augmenting_irregular_images.py

# -*- coding: utf-8 -*-
"""Augmenting_Irregularimages.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1bfy3YWUQ0ZXlkjeWWDPXtCI7rbIQWY06
"""

# mounting on Google Drive,below 2 lines helps in getting the authorization code by loggin into your Google account
from google.colab import drive
drive.mount('/gdrive')

# Required packages installation
!pip install imgaug==0.4.0

# Importing all the required libraries
import os
import random
import numpy as np
import cv2

import matplotlib.pyplot as plt
import imgaug as ia
import imgaug.augmenters as iaa

"""
  This script is used to augment the images of the irregular images
"""
INPUT_DIRECTORY = "/gdrive/Shareddrives/ALDA_Project/data/web_scraping_meghana/irregular"

RESIZED_OUTPUT_DIRECTORY = "/gdrive/Shareddrives/ALDA_Project/data/web_scraping_meghana/irregular_resized"

AUG_OUTPUT_DIRECTORY = "/gdrive/Shareddrives/ALDA_Project/data/web_scraping_meghana/irregular_aug"

IMAGE_SIDE_LENGTH = 128
NUMBER_IMAGES =1
GROUP_NUMBER = 11

# Resizing all images in input_dir
filenames = os.listdir(INPUT_DIRECTORY)
for filename in filenames:
  temp = cv2.imread(os.path.join(INPUT_DIRECTORY, filename))
  temp = cv2.resize(temp, (IMAGE_SIDE_LENGTH, IMAGE_SIDE_LENGTH))
  cv2.imwrite(os.path.join(RESIZED_OUTPUT_DIRECTORY, filename), temp)

# Loading a thousand images into memory
images = np.zeros((1, IMAGE_SIDE_LENGTH, IMAGE_SIDE_LENGTH, 3), dtype = np.uint8)
filenames = os.listdir(RESIZED_OUTPUT_DIRECTORY)

original_images = len(filenames)

counter = 0
while(images.shape[0] < NUMBER_IMAGES +1):
  # Choose a random index
  choice = random.randint(0, original_images-1)
  # Image file to read (from random index chosen)
  temp = cv2.imread(os.path.join(RESIZED_OUTPUT_DIRECTORY, filenames[choice]))
  # Expand the dimensions of the image you have read in
  temp = np.expand_dims(temp, axis = 0)
  
  counter +=1
  # Concatenate to image directory
  images = np.concatenate((images, temp), axis = 0)

  if counter % 100 ==0:
    print(str(counter), "images loaded.")
images = images[1:]

print(images.shape)

# Sample 15 images
cols = 9
rows = 5

row_images = []

for i in range(rows):
  indices = np.random.rand(cols)
  indices = (indices*NUMBER_IMAGES).astype(np.int64)  

  row = images[indices[0], ...]
  for j in range(1, cols):
    row = np.hstack((row, images[indices[j]]))
  
  row_images.append(row[:])

image = row_images[0]
for i in range(1, rows):
  image = np.vstack((image, row_images[i]))
# Display 
plt.figure(figsize=(cols*3, rows*3))
plt.imshow(image)

ia.seed(GROUP_NUMBER)
#Using augmenter to augment the image
sequential_augmenter = iaa.Sequential([
                                        iaa.ChannelShuffle(0.35),
                                        iaa.ScaleX((0.9, 1.1)),
                                        iaa.ScaleY((0.9, 1.1)),
                                        iaa.Sometimes(0.5, iaa.OneOf([iaa.Fliplr(), iaa.Flipud()])),
                                        iaa.Sometimes(0.5, iaa.Rot90((1,3))),
                                       ], random_order = True)

augmented_images = sequential_augmenter.augment_images(images)

# Sample 15 images
cols = 9
rows = 5

row_images = []

for i in range(rows):
  indices = np.random.rand(cols)
  indices = (indices*NUMBER_IMAGES).astype(np.int64)  

  row = augmented_images[indices[0], ...]
  for j in range(1, cols):
    row = np.hstack((row, augmented_images[indices[j]]))
  
  row_images.append(row[:])

image = row_images[0]
for i in range(1, rows):
  image = np.vstack((image, row_images[i]))

plt.figure(figsize=(cols*3, rows*3))
plt.imshow(image)

#writing the images on the prescribed file path
for i in range(NUMBER_IMAGES):
  fname = "irregular_" + str(i) + ".jpg"
  cv2.imwrite(os.path.join(AUG_OUTPUT_DIRECTORY, fname), augmented_images[i])

  if i % 100 == 0:
    print(str(i), "images written to disk")