imgaug/augmenters/arithmetic.py
def Dropout(p=0, per_channel=False, name=None, deterministic=False, random_state=None):
    """
    Augmenter that sets a certain fraction of pixels in images to zero.

    dtype support::

        See ``imgaug.augmenters.arithmetic.MultiplyElementwise``.

    Parameters
    ----------
    p : float or tuple of float or imgaug.parameters.StochasticParameter, optional
        The probability of any pixel being dropped (i.e. set to zero).

            * If a float, then that value will be used for all images. A value
              of 1.0 would mean that all pixels will be dropped and 0.0 that
              no pixels would be dropped. A value of 0.05 corresponds to 5
              percent of all pixels dropped.
            * If a tuple ``(a, b)``, then a value p will be sampled from the
              range ``a <= p <= b`` per image and be used as the pixel's dropout
              probability.
            * If a StochasticParameter, then this parameter will be used to
              determine per pixel whether it should be dropped (sampled value
              of 0) or shouldn't (sampled value of 1).
              If you instead want to provide the probability as a stochastic
              parameter, you can usually do ``imgaug.parameters.Binomial(1-p)``
              to convert parameter `p` to a 0/1 representation.

    per_channel : bool or float, optional
        Whether to use the same value (is dropped / is not dropped)
        for all channels of a pixel (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
    --------
    >>> aug = iaa.Dropout(0.02)

    drops 2 percent of all pixels.

    >>> aug = iaa.Dropout((0.0, 0.05))

    drops in each image a random fraction of all pixels, where the fraction
    is in the range ``0.0 <= x <= 0.05``.

    >>> aug = iaa.Dropout(0.02, per_channel=True)

    drops 2 percent of all pixels in a channel-wise fashion, i.e. it is unlikely
    for any pixel to have all channels set to zero (black pixels).

    >>> aug = iaa.Dropout(0.02, per_channel=0.5)

    same as previous example, but the `per_channel` feature is only active
    for 50 percent of all images.

    """
    if ia.is_single_number(p):
        p2 = iap.Binomial(1 - p)
    elif ia.is_iterable(p):
        ia.do_assert(len(p) == 2)
        ia.do_assert(p[0] < p[1])
        ia.do_assert(0 <= p[0] <= 1.0)
        ia.do_assert(0 <= p[1] <= 1.0)
        p2 = iap.Binomial(iap.Uniform(1 - p[1], 1 - p[0]))
    elif isinstance(p, iap.StochasticParameter):
        p2 = p
    else:
        raise Exception("Expected p to be float or int or StochasticParameter, got %s." % (type(p),))

    if name is None:
        name = "Unnamed%s" % (ia.caller_name(),)

    return MultiplyElementwise(p2, per_channel=per_channel, name=name, deterministic=deterministic,
                               random_state=random_state)
def Dropout(p=0, per_channel=False, name=None, deterministic=False, random_state=None):
    """
    Augmenter that sets a certain fraction of pixels in images to zero.

    dtype support::

        See ``imgaug.augmenters.arithmetic.MultiplyElementwise``.

    Parameters
    ----------
    p : float or tuple of float or imgaug.parameters.StochasticParameter, optional
        The probability of any pixel being dropped (i.e. set to zero).

            * If a float, then that value will be used for all images. A value
              of 1.0 would mean that all pixels will be dropped and 0.0 that
              no pixels would be dropped. A value of 0.05 corresponds to 5
              percent of all pixels dropped.
            * If a tuple ``(a, b)``, then a value p will be sampled from the
              range ``a <= p <= b`` per image and be used as the pixel's dropout
              probability.
            * If a StochasticParameter, then this parameter will be used to
              determine per pixel whether it should be dropped (sampled value
              of 0) or shouldn't (sampled value of 1).
              If you instead want to provide the probability as a stochastic
              parameter, you can usually do ``imgaug.parameters.Binomial(1-p)``
              to convert parameter `p` to a 0/1 representation.

    per_channel : bool or float, optional
        Whether to use the same value (is dropped / is not dropped)
        for all channels of a pixel (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
    --------
    >>> aug = iaa.Dropout(0.02)

    drops 2 percent of all pixels.

