Module: color
| Stain to RGB color space conversion. |
| Convert an image array to a new color space. |
| Euclidean distance between two points in Lab color space |
| Color difference as given by the CIEDE 2000 standard. |
| Color difference according to CIEDE 94 standard |
| Color difference from the CMC l:c standard. |
| Create an RGB representation of a gray-level image. |
| Create a RGBA representation of a gray-level image. |
| Create an RGB representation of a gray-level image. |
Haematoxylin-Eosin-DAB (HED) to RGB color space conversion. | |
HSV to RGB color space conversion. | |
CIE-LAB to CIE-LCH color space conversion. | |
| Lab to RGB color space conversion. |
| CIE-LAB to XYZcolor space conversion. |
| Return an RGB image where color-coded labels are painted over the image. |
CIE-LCH to CIE-LAB color space conversion. | |
Compute luminance of an RGB image. | |
Compute luminance of an RGB image. | |
RGB to Haematoxylin-Eosin-DAB (HED) color space conversion. | |
RGB to HSV color space conversion. | |
| Conversion from the sRGB color space (IEC 61966-2-1:1999) to the CIE Lab colorspace under the given illuminant and observer. |
RGB to RGB CIE color space conversion. | |
RGB to XYZ color space conversion. | |
RGB to YCbCr color space conversion. | |
RGB to YDbDr color space conversion. | |
RGB to YIQ color space conversion. | |
RGB to YPbPr color space conversion. | |
RGB to YUV color space conversion. | |
| RGBA to RGB conversion using alpha blending [1]. |
| RGB CIE to RGB color space conversion. |
| RGB to stain color space conversion. |
| XYZ to CIE-LAB color space conversion. |
XYZ to RGB color space conversion. | |
| YCbCr to RGB color space conversion. |
| YDbDr to RGB color space conversion. |
YIQ to RGB color space conversion. | |
| YPbPr to RGB color space conversion. |
YUV to RGB color space conversion. |
combine_stains
-
skimage.color.combine_stains(stains, conv_matrix)
[source] -
Stain to RGB color space conversion.
- Parameters
-
-
stains(…, 3) array_like
-
The image in stain color space. Final dimension denotes channels.
- conv_matrix: ndarray
-
The stain separation matrix as described by G. Landini [1].
-
- Returns
-
-
out(…, 3) ndarray
-
The image in RGB format. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
stains
is not at least 2-D with 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
-
1
-
2
-
A. C. Ruifrok and D. A. Johnston, “Quantification of histochemical staining by color deconvolution,” Anal. Quant. Cytol. Histol., vol. 23, no. 4, pp. 291–299, Aug. 2001.
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.
- Valid color spaces are:
-
‘RGB’, ‘HSV’, ‘RGB CIE’, ‘XYZ’, ‘YUV’, ‘YIQ’, ‘YPbPr’, ‘YCbCr’, ‘YDbDr’
- Parameters
-
-
arr(…, 3) array_like
-
The image to convert. Final dimension denotes channels.
-
fromspacestr
-
The color space to convert from. Can be specified in lower case.
-
tospacestr
-
The color space to convert to. Can be specified in lower case.
-
- Returns
-
-
out(…, 3) ndarray
-
The converted image. Same dimensions as input.
