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test_texture.py
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test_texture.py
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import numpy as np
from skimage.feature import (greycomatrix,
greycoprops,
local_binary_pattern,
multiblock_lbp)
from skimage._shared.testing import test_parallel
from skimage.transform import integral_image
from skimage._shared import testing
class TestGLCM():
def setup(self):
self.image = np.array([[0, 0, 1, 1],
[0, 0, 1, 1],
[0, 2, 2, 2],
[2, 2, 3, 3]], dtype=np.uint8)
@test_parallel()
def test_output_angles(self):
result = greycomatrix(self.image, [1], [0, np.pi / 4, np.pi / 2, 3 * np.pi / 4], 4)
assert result.shape == (4, 4, 1, 4)
expected1 = np.array([[2, 2, 1, 0],
[0, 2, 0, 0],
[0, 0, 3, 1],
[0, 0, 0, 1]], dtype=np.uint32)
np.testing.assert_array_equal(result[:, :, 0, 0], expected1)
expected2 = np.array([[1, 1, 3, 0],
[0, 1, 1, 0],
[0, 0, 0, 2],
[0, 0, 0, 0]], dtype=np.uint32)
np.testing.assert_array_equal(result[:, :, 0, 1], expected2)
expected3 = np.array([[3, 0, 2, 0],
[0, 2, 2, 0],
[0, 0, 1, 2],
[0, 0, 0, 0]], dtype=np.uint32)
np.testing.assert_array_equal(result[:, :, 0, 2], expected3)
expected4 = np.array([[2, 0, 0, 0],
[1, 1, 2, 0],
[0, 0, 2, 1],
[0, 0, 0, 0]], dtype=np.uint32)
np.testing.assert_array_equal(result[:, :, 0, 3], expected4)
def test_output_symmetric_1(self):
result = greycomatrix(self.image, [1], [np.pi / 2], 4,
symmetric=True)
assert result.shape == (4, 4, 1, 1)
expected = np.array([[6, 0, 2, 0],
[0, 4, 2, 0],
[2, 2, 2, 2],
[0, 0, 2, 0]], dtype=np.uint32)
np.testing.assert_array_equal(result[:, :, 0, 0], expected)
def test_error_raise_float(self):
for dtype in [
float, np.double, np.float16, np.float32, np.float64
]:
with testing.raises(ValueError):
greycomatrix(self.image.astype(dtype), [1], [np.pi], 4)
def test_error_raise_int_types(self):
for dtype in [
np.int16, np.int32, np.int64, np.uint16, np.uint32, np.uint64
]:
with testing.raises(ValueError):
greycomatrix(self.image.astype(dtype), [1], [np.pi])
def test_error_raise_negative(self):
with testing.raises(ValueError):
greycomatrix(self.image.astype(np.int16) - 1, [1], [np.pi], 4)
def test_error_raise_levels_smaller_max(self):
with testing.raises(ValueError):
greycomatrix(self.image - 1, [1], [np.pi], 3)
def test_image_data_types(self):
for dtype in [np.uint16, np.uint32, np.uint64, np.int16, np.int32, np.int64]:
img = self.image.astype(dtype)
result = greycomatrix(img, [1], [np.pi / 2], 4,
symmetric=True)
assert result.shape == (4, 4, 1, 1)
expected = np.array([[6, 0, 2, 0],
[0, 4, 2, 0],
[2, 2, 2, 2],
[0, 0, 2, 0]], dtype=np.uint32)
np.testing.assert_array_equal(result[:, :, 0, 0], expected)
return
def test_output_distance(self):
im = np.array([[0, 0, 0, 0],
[1, 0, 0, 1],
[2, 0, 0, 2],
[3, 0, 0, 3]], dtype=np.uint8)
result = greycomatrix(im, [3], [0], 4, symmetric=False)
expected = np.array([[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]], dtype=np.uint32)
