zllrunning / imgaug

Image augmentation for machine learning experiments.

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.

	Image	Heatmaps	Seg. Maps	Keypoints	Bounding Boxes
Original Input
Gauss. Noise + Contrast + Sharpen
Affine
Crop + Pad
Fliplr + Perspective

More (strong) example augmentations of one input image:

Features of the library

• Supports both common and exotic augmentation techniques.
  • E.g. affine transformations, perspective transformations, contrast changes, gaussian noise, dropout of regions, hue/saturation changes, cropping/padding, blurring, ...
• Supports augmentation of:
  • Images (full support for uint8, for other dtypes see documentation)
  • Heatmaps (float32)
  • Segmentation maps (integer-based, bool, float-based)
  • Keypoints/Landmarks (int or float coordinates)
  • Bounding Boxes (int or float coordinates)
  • Can augment all of the above automatically with the same sampled values. E.g. rotate both images and the segmentation maps on them by the same random value sampled from uniform(0°, 30°).
  • Native support for heatmaps and segmentation maps that are smaller than their corresponding images.
• Define flexible stochastic ranges for each augmentation parameter.
  • E.g. "rotate each image by a value between -45 and 45 degrees".
  • E.g. "rotate each image by 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.
• Offers many helper functions.
  • E.g. for drawing heatmaps, segmentation maps, keypoints and bounding boxes.
  • E.g. for scaling segmentation maps, average/max pooling of images/maps or for padding images to desired aspect ratios (e.g. to square them).
• Define your augmentation sequence once at the start of the experiment, then apply it many times.
• Supports augmentation on multiple CPU cores.

Documentation

• http://imgaug.readthedocs.io/en/latest/source/examples_basics.html - Quick example code to use the library.
• http://imgaug.readthedocs.io/en/latest/source/augmenters.html - Example code for each augmentation technique.
• http://imgaug.readthedocs.io/en/latest/source/api.html - API.
• This README contains more examples. See further below.

Installation

The library supports python 2.7 and 3.4+.

To install the library, first install all requirements:

pip install six numpy scipy Pillow matplotlib scikit-image opencv-python imageio Shapely

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

Alternatively, you can download the repository via git clone https://github.com/aleju/imgaug and install manually via cd imgaug && python setup.py install.

To deinstall the library, just execute pip uninstall imgaug.

Overview of most augmenters

The images below show examples for most augmentation techniques (values written in the form (a, b) mean that a value was randomly picked from the range a <= x <= b):

meta
Noop	ChannelShuffle
arithmetic
Add	Add (per_channel=True)	AdditiveGaussianNoise	AdditiveGaussianNoise (per_channel=True)	AdditiveLaplaceNoise
AdditiveLaplaceNoise (per_channel=True)	AdditivePoissonNoise	AdditivePoissonNoise (per_channel=True)	Multiply	Multiply (per_channel=True)
Dropout	Dropout (per_channel=True)	CoarseDropout (p=0.2)	CoarseDropout (p=0.2, per_channel=True)	ImpulseNoise
SaltAndPepper	Salt	Pepper	CoarseSaltAndPepper (p=0.2)	CoarseSalt (p=0.2)
CoarsePepper (p=0.2)	Invert	Invert (per_channel=True)	JpegCompression
blur
GaussianBlur	AverageBlur	MedianBlur	BilateralBlur (sigma_color=250, sigma_space=250)	MotionBlur (angle=0)
MotionBlur (k=5)
color
AddToHueAndSaturation	Grayscale
contrast
GammaContrast	GammaContrast (per_channel=True)	SigmoidContrast (cutoff=0.5)	SigmoidContrast (gain=10)	SigmoidContrast (per_channel=True)
LogContrast	LogContrast (per_channel=True)	LinearContrast	LinearContrast (per_channel=True)	AllChannels- HistogramEqualization
HistogramEqualization	AllChannelsCLAHE	AllChannelsCLAHE (per_channel=True)	CLAHE
convolutional
Sharpen (alpha=1)	Emboss (alpha=1)	EdgeDetect	DirectedEdgeDetect (alpha=1)
flip
Fliplr		Flipud
geometric
Affine		Affine: Modes
Affine: cval		PiecewiseAffine
PerspectiveTransform		ElasticTransformation (sigma=0.2)
ElasticTransformation (sigma=5.0)		Rot90
overlay
Alpha with EdgeDetect(1.0)	Alpha with EdgeDetect(1.0) (per_channel=True)	SimplexNoiseAlpha with EdgeDetect(1.0)	FrequencyNoiseAlpha with EdgeDetect(1.0)
segmentation
Superpixels (p_replace=1)	Superpixels (n_segments=100)
size
CropAndPad		Crop
Pad		PadToFixedSize (height'=height+32, width'=width+32)
CropToFixedSize (height'=height-32, width'=width-32)
weather
FastSnowyLandscape (lightness_multiplier=2.0)	Clouds	Fog	Snowflakes

