Transforms Module

class bio_volumentations.augmentations.transforms.AffineTransform(angles: Tuple[float, float, float] = (0, 0, 0), translation: Tuple[float, float, float] = (0, 0, 0), scale: Tuple[float, float, float] = (1, 1, 1), spacing: Tuple[float, float, float] = (1, 1, 1), change_to_isotropic: bool = False, interpolation: int = 1, border_mode: str = 'constant', ival: float = 0, mval: float = 0, ignore_index: float | None = None, always_apply: bool = False, p: float = 0.5)

Bases: DualTransform

Affine transformation of the input image with given parameters.

Parameters:

• angles (Tuple[float], optional) –

  Angles of rotation for the spatial axes.

  Must be: (A_Z, A_Y, A_X).

  Defaults to (0, 0, 0).

• translation (Tuple[float], optional) –

  Translation vector for the spatial axes.

  Must be: (T_Z, T_Y, T_X).

  Defaults to (0, 0, 0).

• scale (Tuple[float], optional) –

  Scales for the spatial axes.

  Must be: (S_Z, S_Y, S_X).

  Defaults to (1, 1, 1).

• spacing (Tuple[float, float, float], optional) –

  Voxel spacing for individual spatial dimensions.

  Must be: (S1, S2, S3) (a scale for each spatial dimension must be given).

  Defaults to (1, 1, 1).

• change_to_isotropic (bool, optional) –

  Change data from anisotropic to isotropic.

  Defaults to False.

• interpolation (int, optional) –

  Order of spline interpolation.

  Defaults to 1.

• border_mode (str, optional) –

  Values outside image domain are filled according to this mode.

  Defaults to 'constant'.

• ival (float, optional) –

  Value of image voxels outside of the image domain. Only applied when border_mode = 'constant'.

  Defaults to 0.

• mval (float, optional) –

  Value of mask voxels outside of the mask domain. Only applied when border_mode = 'constant'.

  Defaults to 0.

• ignore_index (float | None, optional) –

  If a float, then transformation of mask is done with border_mode = 'constant' and mval = ignore_index.

  If None, this argument is ignored.

  Defaults to None.

• always_apply (bool, optional) –

  Always apply this transformation in composition.

  Defaults to False.

• p (float, optional) –

  Chance of applying this transformation in composition.

  Defaults to 0.5.

Targets:

image, mask, float_mask

apply(img, **params)

apply_to_float_mask(mask, **params)

apply_to_mask(mask, **params)

class bio_volumentations.augmentations.transforms.CenterCrop(shape: Tuple[int], border_mode: str = 'reflect', ival: Sequence[float] | float = (0, 0), mval: Sequence[float] | float = (0, 0), ignore_index: float | None = None, always_apply: bool = False, p: float = 1.0)

Bases: DualTransform

Crops the central region of the input of given size.

Unlike CenterCrop from Albumentations, this transform pads the input in dimensions where the input is smaller than the shape with numpy.pad. The border_mode, ival and mval arguments are forwarded to numpy.pad if padding is necessary. More details at: https://numpy.org/doc/stable/reference/generated/numpy.pad.html.

Parameters:

• shape (Tuple[int]) –

  The desired shape of input.

  Must be either of: [Z, Y, X] or [Z, Y, X, T].

• border_mode (str, optional) –

  Values outside image domain are filled according to this mode.

  Defaults to 'reflect'.

• ival (float | Sequence, optional) –

  Values of image voxels outside of the image domain. Only applied when border_mode = 'constant' or border_mode = 'linear_ramp'.

  Defaults to (0, 0).

• mval (float | Sequence, optional) –

  Values of mask voxels outside of the mask domain. Only applied when border_mode = 'constant' or border_mode = 'linear_ramp'.

  Defaults to (0, 0).

• ignore_index (float | None, optional) –

  If a float, then transformation of mask is done with border_mode = 'constant' and mval = ignore_index.

  If None, this argument is ignored.

  Defaults to None.

• always_apply (bool, optional) –

  Always apply this transformation in composition.

  Defaults to False.

• p (float, optional) –

  Chance of applying this transformation in composition.

  Defaults to 1.

