def LogContrast(gain=1, per_channel=False, name=None, deterministic=False, random_state=None):
    """
    Adjust contrast by scaling each pixel value to ``255 * gain * log_2(1 + I_ij/255)``.

    dtype support::

        See :func:`imgaug.augmenters.contrast.adjust_contrast_log`.

    Parameters
    ----------
    gain : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
        Multiplier for the logarithm result. Values around 1.0 lead to a contrast-adjusted
        images. Values above 1.0 quickly lead to partially broken images due to exceeding the
        datatype's value range.

            * If a number, then that value will be used for all images.
            * If a tuple ``(a, b)``, then a value from the range ``[a, b]`` will be used per image.
            * If a list, then a random value will be sampled from that list per image.
            * If a StochasticParameter, then a value will be sampled per image from that parameter.

    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__`.

    Returns
    -------
    _ContrastFuncWrapper
        Augmenter to perform logarithmic contrast adjustment.

    """
    # TODO add inv parameter?
    params1d = [iap.handle_continuous_param(gain, "gain", value_range=(0, None), tuple_to_uniform=True,
                                            list_to_choice=True)]
    func = adjust_contrast_log
    return _ContrastFuncWrapper(
        func, params1d, per_channel,
        dtypes_allowed=["uint8", "uint16", "uint32", "uint64",
                        "int8", "int16", "int32", "int64",
                        "float16", "float32", "float64"],
        dtypes_disallowed=["float96", "float128", "float256", "bool"],
        name=name if name is not None else ia.caller_name(),
        deterministic=deterministic,
        random_state=random_state
    )
def LogContrast(gain=1, per_channel=False, name=None, deterministic=False, random_state=None):
    """
    Adjust contrast by scaling each pixel value to ``255 * gain * log_2(1 + I_ij/255)``.

    dtype support::

        See :func:`imgaug.augmenters.contrast.adjust_contrast_log`.

    Parameters
    ----------
    gain : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
        Multiplier for the logarithm result. Values around 1.0 lead to a contrast-adjusted
        images. Values above 1.0 quickly lead to partially broken images due to exceeding the
        datatype's value range.

            * If a number, then that value will be used for all images.
            * If a tuple ``(a, b)``, then a value from the range ``[a, b]`` will be used per image.
            * If a list, then a random value will be sampled from that list per image.
            * If a StochasticParameter, then a value will be sampled per image from that parameter.

    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__`.

    Returns
    -------
    _ContrastFuncWrapper
        Augmenter to perform logarithmic contrast adjustment.

    """
    # TODO add inv parameter?
    params1d = [iap.handle_continuous_param(gain, "gain", value_range=(0, None), tuple_to_uniform=True,
                                            list_to_choice=True)]
    func = adjust_contrast_log
    return _ContrastFuncWrapper(
        func, params1d, per_channel,
        dtypes_allowed=["uint8", "uint16", "uint32", "uint64",
                        "int8", "int16", "int32", "int64",
                        "float16", "float32", "float64"],
        dtypes_disallowed=["float96", "float128", "float256", "bool"],
        name=name if name is not None else ia.caller_name(),
        deterministic=deterministic,
        random_state=random_state
    )
def LogContrast ( gain = 1 , per_channel = False , name = None , deterministic = False , random_state = None ) : # TODO add inv parameter? params1d = [ iap . handle_continuous_param ( gain , "gain" , value_range = ( 0 , None ) , tuple_to_uniform = True , list_to_choice = True ) ] func = adjust_contrast_log return _ContrastFuncWrapper ( func , params1d , per_channel , dtypes_allowed = [ "uint8" , "uint16" , "uint32" , "uint64" , "int8" , "int16" , "int32" , "int64" , "float16" , "float32" , "float64" ] , dtypes_disallowed = [ "float96" , "float128" , "float256" , "bool" ] , name = name if name is not None else ia . caller_name ( ) , deterministic = deterministic , random_state = random_state )
Adjust contrast by scaling each pixel value to ``255 * gain * log_2(1 + I_ij/255)``.

    dtype support::

        See :func:`imgaug.augmenters.contrast.adjust_contrast_log`.

    Parameters
    ----------
    gain : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
        Multiplier for the logarithm result. Values around 1.0 lead to a contrast-adjusted
        images. Values above 1.0 quickly lead to partially broken images due to exceeding the
        datatype's value range.

            * If a number, then that value will be used for all images.
            * If a tuple ``(a, b)``, then a value from the range ``[a, b]`` will be used per image.
            * If a list, then a random value will be sampled from that list per image.
            * If a StochasticParameter, then a value will be sampled per image from that parameter.

    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__`.

    Returns
    -------
    _ContrastFuncWrapper
        Augmenter to perform logarithmic contrast adjustment.
Adjust contrast by scaling each pixel value to 255 * gain * log_2 ( 1 + I_ij / 255 ) .