aleju/imgaug

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

pip install imgaug

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

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)

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)

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])

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)

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)

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)

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)

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)

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)

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)

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)

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))

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)

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)

@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}
}