AutoAndroidController/py/venv/Lib/site-packages/imgaug/augmenters/blur.py

"""
Augmenters that blur images.

List of augmenters:

    * :class:`GaussianBlur`
    * :class:`AverageBlur`
    * :class:`MedianBlur`
    * :class:`BilateralBlur`
    * :class:`MotionBlur`
    * :class:`MeanShiftBlur`

"""
from __future__ import print_function, division, absolute_import

import numpy as np
from scipy import ndimage
import cv2
import six.moves as sm

import imgaug as ia
from imgaug.imgaug import _normalize_cv2_input_arr_
from . import meta
from . import convolutional as iaa_convolutional
from .. import parameters as iap
from .. import dtypes as iadt


# TODO add border mode, cval
def blur_gaussian_(image, sigma, ksize=None, backend="auto", eps=1e-3):
    """Blur an image using gaussian blurring in-place.

    This operation *may* change the input image in-place.

    **Supported dtypes**:

    if (backend="auto"):

        * ``uint8``: yes; fully tested (1)
        * ``uint16``: yes; tested (1)
        * ``uint32``: yes; tested (2)
        * ``uint64``: yes; tested (2)
        * ``int8``: yes; tested (1)
        * ``int16``: yes; tested (1)
        * ``int32``: yes; tested (1)
        * ``int64``: yes; tested (2)
        * ``float16``: yes; tested (1)
        * ``float32``: yes; tested (1)
        * ``float64``: yes; tested (1)
        * ``float128``: no
        * ``bool``: yes; tested (1)

        - (1) Handled by ``cv2``. See ``backend="cv2"``.
        - (2) Handled by ``scipy``. See ``backend="scipy"``.

    if (backend="cv2"):

        * ``uint8``: yes; fully tested
        * ``uint16``: yes; tested
        * ``uint32``: no (2)
        * ``uint64``: no (3)
        * ``int8``: yes; tested (4)
        * ``int16``: yes; tested
        * ``int32``: yes; tested (5)
        * ``int64``: no (6)
        * ``float16``: yes; tested (7)
        * ``float32``: yes; tested
        * ``float64``: yes; tested
        * ``float128``: no (8)
        * ``bool``: yes; tested (1)

        - (1) Mapped internally to ``float32``. Otherwise causes
              ``TypeError: src data type = 0 is not supported``.
        - (2) Causes ``TypeError: src data type = 6 is not supported``.
        - (3) Causes ``cv2.error: OpenCV(3.4.5) (...)/filter.cpp:2957:
              error: (-213:The function/feature is not implemented)
              Unsupported combination of source format (=4), and buffer
              format (=5) in function 'getLinearRowFilter'``.
        - (4) Mapped internally to ``int16``. Otherwise causes
              ``cv2.error: OpenCV(3.4.5) (...)/filter.cpp:2957: error:
              (-213:The function/feature is not implemented) Unsupported
              combination of source format (=1), and buffer format (=5)
              in function 'getLinearRowFilter'``.
        - (5) Mapped internally to ``float64``. Otherwise causes
              ``cv2.error: OpenCV(3.4.5) (...)/filter.cpp:2957: error:
              (-213:The function/feature is not implemented) Unsupported
              combination of source format (=4), and buffer format (=5)
              in function 'getLinearRowFilter'``.
        - (6) Causes ``cv2.error: OpenCV(3.4.5) (...)/filter.cpp:2957:
              error: (-213:The function/feature is not implemented)
              Unsupported combination of source format (=4), and buffer
              format (=5) in function 'getLinearRowFilter'``.
        - (7) Mapped internally to ``float32``. Otherwise causes
              ``TypeError: src data type = 23 is not supported``.
        - (8) Causes ``TypeError: src data type = 13 is not supported``.

    if (backend="scipy"):

        * ``uint8``: yes; fully tested
        * ``uint16``: yes; tested
        * ``uint32``: yes; tested
        * ``uint64``: yes; tested
        * ``int8``: yes; tested
        * ``int16``: yes; tested
        * ``int32``: yes; tested
        * ``int64``: yes; tested
        * ``float16``: yes; tested (1)
        * ``float32``: yes; tested
        * ``float64``: yes; tested
        * ``float128``: no (2)
        * ``bool``: yes; tested (3)

        - (1) Mapped internally to ``float32``. Otherwise causes
              ``RuntimeError: array type dtype('float16') not supported``.
        - (2) Causes ``RuntimeError: array type dtype('float128') not
              supported``.
        - (3) Mapped internally to ``float32``. Otherwise too inaccurate.

    Parameters
    ----------
    image : numpy.ndarray
        The image to blur. Expected to be of shape ``(H, W)`` or ``(H, W, C)``.

    sigma : number
        Standard deviation of the gaussian blur. Larger numbers result in
        more large-scale blurring, which is overall slower than small-scale
        blurring.

    ksize : None or int, optional
        Size in height/width of the gaussian kernel. This argument is only
        understood by the ``cv2`` backend. If it is set to ``None``, an
        appropriate value for `ksize` will automatically be derived from
        `sigma`. The value is chosen tighter for larger sigmas to avoid as
        much as possible very large kernel sizes and therey improve
        performance.

    backend : {'auto', 'cv2', 'scipy'}, optional
        Backend library to use. If ``auto``, then the likely best library
        will be automatically picked per image. That is usually equivalent
        to ``cv2`` (OpenCV) and it will fall back to ``scipy`` for datatypes
        not supported by OpenCV.

    eps : number, optional
        A threshold used to decide whether `sigma` can be considered zero.

