color
Module: color
skimage.color.combine_stains (stains, conv_matrix) | Stain to RGB color space conversion. |
skimage.color.convert_colorspace (arr, ...) | Convert an image array to a new color space. |
skimage.color.deltaE_cie76 (lab1, lab2) | Euclidean distance between two points in Lab color space |
skimage.color.deltaE_ciede2000 (lab1, lab2[, ...]) | Color difference as given by the CIEDE 2000 standard. |
skimage.color.deltaE_ciede94 (lab1, lab2[, ...]) | Color difference according to CIEDE 94 standard |
skimage.color.deltaE_cmc (lab1, lab2[, kL, kC]) | Color difference from the CMC l:c standard. |
skimage.color.gray2rgb (image[, alpha]) | Create an RGB representation of a gray-level image. |
skimage.color.guess_spatial_dimensions (image) | Make an educated guess about whether an image has a channels dimension. |
skimage.color.hed2rgb (hed) | Haematoxylin-Eosin-DAB (HED) to RGB color space conversion. |
skimage.color.hsv2rgb (hsv) | HSV to RGB color space conversion. |
skimage.color.lab2lch (lab) | CIE-LAB to CIE-LCH color space conversion. |
skimage.color.lab2rgb (lab) | Lab to RGB color space conversion. |
skimage.color.lab2xyz (lab[, illuminant, ...]) | CIE-LAB to XYZcolor space conversion. |
skimage.color.label2rgb (label[, image, ...]) | Return an RGB image where color-coded labels are painted over the image. |
skimage.color.lch2lab (lch) | CIE-LCH to CIE-LAB color space conversion. |
skimage.color.luv2rgb (luv) | Luv to RGB color space conversion. |
skimage.color.luv2xyz (luv[, illuminant, ...]) | CIE-Luv to XYZ color space conversion. |
skimage.color.rgb2gray (rgb) | Compute luminance of an RGB image. |
skimage.color.rgb2grey (rgb) | Compute luminance of an RGB image. |
skimage.color.rgb2hed (rgb) | RGB to Haematoxylin-Eosin-DAB (HED) color space conversion. |
skimage.color.rgb2hsv (rgb) | RGB to HSV color space conversion. |
skimage.color.rgb2lab (rgb) | RGB to lab color space conversion. |
skimage.color.rgb2luv (rgb) | RGB to CIE-Luv color space conversion. |
skimage.color.rgb2rgbcie (rgb) | RGB to RGB CIE color space conversion. |
skimage.color.rgb2xyz (rgb) | RGB to XYZ color space conversion. |
skimage.color.rgbcie2rgb (rgbcie) | RGB CIE to RGB color space conversion. |
skimage.color.separate_stains (rgb, conv_matrix) | RGB to stain color space conversion. |
skimage.color.xyz2lab (xyz[, illuminant, ...]) | XYZ to CIE-LAB color space conversion. |
skimage.color.xyz2luv (xyz[, illuminant, ...]) | XYZ to CIE-Luv color space conversion. |
skimage.color.xyz2rgb (xyz) | XYZ to RGB color space conversion. |
combine_stains
-
skimage.color.combine_stains(stains, conv_matrix)
[source] -
Stain to RGB color space conversion.
Parameters: stains : array_like
The image in stain color space, in a 3-D array of shape
(.., .., 3)
.conv_matrix: ndarray
The stain separation matrix as described by G. Landini [R31].
