Geometric transforms (augmentations.geometric.transforms)¶
class Affine (scale=None, translate_percent=None, translate_px=None, rotate=None, shear=None, interpolation=1, mask_interpolation=0, cval=0, cval_mask=0, mode=0, fit_output=False, keep_ratio=False, rotate_method='largest_box', always_apply=False, p=0.5) [view source on GitHub] ¶
Augmentation to apply affine transformations to images. This is mostly a wrapper around the corresponding classes and functions in OpenCV.
Affine transformations involve:
- Translation ("move" image on the x-/y-axis)
- Rotation
- Scaling ("zoom" in/out)
- Shear (move one side of the image, turning a square into a trapezoid)
All such transformations can create "new" pixels in the image without a defined content, e.g. if the image is translated to the left, pixels are created on the right. A method has to be defined to deal with these pixel values. The parameters cval and mode of this class deal with this.
Some transformations involve interpolations between several pixels of the input image to generate output pixel values. The parameters interpolation and mask_interpolation deals with the method of interpolation used for this.
Parameters:
| Name | Type | Description |
|---|---|---|
scale | number, tuple of number or dict | Scaling factor to use, where |
translate_percent | None, number, tuple of number or dict | Translation as a fraction of the image height/width (x-translation, y-translation), where |
translate_px | None, int, tuple of int or dict | Translation in pixels. * If |
rotate | number or tuple of number | Rotation in degrees (NOT radians), i.e. expected value range is around |
shear | number, tuple of number or dict | Shear in degrees (NOT radians), i.e. expected value range is around |
interpolation | int | OpenCV interpolation flag. |
mask_interpolation | int | OpenCV interpolation flag. |
cval | number or sequence of number | The constant value to use when filling in newly created pixels. (E.g. translating by 1px to the right will create a new 1px-wide column of pixels on the left of the image). The value is only used when |
cval_mask | number or tuple of number | Same as cval but only for masks. |
mode | int | OpenCV border flag. |
fit_output | bool | If True, the image plane size and position will be adjusted to tightly capture the whole image after affine transformation ( |
keep_ratio | bool | When True, the original aspect ratio will be kept when the random scale is applied. Default: False. |
rotate_method | str | rotation method used for the bounding boxes. Should be one of "largest_box" or "ellipse"[1]. Default: "largest_box" |
p | float | probability of applying the transform. Default: 0.5. |
Targets
image, mask, keypoints, bboxes
Image types: uint8, float32
Reference
Source code in albumentations/augmentations/geometric/transforms.py
Python
class Affine(DualTransform):
"""Augmentation to apply affine transformations to images.
This is mostly a wrapper around the corresponding classes and functions in OpenCV.
Affine transformations involve:
- Translation ("move" image on the x-/y-axis)
- Rotation
- Scaling ("zoom" in/out)
- Shear (move one side of the image, turning a square into a trapezoid)
All such transformations can create "new" pixels in the image without a defined content, e.g.
if the image is translated to the left, pixels are created on the right.
A method has to be defined to deal with these pixel values.
The parameters `cval` and `mode` of this class deal with this.
Some transformations involve interpolations between several pixels
of the input image to generate output pixel values. The parameters `interpolation` and
`mask_interpolation` deals with the method of interpolation used for this.
Args:
scale (number, tuple of number or dict): Scaling factor to use, where ``1.0`` denotes "no change" and
``0.5`` is zoomed out to ``50`` percent of the original size.
* If a single number, then that value will be used for all images.
* If a tuple ``(a, b)``, then a value will be uniformly sampled per image from the interval ``[a, b]``.
That the same range will be used for both x- and y-axis. To keep the aspect ratio, set
``keep_ratio=True``, then the same value will be used for both x- and y-axis.
* If a dictionary, then it is expected to have the keys ``x`` and/or ``y``.
Each of these keys can have the same values as described above.
Using a dictionary allows to set different values for the two axis and sampling will then happen
*independently* per axis, resulting in samples that differ between the axes. Note that when
the ``keep_ratio=True``, the x- and y-axis ranges should be the same.
translate_percent (None, number, tuple of number or dict): Translation as a fraction of the image height/width
(x-translation, y-translation), where ``0`` denotes "no change"
and ``0.5`` denotes "half of the axis size".
* If ``None`` then equivalent to ``0.0`` unless `translate_px` has a value other than ``None``.
* If a single number, then that value will be used for all images.
* If a tuple ``(a, b)``, then a value will be uniformly sampled per image from the interval ``[a, b]``.
That sampled fraction value will be used identically for both x- and y-axis.
* If a dictionary, then it is expected to have the keys ``x`` and/or ``y``.
Each of these keys can have the same values as described above.
Using a dictionary allows to set different values for the two axis and sampling will then happen
*independently* per axis, resulting in samples that differ between the axes.
translate_px (None, int, tuple of int or dict): Translation in pixels.
* If ``None`` then equivalent to ``0`` unless `translate_percent` has a value other than ``None``.
* If a single int, then that value will be used for all images.
* If a tuple ``(a, b)``, then a value will be uniformly sampled per image from
the discrete interval ``[a..b]``. That number will be used identically for both x- and y-axis.
* If a dictionary, then it is expected to have the keys ``x`` and/or ``y``.
Each of these keys can have the same values as described above.
Using a dictionary allows to set different values for the two axis and sampling will then happen
*independently* per axis, resulting in samples that differ between the axes.
rotate (number or tuple of number): Rotation in degrees (**NOT** radians), i.e. expected value range is
around ``[-360, 360]``. Rotation happens around the *center* of the image,
not the top left corner as in some other frameworks.
* If a number, then that value will be used for all images.
* If a tuple ``(a, b)``, then a value will be uniformly sampled per image from the interval ``[a, b]``
and used as the rotation value.
shear (number, tuple of number or dict): Shear in degrees (**NOT** radians), i.e. expected value range is
around ``[-360, 360]``, with reasonable values being in the range of ``[-45, 45]``.
* If a number, then that value will be used for all images as
the shear on the x-axis (no shear on the y-axis will be done).
* If a tuple ``(a, b)``, then two value will be uniformly sampled per image
from the interval ``[a, b]`` and be used as the x- and y-shear value.
* If a dictionary, then it is expected to have the keys ``x`` and/or ``y``.
Each of these keys can have the same values as described above.
Using a dictionary allows to set different values for the two axis and sampling will then happen
*independently* per axis, resulting in samples that differ between the axes.
interpolation (int): OpenCV interpolation flag.
mask_interpolation (int): OpenCV interpolation flag.
cval (number or sequence of number): The constant value to use when filling in newly created pixels.
(E.g. translating by 1px to the right will create a new 1px-wide column of pixels
on the left of the image).
The value is only used when `mode=constant`. The expected value range is ``[0, 255]`` for ``uint8`` images.
cval_mask (number or tuple of number): Same as cval but only for masks.
mode (int): OpenCV border flag.
fit_output (bool): If True, the image plane size and position will be adjusted to tightly capture
the whole image after affine transformation (`translate_percent` and `translate_px` are ignored).
Otherwise (``False``), parts of the transformed image may end up outside the image plane.
Fitting the output shape can be useful to avoid corners of the image being outside the image plane
after applying rotations. Default: False
keep_ratio (bool): When True, the original aspect ratio will be kept when the random scale is applied.
Default: False.
rotate_method (str): rotation method used for the bounding boxes. Should be one of "largest_box" or
"ellipse"[1].
Default: "largest_box"
p (float): probability of applying the transform. Default: 0.5.
Targets:
image, mask, keypoints, bboxes
Image types:
uint8, float32
Reference:
[1] https://arxiv.org/abs/2109.13488
"""
_targets = (Targets.IMAGE, Targets.MASK, Targets.BBOXES, Targets.KEYPOINTS)
class InitSchema(BaseTransformInitSchema):
scale: Optional[Union[ScaleFloatType, Dict[str, Any]]] = Field(
default=None,
description="Scaling factor or dictionary for independent axis scaling.",
)
translate_percent: Optional[Union[ScaleFloatType, Dict[str, Any]]] = Field(
default=None,
description="Translation as a fraction of the image dimension.",
)
translate_px: Optional[Union[ScaleIntType, Dict[str, Any]]] = Field(
default=None,
description="Translation in pixels.",
)
rotate: Optional[ScaleFloatType] = Field(default=None, description="Rotation angle in degrees.")
shear: Optional[Union[ScaleFloatType, Dict[str, Any]]] = Field(
default=None,
description="Shear angle in degrees.",
)
interpolation: InterpolationType = cv2.INTER_LINEAR
mask_interpolation: InterpolationType = cv2.INTER_NEAREST
cval: ColorType = Field(default=0, description="Value used for constant padding.")
cval_mask: ColorType = Field(default=0, description="Value used for mask constant padding.")
mode: BorderModeType = cv2.BORDER_CONSTANT
fit_output: Annotated[bool, Field(default=False, description="Adjust output to capture whole image.")]
keep_ratio: Annotated[bool, Field(default=False, description="Maintain aspect ratio when scaling.")]
rotate_method: Literal["largest_box", "ellipse"] = "largest_box"
def __init__(
self,
scale: Optional[Union[ScaleFloatType, Dict[str, Any]]] = None,
translate_percent: Optional[Union[ScaleFloatType, Dict[str, Any]]] = None,
translate_px: Optional[Union[ScaleIntType, Dict[str, Any]]] = None,
rotate: Optional[ScaleFloatType] = None,
shear: Optional[Union[ScaleFloatType, Dict[str, Any]]] = None,
interpolation: int = cv2.INTER_LINEAR,
mask_interpolation: int = cv2.INTER_NEAREST,
cval: ColorType = 0,
cval_mask: ColorType = 0,
mode: int = cv2.BORDER_CONSTANT,
fit_output: bool = False,
keep_ratio: bool = False,
rotate_method: Literal["largest_box", "ellipse"] = "largest_box",
always_apply: bool = False,
p: float = 0.5,
):
super().__init__(always_apply=always_apply, p=p)
params = [scale, translate_percent, translate_px, rotate, shear]
if all(p is None for p in params):
scale = {"x": (0.9, 1.1), "y": (0.9, 1.1)}
translate_percent = {"x": (-0.1, 0.1), "y": (-0.1, 0.1)}
rotate = (-15, 15)
shear = {"x": (-10, 10), "y": (-10, 10)}
else:
scale = scale if scale is not None else 1.0
rotate = rotate if rotate is not None else 0.0
shear = shear if shear is not None else 0.0
self.interpolation = interpolation
self.mask_interpolation = mask_interpolation
self.cval = cval
self.cval_mask = cval_mask
self.mode = mode
self.scale = self._handle_dict_arg(scale, "scale")
self.translate_percent, self.translate_px = self._handle_translate_arg(translate_px, translate_percent)
self.rotate = to_tuple(rotate, rotate)
self.fit_output = fit_output
self.shear = self._handle_dict_arg(shear, "shear")
self.keep_ratio = keep_ratio
self.rotate_method = rotate_method
if self.keep_ratio and self.scale["x"] != self.scale["y"]:
raise ValueError(f"When keep_ratio is True, the x and y scale range should be identical. got {self.scale}")
def get_transform_init_args_names(self) -> Tuple[str, ...]:
return (
"interpolation",
"mask_interpolation",
"cval",
"mode",
"scale",
"translate_percent",
"translate_px",
"rotate",
"fit_output",
"shear",
"cval_mask",
"keep_ratio",
"rotate_method",
)
@staticmethod
def _handle_dict_arg(
val: Union[float, Tuple[float, float], Dict[str, Any]],
name: str,
default: float = 1.0,
) -> Dict[str, Any]:
if isinstance(val, dict):
if "x" not in val and "y" not in val:
raise ValueError(
f'Expected {name} dictionary to contain at least key "x" or key "y". Found neither of them.',
)
x = val.get("x", default)
y = val.get("y", default)
return {"x": to_tuple(x, x), "y": to_tuple(y, y)}
return {"x": to_tuple(val, val), "y": to_tuple(val, val)}
@classmethod
def _handle_translate_arg(
cls,
translate_px: Optional[Union[ScaleFloatType, Dict[str, Any]]],
translate_percent: Optional[Union[ScaleFloatType, Dict[str, Any]]],
) -> Any:
if translate_percent is None and translate_px is None:
translate_px = 0
if translate_percent is not None and translate_px is not None:
msg = "Expected either translate_percent or translate_px to be provided, but both were provided."
raise ValueError(msg)
if translate_percent is not None:
# translate by percent
return cls._handle_dict_arg(translate_percent, "translate_percent", default=0.0), translate_px
if translate_px is None:
msg = "translate_px is None."