    Returns
    -------
    numpy.ndarray
        The blurred image. Same shape and dtype as the input.
        (Input image *might* have been altered in-place.)

    """
    has_zero_sized_axes = (image.size == 0)
    if sigma > 0 + eps and not has_zero_sized_axes:
        dtype = image.dtype

        iadt.gate_dtypes(image,
                         allowed=["bool",
                                  "uint8", "uint16", "uint32",
                                  "int8", "int16", "int32", "int64", "uint64",
                                  "float16", "float32", "float64"],
                         disallowed=["uint128", "uint256",
                                     "int128", "int256",
                                     "float96", "float128", "float256"],
                         augmenter=None)

        dts_not_supported_by_cv2 = ["uint32", "uint64", "int64", "float128"]
        backend_to_use = backend
        if backend == "auto":
            backend_to_use = (
                "cv2"
                if image.dtype.name not in dts_not_supported_by_cv2
                else "scipy")
        elif backend == "cv2":
            assert image.dtype.name not in dts_not_supported_by_cv2, (
                "Requested 'cv2' backend, but provided %s input image, which "
                "cannot be handled by that backend. Choose a different "
                "backend or set backend to 'auto' or use a different "
                "datatype." % (
                    image.dtype.name,))
        elif backend == "scipy":
            # can handle all dtypes that were allowed in gate_dtypes()
            pass

        if backend_to_use == "scipy":
            if dtype.name == "bool":
                # We convert bool to float32 here, because gaussian_filter()
                # seems to only return True when the underlying value is
                # approximately 1.0, not when it is above 0.5. So we do that
                # here manually. cv2 does not support bool for gaussian blur.
                image = image.astype(np.float32, copy=False)
            elif dtype.name == "float16":
                image = image.astype(np.float32, copy=False)

            # gaussian_filter() has no ksize argument
            # TODO it does have a truncate argument that truncates at x
            #      standard deviations -- maybe can be used similarly to ksize
            if ksize is not None:
                ia.warn(
                    "Requested 'scipy' backend or picked it automatically by "
                    "backend='auto' n blur_gaussian_(), but also provided "
                    "'ksize' argument, which is not understood by that "
                    "backend and will be ignored.")

            # Note that while gaussian_filter can be applied to all channels
            # at the same time, that should not be done here, because then
            # the blurring would also happen across channels (e.g. red values
            # might be mixed with blue values in RGB)
            if image.ndim == 2:
                image[:, :] = ndimage.gaussian_filter(image[:, :], sigma,
                                                      mode="mirror")
            else:
                nb_channels = image.shape[2]
                for channel in sm.xrange(nb_channels):
                    image[:, :, channel] = ndimage.gaussian_filter(
                        image[:, :, channel], sigma, mode="mirror")
        else:
            if dtype.name == "bool":
                image = image.astype(np.float32, copy=False)
            elif dtype.name == "float16":
                image = image.astype(np.float32, copy=False)
            elif dtype.name == "int8":
                image = image.astype(np.int16, copy=False)
            elif dtype.name == "int32":
                image = image.astype(np.float64, copy=False)

            # ksize here is derived from the equation to compute sigma based
            # on ksize, see
            # https://docs.opencv.org/3.1.0/d4/d86/group__imgproc__filter.html
            # -> cv::getGaussianKernel()
            # example values:
            #   sig = 0.1 -> ksize = -1.666
            #   sig = 0.5 -> ksize = 0.9999
            #   sig = 1.0 -> ksize = 1.0
            #   sig = 2.0 -> ksize = 11.0
            #   sig = 3.0 -> ksize = 17.666
            # ksize = ((sig - 0.8)/0.3 + 1)/0.5 + 1

            if ksize is None:
                ksize = _compute_gaussian_blur_ksize(sigma)
            else:
                assert ia.is_single_integer(ksize), (
                    "Expected 'ksize' argument to be a number, "
                    "got %s." % (type(ksize),))

            ksize = ksize + 1 if ksize % 2 == 0 else ksize

            if ksize > 0:
                image_warped = cv2.GaussianBlur(
                    _normalize_cv2_input_arr_(image),
                    (ksize, ksize),
                    sigmaX=sigma,
                    sigmaY=sigma,
                    borderType=cv2.BORDER_REFLECT_101)

                # re-add channel axis removed by cv2 if input was (H, W, 1)
                image = (
                    image_warped[..., np.newaxis]
                    if image.ndim == 3 and image_warped.ndim == 2
                    else image_warped)

        if dtype.name == "bool":
            image = image > 0.5
        elif dtype.name != image.dtype.name:
            image = iadt.restore_dtypes_(image, dtype)

    return image


def blur_mean_shift_(image, spatial_window_radius, color_window_radius):
    """Apply a pyramidic mean shift filter to the input image in-place.

    This produces an output image that has similarity with one modified by
    a bilateral filter. That is different from mean shift *segmentation*,
    which averages the colors in segments found by mean shift clustering.

    This function is a thin wrapper around ``cv2.pyrMeanShiftFiltering``.

    .. note::

        This function does *not* change the image's colorspace to ``RGB``
        before applying the mean shift filter. A non-``RGB`` colorspace will
        hence influence the results.

    .. note::

        This function is quite slow.

    Added in 0.4.0.

