def test_leave_labels_alone():
labels = np.array([-1, 0, 1])
labels_saved = labels.copy()
label2rgb(labels)
label2rgb(labels, bg_label=1)
assert_array_equal(labels, labels_saved)
def test_avg():
label_field = np.array([[1, 1, 1, 2],
[1, 2, 2, 2],
[3, 3, 3, 3]], dtype=np.uint8)
r = np.array([[1., 1., 0., 0.],
[0., 0., 1., 1.],
[0., 0., 0., 0.]])
g = np.array([[0., 0., 0., 1.],
[1., 1., 1., 0.],
[0., 0., 0., 0.]])
b = np.array([[0., 0., 0., 1.],
[0., 1., 1., 1.],
[0., 0., 1., 1.]])
image = np.dstack((r, g, b))
out = label2rgb(label_field, image, kind='avg')
rout = np.array([[0.5, 0.5, 0.5, 0.5],
[0.5, 0.5, 0.5, 0.5],
[0. , 0. , 0. , 0. ]])
gout = np.array([[0.25, 0.25, 0.25, 0.75],
[0.25, 0.75, 0.75, 0.75],
[0. , 0. , 0. , 0. ]])
bout = np.array([[0. , 0. , 0. , 1. ],
[0. , 1. , 1. , 1. ],
[0.5, 0.5, 0.5, 0.5]])
expected_out = np.dstack((rout, gout, bout))
assert_array_equal(out, expected_out)
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)
def test_label_consistency():
"""Assert that the same labels map to the same colors."""
label_1 = np.arange(5).reshape(1, -1)
label_2 = np.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)
rgb_2 = label2rgb(label_2, colors=colors)
for label_id in label_2.flat:
assert_close(rgb_1[label_1 == label_id], rgb_2[label_2 == label_id])
def test_image_alpha():
image = np.random.uniform(size=(1, 3))
label = np.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)
assert_close(rgb, [colors])
def test_rgb():
image = np.ones((1, 3))
label = np.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)
assert_close(rgb, [colors])
def overlay_labels(labels, image, colormap, alpha):
"""
Overlay the labels on the image using the colormap
:param
labels : (M,N)
A numpy array with integer entries which contains the labels.
Labels must start from 0
image : (M,N,3)
A numpy array of type uint8 which contains the image data
colormap : (N,3)
A list which contains the colormaps. The entries are of type uint8
alpha : float
The opacity of the labelmap
bglabel : int
label of background class.
Pixel labels as bg won't be overlayed with color
Returns
-------
overlayedImg : (M,N,3)
A numpy array of type uint8
"""
colors = label2rgb(labels, colors=colormap)
colors = colors.astype(np.float32)
image = image.astype(np.float32)
overlayedImg = colors*alpha + (1-alpha)*image
overlayedImg = overlayedImg.astype(np.uint8)
return overlayedImg
def test_alpha():
image = np.random.uniform(size=(3, 3))
label = np.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)
assert_close(rgb[..., 0], image)
assert_close(rgb[..., 1], image)
assert_close(rgb[..., 2], image)
def test_avg():
# label image
label_field = np.array([[1, 1, 1, 2],
[1, 2, 2, 2],
[3, 3, 3, 3]], dtype=np.uint8)
# color image
r = np.array([[1., 1., 0., 0.],
[0., 0., 1., 1.],
[0., 0., 0., 0.]])
g = np.array([[0., 0., 0., 1.],
[1., 1., 1., 0.],
[0., 0., 0., 0.]])
b = np.array([[0., 0., 0., 1.],
[0., 1., 1., 1.],
[0., 0., 1., 1.]])
image = np.dstack((r, g, b))
# reference label-colored image
rout = np.array([[0.5, 0.5, 0.5, 0.5],
[0.5, 0.5, 0.5, 0.5],
[0. , 0. , 0. , 0. ]])
gout = np.array([[0.25, 0.25, 0.25, 0.75],
[0.25, 0.75, 0.75, 0.75],
[0. , 0. , 0. , 0. ]])
bout = np.array([[0. , 0. , 0. , 1. ],
[0. , 1. , 1. , 1. ],
[0.5, 0.5, 0.5, 0.5]])
expected_out = np.dstack((rout, gout, bout))
# test standard averaging
out = label2rgb(label_field, image, kind='avg')
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_bg_and_color_cycle():
image = np.zeros((1, 10)) # dummy image
label = np.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_close(rgb[0, 0], bg_color)
for pixel, color in zip(rgb[0, 1:], itertools.cycle(colors)):
assert_close(pixel, color)
def test_bg_and_color_cycle():
image = np.zeros((1, 10)) # dummy image
label = np.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_close(rgb[0, 0], bg_color)
for pixel, color in zip(rgb[0, 1:], itertools.cycle(colors)):
assert_close(pixel, color)
def test_avg():
label_field = np.array([[1, 1, 1, 2], [1, 2, 2, 2], [3, 3, 3, 3]],
dtype=np.uint8)
r = np.array([[1., 1., 0., 0.], [0., 0., 1., 1.], [0., 0., 0., 0.]])
g = np.array([[0., 0., 0., 1.], [1., 1., 1., 0.], [0., 0., 0., 0.]])
b = np.array([[0., 0., 0., 1.], [0., 1., 1., 1.], [0., 0., 1., 1.]])
image = np.dstack((r, g, b))
out = label2rgb(label_field, image, kind='avg')
rout = np.array([[0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5],
[0., 0., 0., 0.]])
