Breaking changes
- API for
A.FDA
is changed to resemble API ofA.HistogramMatching
. Now, both transformations expect to receive a list of reference images, a function to read those image, and additional augmentation parameters. (#734) A.HistogramMatching
now usesread_rgb_image
as a defaultread_fn
. This function reads an image from the disk as an RGB NumPy array. Previously, the defaultread_fn
wascv2.imread
which read an image as a BGR NumPy array. (#734)
New transformations
A.Sequential
transform that can apply augmentations in a sequence. This transform is not intended to be a replacement forA.Compose
. Instead, it should be used insideA.Compose
the same wayA.OneOf
orA.OneOrOther
. For instance, you can combineA.OneOf
withA.Sequential
to create an augmentation pipeline containing multiple sequences of augmentations and apply one randomly chosen sequence to input data. (#735)
Minor changes
A.ShiftScaleRotate
now has two additional optional parameters:shift_limit_x
andshift_limit_y
. If either of those parameters (or both of them) is setA.ShiftScaleRotate
will use the set values to shift images on the respective axis. (#735)A.ToTensorV2
now supports an additional argumenttranspose_mask
(False
by default). If the argument is set toTrue
and an input mask has 3 dimensions,A.ToTensorV2
will transpose dimensions of a mask tensor in addition to transposing dimensions of an image tensor. (#735)
Bugfixes
A.FDA
now correctly uses coordinates of the center of an image. (#730)- Fixed problems with grayscale images for
A.HistogramMatching
. (#734) - Fixed a bug that led to an exception when
A.load()
was called to deserialize a pipeline that containedA.ToTensor
orA.ToTensorV2
, but those transforms were not imported in the code before the call. (#735)