    **Supported dtypes**:

        * ``uint8``: yes; fully tested
        * ``uint16``: no (1)
        * ``uint32``: no (1)
        * ``uint64``: no (1)
        * ``int8``: no (1)
        * ``int16``: no (1)
        * ``int32``: no (1)
        * ``int64``: no (1)
        * ``float16``: no (1)
        * ``float32``: no (1)
        * ``float64``: no (1)
        * ``float128``: no (1)
        * ``bool``: no (1)

        - (1) Not supported by ``cv2.pyrMeanShiftFiltering``.

    Parameters
    ----------
    image : ndarray
        ``(H,W)`` or ``(H,W,1)`` or ``(H,W,3)`` image to blur.
        Images with no or one channel will be temporarily tiled to have
        three channels.

    spatial_window_radius : number
        Spatial radius for pixels that are assumed to be similar.

    color_window_radius : number
        Color radius for pixels that are assumed to be similar.

    Returns
    -------
    ndarray
        Blurred input image. Same shape and dtype as the input.
        (Input image *might* have been altered in-place.)

    """
    if 0 in image.shape[0:2]:
        return image

    # opencv method only supports uint8
    assert image.dtype.name == "uint8", (
        "Expected image with dtype \"uint8\", "
        "got \"%s\"." % (image.dtype.name,))

    shape_is_hw = (image.ndim == 2)
    shape_is_hw1 = (image.ndim == 3 and image.shape[-1] == 1)
    shape_is_hw3 = (image.ndim == 3 and image.shape[-1] == 3)

    assert shape_is_hw or shape_is_hw1 or shape_is_hw3, (
        "Expected (H,W) or (H,W,1) or (H,W,3) image, "
        "got shape %s." % (image.shape,))

    # opencv method only supports (H,W,3), so we have to tile here for (H,W)
    # and (H,W,1)
    if shape_is_hw:
        image = np.tile(image[..., np.newaxis], (1, 1, 3))
    elif shape_is_hw1:
        image = np.tile(image, (1, 1, 3))

    spatial_window_radius = max(spatial_window_radius, 0)
    color_window_radius = max(color_window_radius, 0)

    image = _normalize_cv2_input_arr_(image)
    image = cv2.pyrMeanShiftFiltering(
        image,
        sp=spatial_window_radius,
        sr=color_window_radius,
        dst=image)

    if shape_is_hw:
        image = image[..., 0]
    elif shape_is_hw1:
        image = image[..., 0:1]

    return image


def _compute_gaussian_blur_ksize(sigma):
    if sigma < 3.0:
        ksize = 3.3 * sigma  # 99% of weight
    elif sigma < 5.0:
        ksize = 2.9 * sigma  # 97% of weight
    else:
        ksize = 2.6 * sigma  # 95% of weight

    # we use 5x5 here as the minimum size as that simplifies
    # comparisons with gaussian_filter() in the tests
    # TODO reduce this to 3x3
    ksize = int(max(ksize, 5))
    return ksize


# TODO offer different values for sigma on x/y-axis, supported by cv2 but not
#      by scipy
# TODO add channelwise flag - channelwise=False would be supported by scipy
class GaussianBlur(meta.Augmenter):
    """Augmenter to blur images using gaussian kernels.

    **Supported dtypes**:

    See ``~imgaug.augmenters.blur.blur_gaussian_(backend="auto")``.

    Parameters
    ----------
    sigma : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
        Standard deviation of the gaussian kernel.
        Values in the range ``0.0`` (no blur) to ``3.0`` (strong blur) are
        common.

            * If a single ``float``, that value will always be used as the
              standard deviation.
            * If a tuple ``(a, b)``, then a random value from the interval
              ``[a, b]`` will be picked per image.
            * If a list, then a random value will be sampled per image from
              that list.
            * If a ``StochasticParameter``, then ``N`` samples will be drawn
              from that parameter per ``N`` input images.

    seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
        See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.

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

    random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
        Old name for parameter `seed`.
        Its usage will not yet cause a deprecation warning,
        but it is still recommended to use `seed` now.
        Outdated since 0.4.0.

    deterministic : bool, optional
        Deprecated since 0.4.0.
        See method ``to_deterministic()`` for an alternative and for
        details about what the "deterministic mode" actually does.

    Examples
    --------
    >>> import imgaug.augmenters as iaa
    >>> aug = iaa.GaussianBlur(sigma=1.5)

    Blur all images using a gaussian kernel with a standard deviation of
    ``1.5``.

    >>> aug = iaa.GaussianBlur(sigma=(0.0, 3.0))

    Blur images using a gaussian kernel with a random standard deviation
    sampled uniformly (per image) from the interval ``[0.0, 3.0]``.

    """

    def __init__(self, sigma=(0.0, 3.0),
                 seed=None, name=None,
                 random_state="deprecated", deterministic="deprecated"):
        super(GaussianBlur, self).__init__(
            seed=seed, name=name,
            random_state=random_state, deterministic=deterministic)

        self.sigma = iap.handle_continuous_param(
            sigma, "sigma", value_range=(0, None), tuple_to_uniform=True,
            list_to_choice=True)

        # epsilon value to estimate whether sigma is sufficently above 0 to
        # apply the blur
        self.eps = 1e-3

    # Added in 0.4.0.
    def _augment_batch_(self, batch, random_state, parents, hooks):
        if batch.images is None:
            return batch

        images = batch.images
        nb_images = len(images)
        samples = self.sigma.draw_samples((nb_images,),
                                          random_state=random_state)
        for image, sig in zip(images, samples):
            image[...] = blur_gaussian_(image, sigma=sig, eps=self.eps)
        return batch

    def get_parameters(self):
        """See :func:`~imgaug.augmenters.meta.Augmenter.get_parameters`."""
        return [self.sigma]


class AverageBlur(meta.Augmenter):
    """Blur an image by computing simple means over neighbourhoods.

