0.4.0

Table of Contents

• New transforms

  • ISONoise
  • Solarize
  • Equilize
  • Posterize
  • ImageCompression
  • Downscale
  • RandomResizedCrop
  • RandomGridShuffle
  • CropNonEmptyMaskIfExists
  • ToTensorV2

• New features

  • Added YOLO format to bounding boxes
  • Deterministic / Replay mode

• Improvements

  • Added fill_value to Cutout
  • Separate fill_value for image and mask targets
  • Speedup in RGBShift transform
  • Speedup in HueSaturationValue
  • Speedup in RandomBrightnessContrast
  • Speedup in RandomGamma
  • Added support for images and masks with more than 3 channels
  • Added key points support to Crop, CropNonEmptyMaskIfExists, LongestMaxSize, RandomCropNearBBox, Resize, SmallestMaxSize, and Transpose
  • Add per channel transform composition

• Bug Fixes

  • Bugfix in GaussNoise
  • Bugfix in RandomGamma
  • Bugfix in RandomSizedBBoxSafeCrop

• Documentation Updated

  • Added page that lists pre-prints and papers that cite albumentations
  • Added page that contains competitions in which top teams used albumentations

New transforms

ISONoise

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Target: image

This transform mimics the noise that images will have if the ISO parameter of the camera is high. Wiki

Solarize

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Targets: image

Solarize inverts all pixels above some threshold. It is an essential part of the work AutoAugment: Learning Augmentation Policies from Data.

Equilize

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Target: image

Equalizes image histogram. It is an essential part of the work AutoAugment: Learning Augmentation Policies from Data.

Posterize

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Target: image

Reduce the number of bits for each pixel. It is an essential part of the work AutoAugment: Learning Augmentation Policies from Data.

ImageCompression

Target: image
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Decrease Jpeg or WebP compression to the image.

Downscale

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Target: image

Decreases image quality by downscaling and upscaling back.

RandomResizedCrop

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Targets: image, mask, bboxes, keypoints

Crop the given Image to the random size and aspect ratio. This transform is an essential part of many image classification pipelines. Very popular for ImageNet classification.

It has the same API as RandomResizedCrop in torchvision.

RandomGridShuffle

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Targets: image, mask

Partition an image into tiles. Shuffle them and merge back.

CropNonEmptyMaskIfExists

Targets: image, mask, bboxes, keypoints

Crop area with a mask if the mask is non-empty, else make a random crop.

ToTensorV2

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Targets: image, mask

Convert image and mask to torch.Tensor

New features

Added YOLO format to bounding boxes.

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The Yolo format of a bounding box has a format [x, y, width, height], where values normalized to the size of the image. Ex: [0.3, 0.1, 0.05, 0.07]

Added Deterministic / Replay mode

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Augmentations pipeline has a lot of randomnesses, which is hard to debug. We added Determentsic / Replay mode in which you can track what parameters were applied to the input and use precisely the same transform to another input if necessary.

Jupyter notebook with an example.

Added fill_value to the Cutout transform.

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Separated fill_value for images and masks

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One of the use cases is it to use mask_value, which is equal to the ignore_index of your loss. This will decrease the level of noise and may improve convergence.

Speedup in the RGBShift

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3.2 times faster for uint8 images.

Speedup in HueSaturationValue

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2 times faster for uint8 images.

Speedup in [RandomBrightnessContrast](https://albumentations.readthedocs.io/en/latest/api/augmentations.html#albumentations.augmentations.transforms.RandomBrightnessCont...

0.3.0 Release

Added serialization / deserialization

• Now we can define transformations in a python dictionary, json, yaml files and they will be deserialized and used in the code.
• Now we can define transformations in the code and serialize them in python dictionary, json and yaml files.

Jupyter notebook with an example

Special thanks to @creafz

Added new transformations

• Lambda
• GaussianBlur
• ChannelDropout
• CoarseDropout
• RandomSnow
• RandomRain
• RandomFog
• RandomSunFlare
• RandomShadow

Special thanks to @vfdev-5 @ternaus @BloodAxe @kirillbobyrev

Bugfixes and improvements

• Bugfix in ToGray
• Bugfix in ShiftScaleRotate
• Bugfix in GaussNoise
• Added fill_value parameter to CutOut
• SpeedUp in RandomBrightnessContrast

Special thanks to @qubvel @ternaus @albu @BloodAxe

0.2.0 Release

Added support for the keypoint transformations to

• CenterCrop
• Flip
• HorizontalFlip
• IAAAffine
• IAACropAndPad
• IAAFliplr
• IAAFlipud
• IAAPerspective
• IAAPiecewiseAffine
• PadIfNeeded
• RandomCrop
• RandomRotate90
• RandomScale
• RandomSizedCrop
• Rotate
• ShiftScaleRotate
• VerticalFlip

Notebook with an example

Special thanks to the Evegene Khvedchenya (@BloodAxe) for the work.

Added an option to apply the same transformation to the more than one target of the same type.

The possible use case are image2image or stereo-image pipelines.

Notebook with an example

Special thanks to Alexander Buslaev (@albu) for the work.

Added new transformations

• RandomCropNearBBox
• RandomSizedBBoxSafeCrop
• SmallestMaxSize

Speed up in

• Normalize
• Flip
• HorizontalFlip
• VerticalFlip
• ElasticTransform

Bug fixes

• Fix for Compose with multiprocessing DataLoaders.
• Fix in SmallestMaxSize for multiclass masks.
• Fix in RandomBrightness
• Fix in RandomContrast

And many others.

Additional

• Performance benchmark was extended to the Augmentor and Solt libraries.
• Added table to Readme that shows all implemented transformations with the set of possible targets: images, bounding boxes, masks, key points. (Special thanks to Alex Parinov @creafz )
• The library can be installed in anaconda.

Contributors

@BloodAxe @albu @creafz @ternaus @erikgaas @marcocaccin @libfun @DBusAI @alexobednikov @StrikerRUS @IlyaOvodov @ZFTurbo @Vcv85 @georgymironov @LinaShiryaeva @vfdev-5 @daisukelab @cdicle

Extended bounding boxes support. New transformations. New notebooks with examples. A lot of bugfixes.

Bounding boxes support

Transformations that support bounding boxes

The main change in this release is the addition of the operations on bounding boxes to the

• Flip
• VerticalFlip
• HorizontalFlip
• Transpose
• RandomRotate90
• LongestMaxSize
• Resize
• RandomScale
• Crop
• RandomCrop
• CenterCrop
• RandomSizedCrop
• IAAAffine

Supported formats

Currently supported the following formats for the bounding boxes:

1. COCO: [x_min, y_min, width, height], ex [97, 12, 150, 200]
2. Pascal VOC: [x_min, y_min, x_max, y_max], ex [97, 12, 247, 212]

Bounding box filtering

It may happen that after the transformation a big part of the bounding box was cropped and it is needed to exclude such boxes.

We support such a bounding box filtering based on the:

• Bounding box area, measured in pixels.
• Visible box area, measured in percent.

Smaller changes

• Added support for 8-bit images.
• We changed all np.random occurrences to random due to the numpy behavior reported in pytorch/pytorch#5059
• Multiple bugfixes.

Added notebooks with examples

• How to migrate from torchvision to albumentations.
• How to apply the transformation to the classification problems.
• How to apply transformations to the detection problems.
• How to apply transformations to the segmentation problems.
• How to apply transformations to the non 8-bit images
• All in one showcase