raise ValueError(msg)
# translate by pixels
return translate_percent, cls._handle_dict_arg(translate_px, "translate_px")
def apply(
self,
img: np.ndarray,
matrix: skimage.transform.ProjectiveTransform = None,
output_shape: Sequence[int] = (),
**params: Any,
) -> np.ndarray:
return F.warp_affine(
img,
matrix,
interpolation=cast(int, self.interpolation),
cval=self.cval,
mode=self.mode,
output_shape=output_shape,
)
def apply_to_mask(
self,
mask: np.ndarray,
matrix: skimage.transform.ProjectiveTransform = None,
output_shape: Sequence[int] = (),
**params: Any,
) -> np.ndarray:
return F.warp_affine(
mask,
matrix,
interpolation=self.mask_interpolation,
cval=self.cval_mask,
mode=self.mode,
output_shape=output_shape,
)
def apply_to_bbox(
self,
bbox: BoxInternalType,
matrix: skimage.transform.ProjectiveTransform = None,
rows: int = 0,
cols: int = 0,
output_shape: Sequence[int] = (),
**params: Any,
) -> BoxInternalType:
return F.bbox_affine(bbox, matrix, self.rotate_method, rows, cols, output_shape)
def apply_to_keypoint(
self,
keypoint: KeypointInternalType,
matrix: Optional[skimage.transform.ProjectiveTransform] = None,
scale: Optional[Dict[str, Any]] = None,
**params: Any,
) -> KeypointInternalType:
if scale is None:
msg = "Expected scale to be provided, but got None."
raise ValueError(msg)
if matrix is None:
msg = "Expected matrix to be provided, but got None."
raise ValueError(msg)
return F.keypoint_affine(keypoint, matrix=matrix, scale=scale)
@property
def targets_as_params(self) -> List[str]:
return ["image"]
def get_params_dependent_on_targets(self, params: Dict[str, Any]) -> Dict[str, Any]:
height, width = params["image"].shape[:2]
translate: Dict[str, Union[int, float]]
if self.translate_px is not None:
translate = {key: random.randint(*value) for key, value in self.translate_px.items()}
elif self.translate_percent is not None:
translate = {key: random.uniform(*value) for key, value in self.translate_percent.items()}
translate["x"] = translate["x"] * width
translate["y"] = translate["y"] * height
else:
translate = {"x": 0, "y": 0}
# Look to issue https://github.com/albumentations-team/albumentations/issues/1079
shear = {key: -random.uniform(*value) for key, value in self.shear.items()}
scale = {key: random.uniform(*value) for key, value in self.scale.items()}
if self.keep_ratio:
scale["y"] = scale["x"]
# Look to issue https://github.com/albumentations-team/albumentations/issues/1079
rotate = -random.uniform(*self.rotate)
# for images we use additional shifts of (0.5, 0.5) as otherwise
# we get an ugly black border for 90deg rotations
shift_x = width / 2 - 0.5
shift_y = height / 2 - 0.5
matrix_to_topleft = skimage.transform.SimilarityTransform(translation=[-shift_x, -shift_y])
matrix_shear_y_rot = skimage.transform.AffineTransform(rotation=-np.pi / 2)
matrix_shear_y = skimage.transform.AffineTransform(shear=np.deg2rad(shear["y"]))
matrix_shear_y_rot_inv = skimage.transform.AffineTransform(rotation=np.pi / 2)
matrix_transforms = skimage.transform.AffineTransform(
scale=(scale["x"], scale["y"]),
translation=(translate["x"], translate["y"]),
rotation=np.deg2rad(rotate),
shear=np.deg2rad(shear["x"]),
)
matrix_to_center = skimage.transform.SimilarityTransform(translation=[shift_x, shift_y])
matrix = (
matrix_to_topleft
+ matrix_shear_y_rot
+ matrix_shear_y
+ matrix_shear_y_rot_inv
+ matrix_transforms
+ matrix_to_center
)
if self.fit_output:
matrix, output_shape = self._compute_affine_warp_output_shape(matrix, params["image"].shape)
else:
output_shape = params["image"].shape
return {
"rotate": rotate,
"scale": scale,
"matrix": matrix,
"output_shape": output_shape,
}
@staticmethod
def _compute_affine_warp_output_shape(
matrix: skimage.transform.ProjectiveTransform,
input_shape: Sequence[int],
) -> Tuple[skimage.transform.ProjectiveTransform, Sequence[int]]:
height, width = input_shape[:2]
if height == 0 or width == 0:
return matrix, input_shape
# determine shape of output image
corners = np.array([[0, 0], [0, height - 1], [width - 1, height - 1], [width - 1, 0]])
corners = matrix(corners)
minc = corners[:, 0].min()
minr = corners[:, 1].min()
maxc = corners[:, 0].max()
maxr = corners[:, 1].max()
out_height = maxr - minr + 1
out_width = maxc - minc + 1
if len(input_shape) == THREE:
output_shape = np.ceil((out_height, out_width, input_shape[2]))
else:
output_shape = np.ceil((out_height, out_width))
output_shape_tuple = tuple([int(v) for v in output_shape.tolist()])
# fit output image in new shape
translation = (-minc, -minr)
matrix_to_fit = skimage.transform.SimilarityTransform(translation=translation)
matrix = matrix + matrix_to_fit
return matrix, output_shape_tuple
targets_as_params: List[str] property readonly ¶
Targets used to get params
apply (self, img, matrix=None, output_shape=(), **params) ¶
Apply transform on image.
Source code in albumentations/augmentations/geometric/transforms.py
Python
def apply(
self,
img: np.ndarray,
matrix: skimage.transform.ProjectiveTransform = None,
output_shape: Sequence[int] = (),
**params: Any,
) -> np.ndarray:
return F.warp_affine(
img,
matrix,
interpolation=cast(int, self.interpolation),
cval=self.cval,
mode=self.mode,
output_shape=output_shape,
)
get_params_dependent_on_targets (self, params) ¶
Returns parameters dependent on targets. Dependent target is defined in self.targets_as_params
Source code in albumentations/augmentations/geometric/transforms.py
Python
def get_params_dependent_on_targets(self, params: Dict[str, Any]) -> Dict[str, Any]:
height, width = params["image"].shape[:2]
translate: Dict[str, Union[int, float]]
if self.translate_px is not None:
translate = {key: random.randint(*value) for key, value in self.translate_px.items()}
elif self.translate_percent is not None:
translate = {key: random.uniform(*value) for key, value in self.translate_percent.items()}
translate["x"] = translate["x"] * width
translate["y"] = translate["y"] * height
else:
translate = {"x": 0, "y": 0}
# Look to issue https://github.com/albumentations-team/albumentations/issues/1079
shear = {key: -random.uniform(*value) for key, value in self.shear.items()}
scale = {key: random.uniform(*value) for key, value in self.scale.items()}
if self.keep_ratio:
scale["y"] = scale["x"]
# Look to issue https://github.com/albumentations-team/albumentations/issues/1079
rotate = -random.uniform(*self.rotate)
# for images we use additional shifts of (0.5, 0.5) as otherwise
# we get an ugly black border for 90deg rotations
shift_x = width / 2 - 0.5
shift_y = height / 2 - 0.5
matrix_to_topleft = skimage.transform.SimilarityTransform(translation=[-shift_x, -shift_y])
matrix_shear_y_rot = skimage.transform.AffineTransform(rotation=-np.pi / 2)
matrix_shear_y = skimage.transform.AffineTransform(shear=np.deg2rad(shear["y"]))
matrix_shear_y_rot_inv = skimage.transform.AffineTransform(rotation=np.pi / 2)
matrix_transforms = skimage.transform.AffineTransform(
scale=(scale["x"], scale["y"]),
translation=(translate["x"], translate["y"]),
rotation=np.deg2rad(rotate),
shear=np.deg2rad(shear["x"]),
)
matrix_to_center = skimage.transform.SimilarityTransform(translation=[shift_x, shift_y])
matrix = (
matrix_to_topleft
+ matrix_shear_y_rot
+ matrix_shear_y
+ matrix_shear_y_rot_inv
+ matrix_transforms
+ matrix_to_center
)
if self.fit_output:
matrix, output_shape = self._compute_affine_warp_output_shape(matrix, params["image"].shape)
else:
output_shape = params["image"].shape
return {
"rotate": rotate,
"scale": scale,
"matrix": matrix,
"output_shape": output_shape,
}
get_transform_init_args_names (self) ¶
Returns names of arguments that are used in init method of the transform
Source code in albumentations/augmentations/geometric/transforms.py
class D4 (always_apply=False, p=1) [view source on GitHub] ¶
Applies one of the eight possible D4 dihedral group transformations to a square-shaped input, maintaining the square shape. These transformations correspond to the symmetries of a square, including rotations and reflections.
The D4 group transformations include: - 'e' (identity): No transformation is applied. - 'r90' (rotation by 90 degrees counterclockwise) - 'r180' (rotation by 180 degrees) - 'r270' (rotation by 270 degrees counterclockwise) - 'v' (reflection across the vertical midline) - 'hvt' (reflection across the anti-diagonal) - 'h' (reflection across the horizontal midline) - 't' (reflection across the main diagonal)
Even if the probability (p) of applying the transform is set to 1, the identity transformation 'e' may still occur, which means the input will remain unchanged in one out of eight cases.
Parameters:
| Name | Type | Description |
|---|---|---|
p | float | Probability of applying the transform. Default is 1, meaning the transform is applied every time it is called. |
Targets
image, mask, bboxes, keypoints
Image types: uint8, float32
Note
This transform is particularly useful when augmenting data that does not have a clear orientation: - Top view satellite or drone imagery - Medical images
Source code in albumentations/augmentations/geometric/transforms.py
Python
class D4(DualTransform):
"""Applies one of the eight possible D4 dihedral group transformations to a square-shaped input,
maintaining the square shape. These transformations correspond to the symmetries of a square,
including rotations and reflections.
The D4 group transformations include:
- 'e' (identity): No transformation is applied.
- 'r90' (rotation by 90 degrees counterclockwise)
- 'r180' (rotation by 180 degrees)
- 'r270' (rotation by 270 degrees counterclockwise)
- 'v' (reflection across the vertical midline)
- 'hvt' (reflection across the anti-diagonal)
- 'h' (reflection across the horizontal midline)
- 't' (reflection across the main diagonal)
Even if the probability (`p`) of applying the transform is set to 1, the identity transformation
'e' may still occur, which means the input will remain unchanged in one out of eight cases.
Args:
p (float): Probability of applying the transform. Default is 1, meaning the
transform is applied every time it is called.
Targets:
image, mask, bboxes, keypoints
Image types:
uint8, float32
Note:
This transform is particularly useful when augmenting data that does not have a clear orientation:
- Top view satellite or drone imagery
- Medical images
"""
_targets = (Targets.IMAGE, Targets.MASK, Targets.BBOXES, Targets.KEYPOINTS)
class InitSchema(BaseTransformInitSchema):
p: ProbabilityType = 1
def __init__(
self,
always_apply: bool = False,
p: float = 1,
):
super().__init__(always_apply, p)
def apply(self, img: np.ndarray, group_element: D4Type, **params: Any) -> np.ndarray:
return F.d4(img, group_element)
def apply_to_bbox(self, bbox: BoxInternalType, group_element: D4Type, **params: Any) -> BoxInternalType:
return F.bbox_d4(bbox, group_element, **params)
def apply_to_keypoint(
self,
keypoint: KeypointInternalType,
group_element: D4Type,
**params: Any,
) -> KeypointInternalType:
return F.keypoint_d4(keypoint, group_element, **params)
def get_params(self) -> Dict[str, D4Type]:
return {
"group_element": random_utils.choice(d4_group_elements),
}
def get_transform_init_args_names(self) -> Tuple[()]:
return ()
class ElasticTransform (alpha=1, sigma=50, alpha_affine=50, interpolation=1, border_mode=4, value=None, mask_value=None, always_apply=False, approximate=False, same_dxdy=False, p=0.5) [view source on GitHub] ¶
Elastic deformation of images as described in [Simard2003]_ (with modifications).
.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for Convolutional Neural Networks applied to Visual Document Analysis", in Proc. of the International Conference on Document Analysis and Recognition, 2003.