    The padding behaviour around the image borders is cv2's
    ``BORDER_REFLECT_101``.

    **Supported dtypes**:

        * ``uint8``: yes; fully tested
        * ``uint16``: yes; tested
        * ``uint32``: no (1)
        * ``uint64``: no (2)
        * ``int8``: yes; tested (3)
        * ``int16``: yes; tested
        * ``int32``: no (4)
        * ``int64``: no (5)
        * ``float16``: yes; tested (6)
        * ``float32``: yes; tested
        * ``float64``: yes; tested
        * ``float128``: no
        * ``bool``: yes; tested (7)

        - (1) rejected by ``cv2.blur()``
        - (2) loss of resolution in ``cv2.blur()`` (result is ``int32``)
        - (3) ``int8`` is mapped internally to ``int16``, ``int8`` itself
              leads to cv2 error "Unsupported combination of source format
              (=1), and buffer format (=4) in function 'getRowSumFilter'" in
              ``cv2``
        - (4) results too inaccurate
        - (5) loss of resolution in ``cv2.blur()`` (result is ``int32``)
        - (6) ``float16`` is mapped internally to ``float32``
        - (7) ``bool`` is mapped internally to ``float32``

    Parameters
    ----------
    k : int or tuple of int or tuple of tuple of int or imgaug.parameters.StochasticParameter or tuple of StochasticParameter, optional
        Kernel size to use.

            * If a single ``int``, then that value will be used for the height
              and width of the kernel.
            * If a tuple of two ``int`` s ``(a, b)``, then the kernel size will
              be sampled from the interval ``[a..b]``.
            * If a tuple of two tuples of ``int`` s ``((a, b), (c, d))``,
              then per image a random kernel height will be sampled from the
              interval ``[a..b]`` and a random kernel width will be sampled
              from the interval ``[c..d]``.
            * If a ``StochasticParameter``, then ``N`` samples will be drawn
              from that parameter per ``N`` input images, each representing
              the kernel size for the n-th image.
            * If a tuple ``(a, b)``, where either ``a`` or ``b`` is a tuple,
              then ``a`` and ``b`` will be treated according to the rules
              above. This leads to different values for height and width of
              the kernel.

    seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
        See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.

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

    random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
        Old name for parameter `seed`.
        Its usage will not yet cause a deprecation warning,
        but it is still recommended to use `seed` now.
        Outdated since 0.4.0.

    deterministic : bool, optional
        Deprecated since 0.4.0.
        See method ``to_deterministic()`` for an alternative and for
        details about what the "deterministic mode" actually does.

    Examples
    --------
    >>> import imgaug.augmenters as iaa
    >>> aug = iaa.AverageBlur(k=5)

    Blur all images using a kernel size of ``5x5``.

    >>> aug = iaa.AverageBlur(k=(2, 5))

    Blur images using a varying kernel size, which is sampled (per image)
    uniformly from the interval ``[2..5]``.

    >>> aug = iaa.AverageBlur(k=((5, 7), (1, 3)))

    Blur images using a varying kernel size, which's height is sampled
    (per image) uniformly from the interval ``[5..7]`` and which's width is
    sampled (per image) uniformly from ``[1..3]``.

    """

    def __init__(self, k=(1, 7),
                 seed=None, name=None,
                 random_state="deprecated", deterministic="deprecated"):
        super(AverageBlur, self).__init__(
            seed=seed, name=name,
            random_state=random_state, deterministic=deterministic)

        # TODO replace this by iap.handle_discrete_kernel_size()
        self.mode = "single"
        if ia.is_single_number(k):
            self.k = iap.Deterministic(int(k))
        elif ia.is_iterable(k):
            assert len(k) == 2, (
                "Expected iterable 'k' to contain exactly 2 entries, "
                "got %d." % (len(k),))
            if all([ia.is_single_number(ki) for ki in k]):
                self.k = iap.DiscreteUniform(int(k[0]), int(k[1]))
            elif all([isinstance(ki, iap.StochasticParameter) for ki in k]):
                self.mode = "two"
                self.k = (k[0], k[1])
            else:
                k_tuple = [None, None]
                if ia.is_single_number(k[0]):
                    k_tuple[0] = iap.Deterministic(int(k[0]))
                elif (ia.is_iterable(k[0])
                      and all([ia.is_single_number(ki) for ki in k[0]])):
                    k_tuple[0] = iap.DiscreteUniform(int(k[0][0]),
                                                     int(k[0][1]))
                else:
                    raise Exception(
                        "k[0] expected to be int or tuple of two ints, "
                        "got %s" % (type(k[0]),))

                if ia.is_single_number(k[1]):
                    k_tuple[1] = iap.Deterministic(int(k[1]))
                elif (ia.is_iterable(k[1])
                      and all([ia.is_single_number(ki) for ki in k[1]])):
                    k_tuple[1] = iap.DiscreteUniform(int(k[1][0]),
                                                     int(k[1][1]))
                else:
                    raise Exception(
                        "k[1] expected to be int or tuple of two ints, "
                        "got %s" % (type(k[1]),))

                self.mode = "two"
                self.k = k_tuple
        elif isinstance(k, iap.StochasticParameter):
            self.k = k
        else:
            raise Exception(
                "Expected int, tuple/list with 2 entries or "
                "StochasticParameter. Got %s." % (type(k),))

