
[docs]
@deprecated_positional_kwargs(since='3.0', until='4.0')
def remove_border_labels(self, border_width, partial_overlap=True,
relabel=False):
"""
Remove labeled segments near the array 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 array will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils.segmentation import SegmentationImage
>>> data = np.array([[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 = SegmentationImage(data)
>>> 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]])
>>> data = np.array([[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 = SegmentationImage(data)
>>> 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:
msg = ('border_width must be smaller than half the array size '
'in any dimension')
raise ValueError(msg)
border_mask = np.zeros(self.shape, dtype=bool)
for i in range(border_mask.ndim):
border_mask = border_mask.swapaxes(0, i)
border_mask[:border_width] = True
border_mask[-border_width:] = True
border_mask = border_mask.swapaxes(0, i)
self.remove_masked_labels(border_mask,
partial_overlap=partial_overlap,
relabel=relabel)
[docs]
@deprecated_positional_kwargs(since='3.0', until='4.0')
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
array, where `True` values indicate masked pixels.
partial_overlap : bool, optional
If this is set to `True` (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 array will be relabeled
such that the labels are in consecutive order starting from
1.
Examples
--------
>>> from photutils.segmentation import SegmentationImage
>>> data = np.array([[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 = SegmentationImage(data)
>>> mask = np.zeros(segm.data.shape, dtype=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]])
>>> data = np.array([[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 = SegmentationImage(data)
>>> 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:
msg = 'mask must have the same shape as the segmentation array'
raise ValueError(msg)
remove_labels = self._get_labels(self.data[mask])
if not partial_overlap:
interior_labels = self._get_labels(self.data[~mask])
remove_labels = list(set(remove_labels) - set(interior_labels))
self.remove_labels(remove_labels, relabel=relabel)
[docs]
def make_source_mask(self, *, size=None, footprint=None):
"""
Make a source mask from the segmentation image.
Use the ``size`` or ``footprint`` keyword to perform binary
dilation on the segmentation image mask.
Parameters
----------
size : int or tuple of int, optional
The size along each axis of the rectangular footprint used
for the source dilation. If ``size`` is a scalar, then a
square footprint of ``size`` will be used. If ``size`` has
two elements, they must be in ``(ny, nx)`` order. ``size``
should have odd values for each axis. To perform source
dilation, either ``size`` or ``footprint`` must be defined.
If they are both defined, then ``footprint`` overrides
``size``.
footprint : 2D `~numpy.ndarray`, optional
The local footprint used for the source dilation. Non-zero
elements are considered `True`. ``size=(n, m)`` is
equivalent to ``footprint=np.ones((n, m))``. To perform
source dilation, either ``size`` or ``footprint`` must
be defined. If they are both defined, then ``footprint``
overrides ``size``.
Returns
-------
mask : 2D bool `~numpy.ndarray`
A 2D boolean image containing the source mask.
Notes
-----
When performing source dilation, using a square footprint
will be much faster than using other shapes (e.g., a circular
footprint). Source dilation also is slower for larger images and
larger footprints.
Examples
--------
>>> import numpy as np
>>> from photutils.segmentation import SegmentationImage
>>> from photutils.utils import circular_footprint
>>> data = np.zeros((7, 7), dtype=int)
>>> data[3, 3] = 1
>>> segm = SegmentationImage(data)
>>> segm.data
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, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]])
>>> mask0 = segm.make_source_mask()
>>> mask0
array([[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, True, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False]])
>>> mask1 = segm.make_source_mask(size=3)
>>> mask1
array([[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, True, True, True, False, False],
[False, False, True, True, True, False, False],
[False, False, True, True, True, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False]])
>>> footprint = circular_footprint(radius=3)
>>> mask2 = segm.make_source_mask(footprint=footprint)
>>> mask2
array([[False, False, False, True, False, False, False],
[False, True, True, True, True, True, False],
[False, True, True, True, True, True, False],
[ True, True, True, True, True, True, True],
[False, True, True, True, True, True, False],
[False, True, True, True, True, True, False],
[False, False, False, True, False, False, False]])
"""
mask = self._data.astype(bool)
if footprint is None:
if size is None:
return mask
size = as_pair('size', size, check_odd=False)
footprint = np.ones(size, dtype=bool)
footprint = footprint.astype(bool)
if np.all(footprint):