Targets:

image, mask, float_mask

apply(img, **params)

apply_to_mask(mask, **params)

class bio_volumentations.augmentations.transforms.Contiguous(always_apply: bool = True, p: float = 1.0)

Bases: DualTransform

Transform the image data to a contiguous array.

Parameters:

• always_apply (bool, optional) –

  Always apply this transformation in composition.

  Defaults to True.

• p (float, optional) –

  Chance of applying this transformation in composition.

  Defaults to 1.

Targets:

image, mask, float_mask

apply(image, **params)

apply_to_mask(mask, **params)

class bio_volumentations.augmentations.transforms.Flip(axes: List[int] | None = None, always_apply=False, p=1)

Bases: DualTransform

Flip input around the specified spatial axes.

Parameters:

• axes (List[int], optional) –

  List of axes around which is flip done. Recognised axis symbols are 1 for Z, 2 for Y, and 3 for X.

  Defaults to [1,2,3].

• always_apply (bool, optional) –

  Always apply this transformation in composition.

  Defaults to False.

• p (float, optional) –

  Chance of applying this transformation in composition.

  Defaults to 1.

Targets:

image, mask, float_mask

apply(img, **params)

apply_to_mask(mask, **params)

get_params(**data)

class bio_volumentations.augmentations.transforms.Float(always_apply: bool = True, p: float = 1.0)

Bases: DualTransform

Change datatype to np.float32 without changing intensities.

Parameters:

• always_apply (bool, optional) –

  Always apply this transformation in composition.

  Defaults to True.

• p (float, optional) –

  Chance of applying this transformation in composition.

  Defaults to 1.

Targets:

image, mask, float_mask

apply(image, **params)

apply_to_mask(mask, **params)

class bio_volumentations.augmentations.transforms.GaussianBlur(sigma: float | Tuple[float] | List[Tuple[float] | float] = 0.8, border_mode: str = 'reflect', cval: float = 0, always_apply: bool = False, p: float = 0.5)

Bases: ImageOnlyTransform

Performs Gaussian blurring of the image. In case of a multi-channel image, individual channels are blured separately.

Internally, the scipy.ndimage.gaussian_filter function is used. The border_mode and cval arguments are forwarded to it. More details at: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.gaussian_filter.html.

Parameters:

• sigma (float, Tuple(float), List[Tuple(float) | float] , optional) –

  Gaussian sigma.

  Must be either of: S, (S_Z, S_Y, S_X), (S_Z, S_Y, S_X, S_T), [S_1, S_2, ..., S_C], [(S_Z1, S_Y1, S_X1), (S_Z2, S_Y2, S_X2), ..., (S_ZC, S_YC, S_XC)], or [(S_Z1, S_Y1, S_X1, S_T1), (S_Z2, S_Y2, S_X2, S_T2), ..., (S_ZC, S_YC, S_XC, S_TC)].

  If a float, the spatial dimensions are blurred with the same strength (equivalent to (S, S, S)).

  If a tuple, the sigmas for spatial dimensions and possibly the time dimension must be specified.

  If a list, sigmas for each channel must be specified either as a single number or as a tuple.

  Defaults to 0.8.

• border_mode (str, optional) –

  Values outside image domain are filled according to this mode.

  Defaults to 'reflect'.

• cval (float, optional) –

  Value to fill past edges of image. Only applied when border_mode = 'constant'.

  Defaults to 0.

• always_apply (bool, optional) –

  Always apply this transformation in composition.

  Defaults to False.

• p (float, optional) –

  Chance of applying this transformation in composition.

  Defaults to 0.5.

Targets:

image

apply(img, **params)

class bio_volumentations.augmentations.transforms.GaussianNoise(var_limit: tuple = (0.001, 0.1), mean: float = 0, always_apply: bool = False, p: float = 0.5)

Bases: ImageOnlyTransform

Adds Gaussian noise to the image. The noise is drawn from normal distribution with given parameters.

Parameters:

• var_limit (tuple, optional) –

  Variance of normal distribution is randomly chosen from this interval.

  Defaults to (0.001, 0.1).

• mean (float, optional) –

  Mean of normal distribution.