Returns: out : ndarray
The image in RGB format, in a 3-D array of shape
(.., .., 3)
.Raises: ValueError
If
stains
is not a 3-D array of shape(.., .., 3)
.Notes
Stain combination matrices available in the
color
module and their respective colorspace:-
rgb_from_hed
: Hematoxylin + Eosin + DAB -
rgb_from_hdx
: Hematoxylin + DAB -
rgb_from_fgx
: Feulgen + Light Green -
rgb_from_bex
: Giemsa stain : Methyl Blue + Eosin -
rgb_from_rbd
: FastRed + FastBlue + DAB -
rgb_from_gdx
: Methyl Green + DAB -
rgb_from_hax
: Hematoxylin + AEC -
rgb_from_bro
: Blue matrix Anilline Blue + Red matrix Azocarmine + Orange matrix Orange-G -
rgb_from_bpx
: Methyl Blue + Ponceau Fuchsin -
rgb_from_ahx
: Alcian Blue + Hematoxylin -
rgb_from_hpx
: Hematoxylin + PAS
References
[R31] (1, 2) http://www.dentistry.bham.ac.uk/landinig/software/cdeconv/cdeconv.html Examples
>>> from skimage import data >>> from skimage.color import (separate_stains, combine_stains, ... hdx_from_rgb, rgb_from_hdx) >>> ihc = data.immunohistochemistry() >>> ihc_hdx = separate_stains(ihc, hdx_from_rgb) >>> ihc_rgb = combine_stains(ihc_hdx, rgb_from_hdx)
-
convert_colorspace
-
skimage.color.convert_colorspace(arr, fromspace, tospace)
[source] -
Convert an image array to a new color space.
Parameters: arr : array_like
The image to convert.
fromspace : str
The color space to convert from. Valid color space strings are
['RGB', 'HSV', 'RGB CIE', 'XYZ']
. Value may also be specified as lower case.tospace : str
The color space to convert to. Valid color space strings are
['RGB', 'HSV', 'RGB CIE', 'XYZ']
. Value may also be specified as lower case.Returns: newarr : ndarray
The converted image.
Notes
Conversion occurs through the “central” RGB color space, i.e. conversion from XYZ to HSV is implemented as
XYZ -> RGB -> HSV
instead of directly.Examples
>>> from skimage import data >>> img = data.astronaut() >>> img_hsv = convert_colorspace(img, 'RGB', 'HSV')
deltaE_cie76
-
skimage.color.deltaE_cie76(lab1, lab2)
[source] -
Euclidean distance between two points in Lab color space
Parameters: lab1 : array_like
reference color (Lab colorspace)
lab2 : array_like
comparison color (Lab colorspace)
Returns: dE : array_like
distance between colors
lab1
andlab2
References
[R32] http://en.wikipedia.org/wiki/Color_difference [R33] A. R. Robertson, “The CIE 1976 color-difference formulae,” Color Res. Appl. 2, 7-11 (1977).
deltaE_ciede2000
-
skimage.color.deltaE_ciede2000(lab1, lab2, kL=1, kC=1, kH=1)
[source] -
Color difference as given by the CIEDE 2000 standard.
CIEDE 2000 is a major revision of CIDE94. The perceptual calibration is largely based on experience with automotive paint on smooth surfaces.
Parameters: lab1 : array_like
reference color (Lab colorspace)
lab2 : array_like
comparison color (Lab colorspace)
kL : float (range), optional
lightness scale factor, 1 for “acceptably close”; 2 for “imperceptible” see deltaE_cmc
kC : float (range), optional
chroma scale factor, usually 1
kH : float (range), optional
hue scale factor, usually 1
Returns: deltaE : array_like
The distance between
lab1
andlab2
Notes
CIEDE 2000 assumes parametric weighting factors for the lightness, chroma, and hue (
kL
,kC
,kH
respectively). These default to 1.References
[R34] http://en.wikipedia.org/wiki/Color_difference [R35] http://www.ece.rochester.edu/~gsharma/ciede2000/ciede2000noteCRNA.pdf (doi:10.1364/AO.33.008069) [R36] M. Melgosa, J. Quesada, and E. Hita, “Uniformity of some recent color metrics tested with an accurate color-difference tolerance dataset,” Appl. Opt. 33, 8069-8077 (1994).