# With a rectangular footprint, scipy's grey_dilation is
# currently much faster than binary_dilation (separable
# footprint). grey_dilation and binary_dilation are identical
# for binary inputs (equivalent to a 2D maximum filter).
return grey_dilation(mask, footprint=footprint)
# Binary dilation is very slow, especially for large
# footprints. The following is a faster implementation
# using fast Fourier transforms (FFTs) that gives identical
# results to binary_dilation. Based on the following paper:
# "Dilation and Erosion of Gray Images with Spherical
# Masks", J. Kukal, D. Majerova, A. Prochazka (Jan 2007).
# https://www.researchgate.net/publication/238778666_DILATION_AND_EROSION_OF_GRAY_IMAGES_WITH_SPHERICAL_MASKS
return fftconvolve(mask, footprint, 'same') > 0.5
@lazyproperty
def _geojson_polygons(self):
"""
A dictionary of GeoJSON-like polygons representing each source
segment.
The keys are the unique label numbers in the segmentation image,
and the values are lists of polygons for each label.
Each item in the dictionary is list containing tuples of
(polygon, value) where the polygon is a GeoJSON-like dict and
the value is the label from the segmentation image. Non-
contiguous segments for a single label will have multiple tuples
in the list (e.g., from slicing the segmentation image where a
segment label is split into non-contiguous segments). Segments
with holes will have a single tuple with a polygon containing
the outer ring and the inner rings (holes) as a list of lists.
Note that the coordinates of these polygon vertices are
transformed to a reference frame with the (0, 0) origin at the
center of the lower-left pixel. This is done by shifting the
vertices by 0.5 pixels in both x and y directions, so that the
origin is at the center of the lower-left pixel. By default,
rasterio and GeoJSON use the corner of the lower-left pixel as
the origin, which is not compatible with the pixel coordinates
used in Photutils.
"""
from rasterio.features import shapes
from rasterio.transform import Affine
rasterio_int_dtypes = {np.dtype('uint8'), np.dtype('int8'),
np.dtype('uint16'), np.dtype('int16'),
np.dtype('int32')}
# Try to convert the data to int32 if it has an unsupported
# dtype
if self.data.dtype not in rasterio_int_dtypes:
min_val, max_val = self.data.min(), self.data.max()
int32_info = np.iinfo(np.int32)
if min_val >= int32_info.min and max_val <= int32_info.max:
dtype = np.int32
else:
msg = (f'The segmentation image dtype is {self.data.dtype} '
'with values outside the safe np.int32 range '
f'[{int32_info.min}, {int32_info.max}]. The rasterio '
'library cannot create polygons in this case. You may '
'try to relabel your data to fit within an int32 '
'range.')
raise ValueError(msg)
else:
dtype = self.data.dtype
# Shift the vertices so that the (0, 0) origin is at the
# center of the lower-left pixel
transform = Affine(1.0, 0.0, -0.5, 0.0, 1.0, -0.5)
mask = self.data > 0 # mask out the background pixels
polygons = list(shapes(self.data.astype(dtype), connectivity=8,
mask=mask, transform=transform))
polygons.sort(key=lambda x: x[1]) # sort in label order
# Group polygons by label
polygon_dict = defaultdict(list)
for polygon, label in polygons:
polygon_dict[int(label)].append(polygon)
# Check that the polygon labels match the segmentation image
# labels; this is a sanity check to ensure that the rasterio
# library is working correctly.
# Note that polygons have been sorted by label.
if not np.all(np.array(list(polygon_dict.keys())) == self.labels):
msg = ('The segmentation image labels do not match the '
'polygon labels. This may be due to a bug in the '
'rasterio library or an unexpected data type in the '
'segmentation image.')
raise ValueError(msg)
return polygon_dict
@lazyproperty
def polygons(self):
"""
A list of `Shapely <https://shapely.readthedocs.io/en/stable/>`_
polygons representing each source segment.
