neulab/docprompting-conala huggingface dataset:
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)
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)
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)
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)
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
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)
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)
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
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
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).
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
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
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)
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)
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)
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)
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)
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
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
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).
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
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
skimage.data.astronaut() [source]
 
Color image of the astronaut Eileen Collins. Photograph of Eileen Collins, an American astronaut. She was selected as an astronaut in 1992 and first piloted the space shuttle STS-63 in 1995. She retired in 2006 after spending a total of 38 days, 8 hours and 10 minutes in outer space. This image was downloaded from the NASA Great Images database <https://flic.kr/p/r9qvLn>`__. No known copyright restrictions, released into the public domain.  Returns 
 
astronaut(512, 512, 3) uint8 ndarray 

Astronaut image.
skimage.data.binary_blobs(length=512, blob_size_fraction=0.1, n_dim=2, volume_fraction=0.5, seed=None) [source]
 
Generate synthetic binary image with several rounded blob-like objects.  Parameters 
 
lengthint, optional 

Linear size of output image.  
blob_size_fractionfloat, optional 

Typical linear size of blob, as a fraction of length, should be smaller than 1.  
n_dimint, optional 

Number of dimensions of output image.  
volume_fractionfloat, default 0.5 

Fraction of image pixels covered by the blobs (where the output is 1). Should be in [0, 1].  
seedint, optional 

Seed to initialize the random number generator. If None, a random seed from the operating system is used.    Returns 
 
blobsndarray of bools 

Output binary image     Examples >>> from skimage import data
>>> data.binary_blobs(length=5, blob_size_fraction=0.2, seed=1)
array([[ True, False,  True,  True,  True],
       [ True,  True,  True, False,  True],
       [False,  True, False,  True,  True],
       [ True, False, False,  True,  True],
       [ True, False, False, False,  True]])
>>> blobs = data.binary_blobs(length=256, blob_size_fraction=0.1)
>>> # Finer structures
>>> blobs = data.binary_blobs(length=256, blob_size_fraction=0.05)
>>> # Blobs cover a smaller volume fraction of the image
>>> blobs = data.binary_blobs(length=256, volume_fraction=0.3)
skimage.data.brain() [source]
 
Subset of data from the University of North Carolina Volume Rendering Test Data Set. The full dataset is available at [1].  Returns 
 
image(10, 256, 256) uint16 ndarray 
   Notes The 3D volume consists of 10 layers from the larger volume. References  
1  
https://graphics.stanford.edu/data/voldata/
skimage.data.brick() [source]
 
Brick wall.  Returns 
 
brick(512, 512) uint8 image 

A small section of a brick wall.     Notes The original image was downloaded from CC0Textures and licensed under the Creative Commons CC0 License. A perspective transform was then applied to the image, prior to rotating it by 90 degrees, cropping and scaling it to obtain the final image.
skimage.data.camera() [source]
 
Gray-level “camera” image. Can be used for segmentation and denoising examples.  Returns 
 
camera(512, 512) uint8 ndarray 

Camera image.     Notes No copyright restrictions. CC0 by the photographer (Lav Varshney).  Changed in version 0.18: This image was replaced due to copyright restrictions. For more information, please see [1].  References  
1  
https://github.com/scikit-image/scikit-image/issues/3927
skimage.data.cat() [source]
 
Chelsea the cat. An example with texture, prominent edges in horizontal and diagonal directions, as well as features of differing scales.  Returns 
 
chelsea(300, 451, 3) uint8 ndarray 

Chelsea image.     Notes No copyright restrictions. CC0 by the photographer (Stefan van der Walt).
skimage.data.cell() [source]
 
Cell floating in saline. This is a quantitative phase image retrieved from a digital hologram using the Python library qpformat. The image shows a cell with high phase value, above the background phase. Because of a banding pattern artifact in the background, this image is a good test of thresholding algorithms. The pixel spacing is 0.107 µm. These data were part of a comparison between several refractive index retrieval techniques for spherical objects as part of [1]. This image is CC0, dedicated to the public domain. You may copy, modify, or distribute it without asking permission.  Returns 
 
cell(660, 550) uint8 array 

Image of a cell.     References  
1  
Paul Müller, Mirjam Schürmann, Salvatore Girardo, Gheorghe Cojoc, and Jochen Guck. “Accurate evaluation of size and refractive index for spherical objects in quantitative phase imaging.” Optics Express 26(8): 10729-10743 (2018). DOI:10.1364/OE.26.010729
skimage.data.cells3d() [source]
 
3D fluorescence microscopy image of cells. The returned data is a 3D multichannel array with dimensions provided in (z, c, y, x) order. Each voxel has a size of (0.29 0.26 0.26) micrometer. Channel 0 contains cell membranes, channel 1 contains nuclei.  Returns 
 cells3d: (60, 2, 256, 256) uint16 ndarray