  Defaults to 0.

• always_apply (bool, optional) –

  Always apply this transformation in composition.

  Defaults to False.

• p (float, optional) –

  Chance of applying this transformation in composition.

  Defaults to 0.5.

Targets:

image

apply(img, **params)

get_params(**params)

class bio_volumentations.augmentations.transforms.HistogramEqualization(bins: int = 256, always_apply: bool = False, p: float = 1)

Bases: ImageOnlyTransform

Performs equalization of histogram. The equalization is done channel-wise, meaning that each channel is equalized separately.

Warning! Images are normalized over both spatial and temporal domains together. The output is in the range [0, 1].

Parameters:

• bins (int, optional) –

  Number of bins for image histogram.

  Defaults to 256.

• always_apply (bool, optional) –

  Always apply this transformation in composition.

  Defaults to False.

• p (float, optional) –

  Chance of applying this transformation in composition.

  Defaults to 1.

Targets:

image

apply(img, **params)

class bio_volumentations.augmentations.transforms.Normalize(mean: float | List[float] = 0, std: float | List[float] = 1, always_apply: bool = True, p: float = 1.0)

Bases: ImageOnlyTransform

Change image mean and standard deviation to the given values (channel-wise).

Parameters:

• mean (float | List[float], optional) –

  The desired channel-wise means.

  Must be either of: M, [M_1, M_2, ..., M_C].

  Defaults to 0.

• std (float | List[float], optional) –

  The desired channel-wise standard deviations.

  Must be either of: S, [S_1, S_2, ..., S_C].

  Defaults to 1.

• always_apply (bool, optional) –

  Always apply this transformation in composition.

  Defaults to True.

• p (float, optional) –

  Chance of applying this transformation in composition.

  Defaults to 1.

Targets:

image

apply(img, **params)

class bio_volumentations.augmentations.transforms.NormalizeMeanStd(mean: List[float] | float, std: List[float] | float, always_apply: bool = True, p: float = 1.0)

Bases: ImageOnlyTransform

Normalize image values to have mean 0 and standard deviation 1, given channel-wise means and standard deviations.

For a single-channel image, the normalization is applied by the formula: \(img = (img - mean) / std\). If the image contains more channels, then the previous formula is used for each channel separately.

It is recommended to input dataset-wide means and standard deviations.

Parameters:

• mean (float | List[float]) –

  Channel-wise image mean.

  Must be either of: M, (M_1, M_2, ..., M_C).

• std (float | List[float]) –

  Channel-wise image standard deviation.

  Must be either of: S, (S_1, S_2, ..., S_C).

• always_apply (bool, optional) –

  Always apply this transformation in composition.

  Defaults to True.

• p (float, optional) –

  Chance of applying this transformation in composition.

  Defaults to 1.

Targets:

image

apply(image, **params)

class bio_volumentations.augmentations.transforms.Pad(pad_size: int | Tuple[int] | List[int | Tuple[int]], border_mode: str = 'constant', ival: float | Sequence = 0, mval: float | Sequence = 0, ignore_index: float | None = None, always_apply: bool = True, p: float = 1)

Bases: DualTransform

Pads the input.

Parameters:

• pad_size (int | Tuple[int] | List[int | Tuple[int]]) –

  Number of pixels padded to the edges of each axis.

  Must be either of: P, (P1, P2), [P_Z, P_Y, P_X], [P_Z, P_Y, P_X, P_T], [(P_Z1, P_Z2), (P_Y1, P_Y2), (P_X1, P_X2)], or [(P_Z1, P_Z2), (P_Y1, P_Y2), (P_X1, P_X2), (P_T1, P_T2)].

  If an integer, it is equivalent to [(P, P), (P, P), (P, P)].

  If a tuple, it is equivalent to [(P1, P2), (P1, P2), (P1, P2)].

  If a list, it must specify padding for all spatial dimensions and possibly also for the time dimension.

  The unspecified dimensions (C and possibly T) are not affected.

• border_mode (str, optional) –

  Values outside image domain are filled according to this mode.

  Defaults to 'constant'.