-
- Raises
-
- ValueError
-
If fromspace is not a valid color space
- ValueError
-
If tospace is not a valid color space
Notes
Conversion is performed 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
-
-
lab1array_like
-
reference color (Lab colorspace)
-
lab2array_like
-
comparison color (Lab colorspace)
-
- Returns
-
-
dEarray_like
-
distance between colors
lab1
andlab2
-
References
-
1
-
2
-
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
-
-
lab1array_like
-
reference color (Lab colorspace)
-
lab2array_like
-
comparison color (Lab colorspace)
-
kLfloat (range), optional
-
lightness scale factor, 1 for “acceptably close”; 2 for “imperceptible” see deltaE_cmc
-
kCfloat (range), optional
-
chroma scale factor, usually 1
-
kHfloat (range), optional
-
hue scale factor, usually 1
-
- Returns
-
-
deltaEarray_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
-
1
-
2
-
http://www.ece.rochester.edu/~gsharma/ciede2000/ciede2000noteCRNA.pdf DOI:10.1364/AO.33.008069
-
3
-
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
-
-
lab1array_like
-
reference color (Lab colorspace)
-
lab2array_like
-
comparison color (Lab colorspace)
-
kHfloat, optional
-
Hue scale
-
kCfloat, optional
-
Chroma scale
-
kLfloat, optional
-
Lightness scale
-
k1float, optional
-
first scale parameter
-
k2float, optional
-
second scale parameter
-
- Returns
-
-
dEarray_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
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
-
-
lab1array_like
-
reference color (Lab colorspace)
-
lab2array_like
-
comparison color (Lab colorspace)
-
- Returns
-
-
dEarray_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
-
1
-
2
-
http://www.brucelindbloom.com/index.html?Eqn_DeltaE_CIE94.html
-
3
-
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
-
-
imagearray_like
-
Input image.
-
alphabool, optional
-
Ensure that the output image has an alpha layer. If None, alpha layers are passed through but not created.
-
- Returns
-
-
rgb(…, 3) ndarray
-
RGB image. A new dimension of length 3 is added to input image.
-
Notes
If the input is a 1-dimensional image of shape
(M, )
, the output will be shape(M, 3)
.
Examples using skimage.color.gray2rgb
gray2rgba
-
skimage.color.gray2rgba(image, alpha=None)
[source] -
Create a RGBA representation of a gray-level image.
- Parameters
-
-
imagearray_like
-
Input image.
-
alphaarray_like, optional
-
Alpha channel of the output image. It may be a scalar or an array that can be broadcast to
image
. If not specified it is set to the maximum limit corresponding to theimage
dtype.
-
- Returns
-
-
rgbandarray
-
RGBA image. A new dimension of length 4 is added to input image shape.
-
grey2rgb
-
skimage.color.grey2rgb(image, alpha=None)
[source] -
Create an RGB representation of a gray-level image.
- Parameters
-
-
imagearray_like
-
Input image.
-
alphabool, optional
-
Ensure that the output image has an alpha layer. If None, alpha layers are passed through but not created.
-
- Returns
-
-
rgb(…, 3) ndarray
-
RGB image. A new dimension of length 3 is added to input image.
-
Notes
If the input is a 1-dimensional image of shape
(M, )
, the output will be shape(M, 3)
.
hed2rgb
-
skimage.color.hed2rgb(hed)
[source] -
Haematoxylin-Eosin-DAB (HED) to RGB color space conversion.
- Parameters
-
-
hed(…, 3) array_like
-
The image in the HED color space. Final dimension denotes channels.
-
- Returns
-
-
out(…, 3) ndarray
-
The image in RGB. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
hed
is not at least 2-D with shape (…, 3).
References
-
1
-
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(…, 3) array_like
-
The image in HSV format. Final dimension denotes channels.
-
- Returns
-
-
out(…, 3) ndarray
-
The image in RGB format. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
hsv
is not at least 2-D with shape (…, 3).
Notes
Conversion between RGB and HSV color spaces results in some loss of precision, due to integer arithmetic and rounding [1].
References
Examples
>>> from skimage import data >>> img = data.astronaut() >>> img_hsv = rgb2hsv(img) >>> img_rgb = hsv2rgb(img_hsv)
Examples using skimage.color.hsv2rgb
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(…, 3) 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(…, 3) 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, illuminant='D65', observer='2')
[source] -
Lab to RGB color space conversion.
- Parameters
-
-
lab(…, 3) array_like
-
The image in Lab 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(…, 3) ndarray
-
The image in RGB format. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
lab
is not at least 2-D with shape (…, 3).
Notes
This function uses lab2xyz and xyz2rgb. 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
lab2xyz
-
skimage.color.lab2xyz(lab, illuminant='D65', observer='2')
[source] -
CIE-LAB to XYZcolor space conversion.