Polygon or MultiPolygon objects are returned, depending on
whether the source segment is a single polygon or multiple
polygons (e.g., holes or non-contiguous) for the same label.
"""
from shapely import MultiPolygon
from shapely.geometry import shape
polygons = []
for label, geo_polys in self._geojson_polygons.items():
if len(geo_polys) == 0:
msg = f'Could not create a polygon for label {label}'
raise ValueError(msg)
if len(geo_polys) == 1:
polygons.append(shape(geo_polys[0]))
elif len(geo_polys) > 1:
# Merge multiple polygons for the same label
polys = [shape(poly) for poly in geo_polys]
polygons.append(MultiPolygon(polys))
# NOTE: the returned polygons may return False for
# is_valid due to ring self-intersections (e.g.,
# for corner-only intersections of two pixels). The
# shapely.validation.explain_validity function can be
# used to explain the validity of the polygons. The
# shapely.validation.make_valid function can be used to make the
# polygons valid, usually by converting Polygon objects into
# MultiPolyon objects.
return polygons
[docs]
def get_polygon(self, label):
"""
Return the `Shapely
<https://shapely.readthedocs.io/en/stable/>`_ polygon for the
given label.
Parameters
----------
label : int
The label number.
Returns
-------
polygon : `shapely.Polygon` or `shapely.MultiPolygon` or `None`
A Shapely Polygon or MultiPolygon object, or `None` if
rasterio and shapely are not available.
Raises
------
TypeError
If ``label`` is not a scalar.
ValueError
If ``label`` is invalid.
"""
if np.ndim(label) != 0:
msg = 'label must be a scalar value'
raise TypeError(msg)
return self.get_polygons(label)[0]
[docs]
def get_polygons(self, labels):
"""
Return a list of `Shapely
<https://shapely.readthedocs.io/en/stable/>`_ polygons for the
given labels.
Parameters
----------
labels : int, array_like (1D, int)
The label number(s).
Returns
-------
polygons : list of `shapely.Polygon`, `shapely.MultiPolygon`, \
or `None`
A list of Shapely Polygon or MultiPolygon objects, or `None`
elements if rasterio and shapely are not available.
Raises
------
ValueError
If any input ``labels`` are invalid.
"""
labels = np.atleast_1d(labels)
self.check_labels(labels)
return [self._make_polygon(label, self._raw_slices[label - 1])
for label in labels]
@staticmethod
def _convert_ring_to_path(ring):
"""
Helper function to process a single Shapely ring (exterior or
interior) into vertices and Matplotlib path codes.
"""
from matplotlib import path
coords = np.array(ring.coords)
# A closed polygon path in Matplotlib starts with MOVETO,
# is followed by LINETO for each subsequent vertex,
# and ends with a CLOSEPOLY.
codes = ([path.Path.MOVETO] + [path.Path.LINETO] * (len(coords) - 2)
+ [path.Path.CLOSEPOLY])
return coords, codes
def _convert_shapely_to_pathpatch(self, geometry, *, origin=(0, 0),
scale=1.0, **kwargs):
"""
Create a single Matplotlib PathPatch from a Shapely geometry.
Parameters
----------
geometry : `shapely.geometry.base.BaseGeometry`
The Shapely geometry to convert to a PathPatch.
**kwargs : dict, optional
Any keyword arguments accepted by
`matplotlib.patches.PathPatch`.
Returns
-------
patch : `matplotlib.patches.PathPatch` or `None`
A Matplotlib PathPatch representing the geometry, or `None`
if the geometry is empty.
