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.grey2rgb (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[, illuminant, …]) | 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[, illuminant, …]) | 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.rgb2ycbcr (rgb) | RGB to YCbCr color space conversion. |
skimage.color.rgb2yiq (rgb) | RGB to YIQ color space conversion. |
skimage.color.rgb2ypbpr (rgb) | RGB to YPbPr color space conversion. |
skimage.color.rgb2yuv (rgb) | RGB to YUV color space conversion. |
skimage.color.rgba2rgb (rgba[, background]) | RGBA to RGB 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. |
skimage.color.ycbcr2rgb (ycbcr) | YCbCr to RGB color space conversion. |
skimage.color.yiq2rgb (yiq) | YIQ to RGB color space conversion. |
skimage.color.ypbpr2rgb (ypbpr) | YPbPr to RGB color space conversion. |
skimage.color.yuv2rgb (yuv) | YUV to RGB color space conversion. |
skimage.color.colorconv | Functions for converting between color spaces. |
skimage.color.colorlabel | |
skimage.color.delta_e | Functions for calculating the “distance” between colors. |
skimage.color.rgb_colors |
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 conv_matrix: ndarray The stain separation matrix as described by G. Landini [R47]. |
---|---|
Returns: |
out : ndarray The image in RGB format, in a 3-D array of shape |
Raises: |
ValueError If |
Stain combination matrices available in the color
module and their respective colorspace:
rgb_from_hed
: Hematoxylin + Eosin + DABrgb_from_hdx
: Hematoxylin + DABrgb_from_fgx
: Feulgen + Light Greenrgb_from_bex
: Giemsa stain : Methyl Blue + Eosinrgb_from_rbd
: FastRed + FastBlue + DABrgb_from_gdx
: Methyl Green + DABrgb_from_hax
: Hematoxylin + AECrgb_from_bro
: Blue matrix Anilline Blue + Red matrix Azocarmine + Orange matrix Orange-Grgb_from_bpx
: Methyl Blue + Ponceau Fuchsinrgb_from_ahx
: Alcian Blue + Hematoxylinrgb_from_hpx
: Hematoxylin + PAS[R47] | (1, 2) http://www.dentistry.bham.ac.uk/landinig/software/cdeconv/cdeconv.html |
>>> 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)
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 tospace : str The color space to convert to. Valid color space strings are |
---|---|
Returns: |
newarr : ndarray The converted image. |
Conversion occurs through the “central” RGB color space, i.e. conversion from XYZ to HSV is implemented as XYZ -> RGB -> HSV
instead of directly.
>>> from skimage import data >>> img = data.astronaut() >>> img_hsv = convert_colorspace(img, 'RGB', 'HSV')
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 |
[R48] | http://en.wikipedia.org/wiki/Color_difference |
[R49] | A. R. Robertson, “The CIE 1976 color-difference formulae,” Color Res. Appl. 2, 7-11 (1977). |
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 |
CIEDE 2000 assumes parametric weighting factors for the lightness, chroma, and hue (kL
, kC
, kH
respectively). These default to 1.
[R50] | http://en.wikipedia.org/wiki/Color_difference |
[R51] | http://www.ece.rochester.edu/~gsharma/ciede2000/ciede2000noteCRNA.pdf (doi:10.1364/AO.33.008069) |
[R52] | 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). |
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: |
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 |
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 |
[R53] | http://en.wikipedia.org/wiki/Color_difference |
[R54] | http://www.brucelindbloom.com/index.html?Eqn_DeltaE_CIE94.html |
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: |
lab1 : array_like reference color (Lab colorspace) lab2 : array_like comparison color (Lab colorspace) |
---|---|
Returns: |
dE : array_like distance between colors |
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)
[R55] | http://en.wikipedia.org/wiki/Color_difference |
[R56] | http://www.brucelindbloom.com/index.html?Eqn_DeltaE_CIE94.html |
[R57] | F. J. J. Clarke, R. McDonald, and B. Rigg, “Modification to the JPC79 colour-difference formula,” J. Soc. Dyers Colour. 100, 128-132 (1984). |
skimage.color.gray2rgb(image, alpha=None)
[source]
Create an RGB representation of a gray-level image.
Parameters: |
image : array_like Input image of shape 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 |
Raises: |
ValueError If the input is not a 2- or 3-dimensional image. |
skimage.color.grey2rgb(image, alpha=None)
[source]
Create an RGB representation of a gray-level image.
