def test_leave_labels_alone():
labels = cp.array([-1, 0, 1])
labels_saved = labels.copy()
label2rgb(labels, bg_label=-1)
label2rgb(labels, bg_label=1)
assert_array_equal(labels, labels_saved)
def test_wrong_kind():
label = cp.ones((3, 3))
# Must not raise an error.
label2rgb(label, bg_label=-1)
# kind='foo' is wrong.
with pytest.raises(ValueError):
label2rgb(label, kind="foo", bg_label=-1)
def test_deprecation_warning():
image = cp.ones((3, 3))
label = cp.ones((3, 3))
with pytest.warns(FutureWarning) as record:
label2rgb(image, label)
expected_msg = "The new recommended value"
assert str(record[0].message).startswith(expected_msg)
def test_label_consistency():
"""Assert that the same labels map to the same colors."""
label_1 = cp.arange(5).reshape(1, -1)
label_2 = cp.array([0, 1])
colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1), (1, 1, 0), (1, 0, 1)]
# Set alphas just in case the defaults change
rgb_1 = label2rgb(label_1, colors=colors, bg_label=-1)
rgb_2 = label2rgb(label_2, colors=colors, bg_label=-1)
for label_id in label_2.ravel():
assert_array_almost_equal(
rgb_1[label_1 == label_id], rgb_2[label_2 == label_id]
)
def test_avg():
# label image
# ftm: off
label_field = cp.asarray(
[[1, 1, 1, 2], [1, 2, 2, 2], [3, 3, 4, 4]], dtype=np.uint8
)
# color image
r = cp.asarray(
[[1.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0]]
)
g = cp.asarray(
[[0.0, 0.0, 0.0, 1.0], [1.0, 1.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0]]
)
b = cp.asarray(
[[0.0, 0.0, 0.0, 1.0], [0.0, 1.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]]
)
image = cp.dstack((r, g, b))
# reference label-colored image
rout = cp.asarray(
[[0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5], [0.0, 0.0, 0.0, 0.0]]
)
gout = cp.asarray(
[
[0.25, 0.25, 0.25, 0.75],
[0.25, 0.75, 0.75, 0.75],
[0.0, 0.0, 0.0, 0.0],
]
)
bout = cp.asarray(
[[0.0, 0.0, 0.0, 1.0], [0.0, 1.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]]
)
expected_out = cp.dstack((rout, gout, bout))
# ftm: on
# test standard averaging
out = label2rgb(label_field, image, kind="avg", bg_label=-1)
assert_array_equal(out, expected_out)
# test averaging with custom background value
out_bg = label2rgb(
label_field, image, bg_label=2, bg_color=(0, 0, 0), kind="avg"
)
expected_out_bg = expected_out.copy()
expected_out_bg[label_field == 2] = 0
assert_array_equal(out_bg, expected_out_bg)
# test default background color
out_bg = label2rgb(label_field, image, bg_label=2, kind="avg")
assert_array_equal(out_bg, expected_out_bg)
def test_alpha():
image = cp.random.uniform(size=(3, 3))
label = cp.random.randint(0, 9, size=(3, 3))
# If we set `alpha = 0`, then rgb should match image exactly.
rgb = label2rgb(label, image=image, alpha=0, image_alpha=1, bg_label=-1)
assert_array_almost_equal(rgb[..., 0], image)
assert_array_almost_equal(rgb[..., 1], image)
assert_array_almost_equal(rgb[..., 2], image)
def test_nonconsecutive():
labels = cp.array([0, 2, 4, 0])
colors = [(1, 0, 0), (0, 0, 1)]
rout = cp.array(
[(1.0, 0.0, 0.0), (0.0, 0.0, 1.0), (1.0, 0.0, 0.0), (1.0, 0.0, 0.0)]
)
r = label2rgb(labels, colors=colors, alpha=1, image_alpha=1, bg_label=-1)
assert_array_almost_equal(rout, r)
def test_negative_labels():
labels = cp.array([0, -1, -2, 0])
rout = cp.array(
[(0.0, 0.0, 0.0), (0.0, 0.0, 1.0), (1.0, 0.0, 0.0), (0.0, 0.0, 0.0)]
)
assert_array_almost_equal(
rout, label2rgb(labels, bg_label=0, alpha=1, image_alpha=1)
)
def test_image_alpha():
image = cp.random.uniform(size=(1, 3))
label = cp.arange(3).reshape(1, -1)
colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)]
# If we set `image_alpha = 0`, then rgb should match label colors exactly.
rgb = label2rgb(
label, image=image, colors=colors, alpha=1, image_alpha=0, bg_label=-1
)
assert_array_almost_equal(rgb, [colors])
def test_rgb():
image = cp.ones((1, 3))
label = cp.arange(3).reshape(1, -1)
colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)]
# Set alphas just in case the defaults change
rgb = label2rgb(
label, image=image, colors=colors, alpha=1, image_alpha=1, bg_label=-1
)
assert_array_almost_equal(rgb, [colors])
def test_uint_image():
img = cp.random.randint(0, 255, (10, 10), dtype=cp.uint8)
labels = cp.zeros((10, 10), dtype=cp.int64)
labels[1:3, 1:3] = 1
labels[6:9, 6:9] = 2
output = label2rgb(labels, image=img, bg_label=0)
# Make sure that the output is made of floats and in the correct range
assert cp.issubdtype(output.dtype, cp.floating)
assert output.max() <= 1
def test_bg_color_rgb_string():
img = np.random.randint(0, 255, (10, 10), dtype=np.uint8)
labels = np.zeros((10, 10), dtype=np.int64)
labels[1:3, 1:3] = 1
labels[6:9, 6:9] = 2
img = cp.asarray(img)
labels = cp.asarray(labels)
output = label2rgb(labels, image=img, alpha=0.9, bg_label=0, bg_color="red")
assert output[0, 0, 0] > 0.9 # red channel
def test_bg_and_color_cycle():
image = cp.zeros((1, 10)) # dummy image
label = cp.arange(10).reshape(1, -1)
colors = [(1, 0, 0), (0, 0, 1)]
bg_color = (0, 0, 0)
rgb = label2rgb(
label,
image=image,
bg_label=0,
bg_color=bg_color,
colors=colors,
alpha=1,
)
assert_array_almost_equal(rgb[0, 0], bg_color)
for pixel, color in zip(rgb[0, 1:], itertools.cycle(colors)):
assert_array_almost_equal(pixel, color)
def test_no_input_image():
label = cp.arange(3).reshape(1, -1)
colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)]
rgb = label2rgb(label, colors=colors, bg_label=-1)
assert_array_almost_equal(rgb, [colors])
def test_shape_mismatch():
image = cp.ones((3, 3))
label = cp.ones((2, 2))
with pytest.raises(ValueError):
label2rgb(image, label, bg_label=-1)
def test_negative_intensity():
labels = cp.arange(100).reshape(10, 10)
image = cp.full((10, 10), -1, dtype="float64")
with pytest.warns(UserWarning):
label2rgb(labels, image, bg_label=-1)
