Module: data Standard test images. For more images, see  http://sipi.usc.edu/database/database.php   
skimage.data.astronaut() Color image of the astronaut Eileen Collins.  
skimage.data.binary_blobs([length, …]) Generate synthetic binary image with several rounded blob-like objects.  
skimage.data.brain() Subset of data from the University of North Carolina Volume Rendering Test Data Set.  
skimage.data.brick() Brick wall.  
skimage.data.camera() Gray-level “camera” image.  
skimage.data.cat() Chelsea the cat.  
skimage.data.cell() Cell floating in saline.  
skimage.data.cells3d() 3D fluorescence microscopy image of cells.  
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.colorwheel() Color Wheel.  
skimage.data.download_all([directory]) Download all datasets for use with scikit-image offline.  
skimage.data.eagle() A golden eagle.  
skimage.data.grass() Grass.  
skimage.data.gravel() Gravel  
skimage.data.horse() Black and white silhouette of a horse.  
skimage.data.hubble_deep_field() Hubble eXtreme Deep Field.  
skimage.data.human_mitosis() Image of human cells undergoing mitosis.  
skimage.data.immunohistochemistry() Immunohistochemical (IHC) staining with hematoxylin counterstaining.  
skimage.data.kidney() Mouse kidney tissue.  
skimage.data.lbp_frontal_face_cascade_filename() Return the path to the XML file containing the weak classifier cascade.  
skimage.data.lfw_subset() Subset of data from the LFW dataset.  
skimage.data.lily() Lily of the valley plant stem.  
skimage.data.logo() Scikit-image logo, a RGBA image.  
skimage.data.microaneurysms() Gray-level “microaneurysms” image.  
skimage.data.moon() Surface of the moon.  
skimage.data.page() Scanned page.  
skimage.data.retina() Human retina.  
skimage.data.rocket() Launch photo of DSCOVR on Falcon 9 by SpaceX.  
skimage.data.shepp_logan_phantom() Shepp Logan Phantom.  
skimage.data.skin() Microscopy image of dermis and epidermis (skin layers).  
skimage.data.stereo_motorcycle() Rectified stereo image pair with ground-truth disparities.  
skimage.data.text() Gray-level “text” image used for corner detection.   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.     
 Examples using skimage.data.astronaut
 
  Flood Fill   binary_blobs  
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)
 
 brain  
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/   
 Examples using skimage.data.brain
 
  Local Histogram Equalization  

  Rank filters   brick  
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. 
 camera  
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   
 Examples using skimage.data.camera
 
  Tinting gray-scale images  

  Masked Normalized Cross-Correlation  

  Entropy  

  GLCM Texture Features  

  Multi-Otsu Thresholding  

  Flood Fill  

  Rank filters   cat  
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). 
 cell  
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   
 cells3d  
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).  
 Examples using skimage.data.cells3d
 
  3D adaptive histogram equalization  

  Use rolling-ball algorithm for estimating background intensity  

  Explore 3D images (of cells)   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.     
 Examples using skimage.data.checkerboard
 
  Flood Fill   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). 
 Examples using skimage.data.chelsea
 
  Phase Unwrapping  

  Flood Fill   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. 
 Examples using skimage.data.coins
 
  Finding local maxima  

  Measure region properties  

  Use rolling-ball algorithm for estimating background intensity   colorwheel  
skimage.data.colorwheel() [source]
 
Color Wheel.  Returns 
 
colorwheel(370, 371, 3) uint8 image 

A colorwheel.     
 download_all  
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. 
 eagle  
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). 
 Examples using skimage.data.eagle
 
  Markers for watershed transform   grass  
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. 
 gravel  
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. 
 horse  
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.     
 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. 
 human_mitosis  
skimage.data.human_mitosis() [source]
 
Image of human cells undergoing mitosis.  Returns 
 human_mitosis: (512, 512) uint8 ndimage

Data of human cells undergoing mitosis taken during the preperation of the manuscript in [1].     Notes Copyright David Root. Licensed under CC-0 [2]. References  
1  
Moffat J, Grueneberg DA, Yang X, Kim SY, Kloepfer AM, Hinkle G, Piqani B, Eisenhaure TM, Luo B, Grenier JK, Carpenter AE, Foo SY, Stewart SA, Stockwell BR, Hacohen N, Hahn WC, Lander ES, Sabatini DM, Root DE (2006) A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen. Cell, 124(6):1283-98 / :DOI: 10.1016/j.cell.2006.01.040 PMID 16564017  
2  
GitHub licensing discussion https://github.com/CellProfiler/examples/issues/41   
 Examples using skimage.data.human_mitosis
 
  Segment human cells (in mitosis)   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.     
 kidney  
skimage.data.kidney() [source]
 
Mouse kidney tissue. This biological tissue on a pre-prepared slide was imaged with confocal fluorescence microscopy (Nikon C1 inverted microscope). Image shape is (16, 512, 512, 3). That is 512x512 pixels in X-Y, 16 image slices in Z, and 3 color channels (emission wavelengths 450nm, 515nm, and 605nm, respectively). Real-space voxel size is 1.24 microns in X-Y, and 1.25 microns in Z. Data type is unsigned 16-bit integers.  Returns 
 
kidney(16, 512, 512, 3) uint16 ndarray 

Kidney 3D multichannel image.     Notes This image was acquired by Genevieve Buckley at Monasoh Micro Imaging in 2018. License: CC0 
 lbp_frontal_face_cascade_filename  
skimage.data.lbp_frontal_face_cascade_filename() [source]
 