Parameters: |
image : array_like Input image of shape 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 |
Raises: |
ValueError If the input is not a 2- or 3-dimensional image. |
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 |
Raises: |
ValueError If the image array has less than two or more than four dimensions. |
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 |
---|---|
Returns: |
out : ndarray The image in RGB, in a 3-D array of shape |
Raises: |
ValueError If |
[R58] | 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. |
>>> from skimage import data >>> from skimage.color import rgb2hed, hed2rgb >>> ihc = data.immunohistochemistry() >>> ihc_hed = rgb2hed(ihc) >>> ihc_rgb = hed2rgb(ihc_hed)
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 |
---|---|
Returns: |
out : ndarray The image in RGB format, in a 3-D array of shape |
Raises: |
ValueError If |
Conversion between RGB and HSV color spaces results in some loss of precision, due to integer arithmetic and rounding [R59].
[R59] | (1, 2) http://en.wikipedia.org/wiki/HSL_and_HSV |
>>> from skimage import data >>> img = data.astronaut() >>> img_hsv = rgb2hsv(img) >>> img_rgb = hsv2rgb(img_hsv)
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 ( |
---|---|
Returns: |
out : ndarray The image in LCH format, in a N-D array with same shape as input |
Raises: |
ValueError If |
The Hue is expressed as an angle between (0, 2*pi)
>>> from skimage import data >>> from skimage.color import rgb2lab, lab2lch >>> img = data.astronaut() >>> img_lab = rgb2lab(img) >>> img_lch = lab2lch(img_lab)
skimage.color.lab2rgb(lab, illuminant='D65', observer='2')
[source]
Lab to RGB color space conversion.
Parameters: |
lab : array_like The image in Lab format, in a 3-D array of shape 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 RGB format, in a 3-D array of shape |
Raises: |
ValueError If |
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.
[R60] | https://en.wikipedia.org/wiki/Standard_illuminant |
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 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 |
Raises: |
ValueError If ValueError If either the illuminant or the observer angle are not supported or unknown. UserWarning If any of the pixels are invalid (Z < 0). |
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.
[R61] | http://www.easyrgb.com/index.php?X=MATH&H=07#text7 |
[R62] | http://en.wikipedia.org/wiki/Lab_color_space |
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 : 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 bg_label : int, optional Label that’s treated as the background. bg_color : str or array, optional Background color. Must be a name in 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 ( |
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 ( |
---|---|
Returns: |
out : ndarray The image in LAB format, with same shape as input |
Raises: |
ValueError If |
>>> 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)
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 |
This function uses luv2xyz and xyz2rgb.
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 ValueError If either the illuminant or the observer angle are not supported or unknown. |
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.
[R63] | http://www.easyrgb.com/index.php?X=MATH&H=16#text16 |
[R64] | http://en.wikipedia.org/wiki/CIELUV |
skimage.color.rgb2gray(rgb)
[source]
Compute luminance of an RGB image.
Parameters: |
rgb : array_like The image in RGB format, in a 3-D or 4-D array of shape |
---|---|
Returns: |
out : ndarray The luminance image - an array which is the same size as the input array, but with the channel dimension removed. |
Raises: |
ValueError If |
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.
[R65] | http://www.poynton.com/PDFs/ColorFAQ.pdf |
>>> from skimage.color import rgb2gray >>> from skimage import data >>> img = data.astronaut() >>> img_gray = rgb2gray(img)
skimage.color.rgb2grey(rgb)
[source]
Compute luminance of an RGB image.
Parameters: |
rgb : array_like The image in RGB format, in a 3-D or 4-D array of shape |
---|---|
Returns: |
out : ndarray The luminance image - an array which is the same size as the input array, but with the channel dimension removed. |
Raises: |
ValueError If |
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.
[R66] | http://www.poynton.com/PDFs/ColorFAQ.pdf |
>>> from skimage.color import rgb2gray >>> from skimage import data >>> img = data.astronaut() >>> img_gray = rgb2gray(img)
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 |
---|---|
Returns: |
out : ndarray The image in HED format, in a 3-D array of shape |
Raises: |
ValueError If |
[R67] | 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. |
>>> from skimage import data >>> from skimage.color import rgb2hed >>> ihc = data.immunohistochemistry() >>> ihc_hed = rgb2hed(ihc)
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 |
---|---|
Returns: |
out : ndarray The image in HSV format, in a 3-D array of shape |
Raises: |
ValueError If |
Conversion between RGB and HSV color spaces results in some loss of precision, due to integer arithmetic and rounding [R68].
