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imgaug

by aleju

aleju /imgaug

Image augmentation for machine learning experiments.

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imgaug

This python library helps you with augmenting images for your machine learning projects. It converts a set of input images into a new, much larger set of slightly altered images.

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|   | Image | Heatmaps | Seg. Maps | Keypoints | Bounding Boxes,
Polygons | | --- | --- | --- | --- | --- | --- |

| _Original Input_ | ![input images](https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/noop_image.jpg?raw=true) | ![input heatmaps](https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/noop_heatmap.jpg?raw=true) | ![input segmentation maps](https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/noop_segmap.jpg?raw=true) | ![input keypoints](https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/noop_kps.jpg?raw=true) | ![input bounding boxes](https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/noop_bbs.jpg?raw=true) | | Gauss. Noise

+ Contrast
+ Sharpen | non geometric augmentations, applied to images | non geometric augmentations, applied to heatmaps | non geometric augmentations, applied to segmentation maps | non geometric augmentations, applied to keypoints | non geometric augmentations, applied to bounding boxes |

| Affine | ![affine augmentations, applied to images](https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/affine_image.jpg?raw=true) | ![affine augmentations, applied to heatmaps](https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/affine_heatmap.jpg?raw=true) | ![affine augmentations, applied to segmentation maps](https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/affine_segmap.jpg?raw=true) | ![affine augmentations, applied to keypoints](https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/affine_kps.jpg?raw=true) | ![affine augmentations, applied to bounding boxes](https://raw.githubusercontent.com/aleju/imgaug-doc/master/readme_images/small_overview/affine_bbs.jpg?raw=true) | | Crop

+ Pad | crop and pad augmentations, applied to images | crop and pad augmentations, applied to heatmaps | crop and pad augmentations, applied to segmentation maps | crop and pad augmentations, applied to keypoints | crop and pad augmentations, applied to bounding boxes |

| Fliplr

+ Perspective | Horizontal flip and perspective transform augmentations, applied to images | Horizontal flip and perspective transform augmentations, applied to heatmaps | Horizontal flip and perspective transform augmentations, applied to segmentation maps | Horizontal flip and perspective transform augmentations, applied to keypoints | Horizontal flip and perspective transform augmentations, applied to bounding boxes |

More (strong) example augmentations of one input image:

64 quokkas

Table of Contents

  1. Features
  2. Installation
  3. Documentation
  4. Recent Changes
  5. Example Images
  6. Code Examples
  7. Citation

Features

  • Many augmentation techniques
    • E.g. affine transformations, perspective transformations, contrast changes, gaussian noise, dropout of regions, hue/saturation changes, cropping/padding, blurring, ...
    • Optimized for high performance
    • Easy to apply augmentations only to some images
    • Easy to apply augmentations in random order
  • Support for
    • Images (full support for uint8, for other dtypes see documentation)
    • Heatmaps (float32), Segmentation Maps (int), Masks (bool)
    • May be smaller/larger than their corresponding images. No extra lines of code needed for e.g. crop.
    • Keypoints/Landmarks (int/float coordinates)
    • Bounding Boxes (int/float coordinates)
    • Polygons (int/float coordinates)
    • Line Strings (int/float coordinates)
  • Automatic alignment of sampled random values
    • Example: Rotate image and segmentation map on it by the same value sampled from
      uniform(-10°, 45°)
      . (0 extra lines of code.)
  • Probability distributions as parameters
    • Example: Rotate images by values sampled from
      uniform(-10°, 45°)
      .
    • Example: Rotate images by values sampled from
      ABS(N(0, 20.0))\*(1+B(1.0, 1.0))
      ", where
      ABS(.)
      is the absolute function,
      N(.)
      the gaussian distribution and
      B(.)
      the beta distribution.
  • Many helper functions
    • Example: Draw heatmaps, segmentation maps, keypoints, bounding boxes, ...
    • Example: Scale segmentation maps, average/max pool of images/maps, pad images to aspect ratios (e.g. to square them)
    • Example: Convert keypoints to distance maps, extract pixels within bounding boxes from images, clip polygon to the image plane, ...
  • Support for augmentation on multiple CPU cores

Installation

The library supports python 2.7 and 3.4+.

