def test_lbp_values():
image = Image([[0.0, 6.0, 0.0], [5.0, 18.0, 13.0], [0.0, 20.0, 0.0]])
lbp_img = lbp(image, radius=1, samples=4, mapping_type="none", padding=False)
assert_allclose(lbp_img.pixels, 8.0)
image = Image([[0.0, 6.0, 0.0], [5.0, 25.0, 13.0], [0.0, 20.0, 0.0]])
lbp_img = lbp(image, radius=1, samples=4, mapping_type="riu2", padding=False)
assert_allclose(lbp_img.pixels, 0.0)
image = Image([[0.0, 6.0, 0.0], [5.0, 13.0, 13.0], [0.0, 20.0, 0.0]])
lbp_img = lbp(image, radius=1, samples=4, mapping_type="u2", padding=False)
assert_allclose(lbp_img.pixels, 8.0)
image = Image([[0.0, 6.0, 0.0], [5.0, 6.0, 13.0], [0.0, 20.0, 0.0]])
lbp_img = lbp(image, radius=1, samples=4, mapping_type="ri", padding=False)
assert_allclose(lbp_img.pixels, 4.0)
def test_lbp_values():
image = Image([[0., 6., 0.], [5., 18., 13.], [0., 20., 0.]])
lbp_img = lbp(image, radius=1, samples=4, mapping_type='none',
padding=False)
assert_allclose(lbp_img.pixels, 8.)
image = Image([[0., 6., 0.], [5., 25., 13.], [0., 20., 0.]])
lbp_img = lbp(image, radius=1, samples=4, mapping_type='riu2',
padding=False)
assert_allclose(lbp_img.pixels, 0.)
image = Image([[0., 6., 0.], [5., 13., 13.], [0., 20., 0.]])
lbp_img = lbp(image, radius=1, samples=4, mapping_type='u2', padding=False)
assert_allclose(lbp_img.pixels, 8.)
image = Image([[0., 6., 0.], [5., 6., 13.], [0., 20., 0.]])
lbp_img = lbp(image, radius=1, samples=4, mapping_type='ri', padding=False)
assert_allclose(lbp_img.pixels, 4.)
def test_lbp_values():
image = Image([[0., 6., 0.], [5., 18., 13.], [0., 20., 0.]])
lbp_img = lbp(image, radius=1, samples=4, mapping_type='none',
padding=False)
assert_allclose(lbp_img.pixels, 8.)
image = Image([[0., 6., 0.], [5., 25., 13.], [0., 20., 0.]])
lbp_img = lbp(image, radius=1, samples=4, mapping_type='riu2',
padding=False)
assert_allclose(lbp_img.pixels, 0.)
image = Image([[0., 6., 0.], [5., 13., 13.], [0., 20., 0.]])
lbp_img = lbp(image, radius=1, samples=4, mapping_type='u2', padding=False)
assert_allclose(lbp_img.pixels, 8.)
image = Image([[0., 6., 0.], [5., 6., 13.], [0., 20., 0.]])
lbp_img = lbp(image, radius=1, samples=4, mapping_type='ri', padding=False)
assert_allclose(lbp_img.pixels, 4.)