- Parameters
-
-
lab(…, 3) array_like
-
The image in Lab 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(…, 3) ndarray
-
The image in XYZ format. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
lab
is not at least 2-D with 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
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
-
-
labelarray, shape (M, N)
-
Integer array of labels with the same shape as
image
. -
imagearray, 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.
-
colorslist, optional
-
List of colors. If the number of labels exceeds the number of colors, then the colors are cycled.
-
alphafloat [0, 1], optional
-
Opacity of colorized labels. Ignored if image is
None
. -
bg_labelint, optional
-
Label that’s treated as the background. If
bg_label
is specified,bg_color
isNone
, andkind
isoverlay
, background is not painted by any colors. -
bg_colorstr or array, optional
-
Background color. Must be a name in
color_dict
or RGB float values between [0, 1]. -
image_alphafloat [0, 1], optional
-
Opacity of the image.
-
kindstring, 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
-
-
resultarray 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.
-
Examples using skimage.color.label2rgb
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(…, 3) 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(…, 3) 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)
rgb2gray
-
skimage.color.rgb2gray(rgb)
[source] -
Compute luminance of an RGB image.
- Parameters
-
-
rgb(…, 3) array_like
-
The image in RGB format. Final dimension denotes channels.
-
- Returns
-
-
outndarray
-
The luminance image - an array which is the same size as the input array, but with the channel dimension removed.
-
- Raises
-
- ValueError
-
If
rgb
is not at least 2-D with shape (…, 3).
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
Examples
>>> from skimage.color import rgb2gray >>> from skimage import data >>> img = data.astronaut() >>> img_gray = rgb2gray(img)
Examples using skimage.color.rgb2gray
rgb2grey
-
skimage.color.rgb2grey(rgb)
[source] -
Compute luminance of an RGB image.
- Parameters
-
-
rgb(…, 3) array_like
-
The image in RGB format. Final dimension denotes channels.
-
- Returns
-
-
outndarray
-
The luminance image - an array which is the same size as the input array, but with the channel dimension removed.
-
- Raises
-
- ValueError
-
If
rgb
is not at least 2-D with shape (…, 3).
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
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(…, 3) array_like
-
The image in RGB format. Final dimension denotes channels.
-
- Returns
-
-
out(…, 3) ndarray
-
The image in HED format. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
rgb
is not at least 2-D with shape (…, 3).
References
-
1
-
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(…, 3) array_like
-
The image in RGB format. Final dimension denotes channels.
-
- Returns
-
-
out(…, 3) ndarray
-
The image in HSV format. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
rgb
is not at least 2-D with shape (…, 3).
Notes
Conversion between RGB and HSV color spaces results in some loss of precision, due to integer arithmetic and rounding [1].
References
Examples
>>> from skimage import color >>> from skimage import data >>> img = data.astronaut() >>> img_hsv = color.rgb2hsv(img)
Examples using skimage.color.rgb2hsv
rgb2lab
-
skimage.color.rgb2lab(rgb, illuminant='D65', observer='2')
[source] -
Conversion from the sRGB color space (IEC 61966-2-1:1999) to the CIE Lab colorspace under the given illuminant and observer.
- Parameters
-
-
rgb(…, 3) array_like
-
The image in RGB 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(…, 3) ndarray
-
The image in Lab format. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
rgb
is not at least 2-D with shape (…, 3).
Notes
RGB is a device-dependent color space so, if you use this function, be sure that the image you are analyzing has been mapped to the sRGB color space.
This function uses rgb2xyz and xyz2lab. 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
rgb2rgbcie
-
skimage.color.rgb2rgbcie(rgb)
[source] -
RGB to RGB CIE color space conversion.
- Parameters
-
-
rgb(…, 3) array_like
-
The image in RGB format. Final dimension denotes channels.
-
- Returns
-
-
out(…, 3) ndarray
-
The image in RGB CIE format. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
rgb
is not at least 2-D with shape (…, 3).
