test/test_tomopy/test_misc/test_corr.py
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import unittest
import numpy as np
import scipy
from numpy.testing import assert_allclose, assert_equal
from tomopy.misc.corr import (
gaussian_filter,
median_filter,
median_filter3d,
remove_outlier3d,
median_filter_nonfinite,
remove_neg,
remove_nan,
remove_outlier,
circ_mask,
inpainter_morph,
)
from tomopy.misc.phantom import peppers
from ..util import read_file, loop_dim
__author__ = "Doga Gursoy, William Judge"
__copyright__ = "Copyright (c) 2015, UChicago Argonne, LLC."
__docformat__ = "restructuredtext en"
class ImageFilterTestCase(unittest.TestCase):
def test_gaussian_filter(self):
loop_dim(gaussian_filter, read_file("cube.npy"))
def test_median_filter(self):
loop_dim(median_filter, read_file("cube.npy"))
def test_median_filter_nonfinite(self):
# Set a standard random value to make reproducible
np.random.seed(1)
data_org = np.ones(shape=(100, 100, 100))
# Add some random non-finite values to an array of all ones
for non_finite in [np.nan, np.inf, -np.inf]:
for i in range(50):
x = np.random.randint(0, 100)
y = np.random.randint(0, 100)
z = np.random.randint(0, 100)
data_org[z, x, y] = non_finite
data_post_corr = median_filter_nonfinite(
data_org.copy(),
size=5,
callback=None,
)
# All the post filtering values should be 1 because all of the finite
# values are 1.
assert np.all(data_post_corr == 1.0)
# Making sure filter raises ValueError when function finds a filter
# filled with non-finite values.
for non_finite in [np.nan, np.inf, -np.inf]:
data_org = np.empty((1, 3, 3))
data_org[:, -2:, -2:] = non_finite
with self.assertRaises(ValueError):
result = median_filter_nonfinite(
data_org.copy(),
size=3,
callback=None,
)
def test_median_filter3d(self):
A = np.arange(4 * 5 * 6).reshape(4, 5, 6)
assert_equal(
scipy.ndimage.median_filter(np.float32(A), size=3),
median_filter3d(np.float32(A), size=3),
)
def test_remove_outlier3d(self):
A = np.arange(4 * 5 * 6).reshape(4, 5, 6)
A[2, 2, 2] = 1000.0 # introduce an outlier
A_dezinged = remove_outlier3d(np.float32(A), dif=500, size=3)
A[2, 2, 2] = 75 # substituted value by dezinger
assert_equal(np.float32(A), A_dezinged)
def test_remove_neg(self):
assert_allclose(remove_neg([-2, -1, 0, 1, 2]), [0, 0, 0, 1, 2])
def test_remove_nan(self):
assert_allclose(remove_nan([np.nan, 1.5, 2, np.nan, 1]), [0, 1.5, 2, 0, 1])
def test_remove_outlier(self):
proj = read_file("proj.npy")
proj[8][4][6] = 20
assert_allclose(remove_outlier(proj, dif=10), read_file("proj.npy"))
def test_circ_mask(self):
loop_dim(circ_mask, read_file("obj.npy"))
def test_inpainter2d(self):
input_image = peppers(size=512)[0, :, :]
mask = np.zeros((512, 512))
mask[270:285, :] = 1 # crop out the horizontal region
mask = np.array(mask, dtype="bool")
inpainted2d_mean = inpainter_morph(
input_image, mask, size=3, iterations=2, inpainting_type="mean", axis=None
)
inpainted2d_median = inpainter_morph(
input_image, mask, size=3, iterations=2, inpainting_type="median", axis=None
)
inpainted2d_random = inpainter_morph(
input_image, mask, size=3, iterations=2, inpainting_type="random", axis=None
)
assert_allclose(
np.mean(inpainted2d_mean, axis=(0, 1)).sum(), 104.20617, rtol=1e-6
)
assert_allclose(
np.mean(inpainted2d_median, axis=(0, 1)).sum(), 104.26762, rtol=1e-6
)
# providing a range as the method is probabilistic
assert 103.0 <= np.mean(inpainted2d_random, axis=(0, 1)).sum() <= 106.0
def test_inpainter3d_as_2d(self):
input_vol3d = np.float32(np.zeros((512, 3, 512)))
mask3d = np.zeros((512, 3, 512))
mask2d = np.zeros((512, 512))
mask2d[270:285, :] = 1 # crop out the horizontal region
for j in range(3):
input_vol3d[:, j, :] = peppers(size=512)[0, :, :]
mask3d[:, j, :] = mask2d
mask3d = np.array(mask3d, dtype="bool")
inpainted3d_mean = inpainter_morph(
input_vol3d, mask3d, size=3, iterations=2, inpainting_type="mean", axis=1
)
inpainted3d_median = inpainter_morph(
input_vol3d, mask3d, size=3, iterations=2, inpainting_type="median", axis=1
)
inpainted3d_random = inpainter_morph(
input_vol3d, mask3d, size=3, iterations=2, inpainting_type="random", axis=1
)
assert_allclose(np.mean(inpainted3d_mean), 104.20617, rtol=1e-6)
assert_allclose(np.mean(inpainted3d_median), 104.26761, rtol=1e-6)
# providing a range as the method is probabilistic
assert 103.0 <= np.mean(inpainted3d_random) <= 106.0
def test_inpainter3d(self):
input_vol3d = np.float32(np.zeros((512, 3, 512)))
mask3d = np.zeros((512, 3, 512))
mask2d = np.zeros((512, 512))
mask2d[270:285, :] = 1 # crop out the horizontal region
for j in range(3):
input_vol3d[:, j, :] = peppers(size=512)[0, :, :]
mask3d[:, j, :] = mask2d
mask3d = np.array(mask3d, dtype="bool")
inpainted3d_mean = inpainter_morph(
input_vol3d, mask3d, size=3, iterations=0, inpainting_type="mean"
)
inpainted3d_random = inpainter_morph(
input_vol3d, mask3d, size=1, iterations=0, inpainting_type="random"
)
assert_allclose(np.mean(inpainted3d_mean), 104.20623, rtol=1e-6)
# pproviding a range as the method is probabilistic
assert 103.0 <= np.mean(inpainted3d_random) <= 106.0