"""
from matplotlib import path
from matplotlib.patches import PathPatch
if geometry.is_empty:
return None
if geometry.geom_type == 'Polygon':
polygons = [geometry]
else:
polygons = list(geometry.geoms)
all_vertices = []
all_codes = []
for poly in polygons:
# For each polygon, process its exterior and all its
# interior rings. This loop structure avoids repeating the
# call to the helper function.
for ring in [poly.exterior, *list(poly.interiors)]:
vertices, codes = self._convert_ring_to_path(ring)
vertices = scale * (vertices + 0.5) - 0.5
vertices -= origin
all_vertices.append(vertices)
all_codes.extend(codes)
if not all_vertices:
return None
final_path = path.Path(np.concatenate(all_vertices), all_codes)
return PathPatch(final_path, **kwargs)
[docs]
def to_patches(self, *, origin=(0, 0), scale=1.0, **kwargs):
"""
Return a list of `~matplotlib.patches.PathPatch` objects
representing each source segment.
By default, the patch will have a white edge color and no face
color.
Parameters
----------
origin : array_like, optional
The ``(x, y)`` position of the origin of the displayed
image. This effectively translates the position of the
polygons.
scale : float, optional
The scale factor applied to the polygon vertices.
**kwargs : dict, optional
Any keyword arguments accepted by
`matplotlib.patches.PathPatch`.
Returns
-------
patches : list of `~matplotlib.patches.PathPatch`
A list of matplotlib patches for the source segments.
"""
origin = np.array(origin)
patch_kwargs = {'edgecolor': 'white', 'facecolor': 'none'}
patch_kwargs.update(kwargs)
return [self._convert_shapely_to_pathpatch(geometry, origin=origin,
scale=scale, **patch_kwargs)
for geometry in self.polygons]
[docs]
def get_patch(self, label, *, origin=(0, 0), scale=1.0, **kwargs):
"""
Return a `~matplotlib.patches.PathPatch` for the given label.
By default, the patch will have a white edge color and no face
color.
Parameters
----------
label : int
The label number.
origin : array_like, optional
The ``(x, y)`` position of the origin of the displayed
image. This effectively translates the position of the
polygon.
scale : float, optional
The scale factor applied to the polygon vertices.
**kwargs : dict, optional
Any keyword arguments accepted by
`matplotlib.patches.PathPatch`.
Returns
-------
patch : `~matplotlib.patches.PathPatch` or `None`
A matplotlib patch for the source segment, or `None` if the
geometry is empty or rasterio and shapely are not available.
Raises
------
TypeError
If ``label`` is not a scalar.
ValueError
If ``label`` is invalid.
"""
if np.ndim(label) != 0:
msg = 'label must be a scalar value'
raise TypeError(msg)
return self.get_patches(label, origin=origin, scale=scale, **kwargs)[0]
[docs]
def get_patches(self, labels, *, origin=(0, 0), scale=1.0, **kwargs):
"""
Return a list of `~matplotlib.patches.PathPatch` objects for the
given labels.
By default, the patches will have a white edge color and no face
color.
Parameters
----------
labels : int, array_like (1D, int)
The label number(s).
origin : array_like, optional
The ``(x, y)`` position of the origin of the displayed
image. This effectively translates the position of the
polygons.
scale : float, optional
The scale factor applied to the polygon vertices.
**kwargs : dict, optional
Any keyword arguments accepted by
`matplotlib.patches.PathPatch`.
Returns
-------
patches : list of `~matplotlib.patches.PathPatch`
A list of matplotlib patches for the source segments.
Raises
------
ValueError
If any input ``labels`` are invalid.
"""
labels = np.atleast_1d(labels)
self.check_labels(labels)
origin = np.array(origin)
patch_kwargs = {'edgecolor': 'white', 'facecolor': 'none'}
patch_kwargs.update(kwargs)
patches = []
for label in labels:
poly = self._make_polygon(label, self._raw_slices[label - 1])
patches.append(self._convert_shapely_to_pathpatch(
poly, origin=origin, scale=scale, **patch_kwargs))
return patches
[docs]
def plot_patches(self, *, ax=None, origin=(0, 0), scale=1.0, labels=None,
**kwargs):
"""
Plot the `~matplotlib.patches.PathPatch` objects for the source
segments on a matplotlib `~matplotlib.axes.Axes` instance.