References
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(…, 3) array_like
-
The image in RGB format. Final dimension denotes channels.
-
- Returns
-
-
out(…, 3) ndarray
-
The image in XYZ format. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
rgb
is not at least 2-D with 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
Examples
>>> from skimage import data >>> img = data.astronaut() >>> img_xyz = rgb2xyz(img)
rgb2ycbcr
-
skimage.color.rgb2ycbcr(rgb)
[source] -
RGB to YCbCr color space conversion.
- Parameters
-
-
rgb(…, 3) array_like
-
The image in RGB format. Final dimension denotes channels.
-
- Returns
-
-
out(…, 3) ndarray
-
The image in YCbCr format. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
rgb
is not at least 2-D with shape (…, 3).
Notes
Y is between 16 and 235. This is the color space commonly used by video codecs; it is sometimes incorrectly called “YUV”.
References
rgb2ydbdr
-
skimage.color.rgb2ydbdr(rgb)
[source] -
RGB to YDbDr color space conversion.
- Parameters
-
-
rgb(…, 3) array_like
-
The image in RGB format. Final dimension denotes channels.
-
- Returns
-
-
out(…, 3) ndarray
-
The image in YDbDr format. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
rgb
is not at least 2-D with shape (…, 3).
Notes
This is the color space commonly used by video codecs. It is also the reversible color transform in JPEG2000.
References
rgb2yiq
-
skimage.color.rgb2yiq(rgb)
[source] -
RGB to YIQ color space conversion.
- Parameters
-
-
rgb(…, 3) array_like
-
The image in RGB format. Final dimension denotes channels.
-
- Returns
-
-
out(…, 3) ndarray
-
The image in YIQ format. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
rgb
is not at least 2-D with shape (…, 3).
rgb2ypbpr
-
skimage.color.rgb2ypbpr(rgb)
[source] -
RGB to YPbPr color space conversion.
- Parameters
-
-
rgb(…, 3) array_like
-
The image in RGB format. Final dimension denotes channels.
-
- Returns
-
-
out(…, 3) ndarray
-
The image in YPbPr format. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
rgb
is not at least 2-D with shape (…, 3).
References
rgb2yuv
-
skimage.color.rgb2yuv(rgb)
[source] -
RGB to YUV color space conversion.
- Parameters
-
-
rgb(…, 3) array_like
-
The image in RGB format. Final dimension denotes channels.
-
- Returns
-
-
out(…, 3) ndarray
-
The image in YUV format. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
rgb
is not at least 2-D with shape (…, 3).
Notes
Y is between 0 and 1. Use YCbCr instead of YUV for the color space commonly used by video codecs, where Y ranges from 16 to 235.
References
rgba2rgb
-
skimage.color.rgba2rgb(rgba, background=(1, 1, 1))
[source] -
RGBA to RGB conversion using alpha blending [1].
- Parameters
-
-
rgba(…, 4) array_like
-
The image in RGBA format. Final dimension denotes channels.
-
backgroundarray_like
-
The color of the background to blend the image with (3 floats between 0 to 1 - the RGB value of the background).
-
- Returns
-
-
out(…, 3) ndarray
-
The image in RGB format. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
rgba
is not at least 2-D with shape (…, 4).
References
Examples
>>> from skimage import color >>> from skimage import data >>> img_rgba = data.logo() >>> img_rgb = color.rgba2rgb(img_rgba)
rgbcie2rgb
-
skimage.color.rgbcie2rgb(rgbcie)
[source] -
RGB CIE to RGB color space conversion.
- Parameters
-
-
rgbcie(…, 3) array_like
-
The image in RGB CIE format. Final dimension denotes channels.
-
- Returns
-
-
out(…, 3) ndarray
-
The image in RGB format. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
rgbcie
is not at least 2-D with shape (…, 3).
References
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(…, 3) array_like
-
The image in RGB format. Final dimension denotes channels.
- conv_matrix: ndarray
-
The stain separation matrix as described by G. Landini [1].