deltaE_ciede94
-
skimage.color.deltaE_ciede94(lab1, lab2, kH=1, kC=1, kL=1, k1=0.045, k2=0.015)
[source] -
Color difference according to CIEDE 94 standard
Accommodates perceptual non-uniformities through the use of application specific scale factors (
kH
,kC
,kL
,k1
, andk2
).Parameters: lab1 : array_like
reference color (Lab colorspace)
lab2 : array_like
comparison color (Lab colorspace)
kH : float, optional
Hue scale
kC : float, optional
Chroma scale
kL : float, optional
Lightness scale
k1 : float, optional
first scale parameter
k2 : float, optional
second scale parameter
Returns: dE : array_like
color difference between
lab1
andlab2
Notes
deltaE_ciede94 is not symmetric with respect to lab1 and lab2. CIEDE94 defines the scales for the lightness, hue, and chroma in terms of the first color. Consequently, the first color should be regarded as the “reference” color.
kL
,k1
,k2
depend on the application and default to the values suggested for graphic artsParameter Graphic Arts Textiles kL
1.000 2.000 k1
0.045 0.048 k2
0.015 0.014 References
[R37] http://en.wikipedia.org/wiki/Color_difference [R38] http://www.brucelindbloom.com/index.html?Eqn_DeltaE_CIE94.html
deltaE_cmc
-
skimage.color.deltaE_cmc(lab1, lab2, kL=1, kC=1)
[source] -
Color difference from the CMC l:c standard.
This color difference was developed by the Colour Measurement Committee (CMC) of the Society of Dyers and Colourists (United Kingdom). It is intended for use in the textile industry.
The scale factors
kL
,kC
set the weight given to differences in lightness and chroma relative to differences in hue. The usual values arekL=2
,kC=1
for “acceptability” andkL=1
,kC=1
for “imperceptibility”. Colors withdE > 1
are “different” for the given scale factors.Parameters: lab1 : array_like
reference color (Lab colorspace)
lab2 : array_like
comparison color (Lab colorspace)
Returns: dE : array_like
distance between colors
lab1
andlab2
Notes
deltaE_cmc the defines the scales for the lightness, hue, and chroma in terms of the first color. Consequently
deltaE_cmc(lab1, lab2) != deltaE_cmc(lab2, lab1)
References
[R39] http://en.wikipedia.org/wiki/Color_difference [R40] http://www.brucelindbloom.com/index.html?Eqn_DeltaE_CIE94.html [R41] F. J. J. Clarke, R. McDonald, and B. Rigg, “Modification to the JPC79 colour-difference formula,” J. Soc. Dyers Colour. 100, 128-132 (1984).
gray2rgb
-
skimage.color.gray2rgb(image, alpha=None)
[source] -
Create an RGB representation of a gray-level image.
Parameters: image : array_like
Input image of shape
(M, N [, P])
.alpha : bool, optional
Ensure that the output image has an alpha layer. If None, alpha layers are passed through but not created.
Returns: rgb : ndarray
RGB image of shape
(M, N, [, P], 3)
.Raises: ValueError
If the input is not a 2- or 3-dimensional image.
guess_spatial_dimensions
-
skimage.color.guess_spatial_dimensions(image)
[source] -
Make an educated guess about whether an image has a channels dimension.
Parameters: image : ndarray
The input image.
Returns: spatial_dims : int or None
The number of spatial dimensions of
image
. If ambiguous, the value isNone
.Raises: ValueError
If the image array has less than two or more than four dimensions.
hed2rgb
-
skimage.color.hed2rgb(hed)
[source] -
Haematoxylin-Eosin-DAB (HED) to RGB color space conversion.
Parameters: hed : array_like
The image in the HED color space, in a 3-D array of shape
(.., .., 3)
.Returns: out : ndarray
The image in RGB, in a 3-D array of shape
(.., .., 3)
.Raises: ValueError
If
hed
is not a 3-D array of shape(.., .., 3)
.References
[R42] A. C. Ruifrok and D. A. Johnston, “Quantification of histochemical staining by color deconvolution.,” Analytical and quantitative cytology and histology / the International Academy of Cytology [and] American Society of Cytology, vol. 23, no. 4, pp. 291-9, Aug. 2001. Examples
>>> from skimage import data >>> from skimage.color import rgb2hed, hed2rgb >>> ihc = data.immunohistochemistry() >>> ihc_hed = rgb2hed(ihc) >>> ihc_rgb = hed2rgb(ihc_hed)
hsv2rgb
-
skimage.color.hsv2rgb(hsv)
[source] -
HSV to RGB color space conversion.