Parameters
----------
ax : `matplotlib.axes.Axes` or `None`, optional
The matplotlib axes on which to plot. If `None`, then the
current `~matplotlib.axes.Axes` instance is used.
origin : array_like, optional
The ``(x, y)`` position of the origin of the displayed
image.
scale : float, optional
The scale factor applied to the polygon vertices.
labels : int or array of int, optional
The label numbers whose polygons are to be plotted. If
`None`, the polygons for all labels will be plotted.
**kwargs : dict, optional
Any keyword arguments accepted by
`matplotlib.patches.PathPatch`.
Returns
-------
patches : list of `~matplotlib.patches.PathPatch`
A list of matplotlib patches for the plotted polygons. The
patches can be used, for example, when adding a plot legend.
Examples
--------
.. plot::
:include-source:
import numpy as np
from photutils.segmentation import SegmentationImage
data = np.array([[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 = SegmentationImage(data)
segm.imshow(figsize=(5, 5))
segm.plot_patches(edgecolor='white', lw=2)
"""
import matplotlib.pyplot as plt
if ax is None:
ax = plt.gca()
patches = self.to_patches(origin=origin, scale=scale, **kwargs)
if labels is not None:
patches = np.array(patches)
indices = self.get_indices(labels)
patches = patches[indices]
if np.isscalar(labels):
patches = [patches]
for patch in patches:
patch = copy(patch)
ax.add_patch(patch)
if labels is not None:
patches = list(patches)
return patches
[docs]
def to_regions(self, *, group=False, **kwargs):
"""
Return the `regions.Region` objects representing the source
segments.
The returned polygon region objects are defined as the exteriors
of the source segments. Interior holes within the source
segments are not included.
See the ``group`` keyword below for details about how
non-contiguous segments for a single label are handled.
Parameters
----------
group : bool, optional
If `False` (the default), then a `regions.Regions`
object will be returned with a flattened list of
`~regions.PolygonPixelRegion` objects. Note that in this
case, there will be multiple `~regions.PolygonPixelRegion`
objects for a single label if the label has non-contiguous
segments. Because of this, the number of regions returned
may not be equal to the number of unique labels in the
segmentation image.
If `True`, then a list of `~regions.PolygonPixelRegion`
or `~regions.Regions` objects will be returned. There
will be one item in the list for each label. If a
label has non-contiguous segments, then the item will
be a `~regions.Regions` object containing multiple
`~regions.PolygonPixelRegion` objects for that label.
**kwargs : dict, optional
Any keyword arguments accepted by
`regions.RegionVisual`. Common keywords include
``edgecolor``, ``facecolor``, ``color``, ``linewidth``,
and ``linestyle``.
Returns
-------
regions : `~regions.Regions`
A list of `~regions.Region` objects or a `~regions.Regions`
object, depending on the value of ``group`` (see above).
Notes
-----
If ``group=False``, then the number of regions returned may not
be equal to the number of unique labels in the segmentation
image. This occurs when the segmentation image contains
non-contiguous segments for a single label. That can happen as a
result of slicing the segmentation image where a segment label
is split into non-contiguous segments.
The meta attribute of the `~regions.PolygonPixelRegion` objects
will contain the label number as an integer value under the
'label' key. This can be used to identify the label of the
region.
"""
from regions import Regions
visual_kwargs = kwargs or None
regions = []
for label, poly in zip(self.labels, self.polygons, strict=True):
regions.append(_shapely_polygon_to_region(
poly, label=int(label), visual_kwargs=visual_kwargs))
if group:
return regions
# If group=False, return a Regions object with a flattened list
# of region objects
flat_regions = []
for region in regions:
if isinstance(region, Regions):
flat_regions.extend(region.regions)
else:
flat_regions.append(region)
return Regions(flat_regions)
[docs]
def get_region(self, label, **kwargs):
"""
Return the `regions <https://astropy-regions.readthedocs.io>`_
region object for the given label.
The returned polygon region is defined as the exterior of the
source segment. Interior holes within the source segment are not
included.
