Image data augmentation using openCV

image_data_augmentation.py

# Image Data augmentation
# Use this program to perform image data augmentation in case you do not want to use a generator for some reason
# Check out ImageDataGenerator if you are looking for a generator in TensorFlow: https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator

import cv2
import numpy as np
import glob
import hashlib

search_pattern = "images/*.jpg"

for each in glob.glob(search_pattern):
  image = cv2.imread(each)

  # --------------------------------------------------------------------------------
  # Scaling using cubic interpolation

  image = cv2.resize(image, None, fx=.75, fy=.75, interpolation = cv2.INTER_CUBIC)

  # --------------------------------------------------------------------------------
  # Flip

  # Horizontal
  image = cv2.flip(image, 1)
  # Vertical 
  image = cv2.flip(image, 0)
  # Both vertical and horizontal
  image = cv2.flip(image, -1)

  # --------------------------------------------------------------------------------
  # Brightness
  matrix = np.ones(image.shape, dtype = "uint8") * 120 

  # Increase
  image = cv2.add(image, matrix)
  # Decrease
  image = cv2.subtract(image, matrix)

  # --------------------------------------------------------------------------------
  # Contrast 

  # Blurring
  image = cv2.blur(image, (9,9))
  # Sharpening
  kernel = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])
  image = cv2.filter2D(image, -1, kernel)

  # --------------------------------------------------------------------------------
  # Display the image and save the image in the same directory as this program
  
  # cv2.imshow("Output", image)
  # cv2.waitKey(0)

  h = hashlib.sha1()
  h.update(each.encode())
  filename_hash = h.hexdigest()
  cv2.imwrite(f"{filename_hash[:10]}.jpg", image)