• ival (float | Sequence, optional) –

  Values of image voxels outside of the image domain. Only applied when border_mode = 'constant' or border_mode = 'linear_ramp'.

  Defaults to 0.

• mval (float | Sequence, optional) –

  Values of mask voxels outside of the mask domain. Only applied when border_mode = 'constant' or border_mode = 'linear_ramp'.

  Defaults to 0.

• ignore_index (float | None, optional) –

  If a float, then transformation of mask is done with border_mode = 'constant' and mval = ignore_index.

  If None, this argument is ignored.

  Defaults to None.

• always_apply (bool, optional) –

  Always apply this transformation in composition.

  Defaults to True.

• p (float, optional) –

  Chance of applying this transformation in composition.

  Defaults to 1.

Targets:

image, mask, float_mask

apply(img, **params)

apply_to_mask(mask, **params)

class bio_volumentations.augmentations.transforms.PoissonNoise(intensity_limit=(1, 10), always_apply: bool = False, p: float = 0.5)

Bases: ImageOnlyTransform

Adds Poisson noise to the image.

Parameters:

• intensity_limit (tuple) –

  Range to sample the expected intensity of Poisson noise.

  Defaults to (1, 10).

• always_apply (bool, optional) –

  Always apply this transformation in composition.

  Defaults to False.

• p (float, optional) –

  Chance of applying this transformation in composition.

  Defaults to 0.5.

Targets:

image

apply(img, **params)

get_params(**params)

class bio_volumentations.augmentations.transforms.RandomAffineTransform(angle_limit: float | Tuple[float, float] | Tuple[float, float, float, float, float, float] = (15, 15, 15), translation_limit: float | Tuple[float, float] | Tuple[float, float, float, float, float, float] = (0, 0, 0), scaling_limit: float | Tuple[float, float] | Tuple[float, float, float, float, float, float] = (0.2, 0.2, 0.2), spacing: float | Tuple[float, float, float] | None = None, change_to_isotropic: bool = False, interpolation: int = 1, border_mode: str = 'constant', ival: float = 0, mval: float = 0, ignore_index: float | None = None, always_apply: bool = False, p: float = 0.5)

Bases: DualTransform

Affine transformation of the input image with randomly chosen parameters.

Parameters:

• angle_limit (Tuple[float] | float, optional) –

  Intervals in degrees from which angles of rotation for the spatial axes are chosen.

  Must be either of: A, (A1, A2), or (A_Z1, A_Z2, A_Y1, A_Y2, A_X1, A_X2).

  If a float, equivalent to (-A, A, -A, A, -A, A).

  If a tuple with 2 items, equivalent to (A1, A2, A1, A2, A1, A2).

  If a tuple with 6 items, angle of rotation is randomly chosen from an interval [A_a1, A_a2] for each spatial axis.

  Defaults to (15, 15, 15).

• translation_limit (Tuple[int] | int | None, optional) –

  Intervals from which the translation parameters for the spatial axes are chosen.

  Must be either of: T, (T1, T2), or (T_Z1, T_Z2, T_Y1, T_Y2, T_X1, T_X2).

  If a float, equivalent to (-T, T, -T, T, -T, T).

  If a tuple with 2 items, equivalent to (T1, T2, T1, T2, T1, T2).

  If a tuple with 6 items, the translation parameter is randomly chosen from an interval [T_a1, T_a2] for each spatial axis.

  Defaults to (0, 0, 0).

• scaling_limit (Tuple[float] | float, optional) –

  Intervals from which the scales for the spatial axes are chosen.

  Must be either of: S, (S1, S2), or (S_Z1, S_Z2, S_Y1, S_Y2, S_X1, S_X2).

  If a float, equivalent to (1-S, 1+S, 1-S, 1+S, 1-S, 1+S).

  If a tuple with 2 items, equivalent to (S1, S2, S1, S2, S1, S2).

  If a tuple with 6 items, the scale is randomly chosen from an interval [S_a1, S_a2] for each spatial axis.

  Defaults to (0.2, 0.2, 0.2).

• spacing (float | Tuple[float, float, float] | None, optional) –

  Voxel spacing for individual spatial dimensions.

  Must be either of: S, (S1, S2, S3), or None.

