# coding: utf-8 """Standard test images. For more images, see - http://sipi.usc.edu/database/database.php """ import os as _os import numpy as _np from ..io import imread, use_plugin from .._shared._warnings import expected_warnings, warn from ..util.dtype import img_as_bool from ._binary_blobs import binary_blobs import os.path as osp data_dir = osp.abspath(osp.dirname(__file__)) __all__ = ['data_dir', 'load', 'astronaut', 'binary_blobs', 'camera', 'checkerboard', 'chelsea', 'clock', 'coffee', 'coins', 'horse', 'hubble_deep_field', 'immunohistochemistry', 'lfw_subset', 'logo', 'moon', 'page', 'text', 'rocket', 'stereo_motorcycle'] def load(f, as_gray=False, as_grey=None): """Load an image file located in the data directory. Parameters ---------- f : string File name. as_gray : bool, optional Convert to grayscale. as_grey : bool or None, optional Deprecated keyword argument. Use `as_gray` instead. If None, `as_gray` is used. Convert to grayscale. Returns ------- img : ndarray Image loaded from ``skimage.data_dir``. """ if as_grey is not None: as_gray = as_grey warn('`as_grey` has been deprecated in favor of `as_gray`' ' and will be removed in v0.16.') use_plugin('pil') return imread(_os.path.join(data_dir, f), as_gray=as_gray) def camera(): """Gray-level "camera" image. Often used for segmentation and denoising examples. Returns ------- camera : (512, 512) uint8 ndarray Camera image. """ return load("camera.png") def astronaut(): """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 `__. No known copyright restrictions, released into the public domain. Returns ------- astronaut : (512, 512, 3) uint8 ndarray Astronaut image. """ return load("astronaut.png") def text(): """Gray-level "text" image used for corner detection. Notes ----- This image was downloaded from Wikipedia `__. No known copyright restrictions, released into the public domain. Returns ------- text : (172, 448) uint8 ndarray Text image. """ return load("text.png") def checkerboard(): """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. """ return load("chessboard_GRAY.png") def coins(): """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. Notes ----- This image was downloaded from the `Brooklyn Museum Collection `__. No known copyright restrictions. Returns ------- coins : (303, 384) uint8 ndarray Coins image. """ return load("coins.png") def logo(): """Scikit-image logo, a RGBA image. Returns ------- logo : (500, 500, 4) uint8 ndarray Logo image. """ return load("logo.png") def moon(): """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. """ return load("moon.png") def page(): """Scanned page. This image of printed text is useful for demonstrations requiring uneven background illumination. Returns ------- page : (191, 384) uint8 ndarray Page image. """ return load("page.png") def horse(): """Black and white silhouette of a horse. This image was downloaded from `openclipart ` Released into public domain and drawn and uploaded by Andreas Preuss (marauder). Returns ------- horse : (328, 400) bool ndarray Horse image. """ with expected_warnings(['Possible precision loss', 'Possible sign loss']): return img_as_bool(load("horse.png", as_gray=True)) def clock(): """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. """ return load("clock_motion.png") def immunohistochemistry(): """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. """ return load("ihc.png") def chelsea(): """Chelsea the cat. An example with texture, prominent edges in horizontal and diagonal directions, as well as features of differing scales. Notes ----- No copyright restrictions. CC0 by the photographer (Stefan van der Walt). Returns ------- chelsea : (300, 451, 3) uint8 ndarray Chelsea image. """ return load("chelsea.png") def coffee(): """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). Notes ----- No copyright restrictions. CC0 by the photographer (Rachel Michetti). Returns ------- coffee : (400, 600, 3) uint8 ndarray Coffee image. """ return load("coffee.png") def hubble_deep_field(): """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. Notes ----- This image was downloaded from `HubbleSite `__. The image was captured by NASA and `may be freely used in the public domain `_. Returns ------- hubble_deep_field : (872, 1000, 3) uint8 ndarray Hubble deep field image. """ return load("hubble_deep_field.jpg") def rocket(): """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. Notes ----- This image was downloaded from `SpaceX Photos `__. The image was captured by SpaceX and `released in the public domain `_. Returns ------- rocket : (427, 640, 3) uint8 ndarray Rocket image. """ return load("rocket.jpg") def stereo_motorcycle(): """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/ """ return (load("motorcycle_left.png"), load("motorcycle_right.png"), _np.load(_os.path.join(data_dir, "motorcycle_disp.npz"))["arr_0"]) def lfw_subset(): """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/ """ return _np.load(_os.path.join(data_dir, 'lfw_subset.npy'))