-
- Returns
-
-
out(…, 3) ndarray
-
The image in stain color space. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
rgb
is not at least 2-D with 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
This implementation borrows some ideas from DIPlib [2], e.g. the compensation using a small value to avoid log artifacts when calculating the Beer-Lambert law.
References
-
1
-
2
-
3
-
A. C. Ruifrok and D. A. Johnston, “Quantification of histochemical staining by color deconvolution,” Anal. Quant. Cytol. Histol., vol. 23, no. 4, pp. 291–299, Aug. 2001.
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(…, 3) array_like
-
The 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(…, 3) ndarray
-
The image in CIE-LAB format. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
xyz
is not at least 2-D with 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
Examples
>>> from skimage import data >>> from skimage.color import rgb2xyz, xyz2lab >>> img = data.astronaut() >>> img_xyz = rgb2xyz(img) >>> img_lab = xyz2lab(img_xyz)
xyz2rgb
-
skimage.color.xyz2rgb(xyz)
[source] -
XYZ to RGB color space conversion.
- Parameters
-
-
xyz(…, 3) array_like
-
The image in XYZ format. Final dimension denotes channels.
-
- Returns
-
-
out(…, 3) ndarray
-
The image in RGB format. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
xyz
is not at least 2-D with 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
Examples
>>> from skimage import data >>> from skimage.color import rgb2xyz, xyz2rgb >>> img = data.astronaut() >>> img_xyz = rgb2xyz(img) >>> img_rgb = xyz2rgb(img_xyz)
ycbcr2rgb
-
skimage.color.ycbcr2rgb(ycbcr)
[source] -
YCbCr to RGB color space conversion.
- Parameters
-
-
ycbcr(…, 3) array_like
-
The image in YCbCr format. Final dimension denotes channels.
-
- Returns
-
-
out(…, 3) ndarray
-
The image in RGB format. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
ycbcr
is not at least 2-D with shape (…, 3).
Notes
Y is between 16 and 235. This is the color space commonly used by video codecs; it is sometimes incorrectly called “YUV”.
References
ydbdr2rgb
-
skimage.color.ydbdr2rgb(ydbdr)
[source] -
YDbDr to RGB color space conversion.
- Parameters
-
-
ydbdr(…, 3) array_like
-
The image in YDbDr format. Final dimension denotes channels.
-
- Returns
-
-
out(…, 3) ndarray
-
The image in RGB format. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
ydbdr
is not at least 2-D with shape (…, 3).
Notes
This is the color space commonly used by video codecs, also called the reversible color transform in JPEG2000.
References
yiq2rgb
-
skimage.color.yiq2rgb(yiq)
[source] -
YIQ to RGB color space conversion.
- Parameters
-
-
yiq(…, 3) array_like
-
The image in YIQ format. Final dimension denotes channels.
-
- Returns
-
-
out(…, 3) ndarray
-
The image in RGB format. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
yiq
is not at least 2-D with shape (…, 3).
ypbpr2rgb
-
skimage.color.ypbpr2rgb(ypbpr)
[source] -
YPbPr to RGB color space conversion.
- Parameters
-
-
ypbpr(…, 3) array_like
-
The image in YPbPr format. Final dimension denotes channels.
-
- Returns
-
-
out(…, 3) ndarray
-
The image in RGB format. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
ypbpr
is not at least 2-D with shape (…, 3).
References
yuv2rgb
-
skimage.color.yuv2rgb(yuv)
[source] -
YUV to RGB color space conversion.
- Parameters
-
-
yuv(…, 3) array_like
-
The image in YUV format. Final dimension denotes channels.
-
- Returns
-
-
out(…, 3) ndarray
-
The image in RGB format. Same dimensions as input.
-
- Raises
-
- ValueError
-
If
yuv
is not at least 2-D with shape (…, 3).
References
© 2019 the scikit-image team
Licensed under the BSD 3-clause License.
https://scikit-image.org/docs/0.18.x/api/skimage.color.html