Parameters: hsv : array_like
The image in HSV format, in a 3-D array of shape
(.., .., 3)
.Returns: out : ndarray
The image in RGB format, in a 3-D array of shape
(.., .., 3)
.Raises: ValueError
If
hsv
is not a 3-D array of shape(.., .., 3)
.Notes
The conversion assumes an input data range of
[0, 1]
for all color components.Conversion between RGB and HSV color spaces results in some loss of precision, due to integer arithmetic and rounding [R43].
References
[R43] (1, 2) http://en.wikipedia.org/wiki/HSL_and_HSV Examples
>>> from skimage import data >>> img = data.astronaut() >>> img_hsv = rgb2hsv(img) >>> img_rgb = hsv2rgb(img_hsv)
lab2lch
-
skimage.color.lab2lch(lab)
[source] -
CIE-LAB to CIE-LCH color space conversion.
LCH is the cylindrical representation of the LAB (Cartesian) colorspace
Parameters: lab : array_like
The N-D image in CIE-LAB format. The last (
N+1
-th) dimension must have at least 3 elements, corresponding to theL
,a
, andb
color channels. Subsequent elements are copied.Returns: out : ndarray
The image in LCH format, in a N-D array with same shape as input
lab
.Raises: ValueError
If
lch
does not have at least 3 color channels (i.e. l, a, b).Notes
The Hue is expressed as an angle between
(0, 2*pi)
Examples
>>> from skimage import data >>> from skimage.color import rgb2lab, lab2lch >>> img = data.astronaut() >>> img_lab = rgb2lab(img) >>> img_lch = lab2lch(img_lab)
lab2rgb
-
skimage.color.lab2rgb(lab)
[source] -
Lab to RGB color space conversion.
Parameters: lab : array_like
The image in Lab format, in a 3-D array of shape
(.., .., 3)
.Returns: out : ndarray
The image in RGB format, in a 3-D array of shape
(.., .., 3)
.Raises: ValueError
If
lab
is not a 3-D array of shape(.., .., 3)
.Notes
This function uses lab2xyz and xyz2rgb.
lab2xyz
-
skimage.color.lab2xyz(lab, illuminant='D65', observer='2')
[source] -
CIE-LAB to XYZcolor space conversion.
Parameters: lab : array_like
The image in lab format, in a 3-D array of shape
(.., .., 3)
.illuminant : {“A”, “D50”, “D55”, “D65”, “D75”, “E”}, optional
The name of the illuminant (the function is NOT case sensitive).
observer : {“2”, “10”}, optional
The aperture angle of the observer.
Returns: out : ndarray
The image in XYZ format, in a 3-D array of shape
(.., .., 3)
.Raises: ValueError
If
lab
is not a 3-D array of shape(.., .., 3)
.ValueError
If either the illuminant or the observer angle are not supported or unknown.
UserWarning
If any of the pixels are invalid (Z < 0).
Notes
By default Observer= 2A, Illuminant= D65. CIE XYZ tristimulus values x_ref = 95.047, y_ref = 100., z_ref = 108.883. See function ‘get_xyz_coords’ for a list of supported illuminants.
References
[R44] http://www.easyrgb.com/index.php?X=MATH&H=07#text7 [R45] http://en.wikipedia.org/wiki/Lab_color_space
label2rgb
-
skimage.color.label2rgb(label, image=None, colors=None, alpha=0.3, bg_label=-1, bg_color=(0, 0, 0), image_alpha=1, kind='overlay')
[source] -
Return an RGB image where color-coded labels are painted over the image.
