Table Of Contents
Table Of Contents

Image Augmentation

Image augmentation technology expands the scale of training data sets by making a series of random changes to the training images to produce similar, but different, training examples. Given its popularity in computer vision, the model provides multiple pre-defined image augmentation methods. In this section we will briefly go through this module.

First, import the module required for this section.

In [1]:
from matplotlib import pyplot as plt
from mxnet import image
from mxnet.gluon import data as gdata, utils

Then read the sample \(400\times 500\) image.

In [2]:'')
img = image.imread('cat.jpg')

In addition, we define a function to draw a list of images.

In [3]:
def show_images(imgs, num_rows, num_cols, scale=2):
    figsize = (num_cols * scale, num_rows * scale)
    _, axes = plt.subplots(num_rows, num_cols, figsize=figsize)
    for i in range(num_rows):
        for j in range(num_cols):
            axes[i][j].imshow(imgs[i * num_cols + j].asnumpy())
    return axes

Most image augmentation methods have a certain degree of randomness. To make it easier for us to observe the effect of image augmentation, we next define the auxiliary function apply. This function runs the image augmentation method aug multiple times on the input image img and shows all results.

In [4]:
def apply(img, aug, num_rows=2, num_cols=4, scale=3):
    Y = [aug(img) for _ in range(num_rows * num_cols)]
    show_images(Y, num_rows, num_cols, scale)

Flip and Crop

Flipping the image left and right usually does not change the category of the object. This is one of the earliest and most widely used methods of image augmentation. Next, we use the transforms module to create the RandomFlipLeftRight instance, which introduces a 50% chance that the image is flipped left and right.

In [5]:

Flipping up and down is not as commonly used as flipping left and right. However, at least for this example image, flipping up and down does not hinder recognition. Next, we create a RandomFlipTopBottom instance for a 50% chance of flipping the image up and down.

In [6]:

In the example image we used, the cat is in the middle of the image, but this may not be the case for all images. In the “Pooling Layer” section, we explained that the pooling layer can reduce the sensitivity of the convolutional layer to the target location. In addition, we can make objects appear at different positions in the image in different proportions by randomly cropping the image. This can also reduce the sensitivity of the model to the target position.

In the following code, we randomly crop a region with an area of 10% to 100% of the original area, and the ratio of width to height of the region is randomly selected from between 0.5 and 2. Then, the width and height of the region are both scaled to 200 pixels. Unless otherwise stated, the random number between \(a\) and \(b\) in this section refers to a continuous value obtained by uniform sampling in the interval \([a,b]\).

In [7]:
shape_aug =
    (200, 200), scale=(0.1, 1), ratio=(0.5, 2))
apply(img, shape_aug)

Change Color

Another augmentation method is changing colors. We can change four aspects of the image color: brightness, contrast, saturation, and hue. In the example below, we randomly change the brightness of the image to a value between 50% (\(1-0.5\)) and 150% (\(1+0.5\)) of the original image.

In [8]:

Similarly, we can randomly change the hue of the image.

In [9]:

We can also create a RandomColorJitter instance and set how to randomly change the brightness, contrast, saturation, and hue of the image at the same time.

In [10]:
color_aug =
    brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5)
apply(img, color_aug)

Overlying Multiple Image Augmentation Methods

In practice, we will overlay multiple image augmentation methods. We can overlay the different image augmentation methods defined above and apply them to each image by using a Compose instance.

In [11]:
augs =[, color_aug, shape_aug])
apply(img, augs)