Polygons
+ Contrast
+ Sharpen
+ Pad
+ Perspective
| Image | Heatmaps | Seg. Maps | Keypoints | Bounding Boxes, Polygons |
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|---|---|---|---|---|---|
| Original Input | |
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| Gauss. Noise + Contrast + Sharpen |
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| Affine | |
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| Crop + Pad |
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| Fliplr + Perspective |
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|
## Cartoon
A new cartoon style filter is introduced, shown below. Each row starts with
the input image.
## ChangeColorTemperature
The color temperature of images can now be modified. The example below
shows `ChangeColorTemperature(kelvin=1000)` to
`ChangeColorTemperature(kelvin=5000)`, with the first image being the input.
## Cutout
Cutout is added to the library. The first row shows the hyperparameters that
were used in the corresponding paper. The second row shows two cutout
iterations per image, using intensity values, random RGB values and gaussian
noise to fill in the pixels. First image in each row is the input.
## Dropout2d and TotalDropout
Two new dropout augmenters, `Dropout2d` and `TotalDropout`, are added.
The example below shows `Dropout2d`. First image is the input.
## Jigsaw
A jigsaw puzzle augmenter is added. The first row below shows its effects
using a grid size of `5x5`. The second row shows `10x10`.
First image in each row is the input.
## MeanShiftBlur
A mean shift-based blur augmenter is added. First image below shows the input,
followed by `MeanShiftBlur(5.0)` to `MeanShiftBlur(40.0)`.
## Posterize
The example below shows `Posterize` (aka `UniformQuantizationToNBits`)
with `n_bits=8` to `n_bits=1`. First image is the input.
## Solarize
The example below shows `Solarize`, which is the same as `Invert` with a
threshold. First image is the input.
## Rain
The example below shows the new `Rain` augmenter. First image is the input.
## RandAugment
This release adds an implementation of `RandAugment` the following example
shows `RandAugment(n=2, m=20)`. First image is the input.
## WithPolarWarping
The example below shows `WithPolarWarping(
The augmenter supports all input types, but bounding boxes and polygons
should be used with caution. (Bounding boxes, because they tend to produce
unintuitive results in combination with rotation-like augmentations.
Polygons, because they can become invalid under geometric augmentations
and will have to be repaired, which can easily mess them up.)
## imagecorruptions wrappers
Wrappers around the library `imagecorruptions` are added, which contains
augmentation methods introduced by `Hendrycks and Dietterich - Benchmarking
Neural Network Robustness to Common Corruptions and Surface Variations`.
The methods were used in some recent papers. The example below shows their
effects, always with `severity=3`.
## PIL wrappers
Various wrappers around popular `PIL` methods are added.
The image below shows in the first row `Autocontrast`, in the second
`EnhanceColor` (strength of `0.1` to `1.9`), the third
`EnhanceSharpness` (strength of `0.1` to `1.9`), the fourth shows
various convolution-based filters (`FilterBlur`, `FilterSmooth`,
`FilterEdgeEnhance`, `FilterFindEdges`, `FilterContour`, `FilterSharpen`,
`FilterDetail` -- in that order) and the fourth row shows `pillike.Affine`
with the top-left as the transformation origin.
The first image in each row is the input.
## More Blending Augmenters
Various new (alpha-)blending augmenters are introduced in this patch.
The following example makes use of a segmentation map in which all cars
are marked with a segmentation class id.
It uses roughly
`BlendAlphaSegMapClassIds(BlendAlphaSomeColors(AddToHueAndSaturation(...)))`
in order to modify some colors within the car classes.
Left is the input image, right is the output:
The next example applies blending to some non color-based augmenters. It uses
roughly `BlendAlphaSegMapClassIds(AdditiveGaussianNoise(...))` (left)
and `BlendAlphaSegMapClassIds(Emboss(...))` (right). The street has a separate
class in the segmentation map.
This release also adds `BlendAlphaBoundingBoxes`,
`BlendAlphaHorizontalLinearGradient` and
`BlendAlphaVerticalLinearGradient`. These are not visualized here.