Parameters
----------
label : int
The label number.
**kwargs : dict, optional
Any keyword arguments accepted by
`regions.RegionVisual`. Common keywords include
``edgecolor``, ``facecolor``, ``color``, ``linewidth``,
and ``linestyle``.
Returns
-------
region : `~regions.PolygonPixelRegion` or `~regions.Regions`
A `~regions.PolygonPixelRegion` object, or a
`~regions.Regions` object if the segment is a MultiPolygon
(e.g., non-contiguous).
Raises
------
TypeError
If ``label`` is not a scalar.
ValueError
If ``label`` is invalid.
"""
if np.ndim(label) != 0:
msg = 'label must be a scalar value'
raise TypeError(msg)
return self.get_regions(label, **kwargs)[0]
[docs]
def get_regions(self, labels, **kwargs):
"""
Return a list of `regions
<https://astropy-regions.readthedocs.io>`_ region objects for
the given labels.
The returned polygon regions are defined as the exteriors of the
source segments. Interior holes within the source segments are
not included.
Parameters
----------
labels : int, array_like (1D, int)
The label number(s).
**kwargs : dict, optional
Any keyword arguments accepted by
`regions.RegionVisual`. Common keywords include
``edgecolor``, ``facecolor``, ``color``, ``linewidth``,
and ``linestyle``.
Returns
-------
regions : list of `~regions.PolygonPixelRegion` or `~regions.Regions`
A list of `~regions.PolygonPixelRegion` objects, or
`~regions.Regions` objects for labels with MultiPolygon
segments (e.g., non-contiguous).
Raises
------
ValueError
If any input ``labels`` are invalid.
"""
labels = np.atleast_1d(labels)
self.check_labels(labels)
visual_kwargs = kwargs or None
regions = []
for label in labels:
poly = self._make_polygon(label, self._raw_slices[label - 1])
regions.append(_shapely_polygon_to_region(
poly, label=int(label), visual_kwargs=visual_kwargs))
return regions
[docs]
@deprecated_positional_kwargs(since='3.0', until='4.0')
def imshow(self, ax=None, figsize=None, dpi=None, cmap=None, alpha=None):
"""
Display the segmentation image in a matplotlib
`~matplotlib.axes.Axes` instance.
The segmentation image will be displayed with "nearest"
interpolation and with the origin set to "lower".
Parameters
----------
ax : `matplotlib.axes.Axes` or `None`, optional
The matplotlib axes on which to plot. If `None`, then a new
`~matplotlib.axes.Axes` instance will be created.
figsize : 2-tuple of floats or `None`, optional
The figure dimension (width, height) in inches when creating
a new Axes. This keyword is ignored if ``axes`` is input.
dpi : float or `None`, optional
The figure dots per inch when creating a new Axes. This
keyword is ignored if ``axes`` is input.
cmap : `matplotlib.colors.Colormap`, str, or `None`, optional
The `~matplotlib.colors.Colormap` instance or a registered
matplotlib colormap name used to map scalar data to colors.
If `None`, then the colormap defined by the `cmap` attribute
will be used.
alpha : float, array_like, or `None`, optional
The alpha blending value, between 0 (transparent) and 1
(opaque). If alpha is an array, the alpha blending values
are applied pixel by pixel, and alpha must have the same
shape as the segmentation image.
Returns
-------
result : `matplotlib.image.AxesImage`
An image attached to an `matplotlib.axes.Axes`.
Examples
--------
.. plot::
:include-source:
import matplotlib.pyplot as plt
import numpy as np
from photutils.segmentation import SegmentationImage
data = np.array([[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 = SegmentationImage(data)
fig, ax = plt.subplots()
im = segm.imshow(ax=ax)
fig.colorbar(im, ax=ax)
"""
import matplotlib.pyplot as plt
if ax is None:
_, ax = plt.subplots(figsize=figsize, dpi=dpi)
if cmap is None:
cmap = self.cmap
return ax.imshow(self.data, cmap=cmap, interpolation='nearest',
origin='lower', alpha=alpha, vmin=-0.5,
vmax=self.max_label + 0.5)
[docs]
@deprecated_positional_kwargs(since='3.0', until='4.0')
def imshow_map(self, ax=None, figsize=None, dpi=None, cmap=None,
alpha=None, max_labels=25, cbar_labelsize=None):
"""
Display the segmentation image in a matplotlib
`~matplotlib.axes.Axes` instance with a colorbar.