  If None, equivalent to (1, 1, 1).

  If a float S, equivalent to (S, S, S).

  Otherwise, a scale for each spatial dimension must be given.

  Defaults to None.

• change_to_isotropic (bool, optional) –

  Change data from anisotropic to isotropic.

  Defaults to False.

• interpolation (int, optional) –

  Order of spline interpolation.

  Defaults to 1.

• border_mode (str, optional) –

  Values outside image domain are filled according to this mode.

  Defaults to 'constant'.

• ival (float, optional) –

  Value of image voxels outside of the image domain. Only applied when border_mode = 'constant'.

  Defaults to 0.

• mval (float, optional) –

  Value of mask voxels outside of the mask domain. Only applied when border_mode = 'constant'.

  Defaults to 0.

• ignore_index (float | None, optional) –

  If a float, then transformation of mask is done with border_mode = 'constant' and mval = ignore_index.

  If None, this argument is ignored.

  Defaults to None.

• always_apply (bool, optional) –

  Always apply this transformation in composition.

  Defaults to False.

• p (float, optional) –

  Chance of applying this transformation in composition.

  Defaults to 0.5.

Targets:

image, mask, float_mask

apply(img, **params)

apply_to_float_mask(mask, **params)

apply_to_mask(mask, **params)

get_params(**data)

class bio_volumentations.augmentations.transforms.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, always_apply=False, p=0.5)

Bases: ImageOnlyTransform

Randomly change brightness and contrast of the input image.

Unlike RandomBrightnessContrast from Albumentations, this transform is using the formula \(f(a) = (c+1) * a + b\), where \(c\) is contrast and \(b\) is brightness.

Parameters:

• brightness_limit ((float, float) | float, optional) –

  Interval from which the change in brightness is randomly drawn. If the change in brightness is 0, the brightness will not change.

  Must be either of: B, (B1, B2).

  If a float, the interval will be (-B, B).

  Defaults to 0.2.

• contrast_limit ((float, float) | float, optional) –

  Interval from which the change in contrast is randomly drawn. If the change in contrast is 1, the contrast will not change.

  Must be either of: C, (C1, C2).

  If a float, the interval will be (-C, C).

  Defaults to 0.2.

• always_apply (bool, optional) –

  Always apply this transformation in composition.

  Defaults to False.

• p (float, optional) –

  Chance of applying this transformation in composition.

  Defaults to 0.5.

Targets:

image

apply(img, **params)

get_params(**data)

class bio_volumentations.augmentations.transforms.RandomCrop(shape: tuple, border_mode: str = 'reflect', ival: Sequence[float] | float = (0, 0), mval: Sequence[float] | float = (0, 0), ignore_index: float | None = None, always_apply: bool = False, p: float = 1.0)

Bases: DualTransform

Randomly crops a region of given size from the input.

Unlike RandomCrop from Albumentations, this transform pads the input in dimensions where the input is smaller than the shape with numpy.pad. The border_mode, ival and mval arguments are forwarded to numpy.pad if padding is necessary. More details at: https://numpy.org/doc/stable/reference/generated/numpy.pad.html.

Parameters:

• shape (Tuple[int]) –

  The desired shape of input.

  Must be either of: [Z, Y, X] or [Z, Y, X, T].

• border_mode (str, optional) –

  Values outside image domain are filled according to this mode.

  Defaults to 'reflect'.

• ival (float | Sequence, optional) –

  Values of image voxels outside of the image domain. Only applied when border_mode = 'constant' or border_mode = 'linear_ramp'.

  Defaults to (0, 0).

• mval (float | Sequence, optional) –

  Values of mask voxels outside of the mask domain. Only applied when border_mode = 'constant' or border_mode = 'linear_ramp'.

  Defaults to (0, 0).

• ignore_index (float | None, optional) –

  If a float, then transformation of mask is done with border_mode = 'constant' and mval = ignore_index.

  If None, this argument is ignored.

  Defaults to None.

• always_apply (bool, optional) –

  Always apply this transformation in composition.

  Defaults to False.

• p (float, optional) –

  Chance of applying this transformation in composition.

  Defaults to 1.

