def test_uniform_properties(self):
im = np.ones((4, 4), dtype=np.uint8)
result = graycomatrix(im, [1, 2, 8], [0, np.pi / 2], 4, normed=True,
symmetric=True)
for prop in ['contrast', 'dissimilarity', 'homogeneity',
'energy', 'correlation', 'ASM']:
graycoprops(result, prop)
def test_energy(self):
result = graycomatrix(self.image, [1], [0, 4],
4,
normed=True,
symmetric=True)
energy = graycoprops(result, 'energy')[0, 0]
np.testing.assert_almost_equal(energy, 0.38188131)
def test_homogeneity(self):
result = graycomatrix(self.image, [1], [0, 6],
4,
normed=True,
symmetric=True)
homogeneity = graycoprops(result, 'homogeneity')[0, 0]
np.testing.assert_almost_equal(homogeneity, 0.80833333)
def test_correlation(self):
result = graycomatrix(self.image, [1, 2], [0],
4,
normed=True,
symmetric=True)
energy = graycoprops(result, 'correlation')
np.testing.assert_almost_equal(energy[0, 0], 0.71953255)
np.testing.assert_almost_equal(energy[1, 0], 0.41176470)
def test_dissimilarity_2(self):
result = graycomatrix(self.image, [1, 3], [np.pi / 2],
4,
normed=True,
symmetric=True)
result = np.round(result, 3)
dissimilarity = graycoprops(result, 'dissimilarity')[0, 0]
np.testing.assert_almost_equal(dissimilarity, 0.665, decimal=3)
def test_contrast(self):
result = graycomatrix(self.image, [1, 2], [0],
4,
normed=True,
symmetric=True)
result = np.round(result, 3)
contrast = graycoprops(result, 'contrast')
np.testing.assert_almost_equal(contrast[0, 0], 0.585, decimal=3)
def test_greycomatrix_and_greycoprops_deprecations(self):
expected = graycomatrix(self.image, [1], [0, np.pi / 2], 4,
normed=True, symmetric=True)
with expected_warnings(["Function ``greycomatrix``"]):
result = greycomatrix(self.image, [1], [0, np.pi / 2], 4,
normed=True, symmetric=True)
np.testing.assert_array_equal(expected, result)
result = np.round(result, 3)
dissimilarity_expected = graycoprops(result, 'dissimilarity')
with expected_warnings(["Function ``greycoprops``"]):
dissimilarity_result = greycoprops(result, 'dissimilarity')
np.testing.assert_array_equal(
dissimilarity_expected, dissimilarity_result
)
loc[1]:loc[1] + PATCH_SIZE])
# select some patches from sky areas of the image
sky_locations = [(38, 34), (139, 28), (37, 437), (145, 379)]
sky_patches = []
for loc in sky_locations:
sky_patches.append(image[loc[0]:loc[0] + PATCH_SIZE,
loc[1]:loc[1] + PATCH_SIZE])
# compute some GLCM properties each patch
xs = []
ys = []
for patch in (grass_patches + sky_patches):
glcm = graycomatrix(patch, distances=[5], angles=[0], levels=256,
symmetric=True, normed=True)
xs.append(graycoprops(glcm, 'dissimilarity')[0, 0])
ys.append(graycoprops(glcm, 'correlation')[0, 0])
# create the figure
fig = plt.figure(figsize=(8, 8))
# display original image with locations of patches
ax = fig.add_subplot(3, 2, 1)
ax.imshow(image, cmap=plt.cm.gray,
vmin=0, vmax=255)
for (y, x) in grass_locations:
ax.plot(x + PATCH_SIZE / 2, y + PATCH_SIZE / 2, 'gs')
for (y, x) in sky_locations:
ax.plot(x + PATCH_SIZE / 2, y + PATCH_SIZE / 2, 'bs')
ax.set_xlabel('Original Image')
ax.set_xticks([])
def test_invalid_property(self):
result = graycomatrix(self.image, [1], [0], 4)
with pytest.raises(ValueError):
graycoprops(result, 'ABC')
def test_non_normalized_glcm(self):
img = (np.random.random((100, 100)) * 8).astype(np.uint8)
p = graycomatrix(img, [1, 2, 4, 5], [0, 0.25, 1, 1.5], levels=8)
np.testing.assert_(np.max(graycoprops(p, 'correlation')) < 1.0)