Return the path to the XML file containing the weak classifier cascade. These classifiers were trained using LBP features. The file is part of the OpenCV repository [1]. References  
1  
OpenCV lbpcascade trained files https://github.com/opencv/opencv/tree/master/data/lbpcascades   
 lfw_subset  
skimage.data.lfw_subset() [source]
 
Subset of data from the LFW dataset. This database is a subset of the LFW database containing:  100 faces 100 non-faces  The full dataset is available at [2].  Returns 
 
images(200, 25, 25) uint8 ndarray 

100 first images are faces and subsequent 100 are non-faces.     Notes The faces were randomly selected from the LFW dataset and the non-faces were extracted from the background of the same dataset. The cropped ROIs have been resized to a 25 x 25 pixels. References  
1  
Huang, G., Mattar, M., Lee, H., & Learned-Miller, E. G. (2012). Learning to align from scratch. In Advances in Neural Information Processing Systems (pp. 764-772).  
2  
http://vis-www.cs.umass.edu/lfw/   
 Examples using skimage.data.lfw_subset
 
  Specific images   lily  
skimage.data.lily() [source]
 
Lily of the valley plant stem. This plant stem on a pre-prepared slide was imaged with confocal fluorescence microscopy (Nikon C1 inverted microscope). Image shape is (922, 922, 4). That is 922x922 pixels in X-Y, with 4 color channels. Real-space voxel size is 1.24 microns in X-Y. Data type is unsigned 16-bit integers.  Returns 
 
lily(922, 922, 4) uint16 ndarray 

Lily 2D multichannel image.     Notes This image was acquired by Genevieve Buckley at Monasoh Micro Imaging in 2018. License: CC0 
 logo  
skimage.data.logo() [source]
 
Scikit-image logo, a RGBA image.  Returns 
 
logo(500, 500, 4) uint8 ndarray 

Logo image.     
 microaneurysms  
skimage.data.microaneurysms() [source]
 
Gray-level “microaneurysms” image. Detail from an image of the retina (green channel). The image is a crop of image 07_dr.JPG from the High-Resolution Fundus (HRF) Image Database: https://www5.cs.fau.de/research/data/fundus-images/  Returns 
 
microaneurysms(102, 102) uint8 ndarray 

Retina image with lesions.     Notes No copyright restrictions. CC0 given by owner (Andreas Maier). References  
1  
Budai, A., Bock, R, Maier, A., Hornegger, J., Michelson, G. (2013). Robust Vessel Segmentation in Fundus Images. International Journal of Biomedical Imaging, vol. 2013, 2013. DOI:10.1155/2013/154860   
 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.     
 Examples using skimage.data.moon
 
  Local Histogram Equalization   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.     
 Examples using skimage.data.page
 
  Use rolling-ball algorithm for estimating background intensity  

  Rank filters   retina  
skimage.data.retina() [source]
 
Human retina. This image of a retina is useful for demonstrations requiring circular images.  Returns 
 
retina(1411, 1411, 3) uint8 ndarray 

Retina image in RGB.     Notes This image was downloaded from wikimedia. This file is made available under the Creative Commons CC0 1.0 Universal Public Domain Dedication. References  
1  
Häggström, Mikael (2014). “Medical gallery of Mikael Häggström 2014”. WikiJournal of Medicine 1 (2). DOI:10.15347/wjm/2014.008. ISSN 2002-4436. 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. 
 shepp_logan_phantom  
skimage.data.shepp_logan_phantom() [source]
 
Shepp Logan Phantom.  Returns 
 
phantom(400, 400) float64 image 

Image of the Shepp-Logan phantom in grayscale.     References  
1  
L. A. Shepp and B. F. Logan, “The Fourier reconstruction of a head section,” in IEEE Transactions on Nuclear Science, vol. 21, no. 3, pp. 21-43, June 1974. DOI:10.1109/TNS.1974.6499235   
 skin  
skimage.data.skin() [source]
 
Microscopy image of dermis and epidermis (skin layers). Hematoxylin and eosin stained slide at 10x of normal epidermis and dermis with a benign intradermal nevus.  Returns 
 
skin(960, 1280, 3) RGB image of uint8 
   Notes This image requires an Internet connection the first time it is called, and to have the pooch package installed, in order to fetch the image file from the scikit-image datasets repository. The source of this image is https://en.wikipedia.org/wiki/File:Normal_Epidermis_and_Dermis_with_Intradermal_Nevus_10x.JPG The image was released in the public domain by its author Kilbad. 
 Examples using skimage.data.skin
 
  Trainable segmentation using local features and random forests   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 [1]. 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 [2]. References  
1  
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.  
2  
http://vision.middlebury.edu/stereo/data/scenes2014/   
 Examples using skimage.data.stereo_motorcycle
 
  Specific images  

  Registration using optical flow   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 <https://en.wikipedia.org/wiki/File:Corner.png>`__. No known copyright restrictions, released into the public domain.