Instruction:
Can you create a Python 3 function that
draws the lines of the line string on a given image.

Response:
def draw_lines_on_image(self, image, color=(0, 255, 0),
                            alpha=1.0, size=3,
                            antialiased=True,
                            raise_if_out_of_image=False):
        """
        Draw the line segments of the line string on a given image.

        Parameters
        ----------
        image : ndarray or tuple of int
            The image onto which to draw.
            Expected to be ``uint8`` and of shape ``(H, W, C)`` with ``C``
            usually being ``3`` (other values are not tested).
            If a tuple, expected to be ``(H, W, C)`` and will lead to a new
            ``uint8`` array of zeros being created.

        color : int or iterable of int
            Color to use as RGB, i.e. three values.

        alpha : float, optional
            Opacity of the line string. Higher values denote a more visible
            line string.

        size : int, optional
            Thickness of the line segments.

        antialiased : bool, optional
            Whether to draw the line with anti-aliasing activated.

        raise_if_out_of_image : bool, optional
            Whether to raise an error if the line string is fully
            outside of the image. If set to False, no error will be raised and
            only the parts inside the image will be drawn.

        Returns
        -------
        ndarray
            `image` with line drawn on it.

        """
        from .. import dtypes as iadt
        from ..augmenters import blend as blendlib

        image_was_empty = False
        if isinstance(image, tuple):
            image_was_empty = True
            image = np.zeros(image, dtype=np.uint8)
        assert image.ndim in [2, 3], (
            ("Expected image or shape of form (H,W) or (H,W,C), "
             + "got shape %s.") % (image.shape,))

        if len(self.coords) <= 1 or alpha < 0 + 1e-4 or size < 1:
            return np.copy(image)

        if raise_if_out_of_image \
                and self.is_out_of_image(image, partly=False, fully=True):
            raise Exception(
                "Cannot draw line string '%s' on image with shape %s, because "
                "it would be out of bounds." % (
                    self.__str__(), image.shape))

        if image.ndim == 2:
            assert ia.is_single_number(color), (
                "Got a 2D image. Expected then 'color' to be a single number, "
                "but got %s." % (str(color),))
            color = [color]
        elif image.ndim == 3 and ia.is_single_number(color):
            color = [color] * image.shape[-1]

        image = image.astype(np.float32)
        height, width = image.shape[0:2]

        # We can't trivially exclude lines outside of the image here, because
        # even if start and end point are outside, there can still be parts of
        # the line inside the image.
        # TODO Do this with edge-wise intersection tests
        lines = []
        for line_start, line_end in zip(self.coords[:-1], self.coords[1:]):
            # note that line() expects order (y1, x1, y2, x2), hence ([1], [0])
            lines.append((line_start[1], line_start[0],
                          line_end[1], line_end[0]))

        # skimage.draw.line can only handle integers
        lines = np.round(np.float32(lines)).astype(np.int32)

        # size == 0 is already covered above
        # Note here that we have to be careful not to draw lines two times
        # at their intersection points, e.g. for (p0, p1), (p1, 2) we could
        # end up drawing at p1 twice, leading to higher values if alpha is used.
        color = np.float32(color)
        heatmap = np.zeros(image.shape[0:2], dtype=np.float32)
        for line in lines:
            if antialiased:
                rr, cc, val = skimage.draw.line_aa(*line)
            else:
                rr, cc = skimage.draw.line(*line)
                val = 1.0

            # mask check here, because line() can generate coordinates
            # outside of the image plane
            rr_mask = np.logical_and(0 <= rr, rr < height)
            cc_mask = np.logical_and(0 <= cc, cc < width)
            mask = np.logical_and(rr_mask, cc_mask)

            if np.any(mask):
                rr = rr[mask]
                cc = cc[mask]
                val = val[mask] if not ia.is_single_number(val) else val
                heatmap[rr, cc] = val * alpha

        if size > 1:
            kernel = np.ones((size, size), dtype=np.uint8)
            heatmap = cv2.dilate(heatmap, kernel)

        if image_was_empty:
            image_blend = image + heatmap * color
        else:
            image_color_shape = image.shape[0:2]
            if image.ndim == 3:
                image_color_shape = image_color_shape + (1,)
            image_color = np.tile(color, image_color_shape)
            image_blend = blendlib.blend_alpha(image_color, image, heatmap)

        image_blend = iadt.restore_dtypes_(image_blend, np.uint8)
        return image_blend


Instruction:
Explain what the following Python 3 code does
def draw_points_on_image(self, image, color=(0, 128, 0),
                             alpha=1.0, size=3,
                             copy=True, raise_if_out_of_image=False):
        """
        Draw the points of the line string on a given image.

        Parameters
        ----------
        image : ndarray or tuple of int
            The image onto which to draw.
            Expected to be ``uint8`` and of shape ``(H, W, C)`` with ``C``
            usually being ``3`` (other values are not tested).
            If a tuple, expected to be ``(H, W, C)`` and will lead to a new
            ``uint8`` array of zeros being created.

        color : iterable of int
            Color to use as RGB, i.e. three values.

        alpha : float, optional
            Opacity of the line string points. Higher values denote a more
            visible points.

        size : int, optional
            Size of the points in pixels.

        copy : bool, optional
            Whether it is allowed to draw directly in the input
            array (``False``) or it has to be copied (``True``).
            The routine may still have to copy, even if ``copy=False`` was
            used. Always use the return value.

        raise_if_out_of_image : bool, optional
            Whether to raise an error if the line string is fully
            outside of the image. If set to False, no error will be raised and
            only the parts inside the image will be drawn.

        Returns
        -------
        ndarray
            Float array of shape `image_shape` (no channel axis) with drawn
            line string points. All values are in the interval ``[0.0, 1.0]``.

        """
        from .kps import KeypointsOnImage
        kpsoi = KeypointsOnImage.from_xy_array(self.coords, shape=image.shape)
        image = kpsoi.draw_on_image(
            image, color=color, alpha=alpha,
            size=size, copy=copy,
            raise_if_out_of_image=raise_if_out_of_image)

        return image

Response: Draw the points of the line string on a given image.


Instruction:
Implement a function in Python 3 to
draw the line string on an image.

Response:
def draw_on_image(self, image,
                      color=(0, 255, 0), color_lines=None, color_points=None,
                      alpha=1.0, alpha_lines=None, alpha_points=None,
                      size=1, size_lines=None, size_points=None,
                      antialiased=True,
                      raise_if_out_of_image=False):
        """
        Draw the line string on an image.

        Parameters
        ----------
        image : ndarray
            The `(H,W,C)` `uint8` image onto which to draw the line string.

        color : iterable of int, optional
            Color to use as RGB, i.e. three values.
            The color of the line and points are derived from this value,
            unless they are set.

        color_lines : None or iterable of int
            Color to use for the line segments as RGB, i.e. three values.
            If ``None``, this value is derived from `color`.

        color_points : None or iterable of int
            Color to use for the points as RGB, i.e. three values.
            If ``None``, this value is derived from ``0.5 * color``.

        alpha : float, optional
            Opacity of the line string. Higher values denote more visible
            points.
            The alphas of the line and points are derived from this value,
            unless they are set.

        alpha_lines : None or float, optional
            Opacity of the line string. Higher values denote more visible
            line string.
            If ``None``, this value is derived from `alpha`.

        alpha_points : None or float, optional
            Opacity of the line string points. Higher values denote more
            visible points.
            If ``None``, this value is derived from `alpha`.

        size : int, optional
            Size of the line string.
            The sizes of the line and points are derived from this value,
            unless they are set.

        size_lines : None or int, optional
            Thickness of the line segments.
            If ``None``, this value is derived from `size`.

        size_points : None or int, optional
            Size of the points in pixels.
            If ``None``, this value is derived from ``3 * size``.

        antialiased : bool, optional
            Whether to draw the line with anti-aliasing activated.
            This does currently not affect the point drawing.

        raise_if_out_of_image : bool, optional
            Whether to raise an error if the line string is fully
            outside of the image. If set to False, no error will be raised and
            only the parts inside the image will be drawn.

        Returns
        -------
        ndarray
            Image with line string drawn on it.

        """
        assert color is not None
        assert alpha is not None
        assert size is not None

        color_lines = color_lines if color_lines is not None \
            else np.float32(color)
        color_points = color_points if color_points is not None \
            else np.float32(color) * 0.5

        alpha_lines = alpha_lines if alpha_lines is not None \
            else np.float32(alpha)
        alpha_points = alpha_points if alpha_points is not None \
            else np.float32(alpha)

        size_lines = size_lines if size_lines is not None else size
        size_points = size_points if size_points is not None else size * 3

        image = self.draw_lines_on_image(
            image, color=np.array(color_lines).astype(np.uint8),
            alpha=alpha_lines, size=size_lines,
            antialiased=antialiased,
            raise_if_out_of_image=raise_if_out_of_image)

        image = self.draw_points_on_image(
            image, color=np.array(color_points).astype(np.uint8),
            alpha=alpha_points, size=size_points,
            copy=False,
            raise_if_out_of_image=raise_if_out_of_image)

        return image


Instruction:
Write a Python 3 function for
extracting the image pixels covered by the line string. It will only extract pixels overlapped by the line string. This function will by default zero-pad the image if the line string is partially/fully outside of the image. This is for consistency with the same implementations for bounding boxes and polygons. Parameters ---------- image : ndarray The image of shape `(H,W,[C])` from which to extract the pixels within the line string. size : int, optional Thickness of the line. pad : bool, optional Whether to zero-pad the image if the object is partially/fully outside of it. pad_max : None or int, optional The maximum number of pixels that may be zero-paded on any side, i.e. if this has value ``N`` the total maximum of added pixels is ``4*N``. This option exists to prevent extremely large images as a result of single points being moved very far away during augmentation. antialiased : bool, optional Whether to apply anti-aliasing to the line string. prevent_zero_size : bool, optional Whether to prevent height or width of the extracted image from becoming zero. If this is set to True and height or width of the line string is below 1, the height/width will be increased to 1. This can be useful to prevent problems, e.g. with image saving or plotting. If it is set to False, images will be returned as ``(H', W')`` or ``(H', W', 3)`` with ``H`` or ``W`` potentially being 0. Returns ------- image : (H',W') ndarray or (H',W',C) ndarray Pixels overlapping with the line string. Zero-padded if the line string is partially/fully outside of the image and ``pad=True``. If `prevent_zero_size` is activated, it is guarantueed that ``H'>0`` and ``W'>0``, otherwise only ``H'>=0`` and ``W'>=0``.