Parameters:
| Name | Type | Description |
|---|---|---|
alpha | float | |
sigma | float | Gaussian filter parameter. |
alpha_affine | float | The range will be (-alpha_affine, alpha_affine) |
interpolation | OpenCV flag | flag that is used to specify the interpolation algorithm. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_LINEAR. |
border_mode | OpenCV flag | flag that is used to specify the pixel extrapolation method. Should be one of: cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101. Default: cv2.BORDER_REFLECT_101 |
value | int, float, list of ints, list of float | padding value if border_mode is cv2.BORDER_CONSTANT. |
mask_value | int, float, list of ints, list of float | padding value if border_mode is cv2.BORDER_CONSTANT applied for masks. |
approximate | boolean | Whether to smooth displacement map with fixed kernel size. Enabling this option gives ~2X speedup on large images. |
same_dxdy | boolean | Whether to use same random generated shift for x and y. Enabling this option gives ~2X speedup. |
Targets
image, mask, bboxes
Image types: uint8, float32
Source code in albumentations/augmentations/geometric/transforms.py
Python
class ElasticTransform(DualTransform):
"""Elastic deformation of images as described in [Simard2003]_ (with modifications).
.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
Convolutional Neural Networks applied to Visual Document Analysis", in
Proc. of the International Conference on Document Analysis and
Recognition, 2003.
Args:
alpha (float):
sigma (float): Gaussian filter parameter.
alpha_affine (float): The range will be (-alpha_affine, alpha_affine)
interpolation (OpenCV flag): flag that is used to specify the interpolation algorithm. Should be one of:
cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4.
Default: cv2.INTER_LINEAR.
border_mode (OpenCV flag): flag that is used to specify the pixel extrapolation method. Should be one of:
cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101.
Default: cv2.BORDER_REFLECT_101
value (int, float, list of ints, list of float): padding value if border_mode is cv2.BORDER_CONSTANT.
mask_value (int, float,
list of ints,
list of float): padding value if border_mode is cv2.BORDER_CONSTANT applied for masks.
approximate (boolean): Whether to smooth displacement map with fixed kernel size.
Enabling this option gives ~2X speedup on large images.
same_dxdy (boolean): Whether to use same random generated shift for x and y.
Enabling this option gives ~2X speedup.
Targets:
image, mask, bboxes
Image types:
uint8, float32
Reference:
https://gist.github.com/ernestum/601cdf56d2b424757de5
"""
_targets = (Targets.IMAGE, Targets.MASK, Targets.BBOXES)
class InitSchema(BaseTransformInitSchema):
alpha: Annotated[float, Field(default=1, description="Alpha parameter.", ge=0)]
sigma: Annotated[float, Field(default=50, description="Sigma parameter for Gaussian filter.", ge=0)]
alpha_affine: Annotated[float, Field(default=50, description="Alpha affine parameter.", ge=0)]
interpolation: InterpolationType = cv2.INTER_LINEAR
border_mode: BorderModeType = cv2.BORDER_REFLECT_101
value: Optional[Union[int, float, List[int], List[float]]] = Field(
default=None,
description="Padding value if border_mode is cv2.BORDER_CONSTANT.",
)
mask_value: Optional[Union[float, List[int], List[float]]] = Field(
default=None,
description="Padding value if border_mode is cv2.BORDER_CONSTANT applied for masks.",
)
approximate: Annotated[bool, Field(default=False, description="Approximate displacement map smoothing.")]
same_dxdy: Annotated[bool, Field(default=False, description="Use same shift for x and y.")]
def __init__(
self,
alpha: float = 1,
sigma: float = 50,
alpha_affine: float = 50,
interpolation: int = cv2.INTER_LINEAR,
border_mode: int = cv2.BORDER_REFLECT_101,
value: Optional[Union[int, float, List[int], List[float]]] = None,
mask_value: Optional[Union[int, float, List[int], List[float]]] = None,
always_apply: bool = False,
approximate: bool = False,
same_dxdy: bool = False,
p: float = 0.5,
):
super().__init__(always_apply, p)
self.alpha = alpha
self.alpha_affine = alpha_affine
self.sigma = sigma
self.interpolation = interpolation
self.border_mode = border_mode
self.value = value
self.mask_value = mask_value
self.approximate = approximate
self.same_dxdy = same_dxdy
def apply(
self,
img: np.ndarray,
random_state: Optional[int] = None,
interpolation: int = cv2.INTER_LINEAR,
**params: Any,
) -> np.ndarray:
return F.elastic_transform(
img,
self.alpha,
self.sigma,
self.alpha_affine,
interpolation,
self.border_mode,
self.value,
np.random.RandomState(random_state),
self.approximate,
self.same_dxdy,
)
def apply_to_mask(self, mask: np.ndarray, random_state: Optional[int] = None, **params: Any) -> np.ndarray:
return F.elastic_transform(
mask,
self.alpha,
self.sigma,
self.alpha_affine,
cv2.INTER_NEAREST,
self.border_mode,
self.mask_value,
np.random.RandomState(random_state),
self.approximate,
self.same_dxdy,
)
def apply_to_bbox(
self,
bbox: BoxInternalType,
random_state: Optional[int] = None,
**params: Any,
) -> BoxInternalType:
rows, cols = params["rows"], params["cols"]
mask = np.zeros((rows, cols), dtype=np.uint8)
bbox_denorm = F.denormalize_bbox(bbox, rows, cols)
x_min, y_min, x_max, y_max = bbox_denorm[:4]
x_min, y_min, x_max, y_max = int(x_min), int(y_min), int(x_max), int(y_max)
mask[y_min:y_max, x_min:x_max] = 1
mask = F.elastic_transform(
mask,
self.alpha,
self.sigma,
self.alpha_affine,
cv2.INTER_NEAREST,
self.border_mode,
self.mask_value,
np.random.RandomState(random_state),
self.approximate,
)
bbox_returned = bbox_from_mask(mask)
return cast(BoxInternalType, F.normalize_bbox(bbox_returned, rows, cols))
def get_params(self) -> Dict[str, int]:
return {"random_state": random.randint(0, 10000)}
def get_transform_init_args_names(self) -> Tuple[str, ...]:
return (
"alpha",
"sigma",
"alpha_affine",
"interpolation",
"border_mode",
"value",
"mask_value",
"approximate",
"same_dxdy",
)
apply (self, img, random_state=None, interpolation=1, **params) ¶
Apply transform on image.
Source code in albumentations/augmentations/geometric/transforms.py
Python
def apply(
self,
img: np.ndarray,
random_state: Optional[int] = None,
interpolation: int = cv2.INTER_LINEAR,
**params: Any,
) -> np.ndarray:
return F.elastic_transform(
img,
self.alpha,
self.sigma,
self.alpha_affine,
interpolation,
self.border_mode,
self.value,
np.random.RandomState(random_state),
self.approximate,
self.same_dxdy,
)
get_params (self) ¶
get_transform_init_args_names (self) ¶
Returns names of arguments that are used in init method of the transform
class Flip [view source on GitHub] ¶
Flip the input either horizontally, vertically or both horizontally and vertically.
Parameters:
| Name | Type | Description |
|---|---|---|
p | float | probability of applying the transform. Default: 0.5. |
Targets
image, mask, bboxes, keypoints
Image types: uint8, float32
Source code in albumentations/augmentations/geometric/transforms.py
Python
class Flip(DualTransform):
"""Flip the input either horizontally, vertically or both horizontally and vertically.
Args:
p (float): probability of applying the transform. Default: 0.5.
Targets:
image, mask, bboxes, keypoints
Image types:
uint8, float32
"""
_targets = (Targets.IMAGE, Targets.MASK, Targets.BBOXES, Targets.KEYPOINTS)
def apply(self, img: np.ndarray, d: int = 0, **params: Any) -> np.ndarray:
"""Args:
d (int): code that specifies how to flip the input. 0 for vertical flipping, 1 for horizontal flipping,
-1 for both vertical and horizontal flipping (which is also could be seen as rotating the input by
180 degrees).
"""
return F.random_flip(img, d)
def get_params(self) -> Dict[str, int]:
# Random int in the range [-1, 1]
return {"d": random.randint(-1, 1)}
def apply_to_bbox(self, bbox: BoxInternalType, **params: Any) -> BoxInternalType:
return F.bbox_flip(bbox, **params)
def apply_to_keypoint(self, keypoint: KeypointInternalType, **params: Any) -> KeypointInternalType:
return F.keypoint_flip(keypoint, **params)
def get_transform_init_args_names(self) -> Tuple[()]:
return ()
apply (self, img, d=0, **params) ¶
d (int): code that specifies how to flip the input. 0 for vertical flipping, 1 for horizontal flipping, -1 for both vertical and horizontal flipping (which is also could be seen as rotating the input by 180 degrees).
Source code in albumentations/augmentations/geometric/transforms.py
Python
def apply(self, img: np.ndarray, d: int = 0, **params: Any) -> np.ndarray:
"""Args:
d (int): code that specifies how to flip the input. 0 for vertical flipping, 1 for horizontal flipping,
-1 for both vertical and horizontal flipping (which is also could be seen as rotating the input by
180 degrees).
"""
return F.random_flip(img, d)
get_params (self) ¶
get_transform_init_args_names (self) ¶
class GridDistortion (num_steps=5, distort_limit=(-0.3, 0.3), interpolation=1, border_mode=4, value=None, mask_value=None, normalized=False, always_apply=False, p=0.5) [view source on GitHub] ¶
Parameters:
| Name | Type | Description |
|---|---|---|
num_steps | int | count of grid cells on each side. |
distort_limit | float, (float, float | If distort_limit is a single float, the range will be (-distort_limit, distort_limit). Default: (-0.03, 0.03). |
interpolation | OpenCV flag | flag that is used to specify the interpolation algorithm. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_LINEAR. |
border_mode | OpenCV flag | flag that is used to specify the pixel extrapolation method. Should be one of: cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101. Default: cv2.BORDER_REFLECT_101 |
value | int, float, list of ints, list of float | padding value if border_mode is cv2.BORDER_CONSTANT. |
mask_value | int, float, list of ints, list of float | padding value if border_mode is cv2.BORDER_CONSTANT applied for masks. |
normalized | bool | if true, distortion will be normalized to do not go outside the image. Default: False See for more information: https://github.com/albumentations-team/albumentations/pull/722 |
Targets
image, mask, bboxes
Image types: uint8, float32
Source code in albumentations/augmentations/geometric/transforms.py
Python
class GridDistortion(DualTransform):
"""Args:
num_steps (int): count of grid cells on each side.
distort_limit (float, (float, float)): If distort_limit is a single float, the range
will be (-distort_limit, distort_limit). Default: (-0.03, 0.03).
interpolation (OpenCV flag): flag that is used to specify the interpolation algorithm. Should be one of:
cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4.
Default: cv2.INTER_LINEAR.
border_mode (OpenCV flag): flag that is used to specify the pixel extrapolation method. Should be one of:
cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101.
Default: cv2.BORDER_REFLECT_101
value (int, float, list of ints, list of float): padding value if border_mode is cv2.BORDER_CONSTANT.
mask_value (int, float,
list of ints,
list of float): padding value if border_mode is cv2.BORDER_CONSTANT applied for masks.
normalized (bool): if true, distortion will be normalized to do not go outside the image. Default: False
See for more information: https://github.com/albumentations-team/albumentations/pull/722
Targets:
image, mask, bboxes
Image types:
uint8, float32
"""
_targets = (Targets.IMAGE, Targets.MASK, Targets.BBOXES)
class InitSchema(BaseTransformInitSchema):
num_steps: Annotated[int, Field(ge=1, description="Count of grid cells on each side.")]