Code Examples

A standard machine learning situation. Train on batches of images and augment each batch via crop, horizontal flip ("Fliplr") and gaussian blur:

from imgaug import augmenters as iaa

seq = iaa.Sequential([
    iaa.Crop(px=(0, 16)), # crop images from each side by 0 to 16px (randomly chosen)
    iaa.Fliplr(0.5), # horizontally flip 50% of the images
    iaa.GaussianBlur(sigma=(0, 3.0)) # blur images with a sigma of 0 to 3.0
])

for batch_idx in range(1000):
    # 'images' should be either a 4D numpy array of shape (N, height, width, channels)
    # or a list of 3D numpy arrays, each having shape (height, width, channels).
    # Grayscale images must have shape (height, width, 1) each.
    # All images must have numpy's dtype uint8. Values are expected to be in
    # range 0-255.
    images = load_batch(batch_idx)  # you have to implement this function
    images_aug = seq.augment_images(images)  # done by the library
    train_on_images(images_aug)  # you have to implement this function

Apply heavy augmentations to images (used to create the image at the very top of this readme):

import imgaug as ia
from imgaug import augmenters as iaa
import numpy as np

# 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.ContrastNormalization((0.5, 2.0))
                    )
                ]),
                iaa.ContrastNormalization((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.augment_images(images)

Quickly show example results of your augmentation sequence:

from imgaug import augmenters as iaa
import numpy as np

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

# Show an image with 8*8 augmented versions of image 0 and 8*8 augmented
# versions of image 1. The identical augmentations will be applied to
# image 0 and 1.
seq.show_grid([images[0], images[1]], cols=8, rows=8)

Augment two batches of images in exactly the same way (e.g. horizontally flip 1st, 2nd and 5th images in both batches, but do not alter 3rd and 4th images):

from imgaug import 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.randint(0, 255, (16, 128, 128, 21), dtype=np.uint8)

seq = iaa.Sequential([iaa.GaussianBlur((0, 3.0)), iaa.Affine(translate_px={"x": (-40, 40)})])

# Convert the stochastic sequence of augmenters to a deterministic one.
# The deterministic sequence will always apply the exactly same effects to the images.
seq_det = seq.to_deterministic() # call this for each batch again, NOT only once at the start
images_aug = seq_det.augment_images(images)
heatmaps_aug = seq_det.augment_images(heatmaps)

Augment images and landmarks/keypoints on these images:

import imgaug as ia
from imgaug import augmenters as iaa
import random
import numpy as np
images = np.random.randint(0, 50, (4, 128, 128, 3), dtype=np.uint8)

# Generate random keypoints.
# The augmenters expect a list of imgaug.KeypointsOnImage.
keypoints_on_images = []
for image in images:
    height, width = image.shape[0:2]
    keypoints = []
    for _ in range(4):
        x = random.randint(0, width-1)
        y = random.randint(0, height-1)
        keypoints.append(ia.Keypoint(x=x, y=y))
    keypoints_on_images.append(ia.KeypointsOnImage(keypoints, shape=image.shape))

seq = iaa.Sequential([iaa.GaussianBlur((0, 3.0)), iaa.Affine(scale=(0.5, 0.7))])
seq_det = seq.to_deterministic() # call this for each batch again, NOT only once at the start