    # Added in 0.4.0.
    def _augment_batch_(self, batch, random_state, parents, hooks):
        if batch.images is None:
            return batch

        images = batch.images

        iadt.gate_dtypes(
            images,
            allowed=["bool",
                     "uint8", "uint16", "int8", "int16",
                     "float16", "float32", "float64"],
            disallowed=["uint32", "uint64", "uint128", "uint256",
                        "int32", "int64", "int128", "int256",
                        "float96", "float128", "float256"],
            augmenter=self)

        nb_images = len(images)
        if self.mode == "single":
            samples = self.k.draw_samples((nb_images,),
                                          random_state=random_state)
            samples = (samples, samples)
        else:
            rss = random_state.duplicate(2)
            samples = (
                self.k[0].draw_samples((nb_images,), random_state=rss[0]),
                self.k[1].draw_samples((nb_images,), random_state=rss[1]),
            )

        gen = enumerate(zip(images, samples[0], samples[1]))
        for i, (image, ksize_h, ksize_w) in gen:
            kernel_impossible = (ksize_h == 0 or ksize_w == 0)
            kernel_does_nothing = (ksize_h == 1 and ksize_w == 1)
            has_zero_sized_axes = (image.size == 0)
            if (not kernel_impossible and not kernel_does_nothing
                    and not has_zero_sized_axes):
                input_dtype = image.dtype
                if image.dtype.name in ["bool", "float16"]:
                    image = image.astype(np.float32, copy=False)
                elif image.dtype.name == "int8":
                    image = image.astype(np.int16, copy=False)

                if image.ndim == 2 or image.shape[-1] <= 512:
                    image_aug = cv2.blur(
                        _normalize_cv2_input_arr_(image),
                        (ksize_h, ksize_w))
                    # cv2.blur() removes channel axis for single-channel images
                    if image_aug.ndim == 2:
                        image_aug = image_aug[..., np.newaxis]
                else:
                    # TODO this is quite inefficient
                    # handling more than 512 channels in cv2.blur()
                    channels = [
                        cv2.blur(
                            _normalize_cv2_input_arr_(image[..., c]),
                            (ksize_h, ksize_w))
                        for c in sm.xrange(image.shape[-1])
                    ]
                    image_aug = np.stack(channels, axis=-1)

                if input_dtype.name == "bool":
                    image_aug = image_aug > 0.5
                elif input_dtype.name in ["int8", "float16"]:
                    image_aug = iadt.restore_dtypes_(image_aug, input_dtype)

                batch.images[i] = image_aug
        return batch

    def get_parameters(self):
        """See :func:`~imgaug.augmenters.meta.Augmenter.get_parameters`."""
        return [self.k]


class MedianBlur(meta.Augmenter):
    """Blur an image by computing median values over neighbourhoods.

    Median blurring can be used to remove small dirt from images.
    At larger kernel sizes, its effects have some similarity with Superpixels.

    **Supported dtypes**:

        * ``uint8``: yes; fully tested
        * ``uint16``: ?
        * ``uint32``: ?
        * ``uint64``: ?
        * ``int8``: ?
        * ``int16``: ?
        * ``int32``: ?
        * ``int64``: ?
        * ``float16``: ?
        * ``float32``: ?
        * ``float64``: ?
        * ``float128``: ?
        * ``bool``: ?

    Parameters
    ----------
    k : int or tuple of int or list of int or imgaug.parameters.StochasticParameter, optional
        Kernel size.

            * If a single ``int``, then that value will be used for the
              height and width of the kernel. Must be an odd value.
            * If a tuple of two ints ``(a, b)``, then the kernel size will be
              an odd value sampled from the interval ``[a..b]``. ``a`` and
              ``b`` must both be odd values.
            * If a list, then a random value will be sampled from that list
              per image.
            * If a ``StochasticParameter``, then ``N`` samples will be drawn
              from that parameter per ``N`` input images, each representing
              the kernel size for the nth image. Expected to be discrete. If
              a sampled value is not odd, then that value will be increased
              by ``1``.

    seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
        See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.

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

    random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
        Old name for parameter `seed`.
        Its usage will not yet cause a deprecation warning,
        but it is still recommended to use `seed` now.
        Outdated since 0.4.0.

    deterministic : bool, optional
        Deprecated since 0.4.0.
        See method ``to_deterministic()`` for an alternative and for
        details about what the "deterministic mode" actually does.

    Examples
    --------
    >>> import imgaug.augmenters as iaa
    >>> aug = iaa.MedianBlur(k=5)

    Blur all images using a kernel size of ``5x5``.

    >>> aug = iaa.MedianBlur(k=(3, 7))

    Blur images using varying kernel sizes, which are sampled uniformly from
    the interval ``[3..7]``. Only odd values will be sampled, i.e. ``3``
    or ``5`` or ``7``.

    """

    def __init__(self, k=(1, 7),
                 seed=None, name=None,
                 random_state="deprecated", deterministic="deprecated"):
        super(MedianBlur, self).__init__(
            seed=seed, name=name,
            random_state=random_state, deterministic=deterministic)

        # TODO replace this by iap.handle_discrete_kernel_size()
        self.k = iap.handle_discrete_param(
            k, "k", value_range=(1, None), tuple_to_uniform=True,
            list_to_choice=True, allow_floats=False)
        if ia.is_single_integer(k):
            assert k % 2 != 0, (
                "Expected k to be odd, got %d. Add or subtract 1." % (
                    int(k),))
        elif ia.is_iterable(k):
            assert all([ki % 2 != 0 for ki in k]), (
                "Expected all values in iterable k to be odd, but at least "
                "one was not. Add or subtract 1 to/from that value.")