distort_limit: SymmetricRangeType = (-0.03, 0.03)
interpolation: InterpolationType = cv2.INTER_LINEAR
border_mode: BorderModeType = cv2.BORDER_REFLECT_101
value: Optional[ColorType] = Field(
default=None,
description="Padding value if border_mode is cv2.BORDER_CONSTANT.",
)
mask_value: Optional[ColorType] = Field(
default=None,
description="Padding value for mask if border_mode is cv2.BORDER_CONSTANT.",
)
normalized: bool = Field(
default=False,
description="If true, distortion will be normalized to not go outside the image.",
)
@field_validator("distort_limit")
@classmethod
def check_limits(cls, v: Tuple[float, float], info: ValidationInfo) -> Tuple[float, float]:
bounds = -1, 1
result = to_tuple(v)
check_range(result, *bounds, info.field_name)
return result
def __init__(
self,
num_steps: int = 5,
distort_limit: ScaleFloatType = (-0.3, 0.3),
interpolation: int = cv2.INTER_LINEAR,
border_mode: int = cv2.BORDER_REFLECT_101,
value: Optional[ColorType] = None,
mask_value: Optional[ColorType] = None,
normalized: bool = False,
always_apply: bool = False,
p: float = 0.5,
):
super().__init__(always_apply, p)
self.num_steps = num_steps
self.distort_limit = cast(Tuple[float, float], distort_limit)
self.interpolation = interpolation
self.border_mode = border_mode
self.value = value
self.mask_value = mask_value
self.normalized = normalized
def apply(
self,
img: np.ndarray,
stepsx: Tuple[()] = (),
stepsy: Tuple[()] = (),
interpolation: int = cv2.INTER_LINEAR,
**params: Any,
) -> np.ndarray:
return F.grid_distortion(img, self.num_steps, stepsx, stepsy, interpolation, self.border_mode, self.value)
def apply_to_mask(
self,
mask: np.ndarray,
stepsx: Tuple[()] = (),
stepsy: Tuple[()] = (),
**params: Any,
) -> np.ndarray:
return F.grid_distortion(
mask,
self.num_steps,
stepsx,
stepsy,
cv2.INTER_NEAREST,
self.border_mode,
self.mask_value,
)
def apply_to_bbox(
self,
bbox: BoxInternalType,
stepsx: Tuple[()] = (),
stepsy: Tuple[()] = (),
**params: Any,
) -> BoxInternalType:
rows, cols = params["rows"], params["cols"]
mask = np.zeros((rows, cols), dtype=np.uint8)
bbox_denorm = F.denormalize_bbox(bbox, rows, cols)
x_min, y_min, x_max, y_max = bbox_denorm[:4]
x_min, y_min, x_max, y_max = int(x_min), int(y_min), int(x_max), int(y_max)
mask[y_min:y_max, x_min:x_max] = 1
mask = F.grid_distortion(
mask,
self.num_steps,
stepsx,
stepsy,
cv2.INTER_NEAREST,
self.border_mode,
self.mask_value,
)
bbox_returned = bbox_from_mask(mask)
return cast(BoxInternalType, F.normalize_bbox(bbox_returned, rows, cols))
def _normalize(self, h: int, w: int, xsteps: List[float], ysteps: List[float]) -> Dict[str, Any]:
# compensate for smaller last steps in source image.
x_step = w // self.num_steps
last_x_step = min(w, ((self.num_steps + 1) * x_step)) - (self.num_steps * x_step)
xsteps[-1] *= last_x_step / x_step
y_step = h // self.num_steps
last_y_step = min(h, ((self.num_steps + 1) * y_step)) - (self.num_steps * y_step)
ysteps[-1] *= last_y_step / y_step
# now normalize such that distortion never leaves image bounds.
tx = w / math.floor(w / self.num_steps)
ty = h / math.floor(h / self.num_steps)
xsteps = np.array(xsteps) * (tx / np.sum(xsteps))
ysteps = np.array(ysteps) * (ty / np.sum(ysteps))
return {"stepsx": xsteps, "stepsy": ysteps}
@property
def targets_as_params(self) -> List[str]:
return ["image"]
def get_params_dependent_on_targets(self, params: Dict[str, Any]) -> Dict[str, Any]:
height, width = params["image"].shape[:2]
stepsx = [
1 + random_utils.uniform(self.distort_limit[0], self.distort_limit[1]) for _ in range(self.num_steps + 1)
]
stepsy = [
1 + random_utils.uniform(self.distort_limit[0], self.distort_limit[1]) for _ in range(self.num_steps + 1)
]
if self.normalized:
return self._normalize(height, width, stepsx, stepsy)
return {"stepsx": stepsx, "stepsy": stepsy}
def get_transform_init_args_names(self) -> Tuple[str, ...]:
return "num_steps", "distort_limit", "interpolation", "border_mode", "value", "mask_value", "normalized"
targets_as_params: List[str] property readonly ¶
Targets used to get params
apply (self, img, stepsx=(), stepsy=(), interpolation=1, **params) ¶
Apply transform on image.
Source code in albumentations/augmentations/geometric/transforms.py
get_params_dependent_on_targets (self, params) ¶
Returns parameters dependent on targets. Dependent target is defined in self.targets_as_params
Source code in albumentations/augmentations/geometric/transforms.py
Python
def get_params_dependent_on_targets(self, params: Dict[str, Any]) -> Dict[str, Any]:
height, width = params["image"].shape[:2]
stepsx = [
1 + random_utils.uniform(self.distort_limit[0], self.distort_limit[1]) for _ in range(self.num_steps + 1)
]
stepsy = [
1 + random_utils.uniform(self.distort_limit[0], self.distort_limit[1]) for _ in range(self.num_steps + 1)
]
if self.normalized:
return self._normalize(height, width, stepsx, stepsy)
return {"stepsx": stepsx, "stepsy": stepsy}
get_transform_init_args_names (self) ¶
Returns names of arguments that are used in init method of the transform
class HorizontalFlip [view source on GitHub] ¶
Flip the input horizontally around the y-axis.
Parameters:
| Name | Type | Description |
|---|---|---|
p | float | probability of applying the transform. Default: 0.5. |
Targets
image, mask, bboxes, keypoints
Image types: uint8, float32
Source code in albumentations/augmentations/geometric/transforms.py
Python
class HorizontalFlip(DualTransform):
"""Flip the input horizontally around the y-axis.
Args:
p (float): probability of applying the transform. Default: 0.5.
Targets:
image, mask, bboxes, keypoints
Image types:
uint8, float32
"""
_targets = (Targets.IMAGE, Targets.MASK, Targets.BBOXES, Targets.KEYPOINTS)
def apply(self, img: np.ndarray, **params: Any) -> np.ndarray:
if img.ndim == THREE and img.shape[2] > 1 and img.dtype == np.uint8:
# Opencv is faster than numpy only in case of
# non-gray scale 8bits images
return F.hflip_cv2(img)
return F.hflip(img)
def apply_to_bbox(self, bbox: BoxInternalType, **params: Any) -> BoxInternalType:
return F.bbox_hflip(bbox, **params)
def apply_to_keypoint(self, keypoint: KeypointInternalType, **params: Any) -> KeypointInternalType:
return F.keypoint_hflip(keypoint, **params)
def get_transform_init_args_names(self) -> Tuple[()]:
return ()
class OpticalDistortion (distort_limit=(-0.05, 0.05), shift_limit=(-0.05, 0.05), interpolation=1, border_mode=4, value=None, mask_value=None, always_apply=False, p=0.5) [view source on GitHub] ¶
Parameters:
| Name | Type | Description |
|---|---|---|
distort_limit | float, (float, float | If distort_limit is a single float, the range will be (-distort_limit, distort_limit). Default: (-0.05, 0.05). |
shift_limit | float, (float, float | If shift_limit is a single float, the range will be (-shift_limit, shift_limit). Default: (-0.05, 0.05). |
interpolation | OpenCV flag | flag that is used to specify the interpolation algorithm. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_LINEAR. |
border_mode | OpenCV flag | flag that is used to specify the pixel extrapolation method. Should be one of: cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101. Default: cv2.BORDER_REFLECT_101 |
value | int, float, list of ints, list of float | padding value if border_mode is cv2.BORDER_CONSTANT. |
mask_value | int, float, list of ints, list of float | padding value if border_mode is cv2.BORDER_CONSTANT applied for masks. |
Targets
image, mask, bboxes
Image types: uint8, float32
Source code in albumentations/augmentations/geometric/transforms.py
Python
class OpticalDistortion(DualTransform):
"""Args:
distort_limit (float, (float, float)): If distort_limit is a single float, the range
will be (-distort_limit, distort_limit). Default: (-0.05, 0.05).
shift_limit (float, (float, float))): If shift_limit is a single float, the range
will be (-shift_limit, shift_limit). Default: (-0.05, 0.05).
interpolation (OpenCV flag): flag that is used to specify the interpolation algorithm. Should be one of:
cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4.
Default: cv2.INTER_LINEAR.
border_mode (OpenCV flag): flag that is used to specify the pixel extrapolation method. Should be one of:
cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101.
Default: cv2.BORDER_REFLECT_101
value (int, float, list of ints, list of float): padding value if border_mode is cv2.BORDER_CONSTANT.
mask_value (int, float,
list of ints,
list of float): padding value if border_mode is cv2.BORDER_CONSTANT applied for masks.
Targets:
image, mask, bboxes
Image types:
uint8, float32
"""
_targets = (Targets.IMAGE, Targets.MASK, Targets.BBOXES)
class InitSchema(BaseTransformInitSchema):
distort_limit: SymmetricRangeType = (-0.05, 0.05)
shift_limit: SymmetricRangeType = (-0.05, 0.05)
interpolation: InterpolationType = cv2.INTER_LINEAR
border_mode: BorderModeType = cv2.BORDER_REFLECT_101
value: Optional[ColorType] = Field(
default=None,
description="Padding value if border_mode is cv2.BORDER_CONSTANT.",
)
mask_value: Optional[ColorType] = Field(
default=None,
description="Padding value for mask if border_mode is cv2.BORDER_CONSTANT.",
)
def __init__(
self,
distort_limit: ScaleFloatType = (-0.05, 0.05),
shift_limit: ScaleFloatType = (-0.05, 0.05),
interpolation: int = cv2.INTER_LINEAR,
border_mode: int = cv2.BORDER_REFLECT_101,
value: Optional[ColorType] = None,
mask_value: Optional[ColorType] = None,
always_apply: bool = False,
p: float = 0.5,
):
super().__init__(always_apply, p)
self.shift_limit = cast(Tuple[float, float], shift_limit)
self.distort_limit = cast(Tuple[float, float], distort_limit)
self.interpolation = interpolation
self.border_mode = border_mode
self.value = value
self.mask_value = mask_value
def apply(
self,
img: np.ndarray,
k: int = 0,
dx: int = 0,
dy: int = 0,
interpolation: int = cv2.INTER_LINEAR,
**params: Any,
) -> np.ndarray:
return F.optical_distortion(img, k, dx, dy, interpolation, self.border_mode, self.value)
def apply_to_mask(self, mask: np.ndarray, k: int = 0, dx: int = 0, dy: int = 0, **params: Any) -> np.ndarray:
return F.optical_distortion(mask, k, dx, dy, cv2.INTER_NEAREST, self.border_mode, self.mask_value)
def apply_to_bbox(
self,
bbox: BoxInternalType,
k: int = 0,
dx: int = 0,
dy: int = 0,
**params: Any,
) -> BoxInternalType:
rows, cols = params["rows"], params["cols"]
mask = np.zeros((rows, cols), dtype=np.uint8)
bbox_denorm = F.denormalize_bbox(bbox, rows, cols)
x_min, y_min, x_max, y_max = bbox_denorm[:4]
x_min, y_min, x_max, y_max = int(x_min), int(y_min), int(x_max), int(y_max)
mask[y_min:y_max, x_min:x_max] = 1
mask = F.optical_distortion(mask, k, dx, dy, cv2.INTER_NEAREST, self.border_mode, self.mask_value)
bbox_returned = bbox_from_mask(mask)
return cast(BoxInternalType, F.normalize_bbox(bbox_returned, rows, cols))
def get_params(self) -> Dict[str, Any]:
return {
"k": random.uniform(self.distort_limit[0], self.distort_limit[1]),
"dx": round(random.uniform(self.shift_limit[0], self.shift_limit[1])),
"dy": round(random.uniform(self.shift_limit[0], self.shift_limit[1])),
}
def get_transform_init_args_names(self) -> Tuple[str, ...]:
return (
"distort_limit",
"shift_limit",
"interpolation",
"border_mode",
"value",
"mask_value",
)
apply (self, img, k=0, dx=0, dy=0, interpolation=1, **params) ¶
Apply transform on image.
Source code in albumentations/augmentations/geometric/transforms.py
get_params (self) ¶
Returns parameters independent of input
Source code in albumentations/augmentations/geometric/transforms.py
get_transform_init_args_names (self) ¶
Returns names of arguments that are used in init method of the transform
class PadIfNeeded (min_height=1024, min_width=1024, pad_height_divisor=None, pad_width_divisor=None, position=<PositionType.CENTER: 'center'>, border_mode=4, value=None, mask_value=None, always_apply=False, p=1.0) [view source on GitHub] ¶
Pad side of the image / max if side is less than desired number.