## SaveDebugImageEveryNBatches
A new debug helper -- `SaveDebugImageEveryNBatches` -- was added.
The example below shows one of its outputs for a batch containing images,
segmentation maps and bounding boxes.
Note that this augmenter must be called with all inputs at the same time,
e.g. via `augmenters(images=..., segmentation_maps...)` for image + segmap
inputs.
# Mixed-Category Patches
## Reworked Augmentation Methods [#451](https://github.com/aleju/imgaug/pull/451) [#566](https://github.com/aleju/imgaug/pull/566)
The internal backend of the library was changed so that augmentation now
happens batchwise instead of input-type-wise. Child augmenters still have
the option of using input-type-wise augmentation. All calls are now at some
point routed through `Augmenter.augment_batch_()` and child augmenters are
expected to implement `_augment_batch_()`. This change allows to re-use
information between different input types within the same batch, which in
turn improves performance and extends the space of possible augmentations.
Note: It is now recommended to use a batch-wise augmentation call. I.e.
use `.augment_batch_()` or `.augment()` or `.__call__()`. These calls provide
all inputs of a batch at the same time and several of the new augmenters now
explicitly require that (e.g. `BlendAlphaBoundingBoxes`). Example:
```python
import numpy as np
import imgaug as ia
import imgaug.augmenters as iaa
images = [np.zeros((32, 32, 3), dtype=np.uint8),
np.zeros((64, 64, 3), dtype=np.uint8)]
bbs = [
[ia.BoundingBox(x1=0, y1=1, x2=2, y2=3)],
[ia.BoundingBox(x1=1, y1=2, x2=3, y2=4),
ia.BoundingBox(x1=2, y1=3, x2=4, y2=5)],
]
bbsois = [ia.BoundingBoxesOnImage(bbs[0], shape=images[0]),
ia.BoundingBoxesOnImage(bbs[1], shape=images[1])]
aug = iaa.Affine(rotate=(-30, 30))
# No longer recommended:
aug_det = aug.to_deterministic()
images_aug = aug_det.augment_images(images)
bbsois_aug = aug_det.augment_bounding_boxes(bbsois)
# Now recommended:
images_aug, bbs_aug = aug(images=images, bounding_boxes=bbs)
```
* Added methods:
* `augmentables.batches.Batch.to_normalized_batch()`.
* `augmentables.batches.Batch.get_augmentables()`.
* `augmentables.batches.UnnormalizedBatch.get_augmentables()`.
* `augmentables.batches.Batch.get_augmentable_names()`.
* `augmentables.batches.UnnormalizedBatch.get_augmentable_names()`.
* `augmentables.batches.Batch.to_batch_in_augmentation()`.
* `augmentables.batches.Batch.fill_from_batch_in_augmentation_()`.
* `augmentables.batches.UnnormalizedBatch.fill_from_augmented_normalized_batch()`.
* `augmenters.meta.Augmenter.augment_batch_()`, ,
similar to `augment_batch()`, but explicitly works in-place and has a
`parent` parameter.
* `augmenters.meta.Augmenter._augment_batch_()`.
* `augmentables.polys.recover_psois_()`.
* `augmentables.utils.convert_cbaois_to_kpsois()`.
* `augmentables.utils.invert_convert_cbaois_to_kpsois_()`.
* `augmentables.utils.deepcopy_fast()`.
* `augmentables.bbs.BoundingBox.from_point_soup()`.
* `augmentables.bbs.BoundingBoxesOnImages.from_point_soups()`.
* Added method `to_xy_array()` to:
* `augmentables.bbs.BoundingBoxesOnImage`.
* `augmentables.polys.PolygonsOnImage`.
* `augmentables.lines.LineStringsOnImage`.
* Added methods `to_keypoints_on_image()`,
`invert_to_keypoints_on_image_()` and `fill_from_xy_array_()` to:
* `augmentables.kps.KeypointsOnImage`.
* `augmentables.bbs.BoundingBoxesOnImage`.
* `augmentables.polys.PolygonsOnImage`.