Parameters: label : array, shape (M, N)
Integer array of labels with the same shape as
image
.image : array, shape (M, N, 3), optional
Image used as underlay for labels. If the input is an RGB image, it’s converted to grayscale before coloring.
colors : list, optional
List of colors. If the number of labels exceeds the number of colors, then the colors are cycled.
alpha : float [0, 1], optional
Opacity of colorized labels. Ignored if image is
None
.bg_label : int, optional
Label that’s treated as the background.
bg_color : str or array, optional
Background color. Must be a name in
color_dict
or RGB float values between [0, 1].image_alpha : float [0, 1], optional
Opacity of the image.
kind : string, one of {‘overlay’, ‘avg’}
The kind of color image desired. ‘overlay’ cycles over defined colors and overlays the colored labels over the original image. ‘avg’ replaces each labeled segment with its average color, for a stained-class or pastel painting appearance.
Returns: result : array of float, shape (M, N, 3)
The result of blending a cycling colormap (
colors
) for each distinct value inlabel
with the image, at a certain alpha value.
lch2lab
-
skimage.color.lch2lab(lch)
[source] -
CIE-LCH to CIE-LAB color space conversion.
LCH is the cylindrical representation of the LAB (Cartesian) colorspace
Parameters: lch : array_like
The N-D image in CIE-LCH format. The last (
N+1
-th) dimension must have at least 3 elements, corresponding to theL
,a
, andb
color channels. Subsequent elements are copied.Returns: out : ndarray
The image in LAB format, with same shape as input
lch
.Raises: ValueError
If
lch
does not have at least 3 color channels (i.e. l, c, h).Examples
>>> from skimage import data >>> from skimage.color import rgb2lab, lch2lab >>> img = data.astronaut() >>> img_lab = rgb2lab(img) >>> img_lch = lab2lch(img_lab) >>> img_lab2 = lch2lab(img_lch)
luv2rgb
-
skimage.color.luv2rgb(luv)
[source] -
Luv to RGB color space conversion.
Parameters: luv : (M, N, [P,] 3) array_like
The 3 or 4 dimensional image in CIE Luv format. Final dimension denotes channels.
Returns: out : (M, N, [P,] 3) ndarray
The image in RGB format. Same dimensions as input.
Raises: ValueError
If
luv
is not a 3-D or 4-D array of shape(M, N, [P,] 3)
.Notes
This function uses luv2xyz and xyz2rgb.
luv2xyz
-
skimage.color.luv2xyz(luv, illuminant='D65', observer='2')
[source] -
CIE-Luv to XYZ color space conversion.
Parameters: luv : (M, N, [P,] 3) array_like
The 3 or 4 dimensional image in CIE-Luv format. Final dimension denotes channels.
illuminant : {“A”, “D50”, “D55”, “D65”, “D75”, “E”}, optional
The name of the illuminant (the function is NOT case sensitive).
observer : {“2”, “10”}, optional
The aperture angle of the observer.
Returns: out : (M, N, [P,] 3) ndarray
The image in XYZ format. Same dimensions as input.
Raises: ValueError
If
luv
is not a 3-D or 4-D array of shape(M, N, [P,] 3)
.ValueError
If either the illuminant or the observer angle are not supported or unknown.
Notes
XYZ conversion weights use observer=2A. Reference whitepoint for D65 Illuminant, with XYZ tristimulus values of
(95.047, 100., 108.883)
. See function ‘get_xyz_coords’ for a list of supported illuminants.References
[R46] http://www.easyrgb.com/index.php?X=MATH&H=16#text16 [R47] http://en.wikipedia.org/wiki/CIELUV
rgb2gray
-
skimage.color.rgb2gray(rgb)
[source] -
Compute luminance of an RGB image.
Parameters: rgb : array_like
The image in RGB format, in a 3-D array of shape
(.., .., 3)
, or in RGBA format with shape(.., .., 4)
.Returns: out : ndarray
The luminance image, a 2-D array.
Raises: ValueError
If
rgb2gray
is not a 3-D array of shape(.., .., 3)
or(.., .., 4)
.Notes
The weights used in this conversion are calibrated for contemporary CRT phosphors:
Y = 0.2125 R + 0.7154 G + 0.0721 B
If there is an alpha channel present, it is ignored.