Parameters:
| Name | Type | Description |
|---|---|---|
min_height | int | minimal result image height. |
min_width | int | minimal result image width. |
pad_height_divisor | int | if not None, ensures image height is dividable by value of this argument. |
pad_width_divisor | int | if not None, ensures image width is dividable by value of this argument. |
position | Union[str, PositionType] | Position of the image. should be PositionType.CENTER or PositionType.TOP_LEFT or PositionType.TOP_RIGHT or PositionType.BOTTOM_LEFT or PositionType.BOTTOM_RIGHT. or PositionType.RANDOM. Default: PositionType.CENTER. |
border_mode | OpenCV flag | OpenCV border mode. |
value | int, float, list of int, list of float | padding value if border_mode is cv2.BORDER_CONSTANT. |
mask_value | int, float, list of int, list of float | padding value for mask if border_mode is cv2.BORDER_CONSTANT. |
p | float | probability of applying the transform. Default: 1.0. |
Targets
image, mask, bboxes, keypoints
Image types: uint8, float32
Source code in albumentations/augmentations/geometric/transforms.py
Python
class PadIfNeeded(DualTransform):
"""Pad side of the image / max if side is less than desired number.
Args:
min_height (int): minimal result image height.
min_width (int): minimal result image width.
pad_height_divisor (int): if not None, ensures image height is dividable by value of this argument.
pad_width_divisor (int): if not None, ensures image width is dividable by value of this argument.
position (Union[str, PositionType]): Position of the image. should be PositionType.CENTER or
PositionType.TOP_LEFT or PositionType.TOP_RIGHT or PositionType.BOTTOM_LEFT or PositionType.BOTTOM_RIGHT.
or PositionType.RANDOM. Default: PositionType.CENTER.
border_mode (OpenCV flag): OpenCV border mode.
value (int, float, list of int, list of float): padding value if border_mode is cv2.BORDER_CONSTANT.
mask_value (int, float,
list of int,
list of float): padding value for mask if border_mode is cv2.BORDER_CONSTANT.
p (float): probability of applying the transform. Default: 1.0.
Targets:
image, mask, bboxes, keypoints
Image types:
uint8, float32
"""
class PositionType(Enum):
"""Enumerates the types of positions for placing an object within a container.
This Enum class is utilized to define specific anchor positions that an object can
assume relative to a container. It's particularly useful in image processing, UI layout,
and graphic design to specify the alignment and positioning of elements.
Attributes:
CENTER (str): Specifies that the object should be placed at the center.
TOP_LEFT (str): Specifies that the object should be placed at the top-left corner.
TOP_RIGHT (str): Specifies that the object should be placed at the top-right corner.
BOTTOM_LEFT (str): Specifies that the object should be placed at the bottom-left corner.
BOTTOM_RIGHT (str): Specifies that the object should be placed at the bottom-right corner.
RANDOM (str): Indicates that the object's position should be determined randomly.
"""
CENTER = "center"
TOP_LEFT = "top_left"
TOP_RIGHT = "top_right"
BOTTOM_LEFT = "bottom_left"
BOTTOM_RIGHT = "bottom_right"
RANDOM = "random"
_targets = (Targets.IMAGE, Targets.MASK, Targets.BBOXES, Targets.KEYPOINTS)
class InitSchema(BaseTransformInitSchema):
min_height: Optional[int] = Field(default=None, ge=1, description="Minimal result image height.")
min_width: Optional[int] = Field(default=None, ge=1, description="Minimal result image width.")
pad_height_divisor: Optional[int] = Field(
default=None,
ge=1,
description="Ensures image height is divisible by this value.",
)
pad_width_divisor: Optional[int] = Field(
default=None,
ge=1,
description="Ensures image width is divisible by this value.",
)
position: str = Field(default="center", description="Position of the padded image.")
border_mode: BorderModeType = cv2.BORDER_REFLECT_101
value: Optional[ColorType] = Field(default=None, description="Value for border if BORDER_CONSTANT is used.")
mask_value: Optional[ColorType] = Field(
default=None,
description="Value for mask border if BORDER_CONSTANT is used.",
)
p: ProbabilityType = 1.0
@model_validator(mode="after")
def validate_divisibility(self) -> Self:
if (self.min_height is None) == (self.pad_height_divisor is None):
msg = "Only one of 'min_height' and 'pad_height_divisor' parameters must be set"
raise ValueError(msg)
if (self.min_width is None) == (self.pad_width_divisor is None):
msg = "Only one of 'min_width' and 'pad_width_divisor' parameters must be set"
raise ValueError(msg)
return self
def __init__(
self,
min_height: Optional[int] = 1024,
min_width: Optional[int] = 1024,
pad_height_divisor: Optional[int] = None,
pad_width_divisor: Optional[int] = None,
position: Union[PositionType, str] = PositionType.CENTER,
border_mode: int = cv2.BORDER_REFLECT_101,
value: Optional[ColorType] = None,
mask_value: Optional[ColorType] = None,
always_apply: bool = False,
p: float = 1.0,
):
super().__init__(always_apply, p)
self.min_height = min_height
self.min_width = min_width
self.pad_width_divisor = pad_width_divisor
self.pad_height_divisor = pad_height_divisor
self.position = PadIfNeeded.PositionType(position)
self.border_mode = border_mode
self.value = value
self.mask_value = mask_value
def update_params(self, params: Dict[str, Any], **kwargs: Any) -> Dict[str, Any]:
params = super().update_params(params, **kwargs)
rows = params["rows"]
cols = params["cols"]
if self.min_height is not None:
if rows < self.min_height:
h_pad_top = int((self.min_height - rows) / 2.0)
h_pad_bottom = self.min_height - rows - h_pad_top
else:
h_pad_top = 0
h_pad_bottom = 0
else:
pad_remained = rows % self.pad_height_divisor
pad_rows = self.pad_height_divisor - pad_remained if pad_remained > 0 else 0
h_pad_top = pad_rows // 2
h_pad_bottom = pad_rows - h_pad_top
if self.min_width is not None:
if cols < self.min_width:
w_pad_left = int((self.min_width - cols) / 2.0)
w_pad_right = self.min_width - cols - w_pad_left
else:
w_pad_left = 0
w_pad_right = 0
else:
pad_remainder = cols % self.pad_width_divisor
pad_cols = self.pad_width_divisor - pad_remainder if pad_remainder > 0 else 0
w_pad_left = pad_cols // 2
w_pad_right = pad_cols - w_pad_left
h_pad_top, h_pad_bottom, w_pad_left, w_pad_right = self.__update_position_params(
h_top=h_pad_top,
h_bottom=h_pad_bottom,
w_left=w_pad_left,
w_right=w_pad_right,
)
params.update(
{
"pad_top": h_pad_top,
"pad_bottom": h_pad_bottom,
"pad_left": w_pad_left,
"pad_right": w_pad_right,
},
)
return params
def apply(
self,
img: np.ndarray,
pad_top: int = 0,
pad_bottom: int = 0,
pad_left: int = 0,
pad_right: int = 0,
**params: Any,
) -> np.ndarray:
return F.pad_with_params(
img,
pad_top,
pad_bottom,
pad_left,
pad_right,
border_mode=self.border_mode,
value=self.value,
)
def apply_to_mask(
self,
mask: np.ndarray,
pad_top: int = 0,
pad_bottom: int = 0,
pad_left: int = 0,
pad_right: int = 0,
**params: Any,
) -> np.ndarray:
return F.pad_with_params(
mask,
pad_top,
pad_bottom,
pad_left,
pad_right,
border_mode=self.border_mode,
value=self.mask_value,
)
def apply_to_bbox(
self,
bbox: BoxInternalType,
pad_top: int = 0,
pad_bottom: int = 0,
pad_left: int = 0,
pad_right: int = 0,
rows: int = 0,
cols: int = 0,
**params: Any,
) -> BoxInternalType:
x_min, y_min, x_max, y_max = denormalize_bbox(bbox, rows, cols)[:4]
bbox = x_min + pad_left, y_min + pad_top, x_max + pad_left, y_max + pad_top
return cast(BoxInternalType, normalize_bbox(bbox, rows + pad_top + pad_bottom, cols + pad_left + pad_right))
def apply_to_keypoint(
self,
keypoint: KeypointInternalType,
pad_top: int = 0,
pad_bottom: int = 0,
pad_left: int = 0,
pad_right: int = 0,
**params: Any,
) -> KeypointInternalType:
x, y, angle, scale = keypoint[:4]
return x + pad_left, y + pad_top, angle, scale
def get_transform_init_args_names(self) -> Tuple[str, ...]:
return (
"min_height",
"min_width",
"pad_height_divisor",
"pad_width_divisor",
"position",
"border_mode",
"value",
"mask_value",
)
def __update_position_params(
self,
h_top: int,
h_bottom: int,
w_left: int,
w_right: int,
) -> Tuple[int, int, int, int]:
if self.position == PadIfNeeded.PositionType.TOP_LEFT:
h_bottom += h_top
w_right += w_left
h_top = 0
w_left = 0
elif self.position == PadIfNeeded.PositionType.TOP_RIGHT:
h_bottom += h_top
w_left += w_right
h_top = 0
w_right = 0
elif self.position == PadIfNeeded.PositionType.BOTTOM_LEFT:
h_top += h_bottom
w_right += w_left
h_bottom = 0
w_left = 0
elif self.position == PadIfNeeded.PositionType.BOTTOM_RIGHT:
h_top += h_bottom
w_left += w_right
h_bottom = 0
w_right = 0
elif self.position == PadIfNeeded.PositionType.RANDOM:
h_pad = h_top + h_bottom
w_pad = w_left + w_right
h_top = random.randint(0, h_pad)
h_bottom = h_pad - h_top
w_left = random.randint(0, w_pad)
w_right = w_pad - w_left
return h_top, h_bottom, w_left, w_right
class PositionType ¶
Enumerates the types of positions for placing an object within a container.
This Enum class is utilized to define specific anchor positions that an object can assume relative to a container. It's particularly useful in image processing, UI layout, and graphic design to specify the alignment and positioning of elements.
Attributes:
| Name | Type | Description |
|---|---|---|
CENTER | str | Specifies that the object should be placed at the center. |
TOP_LEFT | str | Specifies that the object should be placed at the top-left corner. |
TOP_RIGHT | str | Specifies that the object should be placed at the top-right corner. |
BOTTOM_LEFT | str | Specifies that the object should be placed at the bottom-left corner. |
BOTTOM_RIGHT | str | Specifies that the object should be placed at the bottom-right corner. |
RANDOM | str | Indicates that the object's position should be determined randomly. |
Source code in albumentations/augmentations/geometric/transforms.py
Python
class PositionType(Enum):
"""Enumerates the types of positions for placing an object within a container.
This Enum class is utilized to define specific anchor positions that an object can
assume relative to a container. It's particularly useful in image processing, UI layout,
and graphic design to specify the alignment and positioning of elements.
Attributes:
CENTER (str): Specifies that the object should be placed at the center.
TOP_LEFT (str): Specifies that the object should be placed at the top-left corner.
TOP_RIGHT (str): Specifies that the object should be placed at the top-right corner.
BOTTOM_LEFT (str): Specifies that the object should be placed at the bottom-left corner.
BOTTOM_RIGHT (str): Specifies that the object should be placed at the bottom-right corner.
RANDOM (str): Indicates that the object's position should be determined randomly.
"""
CENTER = "center"
TOP_LEFT = "top_left"
TOP_RIGHT = "top_right"
BOTTOM_LEFT = "bottom_left"
BOTTOM_RIGHT = "bottom_right"
RANDOM = "random"
apply (self, img, pad_top=0, pad_bottom=0, pad_left=0, pad_right=0, **params) ¶
Apply transform on image.
Source code in albumentations/augmentations/geometric/transforms.py
get_transform_init_args_names (self) ¶
Returns names of arguments that are used in init method of the transform
update_params (self, params, **kwargs) ¶
Update parameters with transform specific params
Source code in albumentations/augmentations/geometric/transforms.py
Python
def update_params(self, params: Dict[str, Any], **kwargs: Any) -> Dict[str, Any]:
params = super().update_params(params, **kwargs)
rows = params["rows"]
cols = params["cols"]
if self.min_height is not None:
if rows < self.min_height:
h_pad_top = int((self.min_height - rows) / 2.0)
h_pad_bottom = self.min_height - rows - h_pad_top
else:
h_pad_top = 0
h_pad_bottom = 0
else:
pad_remained = rows % self.pad_height_divisor
pad_rows = self.pad_height_divisor - pad_remained if pad_remained > 0 else 0
h_pad_top = pad_rows // 2
h_pad_bottom = pad_rows - h_pad_top
if self.min_width is not None:
if cols < self.min_width:
w_pad_left = int((self.min_width - cols) / 2.0)
w_pad_right = self.min_width - cols - w_pad_left
else:
w_pad_left = 0
w_pad_right = 0
else:
pad_remainder = cols % self.pad_width_divisor
pad_cols = self.pad_width_divisor - pad_remainder if pad_remainder > 0 else 0
w_pad_left = pad_cols // 2
w_pad_right = pad_cols - w_pad_left
h_pad_top, h_pad_bottom, w_pad_left, w_pad_right = self.__update_position_params(
h_top=h_pad_top,
h_bottom=h_pad_bottom,
w_left=w_pad_left,
w_right=w_pad_right,
)
params.update(
{
"pad_top": h_pad_top,
"pad_bottom": h_pad_bottom,
"pad_left": w_pad_left,
"pad_right": w_pad_right,
},
)
return params
class Perspective (scale=(0.05, 0.1), keep_size=True, pad_mode=0, pad_val=0, mask_pad_val=0, fit_output=False, interpolation=1, always_apply=False, p=0.5) [view source on GitHub] ¶
Perform a random four point perspective transform of the input.
