/usr/lib/python3/dist-packages/photutils/segmentation/tests/test_core.py is in python3-photutils 0.4-1.
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from __future__ import (absolute_import, division, print_function,
unicode_literals)
import numpy as np
from numpy.testing import assert_allclose
import pytest
from ..core import SegmentationImage
try:
import scipy # noqa
HAS_SCIPY = True
except ImportError:
HAS_SCIPY = False
try:
import skimage # noqa
HAS_SKIMAGE = True
except ImportError:
HAS_SKIMAGE = False
@pytest.mark.skipif('not HAS_SKIMAGE')
@pytest.mark.skipif('not HAS_SCIPY')
class TestSegmentationImage(object):
def setup_class(self):
self.data = [[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]]
self.segm = SegmentationImage(self.data)
def test_array(self):
assert_allclose(self.segm.data, self.segm.array)
assert_allclose(self.segm.data, self.segm.__array__())
def test_copy(self):
segm = SegmentationImage(self.data)
segm2 = segm.copy()
assert segm.data is not segm2.data
assert segm.labels is not segm2.labels
segm.data[0, 0] = 100.
assert segm.data[0, 0] != segm2.data[0, 0]
def test_negative_data(self):
data = np.arange(-1, 8).reshape(3, 3)
with pytest.raises(ValueError):
SegmentationImage(data)
def test_zero_label(self):
with pytest.raises(ValueError):
self.segm.check_label(0)
def test_negative_label(self):
with pytest.raises(ValueError):
self.segm.check_label(-1)
def test_invalid_label(self):
with pytest.raises(ValueError):
self.segm.check_label(2)
def test_data_masked(self):
assert isinstance(self.segm.data_masked, np.ma.MaskedArray)
assert np.ma.count(self.segm.data_masked) == 18
assert np.ma.count_masked(self.segm.data_masked) == 18
def test_labels(self):
assert_allclose(self.segm.labels, [1, 3, 4, 5, 7])
def test_nlabels(self):
assert self.segm.nlabels == 5
def test_max(self):
assert self.segm.max == 7
def test_areas(self):
expected = np.array([18, 2, 0, 2, 3, 6, 0, 5])
assert_allclose(self.segm.areas, expected)
def test_area(self):
expected = np.array([18, 2, 0, 2, 3, 6, 0, 5])
assert self.segm.area(0) == expected[0]
labels = [3, 1, 4]
assert_allclose(self.segm.area(labels), expected[labels])
def test_outline_segments(self):
segm_array = np.zeros((5, 5)).astype(int)
segm_array[1:4, 1:4] = 2
segm = SegmentationImage(segm_array)
segm_array_ref = np.copy(segm_array)
segm_array_ref[2, 2] = 0
assert_allclose(segm.outline_segments(), segm_array_ref)
def test_outline_segments_masked_background(self):
segm_array = np.zeros((5, 5)).astype(int)
segm_array[1:4, 1:4] = 2
segm = SegmentationImage(segm_array)
segm_array_ref = np.copy(segm_array)
segm_array_ref[2, 2] = 0
segm_outlines = segm.outline_segments(mask_background=True)
assert isinstance(segm_outlines, np.ma.MaskedArray)
assert np.ma.count(segm_outlines) == 8
assert np.ma.count_masked(segm_outlines) == 17
def test_relabel(self):
segm = SegmentationImage(self.data)
segm.relabel(labels=[1, 7], new_label=2)
ref_data = np.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]])
assert_allclose(segm.data, ref_data)
assert segm.nlabels == len(segm.slices) - segm.slices.count(None)
@pytest.mark.parametrize('start_label', [1, 5])
def test_relabel_sequential(self, start_label):
segm = SegmentationImage(self.data)
ref_data = np.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]])
ref_data[ref_data != 0] += (start_label - 1)
segm.relabel_sequential(start_label=start_label)
assert_allclose(segm.data, ref_data)
# relabel_sequential should do nothing if already sequential
segm.relabel_sequential(start_label=start_label)
assert_allclose(segm.data, ref_data)
assert segm.nlabels == len(segm.slices) - segm.slices.count(None)
@pytest.mark.parametrize('start_label', [0, -1])
def test_relabel_sequential_start_invalid(self, start_label):
with pytest.raises(ValueError):
segm = SegmentationImage(self.data)
segm.relabel_sequential(start_label=start_label)
def test_keep_labels(self):
ref_data = np.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]])
segm = SegmentationImage(self.data)
segm.keep_labels([5, 3])
assert_allclose(segm.data, ref_data)
def test_keep_labels_relabel(self):
ref_data = np.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]])
segm = SegmentationImage(self.data)
segm.keep_labels([5, 3], relabel=True)
assert_allclose(segm.data, ref_data)
def test_remove_labels(self):
ref_data = np.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]])
segm = SegmentationImage(self.data)
segm.remove_labels(labels=[5, 3])
assert_allclose(segm.data, ref_data)
def test_remove_labels_relabel(self):
ref_data = np.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]])
segm = SegmentationImage(self.data)
segm.remove_labels(labels=[5, 3], relabel=True)
assert_allclose(segm.data, ref_data)
def test_remove_border_labels(self):
ref_data = np.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(self.data)
segm.remove_border_labels(border_width=1)
assert_allclose(segm.data, ref_data)
def test_remove_border_labels_border_width(self):
with pytest.raises(ValueError):
segm = SegmentationImage(self.data)
segm.remove_border_labels(border_width=3)
def test_remove_masked_labels(self):
ref_data = np.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(self.data)
mask = np.zeros_like(segm.data, dtype=np.bool)
mask[0, :] = True
segm.remove_masked_labels(mask)
assert_allclose(segm.data, ref_data)
def test_remove_masked_labels_without_partial_overlap(self):
ref_data = np.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]])
segm = SegmentationImage(self.data)
mask = np.zeros_like(segm.data, dtype=np.bool)
mask[0, :] = True
segm.remove_masked_labels(mask, partial_overlap=False)
assert_allclose(segm.data, ref_data)
def test_remove_masked_segments_mask_shape(self):
segm = SegmentationImage(np.ones((5, 5)))
mask = np.zeros((3, 3), dtype=np.bool)
with pytest.raises(ValueError):
segm.remove_masked_labels(mask)
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