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Module: data

Standard test images.

For more images, see

skimage.data.load(f[, as_grey]) Load an image file located in the data directory.
skimage.data.astronaut() Color image of the astronaut Eileen Collins.
skimage.data.camera() Gray-level “camera” image.
skimage.data.checkerboard() Checkerboard image.
skimage.data.chelsea() Chelsea the cat.
skimage.data.clock() Motion blurred clock.
skimage.data.coffee() Coffee cup.
skimage.data.coins() Greek coins from Pompeii.
skimage.data.horse() Black and white silhouette of a horse.
skimage.data.hubble_deep_field() Hubble eXtreme Deep Field.
skimage.data.immunohistochemistry() Immunohistochemical (IHC) staining with hematoxylin counterstaining.
skimage.data.logo() Scikit-image logo, a RGBA image.
skimage.data.moon() Surface of the moon.
skimage.data.page() Scanned page.
skimage.data.text() Gray-level “text” image used for corner detection.
skimage.data.rocket() Launch photo of DSCOVR on Falcon 9 by SpaceX.
skimage.data.stereo_motorcycle() Rectified stereo image pair with ground-truth disparities.

load

skimage.data.load(f, as_grey=False)[source]

Load an image file located in the data directory.

Parameters:

f : string

File name.

as_grey : bool, optional

Convert to greyscale.

Returns:

img : ndarray

Image loaded from skimage.data_dir.

astronaut

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.

camera

skimage.data.camera()[source]

Gray-level “camera” image.

Often used for segmentation and denoising examples.

Returns:

camera : (512, 512) uint8 ndarray

Camera image.

checkerboard

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.

chelsea

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).

clock

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.

coffee

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).

coins

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.

horse

skimage.data.horse()[source]

Black and white silhouette of a horse.

This image was downloaded from openclipart <http://openclipart.org/detail/158377/horse-by-marauder>

Released into public domain and drawn and uploaded by Andreas Preuss (marauder).

Returns:

horse : (328, 400) bool ndarray

Horse image.

hubble_deep_field

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.

immunohistochemistry

skimage.data.immunohistochemistry()[source]

Immunohistochemical (IHC) staining with hematoxylin counterstaining.

This picture shows colonic glands where the IHC expression of FHL2 protein is revealed with DAB. Hematoxylin counterstaining is applied to enhance the negative parts of the tissue.

This image was acquired at the Center for Microscopy And Molecular Imaging (CMMI).

No known copyright restrictions.

Returns:

immunohistochemistry : (512, 512, 3) uint8 ndarray

Immunohistochemistry image.

moon

skimage.data.moon()[source]

Surface of the moon.

This low-contrast image of the surface of the moon is useful for illustrating histogram equalization and contrast stretching.

Returns:

moon : (512, 512) uint8 ndarray

Moon image.

page

skimage.data.page()[source]

Scanned page.

This image of printed text is useful for demonstrations requiring uneven background illumination.

Returns:

page : (191, 384) uint8 ndarray

Page image.

text

skimage.data.text()[source]

Gray-level “text” image used for corner detection.

Returns:

text : (172, 448) uint8 ndarray

Text image.

Notes

This image was downloaded from Wikipedia <http://en.wikipedia.org/wiki/File:Corner.png>`__.

No known copyright restrictions, released into the public domain.

rocket

skimage.data.rocket()[source]

Launch photo of DSCOVR on Falcon 9 by SpaceX.

This is the launch photo of Falcon 9 carrying DSCOVR lifted off from SpaceX’s Launch Complex 40 at Cape Canaveral Air Force Station, FL.

Returns:

rocket : (427, 640, 3) uint8 ndarray

Rocket image.

Notes

This image was downloaded from SpaceX Photos.

The image was captured by SpaceX and released in the public domain.

stereo_motorcycle

skimage.data.stereo_motorcycle()[source]

Rectified stereo image pair with ground-truth disparities.

The two images are rectified such that every pixel in the left image has its corresponding pixel on the same scanline in the right image. That means that both images are warped such that they have the same orientation but a horizontal spatial offset (baseline). The ground-truth pixel offset in column direction is specified by the included disparity map.

The two images are part of the Middlebury 2014 stereo benchmark. The dataset was created by Nera Nesic, Porter Westling, Xi Wang, York Kitajima, Greg Krathwohl, and Daniel Scharstein at Middlebury College. A detailed description of the acquisition process can be found in [R141142].

The images included here are down-sampled versions of the default exposure images in the benchmark. The images are down-sampled by a factor of 4 using the function skimage.transform.downscale_local_mean. The calibration data in the following and the included ground-truth disparity map are valid for the down-sampled images:

Focal length:           994.978px
Principal point x:      311.193px
Principal point y:      254.877px
Principal point dx:      31.086px
Baseline:               193.001mm
Returns:

img_left : (500, 741, 3) uint8 ndarray

Left stereo image.

img_right : (500, 741, 3) uint8 ndarray

Right stereo image.

disp : (500, 741, 3) float ndarray

Ground-truth disparity map, where each value describes the offset in column direction between corresponding pixels in the left and the right stereo images. E.g. the corresponding pixel of img_left[10, 10 + disp[10, 10]] is img_right[10, 10]. NaNs denote pixels in the left image that do not have ground-truth.

Notes

The original resolution images, images with different exposure and lighting, and ground-truth depth maps can be found at the Middlebury website [R142142].

References

[R141142](1, 2) D. Scharstein, H. Hirschmueller, Y. Kitajima, G. Krathwohl, N. Nesic, X. Wang, and P. Westling. High-resolution stereo datasets with subpixel-accurate ground truth. In German Conference on Pattern Recognition (GCPR 2014), Muenster, Germany, September 2014.
[R142142](1, 2) http://vision.middlebury.edu/stereo/data/scenes2014/