References
[R48] http://www.poynton.com/PDFs/ColorFAQ.pdf Examples
>>> from skimage.color import rgb2gray >>> from skimage import data >>> img = data.astronaut() >>> img_gray = rgb2gray(img)
rgb2grey
-
skimage.color.rgb2grey(rgb)
[source] -
Compute luminance of an RGB image.
Parameters: rgb : array_like
The image in RGB format, in a 3-D array of shape
(.., .., 3)
, or in RGBA format with shape(.., .., 4)
.Returns: out : ndarray
The luminance image, a 2-D array.
Raises: ValueError
If
rgb2gray
is not a 3-D array of shape(.., .., 3)
or(.., .., 4)
.Notes
The weights used in this conversion are calibrated for contemporary CRT phosphors:
Y = 0.2125 R + 0.7154 G + 0.0721 B
If there is an alpha channel present, it is ignored.
References
[R49] http://www.poynton.com/PDFs/ColorFAQ.pdf Examples
>>> from skimage.color import rgb2gray >>> from skimage import data >>> img = data.astronaut() >>> img_gray = rgb2gray(img)
rgb2hed
-
skimage.color.rgb2hed(rgb)
[source] -
RGB to Haematoxylin-Eosin-DAB (HED) color space conversion.
Parameters: rgb : array_like
The image in RGB format, in a 3-D array of shape
(.., .., 3)
.Returns: out : ndarray
The image in HED format, in a 3-D array of shape
(.., .., 3)
.Raises: ValueError
If
rgb
is not a 3-D array of shape(.., .., 3)
.References
[R50] A. C. Ruifrok and D. A. Johnston, “Quantification of histochemical staining by color deconvolution.,” Analytical and quantitative cytology and histology / the International Academy of Cytology [and] American Society of Cytology, vol. 23, no. 4, pp. 291-9, Aug. 2001. Examples
>>> from skimage import data >>> from skimage.color import rgb2hed >>> ihc = data.immunohistochemistry() >>> ihc_hed = rgb2hed(ihc)
rgb2hsv
-
skimage.color.rgb2hsv(rgb)
[source] -
RGB to HSV color space conversion.
Parameters: rgb : array_like
The image in RGB format, in a 3-D array of shape
(.., .., 3)
.Returns: out : ndarray
The image in HSV format, in a 3-D array of shape
(.., .., 3)
.Raises: ValueError
If
rgb
is not a 3-D array of shape(.., .., 3)
.Notes
The conversion assumes an input data range of [0, 1] for all color components.
Conversion between RGB and HSV color spaces results in some loss of precision, due to integer arithmetic and rounding [R51].
References
[R51] (1, 2) http://en.wikipedia.org/wiki/HSL_and_HSV Examples
>>> from skimage import color >>> from skimage import data >>> img = data.astronaut() >>> img_hsv = color.rgb2hsv(img)
rgb2lab
-
skimage.color.rgb2lab(rgb)
[source] -
RGB to lab color space conversion.
Parameters: rgb : array_like
The image in RGB format, in a 3- or 4-D array of shape
(.., ..,[ ..,] 3)
.Returns: out : ndarray
The image in Lab format, in a 3- or 4-D array of shape
(.., ..,[ ..,] 3)
.Raises: ValueError
If
rgb
is not a 3- or 4-D array of shape(.., ..,[ ..,] 3)
.Notes
This function uses rgb2xyz and xyz2lab.
rgb2luv
-
skimage.color.rgb2luv(rgb)
[source] -
RGB to CIE-Luv color space conversion.
Parameters: rgb : (M, N, [P,] 3) array_like
The 3 or 4 dimensional image in RGB format. Final dimension denotes channels.
Returns: out : (M, N, [P,] 3) ndarray
The image in CIE Luv format. Same dimensions as input.