def test_windowiterator_lbp_no_padding():
n_cases = 5
image_width = np.random.randint(50, 250, [n_cases, 1])
image_height = np.random.randint(50, 250, [n_cases, 1])
window_step_horizontal = np.random.randint(1, 10, [n_cases, 1])
window_step_vertical = np.random.randint(1, 10, [n_cases, 1])
radius = np.random.randint(3, 5, [n_cases, 1])
for i in range(n_cases):
image = Image(np.random.randn(1, image_height[i, 0], image_width[i,
0]))
lbp_img = lbp(image,
radius=radius[i, 0],
samples=8,
window_step_vertical=window_step_vertical[i, 0],
window_step_horizontal=window_step_horizontal[i, 0],
window_step_unit='pixels',
padding=False)
window_size = 2 * radius[i, 0] + 1
n_windows_horizontal = len(
range(window_size - 1, image_width[i, 0],
window_step_horizontal[i, 0]))
n_windows_vertical = len(
range(window_size - 1, image_height[i, 0],
window_step_vertical[i, 0]))
assert_allclose(lbp_img.shape,
(n_windows_vertical, n_windows_horizontal))
def test_lbp_channels():
n_cases = 3
n_combs = np.random.randint(1, 6, [n_cases, 1])
channels = np.random.randint(1, 4, [n_cases, 1])
for i in range(n_cases):
radius = random.sample(range(1, 10), n_combs[i, 0])
samples = random.sample(range(4, 12), n_combs[i, 0])
image = MaskedImage(np.random.randn(channels[i, 0], 40, 40))
lbp_img = lbp(image, radius=radius, samples=samples,
window_step_vertical=3, window_step_horizontal=3,
window_step_unit='pixels', padding=True)
assert_allclose(lbp_img.n_channels, n_combs[i, 0] * channels[i, 0])
def test_lbp_channels():
n_cases = 3
n_combs = np.random.randint(1, 6, [n_cases, 1])
channels = np.random.randint(1, 4, [n_cases, 1])
for i in range(n_cases):
radius = random.sample(range(1, 10), n_combs[i, 0])
samples = random.sample(range(4, 12), n_combs[i, 0])
image = MaskedImage(np.random.randn(channels[i, 0], 40, 40))
lbp_img = lbp(image, radius=radius, samples=samples,
window_step_vertical=3, window_step_horizontal=3,
window_step_unit='pixels', padding=True)
assert_allclose(lbp_img.n_channels, n_combs[i, 0] * channels[i, 0])
def test_windowiterator_lbp_padding():
n_cases = 5
image_width = np.random.randint(50, 250, [n_cases, 1])
image_height = np.random.randint(50, 250, [n_cases, 1])
window_step_horizontal = np.random.randint(1, 10, [n_cases, 1])
window_step_vertical = np.random.randint(1, 10, [n_cases, 1])
for i in range(n_cases):
image = MaskedImage(np.random.randn(1, image_height[i, 0],
image_width[i, 0]))
lbp_img = lbp(image, window_step_vertical=window_step_vertical[i, 0],
window_step_horizontal=window_step_horizontal[i, 0],
window_step_unit='pixels', padding=True)
n_windows_horizontal = len(range(0, image_width[i, 0],
window_step_horizontal[i, 0]))
n_windows_vertical = len(range(0, image_height[i, 0],
window_step_vertical[i, 0]))
assert_allclose(lbp_img.shape, (n_windows_vertical,
n_windows_horizontal))
def test_windowiterator_lbp_padding():
n_cases = 5
image_width = np.random.randint(50, 250, [n_cases, 1])
image_height = np.random.randint(50, 250, [n_cases, 1])
window_step_horizontal = np.random.randint(1, 10, [n_cases, 1])
window_step_vertical = np.random.randint(1, 10, [n_cases, 1])
for i in range(n_cases):
image = MaskedImage(np.random.randn(1, image_height[i, 0],
image_width[i, 0]))
lbp_img = lbp(image, window_step_vertical=window_step_vertical[i, 0],
window_step_horizontal=window_step_horizontal[i, 0],
window_step_unit='pixels', padding=True)
n_windows_horizontal = len(range(0, image_width[i, 0],
window_step_horizontal[i, 0]))
n_windows_vertical = len(range(0, image_height[i, 0],
window_step_vertical[i, 0]))
assert_allclose(lbp_img.shape, (n_windows_vertical,
n_windows_horizontal))
def test_windowiterator_lbp_no_padding():
n_cases = 5
image_width = np.random.randint(50, 250, [n_cases, 1])
image_height = np.random.randint(50, 250, [n_cases, 1])
window_step_horizontal = np.random.randint(1, 10, [n_cases, 1])
window_step_vertical = np.random.randint(1, 10, [n_cases, 1])
radius = np.random.randint(3, 5, [n_cases, 1])
for i in range(n_cases):
image = Image(np.random.randn(1, image_height[i, 0],
image_width[i, 0]))
lbp_img = lbp(image, radius=radius[i, 0], samples=8,
window_step_vertical=window_step_vertical[i, 0],
window_step_horizontal=window_step_horizontal[i, 0],
window_step_unit='pixels', padding=False)
window_size = 2 * radius[i, 0] + 1
n_windows_horizontal = len(range(window_size - 1, image_width[i, 0],
window_step_horizontal[i, 0]))
n_windows_vertical = len(range(window_size - 1, image_height[i, 0],
window_step_vertical[i, 0]))
assert_allclose(lbp_img.shape, (n_windows_vertical,
n_windows_horizontal))