Installation: Anaconda

To install the library in anaconda, perform the following commands:

bash conda config --add channels conda-forge conda install imgaug

You can deinstall the library again via

conda remove imgaug

.

Installation: pip

Then install imgaug either via pypi (can lag behind the github version):

bash pip install imgaug

or install the latest version directly from github:

bash pip install git+https://github.com/aleju/imgaug.git

For more details, see the install guide

To deinstall the library, just execute

pip uninstall imgaug

.

Documentation

Example jupyter notebooks: * Load and Augment an Image * Multicore Augmentation * Augment and work with: Keypoints/Landmarks, Bounding Boxes, Polygons, Line Strings, Heatmaps, Segmentation Maps

More notebooks: imgaug-doc/notebooks.

Example ReadTheDocs pages: * Quick example code on how to use the library* Overview of all Augmenters* API

More RTD documentation: imgaug.readthedocs.io.

All documentation related files of this project are hosted in the repository imgaug-doc.

Recent Changes

  • 0.4.0: Added new augmenters, changed backend to batchwise augmentation, support for numpy 1.18 and python 3.8.
  • 0.3.0: Reworked segmentation map augmentation, adapted to numpy 1.17+ random number sampling API, several new augmenters.
  • 0.2.9: Added polygon augmentation, added line string augmentation, simplified augmentation interface.
  • 0.2.8: Improved performance, dtype support and multicore augmentation.

See changelogs/ for more details.

Example Images

The images below show examples for most augmentation techniques.

Values written in the form

(a, b)

denote a uniform distribution, i.e. the value is randomly picked from the interval

[a, b]

. Line strings are supported by (almost) all augmenters, but are not explicitly visualized here.

