Image Data Augmentation. How and Why?
If we have limited amount of Data, We can diversify it using data augmentation. It is like instead of collecting new data elements we just transform which is already there to increase the sample size along with diversity. We will consider unstructured data i.e. Image data for augmentation process.
We will Discuss certain things such as…
- Need of Image Data Augmentation
- Commonly used Image Data Augmentation Techniques and Operations
- Using Keras and Augmentor for Augmenting process
Need of Image Data Augmentation
Suppose we are working on a Deep Learning project which usually needs a large set of data to create a proper prediction model. But it is not convenient for us to collect such a huge set then data augmentation can let us multiply our existing data set with a lot of possibilities of variability and diversity.
Machine Intelligence is the last Invention that humanity will need to make.Nick Bostrom
Commonly used Image Data Augmentation Techniques and Operations
It is about creating variability or diversity on a single Image. Then what are the possibilities ?
These are following possibilities for which we can look for..
- Brightness variations
How can we apply these ? Lets Discuss…
Lets Consider an Image…
It operates over an image to create an image either zoomed in or zoomed out.
Shearing lets us create an sheared image.
Through flipping we can flip an image either vertical or horizontal.
Rotation basically used to rotate an image upto a defined degree.
We can enhance or decrease the brightness of an image using this operation.
Image Data Augmentation using Keras
Code for Keras based Augmentation
# Import keras library and required functions from keras.preprocessing.image import ImageDataGenerator,array_to_img, img_to_array, load_img # Initialise ImageDataGenerator class. # Pass the augmentation parameters in the constructor. datagen = ImageDataGenerator(zoom_range = 0.2,shear_range = 0.3, horizontal_flip = True, rotation_range = 50, brightness_range = (0.5, 1.5)) # Get the sample image img = load_img('image.jpg') # Generate image features as array x = img_to_array(img) # Reshape the input image x = x.reshape((1, ) + x.shape) # Create a loop to generate and save five augumented images #using defined parameters i = 0 for batch in datagen.flow(x, batch_size = 1, save_to_dir ='E:/preview', save_prefix ='image', save_format ='jpeg'): i += 1 if i > 5: break
Image Data Augmentation using Augmentor
Code for augmentor based Augmentation
import sys sys.path.append('C:/Users/vipul/Anaconda3/envs/tensorenv/Lib/site-packages') # Import Augmentor Library #For installation : pip install Augumentor import Augmentor # Locate image Directory p = Augmentor.Pipeline("E:\preview") # Generate Five samples based out of Augumentation Parameters p.flip_left_right(0.5) p.black_and_white(0.1) p.rotate(0.3, 10, 10) p.skew(0.4, 0.5) p.zoom(probability = 0.2, min_factor = 1.1, max_factor = 1.5) p.sample(5)