Parameters:
| Name | Type | Description |
|---|---|---|
scale | Union[float, Tuple[float, float]] | standard deviation of the normal distributions. These are used to sample the random distances of the subimage's corners from the full image's corners. If scale is a single float value, the range will be (0, scale). Default: (0.05, 0.1). |
keep_size | bool | Whether to resize image back to their original size after applying the perspective transform. If set to False, the resulting images may end up having different shapes and will always be a list, never an array. Default: True |
pad_mode | OpenCV flag | OpenCV border mode. |
pad_val | int, float, list of int, list of float | padding value if border_mode is cv2.BORDER_CONSTANT. Default: 0 |
mask_pad_val | int, float, list of int, list of float | padding value for mask if border_mode is cv2.BORDER_CONSTANT. Default: 0 |
fit_output | bool | If True, the image plane size and position will be adjusted to still capture the whole image after perspective transformation. (Followed by image resizing if keep_size is set to True.) Otherwise, parts of the transformed image may be outside of the image plane. This setting should not be set to True when using large scale values as it could lead to very large images. Default: False |
p | float | probability of applying the transform. Default: 0.5. |
Targets
image, mask, keypoints, bboxes
Image types: uint8, float32
Source code in albumentations/augmentations/geometric/transforms.py
Python
class Perspective(DualTransform):
"""Perform a random four point perspective transform of the input.
Args:
scale: standard deviation of the normal distributions. These are used to sample
the random distances of the subimage's corners from the full image's corners.
If scale is a single float value, the range will be (0, scale). Default: (0.05, 0.1).
keep_size: Whether to resize image back to their original size after applying the perspective
transform. If set to False, the resulting images may end up having different shapes
and will always be a list, never an array. Default: True
pad_mode (OpenCV flag): OpenCV border mode.
pad_val (int, float, list of int, list of float): padding value if border_mode is cv2.BORDER_CONSTANT.
Default: 0
mask_pad_val (int, float, list of int, list of float): padding value for mask
if border_mode is cv2.BORDER_CONSTANT. Default: 0
fit_output (bool): If True, the image plane size and position will be adjusted to still capture
the whole image after perspective transformation. (Followed by image resizing if keep_size is set to True.)
Otherwise, parts of the transformed image may be outside of the image plane.
This setting should not be set to True when using large scale values as it could lead to very large images.
Default: False
p (float): probability of applying the transform. Default: 0.5.
Targets:
image, mask, keypoints, bboxes
Image types:
uint8, float32
"""
_targets = (Targets.IMAGE, Targets.MASK, Targets.KEYPOINTS, Targets.BBOXES)
class InitSchema(BaseTransformInitSchema):
scale: NonNegativeFloatRangeType = (0.05, 0.1)
keep_size: Annotated[bool, Field(default=True, description="Keep size after transform.")]
pad_mode: BorderModeType = cv2.BORDER_CONSTANT
pad_val: Optional[ColorType] = Field(
default=0,
description="Padding value if border_mode is cv2.BORDER_CONSTANT.",
)
mask_pad_val: Optional[ColorType] = Field(
default=0,
description="Mask padding value if border_mode is cv2.BORDER_CONSTANT.",
)
fit_output: Annotated[bool, Field(default=False, description="Adjust image plane to capture whole image.")]
interpolation: InterpolationType = cv2.INTER_LINEAR
def __init__(
self,
scale: ScaleFloatType = (0.05, 0.1),
keep_size: bool = True,
pad_mode: int = cv2.BORDER_CONSTANT,
pad_val: Union[ColorType] = 0,
mask_pad_val: Union[ColorType] = 0,
fit_output: bool = False,
interpolation: int = cv2.INTER_LINEAR,
always_apply: bool = False,
p: float = 0.5,
):
super().__init__(always_apply, p)
self.scale = cast(Tuple[float, float], scale)
self.keep_size = keep_size
self.pad_mode = pad_mode
self.pad_val = pad_val
self.mask_pad_val = mask_pad_val
self.fit_output = fit_output
self.interpolation = interpolation
def apply(
self,
img: np.ndarray,
matrix: np.ndarray,
max_height: int,
max_width: int,
**params: Any,
) -> np.ndarray:
return F.perspective(
img,
matrix,
max_width,
max_height,
self.pad_val,
self.pad_mode,
self.keep_size,
params["interpolation"],
)
def apply_to_bbox(
self,
bbox: BoxInternalType,
matrix: np.ndarray,
max_height: int,
max_width: int,
**params: Any,
) -> BoxInternalType:
return F.perspective_bbox(bbox, params["rows"], params["cols"], matrix, max_width, max_height, self.keep_size)
def apply_to_keypoint(
self,
keypoint: KeypointInternalType,
matrix: np.ndarray,
max_height: int,
max_width: int,
**params: Any,
) -> np.ndarray:
return F.perspective_keypoint(
keypoint,
params["rows"],
params["cols"],
matrix,
max_width,
max_height,
self.keep_size,
)
@property
def targets_as_params(self) -> List[str]:
return ["image"]
def get_params_dependent_on_targets(self, params: Dict[str, Any]) -> Dict[str, Any]:
height, width = params["image"].shape[:2]
scale = random_utils.uniform(*self.scale)
points = random_utils.normal(0, scale, [4, 2])
points = np.mod(np.abs(points), 0.32)
# top left -- no changes needed, just use jitter
# top right
points[1, 0] = 1.0 - points[1, 0] # w = 1.0 - jitter
# bottom right
points[2] = 1.0 - points[2] # w = 1.0 - jitt
# bottom left
points[3, 1] = 1.0 - points[3, 1] # h = 1.0 - jitter
points[:, 0] *= width
points[:, 1] *= height
# Obtain a consistent order of the points and unpack them individually.
# Warning: don't just do (tl, tr, br, bl) = _order_points(...)
# here, because the reordered points is used further below.
points = self._order_points(points)
tl, tr, br, bl = points
# compute the width of the new image, which will be the
# maximum distance between bottom-right and bottom-left
# x-coordiates or the top-right and top-left x-coordinates
min_width = None
max_width = None
while min_width is None or min_width < TWO:
width_top = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
width_bottom = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
max_width = int(max(width_top, width_bottom))
min_width = int(min(width_top, width_bottom))
if min_width < TWO:
step_size = (2 - min_width) / 2
tl[0] -= step_size
tr[0] += step_size
bl[0] -= step_size
br[0] += step_size
# compute the height of the new image, which will be the maximum distance between the top-right
# and bottom-right y-coordinates or the top-left and bottom-left y-coordinates
min_height = None
max_height = None
while min_height is None or min_height < TWO:
height_right = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
height_left = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
max_height = int(max(height_right, height_left))
min_height = int(min(height_right, height_left))
if min_height < TWO:
step_size = (2 - min_height) / 2
tl[1] -= step_size
tr[1] -= step_size
bl[1] += step_size
br[1] += step_size
# now that we have the dimensions of the new image, construct
# the set of destination points to obtain a "birds eye view",
# (i.e. top-down view) of the image, again specifying points
# in the top-left, top-right, bottom-right, and bottom-left order
# do not use width-1 or height-1 here, as for e.g. width=3, height=2
# the bottom right coordinate is at (3.0, 2.0) and not (2.0, 1.0)
dst = np.array([[0, 0], [max_width, 0], [max_width, max_height], [0, max_height]], dtype=np.float32)
# compute the perspective transform matrix and then apply it
m = cv2.getPerspectiveTransform(points, dst)
if self.fit_output:
m, max_width, max_height = self._expand_transform(m, (height, width))
return {"matrix": m, "max_height": max_height, "max_width": max_width, "interpolation": self.interpolation}
@classmethod
def _expand_transform(cls, matrix: np.ndarray, shape: SizeType) -> Tuple[np.ndarray, int, int]:
height, width = shape[:2]
# do not use width-1 or height-1 here, as for e.g. width=3, height=2, max_height
# the bottom right coordinate is at (3.0, 2.0) and not (2.0, 1.0)
rect = np.array([[0, 0], [width, 0], [width, height], [0, height]], dtype=np.float32)
dst = cv2.perspectiveTransform(np.array([rect]), matrix)[0]
# get min x, y over transformed 4 points
# then modify target points by subtracting these minima => shift to (0, 0)
dst -= dst.min(axis=0, keepdims=True)
dst = np.around(dst, decimals=0)
matrix_expanded = cv2.getPerspectiveTransform(rect, dst)
max_width, max_height = dst.max(axis=0)
return matrix_expanded, int(max_width), int(max_height)
@staticmethod
def _order_points(pts: np.ndarray) -> np.ndarray:
pts = np.array(sorted(pts, key=lambda x: x[0]))
left = pts[:2] # points with smallest x coordinate - left points
right = pts[2:] # points with greatest x coordinate - right points
if left[0][1] < left[1][1]:
tl, bl = left
else:
bl, tl = left
if right[0][1] < right[1][1]:
tr, br = right
else:
br, tr = right
return np.array([tl, tr, br, bl], dtype=np.float32)
def get_transform_init_args_names(self) -> Tuple[str, ...]:
return "scale", "keep_size", "pad_mode", "pad_val", "mask_pad_val", "fit_output", "interpolation"
targets_as_params: List[str] property readonly ¶
Targets used to get params
apply (self, img, matrix, max_height, max_width, **params) ¶
Apply transform on image.
Source code in albumentations/augmentations/geometric/transforms.py
get_params_dependent_on_targets (self, params) ¶
Returns parameters dependent on targets. Dependent target is defined in self.targets_as_params
Source code in albumentations/augmentations/geometric/transforms.py
Python
def get_params_dependent_on_targets(self, params: Dict[str, Any]) -> Dict[str, Any]:
height, width = params["image"].shape[:2]
scale = random_utils.uniform(*self.scale)
points = random_utils.normal(0, scale, [4, 2])
points = np.mod(np.abs(points), 0.32)
# top left -- no changes needed, just use jitter
# top right
points[1, 0] = 1.0 - points[1, 0] # w = 1.0 - jitter
# bottom right
points[2] = 1.0 - points[2] # w = 1.0 - jitt
# bottom left
points[3, 1] = 1.0 - points[3, 1] # h = 1.0 - jitter
points[:, 0] *= width
points[:, 1] *= height
# Obtain a consistent order of the points and unpack them individually.
# Warning: don't just do (tl, tr, br, bl) = _order_points(...)
# here, because the reordered points is used further below.
points = self._order_points(points)
tl, tr, br, bl = points
# compute the width of the new image, which will be the
# maximum distance between bottom-right and bottom-left
# x-coordiates or the top-right and top-left x-coordinates
min_width = None
max_width = None
while min_width is None or min_width < TWO:
width_top = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
width_bottom = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
max_width = int(max(width_top, width_bottom))
min_width = int(min(width_top, width_bottom))
if min_width < TWO:
step_size = (2 - min_width) / 2
tl[0] -= step_size
tr[0] += step_size
bl[0] -= step_size
br[0] += step_size
# compute the height of the new image, which will be the maximum distance between the top-right
# and bottom-right y-coordinates or the top-left and bottom-left y-coordinates
min_height = None
max_height = None
while min_height is None or min_height < TWO:
height_right = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
height_left = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
max_height = int(max(height_right, height_left))
min_height = int(min(height_right, height_left))
if min_height < TWO:
step_size = (2 - min_height) / 2
tl[1] -= step_size
tr[1] -= step_size
bl[1] += step_size
br[1] += step_size
# now that we have the dimensions of the new image, construct
# the set of destination points to obtain a "birds eye view",
# (i.e. top-down view) of the image, again specifying points
# in the top-left, top-right, bottom-right, and bottom-left order
# do not use width-1 or height-1 here, as for e.g. width=3, height=2
# the bottom right coordinate is at (3.0, 2.0) and not (2.0, 1.0)
dst = np.array([[0, 0], [max_width, 0], [max_width, max_height], [0, max_height]], dtype=np.float32)
# compute the perspective transform matrix and then apply it
m = cv2.getPerspectiveTransform(points, dst)
if self.fit_output:
m, max_width, max_height = self._expand_transform(m, (height, width))
return {"matrix": m, "max_height": max_height, "max_width": max_width, "interpolation": self.interpolation}
get_transform_init_args_names (self) ¶
Returns names of arguments that are used in init method of the transform
class PiecewiseAffine (scale=(0.03, 0.05), nb_rows=4, nb_cols=4, interpolation=1, mask_interpolation=0, cval=0, cval_mask=0, mode='constant', absolute_scale=False, always_apply=False, keypoints_threshold=0.01, p=0.5) [view source on GitHub] ¶
Apply affine transformations that differ between local neighborhoods. This augmentation places a regular grid of points on an image and randomly moves the neighborhood of these point around via affine transformations. This leads to local distortions.
