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README.md

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 input heatmaps input segmentation maps input keypoints input bounding boxes
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 affine augmentations, applied to heatmaps affine augmentations, applied to segmentation maps affine augmentations, applied to keypoints affine augmentations, applied to bounding boxes
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

Installation

The library supports python 2.7 and 3.4+.

Installation: Anaconda

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

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):

pip install imgaug

or install the latest version directly from github:

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:

More notebooks: imgaug-doc/notebooks.

Example ReadTheDocs pages:

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:

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):

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:

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.

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:

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 of imgaug.augmentables.batches.UnnormalizedBatch or imgaug.augmentables.batches.Batch. The following example augments a list of image batches in the background:

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 corresponding multicore augmentation notebook or the API about Augmenter.pool() and imgaug.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:

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:

@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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