Response:
def extract_from_image(self, image, size=1, pad=True, pad_max=None,
                           antialiased=True, prevent_zero_size=True):
        """
        Extract the image pixels covered by the line string.

        It will only extract pixels overlapped by the line string.

        This function will by default zero-pad the image if the line string is
        partially/fully outside of the image. This is for consistency with
        the same implementations for bounding boxes and polygons.

        Parameters
        ----------
        image : ndarray
            The image of shape `(H,W,[C])` from which to extract the pixels
            within the line string.

        size : int, optional
            Thickness of the line.

        pad : bool, optional
            Whether to zero-pad the image if the object is partially/fully
            outside of it.

        pad_max : None or int, optional
            The maximum number of pixels that may be zero-paded on any side,
            i.e. if this has value ``N`` the total maximum of added pixels
            is ``4*N``.
            This option exists to prevent extremely large images as a result of
            single points being moved very far away during augmentation.

        antialiased : bool, optional
            Whether to apply anti-aliasing to the line string.

        prevent_zero_size : bool, optional
            Whether to prevent height or width of the extracted image from
            becoming zero. If this is set to True and height or width of the
            line string is below 1, the height/width will be increased to 1.
            This can be useful to prevent problems, e.g. with image saving or
            plotting. If it is set to False, images will be returned as
            ``(H', W')`` or ``(H', W', 3)`` with ``H`` or ``W`` potentially
            being 0.

        Returns
        -------
        image : (H',W') ndarray or (H',W',C) ndarray
            Pixels overlapping with the line string. Zero-padded if the
            line string is partially/fully outside of the image and
            ``pad=True``. If `prevent_zero_size` is activated, it is
            guarantueed that ``H'>0`` and ``W'>0``, otherwise only
            ``H'>=0`` and ``W'>=0``.

        """
        from .bbs import BoundingBox

        assert image.ndim in [2, 3], (
            "Expected image of shape (H,W,[C]), "
            "got shape %s." % (image.shape,))

        if len(self.coords) == 0 or size <= 0:
            if prevent_zero_size:
                return np.zeros((1, 1) + image.shape[2:], dtype=image.dtype)
            return np.zeros((0, 0) + image.shape[2:], dtype=image.dtype)

        xx = self.xx_int
        yy = self.yy_int

        # this would probably work if drawing was subpixel-accurate
        # x1 = np.min(self.coords[:, 0]) - (size / 2)
        # y1 = np.min(self.coords[:, 1]) - (size / 2)
        # x2 = np.max(self.coords[:, 0]) + (size / 2)
        # y2 = np.max(self.coords[:, 1]) + (size / 2)

        # this works currently with non-subpixel-accurate drawing
        sizeh = (size - 1) / 2
        x1 = np.min(xx) - sizeh
        y1 = np.min(yy) - sizeh
        x2 = np.max(xx) + 1 + sizeh
        y2 = np.max(yy) + 1 + sizeh
        bb = BoundingBox(x1=x1, y1=y1, x2=x2, y2=y2)

        if len(self.coords) == 1:
            return bb.extract_from_image(image, pad=pad, pad_max=pad_max,
                                         prevent_zero_size=prevent_zero_size)

        heatmap = self.draw_lines_heatmap_array(
            image.shape[0:2], alpha=1.0, size=size, antialiased=antialiased)
        if image.ndim == 3:
            heatmap = np.atleast_3d(heatmap)
        image_masked = image.astype(np.float32) * heatmap
        extract = bb.extract_from_image(image_masked, pad=pad, pad_max=pad_max,
                                        prevent_zero_size=prevent_zero_size)
        return np.clip(np.round(extract), 0, 255).astype(np.uint8)


Instruction:
Can you generate the documentation for the following Python 3 function
def concatenate(self, other):
        if not isinstance(other, LineString):
            other = LineString(other)
        return self.deepcopy(
            coords=np.concatenate([self.coords, other.coords], axis=0))

Response: Concatenate this line string with another line string.


Instruction:
Write a Python 3 function for
subdividing this LineString into two separate lines.

Response:
def subdivide(self, points_per_edge):
        """
        Adds ``N`` interpolated points with uniform spacing to each edge.

        For each edge between points ``A`` and ``B`` this adds points
        at ``A + (i/(1+N)) * (B - A)``, where ``i`` is the index of the added
        point and ``N`` is the number of points to add per edge.

        Calling this method two times will split each edge at its center
        and then again split each newly created edge at their center.
        It is equivalent to calling `subdivide(3)`.

        Parameters
        ----------
        points_per_edge : int
            Number of points to interpolate on each edge.

        Returns
        -------
        LineString
            Line string with subdivided edges.

        """
        if len(self.coords) <= 1 or points_per_edge < 1:
            return self.deepcopy()
        coords = interpolate_points(self.coords, nb_steps=points_per_edge,
                                    closed=False)
        return self.deepcopy(coords=coords)


Instruction:
Can you implement a function in Python 3 that
converts the line string points to keypoints.

Response:
def to_keypoints(self):
        """
        Convert the line string points to keypoints.

        Returns
        -------
        list of imgaug.augmentables.kps.Keypoint
            Points of the line string as keypoints.

        """
        # TODO get rid of this deferred import
        from imgaug.augmentables.kps import Keypoint
        return [Keypoint(x=x, y=y) for (x, y) in self.coords]


Instruction:
Create a Python 3 function to
generate a bounding box encapsulating the line string.

Response:
def to_bounding_box(self):
        """
        Generate a bounding box encapsulating the line string.

        Returns
        -------
        None or imgaug.augmentables.bbs.BoundingBox
            Bounding box encapsulating the line string.
            ``None`` if the line string contained no points.

        """
        from .bbs import BoundingBox
        # we don't have to mind the case of len(.) == 1 here, because
        # zero-sized BBs are considered valid
        if len(self.coords) == 0:
            return None
        return BoundingBox(x1=np.min(self.xx), y1=np.min(self.yy),
                           x2=np.max(self.xx), y2=np.max(self.yy),
                           label=self.label)


Instruction:
Given the following Python 3 function, write the documentation
def to_polygon(self):
        from .polys import Polygon
        return Polygon(self.coords, label=self.label)

Response: Generate a polygon from the line string points.


Instruction:
Write a Python 3 script to
generate a heatmap object from the line string.

Response:
def to_heatmap(self, image_shape, size_lines=1, size_points=0,
                   antialiased=True, raise_if_out_of_image=False):
        """
        Generate a heatmap object from the line string.

        This is similar to
        :func:`imgaug.augmentables.lines.LineString.draw_lines_heatmap_array`
        executed with ``alpha=1.0``. The result is wrapped in a
        ``HeatmapsOnImage`` object instead of just an array.
        No points are drawn.

        Parameters
        ----------
        image_shape : tuple of int
            The shape of the image onto which to draw the line mask.

        size_lines : int, optional
            Thickness of the line.

        size_points : int, optional
            Size of the points in pixels.

        antialiased : bool, optional
            Whether to draw the line with anti-aliasing activated.

        raise_if_out_of_image : bool, optional
            Whether to raise an error if the line string is fully
            outside of the image. If set to False, no error will be raised and
            only the parts inside the image will be drawn.

        Returns
        -------
        imgaug.augmentables.heatmaps.HeatmapOnImage
            Heatmap object containing drawn line string.

        """
        from .heatmaps import HeatmapsOnImage
        return HeatmapsOnImage(
            self.draw_heatmap_array(
                image_shape, size_lines=size_lines, size_points=size_points,
                antialiased=antialiased,
                raise_if_out_of_image=raise_if_out_of_image),
            shape=image_shape
        )


Instruction:
Implement a Python 3 function for
generating a segmentation map object from the line string.

Response:
def to_segmentation_map(self, image_shape, size_lines=1, size_points=0,
                            raise_if_out_of_image=False):
        """
        Generate a segmentation map object from the line string.

        This is similar to
        :func:`imgaug.augmentables.lines.LineString.draw_mask`.
        The result is wrapped in a ``SegmentationMapOnImage`` object
        instead of just an array.

        Parameters
        ----------
        image_shape : tuple of int
            The shape of the image onto which to draw the line mask.

        size_lines : int, optional
            Thickness of the line.

        size_points : int, optional
            Size of the points in pixels.

        raise_if_out_of_image : bool, optional
            Whether to raise an error if the line string is fully
            outside of the image. If set to False, no error will be raised and
            only the parts inside the image will be drawn.

