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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
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commit 45fb349a7d
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venv/Lib/site-packages/skimage/measure/tests/test_moments.py Normal file
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import numpy as np
from scipy import ndimage as ndi
from skimage import draw
from skimage.measure import (moments, moments_central, moments_coords,
moments_coords_central, moments_normalized,
moments_hu, centroid, inertia_tensor,
inertia_tensor_eigvals)
from skimage._shared import testing
from skimage._shared.testing import (assert_equal, assert_almost_equal,
assert_allclose)
from skimage._shared._warnings import expected_warnings
def test_moments():
image = np.zeros((20, 20), dtype=np.double)
image[14, 14] = 1
image[15, 15] = 1
image[14, 15] = 0.5
image[15, 14] = 0.5
m = moments(image)
assert_equal(m[0, 0], 3)
assert_almost_equal(m[1, 0] / m[0, 0], 14.5)
assert_almost_equal(m[0, 1] / m[0, 0], 14.5)
def test_moments_central():
image = np.zeros((20, 20), dtype=np.double)
image[14, 14] = 1
image[15, 15] = 1
image[14, 15] = 0.5
image[15, 14] = 0.5
mu = moments_central(image, (14.5, 14.5))
# check for proper centroid computation
mu_calc_centroid = moments_central(image)
assert_equal(mu, mu_calc_centroid)
# shift image by dx=2, dy=2
image2 = np.zeros((20, 20), dtype=np.double)
image2[16, 16] = 1
image2[17, 17] = 1
image2[16, 17] = 0.5
image2[17, 16] = 0.5
mu2 = moments_central(image2, (14.5 + 2, 14.5 + 2))
# central moments must be translation invariant
assert_equal(mu, mu2)
def test_moments_coords():
image = np.zeros((20, 20), dtype=np.double)
image[13:17, 13:17] = 1
mu_image = moments(image)
coords = np.array([[r, c] for r in range(13, 17)
for c in range(13, 17)], dtype=np.double)
mu_coords = moments_coords(coords)
assert_almost_equal(mu_coords, mu_image)
def test_moments_central_coords():
image = np.zeros((20, 20), dtype=np.double)
image[13:17, 13:17] = 1
mu_image = moments_central(image, (14.5, 14.5))
coords = np.array([[r, c] for r in range(13, 17)
for c in range(13, 17)], dtype=np.double)
mu_coords = moments_coords_central(coords, (14.5, 14.5))
assert_almost_equal(mu_coords, mu_image)
# ensure that center is being calculated normally
mu_coords_calc_centroid = moments_coords_central(coords)
assert_almost_equal(mu_coords_calc_centroid, mu_coords)
# shift image by dx=3 dy=3
image = np.zeros((20, 20), dtype=np.double)
image[16:20, 16:20] = 1
mu_image = moments_central(image, (14.5, 14.5))
coords = np.array([[r, c] for r in range(16, 20)
for c in range(16, 20)], dtype=np.double)
mu_coords = moments_coords_central(coords, (14.5, 14.5))
assert_almost_equal(mu_coords, mu_image)
def test_moments_normalized():
image = np.zeros((20, 20), dtype=np.double)
image[13:17, 13:17] = 1
mu = moments_central(image, (14.5, 14.5))
nu = moments_normalized(mu)
# shift image by dx=-3, dy=-3 and scale by 0.5
image2 = np.zeros((20, 20), dtype=np.double)
image2[11:13, 11:13] = 1
mu2 = moments_central(image2, (11.5, 11.5))
nu2 = moments_normalized(mu2)
# central moments must be translation and scale invariant
assert_almost_equal(nu, nu2, decimal=1)
def test_moments_normalized_3d():
image = draw.ellipsoid(1, 1, 10)
mu_image = moments_central(image)
nu = moments_normalized(mu_image)
assert nu[0, 0, 2] > nu[0, 2, 0]
assert_almost_equal(nu[0, 2, 0], nu[2, 0, 0])
coords = np.where(image)
mu_coords = moments_coords_central(coords)
assert_almost_equal(mu_coords, mu_image)
def test_moments_normalized_invalid():
with testing.raises(ValueError):
moments_normalized(np.zeros((3, 3)), 3)
with testing.raises(ValueError):
moments_normalized(np.zeros((3, 3)), 4)
def test_moments_hu():
image = np.zeros((20, 20), dtype=np.double)
image[13:15, 13:17] = 1
mu = moments_central(image, (13.5, 14.5))
nu = moments_normalized(mu)
hu = moments_hu(nu)
# shift image by dx=2, dy=3, scale by 0.5 and rotate by 90deg
image2 = np.zeros((20, 20), dtype=np.double)
image2[11, 11:13] = 1
image2 = image2.T
mu2 = moments_central(image2, (11.5, 11))
nu2 = moments_normalized(mu2)
hu2 = moments_hu(nu2)
# central moments must be translation and scale invariant
assert_almost_equal(hu, hu2, decimal=1)
def test_centroid():
image = np.zeros((20, 20), dtype=np.double)
image[14, 14:16] = 1
image[15, 14:16] = 1/3
image_centroid = centroid(image)
assert_allclose(image_centroid, (14.25, 14.5))
def test_inertia_tensor_2d():
image = np.zeros((40, 40))
image[15:25, 5:35] = 1 # big horizontal rectangle (aligned with axis 1)
T = inertia_tensor(image)
assert T[0, 0] > T[1, 1]
np.testing.assert_allclose(T[0, 1], 0)
v0, v1 = inertia_tensor_eigvals(image, T=T)
np.testing.assert_allclose(np.sqrt(v0/v1), 3, rtol=0.01, atol=0.05)
def test_inertia_tensor_3d():
image = draw.ellipsoid(10, 5, 3)
T0 = inertia_tensor(image)
eig0, V0 = np.linalg.eig(T0)
# principal axis of ellipse = eigenvector of smallest eigenvalue
v0 = V0[:, np.argmin(eig0)]
assert np.allclose(v0, [1, 0, 0]) or np.allclose(-v0, [1, 0, 0])
imrot = ndi.rotate(image.astype(float), 30, axes=(0, 1), order=1)
Tr = inertia_tensor(imrot)
eigr, Vr = np.linalg.eig(Tr)
vr = Vr[:, np.argmin(eigr)]
# Check that axis has rotated by expected amount
pi, cos, sin = np.pi, np.cos, np.sin
R = np.array([[ cos(pi/6), -sin(pi/6), 0],
[ sin(pi/6), cos(pi/6), 0],
[ 0, 0, 1]])
expected_vr = R @ v0
assert (np.allclose(vr, expected_vr, atol=1e-3, rtol=0.01) or
np.allclose(-vr, expected_vr, atol=1e-3, rtol=0.01))
def test_inertia_tensor_eigvals():
# Floating point precision problems could make a positive
# semidefinite matrix have an eigenvalue that is very slightly
# negative. Check that we have caught and fixed this problem.
image = np.array([[1, 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, 1]])
# mu = np.array([[3, 0, 98], [0, 14, 0], [2, 0, 98]])
eigvals = inertia_tensor_eigvals(image=image)
assert (min(eigvals) >= 0)