Raises: ValueError
If
rgb
is not a 3-D or 4-D array of shape(M, N, [P,] 3)
.Notes
This function uses rgb2xyz and xyz2luv.
rgb2rgbcie
-
skimage.color.rgb2rgbcie(rgb)
[source] -
RGB to RGB CIE color space conversion.
Parameters: rgb : array_like
The image in RGB format, in a 3-D array of shape
(.., .., 3)
.Returns: out : ndarray
The image in RGB CIE format, in a 3-D array of shape
(.., .., 3)
.Raises: ValueError
If
rgb
is not a 3-D array of shape(.., .., 3)
.References
[R52] http://en.wikipedia.org/wiki/CIE_1931_color_space Examples
>>> from skimage import data >>> from skimage.color import rgb2rgbcie >>> img = data.astronaut() >>> img_rgbcie = rgb2rgbcie(img)
rgb2xyz
-
skimage.color.rgb2xyz(rgb)
[source] -
RGB to XYZ color space conversion.
Parameters: rgb : array_like
The image in RGB format, in a 3- or 4-D array of shape
(.., ..,[ ..,] 3)
.Returns: out : ndarray
The image in XYZ format, in a 3- or 4-D array of shape
(.., ..,[ ..,] 3)
.Raises: ValueError
If
rgb
is not a 3- or 4-D array of shape(.., ..,[ ..,] 3)
.Notes
The CIE XYZ color space is derived from the CIE RGB color space. Note however that this function converts from sRGB.
References
[R53] http://en.wikipedia.org/wiki/CIE_1931_color_space Examples
>>> from skimage import data >>> img = data.astronaut() >>> img_xyz = rgb2xyz(img)
rgbcie2rgb
-
skimage.color.rgbcie2rgb(rgbcie)
[source] -
RGB CIE to RGB color space conversion.
Parameters: rgbcie : array_like
The image in RGB CIE format, in a 3-D array of shape
(.., .., 3)
.Returns: out : ndarray
The image in RGB format, in a 3-D array of shape
(.., .., 3)
.Raises: ValueError
If
rgbcie
is not a 3-D array of shape(.., .., 3)
.References
[R54] http://en.wikipedia.org/wiki/CIE_1931_color_space Examples
>>> from skimage import data >>> from skimage.color import rgb2rgbcie, rgbcie2rgb >>> img = data.astronaut() >>> img_rgbcie = rgb2rgbcie(img) >>> img_rgb = rgbcie2rgb(img_rgbcie)
separate_stains
-
skimage.color.separate_stains(rgb, conv_matrix)
[source] -
RGB to stain color space conversion.
Parameters: rgb : array_like
The image in RGB format, in a 3-D array of shape
(.., .., 3)
.conv_matrix: ndarray
The stain separation matrix as described by G. Landini [R55].
Returns: out : ndarray
The image in stain color space, in a 3-D array of shape
(.., .., 3)
.Raises: ValueError
If
rgb
is not a 3-D array of shape(.., .., 3)
.Notes
Stain separation matrices available in the
color
module and their respective colorspace:-
hed_from_rgb
: Hematoxylin + Eosin + DAB -
hdx_from_rgb
: Hematoxylin + DAB -
fgx_from_rgb
: Feulgen + Light Green -
bex_from_rgb
: Giemsa stain : Methyl Blue + Eosin -
rbd_from_rgb
: FastRed + FastBlue + DAB -
gdx_from_rgb
: Methyl Green + DAB -
hax_from_rgb
: Hematoxylin + AEC -
bro_from_rgb
: Blue matrix Anilline Blue + Red matrix Azocarmine + Orange matrix Orange-G -
bpx_from_rgb
: Methyl Blue + Ponceau Fuchsin -
ahx_from_rgb
: Alcian Blue + Hematoxylin -
hpx_from_rgb
: Hematoxylin + PAS
References
[R55] (1, 2) http://www.dentistry.bham.ac.uk/landinig/software/cdeconv/cdeconv.html Examples
>>> from skimage import data >>> from skimage.color import separate_stains, hdx_from_rgb >>> ihc = data.immunohistochemistry() >>> ihc_hdx = separate_stains(ihc, hdx_from_rgb)
-
xyz2lab
-
skimage.color.xyz2lab(xyz, illuminant='D65', observer='2')
[source] -
XYZ to CIE-LAB color space conversion.
