Release notes¶

Albumentations 1.4.8 Release Notes¶

• Support our work
• Documentation
• Deprecations
• Improvements and bug fixes

Support Our Work¶

1. Love the library? You can contribute to its development by becoming a sponsor for the library. Your support is invaluable, and every contribution makes a difference.
2. Haven't starred our repo yet? Show your support with a ⭐! It's just only one mouse click.
3. Got ideas or facing issues? We'd love to hear from you. Share your thoughts in our issues or join the conversation on our Discord server for Albumentations

Documentation¶

Added to the documentation links to the UI on HuggingFace to explore hyperparameters visually.

Deprecations¶

RandomSnow¶

Updated interface:

Old way:

transform = A.Compose([A.RandomSnow(
  snow_point_lower=0.1,
  snow_point_upper=0.3,
  p=0.5
)])

New way:

transform = A.Compose([A.RandomSnow(
  snow_point_range=(0.1, 0.3),
  p=0.5
)])

by @MarognaLorenzo

RandomRain¶

Old way

transform = A.Compose([A.RandomSnow(
  slant_lower=-10,
  slant_upper=10,
  p=0.5
)])

New way:

transform = A.Compose([A.RandomRain(
  slant_range=(-10, 10),
  p=0.5
)])

by @MarognaLorenzo

Improvements¶

Created library with core functions albucore. Moved a few helper functions there. We need this library to be sure that transforms are: 1. At least as fast as numpy and opencv. For some functions it is possible to be faster than both of them. 2. Easier to debug. 3. Could be used in other projects, not related to Albumentations.

Bugfixes¶

Bugfix in check_for_updates. Previously, the pipeline would throw an error if it failed to check for updates due to network issues or server unavailability. Now, it handles these exceptions gracefully and continues without interruption. - Bugfix in RandomShadow. Does not create unexpected purple color on bright white regions with shadow overlay anymore. - BugFix in Compose. Now Compose([]) does not throw an error, but just works as NoOp by @ayasyrev - Bugfix in min_max normalization. Now return 0 and not NaN on constant images. by @ternaus Bugfix in CropAndPad. Now we can sample pad/crop values for all sides with interface like ((-0.1, -0.2), (-0.2, -0.3), (0.3, 0.4), (0.4, 0.5)), allowing for more flexible and precise control over padding and cropping dimensions by @christian-steinmeyer - Small refactoring to decrease tech debt by @ternaus and @ayasyrev

Albumentations 1.4.7 Release Notes¶

• Support our work
• Documentation
• Deprecations
• Improvements and bug fixes

Support Our Work¶

1. Love the library? You can contribute to its development by becoming a sponsor for the library. Your support is invaluable, and every contribution makes a difference.
2. Haven't starred our repo yet? Show your support with a ⭐! It's just only one mouse click.
3. Got ideas or facing issues? We'd love to hear from you. Share your thoughts in our issues or join the conversation on our Discord server for Albumentations

Documentation¶

• Added to the website tutorial on how to use Albumentations with Hugginigface for object Detection. Based on the tutorial by @qubvel

Deprecations¶

ImageCompression¶

Old way:

transform = A.Compose([A.ImageCompression(
  quality_lower=75,
  quality_upper=100,
  p=0.5
)])

New way:

transform = A.Compose([A.ImageCompression(
  quality_range=(75, 100),
  p=0.5
)])

Downscale¶

Old way:

transform = A.Compose([A.Downscale(
  scale_min=0.25,
  scale_max=1,
  interpolation= {"downscale": cv2.INTER_AREA, "upscale": cv2.INTER_CUBIC},
  p=0.5
)])

New way:

transform = A.Compose([A.Downscale(
  scale_range=(0.25, 1),
 interpolation_pair = {"downscale": cv2.INTER_AREA, "upscale": cv2.INTER_CUBIC},
  p=0.5
)])

As of now both ways work and will provide the same result, but old functionality will be removed in later releases.

by @ternaus

Improvements¶

• Buggix in Blur.
• Bugfix in bbox clipping, it could be not intuitive, but boxes should be clipped by height, width and not height - 1, width -1 by @ternaus
• Allow to compose only keys, that are required there. Any extra unnecessary key will give an error by @ayasyrev
• In PadIfNeeded if value parameter is not None, but border mode is reflection, border mode is changed to cv2.BORDER_CONSTANT by @ternaus

Albumentations 1.4.6 Release Notes¶

This is out of schedule release with a bugfix that was introduced in version 1.4.5¶

In version 1.4.5 there was a bug that went unnoticed - if you used pipeline that consisted only of ImageOnly transforms but pass bounding boxes into it, you would get an error.

