def label2rgb(label, image=None, colors=None, alpha=0.3,
bg_label=-1, bg_color=None, image_alpha=1):
"""Return an RGB image where color-coded labels are painted over the image.
Parameters
----------
label : array
Integer array of labels with the same shape as `image`.
image : array
Image used as underlay for labels. If the input is an RGB image, it's
converted to grayscale before coloring.
colors : list
List of colors. If the number of labels exceeds the number of colors,
then the colors are cycled.
alpha : float [0, 1]
Opacity of colorized labels. Ignored if image is `None`.
bg_label : int
Label that's treated as the background.
bg_color : str or array
Background color. Must be a name in `color_dict` or RGB float values
between [0, 1].
image_alpha : float [0, 1]
Opacity of the image.
"""
if colors is None:
colors = DEFAULT_COLORS
colors = [_rgb_vector(c) for c in colors]
if image is None:
colorized = np.zeros(label.shape + (3,), dtype=np.float64)
# Opacity doesn't make sense if no image exists.
alpha = 1
else:
if not image.shape[:2] == label.shape:
raise ValueError("`image` and `label` must be the same shape")
if image.min() < 0:
warnings.warn("Negative intensities in `image` are not supported")
image = img_as_float(rgb2gray(image))
colorized = gray2rgb(image) * image_alpha + (1 - image_alpha)
labels = list(set(label.flat))
color_cycle = itertools.cycle(colors)
if bg_label in labels:
labels.remove(bg_label)
if bg_color is not None:
labels.insert(0, bg_label)
bg_color = _rgb_vector(bg_color)
color_cycle = itertools.chain(bg_color, color_cycle)
for c, i in zip(color_cycle, labels):
mask = (label == i)
colorized[mask] = c * alpha + colorized[mask] * (1 - alpha)
return colorized
def label2rgb(label, image=None, colors=None, alpha=0.3,
bg_label=-1, bg_color=None, image_alpha=1):
"""Return an RGB image where color-coded labels are painted over the image.
Parameters
----------
label : array
Integer array of labels with the same shape as `image`.
image : array
Image used as underlay for labels. If the input is an RGB image, it's
converted to grayscale before coloring.
colors : list
List of colors. If the number of labels exceeds the number of colors,
then the colors are cycled.
alpha : float [0, 1]
Opacity of colorized labels. Ignored if image is `None`.
bg_label : int
Label that's treated as the background.
bg_color : str or array
Background color. Must be a name in `color_dict` or RGB float values
between [0, 1].
image_alpha : float [0, 1]
Opacity of the image.
"""
if colors is None:
colors = DEFAULT_COLORS
colors = [_rgb_vector(c) for c in colors]
if image is None:
image = np.zeros(label.shape + (3,), dtype=np.float64)
# Opacity doesn't make sense if no image exists.
alpha = 1
else:
if not image.shape[:2] == label.shape:
raise ValueError("`image` and `label` must be the same shape")
if image.min() < 0:
warnings.warn("Negative intensities in `image` are not supported")
image = img_as_float(rgb2gray(image))
image = gray2rgb(image) * image_alpha + (1 - image_alpha)
# Ensure that all labels are non-negative so we can index into
# `label_to_color` correctly.
offset = min(label.min(), bg_label)
if offset != 0:
label = label - offset # Make sure you don't modify the input array.
bg_label -= offset
new_type = np.min_scalar_type(label.max())
if new_type == np.bool:
new_type = np.uint8
label = label.astype(new_type)
unique_labels, color_cycle = _match_label_with_color(label, colors,
bg_label, bg_color)
if len(unique_labels) == 0:
return image
dense_labels = range(max(unique_labels) + 1)
label_to_color = np.array([c for i, c in zip(dense_labels, color_cycle)])
result = label_to_color[label] * alpha + image * (1 - alpha)
# Remove background label if its color was not specified.
remove_background = bg_label in unique_labels and bg_color is None
if remove_background:
result[label == bg_label] = image[label == bg_label]
return result
def label2rgb(label, image=None, colors=None, alpha=0.3,
bg_label=-1, bg_color=None, image_alpha=1):
"""Return an RGB image where color-coded labels are painted over the image.
