Data Augmentation

Horizontal and Vertical Augmentation

Let’s try horizontal and vertical augmentation. We use width_shift_range and height_shift_range that arguments Image Data Generator.

# example of horizontal shift image augmentation
from numpy import expand_dims
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.preprocessing.image import ImageDataGenerator
from matplotlib import pyplot
# load the image
img = load_img('bird.jpg')
# convert to numpy array
data = img_to_array(img)
# expand dimension to one sample
samples = expand_dims(data, 0)
# create image data augmentation generator
datagen = ImageDataGenerator(width_shift_range=[-200,200])
# prepare iterator
it = datagen.flow(samples, batch_size=1)
# generate samples and plot
for i in range(9):
# define subplot
pyplot.subplot(330 + 1 + i)
# generate batch of images
batch = it.next()
# convert to unsigned integers for viewing
image = batch[0].astype('uint8')
# plot raw pixel data
pyplot.imshow(image)
# show the figure
pyplot.show()

You can also try this code with Online Python Compiler

Run Code

By running the above code a plot of augmented generated with a random horizontal shift.

Output:

Horizontal and vertical flip augmentation

An image flip means reversing the rows and columns of pixels in the case of a horizontal or vertical flip.

# example of horizontal flip image augmentation
from numpy import expand_dims
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.preprocessing.image import ImageDataGenerator
from matplotlib import pyplot
# load the image
img = load_img('bird.jpg')
# convert to numpy array
data = img_to_array(img)
# expand dimension to one sample
samples = expand_dims(data, 0)
# create image data augmentation generator
datagen = ImageDataGenerator(horizontal_flip=True)
# prepare iterator
it = datagen.flow(samples, batch_size=1)
# generate samples and plot
for i in range(9):
# define subplot
pyplot.subplot(330 + 1 + i)
# generate batch of images
batch = it.next()
# convert to unsigned integers for viewing
image = batch[0].astype('uint8')
# plot raw pixel data
pyplot.imshow(image)
# show the figure
pyplot.show()

You can also try this code with Online Python Compiler

Run Code

In the above code, we are plotting the augmented images with a random horizontal flip.

Output:

Brightness Augmentation

The brightness of the image can be augmented by randomly increasing or decreasing the brightness.

# example of brighting image augmentation
from numpy import expand_dims
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.preprocessing.image import ImageDataGenerator
from matplotlib import pyplot
# load the image
img = load_img('bird.jpg')
# convert to numpy array
data = img_to_array(img)
# expand dimension to one sample
samples = expand_dims(data, 0)
# create image data augmentation generator
datagen = ImageDataGenerator(brightness_range=[0.2,1.0])
# prepare iterator
it = datagen.flow(samples, batch_size=1)
# generate samples and plot
for i in range(9):
# define subplot
pyplot.subplot(330 + 1 + i)
# generate batch of images
batch = it.next()
# convert to unsigned integers for viewing
image = batch[0].astype('uint8')
# plot raw pixel data
pyplot.imshow(image)
# show the figure
pyplot.show()

You can also try this code with Online Python Compiler

Run Code

In the above code, we are plotting the augmented images with a random brightness flip.

Output:

Frequently Asked Questions

1. What does data augmentation mean?
   Data augmentation in data analysis is a technique used to increase the amount of data by adding slightly modified copies of already existing data or newly created synthetic data from existing data.
    
2. What is data augmentation in CNN?
   Data augmentation is a technique to artificially create new training data from existing training data. This is done by applying domain-specific techniques to examples from the training data that create new and different training examples.
    
3. Does data augmentation increase accuracy?
   Prediction accuracy can be increased in the range of 1–3% by using data Augmentation. GAN is the preferred model for small sets, while VAE is better for larger ones.

Key Takeaways

In the above blog, we discussed

• Data augmentation
• Sample model
• Horizontal and vertical augmentation
• Horizontal and vertical flip augmentation
• Brightness augmentation

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