If you had in such pipeline at least one non ImageOnly transform, say HorizontalFlip or Crop, everything would work as expected.

We fixed the issue and added tests to be sure that it will not happen in the future.

Albumentations 1.4.5 Release Notes¶

• Support our work
• Highlights
• Deprecations
• Improvements and bug fixes

Support Our Work¶

1. Love the library? You can contribute to its development by becoming a sponsor for the library. Your support is invaluable, and every contribution makes a difference.
2. Haven't starred our repo yet? Show your support with a ⭐! It's just only one mouse click.
3. Got ideas or facing issues? We'd love to hear from you. Share your thoughts in our issues or join the conversation on our Discord server for Albumentations

Highlights¶

Bbox clipping¶

Before version 1.4.5 it was assumed that bounding boxes that are fed into the augmentation pipeline should not extend outside of the image.

Now we added an option to clip boxes to the image size before augmenting them. This makes pipeline more robust to inaccurate labeling

Example:

Will fail if boxes extend outside of the image:

transform = A.Compose([
    A.HorizontalFlip(p=0.5)
], bbox_params=A.BboxParams(format='coco'))

Clipping bounding boxes to the image size:

transform = A.Compose([
    A.HorizontalFlip(p=0.5)
], bbox_params=A.BboxParams(format='coco', clip=True))

by @ternaus

SelectiveChannelTransform¶

Added SelectiveChannelTransform that allows to apply transforms to a selected number of channels.

For example it could be helpful when working with multispectral images, when RGB is a subset of the overall multispectral stack which is common when working with satellite imagery.

Example:

aug = A.Compose(
        [A.HorizontalFlip(p=0.5),
        A.SelectiveChannelTransform(transforms=[A.ColorJItter(p=0.5),
        A.ChromaticAberration(p=0.5))], channels=[1, 2, 18], p=1)],
    )

by @ternaus

Deprecations¶

CoarseDropout¶

Old way:

transform = A.Compose([A.CoarseDropout(
  min_holes = 5,
  max_holes = 8,
  min_width = 3,
  max_width = 12,
  min_height = 4,
  max_height = 5
)])

New way:

transform = A.Compose([A.CoarseDropout(
  num_holes_range=(5, 8),
  hole_width_range=(3, 12),
  hole_height_range=(4, 5)
)])

As of now both ways work and will provide the same result, but old functionality will be removed in later releases.

@ternaus

Improvements and bug fixes¶

• Number of fixes and speedups in the core of the library Compose and BasicTransform by @ayasyrev
• Extended Contributor's guide by @ternaus
• Can use random for fill_value in CoarseDropoutby @ternaus
• Fix in ToGray docstring by @wilderrodrigues
• BufFix in D4 - now works not only with square, but with rectangular images as well. By @ternaus
• BugFix in RandomCropFromBorders by @ternaus

Albumentations 1.4.4 Release Notes¶

• Support our work
• Highlights
• Transforms
• Improvements and bug fixes

Support Our Work¶

1. Love the library? You can contribute to its development by becoming a sponsor for the library. Your support is invaluable, and every contribution makes a difference.
2. Haven't starred our repo yet? Show your support with a ⭐! It's just only one mouse click.
3. Got ideas or facing issues? We'd love to hear from you. Share your thoughts in our issues or join the conversation on our Discord server for Albumentations

Transforms¶

Added D4 transform¶

Applies one of the eight possible D4 dihedral group transformations to a square-shaped input, maintaining the square shape. These transformations correspond to the symmetries of a square, including rotations and reflections by @ternaus

The D4 group transformations include: - e (identity): No transformation is applied. - r90 (rotation by 90 degrees counterclockwise) - r180 (rotation by 180 degrees) - r270 (rotation by 270 degrees counterclockwise) - v (reflection across the vertical midline) - hvt (reflection across the anti-diagonal) - h (reflection across the horizontal midline) - t (reflection across the main diagonal)

Could be applied to: - image - mask - bounding boxes - key points

Does not generate interpolation artifacts as there is no interpolation.