        Returns
        -------
        imgaug.augmentables.segmaps.SegmentationMapOnImage
            Segmentation map object containing drawn line string.

        """
        from .segmaps import SegmentationMapOnImage
        return SegmentationMapOnImage(
            self.draw_mask(
                image_shape, size_lines=size_lines, size_points=size_points,
                raise_if_out_of_image=raise_if_out_of_image),
            shape=image_shape
        )


Instruction:
Explain what the following Python 3 code does
def coords_almost_equals(self, other, max_distance=1e-6, points_per_edge=8):
        if isinstance(other, LineString):
            pass
        elif isinstance(other, tuple):
            other = LineString([other])
        else:
            other = LineString(other)

        if len(self.coords) == 0 and len(other.coords) == 0:
            return True
        elif 0 in [len(self.coords), len(other.coords)]:
            # only one of the two line strings has no coords
            return False

        self_subd = self.subdivide(points_per_edge)
        other_subd = other.subdivide(points_per_edge)

        dist_self2other = self_subd.compute_pointwise_distances(other_subd)
        dist_other2self = other_subd.compute_pointwise_distances(self_subd)
        dist = max(np.max(dist_self2other), np.max(dist_other2self))
        return  dist < max_distance

Response: Compare two line strings and return True if the two line strings are almost identical.


Instruction:
Can you write a function in Python 3 where it
compares this line string and another line string.

Response:
def almost_equals(self, other, max_distance=1e-4, points_per_edge=8):
        """
        Compare this and another LineString.

        Parameters
        ----------
        other: imgaug.augmentables.lines.LineString
            The other line string. Must be a LineString instance, not just
            its coordinates.

        max_distance : float, optional
            See :func:`imgaug.augmentables.lines.LineString.coords_almost_equals`.

        points_per_edge : int, optional
            See :func:`imgaug.augmentables.lines.LineString.coords_almost_equals`.

        Returns
        -------
        bool
            ``True`` if the coordinates are almost equal according to
            :func:`imgaug.augmentables.lines.LineString.coords_almost_equals`
            and additionally the labels are identical. Otherwise ``False``.

        """
        if self.label != other.label:
            return False
        return self.coords_almost_equals(
            other, max_distance=max_distance, points_per_edge=points_per_edge)


Instruction:
Can you write a function in Python 3 where it
creates a shallow copy of the LineString object.

Response:
def copy(self, coords=None, label=None):
        """
        Create a shallow copy of the LineString object.

        Parameters
        ----------
        coords : None or iterable of tuple of number or ndarray
            If not ``None``, then the coords of the copied object will be set
            to this value.

        label : None or str
            If not ``None``, then the label of the copied object will be set to
            this value.

        Returns
        -------
        imgaug.augmentables.lines.LineString
            Shallow copy.

        """
        return LineString(coords=self.coords if coords is None else coords,
                          label=self.label if label is None else label)


Instruction:
Explain what the following Python 3 code does
def deepcopy(self, coords=None, label=None):
        return LineString(
            coords=np.copy(self.coords) if coords is None else coords,
            label=copylib.deepcopy(self.label) if label is None else label)

Response: Create a deep copy of the BoundingBox object.


Instruction:
Implement a Python 3 function for
projecting bounding boxes from one image to a new one.

Response:
def on(self, image):
        """
        Project bounding boxes from one image to a new one.

        Parameters
        ----------
        image : ndarray or tuple of int
            The new image onto which to project.
            Either an image with shape ``(H,W,[C])`` or a tuple denoting
            such an image shape.

        Returns
        -------
        line_strings : imgaug.augmentables.lines.LineStrings
            Object containing all projected line strings.

        """
        shape = normalize_shape(image)
        if shape[0:2] == self.shape[0:2]:
            return self.deepcopy()
        line_strings = [ls.project(self.shape, shape)
                        for ls in self.line_strings]
        return self.deepcopy(line_strings=line_strings, shape=shape)


Instruction:
Create a Python 3 function to
convert an array of point coordinates or iterable of arrays to a LineStringsOnImage object.

Response:
def from_xy_arrays(cls, xy, shape):
        """
        Convert an `(N,M,2)` ndarray to a LineStringsOnImage object.

        This is the inverse of
        :func:`imgaug.augmentables.lines.LineStringsOnImage.to_xy_array`.

        Parameters
        ----------
        xy : (N,M,2) ndarray or iterable of (M,2) ndarray
            Array containing the point coordinates ``N`` line strings
            with each ``M`` points given as ``(x,y)`` coordinates.
            ``M`` may differ if an iterable of arrays is used.
            Each array should usually be of dtype ``float32``.

        shape : tuple of int
            ``(H,W,[C])`` shape of the image on which the line strings are
            placed.

        Returns
        -------
        imgaug.augmentables.lines.LineStringsOnImage
            Object containing a list of ``LineString`` objects following the
            provided point coordinates.

        """
        lss = []
        for xy_ls in xy:
            lss.append(LineString(xy_ls))
        return cls(lss, shape)


Instruction:
Given the following Python 3 function, write the documentation
def to_xy_arrays(self, dtype=np.float32):
        from .. import dtypes as iadt
        return [iadt.restore_dtypes_(np.copy(ls.coords), dtype)
                for ls in self.line_strings]

Response: Convert this object to an iterable of arrays of point coordinates each given as M 2.


Instruction:
Here you have a function in Python 3, explain what it does
def draw_on_image(self, image,
                      color=(0, 255, 0), color_lines=None, color_points=None,
                      alpha=1.0, alpha_lines=None, alpha_points=None,
                      size=1, size_lines=None, size_points=None,
                      antialiased=True,
                      raise_if_out_of_image=False):
        """
        Draw all line strings onto a given image.

        Parameters
        ----------
        image : ndarray
            The `(H,W,C)` `uint8` image onto which to draw the line strings.

        color : iterable of int, optional
            Color to use as RGB, i.e. three values.
            The color of the lines and points are derived from this value,
            unless they are set.

        color_lines : None or iterable of int
            Color to use for the line segments as RGB, i.e. three values.
            If ``None``, this value is derived from `color`.

        color_points : None or iterable of int
            Color to use for the points as RGB, i.e. three values.
            If ``None``, this value is derived from ``0.5 * color``.

        alpha : float, optional
            Opacity of the line strings. Higher values denote more visible
            points.
            The alphas of the line and points are derived from this value,
            unless they are set.

        alpha_lines : None or float, optional
            Opacity of the line strings. Higher values denote more visible
            line string.
            If ``None``, this value is derived from `alpha`.

        alpha_points : None or float, optional
            Opacity of the line string points. Higher values denote more
            visible points.
            If ``None``, this value is derived from `alpha`.

        size : int, optional
            Size of the line strings.
            The sizes of the line and points are derived from this value,
            unless they are set.

        size_lines : None or int, optional
            Thickness of the line segments.
            If ``None``, this value is derived from `size`.

        size_points : None or int, optional
            Size of the points in pixels.
            If ``None``, this value is derived from ``3 * size``.

        antialiased : bool, optional
            Whether to draw the lines with anti-aliasing activated.
            This does currently not affect the point drawing.

        raise_if_out_of_image : bool, optional
            Whether to raise an error if a line string is fully
            outside of the image. If set to False, no error will be raised and
            only the parts inside the image will be drawn.

        Returns
        -------
        ndarray
            Image with line strings drawn on it.

        """
        # TODO improve efficiency here by copying only once
        for ls in self.line_strings:
            image = ls.draw_on_image(
                image,
                color=color, color_lines=color_lines, color_points=color_points,
                alpha=alpha, alpha_lines=alpha_lines, alpha_points=alpha_points,
                size=size, size_lines=size_lines, size_points=size_points,
                antialiased=antialiased,
                raise_if_out_of_image=raise_if_out_of_image
            )

        return image

Response: Draw all line strings onto a given image.


Instruction:
Write a Python 3 script to
return a reduced set of line strings that are not part of the image.

Response:
def remove_out_of_image(self, fully=True, partly=False):
        """
        Remove all line strings that are fully/partially outside of the image.

        Parameters
        ----------
        fully : bool, optional
            Whether to remove line strings that are fully outside of the image.

        partly : bool, optional
            Whether to remove line strings that are partially outside of the
            image.

        Returns
        -------
        imgaug.augmentables.lines.LineStringsOnImage
            Reduced set of line strings, with those that were fully/partially
            outside of the image removed.

        """
        lss_clean = [ls for ls in self.line_strings
                     if not ls.is_out_of_image(
                         self.shape, fully=fully, partly=partly)]
        return LineStringsOnImage(lss_clean, shape=self.shape)


Instruction:
Make a summary of the following Python 3 code
def clip_out_of_image(self):
        lss_cut = [ls_clipped
                   for ls in self.line_strings
                   for ls_clipped in ls.clip_out_of_image(self.shape)]
        return LineStringsOnImage(lss_cut, shape=self.shape)

Response: Clip off all parts of the line strings that are outside of the image.


Instruction:
Can you tell what is the following Python 3 function doing
def shift(self, top=None, right=None, bottom=None, left=None):
        lss_new = [ls.shift(top=top, right=right, bottom=bottom, left=left)
                   for ls in self.line_strings]
        return LineStringsOnImage(lss_new, shape=self.shape)

Response: Shifts the line strings from one or more image sides.


Instruction:
Can you create a Python 3 function that
creates a shallow copy of the LineStringsOnImage object.

Response:
def copy(self, line_strings=None, shape=None):
        """
        Create a shallow copy of the LineStringsOnImage object.

