Module: color

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

https://web.archive.org/web/20160624145052/http://www.mecourse.com/landinig/software/cdeconv/cdeconv.html

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 and lab2

References

1

https://en.wikipedia.org/wiki/Color_difference

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 and lab2

Notes

CIEDE 2000 assumes parametric weighting factors for the lightness, chroma, and hue (kL, kC, kH respectively). These default to 1.

References

1

https://en.wikipedia.org/wiki/Color_difference

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, and k2).

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 and lab2

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 arts

Parameter	Graphic Arts	Textiles
kL	1.000	2.000
k1	0.045	0.048
k2	0.015	0.014

References

1

https://en.wikipedia.org/wiki/Color_difference

2

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 are kL=2, kC=1 for “acceptability” and kL=1, kC=1 for “imperceptibility”. Colors with dE > 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 and lab2

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

https://en.wikipedia.org/wiki/Color_difference

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 the image 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

1

https://en.wikipedia.org/wiki/HSL_and_HSV

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 the L, a, and b 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

1

https://en.wikipedia.org/wiki/Standard_illuminant

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

1

http://www.easyrgb.com/index.php?X=MATH&H=07

2

https://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

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 is None, and kind is overlay, 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 in label 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 the L, a, and b 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

1

http://poynton.ca/PDFs/ColorFAQ.pdf

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

1

http://poynton.ca/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(…, 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

1

https://en.wikipedia.org/wiki/HSL_and_HSV

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

1

https://en.wikipedia.org/wiki/Standard_illuminant

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

1

https://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(…, 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

1

https://en.wikipedia.org/wiki/CIE_1931_color_space

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

1

https://en.wikipedia.org/wiki/YCbCr

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

1

https://en.wikipedia.org/wiki/YDbDr

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

1

https://en.wikipedia.org/wiki/YPbPr

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

1

https://en.wikipedia.org/wiki/YUV

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

1(1,2)

https://en.wikipedia.org/wiki/Alpha_compositing#Alpha_blending

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

1

https://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(…, 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

https://web.archive.org/web/20160624145052/http://www.mecourse.com/landinig/software/cdeconv/cdeconv.html

2

https://github.com/DIPlib/diplib/

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

1

http://www.easyrgb.com/index.php?X=MATH&H=07

2

https://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)

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

1

https://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)

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

1

https://en.wikipedia.org/wiki/YCbCr

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

1

https://en.wikipedia.org/wiki/YDbDr

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

1

https://en.wikipedia.org/wiki/YPbPr

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

1

https://en.wikipedia.org/wiki/YUV