    # Added in 0.4.0.
    def _augment_batch_(self, batch, random_state, parents, hooks):
        if batch.images is None:
            return batch

        images = batch.images
        nb_images = len(images)
        samples = self.k.draw_samples((nb_images,), random_state=random_state)
        for i, (image, ksize) in enumerate(zip(images, samples)):
            has_zero_sized_axes = (image.size == 0)
            if ksize > 1 and not has_zero_sized_axes:
                ksize = ksize + 1 if ksize % 2 == 0 else ksize
                if image.ndim == 2 or image.shape[-1] <= 512:
                    image_aug = cv2.medianBlur(
                        _normalize_cv2_input_arr_(image), ksize)
                    # cv2.medianBlur() removes channel axis for single-channel
                    # images
                    if image_aug.ndim == 2:
                        image_aug = image_aug[..., np.newaxis]
                else:
                    # TODO this is quite inefficient
                    # handling more than 512 channels in cv2.medainBlur()
                    channels = [
                        cv2.medianBlur(
                            _normalize_cv2_input_arr_(image[..., c]), ksize)
                        for c in sm.xrange(image.shape[-1])
                    ]
                    image_aug = np.stack(channels, axis=-1)

                batch.images[i] = image_aug
        return batch

    def get_parameters(self):
        """See :func:`~imgaug.augmenters.meta.Augmenter.get_parameters`."""
        return [self.k]


# TODO tests
class BilateralBlur(meta.Augmenter):
    """Blur/Denoise an image using a bilateral filter.

    Bilateral filters blur homogenous and textured areas, while trying to
    preserve edges.

    See
    http://docs.opencv.org/2.4/modules/imgproc/doc/filtering.html#bilateralfilter
    for more information regarding the parameters.

    **Supported dtypes**:

        * ``uint8``: yes; not tested
        * ``uint16``: ?
        * ``uint32``: ?
        * ``uint64``: ?
        * ``int8``: ?
        * ``int16``: ?
        * ``int32``: ?
        * ``int64``: ?
        * ``float16``: ?
        * ``float32``: ?
        * ``float64``: ?
        * ``float128``: ?
        * ``bool``: ?

    Parameters
    ----------
    d : int or tuple of int or list of int or imgaug.parameters.StochasticParameter, optional
        Diameter of each pixel neighborhood with value range ``[1 .. inf)``.
        High values for `d` lead to significantly worse performance. Values
        equal or less than ``10`` seem to be good. Use ``<5`` for real-time
        applications.

            * If a single ``int``, then that value will be used for the
              diameter.
            * If a tuple of two ``int`` s ``(a, b)``, then the diameter will
              be a value sampled from the interval ``[a..b]``.
            * If a list, then a random value will be sampled from that list
              per image.
            * If a ``StochasticParameter``, then ``N`` samples will be drawn
              from that parameter per ``N`` input images, each representing
              the diameter for the n-th image. Expected to be discrete.

    sigma_color : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
        Filter sigma in the color space with value range ``[1, inf)``. A
        large value of the parameter means that farther colors within the
        pixel neighborhood (see `sigma_space`) will be mixed together,
        resulting in larger areas of semi-equal color.

            * If a single ``int``, then that value will be used for the
              diameter.
            * If a tuple of two ``int`` s ``(a, b)``, then the diameter will
              be a value sampled from the interval ``[a, b]``.
            * If a list, then a random value will be sampled from that list
              per image.
            * If a ``StochasticParameter``, then ``N`` samples will be drawn
              from that parameter per ``N`` input images, each representing
              the diameter for the n-th image. Expected to be discrete.

    sigma_space : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
        Filter sigma in the coordinate space with value range ``[1, inf)``. A
        large value of the parameter means that farther pixels will influence
        each other as long as their colors are close enough (see
        `sigma_color`).

            * If a single ``int``, then that value will be used for the
              diameter.
            * If a tuple of two ``int`` s ``(a, b)``, then the diameter will
              be a value sampled from the interval ``[a, b]``.
            * If a list, then a random value will be sampled from that list
              per image.
            * If a ``StochasticParameter``, then ``N`` samples will be drawn
              from that parameter per ``N`` input images, each representing
              the diameter for the n-th image. Expected to be discrete.

    seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
        See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.

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

    random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
        Old name for parameter `seed`.
        Its usage will not yet cause a deprecation warning,
        but it is still recommended to use `seed` now.
        Outdated since 0.4.0.

    deterministic : bool, optional
        Deprecated since 0.4.0.
        See method ``to_deterministic()`` for an alternative and for
        details about what the "deterministic mode" actually does.

    Examples
    --------
    >>> import imgaug.augmenters as iaa
    >>> aug = iaa.BilateralBlur(
    >>>     d=(3, 10), sigma_color=(10, 250), sigma_space=(10, 250))

    Blur all images using a bilateral filter with a `max distance` sampled
    uniformly from the interval ``[3, 10]`` and wide ranges for `sigma_color`
    and `sigma_space`.