Parameters: xyz : array_like
The image in XYZ format, in a 3- or 4-D array of shape
(.., ..,[ ..,] 3)
.illuminant : {“A”, “D50”, “D55”, “D65”, “D75”, “E”}, optional
The name of the illuminant (the function is NOT case sensitive).
observer : {“2”, “10”}, optional
The aperture angle of the observer.
Returns: out : ndarray
The image in CIE-LAB format, in a 3- or 4-D array of shape
(.., ..,[ ..,] 3)
.Raises: ValueError
If
xyz
is not a 3-D array of shape(.., ..,[ ..,] 3)
.ValueError
If either the illuminant or the observer angle is unsupported or unknown.
Notes
By default Observer= 2A, Illuminant= D65. CIE XYZ tristimulus values x_ref=95.047, y_ref=100., z_ref=108.883. See function
get_xyz_coords
for a list of supported illuminants.References
[R56] http://www.easyrgb.com/index.php?X=MATH&H=07#text7 [R57] http://en.wikipedia.org/wiki/Lab_color_space Examples
>>> from skimage import data >>> from skimage.color import rgb2xyz, xyz2lab >>> img = data.astronaut() >>> img_xyz = rgb2xyz(img) >>> img_lab = xyz2lab(img_xyz)
xyz2luv
-
skimage.color.xyz2luv(xyz, illuminant='D65', observer='2')
[source] -
XYZ to CIE-Luv color space conversion.
Parameters: xyz : (M, N, [P,] 3) array_like
The 3 or 4 dimensional image in XYZ format. Final dimension denotes channels.
illuminant : {“A”, “D50”, “D55”, “D65”, “D75”, “E”}, optional
The name of the illuminant (the function is NOT case sensitive).
observer : {“2”, “10”}, optional
The aperture angle of the observer.
Returns: out : (M, N, [P,] 3) ndarray
The image in CIE-Luv format. Same dimensions as input.
Raises: ValueError
If
xyz
is not a 3-D or 4-D array of shape(M, N, [P,] 3)
.ValueError
If either the illuminant or the observer angle are not supported or unknown.
Notes
By default XYZ conversion weights use observer=2A. Reference whitepoint for D65 Illuminant, with XYZ tristimulus values of
(95.047, 100., 108.883)
. See function ‘get_xyz_coords’ for a list of supported illuminants.References
[R58] http://www.easyrgb.com/index.php?X=MATH&H=16#text16 [R59] http://en.wikipedia.org/wiki/CIELUV Examples
>>> from skimage import data >>> from skimage.color import rgb2xyz, xyz2luv >>> img = data.astronaut() >>> img_xyz = rgb2xyz(img) >>> img_luv = xyz2luv(img_xyz)
xyz2rgb
-
skimage.color.xyz2rgb(xyz)
[source] -
XYZ to RGB color space conversion.
Parameters: xyz : array_like
The image in XYZ format, in a 3-D array of shape
(.., .., 3)
.Returns: out : ndarray
The image in RGB format, in a 3-D array of shape
(.., .., 3)
.Raises: ValueError
If
xyz
is not a 3-D array of shape(.., .., 3)
.Notes
The CIE XYZ color space is derived from the CIE RGB color space. Note however that this function converts to sRGB.
References
[R60] http://en.wikipedia.org/wiki/CIE_1931_color_space Examples
>>> from skimage import data >>> from skimage.color import rgb2xyz, xyz2rgb >>> img = data.astronaut() >>> img_xyz = rgb2xyz(img) >>> img_rgb = xyz2rgb(img_xyz)
© 2011 the scikit-image team
Licensed under the BSD 3-clause License.
http://scikit-image.org/docs/0.12.x/api/skimage.color.html