# augment keypoints and images
images_aug = seq_det.augment_images(images)
keypoints_aug = seq_det.augment_keypoints(keypoints_on_images)

# Example code to show each image and print the new keypoints coordinates
for img_idx, (image_before, image_after, keypoints_before, keypoints_after) in enumerate(zip(images, images_aug, keypoints_on_images, keypoints_aug)):
    image_before = keypoints_before.draw_on_image(image_before)
    image_after = keypoints_after.draw_on_image(image_after)
    ia.imshow(np.concatenate((image_before, image_after), axis=1)) # before and after
    for kp_idx, keypoint in enumerate(keypoints_after.keypoints):
        keypoint_old = keypoints_on_images[img_idx].keypoints[kp_idx]
        x_old, y_old = keypoint_old.x, keypoint_old.y
        x_new, y_new = keypoint.x, keypoint.y
        print("[Keypoints for image #%d] before aug: x=%d y=%d | after aug: x=%d y=%d" % (img_idx, x_old, y_old, x_new, y_new))

Apply single augmentations to images:

from imgaug import augmenters as iaa
import numpy as np
images = np.random.randint(0, 255, (16, 128, 128, 3), dtype=np.uint8)

flipper = iaa.Fliplr(1.0) # always horizontally flip each input image
images[0] = flipper.augment_image(images[0]) # horizontally flip image 0

vflipper = iaa.Flipud(0.9) # vertically flip each input image with 90% probability
images[1] = vflipper.augment_image(images[1]) # probably vertically flip image 1

blurer = iaa.GaussianBlur(3.0)
images[2] = blurer.augment_image(images[2]) # blur image 2 by a sigma of 3.0
images[3] = blurer.augment_image(images[3]) # blur image 3 by a sigma of 3.0 too

translater = iaa.Affine(translate_px={"x": -16}) # move each input image by 16px to the left
images[4] = translater.augment_image(images[4]) # move image 4 to the left

scaler = iaa.Affine(scale={"y": (0.8, 1.2)}) # scale each input image to 80-120% on the y axis
images[5] = scaler.augment_image(images[5]) # scale image 5 by 80-120% on the y axis

Apply an augmenter to only specific image channels:

from imgaug import augmenters as iaa
import numpy as np

# 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.augment_images(images)

You can use more unusual distributions for the stochastic parameters of each augmenter:

from imgaug import augmenters as iaa
from imgaug import parameters as iap
import numpy as np
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 0.1 <= x < 3.0.
# The convenience shortcut for this is: iaa.GaussianBlur((0.1, 3.0))
blurer = iaa.GaussianBlur(iap.Uniform(0.1, 3.0))
images_aug = blurer.augment_images(images)

# Blur by a value sigma which is sampled from a normal 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.augment_images(images)

# Same again, but this time the mean of the normal distribution is not constant,
# but comes itself from a uniform distribution between 0.5 and 1.5.
blurer = iaa.GaussianBlur(iap.Clip(iap.Normal(iap.Uniform(0.5, 1.5), 0.1), 0.1, 3.0))
images_aug = blurer.augment_images(images)

# Use for sigma one of exactly three allowed values: 0.5, 1.0 or 1.5.
blurer = iaa.GaussianBlur(iap.Choice([0.5, 1.0, 1.5]))
images_aug = blurer.augment_images(images)

# Sample sigma from a discrete uniform distribution of range 1 <= sigma <= 5,
# i.e. sigma will have any of the following values: 1, 2, 3, 4, 5.
blurer = iaa.GaussianBlur(iap.DiscreteUniform(1, 5))
images_aug = blurer.augment_images(images)

You can dynamically deactivate augmenters in an already defined sequence:

import imgaug as ia
from imgaug import augmenters as iaa
import numpy as np

# images and heatmaps, just arrays filled with value 30
images = np.ones((16, 128, 128, 3), dtype=np.uint8) * 30
heatmaps = np.ones((16, 128, 128, 21), dtype=np.uint8) * 30