Provides the most value in tasks where data is invariant to rotations and reflections like: - Top view drone and satellite imagery - Medical images

Example:

Added new normalizations to Normalize transform¶

• standard - subtract fixed mean, divide by fixed std
• image - the same as standard, but mean and std computed for each image independently.
• image_per_channel - the same as before, but per channel
• min_max - subtract min(image)and divide by max(image) - min(image)
• min_max_per_channel - the same, but per channel by @ternaus

Changes in the interface of RandomShadow¶

New, preferred wat is to use num_shadows_limit instead of num_shadows_lower / num_shadows_upper by @ayasyrev

Improvements and bug fixes¶

Added check for input parameters to transforms with Pydantic¶

Now all input parameters are validated and prepared with Pydantic. This will prevent bugs, when transforms are initialized without errors with parameters that are outside of allowed ranges. by @ternaus

Updates in RandomGridShuffle¶

1. Bugfix by @ayasyrev
2. Transform updated to work even if side is not divisible by the number of tiles. by @ternaus

Example:

New way to add additional targets¶

Standard way uses additional_targets

transform = A.Compose(
    transforms=[A.Rotate(limit=(90.0, 90.0), p=1.0)],
    keypoint_params=A.KeypointParams(
        angle_in_degrees=True,
        check_each_transform=True,
        format="xyas",
        label_fields=None,
        remove_invisible=False,
    ),
    additional_targets={"keypoints2": "keypoints"},
)

Now you can also add them using add_targets:

transform = A.Compose(
    transforms=[A.Rotate(limit=(90.0, 90.0), p=1.0)],
    keypoint_params=A.KeypointParams(
        angle_in_degrees=True,
        check_each_transform=True,
        format="xyas",
        label_fields=None,
        remove_invisible=False,
    ),
)
transform.add_targets({"keypoints2": "keypoints"})

by @ayasyrev

Small fixes¶

• Small speedup in the code for transforms that use add_weighted function by @gogetron
• Fix in error message in Affine transform by @matsumotosan
• Bugfix in Sequential by @ayasyrev

Documentation¶

• Updated Contributor's guide. by @ternaus
• Added example notebook on how to apply D4 to images, masks, bounding boxes and key points. by @ternaus
• Added example notebook on how to apply RandomGridShuffle to images, masks and keypoints. by @ternaus

Albumentations 1.4.3 Release Notes¶

• Request
• Highlights
• New transform
• Minor improvements and bug fixes

Request¶

1. If you enjoy using the library as an individual developer or a company representative, please consider becoming a sponsor for the library. Every dollar helps.
2. If you did not give our repo a ⭐, it is only one mouse click
3. If you have feature requests or proposals or encounter issues - submit your request to issues or ask in Discord server for Albumentations

New transform¶

• Added Morphological transform that modifies the structure of the image. Dilation expands the white (foreground) regions in a binary or grayscale image, while erosion shrinks them.

Minor improvements and bug fixes¶

• Updated benchmark for uint8 images, processed on CPU. Added Kornia and Augly. LINK by @ternaus
• Bugfix in FDA transform by @ternaus
• Now RandomSizedCrop supports the same signature as analogous transform in torchvision by @zetyquickly

Albumentations 1.4.2 Release Notes¶

• Request
• Highlights
• New transform
• New functionality
• Improvements and bug fixes

Request¶

1. If you enjoy using the library as an individual developer or as a representative of the company please consider becoming a sponsor for the library. Every dollar helps.
2. If you did not give our repo a ⭐, it is only one mouse click
3. If you have feature requests or proposals or encounter issues - submit your request to issues or ask in Discord server for Albumentations

New transform¶

Left: Original, Middle: Chromatic aberration (default args, mode="green_purple"), Right: Chromatic aberration (default args, mode="red_blue")
(Image is from our internal mobile mapping dataset)

• Added ChromaticAbberation transform that adds chromatic distortion to the image. Wiki by @mrsmrynk

New functionality¶

• Return mixing parameter for MixUp transform by @Dipet. For more details Tutorial on MixUp

Improvements and Bugfixes¶

• Do not throw deprecation warning when people do not use deprecated parameters in AdvancedBlur by @Aloqeely
• Updated CONTRIBUTORS.md for Windows users by @Aloqeely
• Fixed Docstring for DownScale transform by @ryoryon66
• Bugfix in PadIfNeeded serialization @ternaus

