Data Augmentation using Augmentor || Simple steps to perform Data Augmentation

Data augmentation is the technique of increasing the size of data used for training a model. For reliable predictions, the deep learning models often require a lot of training data, which is not always available. Therefore, the existing data is augmented in order to make a better generalized model.
Although data augmentation can be applied in various domains, it's commonly used in computer vision. Some of the most common data augmentation techniques used for images are:
Some of the most common data augmentation techniques used for images are:
Position augmentation. Scaling. Cropping. Flipping. Padding. Rotation. Translation. Affine transformation.
Color augmentation. Brightness. Contrast. Saturation. Hue.
Augmentor is a Python package designed to aid the augmentation and artificial generation of image data for machine learning tasks. It is primarily a data augmentation tool, but will also incorporate basic image pre-processing functionality.
Step 1: Create a New Pipeline
Step 2: Add Operations to the Pipeline
Step 3: Execute and Sample From the Pipeline
import Augmentor

path_to_data = "/home/user/images/dataset1/"

# Create a pipeline
p = Augmentor.Pipeline(path_to_data)
# Add some operations to an existing pipeline.

# First, we add a horizontal flip operation to the pipeline:
p.flip_left_right(probability=0.4)

# Now we add a vertical flip operation to the pipeline:
p.flip_top_bottom(probability=0.8)

# Add a rotate90 operation to the pipeline:
p.rotate90(probability=0.1)
# Now we can sample from the pipeline:
p.sample(num_of_samples)