    >>> aug = iaa.Dropout((0.0, 0.05))

    drops in each image a random fraction of all pixels, where the fraction
    is in the range ``0.0 <= x <= 0.05``.

    >>> aug = iaa.Dropout(0.02, per_channel=True)

    drops 2 percent of all pixels in a channel-wise fashion, i.e. it is unlikely
    for any pixel to have all channels set to zero (black pixels).

    >>> aug = iaa.Dropout(0.02, per_channel=0.5)

    same as previous example, but the `per_channel` feature is only active
    for 50 percent of all images.

    """
    if ia.is_single_number(p):
        p2 = iap.Binomial(1 - p)
    elif ia.is_iterable(p):
        ia.do_assert(len(p) == 2)
        ia.do_assert(p[0] < p[1])
        ia.do_assert(0 <= p[0] <= 1.0)
        ia.do_assert(0 <= p[1] <= 1.0)
        p2 = iap.Binomial(iap.Uniform(1 - p[1], 1 - p[0]))
    elif isinstance(p, iap.StochasticParameter):
        p2 = p
    else:
        raise Exception("Expected p to be float or int or StochasticParameter, got %s." % (type(p),))

    if name is None:
        name = "Unnamed%s" % (ia.caller_name(),)

    return MultiplyElementwise(p2, per_channel=per_channel, name=name, deterministic=deterministic,
                               random_state=random_state)
def Dropout ( p = 0 , per_channel = False , name = None , deterministic = False , random_state = None ) : if ia . is_single_number ( p ) : p2 = iap . Binomial ( 1 - p ) elif ia . is_iterable ( p ) : ia . do_assert ( len ( p ) == 2 ) ia . do_assert ( p [ 0 ] < p [ 1 ] ) ia . do_assert ( 0 <= p [ 0 ] <= 1.0 ) ia . do_assert ( 0 <= p [ 1 ] <= 1.0 ) p2 = iap . Binomial ( iap . Uniform ( 1 - p [ 1 ] , 1 - p [ 0 ] ) ) elif isinstance ( p , iap . StochasticParameter ) : p2 = p else : raise Exception ( "Expected p to be float or int or StochasticParameter, got %s." % ( type ( p ) , ) ) if name is None : name = "Unnamed%s" % ( ia . caller_name ( ) , ) return MultiplyElementwise ( p2 , per_channel = per_channel , name = name , deterministic = deterministic , random_state = random_state )
Augmenter that sets a certain fraction of pixels in images to zero.

    dtype support::

        See ``imgaug.augmenters.arithmetic.MultiplyElementwise``.

    Parameters
    ----------
    p : float or tuple of float or imgaug.parameters.StochasticParameter, optional
        The probability of any pixel being dropped (i.e. set to zero).

            * If a float, then that value will be used for all images. A value
              of 1.0 would mean that all pixels will be dropped and 0.0 that
              no pixels would be dropped. A value of 0.05 corresponds to 5
              percent of all pixels dropped.
            * If a tuple ``(a, b)``, then a value p will be sampled from the
              range ``a <= p <= b`` per image and be used as the pixel's dropout
              probability.
            * If a StochasticParameter, then this parameter will be used to
              determine per pixel whether it should be dropped (sampled value
              of 0) or shouldn't (sampled value of 1).
              If you instead want to provide the probability as a stochastic
              parameter, you can usually do ``imgaug.parameters.Binomial(1-p)``
              to convert parameter `p` to a 0/1 representation.

    per_channel : bool or float, optional
        Whether to use the same value (is dropped / is not dropped)
        for all channels of a pixel (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
    --------
    >>> aug = iaa.Dropout(0.02)

    drops 2 percent of all pixels.

    >>> aug = iaa.Dropout((0.0, 0.05))

    drops in each image a random fraction of all pixels, where the fraction
    is in the range ``0.0 <= x <= 0.05``.

    >>> aug = iaa.Dropout(0.02, per_channel=True)

    drops 2 percent of all pixels in a channel-wise fashion, i.e. it is unlikely
    for any pixel to have all channels set to zero (black pixels).

    >>> aug = iaa.Dropout(0.02, per_channel=0.5)

    same as previous example, but the `per_channel` feature is only active
    for 50 percent of all images.
Augmenter that sets a certain fraction of pixels in images to zero .