| meta | | Identity | ChannelShuffle |   |   |   | | Identity | ChannelShuffle |   |   |   | | | | | See also: Sequential, SomeOf, OneOf, Sometimes, WithChannels, Lambda, AssertLambda, AssertShape, RemoveCBAsByOutOfImageFraction, ClipCBAsToImagePlanes | | arithmetic | | Add | Add
(per_channel=True)
| AdditiveGaussianNoise | AdditiveGaussianNoise
(per_channel=True)
| Multiply | | Add | Add per_channel=True | AdditiveGaussianNoise | AdditiveGaussianNoise per_channel=True | Multiply | | Cutout | Dropout | CoarseDropout
(p=0.2)
| CoarseDropout
(p=0.2, per_channel=True)
| Dropout2d | | Cutout | Dropout | CoarseDropout p=0.2 | CoarseDropout p=0.2, per_channel=True | Dropout2d | | SaltAndPepper | CoarseSaltAndPepper
(p=0.2)
| Invert | Solarize | JpegCompression | | SaltAndPepper | CoarseSaltAndPepper p=0.2 | Invert | Solarize | JpegCompression | | | | | See also: AddElementwise, AdditiveLaplaceNoise, AdditivePoissonNoise, MultiplyElementwise, TotalDropout, ReplaceElementwise, ImpulseNoise, Salt, Pepper, CoarseSalt, CoarsePepper, Solarize | | artistic | | Cartoon |   |   |   |   | | Cartoon |   |   |   |   | | blend | | BlendAlpha
with EdgeDetect(1.0)
| BlendAlphaSimplexNoise
with EdgeDetect(1.0)
| BlendAlphaFrequencyNoise
with EdgeDetect(1.0)
| BlendAlphaSomeColors
with RemoveSaturation(1.0)
| BlendAlphaRegularGrid
with Multiply((0.0, 0.5))
| | BlendAlpha with EdgeDetect1.0 | BlendAlphaSimplexNoise with EdgeDetect1.0 | BlendAlphaFrequencyNoise with EdgeDetect1.0 | BlendAlphaSomeColors with RemoveSaturation1.0 | BlendAlphaRegularGrid with Multiply0.0, 0.5 | | | | | See also: BlendAlphaMask, BlendAlphaElementwise, BlendAlphaVerticalLinearGradient, BlendAlphaHorizontalLinearGradient, BlendAlphaSegMapClassIds, BlendAlphaBoundingBoxes, BlendAlphaCheckerboard, SomeColorsMaskGen, HorizontalLinearGradientMaskGen, VerticalLinearGradientMaskGen, RegularGridMaskGen, CheckerboardMaskGen, SegMapClassIdsMaskGen, BoundingBoxesMaskGen, InvertMaskGen | | blur | | GaussianBlur | AverageBlur | MedianBlur | BilateralBlur
(sigma_color=250,
sigma_space=250)
| MotionBlur
(angle=0)
| | GaussianBlur | AverageBlur | MedianBlur | BilateralBlur sigma_color=250, sigma_space=250 | MotionBlur angle=0 | | MotionBlur
(k=5)
| MeanShiftBlur |   |   |   | | MotionBlur k=5 | MeanShiftBlur |   |   |   | | collections | | RandAugment |   |   |   |   | | RandAugment |   |   |   |   | | color | | MultiplyAndAddToBrightness | MultiplyHueAndSaturation | MultiplyHue | MultiplySaturation | AddToHueAndSaturation | | MultiplyAndAddToBrightness | MultiplyHueAndSaturation | MultiplyHue | MultiplySaturation | AddToHueAndSaturation | | Grayscale | RemoveSaturation | ChangeColorTemperature | KMeansColorQuantization
(to_colorspace=RGB)
| UniformColorQuantization
(to_colorspace=RGB)
| | Grayscale | RemoveSaturation | ChangeColorTemperature | KMeansColorQuantization to_colorspace=RGB | UniformColorQuantization to_colorspace=RGB | | | | | See also: WithColorspace, WithBrightnessChannels, MultiplyBrightness, AddToBrightness, WithHueAndSaturation, AddToHue, AddToSaturation, ChangeColorspace, Posterize | | contrast | | GammaContrast | GammaContrast
(per_channel=True)
| SigmoidContrast
(cutoff=0.5)
| SigmoidContrast
(gain=10)
| LogContrast | | GammaContrast | GammaContrast per_channel=True | SigmoidContrast cutoff=0.5 | SigmoidContrast gain=10 | LogContrast | | LinearContrast | AllChannels-
HistogramEqualization
| HistogramEqualization | AllChannelsCLAHE | CLAHE | | LinearContrast | AllChannels- HistogramEqualization | HistogramEqualization | AllChannelsCLAHE | CLAHE | | | | | See also: Equalize | | convolutional | | Sharpen