Parameters
----------
label : array
Integer array of labels with the same shape as `image`.
image : array
Image used as underlay for labels. If the input is an RGB image, it's
converted to grayscale before coloring.
colors : list
List of colors. If the number of labels exceeds the number of colors,
then the colors are cycled.
alpha : float [0, 1]
Opacity of colorized labels. Ignored if image is `None`.
bg_label : int
Label that's treated as the background.
bg_color : str or array
Background color. Must be a name in `color_dict` or RGB float values
between [0, 1].
image_alpha : float [0, 1]
Opacity of the image.
"""
if colors is None:
colors = DEFAULT_COLORS
colors = [_rgb_vector(c) for c in colors]
if image is None:
img_layer = np.zeros(label.shape + (3,), dtype=np.float64)
# Opacity doesn't make sense if no image exists.
alpha = 1
else:
if not image.shape[:2] == label.shape:
raise ValueError("`image` and `label` must be the same shape")
if image.min() < 0:
warnings.warn("Negative intensities in `image` are not supported")
image = img_as_float(rgb2gray(image))
img_layer = gray2rgb(image) * image_alpha + (1 - image_alpha)
# need to ensure that all labels are ints >= 0
offset = label.min()
if offset != 0:
label -= offset
bg_label -= offset
new_type = np.min_scalar_type(label.max())
if new_type == np.bool:
new_type = np.uint8
label = label.astype(new_type)
labels = list(set(label.flat))
color_cycle = itertools.cycle(colors)
remove_background = bg_label in labels and bg_color is None
if bg_label in labels:
labels.remove(bg_label)
if bg_color is not None:
labels.insert(0, bg_label)
bg_color = _rgb_vector(bg_color)
color_cycle = itertools.chain(bg_color, color_cycle)
if len(labels) == 0:
return img_layer
label_to_color = np.zeros((max(labels) + 1, 3))
for lab, c in zip(labels, color_cycle):
label_to_color[lab] = c
label_layer = label_to_color[label]
result = label_layer * alpha + img_layer * (1 - alpha)
# remove background label if its color was not specified
if remove_background:
result[label == bg_label] = img_layer[label == bg_label]
return result
def label2rgb(label,
image=None,
colors=None,
alpha=0.3,
bg_label=-1,
bg_color=None,
image_alpha=1):
"""Return an RGB image where color-coded labels are painted over the image.
Parameters
----------
label : array
Integer array of labels with the same shape as `image`.
image : array
Image used as underlay for labels. If the input is an RGB image, it's
converted to grayscale before coloring.
colors : list
List of colors. If the number of labels exceeds the number of colors,
then the colors are cycled.
alpha : float [0, 1]
Opacity of colorized labels. Ignored if image is `None`.
bg_label : int
Label that's treated as the background.
bg_color : str or array
Background color. Must be a name in `color_dict` or RGB float values
between [0, 1].
image_alpha : float [0, 1]
Opacity of the image.
"""
if colors is None:
colors = DEFAULT_COLORS
colors = [_rgb_vector(c) for c in colors]
if image is None:
colorized = np.zeros(label.shape + (3, ), dtype=np.float64)
# Opacity doesn't make sense if no image exists.
alpha = 1
else:
if not image.shape[:2] == label.shape:
raise ValueError("`image` and `label` must be the same shape")
if image.min() < 0:
warnings.warn("Negative intensities in `image` are not supported")
image = img_as_float(rgb2gray(image))
colorized = gray2rgb(image) * image_alpha + (1 - image_alpha)
labels = list(set(label.flat))
color_cycle = itertools.cycle(colors)
if bg_label in labels:
labels.remove(bg_label)
if bg_color is not None:
labels.insert(0, bg_label)
bg_color = _rgb_vector(bg_color)
color_cycle = itertools.chain(bg_color, color_cycle)
for c, i in zip(color_cycle, labels):
mask = (label == i)
colorized[mask] = c * alpha + colorized[mask] * (1 - alpha)
return colorized