        Parameters
        ----------
        line_strings : None \
                       or list of imgaug.augmentables.lines.LineString, optional
            List of line strings on the image.
            If not ``None``, then the ``line_strings`` attribute of the copied
            object will be set to this value.

        shape : None or tuple of int or ndarray, optional
            The shape of the image on which the objects are placed.
            Either an image with shape ``(H,W,[C])`` or a tuple denoting
            such an image shape.
            If not ``None``, then the ``shape`` attribute of the copied object
            will be set to this value.

        Returns
        -------
        imgaug.augmentables.lines.LineStringsOnImage
            Shallow copy.

        """
        lss = self.line_strings if line_strings is None else line_strings
        shape = self.shape if shape is None else shape
        return LineStringsOnImage(line_strings=lss, shape=shape)


Instruction:
Create a Python 3 function for
creating a deep copy of the LineStringsOnImage object.

Response:
def deepcopy(self, line_strings=None, shape=None):
        """
        Create a deep copy of the LineStringsOnImage object.

        Parameters
        ----------
        line_strings : None \
                       or list of imgaug.augmentables.lines.LineString, optional
            List of line strings on the image.
            If not ``None``, then the ``line_strings`` attribute of the copied
            object will be set to this value.

        shape : None or tuple of int or ndarray, optional
            The shape of the image on which the objects are placed.
            Either an image with shape ``(H,W,[C])`` or a tuple denoting
            such an image shape.
            If not ``None``, then the ``shape`` attribute of the copied object
            will be set to this value.

        Returns
        -------
        imgaug.augmentables.lines.LineStringsOnImage
            Deep copy.

        """
        lss = self.line_strings if line_strings is None else line_strings
        shape = self.shape if shape is None else shape
        return LineStringsOnImage(
            line_strings=[ls.deepcopy() for ls in lss],
            shape=tuple(shape))


Instruction:
Explain what the following Python 3 code does
def blend_alpha(image_fg, image_bg, alpha, eps=1e-2):
    assert image_fg.shape == image_bg.shape
    assert image_fg.dtype.kind == image_bg.dtype.kind
    # TODO switch to gate_dtypes()
    assert image_fg.dtype.name not in ["float128"]
    assert image_bg.dtype.name not in ["float128"]

    # TODO add test for this
    input_was_2d = (len(image_fg.shape) == 2)
    if input_was_2d:
        image_fg = np.atleast_3d(image_fg)
        image_bg = np.atleast_3d(image_bg)

    input_was_bool = False
    if image_fg.dtype.kind == "b":
        input_was_bool = True
        # use float32 instead of float16 here because it seems to be faster
        image_fg = image_fg.astype(np.float32)
        image_bg = image_bg.astype(np.float32)

    alpha = np.array(alpha, dtype=np.float64)
    if alpha.size == 1:
        pass
    else:
        if alpha.ndim == 2:
            assert alpha.shape == image_fg.shape[0:2]
            alpha = alpha.reshape((alpha.shape[0], alpha.shape[1], 1))
        elif alpha.ndim == 3:
            assert alpha.shape == image_fg.shape or alpha.shape == image_fg.shape[0:2] + (1,)
        else:
            alpha = alpha.reshape((1, 1, -1))
        if alpha.shape[2] != image_fg.shape[2]:
            alpha = np.tile(alpha, (1, 1, image_fg.shape[2]))

    if not input_was_bool:
        if np.all(alpha >= 1.0 - eps):
            return np.copy(image_fg)
        elif np.all(alpha <= eps):
            return np.copy(image_bg)

    # for efficiency reaons, only test one value of alpha here, even if alpha is much larger
    assert 0 <= alpha.item(0) <= 1.0

    dt_images = iadt.get_minimal_dtype([image_fg, image_bg])

    # doing this only for non-float images led to inaccuracies for large floats values
    isize = dt_images.itemsize * 2
    isize = max(isize, 4)  # at least 4 bytes (=float32), tends to be faster than float16
    dt_blend = np.dtype("f%d" % (isize,))

    if alpha.dtype != dt_blend:
        alpha = alpha.astype(dt_blend)
    if image_fg.dtype != dt_blend:
        image_fg = image_fg.astype(dt_blend)
    if image_bg.dtype != dt_blend:
        image_bg = image_bg.astype(dt_blend)

    # the following is equivalent to
    #     image_blend = alpha * image_fg + (1 - alpha) * image_bg
    # but supposedly faster
    image_blend = image_bg + alpha * (image_fg - image_bg)

    if input_was_bool:
        image_blend = image_blend > 0.5
    else:
        # skip clip, because alpha is expected to be in range [0.0, 1.0] and both images must have same dtype
        # dont skip round, because otherwise it is very unlikely to hit the image's max possible value
        image_blend = iadt.restore_dtypes_(image_blend, dt_images, clip=False, round=True)

    if input_was_2d:
        return image_blend[:, :, 0]
    return image_blend

Response: Blends two images using an alpha blending.


Instruction:
Explain what the following Python 3 code does
def OneOf(children, name=None, deterministic=False, random_state=None):
    return SomeOf(n=1, children=children, random_order=False, name=name, deterministic=deterministic,
                  random_state=random_state)

Response: Augmenter that always executes exactly one of its children.


Instruction:
Given the following Python 3 function, write the documentation
def AssertShape(shape, check_images=True, check_heatmaps=True,
                check_keypoints=True, check_polygons=True,
                name=None, deterministic=False, random_state=None):
    """
    Augmenter to make assumptions about the shape of input image(s), heatmaps and keypoints.

    dtype support::

        * ``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
        * ``float32``: yes; tested
        * ``float64``: yes; tested
        * ``float128``: yes; tested
        * ``bool``: yes; tested

    Parameters
    ----------
    shape : tuple
        The expected shape, given as a tuple. The number of entries in the tuple must match the
        number of dimensions, i.e. it must contain four entries for ``(N, H, W, C)``. If only a
        single image is augmented via ``augment_image()``, then ``N`` is viewed as 1 by this
        augmenter. If the input image(s) don't have a channel axis, then ``C`` is viewed as 1
        by this augmenter.
        Each of the four entries may be None or a tuple of two ints or a list of ints.

            * If an entry is None, any value for that dimensions is accepted.
            * If an entry is int, exactly that integer value will be accepted
              or no other value.
            * If an entry is a tuple of two ints with values ``a`` and ``b``, only a
              value ``x`` with ``a <= x < b`` will be accepted for the dimension.
            * If an entry is a list of ints, only a value for the dimension
              will be accepted which is contained in the list.

    check_images : bool, optional
        Whether to validate input images via the given shape.

    check_heatmaps : bool, optional
        Whether to validate input heatmaps via the given shape.
        The number of heatmaps will be checked and for each Heatmaps
        instance its array's height and width, but not the channel
        count as the channel number denotes the expected number of channels
        in images.

    check_keypoints : bool, optional
        Whether to validate input keypoints via the given shape.
        This will check (a) the number of keypoints and (b) for each
        KeypointsOnImage instance the ``.shape``, i.e. the shape of the
        corresponding image.

    check_polygons : bool, optional
        Whether to validate input keypoints via the given shape.
        This will check (a) the number of polygons and (b) for each
        PolygonsOnImage instance the ``.shape``, i.e. the shape of the
        corresponding image.

    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
    --------
    >>> seq = iaa.Sequential([
    >>>     iaa.AssertShape((None, 32, 32, 3)),
    >>>     iaa.Fliplr(0.5)
    >>> ])

    will first check for each image batch, if it contains a variable number of
    ``32x32`` images with 3 channels each. Only if that check succeeds, the
    horizontal flip will be executed (otherwise an assertion error will be
    thrown).

    >>> seq = iaa.Sequential([
    >>>     iaa.AssertShape((None, (32, 64), 32, [1, 3])),
    >>>     iaa.Fliplr(0.5)
    >>> ])

    like above, but now the height may be in the range ``32 <= H < 64`` and
    the number of channels may be either 1 or 3.

    """
    ia.do_assert(len(shape) == 4, "Expected shape to have length 4, got %d with shape: %s." % (len(shape), str(shape)))

    def compare(observed, expected, dimension, image_index):
        if expected is not None:
            if ia.is_single_integer(expected):
                ia.do_assert(observed == expected,
                             "Expected dim %d (entry index: %s) to have value %d, got %d." % (
                                 dimension, image_index, expected, observed))
            elif isinstance(expected, tuple):
                ia.do_assert(len(expected) == 2)
                ia.do_assert(expected[0] <= observed < expected[1],
                             "Expected dim %d (entry index: %s) to have value in range [%d, %d), got %d." % (
                                 dimension, image_index, expected[0], expected[1], observed))
            elif isinstance(expected, list):
                ia.do_assert(any([observed == val for val in expected]),
                             "Expected dim %d (entry index: %s) to have any value of %s, got %d." % (
                                 dimension, image_index, str(expected), observed))
            else:
                raise Exception(("Invalid datatype for shape entry %d, expected each entry to be an integer, "
                                + "a tuple (with two entries) or a list, got %s.") % (dimension, type(expected),))

    def func_images(images, _random_state, _parents, _hooks):
        if check_images:
            if isinstance(images, list):
                if shape[0] is not None:
                    compare(len(images), shape[0], 0, "ALL")