Targets:

image, mask, float_mask

apply(img, crop_start=array([0, 0, 0]))

apply_to_mask(mask, crop_start=array([0, 0, 0]))

get_params(**data)

class bio_volumentations.augmentations.transforms.RandomFlip(axes_to_choose: None | List[Tuple[int]] = None, always_apply=False, p=0.5)

Bases: DualTransform

Flip input around a set of axes randomly chosen from the input list of axis combinations.

Parameters:

• axes_to_choose (List[Tuple[int]] or None, optional) –

  List of axis combinations from which one option is randomly chosen. Recognised axis symbols are 1 for Z, 2 for Y, and 3 for X. The image will be flipped around all axes in the chosen combination.

  If None, a random subset of spatial axes is chosen, corresponding to inputting [(1,), (2,), (3,), (1, 2), (1, 3), (2, 3), (1, 2, 3)].

  Defaults to None.

• always_apply (bool, optional) –

  Always apply this transformation in composition.

  Defaults to False.

• p (float, optional) –

  Chance of applying this transformation in composition.

  Defaults to 0.5.

Targets:

image, mask, float_mask

apply(img, **params)

apply_to_mask(mask, **params)

get_params(**data)

class bio_volumentations.augmentations.transforms.RandomGamma(gamma_limit: Tuple[float] = (0.8, 1.2), always_apply: bool = False, p: float = 0.5)

Bases: ImageOnlyTransform

Performs the gamma transformation with a randomly chosen gamma. If image values (in any channel) are outside the [0,1] interval, this transformation is not performed.

Parameters:

• gamma_limit (Tuple(float), optional) –

  Interval from which gamma is selected.

  Defaults to (0.8, 1.2).

• always_apply (bool, optional) –

  Always apply this transformation in composition.

  Defaults to False.

• p (float, optional) –

  Chance of applying this transformation in composition.

  Defaults to 0.5.

Targets:

image

apply(img, gamma=1, **params)

get_params(**data)

class bio_volumentations.augmentations.transforms.RandomGaussianBlur(max_sigma: float | Tuple[float, float, float] = 0.8, min_sigma: float = 0, border_mode: str = 'reflect', cval: float = 0, always_apply: bool = False, p: float = 0.5)

Bases: ImageOnlyTransform

Performs Gaussian blur on the image with a random strength blurring. In case of a multi-channel image, individual channels are blured separately.

Behaves similarly to GaussianBlur. The Gaussian sigma is randomly drawn from the interval [min_sigma, s] for the respective s from max_sigma for each channel and dimension.

Internally, the scipy.ndimage.gaussian_filter function is used. The border_mode and cval arguments are forwarded to it. More details at: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.gaussian_filter.html.

Parameters:

• max_sigma (float, Tuple(float), List[Tuple(float) | float] , optional) –

  Maximum Gaussian sigma.

  Must be either of: S, (S_Z, S_Y, S_X), (S_Z, S_Y, S_X, S_T), [S_1, S_2, ..., S_C], [(S_Z1, S_Y1, S_X1), (S_Z2, S_Y2, S_X2), ..., (S_ZC, S_YC, S_XC)], or [(S_Z1, S_Y1, S_X1, S_T1), (S_Z2, S_Y2, S_X2, S_T2), ..., (S_ZC, S_YC, S_XC, S_TC)].

  If a float, the spatial dimensions are blurred equivalently (equivalent to (S, S, S)).

  If a tuple, the sigmas for spatial dimensions and possibly the time dimension must be specified.

  If a list, sigmas for each channel must be specified either as a single number or as a tuple.

  Defaults to 0.8.

• min_sigma (float, optional) –

  Minimum Gaussian sigma for all channels and dimensions.

  Defaults to 0.

• border_mode (str, optional) –

  Values outside image domain are filled according to this mode.

  Defaults to 'reflect'.

• cval (float, optional) –

  Value to fill past edges of image. Only applied when border_mode = 'constant'.

  Defaults to 0.

• always_apply (bool, optional) –

  Always apply this transformation in composition.

  Defaults to False.

• p (float, optional) –

  Chance of applying this transformation in composition.

