Blur

Transforms

class Blur(blur_limit: int | Tuple[int, int] = 7, by_slice: bool = False, mode: str = 'constant', cval: float | int = 0, always_apply: bool = False, p: float = 0.5)

Bases: ImageOnlyTransform

Blur the input image using a random-sized kernel.

Parameters:

• blur_limit (int, (int, int)) – maximum kernel size for blurring the input image. Should be in range [3, inf). Default: (3, 7).

• by_slice (bool) – Whether the kernel should be applied by slice or the image as a whole. If true, a 2D kernel is convolved along each slice of the image. Otherwise, a 3D kernel is used. Default: False

• mode (str) –

  scipy parameter to determine how the input image is extended during convolution to maintain image shape. Must be one of the following:

  • reflect (d c b a | a b c d | d c b a): The input is extended by reflecting about the edge of the last pixel. This mode is also sometimes referred to as half-sample symmetric.

  • constant (k k k k | a b c d | k k k k): The input is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.

  • nearest (a a a a | a b c d | d d d d): The input is extended by replicating the last pixel.

  • mirror (d c b | a b c d | c b a): The input is extended by reflecting about the center of the last pixel. This mode is also sometimes referred to as whole-sample symmetric.

  • wrap (a b c d | a b c d | a b c d): The input is extended by wrapping around to the opposite edge.

  Reference: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.median_filter.html

  Default: constant

• cval (int,float) – The fill value when mode = constant. Default: 0

• always_apply (bool) – whether to always apply the transformation. Default: False

• p (float) – probability of applying the transform. Default: 0.5.

Targets:

image

Image types:

uint8, uint16, float32

apply(img: ndarray, ksize: int = 3, **params) → ndarray

Applies the transformation to the image

get_params() → Dict[str, Any]

Returns parameters needed for the apply methods

get_transform_init_args_names() → Tuple[str, ...]

Returns initialization argument names. (e.g. Transform(arg1 = 1, arg2 = 2) -> (‘arg1’, ‘arg2’))

class GaussianBlur(blur_limit: int | Tuple[int, int] = (3, 7), sigma_limit: float | Tuple[float, float] = 0, by_slice: bool = False, always_apply: bool = False, p: float = 0.5, mode: str = 'constant', cval: float | int = 0)

Bases: ImageOnlyTransform

Blur the input image using a Gaussian filter with a random kernel size.

Parameters:

• blur_limit (int, (int, int)) – maximum Gaussian kernel size for blurring the input image. Must be zero or odd and in range [0, inf). If set to 0 it will be computed from sigma as round(sigma * 4 * 2) + 1. If set single value blur_limit will be in range (0, blur_limit). Default: (3, 7).

• sigma_limit (float, (float, float)) – Gaussian kernel standard deviation. Must be in range [0, inf). If set single value sigma_limit will be in range (0, sigma_limit). If set to 0 sigma will be computed as sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8. Default: 0.

• by_slice (bool) – Whether the kernel should be applied by slice or the image as a whole. If true, a 2D kernel is convolved along each slice of the image. Otherwise, a 3D kernel is used. Default: False

• mode (str) –

  scipy parameter to determine how the input image is extended during convolution to maintain image shape. Must be one of the following:

  • reflect (d c b a | a b c d | d c b a): The input is extended by reflecting about the edge of the last pixel. This mode is also sometimes referred to as half-sample symmetric.

  • constant (k k k k | a b c d | k k k k): The input is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.

  • nearest (a a a a | a b c d | d d d d): The input is extended by replicating the last pixel.

  • mirror (d c b | a b c d | c b a): The input is extended by reflecting about the center of the last pixel. This mode is also sometimes referred to as whole-sample symmetric.

  • wrap (a b c d | a b c d | a b c d): The input is extended by wrapping around to the opposite edge.

  Reference: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.median_filter.html

  Default: constant

• cval (int,float) – The fill value when mode = constant. Default: 0

• always_apply (bool) – whether to always apply the transformation. Default: False

• p (float) – probability of applying the transform. Default: 0.5.

Targets:

image

Image types:

uint8, float32

apply(img: ndarray, ksize: int = 3, sigma: float = 0, **params) → ndarray

Applies the transformation to the image

get_params() → Dict[str, Any]

Returns parameters needed for the apply methods

get_transform_init_args_names() → Tuple[str, ...]

Returns initialization argument names. (e.g. Transform(arg1 = 1, arg2 = 2) -> (‘arg1’, ‘arg2’))

class MedianBlur(blur_limit: int | Tuple[int, int] = 7, by_slice: bool = False, mode: str = 'constant', cval: float | int = 0, always_apply: bool = False, p: float = 0.5)

Bases: Blur

Blur the input image using a median filter with a random aperture linear size.

Parameters:

• blur_limit (int) – maximum aperture linear size for blurring the input image. Must be odd and in range [3, inf). Default: (3, 7).

• by_slice (bool) – Whether the kernel should be applied by slice or the image as a whole. If true, a 2D kernel is convolved along each slice of the image. Otherwise, a 3D kernel is used. Default: False

• mode (str) –

  scipy parameter to determine how the input image is extended during convolution to maintain image shape. Must be one of the following:

  • reflect (d c b a | a b c d | d c b a): The input is extended by reflecting about the edge of the last pixel. This mode is also sometimes referred to as half-sample symmetric.