                for i in sm.xrange(len(images)):
                    image = images[i]
                    ia.do_assert(len(image.shape) == 3,
                                 "Expected image number %d to have a shape of length 3, got %d (shape: %s)." % (
                                     i, len(image.shape), str(image.shape)))
                    for j in sm.xrange(len(shape)-1):
                        expected = shape[j+1]
                        observed = image.shape[j]
                        compare(observed, expected, j, i)
            else:
                ia.do_assert(len(images.shape) == 4,
                             "Expected image's shape to have length 4, got %d (shape: %s)." % (
                                 len(images.shape), str(images.shape)))
                for i in range(4):
                    expected = shape[i]
                    observed = images.shape[i]
                    compare(observed, expected, i, "ALL")
        return images

    def func_heatmaps(heatmaps, _random_state, _parents, _hooks):
        if check_heatmaps:
            if shape[0] is not None:
                compare(len(heatmaps), shape[0], 0, "ALL")

            for i in sm.xrange(len(heatmaps)):
                heatmaps_i = heatmaps[i]
                for j in sm.xrange(len(shape[0:2])):
                    expected = shape[j+1]
                    observed = heatmaps_i.arr_0to1.shape[j]
                    compare(observed, expected, j, i)
        return heatmaps

    def func_keypoints(keypoints_on_images, _random_state, _parents, _hooks):
        if check_keypoints:
            if shape[0] is not None:
                compare(len(keypoints_on_images), shape[0], 0, "ALL")

            for i in sm.xrange(len(keypoints_on_images)):
                keypoints_on_image = keypoints_on_images[i]
                for j in sm.xrange(len(shape[0:2])):
                    expected = shape[j+1]
                    observed = keypoints_on_image.shape[j]
                    compare(observed, expected, j, i)
        return keypoints_on_images

    def func_polygons(polygons_on_images, _random_state, _parents, _hooks):
        if check_polygons:
            if shape[0] is not None:
                compare(len(polygons_on_images), shape[0], 0, "ALL")

            for i in sm.xrange(len(polygons_on_images)):
                polygons_on_image = polygons_on_images[i]
                for j in sm.xrange(len(shape[0:2])):
                    expected = shape[j+1]
                    observed = polygons_on_image.shape[j]
                    compare(observed, expected, j, i)
        return polygons_on_images

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

    return Lambda(func_images, func_heatmaps, func_keypoints, func_polygons,
                  name=name, deterministic=deterministic,
                  random_state=random_state)

Response: Augmenter to make assumptions about the shape of input image and keypoints.


Instruction:
Can you implement a function in Python 3 that
shuffles the channels in an image.

Response:
def shuffle_channels(image, random_state, channels=None):
    """
    Randomize the order of (color) channels in an image.

    dtype support::

        * ``uint8``: yes; fully tested
        * ``uint16``: yes; indirectly tested (1)
        * ``uint32``: yes; indirectly tested (1)
        * ``uint64``: yes; indirectly tested (1)
        * ``int8``: yes; indirectly tested (1)
        * ``int16``: yes; indirectly tested (1)
        * ``int32``: yes; indirectly tested (1)
        * ``int64``: yes; indirectly tested (1)
        * ``float16``: yes; indirectly tested (1)
        * ``float32``: yes; indirectly tested (1)
        * ``float64``: yes; indirectly tested (1)
        * ``float128``: yes; indirectly tested (1)
        * ``bool``: yes; indirectly tested (1)

        - (1) Indirectly tested via ``ChannelShuffle``.

    Parameters
    ----------
    image : (H,W,[C]) ndarray
        Image of any dtype for which to shuffle the channels.

    random_state : numpy.random.RandomState
        The random state to use for this shuffling operation.

    channels : None or imgaug.ALL or list of int, optional
        Which channels are allowed to be shuffled with each other.
        If this is ``None`` or ``imgaug.ALL``, then all channels may be shuffled. If it is a list of integers,
        then only the channels with indices in that list may be shuffled. (Values start at 0. All channel indices in
        the list must exist in each image.)

    Returns
    -------
    ndarray
        The input image with shuffled channels.

    """
    if image.ndim < 3 or image.shape[2] == 1:
        return image
    nb_channels = image.shape[2]
    all_channels = np.arange(nb_channels)
    is_all_channels = (
        channels is None
        or channels == ia.ALL
        or len(set(all_channels).difference(set(channels))) == 0
    )
    if is_all_channels:
        # note that if this is the case, then 'channels' may be None or imgaug.ALL, so don't simply move the
        # assignment outside of the if/else
        channels_perm = random_state.permutation(all_channels)
        return image[..., channels_perm]
    else:
        channels_perm = random_state.permutation(channels)
        channels_perm_full = all_channels
        for channel_source, channel_target in zip(channels, channels_perm):
            channels_perm_full[channel_source] = channel_target
        return image[..., channels_perm_full]


Instruction:
Can you write a function in Python 3 where it
blurs an image using gaussian blurring.

Response:
def blur_gaussian_(image, sigma, ksize=None, backend="auto", eps=1e-3):
    """
    Blur an image using gaussian blurring.

    This operation might change the input image in-place.

    dtype support::

        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
    -------
    image : numpy.ndarray
        The blurred image. Same shape and dtype as the input.

    """
    if sigma > 0 + eps:
        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:
                warnings.warn("Requested 'scipy' backend or picked it automatically by backend='auto' "
                              "in 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:
                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))
            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(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


Instruction:
Can you tell what is the following Python 3 function doing
def Fog(name=None, deterministic=False, random_state=None):
    if name is None:
        name = "Unnamed%s" % (ia.caller_name(),)

    return CloudLayer(
        intensity_mean=(220, 255), intensity_freq_exponent=(-2.0, -1.5), intensity_coarse_scale=2,
        alpha_min=(0.7, 0.9), alpha_multiplier=0.3, alpha_size_px_max=(2, 8), alpha_freq_exponent=(-4.0, -2.0),
        sparsity=0.9, density_multiplier=(0.4, 0.9),
        name=name, deterministic=deterministic, random_state=random_state
    )

Response: Augmenter that draws a fairly dense cloud with low - frequency patterns.


Instruction:
Given the following Python 3 function, write the documentation
def Snowflakes(density=(0.005, 0.075), density_uniformity=(0.3, 0.9), flake_size=(0.2, 0.7),
               flake_size_uniformity=(0.4, 0.8), angle=(-30, 30), speed=(0.007, 0.03),
               name=None, deterministic=False, random_state=None):
    """
    Augmenter to add falling snowflakes to images.

    This is a wrapper around ``SnowflakesLayer``. It executes 1 to 3 layers per image.

    dtype support::

        * ``uint8``: yes; 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) Parameters of this augmenter are optimized for the value range of uint8.
              While other dtypes may be accepted, they will lead to images augmented in
              ways inappropriate for the respective dtype.

    Parameters
    ----------
    density : number or tuple of number or list of number or imgaug.parameters.StochasticParameter
        Density of the snowflake layer, as a probability of each pixel in low resolution space to be a snowflake.
        Valid value range is ``(0.0, 1.0)``. Recommended to be around ``(0.01, 0.075)``.

            * If a number, then that value will be used for all images.
            * If a tuple ``(a, b)``, then a value from the continuous range ``[a, b]`` will be used.
            * 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.

    density_uniformity : number or tuple of number or list of number or imgaug.parameters.StochasticParameter
        Size uniformity of the snowflakes. Higher values denote more similarly sized snowflakes.
        Valid value range is ``(0.0, 1.0)``. Recommended to be around ``0.5``.

            * If a number, then that value will be used for all images.
            * If a tuple ``(a, b)``, then a value from the continuous range ``[a, b]`` will be used.
            * 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.

    flake_size : number or tuple of number or list of number or imgaug.parameters.StochasticParameter
        Size of the snowflakes. This parameter controls the resolution at which snowflakes are sampled.
        Higher values mean that the resolution is closer to the input image's resolution and hence each sampled
        snowflake will be smaller (because of the smaller pixel size).

        Valid value range is ``[0.0, 1.0)``. Recommended values:

            * On ``96x128`` a value of ``(0.1, 0.4)`` worked well.
            * On ``192x256`` a value of ``(0.2, 0.7)`` worked well.
            * On ``960x1280`` a value of ``(0.7, 0.95)`` worked well.

        Allowed datatypes:

            * If a number, then that value will be used for all images.
            * If a tuple ``(a, b)``, then a value from the continuous range ``[a, b]`` will be used.
            * 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.

    flake_size_uniformity : number or tuple of number or list of number or imgaug.parameters.StochasticParameter
        Controls the size uniformity of the snowflakes. Higher values mean that the snowflakes are more similarly
        sized. Valid value range is ``(0.0, 1.0)``. Recommended to be around ``0.5``.

            * If a number, then that value will be used for all images.
            * If a tuple ``(a, b)``, then a value from the continuous range ``[a, b]`` will be used.
            * 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.

    angle : number or tuple of number or list of number or imgaug.parameters.StochasticParameter
        Angle in degrees of motion blur applied to the snowflakes, where ``0.0`` is motion blur that points straight
        upwards. Recommended to be around ``(-30, 30)``.
        See also :func:`imgaug.augmenters.blur.MotionBlur.__init__`.

            * If a number, then that value will be used for all images.
            * If a tuple ``(a, b)``, then a value from the continuous range ``[a, b]`` will be used.
            * 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.

    speed : number or tuple of number or list of number or imgaug.parameters.StochasticParameter
        Perceived falling speed of the snowflakes. This parameter controls the motion blur's kernel size.
        It follows roughly the form ``kernel_size = image_size * speed``. Hence,
        Values around ``1.0`` denote that the motion blur should "stretch" each snowflake over the whole image.

