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 d d d d d d d d d d d d d d d d d d g Z e d d „ Z d „  Z d „  Z d  „  Z d! „  Z d" „  Z d# „  Z d$ „  Z  d% „  Z! d& „  Z" d' „  Z# d( „  Z$ d) „  Z% d* „  Z& d+ „  Z' d, „  Z( d- „  Z) d. „  Z* d S(/   s[   Standard test images.

For more images, see

 - http://sipi.usc.edu/database/database.php

iÿÿÿÿNi   (   t   imreadt
   use_plugin(   t   expected_warningst   warn(   t   img_as_booli   (   t   binary_blobst   data_dirt   loadt	   astronautR   t   camerat   checkerboardt   chelseat   clockt   coffeet   coinst   horset   hubble_deep_fieldt   immunohistochemistryt
   lfw_subsett   logot   moont   paget   textt   rockett   stereo_motorcyclec         C   sH   | d k	 r | } t d ƒ n  t d ƒ t t j j t |  ƒ d | ƒS(   s°  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``.
    sQ   `as_grey` has been deprecated in favor of `as_gray` and will be removed in v0.16.t   pilt   as_grayN(   t   NoneR   R   R    t   _ost   patht   joinR   (   t   fR   t   as_grey(    (    s4   lib/python2.7/site-packages/skimage/data/__init__.pyR   -   s
    
c           C   s
   t  d ƒ S(   s­   Gray-level "camera" image.

    Often used for segmentation and denoising examples.

    Returns
    -------
    camera : (512, 512) uint8 ndarray
        Camera image.
    s
   camera.png(   R   (    (    (    s4   lib/python2.7/site-packages/skimage/data/__init__.pyR	   I   s    
c           C   s
   t  d ƒ S(   s=  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.
    s   astronaut.png(   R   (    (    (    s4   lib/python2.7/site-packages/skimage/data/__init__.pyR   V   s    c           C   s
   t  d ƒ S(   sH  Gray-level "text" image used for corner detection.

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

    No known copyright restrictions, released into the public domain.

    Returns
    -------
    text : (172, 448) uint8 ndarray
        Text image.
    s   text.png(   R   (    (    (    s4   lib/python2.7/site-packages/skimage/data/__init__.pyR   l   s    c           C   s
   t  d ƒ S(   s=  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.
    s   chessboard_GRAY.png(   R   (    (    (    s4   lib/python2.7/site-packages/skimage/data/__init__.pyR
      s    c           C   s
   t  d ƒ S(   sx  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
    <https://www.brooklynmuseum.org/opencollection/archives/image/51611>`__.

    No known copyright restrictions.

    Returns
    -------
    coins : (303, 384) uint8 ndarray
        Coins image.
    s	   coins.png(   R   (    (    (    s4   lib/python2.7/site-packages/skimage/data/__init__.pyR   Ž   s    c           C   s
   t  d ƒ S(   sy   Scikit-image logo, a RGBA image.

    Returns
    -------
    logo : (500, 500, 4) uint8 ndarray
        Logo image.
    s   logo.png(   R   (    (    (    s4   lib/python2.7/site-packages/skimage/data/__init__.pyR   §   s    c           C   s
   t  d ƒ S(   sñ   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.
    s   moon.png(   R   (    (    (    s4   lib/python2.7/site-packages/skimage/data/__init__.pyR   ²   s    c           C   s
   t  d ƒ S(   sÎ   Scanned page.

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

    Returns
    -------
    page : (191, 384) uint8 ndarray
        Page image.
    s   page.png(   R   (    (    (    s4   lib/python2.7/site-packages/skimage/data/__init__.pyR   À   s    c        	   C   s3   t  d d g ƒ  t t d d t ƒƒ SWd QXd S(   sF  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.
    s   Possible precision losss   Possible sign losss	   horse.pngR   N(   R   R   R   t   True(    (    (    s4   lib/python2.7/site-packages/skimage/data/__init__.pyR   Î   s    c           C   s
   t  d ƒ S(   sv  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.
    s   clock_motion.png(   R   (    (    (    s4   lib/python2.7/site-packages/skimage/data/__init__.pyR   à   s    c           C   s
   t  d ƒ S(   s  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.
    s   ihc.png(   R   (    (    (    s4   lib/python2.7/site-packages/skimage/data/__init__.pyR   ñ   s    c           C   s
   t  d ƒ S(   sU  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.
    s   chelsea.png(   R   (    (    (    s4   lib/python2.7/site-packages/skimage/data/__init__.pyR     s    c           C   s
   t  d ƒ S(   ss  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.
    s
   coffee.png(   R   (    (    (    s4   lib/python2.7/site-packages/skimage/data/__init__.pyR     s    c           C   s
   t  d ƒ S(   s`  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
    <http://hubblesite.org/newscenter/archive/releases/2012/37/image/a/>`__.

    The image was captured by NASA and `may be freely used in the public domain
    <http://www.nasa.gov/audience/formedia/features/MP_Photo_Guidelines.html>`_.

    Returns
    -------
    hubble_deep_field : (872, 1000, 3) uint8 ndarray
        Hubble deep field image.
    s   hubble_deep_field.jpg(   R   (    (    (    s4   lib/python2.7/site-packages/skimage/data/__init__.pyR   *  s    c           C   s
   t  d ƒ S(   sj  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
    <https://www.flickr.com/photos/spacexphotos/16511594820/in/photostream/>`__.

    The image was captured by SpaceX and `released in the public domain
    <http://arstechnica.com/tech-policy/2015/03/elon-musk-puts-spacex-photos-into-the-public-domain/>`_.

    Returns
    -------
    rocket : (427, 640, 3) uint8 ndarray
        Rocket image.
    s
   rocket.jpg(   R   (    (    (    s4   lib/python2.7/site-packages/skimage/data/__init__.pyR   B  s    c           C   s5   t  d ƒ t  d ƒ t j  t j j t d ƒ ƒ d f S(   s‡	  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/

    s   motorcycle_left.pngs   motorcycle_right.pngs   motorcycle_disp.npzt   arr_0(   R   t   _npR   R   R   R   (    (    (    s4   lib/python2.7/site-packages/skimage/data/__init__.pyR   Y  s    6		c           C   s   t  j t j j t d ƒ ƒ S(   s*  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/

    s   lfw_subset.npy(   R#   R   R   R   R   R   (    (    (    s4   lib/python2.7/site-packages/skimage/data/__init__.pyR   ”  s    (+   t   __doc__t   osR   t   numpyR#   t   ioR    R   t   _shared._warningsR   R   t
   util.dtypeR   t   _binary_blobsR   t   os.pathR   t   ospt   abspatht   dirnamet   __file__R   t   __all__t   FalseR   R   R	   R   R   R
   R   R   R   R   R   R   R   R   R   R   R   R   R   (    (    (    s4   lib/python2.7/site-packages/skimage/data/__init__.pyt   <module>	   s\   																	;