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

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)

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)

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)

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))

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

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)

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)

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)

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))

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()