        Valid value range is ``(0.0, 1.0)``. Recommended values:

            * On ``96x128`` a value of ``(0.01, 0.05)`` worked well.
            * On ``192x256`` a value of ``(0.007, 0.03)`` worked well.
            * On ``960x1280`` a value of ``(0.001, 0.03)`` worked well.

        Allowed datatypes:

            * If a number, then that value will be used for all images.
            * If a tuple ``(a, b)``, then a value from the continuous range ``[a, b]`` will be used.
            * 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.

    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.Snowflakes(flake_size=(0.1, 0.4), speed=(0.01, 0.05))

    Adds snowflakes to small images (around ``96x128``).

    >>> aug = iaa.Snowflakes(flake_size=(0.2, 0.7), speed=(0.007, 0.03))

    Adds snowflakes to medium-sized images (around ``192x256``).

    >>> aug = iaa.Snowflakes(flake_size=(0.7, 0.95), speed=(0.001, 0.03))

    Adds snowflakes to large images (around ``960x1280``).

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

    layer = SnowflakesLayer(
        density=density, density_uniformity=density_uniformity,
        flake_size=flake_size, flake_size_uniformity=flake_size_uniformity,
        angle=angle, speed=speed,
        blur_sigma_fraction=(0.0001, 0.001)
    )

    return meta.SomeOf(
        (1, 3), children=[layer.deepcopy() for _ in range(3)],
        random_order=False, name=name, deterministic=deterministic, random_state=random_state
    )

Response: Augmenter to add falling snowflakes to images.


Instruction:
Write a Python 3 script to
get the segmentation map array as an integer array of shape H W.

Response:
def get_arr_int(self, background_threshold=0.01, background_class_id=None):
        """
        Get the segmentation map array as an integer array of shape (H, W).

        Each pixel in that array contains an integer value representing the pixel's class.
        If multiple classes overlap, the one with the highest local float value is picked.
        If that highest local value is below `background_threshold`, the method instead uses
        the background class id as the pixel's class value.
        By default, class id 0 is the background class. This may only be changed if the original
        input to the segmentation map object was an integer map.

        Parameters
        ----------
        background_threshold : float, optional
            At each pixel, each class-heatmap has a value between 0.0 and 1.0. If none of the
            class-heatmaps has a value above this threshold, the method uses the background class
            id instead.

        background_class_id : None or int, optional
            Class id to fall back to if no class-heatmap passes the threshold at a spatial
            location. May only be provided if the original input was an integer mask and in these
            cases defaults to 0. If the input were float or boolean masks, the background class id
            may not be set as it is assumed that the background is implicitly defined
            as 'any spatial location that has zero-like values in all masks'.

        Returns
        -------
        result : (H,W) ndarray
            Segmentation map array (int32).
            If the original input consisted of boolean or float masks, then the highest possible
            class id is ``1+C``, where ``C`` is the number of provided float/boolean masks. The value
            ``0`` in the integer mask then denotes the background class.

        """
        if self.input_was[0] in ["bool", "float"]:
            ia.do_assert(background_class_id is None,
                         "The background class id may only be changed if the original input to SegmentationMapOnImage "
                         + "was an *integer* based segmentation map.")

        if background_class_id is None:
            background_class_id = 0

        channelwise_max_idx = np.argmax(self.arr, axis=2)
        # for bool and float input masks, we assume that the background is implicitly given,
        # i.e. anything where all masks/channels have zero-like values
        # for int, we assume that the background class is explicitly given and has the index 0
        if self.input_was[0] in ["bool", "float"]:
            result = 1 + channelwise_max_idx
        else:  # integer mask was provided
            result = channelwise_max_idx
        if background_threshold is not None and background_threshold > 0:
            probs = np.amax(self.arr, axis=2)
            result[probs < background_threshold] = background_class_id

        return result.astype(np.int32)


Instruction:
Can you generate a brief explanation for the following Python 3 code
def draw(self, size=None, background_threshold=0.01, background_class_id=None, colors=None,
             return_foreground_mask=False):
        """
        Render the segmentation map as an RGB image.

        Parameters
        ----------
        size : None or float or iterable of int or iterable of float, optional
            Size of the rendered RGB image as ``(height, width)``.
            See :func:`imgaug.imgaug.imresize_single_image` for details.
            If set to None, no resizing is performed and the size of the segmentation map array is used.

        background_threshold : float, optional
            See :func:`imgaug.SegmentationMapOnImage.get_arr_int`.

        background_class_id : None or int, optional
            See :func:`imgaug.SegmentationMapOnImage.get_arr_int`.

        colors : None or list of tuple of int, optional
            Colors to use. One for each class to draw. If None, then default colors will be used.

        return_foreground_mask : bool, optional
            Whether to return a mask of the same size as the drawn segmentation map, containing
            True at any spatial location that is not the background class and False everywhere else.

        Returns
        -------
        segmap_drawn : (H,W,3) ndarray
            Rendered segmentation map (dtype is uint8).

        foreground_mask : (H,W) ndarray
            Mask indicating the locations of foreground classes (dtype is bool).
            This value is only returned if `return_foreground_mask` is True.

        """
        arr = self.get_arr_int(background_threshold=background_threshold, background_class_id=background_class_id)
        nb_classes = 1 + np.max(arr)
        segmap_drawn = np.zeros((arr.shape[0], arr.shape[1], 3), dtype=np.uint8)
        if colors is None:
            colors = SegmentationMapOnImage.DEFAULT_SEGMENT_COLORS
        ia.do_assert(nb_classes <= len(colors),
                     "Can't draw all %d classes as it would exceed the maximum number of %d available colors." % (
                         nb_classes, len(colors),))

        ids_in_map = np.unique(arr)
        for c, color in zip(sm.xrange(nb_classes), colors):
            if c in ids_in_map:
                class_mask = (arr == c)
                segmap_drawn[class_mask] = color

        if return_foreground_mask:
            background_class_id = 0 if background_class_id is None else background_class_id
            foreground_mask = (arr != background_class_id)
        else:
            foreground_mask = None

        if size is not None:
            segmap_drawn = ia.imresize_single_image(segmap_drawn, size, interpolation="nearest")
            if foreground_mask is not None:
                foreground_mask = ia.imresize_single_image(
                    foreground_mask.astype(np.uint8), size, interpolation="nearest") > 0

        if foreground_mask is not None:
            return segmap_drawn, foreground_mask
        return segmap_drawn

Response: Draws the segmentation map as an RGB image.


Instruction:
Make a summary of the following Python 3 code
def draw_on_image(self, image, alpha=0.75, resize="segmentation_map", background_threshold=0.01,
                      background_class_id=None, colors=None, draw_background=False):
        """
        Draw the segmentation map as an overlay over an image.

        Parameters
        ----------
        image : (H,W,3) ndarray
            Image onto which to draw the segmentation map. Dtype is expected to be uint8.

        alpha : float, optional
            Alpha/opacity value to use for the mixing of image and segmentation map.
            Higher values mean that the segmentation map will be more visible and the image less visible.

        resize : {'segmentation_map', 'image'}, optional
            In case of size differences between the image and segmentation map, either the image or
            the segmentation map can be resized. This parameter controls which of the two will be
            resized to the other's size.

        background_threshold : float, optional
            See :func:`imgaug.SegmentationMapOnImage.get_arr_int`.

        background_class_id : None or int, optional
            See :func:`imgaug.SegmentationMapOnImage.get_arr_int`.

        colors : None or list of tuple of int, optional
            Colors to use. One for each class to draw. If None, then default colors will be used.

        draw_background : bool, optional
            If True, the background will be drawn like any other class.
            If False, the background will not be drawn, i.e. the respective background pixels
            will be identical with the image's RGB color at the corresponding spatial location
            and no color overlay will be applied.

        Returns
        -------
        mix : (H,W,3) ndarray
            Rendered overlays (dtype is uint8).

        """
        # assert RGB image
        ia.do_assert(image.ndim == 3)
        ia.do_assert(image.shape[2] == 3)
        ia.do_assert(image.dtype.type == np.uint8)

        ia.do_assert(0 - 1e-8 <= alpha <= 1.0 + 1e-8)
        ia.do_assert(resize in ["segmentation_map", "image"])

        if resize == "image":
            image = ia.imresize_single_image(image, self.arr.shape[0:2], interpolation="cubic")

        segmap_drawn, foreground_mask = self.draw(
            background_threshold=background_threshold,
            background_class_id=background_class_id,
            size=image.shape[0:2] if resize == "segmentation_map" else None,
            colors=colors,
            return_foreground_mask=True
        )

        if draw_background:
            mix = np.clip(
                (1-alpha) * image + alpha * segmap_drawn,
                0,
                255
            ).astype(np.uint8)
        else:
            foreground_mask = foreground_mask[..., np.newaxis]
            mix = np.zeros_like(image)
            mix += (~foreground_mask).astype(np.uint8) * image
            mix += foreground_mask.astype(np.uint8) * np.clip(
                (1-alpha) * image + alpha * segmap_drawn,
                0,
                255
            ).astype(np.uint8)
        return mix

Response: Draw the segmentation map on an image.


Instruction:
Can you generate the documentation for the following Python 3 function
def pad(self, top=0, right=0, bottom=0, left=0, mode="constant", cval=0.0):
        arr_padded = ia.pad(self.arr, top=top, right=right, bottom=bottom, left=left, mode=mode, cval=cval)
        segmap = SegmentationMapOnImage(arr_padded, shape=self.shape)
        segmap.input_was = self.input_was
        return segmap

Response: Pads the segmentation map on its top right bottom and left side.