(alpha=1)
| Emboss
(alpha=1)
| EdgeDetect | DirectedEdgeDetect
(alpha=1)
|   | | Sharpen alpha=1 | Emboss alpha=1 | EdgeDetect | DirectedEdgeDetect alpha=1 |   | | | | | See also: Convolve | | debug | | See also: SaveDebugImageEveryNBatches | | edges | | Canny |   |   |   |   | | Canny |   |   |   |   | | flip | | Fliplr | Flipud |   | | Fliplr | Flipud |   | | | | | See also: HorizontalFlip, VerticalFlip | | geometric | | Affine | Affine: Modes |   | | Affine | Affine: Modes |   | | Affine: cval | PiecewiseAffine |   | | Affine: cval | PiecewiseAffine |   | | PerspectiveTransform | ElasticTransformation
(sigma=1.0)
|   | | PerspectiveTransform | ElasticTransformation sigma=1.0 |   | | ElasticTransformation
(sigma=4.0)
| Rot90 |   | | ElasticTransformation sigma=4.0 | Rot90 |   | | WithPolarWarping
+Affine
| Jigsaw
(5x5 grid)
|   | | WithPolarWarping +Affine | Jigsaw 5x5 grid |   | | | | | See also: ScaleX, ScaleY, TranslateX, TranslateY, Rotate | | imgcorruptlike | | GlassBlur | DefocusBlur | ZoomBlur | Snow | Spatter | | GlassBlur | DefocusBlur | ZoomBlur | Snow | Spatter | | | | | See also: GaussianNoise, ShotNoise, ImpulseNoise, SpeckleNoise, GaussianBlur, MotionBlur, Fog, Frost, Contrast, Brightness, Saturate, JpegCompression, Pixelate, ElasticTransform | | pillike | | Autocontrast | EnhanceColor | EnhanceSharpness | FilterEdgeEnhanceMore | FilterContour | | Autocontrast | EnhanceColor | EnhanceSharpness | FilterEdgeEnhanceMore | FilterContour | | | | | See also: Solarize, Posterize, Equalize, EnhanceContrast, EnhanceBrightness, FilterBlur, FilterSmooth, FilterSmoothMore, FilterEdgeEnhance, FilterFindEdges, FilterEmboss, FilterSharpen, FilterDetail, Affine | | pooling | | AveragePooling | MaxPooling | MinPooling | MedianPooling |   | | AveragePooling | MaxPooling | MinPooling | MedianPooling |   | | segmentation | | Superpixels
(p_replace=1)
| Superpixels
(n_segments=100)
| UniformVoronoi | RegularGridVoronoi: rows/cols
(p_drop_points=0)
| RegularGridVoronoi: p_drop_points
(n_rows=n_cols=30)
| | Superpixels p_replace=1 | Superpixels n_segments=100 | UniformVoronoi | RegularGridVoronoi: rows/cols p_drop_points=0 | RegularGridVoronoi: p_drop_points n_rows=n_cols=30 | | RegularGridVoronoi: p_replace
(n_rows=n_cols=16)
|   |   |   |   | | RegularGridVoronoi: p_replace n_rows=n_cols=16 |   |   |   |   | | | | | See also: Voronoi, RelativeRegularGridVoronoi, RegularGridPointsSampler, RelativeRegularGridPointsSampler, DropoutPointsSampler, UniformPointsSampler, SubsamplingPointsSampler | | size | | CropAndPad | Crop |   | | CropAndPad | Crop |   | | Pad | PadToFixedSize
(height'=height+32,
width'=width+32)
|   | | Pad | PadToFixedSize height'=height+32, width'=width+32 |   | | CropToFixedSize
(height'=height-32,
width'=width-32)
|   |   |   | | CropToFixedSize height'=height-32, width'=width-32 |   |   |   | | | | | See also: Resize, CropToMultiplesOf, PadToMultiplesOf, CropToPowersOf, PadToPowersOf, CropToAspectRatio, PadToAspectRatio, CropToSquare, PadToSquare, CenterCropToFixedSize, CenterPadToFixedSize, CenterCropToMultiplesOf, CenterPadToMultiplesOf, CenterCropToPowersOf, CenterPadToPowersOf, CenterCropToAspectRatio, CenterPadToAspectRatio, CenterCropToSquare, CenterPadToSquare, KeepSizeByResize | | weather | | FastSnowyLandscape
(lightness_multiplier=2.0)
| Clouds | Fog | Snowflakes | Rain | | FastSnowyLandscape lightness_multiplier=2.0 | Clouds | Fog | Snowflakes | Rain | | | | | See also: CloudLayer, SnowflakesLayer, RainLayer |

