Skip to content

gaussian_blur

GaussianBlur

Bases: ImageOnlyAlbumentation

Blur the image with a Gaussian filter of random kernel size.

Parameters:

Name Type Description Default
inputs Union[str, Iterable[str]]

Key(s) of images to be modified.

 required
outputs Union[str, Iterable[str]]

Key(s) into which to write the modified images.

 required
mode Union[None, str, Iterable[str]]

What mode(s) to execute this Op in. For example, "train", "eval", "test", or "infer". To execute regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument like "!infer" or "!train".

 None
ds_id Union[None, str, Iterable[str]]

What dataset id(s) to execute this Op in. To execute regardless of ds_id, pass None. To execute in all ds_ids except for a particular one, you can pass an argument like "!ds1".

 None
blur_limit Union[int, Tuple[int, int]]

Maximum Gaussian kernel size for blurring the input image. Should be odd and in range [3, inf).

 7
sigma_limit Union[float, Tuple[float, float]]

Gaussian kernel standard deviation. Must be greater 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)

 0.0
Image types

uint8, float32

Source code in fastestimator/fastestimator/op/numpyop/univariate/gaussian_blur.py 
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
@traceable()
class GaussianBlur(ImageOnlyAlbumentation):
    """Blur the image with a Gaussian filter of random kernel size.

    Args:
        inputs: Key(s) of images to be modified.
        outputs: Key(s) into which to write the modified images.
        mode: What mode(s) to execute this Op in. For example, "train", "eval", "test", or "infer". To execute
            regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument
            like "!infer" or "!train".
        ds_id: What dataset id(s) to execute this Op in. To execute regardless of ds_id, pass None. To execute in all
            ds_ids except for a particular one, you can pass an argument like "!ds1".
        blur_limit: Maximum Gaussian kernel size for blurring the input image. Should be odd and in range [3, inf).
        sigma_limit: Gaussian kernel standard deviation. Must be greater 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)

    Image types:
        uint8, float32
    """
    def __init__(self,
                 inputs: Union[str, Iterable[str]],
                 outputs: Union[str, Iterable[str]],
                 mode: Union[None, str, Iterable[str]] = None,
                 ds_id: Union[None, str, Iterable[str]] = None,
                 blur_limit: Union[int, Tuple[int, int]] = 7,
                 sigma_limit: Union[float, Tuple[float, float]] = 0.0):

        super().__init__(GaussianBlurAlb(blur_limit=blur_limit, always_apply=True, sigma_limit=sigma_limit),
                         inputs=inputs,
                         outputs=outputs,
                         mode=mode,
                         ds_id=ds_id)