Instruction:
Create a Python 3 function to
pad the segmentation map on its sides so that the image s width and height match the target aspect ratio.

Response:
def pad_to_aspect_ratio(self, aspect_ratio, mode="constant", cval=0.0, return_pad_amounts=False):
        """
        Pad the segmentation map on its sides so that its matches a target aspect ratio.

        Depending on which dimension is smaller (height or width), only the corresponding
        sides (left/right or top/bottom) will be padded. In each case, both of the sides will
        be padded equally.

        Parameters
        ----------
        aspect_ratio : float
            Target aspect ratio, given as width/height. E.g. 2.0 denotes the image having twice
            as much width as height.

        mode : str, optional
            Padding mode to use. See :func:`numpy.pad` for details.

        cval : number, optional
            Value to use for padding if `mode` is ``constant``. See :func:`numpy.pad` for details.

        return_pad_amounts : bool, optional
            If False, then only the padded image will be returned. If True, a tuple with two
            entries will be returned, where the first entry is the padded image and the second
            entry are the amounts by which each image side was padded. These amounts are again a
            tuple of the form (top, right, bottom, left), with each value being an integer.

        Returns
        -------
        segmap : imgaug.SegmentationMapOnImage
            Padded segmentation map as SegmentationMapOnImage object.

        pad_amounts : tuple of int
            Amounts by which the segmentation map was padded on each side, given as a
            tuple ``(top, right, bottom, left)``.
            This tuple is only returned if `return_pad_amounts` was set to True.

        """
        arr_padded, pad_amounts = ia.pad_to_aspect_ratio(self.arr, aspect_ratio=aspect_ratio, mode=mode, cval=cval,
                                                         return_pad_amounts=True)
        segmap = SegmentationMapOnImage(arr_padded, shape=self.shape)
        segmap.input_was = self.input_was
        if return_pad_amounts:
            return segmap, pad_amounts
        else:
            return segmap


Instruction:
Make a summary of the following Python 3 code
def resize(self, sizes, interpolation="cubic"):
        arr_resized = ia.imresize_single_image(self.arr, sizes, interpolation=interpolation)

        # cubic interpolation can lead to values outside of [0.0, 1.0],
        # see https://github.com/opencv/opencv/issues/7195
        # TODO area interpolation too?
        arr_resized = np.clip(arr_resized, 0.0, 1.0)
        segmap = SegmentationMapOnImage(arr_resized, shape=self.shape)
        segmap.input_was = self.input_was
        return segmap

Response: Resizes the segmentation map array to the provided size.


Instruction:
Write a Python 3 function that can
convert the segment map to a list of heatmap channels.

Response:
def to_heatmaps(self, only_nonempty=False, not_none_if_no_nonempty=False):
        """
        Convert segmentation map to heatmaps object.

        Each segmentation map class will be represented as a single heatmap channel.

        Parameters
        ----------
        only_nonempty : bool, optional
            If True, then only heatmaps for classes that appear in the segmentation map will be
            generated. Additionally, a list of these class ids will be returned.

        not_none_if_no_nonempty : bool, optional
            If `only_nonempty` is True and for a segmentation map no channel was non-empty,
            this function usually returns None as the heatmaps object. If however this parameter
            is set to True, a heatmaps object with one channel (representing class 0)
            will be returned as a fallback in these cases.

        Returns
        -------
        imgaug.HeatmapsOnImage or None
            Segmentation map as a heatmaps object.
            If `only_nonempty` was set to True and no class appeared in the segmentation map,
            then this is None.

        class_indices : list of int
            Class ids (0 to C-1) of the classes that were actually added to the heatmaps.
            Only returned if `only_nonempty` was set to True.

        """
        # TODO get rid of this deferred import
        from imgaug.augmentables.heatmaps import HeatmapsOnImage

        if not only_nonempty:
            return HeatmapsOnImage.from_0to1(self.arr, self.shape, min_value=0.0, max_value=1.0)
        else:
            nonempty_mask = np.sum(self.arr, axis=(0, 1)) > 0 + 1e-4
            if np.sum(nonempty_mask) == 0:
                if not_none_if_no_nonempty:
                    nonempty_mask[0] = True
                else:
                    return None, []

            class_indices = np.arange(self.arr.shape[2])[nonempty_mask]
            channels = self.arr[..., class_indices]
            return HeatmapsOnImage(channels, self.shape, min_value=0.0, max_value=1.0), class_indices


Instruction:
Write a Python 3 function for
converting a list of heatmap channels to a segment map.

Response:
def from_heatmaps(heatmaps, class_indices=None, nb_classes=None):
        """
        Convert heatmaps to segmentation map.

        Assumes that each class is represented as a single heatmap channel.

        Parameters
        ----------
        heatmaps : imgaug.HeatmapsOnImage
            Heatmaps to convert.

        class_indices : None or list of int, optional
            List of class indices represented by each heatmap channel. See also the
            secondary output of :func:`imgaug.SegmentationMapOnImage.to_heatmap`.
            If this is provided, it must have the same length as the number of heatmap channels.

        nb_classes : None or int, optional
            Number of classes. Must be provided if class_indices is set.

        Returns
        -------
        imgaug.SegmentationMapOnImage
            Segmentation map derived from heatmaps.

        """
        if class_indices is None:
            return SegmentationMapOnImage(heatmaps.arr_0to1, shape=heatmaps.shape)
        else:
            ia.do_assert(nb_classes is not None)
            ia.do_assert(min(class_indices) >= 0)
            ia.do_assert(max(class_indices) < nb_classes)
            ia.do_assert(len(class_indices) == heatmaps.arr_0to1.shape[2])
            arr_0to1 = heatmaps.arr_0to1
            arr_0to1_full = np.zeros((arr_0to1.shape[0], arr_0to1.shape[1], nb_classes), dtype=np.float32)
            for heatmap_channel, mapped_channel in enumerate(class_indices):
                arr_0to1_full[:, :, mapped_channel] = arr_0to1[:, :, heatmap_channel]
            return SegmentationMapOnImage(arr_0to1_full, shape=heatmaps.shape)


Instruction:
Here you have a function in Python 3, explain what it does
def deepcopy(self):
        segmap = SegmentationMapOnImage(self.arr, shape=self.shape, nb_classes=self.nb_classes)
        segmap.input_was = self.input_was
        return segmap

Response: Create a deep copy of the segmentation map object. Returns ------- segmap


Instruction:
Implement a function in Python 3 to
offer a new event at point p in this queue.

Response:
def offer(self, p, e: Event):
        """
        Offer a new event ``s`` at point ``p`` in this queue.
        """
        existing = self.events_scan.setdefault(
                p, ([], [], [], []) if USE_VERTICAL else
                   ([], [], []))
        # Can use double linked-list for easy insertion at beginning/end
        '''
        if e.type == Event.Type.END:
            existing.insert(0, e)
        else:
            existing.append(e)
        '''

        existing[e.type].append(e)


Instruction:
How would you code a function in Python 3 to
return the array of the internal representation of the object.

Response:
def get_arr(self):
        """
        Get the heatmap's array within the value range originally provided in ``__init__()``.

        The HeatmapsOnImage object saves heatmaps internally in the value range ``(min=0.0, max=1.0)``.
        This function converts the internal representation to ``(min=min_value, max=max_value)``,
        where ``min_value`` and ``max_value`` are provided upon instantiation of the object.

        Returns
        -------
        result : (H,W) ndarray or (H,W,C) ndarray
            Heatmap array. Dtype is float32.

        """
        if self.arr_was_2d and self.arr_0to1.shape[2] == 1:
            arr = self.arr_0to1[:, :, 0]
        else:
            arr = self.arr_0to1

        eps = np.finfo(np.float32).eps
        min_is_zero = 0.0 - eps < self.min_value < 0.0 + eps
        max_is_one = 1.0 - eps < self.max_value < 1.0 + eps
        if min_is_zero and max_is_one:
            return np.copy(arr)
        else:
            diff = self.max_value - self.min_value
            return self.min_value + diff * arr


Instruction:
Make a summary of the following Python 3 code
def draw(self, size=None, cmap="jet"):
        heatmaps_uint8 = self.to_uint8()
        heatmaps_drawn = []

        for c in sm.xrange(heatmaps_uint8.shape[2]):
            # c:c+1 here, because the additional axis is needed by imresize_single_image
            heatmap_c = heatmaps_uint8[..., c:c+1]

            if size is not None:
                heatmap_c_rs = ia.imresize_single_image(heatmap_c, size, interpolation="nearest")
            else:
                heatmap_c_rs = heatmap_c
            heatmap_c_rs = np.squeeze(heatmap_c_rs).astype(np.float32) / 255.0

            if cmap is not None:
                # import only when necessary (faster startup; optional dependency; less fragile -- see issue #225)
                import matplotlib.pyplot as plt

                cmap_func = plt.get_cmap(cmap)
                heatmap_cmapped = cmap_func(heatmap_c_rs)
                heatmap_cmapped = np.delete(heatmap_cmapped, 3, 2)
            else:
                heatmap_cmapped = np.tile(heatmap_c_rs[..., np.newaxis], (1, 1, 3))

            heatmap_cmapped = np.clip(heatmap_cmapped * 255, 0, 255).astype(np.uint8)

            heatmaps_drawn.append(heatmap_cmapped)
        return heatmaps_drawn

Response: Draws the contents of the array as RGB images.


Instruction:
Create a Python 3 function to
draw the heatmaps as overlays over an image.