gout = np.array([[0.25, 0.25, 0.25, 0.75], [0.25, 0.75, 0.75, 0.75],
[0., 0., 0., 0.]])
bout = np.array([[0., 0., 0., 1.], [0., 1., 1., 1.], [0.5, 0.5, 0.5, 0.5]])
expected_out = np.dstack((rout, gout, bout))
assert_array_equal(out, expected_out)
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)
def test_avg():
# label image
label_field = np.array([[1, 1, 1, 2], [1, 2, 2, 2], [3, 3, 3, 3]],
dtype=np.uint8)
# color image
r = np.array([[1., 1., 0., 0.], [0., 0., 1., 1.], [0., 0., 0., 0.]])
g = np.array([[0., 0., 0., 1.], [1., 1., 1., 0.], [0., 0., 0., 0.]])
b = np.array([[0., 0., 0., 1.], [0., 1., 1., 1.], [0., 0., 1., 1.]])
image = np.dstack((r, g, b))
# reference label-colored image
rout = np.array([[0.5, 0.5, 0.5, 0.5], [0.5, 0.5, 0.5, 0.5],
[0., 0., 0., 0.]])
gout = np.array([[0.25, 0.25, 0.25, 0.75], [0.25, 0.75, 0.75, 0.75],
[0., 0., 0., 0.]])
bout = np.array([[0., 0., 0., 1.], [0., 1., 1., 1.], [0.5, 0.5, 0.5, 0.5]])
expected_out = np.dstack((rout, gout, bout))
# test standard averaging
out = label2rgb(label_field, image, kind='avg')
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_uint_image(channel_axis):
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
output = label2rgb(labels,
image=img,
bg_label=0,
channel_axis=channel_axis)
# Make sure that the output is made of floats and in the correct range
assert np.issubdtype(output.dtype, np.floating)
assert output.max() <= 1
# size 3 (RGB) along the specified channel_axis
new_axis = channel_axis % output.ndim
assert output.shape[new_axis] == 3
def test_overlay_custom_saturation():
rgb_img = np.random.uniform(size=(10, 10, 3))
labels = np.ones((10, 10), dtype=np.int64)
labels[5:, 5:] = 2
labels[:3, :3] = 0
alpha = 0.3
saturation = 0.3
rgb = label2rgb(labels,
image=rgb_img,
alpha=alpha,
bg_label=0,
saturation=saturation)
hsv = rgb2hsv(rgb_img)
hsv[..., 1] *= saturation
saturaded_img = hsv2rgb(hsv)
# check that rgb part of input image is saturated, where labels=0
assert_array_almost_equal(saturaded_img[:3, :3] * (1 - alpha), rgb[:3, :3])
def test_label2rgb_shape_errors():
img = np.random.randint(0, 255, (10, 10, 3), dtype=np.uint8)
labels = np.zeros((10, 10), dtype=np.int64)
labels[2:5, 2:5] = 1
# mismatched 2D shape
with pytest.raises(ValueError):
label2rgb(labels, img[1:])
# too many axes in img
with pytest.raises(ValueError):
label2rgb(labels, img[..., np.newaxis])
# too many channels along the last axis
with pytest.raises(ValueError):
label2rgb(labels, np.concatenate((img, img), axis=-1))
def test_negative_labels():
labels = np.array([0, -1, -2, 0])
rout = np.array([(0., 0., 0.), (0., 0., 1.), (1., 0., 0.), (0., 0., 0.)])
assert_close(rout, label2rgb(labels, bg_label=0, alpha=1, image_alpha=1))
def test_no_input_image():
label = np.arange(3).reshape(1, -1)
colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)]
rgb = label2rgb(label, colors=colors)
assert_close(rgb, [colors])
def test_no_input_image():
label = np.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_saturation_warning():
rgb_img = np.random.uniform(size=(10, 10, 3))
labels = np.ones((10, 10), dtype=np.int64)
with pytest.raises(UserWarning):
label2rgb(labels, image=rgb_img, bg_label=0, saturation=2)
label2rgb(labels, image=rgb_img, bg_label=0, saturation=-1)
def test_no_input_image():
label = np.arange(3).reshape(1, -1)
colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)]
rgb = label2rgb(label, colors=colors)
assert_close(rgb, [colors])
def test_nonconsecutive():
labels = np.array([0, 2, 4, 0])
colors = [(1, 0, 0), (0, 0, 1)]
rout = np.array([(1., 0., 0.), (0., 0., 1.), (1., 0., 0.), (1., 0., 0.)])
assert_close(rout, label2rgb(labels, colors=colors, alpha=1,
image_alpha=1))
def test_negative_labels():
labels = np.array([0, -1, -2, 0])
rout = np.array([(0., 0., 0.), (0., 0., 1.), (1., 0., 0.), (0., 0., 0.)])
assert_close(rout, label2rgb(labels, bg_label=0, alpha=1, image_alpha=1))
def test_nonconsecutive():
labels = np.array([0, 2, 4, 0])
colors=[(1, 0, 0), (0, 0, 1)]
rout = np.array([(1., 0., 0.), (0., 0., 1.), (1., 0., 0.), (1., 0., 0.)])
assert_close(rout, label2rgb(labels, colors=colors, alpha=1, image_alpha=1))
def test_shape_mismatch():
image = np.ones((3, 3))
label = np.ones((2, 2))
with pytest.raises(ValueError):
label2rgb(image, label)
def test_shape_mismatch():
image = np.ones((3, 3))
label = np.ones((2, 2))
with testing.raises(ValueError):
label2rgb(image, label, bg_label=-1)