    """

    def __init__(self, d=(1, 9), sigma_color=(10, 250), sigma_space=(10, 250),
                 seed=None, name=None,
                 random_state="deprecated", deterministic="deprecated"):
        # pylint: disable=invalid-name
        super(BilateralBlur, self).__init__(
            seed=seed, name=name,
            random_state=random_state, deterministic=deterministic)

        self.d = iap.handle_discrete_param(
            d, "d", value_range=(1, None), tuple_to_uniform=True,
            list_to_choice=True, allow_floats=False)
        self.sigma_color = iap.handle_continuous_param(
            sigma_color, "sigma_color", value_range=(1, None),
            tuple_to_uniform=True, list_to_choice=True)
        self.sigma_space = iap.handle_continuous_param(
            sigma_space, "sigma_space", value_range=(1, None),
            tuple_to_uniform=True, list_to_choice=True)

    # Added in 0.4.0.
    def _augment_batch_(self, batch, random_state, parents, hooks):
        # pylint: disable=invalid-name
        if batch.images is None:
            return batch

        images = batch.images

        # Make sure that all images have 3 channels
        assert all([image.shape[2] == 3 for image in images]), (
            "BilateralBlur can currently only be applied to images with 3 "
            "channels. Got channels: %s" % (
                [image.shape[2] for image in images],))

        nb_images = len(images)
        rss = random_state.duplicate(3)
        samples_d = self.d.draw_samples((nb_images,), random_state=rss[0])
        samples_sigma_color = self.sigma_color.draw_samples(
            (nb_images,), random_state=rss[1])
        samples_sigma_space = self.sigma_space.draw_samples(
            (nb_images,), random_state=rss[2])
        gen = enumerate(zip(images, samples_d, samples_sigma_color,
                            samples_sigma_space))
        for i, (image, di, sigma_color_i, sigma_space_i) in gen:
            has_zero_sized_axes = (image.size == 0)
            if di != 1 and not has_zero_sized_axes:
                batch.images[i] = cv2.bilateralFilter(
                    _normalize_cv2_input_arr_(image),
                    di, sigma_color_i, sigma_space_i)
        return batch

    def get_parameters(self):
        """See :func:`~imgaug.augmenters.meta.Augmenter.get_parameters`."""
        return [self.d, self.sigma_color, self.sigma_space]


# TODO add k sizing via float/percentage
class MotionBlur(iaa_convolutional.Convolve):
    """Blur images in a way that fakes camera or object movements.

    **Supported dtypes**:

    See :class:`~imgaug.augmenters.convolutional.Convolve`.

    Parameters
    ----------
    k : int or tuple of int or list of int or imgaug.parameters.StochasticParameter, optional
        Kernel size to use.

            * If a single ``int``, then that value will be used for the height
              and width of the kernel.
            * If a tuple of two ``int`` s ``(a, b)``, then the kernel size
              will be sampled from the interval ``[a..b]``.
            * If a list, then a random value will be sampled from that list
              per image.
            * If a ``StochasticParameter``, then ``N`` samples will be drawn
              from that parameter per ``N`` input images, each representing
              the kernel size for the n-th image.

    angle : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
        Angle of the motion blur in degrees (clockwise, relative to top center
        direction).

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

    direction : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
        Forward/backward direction of the motion blur. Lower values towards
        ``-1.0`` will point the motion blur towards the back (with angle
        provided via `angle`). Higher values towards ``1.0`` will point the
        motion blur forward. A value of ``0.0`` leads to a uniformly (but
        still angled) motion blur.

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

    order : int or iterable of int or imgaug.ALL or imgaug.parameters.StochasticParameter, optional
        Interpolation order to use when rotating the kernel according to
        `angle`.
        See :func:`~imgaug.augmenters.geometric.Affine.__init__`.
        Recommended to be ``0`` or ``1``, with ``0`` being faster, but less
        continuous/smooth as `angle` is changed, particularly around multiple
        of ``45`` degrees.

    seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
        See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.

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

    random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
        Old name for parameter `seed`.
        Its usage will not yet cause a deprecation warning,
        but it is still recommended to use `seed` now.
        Outdated since 0.4.0.

    deterministic : bool, optional
        Deprecated since 0.4.0.
        See method ``to_deterministic()`` for an alternative and for
        details about what the "deterministic mode" actually does.

    Examples
    --------
    >>> import imgaug.augmenters as iaa
    >>> aug = iaa.MotionBlur(k=15)

    Apply motion blur with a kernel size of ``15x15`` pixels to images.

    >>> aug = iaa.MotionBlur(k=15, angle=[-45, 45])

    Apply motion blur with a kernel size of ``15x15`` pixels and a blur angle
    of either ``-45`` or ``45`` degrees (randomly picked per image).

    """

    def __init__(self, k=(3, 7), angle=(0, 360), direction=(-1.0, 1.0), order=1,
                 seed=None, name=None,
                 random_state="deprecated", deterministic="deprecated"):
        # TODO allow (1, None) and set to identity matrix if k == 1
        k_param = iap.handle_discrete_param(
            k, "k", value_range=(3, None), tuple_to_uniform=True,
            list_to_choice=True, allow_floats=False)
        angle_param = iap.handle_continuous_param(
            angle, "angle", value_range=None, tuple_to_uniform=True,
            list_to_choice=True)
        direction_param = iap.handle_continuous_param(
            direction, "direction", value_range=(-1.0-1e-6, 1.0+1e-6),
            tuple_to_uniform=True, list_to_choice=True)

        matrix_gen = _MotionBlurMatrixGenerator(k_param, angle_param,
                                                direction_param, order)

        super(MotionBlur, self).__init__(
            matrix_gen,
            seed=seed, name=name,
            random_state=random_state, deterministic=deterministic)