[R68] | (1, 2) http://en.wikipedia.org/wiki/HSL_and_HSV |
>>> from skimage import color >>> from skimage import data >>> img = data.astronaut() >>> img_hsv = color.rgb2hsv(img)
skimage.color.rgb2lab(rgb, illuminant='D65', observer='2')
[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 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 Lab format, in a 3- or 4-D array of shape |
Raises: |
ValueError If |
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.
[R69] | https://en.wikipedia.org/wiki/Standard_illuminant |
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 |
This function uses rgb2xyz and xyz2luv.
[R70] | http://www.easyrgb.com/index.php?X=MATH&H=16#text16 |
[R71] | http://www.easyrgb.com/index.php?X=MATH&H=02#text2 |
[R72] | http://en.wikipedia.org/wiki/CIELUV |
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 |
---|---|
Returns: |
out : ndarray The image in RGB CIE format, in a 3-D array of shape |
Raises: |
ValueError If |
[R73] | http://en.wikipedia.org/wiki/CIE_1931_color_space |
>>> from skimage import data >>> from skimage.color import rgb2rgbcie >>> img = data.astronaut() >>> img_rgbcie = rgb2rgbcie(img)
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 |
---|---|
Returns: |
out : ndarray The image in XYZ format, in a 3- or 4-D array of shape |
Raises: |
ValueError If |
The CIE XYZ color space is derived from the CIE RGB color space. Note however that this function converts from sRGB.
[R74] | http://en.wikipedia.org/wiki/CIE_1931_color_space |
>>> from skimage import data >>> img = data.astronaut() >>> img_xyz = rgb2xyz(img)
skimage.color.rgb2ycbcr(rgb)
[source]
RGB to YCbCr color space conversion.
Parameters: |
rgb : array_like The image in RGB format, in a 3- or 4-D array of shape |
---|---|
Returns: |
out : ndarray The image in YCbCr format, in a 3- or 4-D array of shape |
Raises: |
ValueError If |
Y is between 16 and 235. This is the color space which is commonly used by video codecs, it is sometimes incorrectly called “YUV”
[R75] | https://en.wikipedia.org/wiki/YCbCr |
skimage.color.rgb2yiq(rgb)
[source]
RGB to YIQ color space conversion.
Parameters: |
rgb : array_like The image in RGB format, in a 3- or 4-D array of shape |
---|---|
Returns: |
out : ndarray The image in YIQ format, in a 3- or 4-D array of shape |
Raises: |
ValueError If |
skimage.color.rgb2ypbpr(rgb)
[source]
RGB to YPbPr color space conversion.
Parameters: |
rgb : array_like The image in RGB format, in a 3- or 4-D array of shape |
---|---|
Returns: |
out : ndarray The image in YPbPr format, in a 3- or 4-D array of shape |
Raises: |
ValueError If |
[R76] | https://en.wikipedia.org/wiki/YPbPr |
skimage.color.rgb2yuv(rgb)
[source]
RGB to YUV color space conversion.
Parameters: |
rgb : array_like The image in RGB format, in a 3- or 4-D array of shape |
---|---|
Returns: |
out : ndarray The image in YUV format, in a 3- or 4-D array of shape |
Raises: |
ValueError If |
Y is between 0 and 1. Use YCbCr instead of YUV for the color space which is commonly used by video codecs (where Y ranges from 16 to 235)
[R77] | https://en.wikipedia.org/wiki/YUV |
skimage.color.rgba2rgb(rgba, background=(1, 1, 1))
[source]
RGBA to RGB conversion.
