scikit-image

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import numpy as np
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from .dtype import dtype_limits
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def invert(image, signed_float=False):
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    """Invert an image.
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    Invert the intensity range of the input image, so that the dtype maximum
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    is now the dtype minimum, and vice-versa. This operation is
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    slightly different depending on the input dtype:
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    - unsigned integers: subtract the image from the dtype maximum
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    - signed integers: subtract the image from -1 (see Notes)
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    - floats: subtract the image from 1 (if signed_float is False, so we
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      assume the image is unsigned), or from 0 (if signed_float is True).
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    See the examples for clarification.
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    Parameters
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    ----------
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    image : ndarray
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        Input image.
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    signed_float : bool, optional
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        If True and the image is of type float, the range is assumed to
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        be [-1, 1]. If False and the image is of type float, the range is
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        assumed to be [0, 1].
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    Returns
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    -------
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    inverted : ndarray
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        Inverted image.
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    Notes
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    -----
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    Ideally, for signed integers we would simply multiply by -1. However,
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    signed integer ranges are asymmetric. For example, for np.int8, the range
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    of possible values is [-128, 127], so that -128 * -1 equals -128! By
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    subtracting from -1, we correctly map the maximum dtype value to the
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    minimum.
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    Examples
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    --------
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    >>> img = np.array([[100,  0, 200],
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    ...                 [  0, 50,   0],
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    ...                 [ 30,  0, 255]], np.uint8)
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    >>> invert(img)
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    array([[155, 255,  55],
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           [255, 205, 255],
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           [225, 255,   0]], dtype=uint8)
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    >>> img2 = np.array([[ -2, 0, -128],
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    ...                  [127, 0,    5]], np.int8)
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    >>> invert(img2)
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    array([[   1,   -1,  127],
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           [-128,   -1,   -6]], dtype=int8)
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    >>> img3 = np.array([[ 0., 1., 0.5, 0.75]])
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    >>> invert(img3)
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    array([[1.  , 0.  , 0.5 , 0.25]])
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    >>> img4 = np.array([[ 0., 1., -1., -0.25]])
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    >>> invert(img4, signed_float=True)
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    array([[-0.  , -1.  ,  1.  ,  0.25]])
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    """
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    if image.dtype == 'bool':
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        inverted = ~image
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    elif np.issubdtype(image.dtype, np.unsignedinteger):
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        max_val = dtype_limits(image, clip_negative=False)[1]
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        inverted = np.subtract(max_val, image, dtype=image.dtype)
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    elif np.issubdtype(image.dtype, np.signedinteger):
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        inverted = np.subtract(-1, image, dtype=image.dtype)
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    else:  # float dtype
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        if signed_float:
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            inverted = -image
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        else:
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            inverted = np.subtract(1, image, dtype=image.dtype)
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    return inverted
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