The volumetric images of cells taken with an optical microscope.     Notes The data for this was provided by the Allen Institute for Cell Science. It has been downsampled by a factor of 4 in the row and column dimensions to reduce computational time. The microscope reports the following voxel spacing in microns:  Original voxel size is (0.290, 0.065, 0.065). Scaling factor is (1, 4, 4) in each dimension. After rescaling the voxel size is (0.29 0.26 0.26).
skimage.data.checkerboard() [source]
 
Checkerboard image. Checkerboards are often used in image calibration, since the corner-points are easy to locate. Because of the many parallel edges, they also visualise distortions particularly well.  Returns 
 
checkerboard(200, 200) uint8 ndarray 

Checkerboard image.
skimage.data.chelsea() [source]
 
Chelsea the cat. An example with texture, prominent edges in horizontal and diagonal directions, as well as features of differing scales.  Returns 
 
chelsea(300, 451, 3) uint8 ndarray 

Chelsea image.     Notes No copyright restrictions. CC0 by the photographer (Stefan van der Walt).
skimage.data.clock() [source]
 
Motion blurred clock. This photograph of a wall clock was taken while moving the camera in an aproximately horizontal direction. It may be used to illustrate inverse filters and deconvolution. Released into the public domain by the photographer (Stefan van der Walt).  Returns 
 
clock(300, 400) uint8 ndarray 

Clock image.
skimage.data.coffee() [source]
 
Coffee cup. This photograph is courtesy of Pikolo Espresso Bar. It contains several elliptical shapes as well as varying texture (smooth porcelain to course wood grain).  Returns 
 
coffee(400, 600, 3) uint8 ndarray 

Coffee image.     Notes No copyright restrictions. CC0 by the photographer (Rachel Michetti).
skimage.data.coins() [source]
 
Greek coins from Pompeii. This image shows several coins outlined against a gray background. It is especially useful in, e.g. segmentation tests, where individual objects need to be identified against a background. The background shares enough grey levels with the coins that a simple segmentation is not sufficient.  Returns 
 
coins(303, 384) uint8 ndarray 

Coins image.     Notes This image was downloaded from the Brooklyn Museum Collection. No known copyright restrictions.
skimage.data.colorwheel() [source]
 
Color Wheel.  Returns 
 
colorwheel(370, 371, 3) uint8 image 

A colorwheel.
skimage.data.download_all(directory=None) [source]
 
Download all datasets for use with scikit-image offline. Scikit-image datasets are no longer shipped with the library by default. This allows us to use higher quality datasets, while keeping the library download size small. This function requires the installation of an optional dependency, pooch, to download the full dataset. Follow installation instruction found at https://scikit-image.org/docs/stable/install.html Call this function to download all sample images making them available offline on your machine.  Parameters 
 directory: path-like, optional

The directory where the dataset should be stored.    Raises 
 ModuleNotFoundError:

If pooch is not install, this error will be raised.     Notes scikit-image will only search for images stored in the default directory. Only specify the directory if you wish to download the images to your own folder for a particular reason. You can access the location of the default data directory by inspecting the variable skimage.data.data_dir.
skimage.data.eagle() [source]
 
A golden eagle. Suitable for examples on segmentation, Hough transforms, and corner detection.  Returns 
 
eagle(2019, 1826) uint8 ndarray 

Eagle image.     Notes No copyright restrictions. CC0 by the photographer (Dayane Machado).
skimage.data.grass() [source]
 
Grass.  Returns 
 
grass(512, 512) uint8 image 

Some grass.     Notes The original image was downloaded from DeviantArt and licensed underthe Creative Commons CC0 License. The downloaded image was cropped to include a region of (512, 512) pixels around the top left corner, converted to grayscale, then to uint8 prior to saving the result in PNG format.
skimage.data.gravel() [source]
 
Gravel  Returns 
 
gravel(512, 512) uint8 image 

Grayscale gravel sample.     Notes The original image was downloaded from CC0Textures and licensed under the Creative Commons CC0 License. The downloaded image was then rescaled to (1024, 1024), then the top left (512, 512) pixel region was cropped prior to converting the image to grayscale and uint8 data type. The result was saved using the PNG format.
skimage.data.horse() [source]
 
Black and white silhouette of a horse. This image was downloaded from openclipart No copyright restrictions. CC0 given by owner (Andreas Preuss (marauder)).  Returns 
 
horse(328, 400) bool ndarray 

Horse image.
skimage.data.hubble_deep_field() [source]
 
Hubble eXtreme Deep Field. This photograph contains the Hubble Telescope’s farthest ever view of the universe. It can be useful as an example for multi-scale detection.  Returns 
 
hubble_deep_field(872, 1000, 3) uint8 ndarray 

Hubble deep field image.     Notes This image was downloaded from HubbleSite. The image was captured by NASA and may be freely used in the public domain.