- Support Our Work
- Transforms
- Core Functionality
- Deprecations
- Improvements and Bug Fixes
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Transforms
Added TextImage transform
Allows adding text on top of images. Works with np,unit8
and np.float32
images with any number of channels.
Additional functionalities:
- Insert random stopwords
- Delete random words
- Swap word order
Core functionality
Added images
target
You can now apply the same transform to a list of images of the same shape, not just one image.
Use cases:
- Video: Split video into frames and apply the transform.
- Slices of 3D volumes: For example, in medical imaging.
import albumentations as A
transform = A.Compose([A.Affine(p=1)])
transformed = transform(images=<list of images>)
transformed_images = transformed["images"]
Note:
You can apply the same transform to any number of images, masks, bounding boxes, and sets of keypoints using the additional_targets functionality notebook with examples
Contributors @ternaus, @ayasyrev
get_params_dependent_on data
Relevant for those who build custom transforms.
Old way
@property
def targets_as_params(self) -> list[str]:
return <list of targets>
def get_params_dependent_on_targets(self, params: dict[str, Any]) -> dict[str, np.ndarray]:
image = params["image"]
....
New way
def get_params_dependent_on_data(self, params: dict[str, Any], data: dict[str, Any]) -> dict[str, np.ndarray]:
image = data["image"]
Contributor @ayasyrev
Added shape
to params
Old way:
def get_params_dependent_on_targets(self, params: dict[str, Any]) -> dict[str, np.ndarray]:
image = params["image"]
shape = image.shape
New way:
def get_params_dependent_on_data(self, params: dict[str, Any], data: dict[str, Any]) -> dict[str, np.ndarray]:
shape = params["shape"]
Contributor @ayasyrev
Deprecations
Elastic Transform
Deprecated parameter alpha_affine
in ElasticTransform
. To have Affine effects on your image, use the Affine
transform.
Contributor @ternaus