Albumentations 1.4.1 Release Notes (4 March 2024)¶

• Request
• Highlights
• New transform
• Improvements
• Bug fixes

Request¶

1. If you enjoy using the library as an individual developer or during the day job as a part of the company, please consider becoming a sponsor for the library. Every dollar helps.
2. If you did not give our repo a ⭐, it is only one mouse click
3. If you have feature requests or proposals or encounter issues - submit your request to issues or our new initiative, - Discord server for albumentations

New transform¶

• Added MixUp transform: which linearly combines an input (image, mask, and class label) with another set from a predefined reference dataset. The mixing degree is controlled by a parameter λ (lambda), sampled from a Beta distribution. This method is known for improving model generalization by promoting linear behavior between classes and smoothing decision boundaries.

Minor changes and Bug Fixes¶

• Moved from isort, flake8, black to ruff
• Added extra checks for docstrings to match Google Style.
• Updated Who's using
• Removed quidda dependency, which addresses opencv library inconsistencies issues
• New, updated version of benchmark.

Albumentations 1.4.0 Release Notes (17 February 2024)¶

• Request
• Highlights
• New transform
• Backwards Incompatible Changes
• Improvements
• Bug fixes

Request¶

1. If you enjoy using the library as an individual developer or during the day job as a part of the company, please consider becoming a sponsor for the library. Every dollar helps.
2. If you did not give our repo a ⭐, it is [only one mouse click].(https://github.com/albumentations-team/albumentations)
3. If you have feature requests, proposals, or encounter issues - submit your request to issues or, our new initiative, - Discord server for albumentations

Highlights¶

In this release, we mainly focused on the technical debt as its decrease allows faster iterations and bug fixes in the codebase. We added only one new transform, did not work on speeding up transforms, and other changes are minor.

1. We are removing the dependency on the imgaug library. The library was one of our inspirations when we created Albumentations, but maintainers of imgaug ceased its support which caused inconsistencies in library versions. It was done in 2021, say commit https://github.com/albumentations-team/albumentations/commit/ba44effb0369ba5eae1e8eb4909105eac9709230 by @Dipet .

But, somehow, we are cutting this dependency only in 2024.

1. Added typing in all of the codebase. When we started the library, Python 2 was still widely used; hence, none of the original codebases had types specified for function arguments and return types. Since the end of the support for Python 2, we added types to the new or updated code, but only now have we covered all the codebase.

New transform¶

• Added XYMasking transform: applies masking strips to an image, either horizontally (X axis) or vertically (Y axis), simulating occlusions. This transform is helpful for training models to recognize images with varied visibility conditions. It's particularly effective for spectrogram images, allowing spectral and frequency masking to improve model robustness. As other dropout transforms CoarseDropout, MaskDropout, GridDropout it supports images, masks and keypoints as targets. (https://github.com/albumentations-team/albumentations/commit/004fabbf90794fbc21ee356e2dde6637b7fecbd4 by @ternaus )

Backward Incompatible Changes¶

The deprecated code, including 15 transforms, was removed. Dependency on the imgaug library was removed.

(https://github.com/albumentations-team/albumentations/commit/be6a217b207b3d7ebe792caabb438d660b45f2a5 by @ternaus )

Deleted Transforms¶

1. JpegCompression. Use ImageCompression instead.
2. RandomBrightness. Use RandomBrigtnessContrast instead.
3. RandomContrast. Use RandomBrigtnessContrast instead.
4. Cutout. Use CoarseDropout instead.
5. ToTensor. Use ToTensorV2 instead.
6. IAAAdditiveGaussianNoise. Use GaussNoise instead.
7. IAAAffine. Use Affine instead.
8. IAACropAndPad. Use CropAndPad instead.
9. IAAEmboss. Use Emboss instead.
10. IAAFliplr. Use HorizontalFlip instead.
11. IAAFlipud. Use VerticalFlip instead.
12. IAAPerspective. Use Perspective instead.
13. IAAPiecewiseAffine. Use PiecewiseAffine instead.
14. IAASharpen. Use Sharpen instead.
15. IAASuperpixels. Use Superpixels instead.

Other deprecated functionality¶

• Removed eps parameter in RandomGamma
• Removed lambda_transformsin serialization.from_dict function.