# add vertical lines to see the effect of flip
images[:, 16:128-16, 120:124, :] = 120
heatmaps[:, 16:128-16, 120:124, :] = 120

seq = iaa.Sequential([
  iaa.Fliplr(0.5, name="Flipper"),
  iaa.GaussianBlur((0, 3.0), name="GaussianBlur"),
  iaa.Dropout(0.02, name="Dropout"),
  iaa.AdditiveGaussianNoise(scale=0.01*255, name="MyLittleNoise"),
  iaa.AdditiveGaussianNoise(loc=32, scale=0.0001*255, name="SomeOtherNoise"),
  iaa.Affine(translate_px={"x": (-40, 40)}, name="Affine")
])

# change the activated augmenters for heatmaps,
# we only want to execute horizontal flip, affine transformation and one of
# the gaussian noises
def activator_heatmaps(images, augmenter, parents, default):
    if augmenter.name in ["GaussianBlur", "Dropout", "MyLittleNoise"]:
        return False
    else:
        # default value for all other augmenters
        return default
hooks_heatmaps = ia.HooksImages(activator=activator_heatmaps)

seq_det = seq.to_deterministic() # call this for each batch again, NOT only once at the start
images_aug = seq_det.augment_images(images)
heatmaps_aug = seq_det.augment_images(heatmaps, hooks=hooks_heatmaps)

Images can be augmented in background processes using the method augment_batches(batches, background=True), where batches is expected to be a list of image batches or a list of batches/lists of imgaug.KeypointsOnImage or a list of imgaug.Batch. The following example augments a list of image batches in the background:

import imgaug as ia
from imgaug import augmenters as iaa
import numpy as np
from skimage import data

# 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 = 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(
        np.array(
            [astronaut for _ in range(batch_size)],
            dtype=np.uint8
        )
    )

# 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, cols=8))

Images can also be augmented in background processes using the classes imgaug.BatchLoader and imgaug.BackgroundAugmenter, which offer a bit more flexibility. (augment_batches() is a wrapper around these.) Using these classes is good practice, when you have a lot of images that you don't want to load at the same time.

import imgaug as ia
from imgaug import augmenters as iaa
import numpy as np
from skimage import data

# Example augmentation sequence to run in the background.
augseq = iaa.Sequential([
    iaa.Fliplr(0.5),
    iaa.CoarseDropout(p=0.1, size_percent=0.1)
])

# A generator that loads batches from the hard drive.
def load_batches():
    # Here, load 10 batches of size 4 each.
    # You can also load an infinite amount of batches, if you don't train
    # in epochs.
    batch_size = 4
    nb_batches = 10

    # Here, for simplicity we just always use the same image.
    astronaut = data.astronaut()
    astronaut = ia.imresize_single_image(astronaut, (64, 64))

    for i in range(nb_batches):
        # A list containing all images of the batch.
        batch_images = []
        # A list containing IDs of images in the batch. This is not necessary
        # for the background augmentation and here just used to showcase that
        # you can transfer additional information.
        batch_data = []

        # Add some images to the batch.
        for b in range(batch_size):
            batch_images.append(astronaut)
            batch_data.append((i, b))

        # Create the batch object to send to the background processes.
        batch = ia.Batch(
            images=np.array(batch_images, dtype=np.uint8),
            data=batch_data
        )

        yield batch

# background augmentation consists of two components:
#  (1) BatchLoader, which runs in a Thread and calls repeatedly a user-defined
#      function (here: load_batches) to load batches (optionally with keypoints
#      and additional information) and sends them to a queue of batches.
#  (2) BackgroundAugmenter, which runs several background processes (on other
#      CPU cores). Each process takes batches from the queue defined by (1),
#      augments images/keypoints and sends them to another queue.
# The main process can then read augmented batches from the queue defined
# by (2).
batch_loader = ia.BatchLoader(load_batches)
bg_augmenter = ia.BackgroundAugmenter(batch_loader, augseq)

# Run until load_batches() returns nothing anymore. This also allows infinite
# training.
while True:
    print("Next batch...")
    batch = bg_augmenter.get_batch()
    if batch is None:
        print("Finished epoch.")
        break
    images_aug = batch.images_aug

    print("Image IDs: ", batch.data)

    ia.imshow(np.hstack(list(images_aug)))

batch_loader.terminate()
bg_augmenter.terminate()