  Defaults to 0.5.

Targets:

image

apply(img, **params)

get_params(**data)

class bio_volumentations.augmentations.transforms.RandomRotate90(axes: List[int] | None = None, shuffle_axis: bool = False, always_apply: bool = False, p: float = 0.5)

Bases: DualTransform

Rotation of input by 0, 90, 180, or 270 degrees around the specified spatial axes.

Parameters:

• axes (List[int], optional) –

  List of axes around which the input is rotated. Recognised axis symbols are 1 for Z, 2 for Y, and 3 for X. A single axis can occur multiple times in the list. If shuffle_axis = False, the order of axes determines the order of transformations.

  Defaults to [1, 2, 3].

• shuffle_axis (bool, optional) –

  If set to True, the order of rotations is random.

  Defaults to False.

• always_apply (bool, optional) –

  Always apply this transformation in composition.

  Defaults to False.

• p (float, optional) –

  Chance of applying this transformation in composition.

  Defaults to 0.5.

Targets:

image, mask, float_mask

apply(img, **params)

apply_to_mask(mask, **params)

get_params(**data)

class bio_volumentations.augmentations.transforms.RandomScale(scaling_limit: float | Tuple[float, float] | Tuple[float, float, float] | Tuple[float, float, float, float, float, float] = (0.9, 1.1), interpolation: int = 1, spacing: float | Tuple[float, float, float] | None = None, border_mode: str = 'constant', ival: float = 0, mval: float = 0, ignore_index: float | None = None, always_apply: bool = False, p: float = 0.5)

Bases: DualTransform

Randomly rescale input.

Parameters:

• scaling_limit (float | Tuple[float] | List[Tuple[float]], optional) –

  Limits of scaling factors.

  Must be either of: S, (S1, S2), (S_Z, S_Y, S_X), or (S_Z1, S_Z2, S_Y1, S_Y2, S_X1, S_X2).

  If a float S, then all spatial dimensions are scaled by a random number drawn uniformly from the interval [1-S, 1+S] (equivalent to inputting (1-S, 1+S, 1-S, 1+S, 1-S, 1+S)).

  If a tuple of 2 floats, then all spatial dimensions are scaled by a random number drawn uniformly from the interval [S1, S2] (equivalent to inputting (S1, S2, S1, S2, S1, S2)).

  If a tuple of 3 floats, then an interval [1-S_a, 1+S_a] is constructed for each spatial dimension and the scale is randomly drawn from it (equivalent to inputting (1-S_Z, 1+S_Z, 1-S_Y, 1+S_Y, 1-S_X, 1+S_X)).

  If a tuple of 6 floats, the scales for individual spatial dimensions are randomly drawn from the respective intervals [S_Z1, S_Z2], [S_Y1, S_Y2], [S_X1, S_X2].

  The unspecified dimensions (C and T) are not affected.

  Defaults to (0.9, 1.1).

• interpolation (int, optional) –

  Order of spline interpolation.

  Defaults to 1.

• spacing (float | Tuple[float, float, float] | None, optional) –

  Voxel spacing for individual spatial dimensions.

  Must be either of: S, (S1, S2, S3), or None.

  If None, equivalent to (1, 1, 1).

  If a float S, equivalent to (S, S, S).

  Otherwise, a scale for each spatial dimension must be given.

  Defaults to None.

• border_mode (str, optional) –

  Values outside image domain are filled according to the mode.

  Defaults to 'constant'.

• ival (float, optional) –

  Value of image voxels outside of the image domain. Only applied when border_mode = 'constant'.

  Defaults to 0.

• mval (float, optional) –

  Value of mask voxels outside of the mask domain. Only applied when border_mode = 'constant'.

  Defaults to 0.

• ignore_index (float | None, optional) –

  If a float, then transformation of mask is done with border_mode = 'constant' and mval = ignore_index.

  If None, this argument is ignored.

  Defaults to None.

• always_apply (bool, optional) –

  Always apply this transformation in composition.

  Defaults to False.

• p (float, optional) –

  Chance of applying this transformation in composition.

  Defaults to 0.5.