Response:
def draw_on_image(self, image, alpha=0.75, cmap="jet", resize="heatmaps"):
        """
        Draw the heatmaps as overlays over an image.

        Parameters
        ----------
        image : (H,W,3) ndarray
            Image onto which to draw the heatmaps. Expected to be of dtype uint8.

        alpha : float, optional
            Alpha/opacity value to use for the mixing of image and heatmaps.
            Higher values mean that the heatmaps will be more visible and the image less visible.

        cmap : str or None, optional
            Color map to use. See :func:`imgaug.HeatmapsOnImage.draw` for details.

        resize : {'heatmaps', 'image'}, optional
            In case of size differences between the image and heatmaps, either the image or
            the heatmaps can be resized. This parameter controls which of the two will be resized
            to the other's size.

        Returns
        -------
        mix : list of (H,W,3) ndarray
            Rendered overlays. One per heatmap array channel. Dtype is uint8.

        """
        # assert RGB image
        ia.do_assert(image.ndim == 3)
        ia.do_assert(image.shape[2] == 3)
        ia.do_assert(image.dtype.type == np.uint8)

        ia.do_assert(0 - 1e-8 <= alpha <= 1.0 + 1e-8)
        ia.do_assert(resize in ["heatmaps", "image"])

        if resize == "image":
            image = ia.imresize_single_image(image, self.arr_0to1.shape[0:2], interpolation="cubic")

        heatmaps_drawn = self.draw(
            size=image.shape[0:2] if resize == "heatmaps" else None,
            cmap=cmap
        )

        mix = [
            np.clip((1-alpha) * image + alpha * heatmap_i, 0, 255).astype(np.uint8)
            for heatmap_i
            in heatmaps_drawn
        ]

        return mix


Instruction:
Can you tell what is the following Python 3 function doing
def invert(self):
        arr_inv = HeatmapsOnImage.from_0to1(1 - self.arr_0to1, shape=self.shape, min_value=self.min_value,
                                            max_value=self.max_value)
        arr_inv.arr_was_2d = self.arr_was_2d
        return arr_inv

Response: Inverts each value in the heatmap shifting low towards high values and vice versa.


Instruction:
Can you tell what is the following Python 3 function doing
def pad(self, top=0, right=0, bottom=0, left=0, mode="constant", cval=0.0):
        arr_0to1_padded = ia.pad(self.arr_0to1, top=top, right=right, bottom=bottom, left=left, mode=mode, cval=cval)
        return HeatmapsOnImage.from_0to1(arr_0to1_padded, shape=self.shape, min_value=self.min_value,
                                         max_value=self.max_value)

Response: Pads the heatmaps on the image to the specified dimensions.


Instruction:
How would you implement a function in Python 3 that
pads the heatmaps on the image to match the given aspect ratio.

Response:
def pad_to_aspect_ratio(self, aspect_ratio, mode="constant", cval=0.0, return_pad_amounts=False):
        """
        Pad the heatmaps on their sides so that they match a target aspect ratio.

        Depending on which dimension is smaller (height or width), only the corresponding
        sides (left/right or top/bottom) will be padded. In each case, both of the sides will
        be padded equally.

        Parameters
        ----------
        aspect_ratio : float
            Target aspect ratio, given as width/height. E.g. 2.0 denotes the image having twice
            as much width as height.

        mode : str, optional
            Padding mode to use. See :func:`numpy.pad` for details.

        cval : number, optional
            Value to use for padding if `mode` is ``constant``. See :func:`numpy.pad` for details.

        return_pad_amounts : bool, optional
            If False, then only the padded image will be returned. If True, a tuple with two
            entries will be returned, where the first entry is the padded image and the second
            entry are the amounts by which each image side was padded. These amounts are again a
            tuple of the form (top, right, bottom, left), with each value being an integer.

        Returns
        -------
        heatmaps : imgaug.HeatmapsOnImage
            Padded heatmaps as HeatmapsOnImage object.

        pad_amounts : tuple of int
            Amounts by which the heatmaps were padded on each side, given as a tuple ``(top, right, bottom, left)``.
            This tuple is only returned if `return_pad_amounts` was set to True.

        """
        arr_0to1_padded, pad_amounts = ia.pad_to_aspect_ratio(self.arr_0to1, aspect_ratio=aspect_ratio, mode=mode,
                                                              cval=cval, return_pad_amounts=True)
        heatmaps = HeatmapsOnImage.from_0to1(arr_0to1_padded, shape=self.shape, min_value=self.min_value,
                                             max_value=self.max_value)
        if return_pad_amounts:
            return heatmaps, pad_amounts
        else:
            return heatmaps


Instruction:
How would you explain what the following Python 3 function does
def max_pool(self, block_size):
        arr_0to1_reduced = ia.max_pool(self.arr_0to1, block_size)
        return HeatmapsOnImage.from_0to1(arr_0to1_reduced, shape=self.shape, min_value=self.min_value,
                                         max_value=self.max_value)

Response: Resize the array using max - pooling.


Instruction:
How would you explain what the following Python 3 function does
def resize(self, sizes, interpolation="cubic"):
        arr_0to1_resized = ia.imresize_single_image(self.arr_0to1, sizes, interpolation=interpolation)

        # cubic interpolation can lead to values outside of [0.0, 1.0],
        # see https://github.com/opencv/opencv/issues/7195
        # TODO area interpolation too?
        arr_0to1_resized = np.clip(arr_0to1_resized, 0.0, 1.0)

        return HeatmapsOnImage.from_0to1(arr_0to1_resized, shape=self.shape, min_value=self.min_value,
                                         max_value=self.max_value)

Response: Resize the array to the provided size.


Instruction:
Explain what the following Python 3 code does
def to_uint8(self):
        # TODO this always returns (H,W,C), even if input ndarray was originall (H,W)
        # does it make sense here to also return (H,W) if self.arr_was_2d?
        arr_0to255 = np.clip(np.round(self.arr_0to1 * 255), 0, 255)
        arr_uint8 = arr_0to255.astype(np.uint8)
        return arr_uint8

Response: Convert this heatmaps object to a 0 - to - 255 array.


Instruction:
Can you implement a function in Python 3 that
creates a new imgaug. HeatmapsOnImage object from an array of uint8 values.

Response:
def from_uint8(arr_uint8, shape, min_value=0.0, max_value=1.0):
        """
        Create a heatmaps object from an heatmap array containing values ranging from 0 to 255.

        Parameters
        ----------
        arr_uint8 : (H,W) ndarray or (H,W,C) ndarray
            Heatmap(s) array, where ``H`` is height, ``W`` is width and ``C`` is the number of heatmap channels.
            Expected dtype is uint8.

        shape : tuple of int
            Shape of the image on which the heatmap(s) is/are placed. NOT the shape of the
            heatmap(s) array, unless it is identical to the image shape (note the likely
            difference between the arrays in the number of channels).
            If there is not a corresponding image, use the shape of the heatmaps array.

        min_value : float, optional
            Minimum value for the heatmaps that the 0-to-255 array represents. This will usually
            be 0.0. It is used when calling :func:`imgaug.HeatmapsOnImage.get_arr`, which converts the
            underlying ``(0, 255)`` array to value range ``(min_value, max_value)``.

        max_value : float, optional
            Maximum value for the heatmaps that 0-to-255 array represents.
            See parameter `min_value` for details.

        Returns
        -------
        imgaug.HeatmapsOnImage
            Heatmaps object.

        """
        arr_0to1 = arr_uint8.astype(np.float32) / 255.0
        return HeatmapsOnImage.from_0to1(arr_0to1, shape, min_value=min_value, max_value=max_value)


Instruction:
Create a Python 3 function to
create a new object from an array containing values ranging from 0. 0 to 1. 0.

Response:
def from_0to1(arr_0to1, shape, min_value=0.0, max_value=1.0):
        """
        Create a heatmaps object from an heatmap array containing values ranging from 0.0 to 1.0.

        Parameters
        ----------
        arr_0to1 : (H,W) or (H,W,C) ndarray
            Heatmap(s) array, where ``H`` is height, ``W`` is width and ``C`` is the number of heatmap channels.
            Expected dtype is float32.

        shape : tuple of ints
            Shape of the image on which the heatmap(s) is/are placed. NOT the shape of the
            heatmap(s) array, unless it is identical to the image shape (note the likely
            difference between the arrays in the number of channels).
            If there is not a corresponding image, use the shape of the heatmaps array.

        min_value : float, optional
            Minimum value for the heatmaps that the 0-to-1 array represents. This will usually
            be 0.0. It is used when calling :func:`imgaug.HeatmapsOnImage.get_arr`, which converts the
            underlying ``(0.0, 1.0)`` array to value range ``(min_value, max_value)``.
            E.g. if you started with heatmaps in the range ``(-1.0, 1.0)`` and projected these
            to (0.0, 1.0), you should call this function with ``min_value=-1.0``, ``max_value=1.0``
            so that :func:`imgaug.HeatmapsOnImage.get_arr` returns heatmap arrays having value
            range (-1.0, 1.0).

        max_value : float, optional
            Maximum value for the heatmaps that to 0-to-255 array represents.
            See parameter min_value for details.

        Returns
        -------
        heatmaps : imgaug.HeatmapsOnImage
            Heatmaps object.

        """
        heatmaps = HeatmapsOnImage(arr_0to1, shape, min_value=0.0, max_value=1.0)
        heatmaps.min_value = min_value
        heatmaps.max_value = max_value
        return heatmaps