List of augmenters

The following is a list of available augmenters. Note that most of the below mentioned variables can be set to ranges, e.g. A=(0.0, 1.0) to sample a random value between 0 and 1.0 per image, or A=[0.0, 0.5, 1.0] to sample randomly either 0.0 or 0.5 or 1.0 per image.

arithmetic

Augmenter	Description
Add(V, PCH)	Adds value V to each image. If PCH is true, then the the sampled values may be different per channel.
AddElementwise(V, PCH)	Adds value V to each pixel. If PCH is true, then the the sampled values may be different per channel (and pixel).
AdditiveGaussianNoise(L, S, PCH)	Adds white/gaussian noise pixelwise to an image. The noise comes from the normal distribution N(L,S). If PCH is true, then the sampled values may be different per channel (and pixel).
AdditiveLaplaceNoise(L, S, PCH)	Adds noise sampled from a laplace distribution following Laplace(L, S) to images. If PCH is true, then the sampled values may be different per channel (and pixel).
AdditivePoissonNoise(L, PCH)	Adds noise sampled from a poisson distribution with L being the lambda exponent. If PCH is true, then the sampled values may be different per channel (and pixel).
Multiply(V, PCH)	Multiplies each image by value V, leading to darker/brighter images. If PCH is true, then the sampled values may be different per channel.
MultiplyElementwise(V, PCH)	Multiplies each pixel by value V, leading to darker/brighter pixels. If PCH is true, then the sampled values may be different per channel (and pixel).
Dropout(P, PCH)	Sets pixels to zero with probability P. If PCH is true, then channels may be treated differently, otherwise whole pixels are set to zero.
CoarseDropout(P, SPX, SPC, PCH)	Like Dropout, but samples the locations of pixels that are to be set to zero from a coarser/smaller image, which has pixel size SPX or relative size SPC. I.e. if SPC has a small value, the coarse map is small, resulting in large rectangles being dropped.
ReplaceElementwise(M, R, PCH)	Replaces pixels in an image by replacements R. Replaces the pixels identified by mask M. M can be a probability, e.g. 0.05 to replace 5% of all pixels. If PCH is true, then the mask will be sampled per image, pixel and additionally channel.
ImpulseNoise(P)	Replaces P percent of all pixels with impulse noise, i.e. very light/dark RGB colors. This is an alias for SaltAndPepper(P, PCH=True).
SaltAndPepper(P, PCH)	Replaces P percent of all pixels with very white or black colors. If PCH is true, then different pixels will be replaced per channel.
CoarseSaltAndPepper(P, SPX, SPC, PCH)	Similar to CoarseDropout, but instead of setting regions to zero, they are replaced by very white or black colors. If PCH is true, then the coarse replacement masks are sampled once per image and channel.
Salt(P, PCH)	Similar to SaltAndPepper, but only replaces with very white colors, i.e. no black colors.
CoarseSalt(P, SPX, SPC, PCH)	Similar to CoarseSaltAndPepper, but only replaces with very white colors, i.e. no black colors.
Pepper(P, PCH)	Similar to SaltAndPepper, but only replaces with very black colors, i.e. no white colors.
CoarsePepper(P, SPX, SPC, PCH)	Similar to CoarseSaltAndPepper, but only replaces with very black colors, i.e. no white colors.
Invert(P, PCH)	Inverts with probability P all pixels in an image, i.e. sets them to (1-pixel_value). If PCH is true, each channel is treated individually (leading to only some channels being inverted).
ContrastNormalization(S, PCH)	Changes the contrast in images, by moving pixel values away or closer to 128. The direction and strength is defined by S. If PCH is set to true, the process happens channel-wise with possibly different S.
JpegCompression(C)	Applies JPEG compression of strength C (value range: 0 to 100) to an image. Higher values of C lead to more visual artifacts.