Targets:

image, mask, float_mask

apply(img, **params)

apply_to_float_mask(mask, **params)

apply_to_mask(mask, **params)

get_params(**data)

class bio_volumentations.augmentations.transforms.Resize(shape: tuple, interpolation: int = 1, border_mode: str = 'reflect', ival: float = 0, mval: float = 0, anti_aliasing_downsample: bool = True, ignore_index: float | None = None, always_apply: bool = False, p: float = 1)

Bases: DualTransform

Resize input to the given shape.

Internally, the skimage.transform.resize function is used. The interpolation, border_mode, ival, mval, and anti_aliasing_downsample arguments are forwarded to it. More details at: https://scikit-image.org/docs/stable/api/skimage.transform.html#skimage.transform.resize.

Parameters:

• shape (tuple of ints) –

  The desired image shape.

  Must be of either of: (Z, Y, X) or (Z, Y, X, T).

  The unspecified dimensions (C and possibly T) are not affected.

• interpolation (int, optional) –

  Order of spline interpolation.

  Defaults to 1.

• border_mode (str, optional) –

  Values outside image domain are filled according to this mode.

  Defaults to 'reflect'.

• ival (float, optional) –

  Value of image voxels outside of the image domain. Only applied when border_mode = 'constant'.

  Defaults to 0.

• mval (float, optional) –

  Value of mask voxels outside of the mask domain. Only applied when border_mode = 'constant'.

  Defaults to 0.

• anti_aliasing_downsample (bool, optional) –

  Controls if the Gaussian filter should be applied before downsampling. Recommended.

  Defaults to True.

• ignore_index (float | None, optional) –

  If a float, then transformation of mask is done with border_mode = 'constant' and mval = ignore_index.

  If None, this argument is ignored.

  Defaults to None.

• always_apply (bool, optional) –

  Always apply this transformation in composition.

  Defaults to False.

• p (float, optional) –

  Chance of applying this transformation in composition.

  Defaults to 1.

Targets:

image, mask, float_mask

apply(img, **params)

apply_to_float_mask(mask, **params)

apply_to_mask(mask, **params)

class bio_volumentations.augmentations.transforms.Scale(scales: float | Tuple[float, float, float] = 1, interpolation: int = 1, spacing: float | Tuple[float, float, float] | None = None, border_mode: str = 'constant', ival: float = 0, mval: float = 0, ignore_index: float | None = None, always_apply: bool = False, p: float = 1)

Bases: DualTransform

Rescale input by the given scale.

Parameters:

• scales (float|List[float], optional) –

  Value by which the input should be scaled.

  Must be either of: S, [S_Z, S_Y, S_X], or [S_Z, S_Y, S_X, S_T].

  If a float, then all spatial dimensions are scaled by it (equivalent to [S, S, S]).

  The unspecified dimensions (C and possibly T) are not affected.

  Defaults to 1.

• interpolation (int, optional) –

  Order of spline interpolation.

  Defaults to 1.

• spacing (float | Tuple[float, float, float] | None, optional) –

  Voxel spacing for individual spatial dimensions.

  Must be either of: S, (S1, S2, S3), or None.

  If None, equivalent to (1, 1, 1).

  If a float S, equivalent to (S, S, S).

  Otherwise, a scale for each spatial dimension must be given.

  Defaults to None.

• border_mode (str, optional) –

  Values outside image domain are filled according to this mode.

  Defaults to 'constant'.

• ival (float, optional) –

  Value of image voxels outside of the image domain. Only applied when border_mode = 'constant'.

  Defaults to 0.

• mval (float, optional) –

  Value of mask voxels outside of the mask domain. Only applied when border_mode = 'constant'.

  Defaults to 0.

• ignore_index (float | None, optional) –

  If a float, then transformation of mask is done with border_mode = 'constant' and mval = ignore_index.

  If None, this argument is ignored.

  Defaults to None.

• always_apply (bool, optional) –

  Always apply this transformation in composition.

  Defaults to False.

• p (float, optional) –

  Chance of applying this transformation in composition.

  Defaults to 1.

Targets:

image, mask, float_mask

apply(img, **params)

apply_to_float_mask(mask, **params)

apply_to_mask(mask, **params)