Code Examples

Example: Simple Training Setting

A standard machine learning situation. Train on batches of images and augment each batch via crop, horizontal flip ("Fliplr") and gaussian blur: ```python import numpy as np import imgaug.augmenters as iaa

def load_batch(batch_idx): # dummy function, implement this # Return a numpy array of shape (N, height, width, #channels) # or a list of (height, width, #channels) arrays (may have different image # sizes). # Images should be in RGB for colorspace augmentations. # (cv2.imread() returns BGR!) # Images should usually be in uint8 with values from 0-255. return np.zeros((128, 32, 32, 3), dtype=np.uint8) + (batch_idx % 255)

def train_on_images(images): # dummy function, implement this pass

Pipeline:

(1) Crop images from each side by 1-16px, do not resize the results

images back to the input size. Keep them at the cropped size.

(2) Horizontally flip 50% of the images.

(3) Blur images using a gaussian kernel with sigma between 0.0 and 3.0.

seq = iaa.Sequential([iaa.Crop(px=(1, 16), keep_size=False), iaa.Fliplr(0.5), iaa.GaussianBlur(sigma=(0, 3.0))])

for batch_idx in range(100): images = load_batch(batch_idx) images_aug = seq(images=images) # done by the library train_on_images(images_aug) ```

Example: Very Complex Augmentation Pipeline

Apply a very heavy augmentation pipeline to images (used to create the image at the very top of this readme): ```python import numpy as np import imgaug as ia import imgaug.augmenters as iaa

random example images

images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8)

Sometimes(0.5, ...) applies the given augmenter in 50% of all cases,

e.g. Sometimes(0.5, GaussianBlur(0.3)) would blur roughly every second image.

sometimes = lambda aug: iaa.Sometimes(0.5, aug)

Define our sequence of augmentation steps that will be applied to every image

All augmenters with per_channel=0.5 will sample one value _per image_

in 50% of all cases. In all other cases they will sample new values

per channel.

seq = iaa.Sequential( [# apply the following augmenters to most images iaa.Fliplr(0.5), # horizontally flip 50% of all images iaa.Flipud(0.2), # vertically flip 20% of all images # crop images by -5% to 10% of their height/width sometimes(iaa.CropAndPad( percent=(-0.05, 0.1), pad_mode=ia.ALL, pad_cval=(0, 255) )), sometimes(iaa.Affine( scale={"x": (0.8, 1.2), "y": (0.8, 1.2)}, # scale images to 80-120% of their size, individually per axis translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)}, # translate by -20 to +20 percent (per axis) rotate=(-45, 45), # rotate by -45 to +45 degrees shear=(-16, 16), # shear by -16 to +16 degrees order=[0, 1], # use nearest neighbour or bilinear interpolation (fast) cval=(0, 255), # if mode is constant, use a cval between 0 and 255 mode=ia.ALL # use any of scikit-image's warping modes (see 2nd image from the top for examples) )), # execute 0 to 5 of the following (less important) augmenters per image # don't execute all of them, as that would often be way too strong iaa.SomeOf((0, 5), [sometimes(iaa.Superpixels(p_replace=(0, 1.0), n_segments=(20, 200))), # convert images into their superpixel representation iaa.OneOf([ iaa.GaussianBlur((0, 3.0)), # blur images with a sigma between 0 and 3.0 iaa.AverageBlur(k=(2, 7)), # blur image using local means with kernel sizes between 2 and 7 iaa.MedianBlur(k=(3, 11)), # blur image using local medians with kernel sizes between 2 and 7]), iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)), # sharpen images iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)), # emboss images # search either for all edges or for directed edges, # blend the result with the original image using a blobby mask iaa.SimplexNoiseAlpha(iaa.OneOf([iaa.EdgeDetect(alpha=(0.5, 1.0)), iaa.DirectedEdgeDetect(alpha=(0.5, 1.0), direction=(0.0, 1.0)),])), iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5), # add gaussian noise to images iaa.OneOf([iaa.Dropout((0.01, 0.1), per_channel=0.5), # randomly remove up to 10% of the pixels iaa.CoarseDropout((0.03, 0.15), size_percent=(0.02, 0.05), per_channel=0.2),]), iaa.Invert(0.05, per_channel=True), # invert color channels iaa.Add((-10, 10), per_channel=0.5), # change brightness of images (by -10 to 10 of original value) iaa.AddToHueAndSaturation((-20, 20)), # change hue and saturation # either change the brightness of the whole image (sometimes # per channel) or change the brightness of subareas iaa.OneOf([iaa.Multiply((0.5, 1.5), per_channel=0.5), iaa.FrequencyNoiseAlpha( exponent=(-4, 0), first=iaa.Multiply((0.5, 1.5), per_channel=True), second=iaa.LinearContrast((0.5, 2.0)) )]), iaa.LinearContrast((0.5, 2.0), per_channel=0.5), # improve or worsen the contrast iaa.Grayscale(alpha=(0.0, 1.0)), sometimes(iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25)), # move pixels locally around (with random strengths) sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.05))), # sometimes move parts of the image around sometimes(iaa.PerspectiveTransform(scale=(0.01, 0.1))) ], random_order=True ) ], random_order=True ) images_aug = seq(images=images) ```