blur

Augmenter	Description
GaussianBlur(S)	Blurs images using a gaussian kernel with size S.
AverageBlur(K)	Blurs images using a simple averaging kernel with size K.
MedianBlur(K)	Blurs images using a median over neihbourhoods of size K.
BilateralBlur(D, SC, SS)	Blurs images using a bilateral filter with distance D (like kernel size). SC is a sigma for the (influence) distance in color space, SS a sigma for the spatial distance.
MotionBlur(K, A, D, O)	Blurs an image using a motion blur kernel with size K. A is the angle of the blur in degrees to the y-axis (value range: 0 to 360, clockwise). D is the blur direction (value range: -1.0 to 1.0, 1.0 is forward from the center). O is the interpolation order (O=0 is fast, O=1 slightly slower but more accurate).

color

Augmenter	Description
WithColorspace(T, F, CH)	Converts images from colorspace T to F, applies child augmenters CH and then converts back from F to T.
AddToHueAndSaturation(V, PCH, F, C)	Adds value V to each pixel in HSV space (i.e. modifying hue and saturation). Converts from colorspace F to HSV (default is F=RGB). Selects channels C before augmenting (default is C=[0,1]). If PCH is true, then the the sampled values may be different per channel.
ChangeColorspace(T, F, A)	Converts images from colorspace F to T and mixes with the original image using alpha A. Grayscale remains at three channels. (Fairly untested augmenter, use at own risk.)
Grayscale(A, F)	Converts images from colorspace F (default: RGB) to grayscale and mixes with the original image using alpha A.

contrast

Augmenter	Description
GammaContrast(G, PCH)	Applies gamma contrast adjustment following I_ij' = I_ij**G', where G' is a gamma value sampled from G and I_ij a pixel (converted to 0 to 1.0 space). If PCH is true, a different G' is sampled per image and channel.
SigmoidContrast(G, C, PCH)	Similar to GammaContrast, but applies I_ij' = 1/(1 + exp(G' * (C' - I_ij))), where G' is a gain value sampled from G and C' is a cutoff value sampled from C.
LogContrast(G, PCH)	Similar to GammaContrast, but applies I_ij = G' * log(1 + I_ij), where G' is a gain value sampled from G.
LinearContrast(S, PCH)	Similar to GammaContrast, but applies I_ij = 128 + S' * (I_ij - 128), where S' is a strength value sampled from S. This augmenter is identical to ContrastNormalization (which will be deprecated in the future).
AllChannelsHistogramEqualization()	Applies standard histogram equalization to each channel of each input image.
HistogramEqualization(F, T)	Similar to AllChannelsHistogramEqualization, but expects images to be in colorspace F, converts to colorspace T and normalizes only an intensity-related channel, e.g. L for T=Lab (default for T) or V for T=HSV.
AllChannelsCLAHE(CL, K, Kmin, PCH)	Contrast Limited Adaptive Histogram Equalization (histogram equalization in small image patches), applied to each image channel with clipping limit CL and kernel size K (clipped to range [Kmin, inf)). If PCH is true, different values for CL and K are sampled per channel.
CLAHE(CL, K, Kmin, F, T)	Similar to HistogramEqualization, this applies CLAHE only to intensity-related channels in Lab/HSV/HLS colorspace. (Usually this works significantly better than AllChannelsCLAHE.)

convolutional

Augmenter	Description
Convolve(M)	Convolves images with matrix M, which can be a lambda function.
Sharpen(A, L)	Runs a sharpening kernel over each image with lightness L (low values result in dark images). Mixes the result with the original image using alpha A.
Emboss(A, S)	Runs an emboss kernel over each image with strength S. Mixes the result with the original image using alpha A.
EdgeDetect(A)	Runs an edge detection kernel over each image. Mixes the result with the original image using alpha A.
DirectedEdgeDetect(A, D)	Runs a directed edge detection kernel over each image, which detects each from direction D (default: random direction from 0 to 360 degrees, chosen per image). Mixes the result with the original image using alpha A.

flip

Augmenter	Description
Fliplr(P)	Horizontally flips images with probability P.
Flipud(P)	Vertically flips images with probability P.