* `augmentables.lines.LineStringsOnImage`.
* Added classes:
* `testutils.TemporaryDirectory` (context)
* Changed:
* Changed the following methods to be thin wrappers around
`augment_batch_()`:
* `augmenters.meta.Augmenter.augment_images()`
* `augmenters.meta.Augmenter.augment_heatmaps()`.
* `augmenters.meta.Augmenter.augment_segmentation_maps()`.
* `augmenters.meta.Augmenter.augment_keypoints()`.
* `augmenters.meta.Augmenter.augment_bounding_boxes()`.
* `augmenters.meta.Augmenter.augment_polygons()`.
* `augmenters.meta.Augmenter.augment_line_strings()`.
* Changed `augment_image()`, `augment_images()`, `augment_heatmaps()`,
`augment_segmentation_maps()`, `augment_keypoints()`,
`augment_bounding_boxes()`, `augment_polygons()` and
`augment_line_strings()` to return `None` inputs without change.
Previously they resulted in an exception. This is more consistent with
the behaviour in the other `augment_*` methods.
* Changed `augment_images()` to no longer be abstract. It defaults
to not changing the input images.
* Changed `imgaug.augmentables.BoundingBoxesOnImage.from_xyxy_array()`
to also accept `(N, 2, 2)` arrays instead of only `(N, 4)`.
Deprecated:
* Deprecated `imgaug.augmenters.meta.Augmenter.augment_batch()`.
Use `.augment_batch_()` instead.
Refactored:
* Refactored most augmenters to use single `_augment_batch_()` method.
Other changes:
* Added validation of input arguments to `KeypointsOnImage.from_xy_array()`.
* Improved validation of input arguments to
`BoundingBoxesOnImage.from_xyxy_array()`.
## Reworked Quantization [#467](https://github.com/aleju/imgaug/pull/467)
This patch reworked the quantization routines to also support quantization
to `N` bits instead of `N` colors in a way that is similar to posterization
in `PIL`. The patch added corresponding `UniformColorQuantizationToNBits`
and `Posterize` augmenters, as well as a `quantize_uniform_to_n_bits()`
function.
* Added classes:
* `augmenters.color.UniformColorQuantizationToNBits`.
* `augmenters.color.Posterize` (alias of `UniformColorQuantizationToNBits`).
* Added functions:
* `augmenters.color.quantize_uniform_()`, the in-place
version of `quantize_uniform()`.
* `augmenters.color.quantize_uniform_to_n_bits()`.
* `augmenters.color.quantize_uniform_to_n_bits_()`.
* `augmenters.color.posterize()`, an alias of
`quantize_uniform_to_n_bits()` that produces the same outputs as
`PIL.ImageOps.posterize()`.
* Added parameters:
* Added `to_bin_centers=True` to `quantize_uniform()`, controling
whether each bin `(a, b)` should be quantized to `a + (b-a)/2` or `a`.
* Deprecated:
* Renamed `imgaug.augmenters.color.quantize_colors_uniform(image, n_colors)`
to `imgaug.augmenters.color.quantize_uniform(arr, nb_bins)`. The old name
is now deprecated.
* Renamed `imgaug.augmenters.color.quantize_colors_kmeans(image, n_colors)`
to `imgaug.augmenters.color.quantize_kmeans(arr, nb_clusters)`. The old
name is now deprecated.
* Other changes:
* Improved performance of `quantize_uniform()` by roughly 10x (small images
around 64x64) to 100x (large images around 1024x1024). This also affects
`UniformColorQuantization`.
* Improved performance of `UniformColorQuantization` by using more in-place
functions.
* Fixed:
* Fixed `quantize_uniform()` producing wrong outputs for non-contiguous
arrays.
## Improved Invert [#469](https://github.com/aleju/imgaug/pull/469)
Added thresholds to `Invert` and the corresponding functions. This enables
solarization (inversion with thresholds). The patch also added
a corresponding `Solarize` augmenter and two solarization functions.
* Added augmenter `imgaug.augmenters.Solarize`, a wrapper around `Invert`.