Parameters: |
rgba : array_like The image in RGBA format, in a 3-D array of shape background : array_like The color of the background to blend the image with. A tuple containing 3 floats between 0 to 1 - the RGB value of the background. |
---|---|
Returns: |
out : ndarray The image in RGB format, in a 3-D array of shape |
Raises: |
ValueError If |
[R78] | https://en.wikipedia.org/wiki/Alpha_compositing#Alpha_blending |
>>> from skimage import color >>> from skimage import data >>> img_rgba = data.logo() >>> img_rgb = color.rgba2rgb(img_rgba)
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 |
---|---|
Returns: |
out : ndarray The image in RGB format, in a 3-D array of shape |
Raises: |
ValueError If |
[R79] | http://en.wikipedia.org/wiki/CIE_1931_color_space |
>>> from skimage import data >>> from skimage.color import rgb2rgbcie, rgbcie2rgb >>> img = data.astronaut() >>> img_rgbcie = rgb2rgbcie(img) >>> img_rgb = rgbcie2rgb(img_rgbcie)
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 conv_matrix: ndarray The stain separation matrix as described by G. Landini [R80]. |
---|---|
Returns: |
out : ndarray The image in stain color space, in a 3-D array of shape |
Raises: |
ValueError If |
Stain separation matrices available in the color
module and their respective colorspace:
hed_from_rgb
: Hematoxylin + Eosin + DABhdx_from_rgb
: Hematoxylin + DABfgx_from_rgb
: Feulgen + Light Greenbex_from_rgb
: Giemsa stain : Methyl Blue + Eosinrbd_from_rgb
: FastRed + FastBlue + DABgdx_from_rgb
: Methyl Green + DABhax_from_rgb
: Hematoxylin + AECbro_from_rgb
: Blue matrix Anilline Blue + Red matrix Azocarmine + Orange matrix Orange-Gbpx_from_rgb
: Methyl Blue + Ponceau Fuchsinahx_from_rgb
: Alcian Blue + Hematoxylinhpx_from_rgb
: Hematoxylin + PAS[R80] | (1, 2) http://www.dentistry.bham.ac.uk/landinig/software/cdeconv/cdeconv.html |
>>> from skimage import data >>> from skimage.color import separate_stains, hdx_from_rgb >>> ihc = data.immunohistochemistry() >>> ihc_hdx = separate_stains(ihc, hdx_from_rgb)
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 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 |
Raises: |
ValueError If ValueError If either the illuminant or the observer angle is unsupported or unknown. |
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.
[R81] | http://www.easyrgb.com/index.php?X=MATH&H=07#text7 |
[R82] | http://en.wikipedia.org/wiki/Lab_color_space |
>>> from skimage import data >>> from skimage.color import rgb2xyz, xyz2lab >>> img = data.astronaut() >>> img_xyz = rgb2xyz(img) >>> img_lab = xyz2lab(img_xyz)
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 ValueError If either the illuminant or the observer angle are not supported or unknown. |
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.
[R83] | http://www.easyrgb.com/index.php?X=MATH&H=16#text16 |
[R84] | http://en.wikipedia.org/wiki/CIELUV |
>>> from skimage import data >>> from skimage.color import rgb2xyz, xyz2luv >>> img = data.astronaut() >>> img_xyz = rgb2xyz(img) >>> img_luv = xyz2luv(img_xyz)
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 |
---|---|
Returns: |
out : ndarray The image in RGB format, in a 3-D array of shape |
Raises: |
ValueError If |
The CIE XYZ color space is derived from the CIE RGB color space. Note however that this function converts to sRGB.
[R85] | http://en.wikipedia.org/wiki/CIE_1931_color_space |
>>> from skimage import data >>> from skimage.color import rgb2xyz, xyz2rgb >>> img = data.astronaut() >>> img_xyz = rgb2xyz(img) >>> img_rgb = xyz2rgb(img_xyz)
skimage.color.ycbcr2rgb(ycbcr)
[source]
YCbCr to RGB color space conversion.
Parameters: |
ycbcr : array_like The image in YCbCr format, in a 3- or 4-D array of shape |
---|---|
Returns: |
out : ndarray The image in RGB format, in a 3- or 4-D array of shape |
Raises: |
ValueError If |
Y is between 16 and 235. This is the color space which is commonly used by video codecs, it is sometimes incorrectly called “YUV”
[R86] | https://en.wikipedia.org/wiki/YCbCr |
skimage.color.yiq2rgb(yiq)
[source]
YIQ to RGB color space conversion.
Parameters: |
yiq : array_like The image in YIQ format, in a 3- or 4-D array of shape |
---|---|
Returns: |
out : ndarray The image in RGB format, in a 3- or 4-D array of shape |
Raises: |
ValueError If |
skimage.color.ypbpr2rgb(ypbpr)
[source]
YPbPr to RGB color space conversion.
Parameters: |
ypbpr : array_like The image in YPbPr format, in a 3- or 4-D array of shape |
---|---|
Returns: |
out : ndarray The image in RGB format, in a 3- or 4-D array of shape |
Raises: |
ValueError If |
[R87] | https://en.wikipedia.org/wiki/YPbPr |
skimage.color.yuv2rgb(yuv)
[source]
YUV to RGB color space conversion.
Parameters: |
yuv : array_like The image in YUV format, in a 3- or 4-D array of shape |
---|---|
Returns: |
out : ndarray The image in RGB format, in a 3- or 4-D array of shape |
Raises: |
ValueError If |
[R88] | https://en.wikipedia.org/wiki/YUV |
© 2011 the scikit-image team
Licensed under the BSD 3-clause License.
http://scikit-image.org/docs/0.13.x/api/skimage.color.html