geometric

Augmenter	Description
Affine(S, TPX, TPC, R, SH, O, CVAL, FO, M, B)	Applies affine transformations to images. Scales them by S (>1=zoom in, <1=zoom out), translates them by TPX pixels or TPC percent, rotates them by R degrees and shears them by SH degrees. Interpolation happens with order O (0 or 1 are good and fast). If FO is true, the output image plane size will be fitted to the distorted image size, i.e. images rotated by 45deg will not be partially outside of the image plane. M controls how to handle pixels in the output image plane that have no correspondence in the input image plane. If M='constant' then CVAL defines a constant value with which to fill these pixels. B allows to set the backend framework (currently cv2 or skimage).
AffineCv2(S, TPX, TPC, R, SH, O, CVAL, M, B)	Same as Affine, but uses only cv2 as its backend. Currently does not support FO=true. Might be deprecated in the future.
PiecewiseAffine(S, R, C, O, M, CVAL)	Places a regular grid of points on the image. The grid has R rows and C columns. Then moves the points (and the image areas around them) by amounts that are samples from normal distribution N(0,S), leading to local distortions of varying strengths. O, M and CVAL are defined as in Affine.
PerspectiveTransform(S, KS)	Applies a random four-point perspective transform to the image (kinda like an advanced form of cropping). Each point has a random distance from the image corner, derived from a normal distribution with sigma S. If KS is set to True (default), each image will be resized back to its original size.
ElasticTransformation(S, SM, O, CVAL, M)	Moves each pixel individually around based on distortion fields. SM defines the smoothness of the distortion field and S its strength. O is the interpolation order, CVAL a constant fill value for newly created pixels and M the fill mode (see also augmenter Affine).
Rot90(K, KS)	Rotate images K times clockwise by 90 degrees. (This is faster than Affine.) If KS is true, the resulting image will be resized to have the same size as the original input image.

meta

Augmenter	Description
Sequential(C, R)	Takes a list of child augmenters C and applies them in that order to images. If R is true (default: false), then the order is random (chosen once per batch).
SomeOf(N, C, R)	Applies N randomly selected augmenters from from a list of augmenters C to each image. The augmenters are chosen per image. R is the same as for Sequential. N can be a range, e.g. (1, 3) in order to pick 1 to 3.
OneOf(C)	Identical to SomeOf(1, C).
Sometimes(P, C, D)	Augments images with probability P by using child augmenters C, otherwise uses D. D can be None, then only P percent of all images are augmented via C.
WithColorspace(T, F, C)	Transforms images from colorspace F (default: RGB) to colorspace T, applies augmenters C and then converts back to F.
WithChannels(H, C)	Selects from each image channels H (e.g. [0,1] for red and green in RGB images), applies child augmenters C to these channels and merges the result back into the original images.
Noop()	Does nothing. (Useful for validation/test.)
Lambda(I, K)	Applies lambda function I to images and K to keypoints.
AssertLambda(I, K)	Checks images via lambda function I and keypoints via K and raises an error if false is returned by either of them.
AssertShape(S)	Raises an error if input images are not of shape S.
ChannelShuffle(P, C)	Permutes the order of the color channels for P percent of all images. Shuffles by default all channels, but may restrict to a subset using C (list of channel indices).

overlay

Augmenter	Description
Alpha(F, A, B, PCH)	Augments images using augmenters A and B independently, then overlays the result using alpha F. Both A and B default to doing nothing if not provided. E.g. use Alpha(0.9, A) to augment images via A, then blend the result, keeping 10% of the original image (before A). If PCH is set to true, the process happens channel-wise with possibly different F (A and B are computed once per image).
AlphaElementwise(F, A, B, PCH)	Same as Alpha, but performs the blending pixel-wise using a continuous mask (values 0.0 to 1.0) sampled from F. If PCH is set to true, the process happens both pixel- and channel-wise.
SimplexNoiseAlpha(A, B, PCH, SM, UP, I, AGG, SIG, SIGT)	Similar to Alpha, but uses a mask to blend the results from augmenters A and B. The mask is sampled from simplex noise, which tends to be blobby. The mask is gathered in I iterations (default 1-3), each iteration is combined using aggregation method AGG (default max, i.e. maximum value from all iterations per pixel). Each mask is sampled in low resolution space with max resolution SM (default 2 to 16px) and upscaled to image size using method UP (default: linear or cubic or nearest neighbour upsampling). If SIG is true, a sigmoid is applied to the mask with threshold SIGT, which makes the blobs have values closer to 0.0 or 1.0.
FrequencyNoiseAlpha(E, A, B, PCH, SM, UP, I, AGG, SIG, SIGT)	Similar to SimplexNoiseAlpha, but generates noise masks from the frequency domain. Exponent E is used to increase/decrease frequency components. High values lead to more pronounced high frequency components. Use values in the range -4 to 4, with -2 roughly generated cloud-like patterns.