This is mostly a wrapper around scikit-image's PiecewiseAffine. See also Affine for a similar technique.
Note
This augmenter is very slow. Try to use ElasticTransformation instead, which is at least 10x faster.
Note
For coordinate-based inputs (keypoints, bounding boxes, polygons, ...), this augmenter still has to perform an image-based augmentation, which will make it significantly slower and not fully correct for such inputs than other transforms.
Parameters:
| Name | Type | Description |
|---|---|---|
scale | float, tuple of float | Each point on the regular grid is moved around via a normal distribution. This scale factor is equivalent to the normal distribution's sigma. Note that the jitter (how far each point is moved in which direction) is multiplied by the height/width of the image if |
nb_rows | int, tuple of int | Number of rows of points that the regular grid should have. Must be at least |
nb_cols | int, tuple of int | Number of columns. Analogous to |
interpolation | int | The order of interpolation. The order has to be in the range 0-5: - 0: Nearest-neighbor - 1: Bi-linear (default) - 2: Bi-quadratic - 3: Bi-cubic - 4: Bi-quartic - 5: Bi-quintic |
mask_interpolation | int | same as interpolation but for mask. |
cval | number | The constant value to use when filling in newly created pixels. |
cval_mask | number | Same as cval but only for masks. |
mode | str | {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional Points outside the boundaries of the input are filled according to the given mode. Modes match the behaviour of |
absolute_scale | bool | Take |
keypoints_threshold | float | Used as threshold in conversion from distance maps to keypoints. The search for keypoints works by searching for the argmin (non-inverted) or argmax (inverted) in each channel. This parameters contains the maximum (non-inverted) or minimum (inverted) value to accept in order to view a hit as a keypoint. Use |
Targets
image, mask, keypoints, bboxes
Image types: uint8, float32
Source code in albumentations/augmentations/geometric/transforms.py
Python
class PiecewiseAffine(DualTransform):
"""Apply affine transformations that differ between local neighborhoods.
This augmentation places a regular grid of points on an image and randomly moves the neighborhood of these point
around via affine transformations. This leads to local distortions.
This is mostly a wrapper around scikit-image's ``PiecewiseAffine``.
See also ``Affine`` for a similar technique.
Note:
This augmenter is very slow. Try to use ``ElasticTransformation`` instead, which is at least 10x faster.
Note:
For coordinate-based inputs (keypoints, bounding boxes, polygons, ...),
this augmenter still has to perform an image-based augmentation,
which will make it significantly slower and not fully correct for such inputs than other transforms.
Args:
scale (float, tuple of float): Each point on the regular grid is moved around via a normal distribution.
This scale factor is equivalent to the normal distribution's sigma.
Note that the jitter (how far each point is moved in which direction) is multiplied by the height/width of
the image if ``absolute_scale=False`` (default), so this scale can be the same for different sized images.
Recommended values are in the range ``0.01`` to ``0.05`` (weak to strong augmentations).
* If a single ``float``, then that value will always be used as the scale.
* If a tuple ``(a, b)`` of ``float`` s, then a random value will
be uniformly sampled per image from the interval ``[a, b]``.
nb_rows (int, tuple of int): Number of rows of points that the regular grid should have.
Must be at least ``2``. For large images, you might want to pick a higher value than ``4``.
You might have to then adjust scale to lower values.
* If a single ``int``, then that value will always be used as the number of rows.
* If a tuple ``(a, b)``, then a value from the discrete interval
``[a..b]`` will be uniformly sampled per image.
nb_cols (int, tuple of int): Number of columns. Analogous to `nb_rows`.
interpolation (int): The order of interpolation. The order has to be in the range 0-5:
- 0: Nearest-neighbor
- 1: Bi-linear (default)
- 2: Bi-quadratic
- 3: Bi-cubic
- 4: Bi-quartic
- 5: Bi-quintic
mask_interpolation (int): same as interpolation but for mask.
cval (number): The constant value to use when filling in newly created pixels.
cval_mask (number): Same as cval but only for masks.
mode (str): {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional
Points outside the boundaries of the input are filled according
to the given mode. Modes match the behaviour of `numpy.pad`.
absolute_scale (bool): Take `scale` as an absolute value rather than a relative value.
keypoints_threshold (float): Used as threshold in conversion from distance maps to keypoints.
The search for keypoints works by searching for the
argmin (non-inverted) or argmax (inverted) in each channel. This
parameters contains the maximum (non-inverted) or minimum (inverted) value to accept in order to view a hit
as a keypoint. Use ``None`` to use no min/max. Default: 0.01
Targets:
image, mask, keypoints, bboxes
Image types:
uint8, float32
"""
_targets = (Targets.IMAGE, Targets.MASK, Targets.BBOXES, Targets.KEYPOINTS)
class InitSchema(BaseTransformInitSchema):
scale: NonNegativeFloatRangeType = (0.03, 0.05)
nb_rows: ScaleIntType = Field(default=4, description="Number of rows in the regular grid.")
nb_cols: ScaleIntType = Field(default=4, description="Number of columns in the regular grid.")
interpolation: InterpolationType = cv2.INTER_LINEAR
mask_interpolation: InterpolationType = cv2.INTER_NEAREST
cval: int = Field(default=0, description="Constant value used for newly created pixels.")
cval_mask: int = Field(default=0, description="Constant value used for newly created mask pixels.")
mode: Literal["constant", "edge", "symmetric", "reflect", "wrap"] = "constant"
absolute_scale: bool = Field(
default=False,
description="Whether scale is an absolute value rather than relative.",
)
keypoints_threshold: float = Field(
default=0.01,
description="Threshold for conversion from distance maps to keypoints.",
)
@field_validator("nb_rows", "nb_cols")
@classmethod
def process_range(cls, value: ScaleFloatType, info: ValidationInfo) -> Tuple[float, float]:
bounds = 2, BIG_INTEGER
result = to_tuple(value, value)
check_range(result, *bounds, info.field_name)
return result
def __init__(
self,
scale: ScaleFloatType = (0.03, 0.05),
nb_rows: ScaleIntType = 4,
nb_cols: ScaleIntType = 4,
interpolation: int = cv2.INTER_LINEAR,
mask_interpolation: int = cv2.INTER_NEAREST,
cval: int = 0,
cval_mask: int = 0,
mode: Literal["constant", "edge", "symmetric", "reflect", "wrap"] = "constant",
absolute_scale: bool = False,
always_apply: bool = False,
keypoints_threshold: float = 0.01,
p: float = 0.5,
):
super().__init__(always_apply, p)
warn("This augmenter is very slow. Try to use ``ElasticTransformation`` instead, which is at least 10x faster.")
self.scale = cast(Tuple[float, float], scale)
self.nb_rows = cast(Tuple[int, int], nb_rows)
self.nb_cols = cast(Tuple[int, int], nb_cols)
self.interpolation = interpolation
self.mask_interpolation = mask_interpolation
self.cval = cval
self.cval_mask = cval_mask
self.mode = mode
self.absolute_scale = absolute_scale
self.keypoints_threshold = keypoints_threshold
def get_transform_init_args_names(self) -> Tuple[str, ...]:
return (
"scale",
"nb_rows",
"nb_cols",
"interpolation",
"mask_interpolation",
"cval",
"cval_mask",
"mode",
"absolute_scale",
"keypoints_threshold",
)
@property
def targets_as_params(self) -> List[str]:
return ["image"]
def get_params_dependent_on_targets(self, params: Dict[str, Any]) -> Dict[str, Any]:
height, width = params["image"].shape[:2]
nb_rows = np.clip(random.randint(*self.nb_rows), 2, None)
nb_cols = np.clip(random.randint(*self.nb_cols), 2, None)
nb_cells = nb_cols * nb_rows
scale = random.uniform(*self.scale)
jitter: np.ndarray = random_utils.normal(0, scale, (nb_cells, 2))
if not np.any(jitter > 0):
for _ in range(10): # See: https://github.com/albumentations-team/albumentations/issues/1442
jitter = random_utils.normal(0, scale, (nb_cells, 2))
if np.any(jitter > 0):
break
if not np.any(jitter > 0):
return {"matrix": None}
y = np.linspace(0, height, nb_rows)
x = np.linspace(0, width, nb_cols)
# (H, W) and (H, W) for H=rows, W=cols
xx_src, yy_src = np.meshgrid(x, y)
# (1, HW, 2) => (HW, 2) for H=rows, W=cols
points_src = np.dstack([yy_src.flat, xx_src.flat])[0]
if self.absolute_scale:
jitter[:, 0] = jitter[:, 0] / height if height > 0 else 0.0
jitter[:, 1] = jitter[:, 1] / width if width > 0 else 0.0
jitter[:, 0] = jitter[:, 0] * height
jitter[:, 1] = jitter[:, 1] * width
points_dest = np.copy(points_src)
points_dest[:, 0] = points_dest[:, 0] + jitter[:, 0]
points_dest[:, 1] = points_dest[:, 1] + jitter[:, 1]
# Restrict all destination points to be inside the image plane.
# This is necessary, as otherwise keypoints could be augmented
# outside of the image plane and these would be replaced by
# (-1, -1), which would not conform with the behaviour of the other augmenters.
points_dest[:, 0] = np.clip(points_dest[:, 0], 0, height - 1)
points_dest[:, 1] = np.clip(points_dest[:, 1], 0, width - 1)
matrix = skimage.transform.PiecewiseAffineTransform()
matrix.estimate(points_src[:, ::-1], points_dest[:, ::-1])
return {
"matrix": matrix,
}
def apply(
self,
img: np.ndarray,
matrix: Optional[skimage.transform.PiecewiseAffineTransform] = None,
**params: Any,
) -> np.ndarray:
return F.piecewise_affine(img, matrix, cast(int, self.interpolation), self.mode, self.cval)
def apply_to_mask(
self,
mask: np.ndarray,
matrix: Optional[skimage.transform.PiecewiseAffineTransform] = None,
**params: Any,
) -> np.ndarray:
return F.piecewise_affine(mask, matrix, self.mask_interpolation, self.mode, self.cval_mask)
def apply_to_bbox(
self,
bbox: BoxInternalType,
rows: int = 0,
cols: int = 0,
matrix: Optional[skimage.transform.PiecewiseAffineTransform] = None,
**params: Any,
) -> BoxInternalType:
return F.bbox_piecewise_affine(bbox, matrix, rows, cols, self.keypoints_threshold)
def apply_to_keypoint(
self,
keypoint: KeypointInternalType,
rows: int = 0,
cols: int = 0,
matrix: Optional[skimage.transform.PiecewiseAffineTransform] = None,
**params: Any,
) -> KeypointInternalType:
return F.keypoint_piecewise_affine(keypoint, matrix, rows, cols, self.keypoints_threshold)
targets_as_params: List[str] property readonly ¶
Targets used to get params
apply (self, img, matrix=None, **params) ¶
Apply transform on image.