np.testing.assert_array_equal(result[:, :, 0, 0], expected)
def test_output_combo(self):
im = np.array([[0],
[1],
[2],
[3]], dtype=np.uint8)
result = greycomatrix(im, [1, 2], [0, np.pi / 2], 4)
assert result.shape == (4, 4, 2, 2)
z = np.zeros((4, 4), dtype=np.uint32)
e1 = np.array([[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
[0, 0, 0, 0]], dtype=np.uint32)
e2 = np.array([[0, 0, 1, 0],
[0, 0, 0, 1],
[0, 0, 0, 0],
[0, 0, 0, 0]], dtype=np.uint32)
np.testing.assert_array_equal(result[:, :, 0, 0], z)
np.testing.assert_array_equal(result[:, :, 1, 0], z)
np.testing.assert_array_equal(result[:, :, 0, 1], e1)
np.testing.assert_array_equal(result[:, :, 1, 1], e2)
def test_output_empty(self):
result = greycomatrix(self.image, [10], [0], 4)
np.testing.assert_array_equal(result[:, :, 0, 0],
np.zeros((4, 4), dtype=np.uint32))
result = greycomatrix(self.image, [10], [0], 4, normed=True)
np.testing.assert_array_equal(result[:, :, 0, 0],
np.zeros((4, 4), dtype=np.uint32))
def test_normed_symmetric(self):
result = greycomatrix(self.image, [1, 2, 3],
[0, np.pi / 2, np.pi], 4,
normed=True, symmetric=True)
for d in range(result.shape[2]):
for a in range(result.shape[3]):
np.testing.assert_almost_equal(result[:, :, d, a].sum(),
1.0)
np.testing.assert_array_equal(result[:, :, d, a],
result[:, :, d, a].transpose())
def test_contrast(self):
result = greycomatrix(self.image, [1, 2], [0], 4,
normed=True, symmetric=True)
result = np.round(result, 3)
contrast = greycoprops(result, 'contrast')
np.testing.assert_almost_equal(contrast[0, 0], 0.585, decimal=3)
def test_dissimilarity(self):
result = greycomatrix(self.image, [1], [0, np.pi / 2], 4,
normed=True, symmetric=True)
result = np.round(result, 3)
dissimilarity = greycoprops(result, 'dissimilarity')
np.testing.assert_almost_equal(dissimilarity[0, 0], 0.418, decimal=3)
def test_dissimilarity_2(self):
result = greycomatrix(self.image, [1, 3], [np.pi / 2], 4,
normed=True, symmetric=True)
result = np.round(result, 3)
dissimilarity = greycoprops(result, 'dissimilarity')[0, 0]
np.testing.assert_almost_equal(dissimilarity, 0.665, decimal=3)
def test_non_normalized_glcm(self):
img = (np.random.random((100, 100)) * 8).astype(np.uint8)
p = greycomatrix(img, [1, 2, 4, 5], [0, 0.25, 1, 1.5], levels=8)
np.testing.assert_(np.max(greycoprops(p, 'correlation')) < 1.0)
def test_invalid_property(self):
result = greycomatrix(self.image, [1], [0], 4)
with testing.raises(ValueError):
greycoprops(result, 'ABC')
def test_homogeneity(self):
result = greycomatrix(self.image, [1], [0, 6], 4, normed=True,
symmetric=True)
homogeneity = greycoprops(result, 'homogeneity')[0, 0]
np.testing.assert_almost_equal(homogeneity, 0.80833333)
def test_energy(self):
result = greycomatrix(self.image, [1], [0, 4], 4, normed=True,
symmetric=True)
energy = greycoprops(result, 'energy')[0, 0]
np.testing.assert_almost_equal(energy, 0.38188131)
def test_correlation(self):
result = greycomatrix(self.image, [1, 2], [0], 4, normed=True,
symmetric=True)