  • constant (k k k k | a b c d | k k k k): The input is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.

  • nearest (a a a a | a b c d | d d d d): The input is extended by replicating the last pixel.

  • mirror (d c b | a b c d | c b a): The input is extended by reflecting about the center of the last pixel. This mode is also sometimes referred to as whole-sample symmetric.

  • wrap (a b c d | a b c d | a b c d): The input is extended by wrapping around to the opposite edge.

  Reference: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.median_filter.html

  Default: constant

• cval (int,float) – The fill value when mode = constant. Default: 0

• always_apply (bool) – whether to always apply the transformation. Default: False

• p (float) – probability of applying the transform. Default: 0.5.

Targets:

image

Image types:

uint8, uint16, float32

apply(img: ndarray, ksize: int = 3, **params) → ndarray

Applies the transformation to the image

Functional

blur(img: ndarray, ksize: int, by_slice: bool = False, mode: str = 'constant', cval: float | int = 0) → ndarray

Blur the input image using an mean kernel.

Parameters:

• ksize (int) – The kernel size for blurring the input image.

• by_slice (bool) – Whether the kernel should be applied by slice or the image as a whole. If true, a 2D kernel is convolved along each slice of the image. Otherwise, a 3D kernel is used. Default: False

• mode (str) –

  scipy parameter to determine how the input image is extended during convolution to maintain image shape. Must be one of the following:

  • reflect (d c b a | a b c d | d c b a): The input is extended by reflecting about the edge of the last pixel. This mode is also sometimes referred to as half-sample symmetric.

  • constant (k k k k | a b c d | k k k k): The input is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.

  • nearest (a a a a | a b c d | d d d d): The input is extended by replicating the last pixel.

  • mirror (d c b | a b c d | c b a): The input is extended by reflecting about the center of the last pixel. This mode is also sometimes referred to as whole-sample symmetric.

  • wrap (a b c d | a b c d | a b c d): The input is extended by wrapping around to the opposite edge.

  Reference: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.median_filter.html

  Default: constant

• cval (int,float) – The fill value when mode = constant. Default: 0

gaussian_blur(img: ndarray, ksize: int, sigma: float = 0, by_slice: bool = False, mode: str = 'constant', cval: float | int = 0) → ndarray

Blur the input image using a Gaussian kernel.

Parameters:

• ksize (int) – The kernel size for blurring the input image. If 0, then ksize is estimated as round(sigma * 8) + 1

• sigma (float) – Gaussian kernel standard deviation. If 0, then sigma is estimated as 0.3 * ((ksize - 1) * 0.5 - 1) + 0.8

• by_slice (bool) – Whether the kernel should be applied by slice or the image as a whole. If true, a 2D kernel is convolved along each slice of the image. Otherwise, a 3D kernel is used. Default: False

• mode (str) –

  scipy parameter to determine how the input image is extended during convolution to maintain image shape. Must be one of the following:

  • reflect (d c b a | a b c d | d c b a): The input is extended by reflecting about the edge of the last pixel. This mode is also sometimes referred to as half-sample symmetric.

  • constant (k k k k | a b c d | k k k k): The input is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.

  • nearest (a a a a | a b c d | d d d d): The input is extended by replicating the last pixel.

  • mirror (d c b | a b c d | c b a): The input is extended by reflecting about the center of the last pixel. This mode is also sometimes referred to as whole-sample symmetric.

  • wrap (a b c d | a b c d | a b c d): The input is extended by wrapping around to the opposite edge.

  Reference: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.median_filter.html

  Default: constant

• cval (int,float) – The fill value when mode = constant. Default: 0

median_blur(img: ndarray, ksize: int, by_slice: bool = False, mode: str = 'constant', cval: float | int = 0) → ndarray

Blur the input image using median blue technique.

Parameters:

• ksize (int) – The kernel size for blurring the input image.

• by_slice (bool) – Whether the kernel should be applied by slice or the image as a whole. If true, a 2D kernel is convolved along each slice of the image. Otherwise, a 3D kernel is used. Default: False

• mode (str) –

  scipy parameter to determine how the input image is extended during convolution to maintain image shape. Must be one of the following:

  • reflect (d c b a | a b c d | d c b a): The input is extended by reflecting about the edge of the last pixel. This mode is also sometimes referred to as half-sample symmetric.

  • constant (k k k k | a b c d | k k k k): The input is extended by filling all values beyond the edge with the same constant value, defined by the cval parameter.

  • nearest (a a a a | a b c d | d d d d): The input is extended by replicating the last pixel.

  • mirror (d c b | a b c d | c b a): The input is extended by reflecting about the center of the last pixel. This mode is also sometimes referred to as whole-sample symmetric.

  • wrap (a b c d | a b c d | a b c d): The input is extended by wrapping around to the opposite edge.

  Reference: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.median_filter.html

  Default: constant

• cval (int,float) – The fill value when mode = constant. Default: 0