/usr/lib/python3/dist-packages/photutils/segmentation/core.py is in python3-photutils 0.4-1.
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from __future__ import (absolute_import, division, print_function,
unicode_literals)
from copy import deepcopy
import numpy as np
from astropy.utils import lazyproperty
from ..utils.colormaps import random_cmap
__all__ = ['SegmentationImage']
__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 cmap(self, background_color='#000000', random_state=None):
"""
A matplotlib colormap consisting of random (muted) colors.
This is very useful for plotting the segmentation image.
Parameters
----------
background_color : str or `None`, optional
A hex string in the "#rrggbb" format defining the first
color in the colormap. This color will be used as the
background color (label = 0) when plotting the segmentation
image. The default is black.
random_state : int or `~numpy.random.RandomState`, optional
The pseudo-random number generator state used for random
sampling. Separate function calls with the same
``random_state`` will generate the same colormap.
"""
from matplotlib import colors
cmap = random_cmap(self.max + 1, random_state=random_state)
if background_color is not None:
cmap.colors[0] = colors.hex2color(background_color)
return cmap
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]])
"""
# 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)
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