## Minor changes and Bug Fixes * Added details Contributor's guide * Added support for matrix=None case for Piecewise affine transform (https://github.com/albumentations-team/albumentations/commit/c70e664e060bfd7463c20674927aed217f72d437 @Dipet ) * Bugfix - Eliminated the possibility of the Perspective transform collapsing (https://github.com/albumentations-team/albumentations/commit/a919a772d763e0c62b674ca490a97c89e0b9c5a3 @alicangok ) * Fixes in docstrings (@domef, @aaronzs, @Dipet, @ternaus ) * Added checks for python 3.12

0.5.2 (29 November 2020)¶

Minor changes¶

• ToTensorV2 now automatically expands grayscale images with the shape [H, W] to the shape [H, W, 1]. PR #604 by @Ingwar.
• CropNonEmptyMaskIfExists now also works with multiple masks that are provided by the masks argument to the transform function. Previously this augmentation worked only with a single mask provided by the mask argument. PR #761.

0.5.1 (2 November 2020)¶

Breaking changes¶

• API for A.FDA is changed to resemble API of A.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 default read_fn. This function reads an image from the disk as an RGB NumPy array. Previously, the default read_fn was cv2.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 for A.Compose. Instead, it should be used inside A.Compose the same way A.OneOf or A.OneOrOther. For instance, you can combine A.OneOf with A.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 and shift_limit_y. If either of those parameters (or both of them) is set A.ShiftScaleRotate will use the set values to shift images on the respective axis. (#735)
• A.ToTensorV2 now supports an additional argument transpose_mask (False by default). If the argument is set to True 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 contained A.ToTensor or A.ToTensorV2, but those transforms were not imported in the code before the call. (#735)

0.5.0 (19 October 2020)¶

Breaking changes¶

• Albumentations now explicitly checks that all inputs to augmentations are named arguments and raise an exception otherwise. So if an augmentation receives input like aug(image) instead of aug(image=image), Albumentations will raise an exception. (#560)
• Dropped support of Python 3.5 (#709)
• Keypoints and bboxes are checked for visibility after each transform (#566)

New transformations¶

• A.FDA transform for Fourier-based domain adaptation. (#685)
• A.HistogramMatching transform that applies histogram matching. (#708)
• A.ColorJitter transform that behaves similarly to ColorJitter from torchvision (though there are some minor differences due to different internal logic for working with HSV colorspace in Pillow, which is used in torchvision and OpenCV, which is used in Albumentations). (#705)

Minor changes¶

• A.PadIfNeeded now accepts additional pad_width_divisor, pad_height_divisor (None by default) to ensure image has width & height that is dividable by given values. (#700)
• Added support to apply A.CoarseDropout to masks via mask_fill_value. (#699)
• A.GaussianBlur now supports the sigma parameter that sets standard deviation for Gaussian kernel. (#674, #673) .

Bugfixes¶

• Fixed bugs in A.HueSaturationValue for float dtype. (#696, #710)
• Fixed incorrect rounding error on bboxes in YOLO format. (#688)

0.4.6 (19 July 2020)¶

Improvements¶

• Change the ImgAug dependency version from “imgaug>=0.2.5,<0.2.7” to “imgaug>=0.4.0". Now Albumentations won’t downgrade your existing ImgAug installation to the old version. PR #658.
• Do not try to resize an image if it already has the required height and width. That eliminates the redundant call to the OpenCV function that requires additional copying of the input data. PR #639. ReplayCompose is now serializable. PR #623 by IlyaOvodov
• Documentation fixes and updates.

Bug Fixes¶

• Fix a bug that causes some keypoints and bounding boxes to lie outside the visible part of the augmented image if an augmentation pipeline contained augmentations that increase the height and width of an image (such as PadIfNeeded). That happened because Albumentations checked which bounding boxes and keypoints lie outside the image only after applying all augmentations. Now Albumentations will check and remove keypoints and bounding boxes that lie outside the image after each augmentation. If, for some reason, you need the old behavior, pass check_each_transform=False in your KeypointParams or BboxParams. Issue #565 and PR #566.
• Fix a bug that causes an exception when Albumentations received images with the number of color channels that are even but are not multiples of 4 (such as 6, 10, etc.). PR #638.
• Fix the off-by-one error in applying steps for GridDistortion. Commit 9c225a9
• Fix bugs that prevent serialization of ImageCompression and GaussNoise. PR #569
• Fix a bug that causes errors with some values for label_fields in BboxParams. PR #504 by IlyaOvodov
• Fix a bug that prevents HueSaturationValue for working with grayscale images. PR #500.