Example: Augment Images and Keypoints

Augment images and keypoints/landmarks on the same images: ```python import numpy as np import imgaug.augmenters as iaa

images = np.zeros((2, 128, 128, 3), dtype=np.uint8) # two example images images[:, 64, 64, :] = 255 points = [[(10.5, 20.5)], # points on first image [(50.5, 50.5), (60.5, 60.5), (70.5, 70.5)] # points on second image ]

seq = iaa.Sequential([iaa.AdditiveGaussianNoise(scale=0.05*255), iaa.Affine(translate_px={"x": (1, 5)})])

augment keypoints and images

images_aug, points_aug = seq(images=images, keypoints=points)

print("Image 1 center", np.argmax(images_aug[0, 64, 64:64+6, 0])) print("Image 2 center", np.argmax(images_aug[1, 64, 64:64+6, 0])) print("Points 1", points_aug[0]) print("Points 2", points_aug[1]) ``

Note that all coordinates in

imgaug

are subpixel-accurate, which is why

x=0.5, y=0.5` denotes the center of the top left pixel.

Example: Augment Images and Bounding Boxes

import numpy as np import imgaug as ia import imgaug.augmenters as iaa images = np.zeros((2, 128, 128, 3), dtype=np.uint8) # two example images images[:, 64, 64, :] = 255 bbs = [[ia.BoundingBox(x1=10.5, y1=15.5, x2=30.5, y2=50.5)], [ia.BoundingBox(x1=10.5, y1=20.5, x2=50.5, y2=50.5), ia.BoundingBox(x1=40.5, y1=75.5, x2=70.5, y2=100.5)] ] seq = iaa.Sequential([iaa.AdditiveGaussianNoise(scale=0.05\*255), iaa.Affine(translate\_px={"x": (1, 5)})]) images\_aug, bbs\_aug = seq(images=images, bounding\_boxes=bbs)

Example: Augment Images and Polygons

import numpy as np import imgaug as ia import imgaug.augmenters as iaa images = np.zeros((2, 128, 128, 3), dtype=np.uint8) # two example images images[:, 64, 64, :] = 255 polygons = [[ia.Polygon([(10.5, 10.5), (50.5, 10.5), (50.5, 50.5)])], [ia.Polygon([(0.0, 64.5), (64.5, 0.0), (128.0, 128.0), (64.5, 128.0)])] ] seq = iaa.Sequential([iaa.AdditiveGaussianNoise(scale=0.05\*255), iaa.Affine(translate\_px={"x": (1, 5)})]) images\_aug, polygons\_aug = seq(images=images, polygons=polygons)

Example: Augment Images and LineStrings

LineStrings are similar to polygons, but are not closed, may intersect with themselves and don't have an inner area. ```python import numpy as np import imgaug as ia import imgaug.augmenters as iaa

images = np.zeros((2, 128, 128, 3), dtype=np.uint8) # two example images images[:, 64, 64, :] = 255 ls = [[ia.LineString([(10.5, 10.5), (50.5, 10.5), (50.5, 50.5)])], [ia.LineString([(0.0, 64.5), (64.5, 0.0), (128.0, 128.0), (64.5, 128.0), (128.0, 0.0)])] ]

seq = iaa.Sequential([iaa.AdditiveGaussianNoise(scale=0.05*255), iaa.Affine(translate_px={"x": (1, 5)})])

images_aug, ls_aug = seq(images=images, line_strings=ls) ```

Example: Augment Images and Heatmaps

Heatmaps are dense float arrays with values between

0.0

and

1.0

. They can be used e.g. when training models to predict facial landmark locations. Note that the heatmaps here have lower height and width than the images.

imgaug

handles that case automatically. The crop pixel amounts will be halved for the heatmaps.