# Added in 0.4.0.
class _MotionBlurMatrixGenerator(object):
    # Added in 0.4.0.
    def __init__(self, k, angle, direction, order):
        self.k = k
        self.angle = angle
        self.direction = direction
        self.order = order

    # Added in 0.4.0.
    def __call__(self, _image, nb_channels, random_state):
        # avoid cyclic import between blur and geometric
        from . import geometric as iaa_geometric

        # force discrete for k_sample via int() in case of stochastic
        # parameter
        k_sample = int(
            self.k.draw_sample(random_state=random_state))
        angle_sample = self.angle.draw_sample(
            random_state=random_state)
        direction_sample = self.direction.draw_sample(
            random_state=random_state)

        k_sample = k_sample if k_sample % 2 != 0 else k_sample + 1
        direction_sample = np.clip(direction_sample, -1.0, 1.0)
        direction_sample = (direction_sample + 1.0) / 2.0

        matrix = np.zeros((k_sample, k_sample), dtype=np.float32)
        matrix[:, k_sample//2] = np.linspace(
            float(direction_sample),
            1.0 - float(direction_sample),
            num=k_sample)
        rot = iaa_geometric.Affine(rotate=angle_sample, order=self.order)

        matrix = (
            rot.augment_image(
                (matrix * 255).astype(np.uint8)
            ).astype(np.float32) / 255.0
        )

        return [matrix/np.sum(matrix)] * nb_channels


# TODO add a per_channel flag?
# TODO make spatial_radius a fraction of the input image size?
class MeanShiftBlur(meta.Augmenter):
    """Apply a pyramidic mean shift filter to each image.

    See also :func:`blur_mean_shift_` for details.

    This augmenter expects input images of shape ``(H,W)`` or ``(H,W,1)``
    or ``(H,W,3)``.

    .. note::

        This augmenter is quite slow.

    Added in 0.4.0.

    **Supported dtypes**:

    See :func:`~imgaug.augmenters.blur.blur_mean_shift_`.

    Parameters
    ----------
    spatial_radius : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
        Spatial radius for pixels that are assumed to be similar.

            * If ``number``: Exactly that value will be used for all images.
            * If ``tuple`` ``(a, b)``: A random value will be uniformly
              sampled per image from the interval ``[a, b)``.
            * If ``list``: A random value will be sampled from that ``list``
              per image.
            * If ``StochasticParameter``: The parameter will be queried once
              per batch for ``(N,)`` values with ``N`` denoting the number of
              images.

    color_radius : number or tuple of number or list of number or imgaug.parameters.StochasticParameter, optional
        Color radius for pixels that are assumed to be similar.

            * If ``number``: Exactly that value will be used for all images.
            * If ``tuple`` ``(a, b)``: A random value will be uniformly
              sampled per image from the interval ``[a, b)``.
            * If ``list``: A random value will be sampled from that ``list``
              per image.
            * If ``StochasticParameter``: The parameter will be queried once
              per batch for ``(N,)`` values with ``N`` denoting the number of
              images.

    seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
        See :func:`~imgaug.augmenters.meta.Augmenter.__init__`.

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

    random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional
        Old name for parameter `seed`.
        Its usage will not yet cause a deprecation warning,
        but it is still recommended to use `seed` now.
        Outdated since 0.4.0.

    deterministic : bool, optional
        Deprecated since 0.4.0.
        See method ``to_deterministic()`` for an alternative and for
        details about what the "deterministic mode" actually does.

    Examples
    --------
    >>> import imgaug.augmenters as iaa
    >>> aug = iaa.MeanShiftBlur()

    Create a mean shift blur augmenter.

    """

    # Added in 0.4.0.
    def __init__(self, spatial_radius=(5.0, 40.0), color_radius=(5.0, 40.0),
                 seed=None, name=None,
                 random_state="deprecated", deterministic="deprecated"):
        super(MeanShiftBlur, self).__init__(
            seed=seed, name=name,
            random_state=random_state, deterministic=deterministic)
        self.spatial_window_radius = iap.handle_continuous_param(
            spatial_radius, "spatial_radius",
            value_range=(0.01, None), tuple_to_uniform=True,
            list_to_choice=True)
        self.color_window_radius = iap.handle_continuous_param(
            color_radius, "color_radius",
            value_range=(0.01, None), tuple_to_uniform=True,
            list_to_choice=True)

    # Added in 0.4.0.
    def _augment_batch_(self, batch, random_state, parents, hooks):
        if batch.images is not None:
            samples = self._draw_samples(batch, random_state)
            for i, image in enumerate(batch.images):
                batch.images[i] = blur_mean_shift_(
                    image,
                    spatial_window_radius=samples[0][i],
                    color_window_radius=samples[1][i]
                )

        return batch

    # Added in 0.4.0.
    def _draw_samples(self, batch, random_state):
        nb_rows = batch.nb_rows
        return (
            self.spatial_window_radius.draw_samples((nb_rows,),
                                                    random_state=random_state),
            self.color_window_radius.draw_samples((nb_rows,),
                                                  random_state=random_state)
        )

    # Added in 0.4.0.
    def get_parameters(self):
        """See :func:`~imgaug.augmenters.meta.Augmenter.get_parameters`."""
        return [self.spatial_window_radius, self.color_window_radius]