mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-27 18:25:32 +08:00
Steven Silvester
217 lines
7.4 KiB
Python
217 lines
7.4 KiB
Python
import itertools
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import numpy as np
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from .._shared.utils import warn
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from .. import img_as_float
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from . import rgb_colors
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from .colorconv import rgb2gray, gray2rgb
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import six
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from six.moves import zip
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__all__ = ['color_dict', 'label2rgb', 'DEFAULT_COLORS']
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DEFAULT_COLORS = ('red', 'blue', 'yellow', 'magenta', 'green',
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'indigo', 'darkorange', 'cyan', 'pink', 'yellowgreen')
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color_dict = dict((k, v) for k, v in six.iteritems(rgb_colors.__dict__)
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if isinstance(v, tuple))
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def _rgb_vector(color):
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"""Return RGB color as (1, 3) array.
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This RGB array gets multiplied by masked regions of an RGB image, which are
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partially flattened by masking (i.e. dimensions 2D + RGB -> 1D + RGB).
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Parameters
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----------
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color : str or array
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Color name in `color_dict` or RGB float values between [0, 1].
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"""
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if isinstance(color, six.string_types):
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color = color_dict[color]
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# Slice to handle RGBA colors.
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return np.array(color[:3])
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def _match_label_with_color(label, colors, bg_label, bg_color):
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"""Return `unique_labels` and `color_cycle` for label array and color list.
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Colors are cycled for normal labels, but the background color should only
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be used for the background.
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"""
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# Temporarily set background color; it will be removed later.
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if bg_color is None:
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bg_color = (0, 0, 0)
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bg_color = _rgb_vector([bg_color])
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unique_labels = list(set(label.flat))
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# Ensure that the background label is in front to match call to `chain`.
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if bg_label in unique_labels:
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unique_labels.remove(bg_label)
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unique_labels.insert(0, bg_label)
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# Modify labels and color cycle so background color is used only once.
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color_cycle = itertools.cycle(colors)
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color_cycle = itertools.chain(bg_color, color_cycle)
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return unique_labels, color_cycle
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def label2rgb(label, image=None, colors=None, alpha=0.3,
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bg_label=-1, bg_color=(0, 0, 0), image_alpha=1, kind='overlay'):
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"""Return an RGB image where color-coded labels are painted over the image.
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Parameters
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----------
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label : array, shape (M, N)
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Integer array of labels with the same shape as `image`.
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image : array, shape (M, N, 3), optional
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Image used as underlay for labels. If the input is an RGB image, it's
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converted to grayscale before coloring.
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colors : list, optional
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List of colors. If the number of labels exceeds the number of colors,
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then the colors are cycled.
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alpha : float [0, 1], optional
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Opacity of colorized labels. Ignored if image is `None`.
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bg_label : int, optional
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Label that's treated as the background.
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bg_color : str or array, optional
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Background color. Must be a name in `color_dict` or RGB float values
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between [0, 1].
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image_alpha : float [0, 1], optional
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Opacity of the image.
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kind : string, one of {'overlay', 'avg'}
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The kind of color image desired. 'overlay' cycles over defined colors
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and overlays the colored labels over the original image. 'avg' replaces
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each labeled segment with its average color, for a stained-class or
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pastel painting appearance.
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Returns
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-------
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result : array of float, shape (M, N, 3)
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The result of blending a cycling colormap (`colors`) for each distinct
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value in `label` with the image, at a certain alpha value.
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"""
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if kind == 'overlay':
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return _label2rgb_overlay(label, image, colors, alpha, bg_label,
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bg_color, image_alpha)
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else:
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return _label2rgb_avg(label, image, bg_label, bg_color)
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def _label2rgb_overlay(label, image=None, colors=None, alpha=0.3,
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bg_label=-1, bg_color=None, image_alpha=1):
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"""Return an RGB image where color-coded labels are painted over the image.
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Parameters
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----------
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label : array, shape (M, N)
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Integer array of labels with the same shape as `image`.
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image : array, shape (M, N, 3), optional
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Image used as underlay for labels. If the input is an RGB image, it's
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converted to grayscale before coloring.
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colors : list, optional
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List of colors. If the number of labels exceeds the number of colors,
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then the colors are cycled.
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alpha : float [0, 1], optional
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Opacity of colorized labels. Ignored if image is `None`.
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bg_label : int, optional
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Label that's treated as the background.
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bg_color : str or array, optional
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Background color. Must be a name in `color_dict` or RGB float values
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between [0, 1].
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image_alpha : float [0, 1], optional
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Opacity of the image.
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Returns
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-------
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result : array of float, shape (M, N, 3)
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The result of blending a cycling colormap (`colors`) for each distinct
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value in `label` with the image, at a certain alpha value.
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"""
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if colors is None:
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colors = DEFAULT_COLORS
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colors = [_rgb_vector(c) for c in colors]
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if image is None:
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image = np.zeros(label.shape + (3,), dtype=np.float64)
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# Opacity doesn't make sense if no image exists.
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alpha = 1
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else:
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if not image.shape[:2] == label.shape:
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raise ValueError("`image` and `label` must be the same shape")
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if image.min() < 0:
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warn("Negative intensities in `image` are not supported")
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image = img_as_float(rgb2gray(image))
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image = gray2rgb(image) * image_alpha + (1 - image_alpha)
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# Ensure that all labels are non-negative so we can index into
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# `label_to_color` correctly.
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offset = min(label.min(), bg_label)
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if offset != 0:
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label = label - offset # Make sure you don't modify the input array.
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bg_label -= offset
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new_type = np.min_scalar_type(int(label.max()))
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if new_type == np.bool:
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new_type = np.uint8
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label = label.astype(new_type)
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unique_labels, color_cycle = _match_label_with_color(label, colors,
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bg_label, bg_color)
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if len(unique_labels) == 0:
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return image
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dense_labels = range(max(unique_labels) + 1)
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label_to_color = np.array([c for i, c in zip(dense_labels, color_cycle)])
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result = label_to_color[label] * alpha + image * (1 - alpha)
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# Remove background label if its color was not specified.
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remove_background = bg_label in unique_labels and bg_color is None
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if remove_background:
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result[label == bg_label] = image[label == bg_label]
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return result
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def _label2rgb_avg(label_field, image, bg_label=0, bg_color=(0, 0, 0)):
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"""Visualise each segment in `label_field` with its mean color in `image`.
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Parameters
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----------
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label_field : array of int
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A segmentation of an image.
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image : array, shape ``label_field.shape + (3,)``
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A color image of the same spatial shape as `label_field`.
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bg_label : int, optional
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A value in `label_field` to be treated as background.
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bg_color : 3-tuple of int, optional
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The color for the background label
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Returns
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-------
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out : array, same shape and type as `image`
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The output visualization.
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"""
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out = np.zeros_like(image)
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labels = np.unique(label_field)
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bg = (labels == bg_label)
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if bg.any():
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labels = labels[labels != bg_label]
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out[bg] = bg_color
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for label in labels:
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mask = (label_field == label).nonzero()
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color = image[mask].mean(axis=0)
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out[mask] = color
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return out
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