import numpy as np import imgaug.augmenters as iaa # Standard scenario: You have N RGB-images and additionally 21 heatmaps per # image. You want to augment each image and its heatmaps identically. images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8) heatmaps = np.random.random(size=(16, 64, 64, 1)).astype(np.float32) seq = iaa.Sequential([iaa.GaussianBlur((0, 3.0)), iaa.Affine(translate\_px={"x": (-40, 40)}), iaa.Crop(px=(0, 10))]) images\_aug, heatmaps\_aug = seq(images=images, heatmaps=heatmaps)

Example: Augment Images and Segmentation Maps

This is similar to heatmaps, but the dense arrays have dtype

int32

. Operations such as resizing will automatically use nearest neighbour interpolation.

import numpy as np import imgaug.augmenters as iaa # Standard scenario: You have N=16 RGB-images and additionally one segmentation # map per image. You want to augment each image and its heatmaps identically. images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8) segmaps = np.random.randint(0, 10, size=(16, 64, 64, 1), dtype=np.int32) seq = iaa.Sequential([iaa.GaussianBlur((0, 3.0)), iaa.Affine(translate\_px={"x": (-40, 40)}), iaa.Crop(px=(0, 10))]) images\_aug, segmaps\_aug = seq(images=images, segmentation\_maps=segmaps)

Example: Visualize Augmented Images

Quickly show example results of your augmentation sequence: ```python import numpy as np import imgaug.augmenters as iaa

images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8) seq = iaa.Sequential([iaa.Fliplr(0.5), iaa.GaussianBlur((0, 3.0))])

Show an image with 8_8 augmented versions of image 0 and 8_8 augmented

versions of image 1. Identical augmentations will be applied to

image 0 and 1.

seq.show_grid([images[0], images[1]], cols=8, rows=8) ```

Example: Visualize Augmented Non-Image Data

imgaug

contains many helper function, among these functions to quickly visualize augmented non-image results, such as bounding boxes or heatmaps.

import numpy as np import imgaug as ia image = np.zeros((64, 64, 3), dtype=np.uint8) # points kps = [ia.Keypoint(x=10.5, y=20.5), ia.Keypoint(x=60.5, y=60.5)] kpsoi = ia.KeypointsOnImage(kps, shape=image.shape) image\_with\_kps = kpsoi.draw\_on\_image(image, size=7, color=(0, 0, 255)) ia.imshow(image\_with\_kps) # bbs bbsoi = ia.BoundingBoxesOnImage([ia.BoundingBox(x1=10.5, y1=20.5, x2=50.5, y2=30.5)], shape=image.shape) image\_with\_bbs = bbsoi.draw\_on\_image(image) image\_with\_bbs = ia.BoundingBox( x1=50.5, y1=10.5, x2=100.5, y2=16.5 ).draw\_on\_image(image\_with\_bbs, color=(255, 0, 0), size=3) ia.imshow(image\_with\_bbs) # polygons psoi = ia.PolygonsOnImage([ia.Polygon([(10.5, 20.5), (50.5, 30.5), (10.5, 50.5)]) ], shape=image.shape) image\_with\_polys = psoi.draw\_on\_image( image, alpha\_points=0, alpha\_face=0.5, color\_lines=(255, 0, 0)) ia.imshow(image\_with\_polys) # heatmaps hms = ia.HeatmapsOnImage(np.random.random(size=(32, 32, 1)).astype(np.float32), shape=image.shape) image\_with\_hms = hms.draw\_on\_image(image) ia.imshow(image\_with\_hms)

LineStrings and segmentation maps support similar methods as shown above.