* Added function `imgaug.augmenters.arithmetic.solarize()`, a wrapper around
`solarize_()`.
* Added function `imgaug.augmenters.arithmetic.solarize_()`, a wrapper around
`invert_()`.
* Added function `imgaug.augmenters.arithmetic.invert_()`, an in-place version
of `imgaug.augmenters.arithmetic.invert()`.
* Added parameters `threshold` and `invert_above_threshold` to
`imgaug.augmenters.arithmetic.invert()`
* Added parameters `threshold` and `invert_above_threshold` to
`imgaug.augmenters.arithmetic.Invert`.
* Improved performance of `imgaug.augmenters.arithmetic.invert()` and
`imgaug.augmenters.arithmetic.Invert` for `uint8` images.
## All Augmenters are now Pickle-able [#493](https://github.com/aleju/imgaug/pull/493) [#575](https://github.com/aleju/imgaug/pull/575)
Ensured that all augmenters can be pickled and un-pickled without errors.
* Added function `imgaug.testutils.runtest_pickleable_uint8_img()`.
* Fixed `imgaug.augmenters.blur.MotionBlur` not being pickle-able.
* Fixed `imgaug.augmenters.meta.AssertLambda` not being pickle-able.
* Fixed `imgaug.augmenters.meta.AssertShape` not being pickle-able.
* Fixed `imgaug.augmenters.color.MultiplyHueAndSaturation` not supporting
all standard RNG datatypes for `random_state`.
## Extended Cropping and Padding Augmenters [#459](https://github.com/aleju/imgaug/pull/459)
This patch extended the cropping and padding augmenters. It added
augmenters that crop/pad towards multiples of values (e.g. crop the
width until it is a multiple of `2`), towards powers of values (e.g.
crop the width until it is one of `1, 2, 4, 8, 16, ...`), towards
an aspect ratio (crop the width or height until `width/height = 2.0`) or
towards a squared size (e.g. crop the width or height until they are equal).
These augmenters are wrappers around `CropToFixedSize` and
`PadToFixedSize`. All `*FixedSize` augmenters also have now corresponding
`Center*ToFixedSize` aliases, e.g. `CenterCropToPowersOf`. These
are equivalent to using `position="center"`, e.g. `CenterCropToPowersOf`
is equivalent to `CropToPowersOf(..., position="center")`.
The following functions were moved. Their old names are now deprecated.
* Moved `imgaug.imgaug.pad` to `imgaug.augmenters.size.pad`
* Moved `imgaug.imgaug.pad_to_aspect_ratio` to
`imgaug.augmenters.size.pad_to_aspect_ratio`.
* Moved `imgaug.imgaug.pad_to_multiples_of` to
`imgaug.augmenters.size.pad_to_multiples_of`.
* Moved `imgaug.imgaug.compute_paddings_for_aspect_ratio` to
`imgaug.augmenters.size.compute_paddings_to_reach_aspect_ratio`.
* Moved `imgaug.imgaug.compute_paddings_to_reach_multiples_of`
to `imgaug.augmenters.size.compute_paddings_to_reach_multiples_of`.
The following augmenters were added:
* Added augmenter `CenterCropToFixedSize`.
* Added augmenter `CenterPadToFixedSize`.
* Added augmenter `CropToMultiplesOf`.
* Added augmenter `CenterCropToMultiplesOf`.
* Added augmenter `PadToMultiplesOf`.
* Added augmenter `CenterPadToMultiplesOf`.
* Added augmenter `CropToPowersOf`.
* Added augmenter `CenterCropToPowersOf`.
* Added augmenter `PadToPowersOf`.
* Added augmenter `CenterPadToPowersOf`.
* Added augmenter `CropToAspectRatio`.
* Added augmenter `CenterCropToAspectRatio`.
* Added augmenter `PadToAspectRatio`.
* Added augmenter `CenterPadToAspectRatio`.
* Added augmenter `PadToSquare`.
* Added augmenter `CenterPadToSquare`.
* Added augmenter `CropToSquare`.
* Added augmenter `CenterCropToSquare`.
All `Center
