imgaug/augmenters/arithmetic.py
def ContrastNormalization(alpha=1.0, per_channel=False, name=None, deterministic=False, random_state=None):
    """
    Augmenter that changes the contrast of images.

    dtype support:

        See ``imgaug.augmenters.contrast.LinearContrast``.

    Parameters
    ----------
    alpha : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
        Strength of the contrast normalization. Higher values than 1.0
        lead to higher contrast, lower values decrease the contrast.

            * If a number, then that value will be used for all images.
            * If a tuple ``(a, b)``, then a value will be sampled per image from
              the range ``a <= x <= b`` and be used as the alpha value.
            * If a list, then a random value will be sampled per image from
              that list.
            * If a StochasticParameter, then this parameter will be used to
              sample the alpha value per image.

    per_channel : bool or float, optional
        Whether to use the same value for all channels (False)
        or to sample a new value for each channel (True).
        If this value is a float ``p``, then for ``p`` percent of all images
        `per_channel` will be treated as True, otherwise as False.

    name : None or str, optional
        See :func:`imgaug.augmenters.meta.Augmenter.__init__`.

    deterministic : bool, optional
        See :func:`imgaug.augmenters.meta.Augmenter.__init__`.

    random_state : None or int or numpy.random.RandomState, optional
        See :func:`imgaug.augmenters.meta.Augmenter.__init__`.

    Examples
    --------
    >>> iaa.ContrastNormalization((0.5, 1.5))

    Decreases oder improves contrast per image by a random factor between
    0.5 and 1.5. The factor 0.5 means that any difference from the center value
    (i.e. 128) will be halved, leading to less contrast.

    >>> iaa.ContrastNormalization((0.5, 1.5), per_channel=0.5)

    Same as before, but for 50 percent of all images the normalization is done
    independently per channel (i.e. factors can vary per channel for the same
    image). In the other 50 percent of all images, the factor is the same for
    all channels.

    """
    # placed here to avoid cyclic dependency
    from . import contrast as contrast_lib
    return contrast_lib.LinearContrast(alpha=alpha, per_channel=per_channel, name=name, deterministic=deterministic,
                                       random_state=random_state)
def ContrastNormalization(alpha=1.0, per_channel=False, name=None, deterministic=False, random_state=None):
    """
    Augmenter that changes the contrast of images.

    dtype support:

        See ``imgaug.augmenters.contrast.LinearContrast``.

    Parameters
    ----------
    alpha : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
        Strength of the contrast normalization. Higher values than 1.0
        lead to higher contrast, lower values decrease the contrast.

            * If a number, then that value will be used for all images.
            * If a tuple ``(a, b)``, then a value will be sampled per image from
              the range ``a <= x <= b`` and be used as the alpha value.
            * If a list, then a random value will be sampled per image from
              that list.
            * If a StochasticParameter, then this parameter will be used to
              sample the alpha value per image.

    per_channel : bool or float, optional
        Whether to use the same value for all channels (False)
        or to sample a new value for each channel (True).
        If this value is a float ``p``, then for ``p`` percent of all images
        `per_channel` will be treated as True, otherwise as False.

    name : None or str, optional
        See :func:`imgaug.augmenters.meta.Augmenter.__init__`.

    deterministic : bool, optional
        See :func:`imgaug.augmenters.meta.Augmenter.__init__`.

    random_state : None or int or numpy.random.RandomState, optional
        See :func:`imgaug.augmenters.meta.Augmenter.__init__`.

    Examples
    --------
    >>> iaa.ContrastNormalization((0.5, 1.5))

    Decreases oder improves contrast per image by a random factor between
    0.5 and 1.5. The factor 0.5 means that any difference from the center value
    (i.e. 128) will be halved, leading to less contrast.

    >>> iaa.ContrastNormalization((0.5, 1.5), per_channel=0.5)

    Same as before, but for 50 percent of all images the normalization is done
    independently per channel (i.e. factors can vary per channel for the same
    image). In the other 50 percent of all images, the factor is the same for
    all channels.

    """
    # placed here to avoid cyclic dependency
    from . import contrast as contrast_lib
    return contrast_lib.LinearContrast(alpha=alpha, per_channel=per_channel, name=name, deterministic=deterministic,
                                       random_state=random_state)
def ContrastNormalization ( alpha = 1.0 , per_channel = False , name = None , deterministic = False , random_state = None ) : # placed here to avoid cyclic dependency from . import contrast as contrast_lib return contrast_lib . LinearContrast ( alpha = alpha , per_channel = per_channel , name = name , deterministic = deterministic , random_state = random_state )
Augmenter that changes the contrast of images.

    dtype support:

        See ``imgaug.augmenters.contrast.LinearContrast``.

    Parameters
    ----------
    alpha : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
        Strength of the contrast normalization. Higher values than 1.0
        lead to higher contrast, lower values decrease the contrast.

            * If a number, then that value will be used for all images.
            * If a tuple ``(a, b)``, then a value will be sampled per image from
              the range ``a <= x <= b`` and be used as the alpha value.
            * If a list, then a random value will be sampled per image from
              that list.
            * If a StochasticParameter, then this parameter will be used to
              sample the alpha value per image.

    per_channel : bool or float, optional
        Whether to use the same value for all channels (False)
        or to sample a new value for each channel (True).
        If this value is a float ``p``, then for ``p`` percent of all images
        `per_channel` will be treated as True, otherwise as False.

    name : None or str, optional
        See :func:`imgaug.augmenters.meta.Augmenter.__init__`.

    deterministic : bool, optional
        See :func:`imgaug.augmenters.meta.Augmenter.__init__`.

    random_state : None or int or numpy.random.RandomState, optional
        See :func:`imgaug.augmenters.meta.Augmenter.__init__`.

    Examples
    --------
    >>> iaa.ContrastNormalization((0.5, 1.5))

    Decreases oder improves contrast per image by a random factor between
    0.5 and 1.5. The factor 0.5 means that any difference from the center value
    (i.e. 128) will be halved, leading to less contrast.

    >>> iaa.ContrastNormalization((0.5, 1.5), per_channel=0.5)

    Same as before, but for 50 percent of all images the normalization is done
    independently per channel (i.e. factors can vary per channel for the same
    image). In the other 50 percent of all images, the factor is the same for
    all channels.
Augmenter that changes the contrast of images .