Source code in albumentations/augmentations/geometric/transforms.py
get_params_dependent_on_targets (self, params) ¶
Returns parameters dependent on targets. Dependent target is defined in self.targets_as_params
Source code in albumentations/augmentations/geometric/transforms.py
Python
def get_params_dependent_on_targets(self, params: Dict[str, Any]) -> Dict[str, Any]:
height, width = params["image"].shape[:2]
nb_rows = np.clip(random.randint(*self.nb_rows), 2, None)
nb_cols = np.clip(random.randint(*self.nb_cols), 2, None)
nb_cells = nb_cols * nb_rows
scale = random.uniform(*self.scale)
jitter: np.ndarray = random_utils.normal(0, scale, (nb_cells, 2))
if not np.any(jitter > 0):
for _ in range(10): # See: https://github.com/albumentations-team/albumentations/issues/1442
jitter = random_utils.normal(0, scale, (nb_cells, 2))
if np.any(jitter > 0):
break
if not np.any(jitter > 0):
return {"matrix": None}
y = np.linspace(0, height, nb_rows)
x = np.linspace(0, width, nb_cols)
# (H, W) and (H, W) for H=rows, W=cols
xx_src, yy_src = np.meshgrid(x, y)
# (1, HW, 2) => (HW, 2) for H=rows, W=cols
points_src = np.dstack([yy_src.flat, xx_src.flat])[0]
if self.absolute_scale:
jitter[:, 0] = jitter[:, 0] / height if height > 0 else 0.0
jitter[:, 1] = jitter[:, 1] / width if width > 0 else 0.0
jitter[:, 0] = jitter[:, 0] * height
jitter[:, 1] = jitter[:, 1] * width
points_dest = np.copy(points_src)
points_dest[:, 0] = points_dest[:, 0] + jitter[:, 0]
points_dest[:, 1] = points_dest[:, 1] + jitter[:, 1]
# Restrict all destination points to be inside the image plane.
# This is necessary, as otherwise keypoints could be augmented
# outside of the image plane and these would be replaced by
# (-1, -1), which would not conform with the behaviour of the other augmenters.
points_dest[:, 0] = np.clip(points_dest[:, 0], 0, height - 1)
points_dest[:, 1] = np.clip(points_dest[:, 1], 0, width - 1)
matrix = skimage.transform.PiecewiseAffineTransform()
matrix.estimate(points_src[:, ::-1], points_dest[:, ::-1])
return {
"matrix": matrix,
}
get_transform_init_args_names (self) ¶
Returns names of arguments that are used in init method of the transform
class ShiftScaleRotate (shift_limit=(-0.0625, 0.0625), scale_limit=(-0.1, 0.1), rotate_limit=(-45, 45), interpolation=1, border_mode=4, value=0, mask_value=0, shift_limit_x=None, shift_limit_y=None, rotate_method='largest_box', always_apply=False, p=0.5) [view source on GitHub] ¶
Randomly apply affine transforms: translate, scale and rotate the input.
Parameters:
| Name | Type | Description |
|---|---|---|
shift_limit | float, float) or float | shift factor range for both height and width. If shift_limit is a single float value, the range will be (-shift_limit, shift_limit). Absolute values for lower and upper bounds should lie in range [-1, 1]. Default: (-0.0625, 0.0625). |
scale_limit | float, float) or float | scaling factor range. If scale_limit is a single float value, the range will be (-scale_limit, scale_limit). Note that the scale_limit will be biased by 1. If scale_limit is a tuple, like (low, high), sampling will be done from the range (1 + low, 1 + high). Default: (-0.1, 0.1). |
rotate_limit | int, int) or int | rotation range. If rotate_limit is a single int value, the range will be (-rotate_limit, rotate_limit). Default: (-45, 45). |
interpolation | OpenCV flag | flag that is used to specify the interpolation algorithm. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_LINEAR. |
border_mode | OpenCV flag | flag that is used to specify the pixel extrapolation method. Should be one of: cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101. Default: cv2.BORDER_REFLECT_101 |
value | int, float, list of int, list of float | padding value if border_mode is cv2.BORDER_CONSTANT. |
mask_value | int, float, list of int, list of float | padding value if border_mode is cv2.BORDER_CONSTANT applied for masks. |
shift_limit_x | float, float) or float | shift factor range for width. If it is set then this value instead of shift_limit will be used for shifting width. If shift_limit_x is a single float value, the range will be (-shift_limit_x, shift_limit_x). Absolute values for lower and upper bounds should lie in the range [-1, 1]. Default: None. |
shift_limit_y | float, float) or float | shift factor range for height. If it is set then this value instead of shift_limit will be used for shifting height. If shift_limit_y is a single float value, the range will be (-shift_limit_y, shift_limit_y). Absolute values for lower and upper bounds should lie in the range [-, 1]. Default: None. |
rotate_method | str | rotation method used for the bounding boxes. Should be one of "largest_box" or "ellipse". Default: "largest_box" |
p | float | probability of applying the transform. Default: 0.5. |
Targets
image, mask, keypoints, bboxes
Image types: uint8, float32
Source code in albumentations/augmentations/geometric/transforms.py
Python
class ShiftScaleRotate(Affine):
"""Randomly apply affine transforms: translate, scale and rotate the input.
Args:
shift_limit ((float, float) or float): shift factor range for both height and width. If shift_limit
is a single float value, the range will be (-shift_limit, shift_limit). Absolute values for lower and
upper bounds should lie in range [-1, 1]. Default: (-0.0625, 0.0625).
scale_limit ((float, float) or float): scaling factor range. If scale_limit is a single float value, the
range will be (-scale_limit, scale_limit). Note that the scale_limit will be biased by 1.
If scale_limit is a tuple, like (low, high), sampling will be done from the range (1 + low, 1 + high).
Default: (-0.1, 0.1).
rotate_limit ((int, int) or int): rotation range. If rotate_limit is a single int value, the
range will be (-rotate_limit, rotate_limit). Default: (-45, 45).
interpolation (OpenCV flag): flag that is used to specify the interpolation algorithm. Should be one of:
cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4.
Default: cv2.INTER_LINEAR.
border_mode (OpenCV flag): flag that is used to specify the pixel extrapolation method. Should be one of:
cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101.
Default: cv2.BORDER_REFLECT_101
value (int, float, list of int, list of float): padding value if border_mode is cv2.BORDER_CONSTANT.
mask_value (int, float,
list of int,
list of float): padding value if border_mode is cv2.BORDER_CONSTANT applied for masks.
shift_limit_x ((float, float) or float): shift factor range for width. If it is set then this value
instead of shift_limit will be used for shifting width. If shift_limit_x is a single float value,
the range will be (-shift_limit_x, shift_limit_x). Absolute values for lower and upper bounds should lie in
the range [-1, 1]. Default: None.
shift_limit_y ((float, float) or float): shift factor range for height. If it is set then this value
instead of shift_limit will be used for shifting height. If shift_limit_y is a single float value,
the range will be (-shift_limit_y, shift_limit_y). Absolute values for lower and upper bounds should lie
in the range [-, 1]. Default: None.
rotate_method (str): rotation method used for the bounding boxes. Should be one of "largest_box" or "ellipse".
Default: "largest_box"
p (float): probability of applying the transform. Default: 0.5.
Targets:
image, mask, keypoints, bboxes
Image types:
uint8, float32
"""
_targets = (Targets.IMAGE, Targets.MASK, Targets.KEYPOINTS, Targets.BBOXES)
class InitSchema(BaseTransformInitSchema):
shift_limit: SymmetricRangeType = (-0.0625, 0.0625)
scale_limit: SymmetricRangeType = (-0.1, 0.1)
rotate_limit: SymmetricRangeType = (-45, 45)
interpolation: InterpolationType = cv2.INTER_LINEAR
border_mode: BorderModeType = cv2.BORDER_REFLECT_101
value: ColorType = 0
mask_value: ColorType = 0
shift_limit_x: Optional[ScaleFloatType] = Field(default=None)
shift_limit_y: Optional[ScaleFloatType] = Field(default=None)
rotate_method: Literal["largest_box", "ellipse"] = "largest_box"
@model_validator(mode="after")
def check_shift_limit(self) -> Self:
bounds = -1, 1
self.shift_limit_x = to_tuple(self.shift_limit_x if self.shift_limit_x is not None else self.shift_limit)
check_range(self.shift_limit_x, *bounds, "shift_limit_x")
self.shift_limit_y = to_tuple(self.shift_limit_y if self.shift_limit_y is not None else self.shift_limit)
check_range(self.shift_limit_y, *bounds, "shift_limit_y")
return self
@field_validator("scale_limit")
@classmethod
def check_scale_limit(cls, value: ScaleFloatType, info: ValidationInfo) -> ScaleFloatType:
bounds = 0, float("inf")
result = to_tuple(value, bias=1.0)
check_range(result, *bounds, str(info.field_name))
return result
def __init__(
self,
shift_limit: ScaleFloatType = (-0.0625, 0.0625),
scale_limit: ScaleFloatType = (-0.1, 0.1),
rotate_limit: ScaleFloatType = (-45, 45),
interpolation: int = cv2.INTER_LINEAR,
border_mode: int = cv2.BORDER_REFLECT_101,
value: ColorType = 0,
mask_value: ColorType = 0,
shift_limit_x: Optional[ScaleFloatType] = None,
shift_limit_y: Optional[ScaleFloatType] = None,
rotate_method: Literal["largest_box", "ellipse"] = "largest_box",
always_apply: bool = False,
p: float = 0.5,
):
super().__init__(
scale=scale_limit,
translate_percent={"x": shift_limit_x, "y": shift_limit_y},
rotate=rotate_limit,
shear=(0, 0),
interpolation=interpolation,
mask_interpolation=cv2.INTER_NEAREST,
cval=value,
cval_mask=mask_value,
mode=border_mode,
fit_output=False,
keep_ratio=False,
rotate_method=rotate_method,
always_apply=always_apply,
p=p,
)
warn(
"ShiftScaleRotate is deprecated. Please use Affine transform instead .",
DeprecationWarning,
stacklevel=2,
)
self.shift_limit_x = cast(Tuple[float, float], shift_limit_x)
self.shift_limit_y = cast(Tuple[float, float], shift_limit_y)
self.scale_limit = cast(Tuple[float, float], scale_limit)
self.rotate_limit = cast(Tuple[int, int], rotate_limit)
self.border_mode = border_mode
self.value = value
self.mask_value = mask_value
def get_transform_init_args(self) -> Dict[str, Any]:
return {
"shift_limit_x": self.shift_limit_x,
"shift_limit_y": self.shift_limit_y,
"scale_limit": to_tuple(self.scale_limit, bias=-1.0),
"rotate_limit": self.rotate_limit,
"interpolation": self.interpolation,
"border_mode": self.border_mode,
"value": self.value,
"mask_value": self.mask_value,
"rotate_method": self.rotate_method,
}
class Transpose [view source on GitHub] ¶
Transpose the input by swapping rows and columns.
Parameters:
| Name | Type | Description |
|---|---|---|
p | float | probability of applying the transform. Default: 0.5. |
Targets
image, mask, bboxes, keypoints
Image types: uint8, float32
Source code in albumentations/augmentations/geometric/transforms.py
Python
class Transpose(DualTransform):
"""Transpose the input by swapping rows and columns.
Args:
p (float): probability of applying the transform. Default: 0.5.
Targets:
image, mask, bboxes, keypoints
Image types:
uint8, float32
"""
_targets = (Targets.IMAGE, Targets.MASK, Targets.BBOXES, Targets.KEYPOINTS)
def apply(self, img: np.ndarray, **params: Any) -> np.ndarray:
return F.transpose(img)
def apply_to_bbox(self, bbox: BoxInternalType, **params: Any) -> BoxInternalType:
return F.bbox_transpose(bbox, **params)
def apply_to_keypoint(self, keypoint: KeypointInternalType, **params: Any) -> KeypointInternalType:
return F.keypoint_transpose(keypoint, **params)
def get_transform_init_args_names(self) -> Tuple[()]:
return ()
class VerticalFlip [view source on GitHub] ¶
Flip the input vertically around the x-axis.
Parameters:
| Name | Type | Description |
|---|---|---|
p | float | probability of applying the transform. Default: 0.5. |
Targets
image, mask, bboxes, keypoints
Image types: uint8, float32
Source code in albumentations/augmentations/geometric/transforms.py
Python
class VerticalFlip(DualTransform):
"""Flip the input vertically around the x-axis.
Args:
p (float): probability of applying the transform. Default: 0.5.
Targets:
image, mask, bboxes, keypoints
Image types:
uint8, float32
"""
_targets = (Targets.IMAGE, Targets.MASK, Targets.BBOXES, Targets.KEYPOINTS)
def apply(self, img: np.ndarray, **params: Any) -> np.ndarray:
return F.vflip(img)
def apply_to_bbox(self, bbox: BoxInternalType, **params: Any) -> BoxInternalType:
return F.bbox_vflip(bbox, **params)
def apply_to_keypoint(self, keypoint: KeypointInternalType, **params: Any) -> KeypointInternalType:
return F.keypoint_vflip(keypoint, **params)
def get_transform_init_args_names(self) -> Tuple[()]:
return ()