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Fixed database typo and removed unnecessary class identifier.

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Batuhan Berk Başoğlu 2020-10-14 10:10:37 -04:00
parent 00ad49a143
commit 45fb349a7d
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venv/Lib/site-packages/skimage/morphology/tests/test_reconstruction.py Normal file
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"""
These tests are originally part of CellProfiler, code licensed under both GPL and BSD licenses.
Website: http://www.cellprofiler.org
Copyright (c) 2003-2009 Massachusetts Institute of Technology
Copyright (c) 2009-2011 Broad Institute
All rights reserved.
Original author: Lee Kamentsky
"""
import numpy as np
from skimage.morphology.greyreconstruct import reconstruction
from skimage._shared import testing
from skimage._shared.testing import assert_array_almost_equal
def test_zeros():
"""Test reconstruction with image and mask of zeros"""
assert_array_almost_equal(
reconstruction(np.zeros((5, 7)), np.zeros((5, 7))), 0)
def test_image_equals_mask():
"""Test reconstruction where the image and mask are the same"""
assert_array_almost_equal(
reconstruction(np.ones((7, 5)), np.ones((7, 5))), 1)
def test_image_less_than_mask():
"""Test reconstruction where the image is uniform and less than mask"""
image = np.ones((5, 5))
mask = np.ones((5, 5)) * 2
assert_array_almost_equal(reconstruction(image, mask), 1)
def test_one_image_peak():
"""Test reconstruction with one peak pixel"""
image = np.ones((5, 5))
image[2, 2] = 2
mask = np.ones((5, 5)) * 3
assert_array_almost_equal(reconstruction(image, mask), 2)
def test_two_image_peaks():
"""Test reconstruction with two peak pixels isolated by the mask"""
image = np.array([[1, 1, 1, 1, 1, 1, 1, 1],
[1, 2, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 3, 1],
[1, 1, 1, 1, 1, 1, 1, 1]])
mask = np.array([[4, 4, 4, 1, 1, 1, 1, 1],
[4, 4, 4, 1, 1, 1, 1, 1],
[4, 4, 4, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 4, 4, 4],
[1, 1, 1, 1, 1, 4, 4, 4],
[1, 1, 1, 1, 1, 4, 4, 4]])
expected = np.array([[2, 2, 2, 1, 1, 1, 1, 1],
[2, 2, 2, 1, 1, 1, 1, 1],
[2, 2, 2, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 3, 3, 3],
[1, 1, 1, 1, 1, 3, 3, 3],
[1, 1, 1, 1, 1, 3, 3, 3]])
assert_array_almost_equal(reconstruction(image, mask), expected)
def test_zero_image_one_mask():
"""Test reconstruction with an image of all zeros and a mask that's not"""
result = reconstruction(np.zeros((10, 10)), np.ones((10, 10)))
assert_array_almost_equal(result, 0)
def test_fill_hole():
"""Test reconstruction by erosion, which should fill holes in mask."""
seed = np.array([0, 8, 8, 8, 8, 8, 8, 8, 8, 0])
mask = np.array([0, 3, 6, 2, 1, 1, 1, 4, 2, 0])
result = reconstruction(seed, mask, method='erosion')
assert_array_almost_equal(result, np.array([0, 3, 6, 4, 4, 4, 4, 4, 2, 0]))
def test_invalid_seed():
seed = np.ones((5, 5))
mask = np.ones((5, 5))
with testing.raises(ValueError):
reconstruction(seed * 2, mask,
method='dilation')
with testing.raises(ValueError):
reconstruction(seed * 0.5, mask,
method='erosion')
def test_invalid_selem():
seed = np.ones((5, 5))
mask = np.ones((5, 5))
with testing.raises(ValueError):
reconstruction(seed, mask,
selem=np.ones((4, 4)))
with testing.raises(ValueError):
reconstruction(seed, mask,
selem=np.ones((3, 4)))
reconstruction(seed, mask, selem=np.ones((3, 3)))
def test_invalid_method():
seed = np.array([0, 8, 8, 8, 8, 8, 8, 8, 8, 0])
mask = np.array([0, 3, 6, 2, 1, 1, 1, 4, 2, 0])
with testing.raises(ValueError):
reconstruction(seed, mask, method='foo')
def test_invalid_offset_not_none():
"""Test reconstruction with invalid not None offset parameter"""
image = np.array([[1, 1, 1, 1, 1, 1, 1, 1],
[1, 2, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 3, 1],
[1, 1, 1, 1, 1, 1, 1, 1]])
mask = np.array([[4, 4, 4, 1, 1, 1, 1, 1],
[4, 4, 4, 1, 1, 1, 1, 1],
[4, 4, 4, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 4, 4, 4],
[1, 1, 1, 1, 1, 4, 4, 4],
[1, 1, 1, 1, 1, 4, 4, 4]])
with testing.raises(ValueError):
reconstruction(image, mask, method='dilation',
selem=np.ones((3, 3)), offset=np.array([3, 0]))
def test_offset_not_none():
"""Test reconstruction with valid offset parameter"""
seed = np.array([0, 3, 6, 2, 1, 1, 1, 4, 2, 0])
mask = np.array([0, 8, 6, 8, 8, 8, 8, 4, 4, 0])
expected = np.array([0, 3, 6, 6, 6, 6, 6, 4, 4, 0])
assert_array_almost_equal(
reconstruction(seed, mask, method='dilation',
selem=np.ones(3), offset=np.array([0])), expected)