affine
Affine
¶
Bases: MultiVariateAlbumentation
Perform affine transformations on an image.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
rotate |
Union[Number, Tuple[Number, Number]]
|
How much to rotate an image (in degrees). If a single value is given then images will be rotated by a value sampled from the range [-n, n]. If a tuple (a, b) is given then each image will be rotated by a value sampled from the range [a, b]. |
0
|
scale |
Union[float, Tuple[float, float]]
|
How much to scale an image (in percentage). If a single value is given then all images will be scaled by a value drawn from the range [1.0, n]. If a tuple (a,b) is given then each image will be scaled based on a value drawn from the range [a,b]. |
1.0
|
shear |
Union[Number, Tuple[Number, Number]]
|
How much to shear an image (in degrees). If a single value is given then all images will be sheared on X and Y by two values sampled from the range [-n, n]. If a tuple (a, b) is given then images will be sheared on X and Y by two values randomly sampled from the range [a, b]. |
0
|
translate |
Union[Number, Tuple[Number, Number]]
|
How much to translate an image. If a single value is given then the translation extent will be sampled from the range [0,n]. If a tuple (a,b) is given then the extent will be sampled from the range [a,b]. If integers are given then the translation will be in pixels. If a float then it will be as a fraction of the image size. |
0
|
border_handling |
Union[str, List[str]]
|
What to do in order to fill newly created pixels. Options are 'constant', 'edge', 'symmetric', 'reflect', and 'wrap'. If a list is given, then the method will be randomly selected from the options in the list. |
'constant'
|
fill_value |
Number
|
What pixel value to insert when border_handling is 'constant'. |
0
|
interpolation |
str
|
What interpolation method to use. Options (from fast to slow) are 'nearest_neighbor', 'bilinear', 'bicubic', 'biquartic', and 'biquintic'. |
'bilinear'
|
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
|
image_in |
Optional[str]
|
The key of an image to be modified. |
None
|
mask_in |
Optional[str]
|
The key of a mask to be modified (with the same random factors as the image). |
None
|
masks_in |
Optional[str]
|
The key of masks to be modified (with the same random factors as the image). |
None
|
bbox_in |
Optional[str]
|
The key of a bounding box(es) to be modified (with the same random factors as the image). |
None
|
keypoints_in |
Optional[str]
|
The key of keypoints to be modified (with the same random factors as the image). |
None
|
image_out |
Optional[str]
|
The key to write the modified image (defaults to |
None
|
mask_out |
Optional[str]
|
The key to write the modified mask (defaults to |
None
|
masks_out |
Optional[str]
|
The key to write the modified masks (defaults to |
None
|
bbox_out |
Optional[str]
|
The key to write the modified bounding box(es) (defaults to |
None
|
keypoints_out |
Optional[str]
|
The key to write the modified keypoints (defaults to |
None
|
bbox_params |
Union[BboxParams, str, None]
|
Parameters defining the type of bounding box ('coco', 'pascal_voc', 'albumentations' or 'yolo'). |
None
|
keypoint_params |
Union[KeypointParams, str, None]
|
Parameters defining the type of keypoints ('xy', 'yx', 'xya', 'xys', 'xyas', 'xysa'). |
None
|
Image types
uint8, float32
Source code in fastestimator/fastestimator/op/numpyop/multivariate/affine.py
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