Example: Using Augmenters Only Once

While the interface is adapted towards re-using instances of augmenters many times, you are also free to use them only once. The overhead to instantiate the augmenters each time is usually negligible.

from imgaug import augmenters as iaa import numpy as np images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8) # always horizontally flip each input image images\_aug = iaa.Fliplr(1.0)(images=images) # vertically flip each input image with 90% probability images\_aug = iaa.Flipud(0.9)(images=images) # blur 50% of all images using a gaussian kernel with a sigma of 3.0 images\_aug = iaa.Sometimes(0.5, iaa.GaussianBlur(3.0))(images=images)

Example: Multicore Augmentation

Images can be augmented in background processes using the method

augment\_batches(batches, background=True)

, where

batches

is a list/generator ofimgaug.augmentables.batches.UnnormalizedBatchorimgaug.augmentables.batches.Batch. The following example augments a list of image batches in the background: ```python import skimage.data import imgaug as ia import imgaug.augmenters as iaa from imgaug.augmentables.batches import UnnormalizedBatch

Number of batches and batch size for this example

nb_batches = 10 batch_size = 32

Example augmentation sequence to run in the background

augseq = iaa.Sequential([iaa.Fliplr(0.5), iaa.CoarseDropout(p=0.1, size_percent=0.1)])

For simplicity, we use the same image here many times

astronaut = skimage.data.astronaut() astronaut = ia.imresize_single_image(astronaut, (64, 64))

Make batches out of the example image (here: 10 batches, each 32 times

the example image)

batches = [] for _ in range(nb_batches): batches.append(UnnormalizedBatch(images=[astronaut] * batch_size))

Show the augmented images.

Note that augment_batches() returns a generator.

for images_aug in augseq.augment_batches(batches, background=True): ia.imshow(ia.draw_grid(images_aug.images_aug, cols=8)) ```

If you need more control over the background augmentation, e.g. to set seeds, control the number of used CPU cores or constraint the memory usage, see the correspondingmulticore augmentation notebookor the API aboutAugmenter.pool()andimgaug.multicore.Pool.

Example: Probability Distributions as Parameters

Most augmenters support using tuples

(a, b)

as a shortcut to denote

uniform(a, b)

or lists

[a, b, c]

to denote a set of allowed values from which one will be picked randomly. If you require more complex probability distributions (e.g. gaussians, truncated gaussians or poisson distributions) you can use stochastic parameters from

imgaug.parameters

:

import numpy as np from imgaug import augmenters as iaa from imgaug import parameters as iap images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8) # Blur by a value sigma which is sampled from a uniform distribution # of range 10.1 \<= x \< 13.0. # The convenience shortcut for this is: GaussianBlur((10.1, 13.0)) blurer = iaa.GaussianBlur(10 + iap.Uniform(0.1, 3.0)) images\_aug = blurer(images=images) # Blur by a value sigma which is sampled from a gaussian distribution # N(1.0, 0.1), i.e. sample a value that is usually around 1.0. # Clip the resulting value so that it never gets below 0.1 or above 3.0. blurer = iaa.GaussianBlur(iap.Clip(iap.Normal(1.0, 0.1), 0.1, 3.0)) images\_aug = blurer(images=images)

There are many more probability distributions in the library, e.g. truncated gaussian distribution, poisson distribution or beta distribution.

Example: WithChannels

Apply an augmenter only to specific image channels: ```python import numpy as np import imgaug.augmenters as iaa

fake RGB images

images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8)

add a random value from the range (-30, 30) to the first two channels of

input images (e.g. to the R and G channels)

aug = iaa.WithChannels( channels=[0, 1], children=iaa.Add((-30, 30)) )

images_aug = aug(images=images) ```

Citation

If this library has helped you during your research, feel free to cite it:

latex @misc{imgaug, author = {Jung, Alexander B. and Wada, Kentaro and Crall, Jon and Tanaka, Satoshi and Graving, Jake and Reinders, Christoph and Yadav, Sarthak and Banerjee, Joy and Vecsei, Gábor and Kraft, Adam and Rui, Zheng and Borovec, Jirka and Vallentin, Christian and Zhydenko, Semen and Pfeiffer, Kilian and Cook, Ben and Fernández, Ismael and De Rainville, François-Michel and Weng, Chi-Hung and Ayala-Acevedo, Abner and Meudec, Raphael and Laporte, Matias and others}, title = {{imgaug}}, howpublished = {\url{https://github.com/aleju/imgaug}}, year = {2020}, note = {Online; accessed 01-Feb-2020} }

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