This method is useful for displaying segmentation images
that have a few labels (e.g., from a cutout) that are not
consecutive. It maps the labels to be consecutive integers
starting from 1 before plotting. The plotted image values are
not the label values, but the colorbar tick labels are used to
show the original labels.
The segmentation image will be displayed with "nearest"
interpolation and with the origin set to "lower".
Parameters
----------
ax : `matplotlib.axes.Axes` or `None`, optional
The matplotlib axes on which to plot. If `None`, then a new
`~matplotlib.axes.Axes` instance will be created.
figsize : 2-tuple of floats or `None`, optional
The figure dimension (width, height) in inches when creating
a new Axes. This keyword is ignored if ``axes`` is input.
dpi : float or `None`, optional
The figure dots per inch when creating a new Axes. This
keyword is ignored if ``axes`` is input.
cmap : `matplotlib.colors.Colormap`, str, or `None`, optional
The `~matplotlib.colors.Colormap` instance or a registered
matplotlib colormap name used to map scalar data to colors.
If `None`, then the colormap defined by the `cmap` attribute
will be used.
alpha : float, array_like, or `None`, optional
The alpha blending value, between 0 (transparent) and 1
(opaque). If alpha is an array, the alpha blending values
are applied pixel by pixel, and alpha must have the same
shape as the segmentation image.
max_labels : int, optional
The maximum number of labels to display in the colorbar. If
the number of labels is greater than ``max_labels``, then
the colorbar will not be displayed.
cbar_labelsize : `None` or float, optional
The font size of the colorbar tick labels.
Returns
-------
result : `matplotlib.image.AxesImage`
An image attached to an `matplotlib.axes.Axes`.
cbar_info : tuple or `None`
The colorbar information as a tuple containing the
`~matplotlib.colorbar.Colorbar` instance, a `~numpy.ndarray`
of tick positions, and a `~numpy.ndarray` of tick labels.
`None` is returned if the colorbar was not plotted.
Examples
--------
.. plot::
:include-source:
import matplotlib.pyplot as plt
import numpy as np
from photutils.segmentation import SegmentationImage
data = np.array([[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]])
data *= 1000
segm = SegmentationImage(data)
fig, ax = plt.subplots()
im, cbar = segm.imshow_map(ax=ax)
"""
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
if ax is None:
_, ax = plt.subplots(figsize=figsize, dpi=dpi)
data, idx = np.unique(self.data, return_inverse=True)
idx = idx.reshape(self.data.shape)
vmin = -0.5
vmax = np.max(idx) + 0.5
# Keep the original cmap colors for the labels
if cmap is None:
cmap = ListedColormap(self.cmap.colors[data])
im = ax.imshow(idx, cmap=cmap, interpolation='nearest', origin='lower',
alpha=alpha, vmin=vmin, vmax=vmax)
cbar_info = None
cbar_labels = np.hstack((0, self.labels))
if len(cbar_labels) <= max_labels:
cbar_ticks = np.arange(len(cbar_labels))
cbar = ax.figure.colorbar(im, ax=ax, ticks=cbar_ticks)
cbar.ax.set_yticklabels(cbar_labels)
if cbar_labelsize is not None:
cbar.ax.yaxis.set_tick_params(labelsize=cbar_labelsize)
cbar_info = (cbar, cbar_ticks, cbar_labels)
else:
msg = ('The colorbar was not plotted because the number of '
f'labels is greater than {max_labels=}.')
warnings.warn(msg, AstropyUserWarning)
return im, cbar_info
[docs]
class Segment:
"""
Class for a single labeled region (segment) within a segmentation
image.