segmentation

Augmenter	Description
Superpixels(P, N, M)	Generates N superpixels of the image at (max) resolution M and resizes back to the original size. Then P percent of all superpixel areas in the original image are replaced by the superpixel. (1-P) percent remain unaltered.

size

Augmenter	Description
Scale(S, I)	Resizes images to size S. Common use case would be to use S={"height":H, "width":W} to resize all images to shape HxW. H and W may be floats (e.g. resize to 50% of original size). Either H or W may be "keep-aspect-ratio" to define only one side's new size and resize the other side correspondingly. I is the interpolation to use (default: cubic).
CropAndPad(PX, PC, PM, PCV, KS)	Crops away or pads PX pixels or PC percent of pixels at top/right/bottom/left of images. Negative values result in cropping, positive in padding. PM defines the pad mode (e.g. use uniform color for all added pixels). PCV controls the color of added pixels if PM=constant. If KS is true (default), the resulting image is resized back to the original size.
Pad(PX, PC, PM, PCV, KS)	Shortcut for CropAndPad(), which only adds pixels. Only positive values are allowed for PX and PC.
Crop(PX, PC, KS)	Shortcut for CropAndPad(), which only crops away pixels. Only positive values are allowed for PX and PC (e.g. a value of 5 results in 5 pixels cropped away).
PadToFixedSize(W, H, PM, PCV, POS)	Pads all images up to height H and width W. PM and PCV are the same as in Pad. POS defines the position around which to pad, e.g. POS="center" pads equally on all sides, POS="left-top" pads only the top and left sides.
CropToFixedSize(W, H, POS)	Similar to PadToFixedSize, but crops down to height H and width W instead of padding.
KeepSizeByResize(CH, I, IH)	Applies child augmenters CH (e.g. cropping) and afterwards resizes all images back to their original size. I is the interpolation used for images, IH the interpolation used for heatmaps.

weather

Augmenter	Description
FastSnowyLandscape(LT, LM)	Converts landscape images to snowy landscapes by increasing in HLS colorspace the lightness L of all pixels with L<LT by a factor of LM.
Clouds()	Adds clouds of various shapes and densities to images. Can be senseful to be combined with an overlay augmenter, e.g. SimplexNoiseAlpha.
Fog()	Adds fog-like cloud structures of various shapes and densities to images. Can be senseful to be combined with an overlay augmenter, e.g. SimplexNoiseAlpha.
CloudLayer(IM, IFE, ICS, AMIN, AMUL, ASPXM, AFE, S, DMUL)	Adds a single layer of clouds to an image. IM is the mean intensity of the clouds, IFE a frequency noise exponent for the intensities (leading to non-uniform colors), ICS controls the variance of a gaussian for intensity sampling, AM is the minimum opacity of the clouds (values >0 are typical of fog), AMUL a multiplier for opacity values, ASPXM controls the minimum grid size at which to sample opacity values, AFE is a frequency noise exponent for opacity values, S controls the sparsity of clouds and DMUL is a cloud density multiplier. This interface is not final and will likely change in the future.
Snowflakes(D, DU, FS, FSU, A, S)	Adds snowflakes with density D, density uniformity DU, snowflake size FS, snowflake size uniformity FSU, falling angle A and speed S to an image. One to three layers of snowflakes are added, hence the values should be stochastic.
SnowflakesLayer(D, DU, FS, FSU, A, S, BSF, BSL)	Adds a single layer of snowflakes to an image. See augmenter Snowflakes. BSF and BSL control a gaussian blur applied to the snowflakes.