energy = greycoprops(result, 'correlation')
np.testing.assert_almost_equal(energy[0, 0], 0.71953255)
np.testing.assert_almost_equal(energy[1, 0], 0.41176470)
def test_uniform_properties(self):
im = np.ones((4, 4), dtype=np.uint8)
result = greycomatrix(im, [1, 2, 8], [0, np.pi / 2], 4, normed=True,
symmetric=True)
for prop in ['contrast', 'dissimilarity', 'homogeneity',
'energy', 'correlation', 'ASM']:
greycoprops(result, prop)
class TestLBP():
def setup(self):
self.image = np.array([[255, 6, 255, 0, 141, 0],
[ 48, 250, 204, 166, 223, 63],
[ 8, 0, 159, 50, 255, 30],
[167, 255, 63, 40, 128, 255],
[ 0, 255, 30, 34, 255, 24],
[146, 241, 255, 0, 189, 126]], dtype='double')
@test_parallel()
def test_default(self):
lbp = local_binary_pattern(self.image, 8, 1, 'default')
ref = np.array([[ 0, 251, 0, 255, 96, 255],
[143, 0, 20, 153, 64, 56],
[238, 255, 12, 191, 0, 252],
[129, 64., 62, 159, 199, 0],
[255, 4, 255, 175, 0, 254],
[ 3, 5, 0, 255, 4, 24]])
np.testing.assert_array_equal(lbp, ref)
def test_ror(self):
lbp = local_binary_pattern(self.image, 8, 1, 'ror')
ref = np.array([[ 0, 127, 0, 255, 3, 255],
[ 31, 0, 5, 51, 1, 7],
[119, 255, 3, 127, 0, 63],
[ 3, 1, 31, 63, 31, 0],
[255, 1, 255, 95, 0, 127],
[ 3, 5, 0, 255, 1, 3]])
np.testing.assert_array_equal(lbp, ref)
def test_uniform(self):
lbp = local_binary_pattern(self.image, 8, 1, 'uniform')
ref = np.array([[0, 7, 0, 8, 2, 8],
[5, 0, 9, 9, 1, 3],
[9, 8, 2, 7, 0, 6],
[2, 1, 5, 6, 5, 0],
[8, 1, 8, 9, 0, 7],
[2, 9, 0, 8, 1, 2]])
np.testing.assert_array_equal(lbp, ref)
def test_var(self):
# Test idea: mean of variance is estimate of overall variance.
# Fix random seed for test stability.
np.random.seed(13141516)
# Create random image with known variance.
image = np.random.rand(500, 500)
target_std = 0.3
image = image / image.std() * target_std
# Use P=4 to avoid interpolation effects
P, R = 4, 1
lbp = local_binary_pattern(image, P, R, 'var')
# Take central part to avoid border effect.
lbp = lbp[5:-5, 5:-5]
# The LBP variance is biased (ddof=0), correct for that.
expected = target_std**2 * (P-1)/P
np.testing.assert_almost_equal(lbp.mean(), expected, 4)
def test_nri_uniform(self):
lbp = local_binary_pattern(self.image, 8, 1, 'nri_uniform')
ref = np.array([[ 0, 54, 0, 57, 12, 57],
[34, 0, 58, 58, 3, 22],
[58, 57, 15, 50, 0, 47],
[10, 3, 40, 42, 35, 0],
[57, 7, 57, 58, 0, 56],
[ 9, 58, 0, 57, 7, 14]])
np.testing.assert_array_almost_equal(lbp, ref)
class TestMBLBP():
def test_single_mblbp(self):
# Create dummy matrix where first and fifth rectangles have greater
# value than the central one. Therefore, the following bits
# should be 1.
test_img = np.zeros((9, 9), dtype='uint8')
test_img[3:6, 3:6] = 1
test_img[:3, :3] = 255
test_img[6:, 6:] = 255
# MB-LBP is filled in reverse order. So the first and fifth bits from
# the end should be filled.
correct_answer = 0b10001000
int_img = integral_image(test_img)
lbp_code = multiblock_lbp(int_img, 0, 0, 3, 3)
np.testing.assert_equal(lbp_code, correct_answer)