Parameters
----------
segment_data : int `~numpy.ndarray`
A segmentation array where source regions are labeled by
different positive integer values. A value of zero is reserved
for the background.
label : int
The segment label number.
slices : tuple of two slices
A tuple of two slices representing the minimal box that contains
the labeled region.
bbox : `~photutils.aperture.BoundingBox`
The minimal bounding box that contains the labeled region.
area : float
The area of the segment in pixels**2.
polygon : Shapely polygon, optional
The outline of the segment as a `Shapely
<https://shapely.readthedocs.io/en/stable/>`_ polygon.
Notes
-----
Only the minimal bounding-box cutout of the segmentation array is
stored (as a copy), so `Segment` instances do not prevent garbage
collection of the parent array.
"""
def __init__(self, segment_data, label, slices, bbox, area, *,
polygon=None):
self._segment_data_cutout = np.copy(segment_data[slices])
self._segment_data_shape = segment_data.shape
self.label = label
self.slices = slices
self.bbox = bbox
self.area = area
self.polygon = polygon
def __str__(self):
cls_name = f'<{self.__class__.__module__}.{self.__class__.__name__}>'
params = ['label', 'slices', 'area']
cls_info = [(param, getattr(self, param)) for param in params]
fmt = [f'{key}: {val}' for key, val in cls_info]
return f'{cls_name}\n' + '\n'.join(fmt)
def __repr__(self):
return self.__str__()
# Remove in 4.0
def __getattr__(self, name):
return deprecated_getattr(self, name,
_SEGMENT_DEPRECATED_ATTRIBUTES,
since='3.0', until='4.0')
def _repr_svg_(self):
if self.polygon is not None:
return self.polygon._repr_svg_()
return None
def __array__(self):
"""
Array representation of the labeled region (e.g., for
matplotlib).
"""
return self.data
@lazyproperty
def data(self):
"""
A cutout array of the segment using the minimal bounding box,
where pixels outside the labeled region are set to zero (i.e.,
neighboring segments within the rectangular cutout array are not
shown).
"""
cutout = np.copy(self._segment_data_cutout)
cutout[cutout != self.label] = 0
return cutout
@lazyproperty
def data_masked(self):
"""
A `~numpy.ma.MaskedArray` cutout array of the segment using the
minimal bounding box.
The mask is `True` for pixels outside the source segment (i.e.,
neighboring segments within the rectangular cutout array are
masked).
"""
mask = (self._segment_data_cutout != self.label)
return np.ma.masked_array(self._segment_data_cutout, mask=mask)
[docs]
@deprecated_positional_kwargs(since='3.0', until='4.0')
def make_cutout(self, data, masked_array=False):
"""
Create a (masked) cutout array from the input ``data`` using the
minimal bounding box of the segment (labeled region).
If ``masked_array`` is `False` (default), then the returned
cutout array is simply a `~numpy.ndarray`. The returned cutout
is a view (not a copy) of the input ``data``. No pixels are
altered (e.g., set to zero) within the bounding box.
If ``masked_array`` is `True`, then the returned cutout array is
a `~numpy.ma.MaskedArray`, where the mask is `True` for pixels
outside the segment (labeled region). The data part of the
masked array is a view (not a copy) of the input ``data``.
Parameters
----------
data : 2D `~numpy.ndarray`
The data array from which to create the masked cutout array.
``data`` must have the same shape as the segmentation array.
masked_array : bool, optional
If `True` then a `~numpy.ma.MaskedArray` will be created
where the mask is `True` for pixels outside the segment
(labeled region). If `False`, then a `~numpy.ndarray` will
be generated.
Returns
-------
result : 2D `~numpy.ndarray` or `~numpy.ma.MaskedArray`
The cutout array.
"""
if data.shape != self._segment_data_shape:
msg = 'data must have the same shape as the segmentation array'
raise ValueError(msg)
if masked_array:
mask = (self._segment_data_cutout != self.label)
return np.ma.masked_array(data[self.slices], mask=mask)
return data[self.slices]
