（一）imgaug -- 1 Load and Augment an Image

imgaug
This python lib rary 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.

1. Load and Augment an Image

import imageio
import imgaug as ia
from imgaug import augmenters as iaa

import numpy as np

%matplotlib inline

1.1 Load and Show an Image

image = imageio.imread("img/cat1.png")
print("Original:")
ia.imshow(image)

Output:

Original:

1.2 Augment an Image

ia.seed(4)
rotate = iaa.Affine(rotate=(25,-25))
image_aug = rotate(image=image)

print("Augmented:")
ia.imshow(image_aug)

Output:

Augmented:

1.3 Augment a Batch of Images

images = [image,image,image,image]
images_aug = rotate(images=images)

print("Augmented batch:")
ia.imshow(np.hstack(images_aug))

Output:

Augmented batch:

1.4 Use Many Augmentation Techniques Simultaneously

1.4.1 same order

# combine several augmentation techniques on one image
# augment a batch of images

seq = iaa.Sequential([
    iaa.Affine(rotate=(-25, 25)),
    iaa.AdditiveGaussianNoise(scale=(10, 60)),
    
    """
    Crop by default retains the input image size, 
    i.e. after removing pixels it resizes the remaining image back to the input size. 
    
    If you instead prefer to not resize back to the original image size, 
    instantiate Crop as Crop(..., keep_size=False).
    """
    iaa.Crop(percent=(0, 0.2))
])

images_aug = seq(images=images)

print("Augmented:")
ia.imshow(np.hstack(images_aug))

Output:

Augmented:

1.4.2 random order

# combine several augmentation techniques on one image
# random order is sampled once per batch
# augment an image eight times via a loop

seq = iaa.Sequential([
    iaa.Affine(rotate=(-25, 25)),
    iaa.AdditiveGaussianNoise(scale=(30, 90)),
    iaa.Crop(percent=(0, 0.4))
], random_order=True)

# augment one image as one batch
images_aug = [seq(image=image) for _ in range(8)]

print("Augmented:")
ia.imshow(ia.draw_grid(images_aug, rows=2, cols=4))

Output:

Augmented:

1.5 Augment Images of Different Sizes

seq = iaa.Sequential([
    iaa.CropAndPad(percent=(-0.2, 0.2), pad_mode="edge"),  # crop and pad
    iaa.AddToHueAndSaturation((-60, 60)),  # change color
    iaa.ElasticTransformation(alpha=90, sigma=9),  # water-like effect
    iaa.Cutout()  # replace one squared area within the image by a constant intensity value
], random_order=True)


# load images with different sizes
images_different_sizes = [
    imageio.imread("img/cat2.jpg"),
    imageio.imread("img/cat3.jpeg")
]


# augment them as one batch
images_aug = seq(images=images_different_sizes)


# visualize the results
print("Image 0 (input shape: %s, output shape: %s)" % (images_different_sizes[0].shape, images_aug[0].shape))
ia.imshow(np.hstack([images_different_sizes[0], images_aug[0]]))

print("Image 1 (input shape: %s, output shape: %s)" % (images_different_sizes[1].shape, images_aug[1].shape))
ia.imshow(np.hstack([images_different_sizes[1], images_aug[1]]))

Output:

Image 0 (input shape: (561, 999, 3), output shape: (561, 999, 3))

Image 1 (input shape: (225, 225, 3), output shape: (225, 225, 3))

References

https://github.com/aleju/imgaug
Load and Augment an Image