ó
 ‰\c           @   s,   d  d l  Z d d l m Z e d „ Z d S(   iÿÿÿÿNi   (   t   dtype_limitsc         C   sÊ   |  j  d k r |  } n­ t j |  j  t j ƒ re t |  d t ƒd } t j | |  d |  j  ƒ} na t j |  j  t j ƒ r› t j d |  d |  j  ƒ} n+ | r« |  } n t j d |  d |  j  ƒ} | S(   s†  Invert an image.

    Invert the intensity range of the input image, so that the dtype maximum
    is now the dtype minimum, and vice-versa. This operation is
    slightly different depending on the input dtype:

    - unsigned integers: subtract the image from the dtype maximum
    - signed integers: subtract the image from -1 (see Notes)
    - floats: subtract the image from 1 (if signed_float is False, so we
      assume the image is unsigned), or from 0 (if signed_float is True).

    See the examples for clarification.

    Parameters
    ----------
    image : ndarray
        Input image.
    signed_float : bool, optional
        If True and the image is of type float, the range is assumed to
        be [-1, 1]. If False and the image is of type float, the range is
        assumed to be [0, 1].

    Returns
    -------
    inverted : ndarray
        Inverted image.

    Notes
    -----
    Ideally, for signed integers we would simply multiply by -1. However,
    signed integer ranges are asymmetric. For example, for np.int8, the range
    of possible values is [-128, 127], so that -128 * -1 equals -128! By
    subtracting from -1, we correctly map the maximum dtype value to the
    minimum.

    Examples
    --------
    >>> img = np.array([[100,  0, 200],
    ...                 [  0, 50,   0],
    ...                 [ 30,  0, 255]], np.uint8)
    >>> invert(img)
    array([[155, 255,  55],
           [255, 205, 255],
           [225, 255,   0]], dtype=uint8)
    >>> img2 = np.array([[ -2, 0, -128],
    ...                  [127, 0,    5]], np.int8)
    >>> invert(img2)
    array([[   1,   -1,  127],
           [-128,   -1,   -6]], dtype=int8)
    >>> img3 = np.array([[ 0., 1., 0.5, 0.75]])
    >>> invert(img3)
    array([[ 1.  ,  0.  ,  0.5 ,  0.25]])
    >>> img4 = np.array([[ 0., 1., -1., -0.25]])
    >>> invert(img4, signed_float=True)
    array([[-0.  , -1.  ,  1.  ,  0.25]])
    t   boolt   clip_negativei   t   dtypeiÿÿÿÿ(   R   t   npt
   issubdtypet   unsignedintegerR    t   Falset   subtractt   signedinteger(   t   imaget   signed_floatt   invertedt   max_val(    (    s3   lib/python2.7/site-packages/skimage/util/_invert.pyt   invert   s    9
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(   t   numpyR   R   R    R   R   (    (    (    s3   lib/python2.7/site-packages/skimage/util/_invert.pyt   <module>   s   