[Intel AI DevCamp] image aug

aug.py

#%%bash

#echo "Start resizing to 227x227"
#parallel -j 200 convert {} -resize 227x227 -filter spline -unsharp 0x6+0.5+0 -background black -gravity center -extent 227x227  {} ::: *.jpg
#echo "Resizing done"

#mkdir flop
#echo "Start augmentation 1"
#parallel -j 200 convert {} -flop flop/{.}-flop.jpg ::: *.jpg
#echo "Finish augmetation 1"

#mkdir flip
#echo "Start augmentation 2"
#parallel -j 200 convert {} -transverse -rotate 90 flip/{.}-flip.jpg ::: *.jpg
#echo "Finish augmetation 2"

#mkdir blur
#echo "Start augmentation 3"
#parallel -j 200 convert {} -blur 0x1 blur/{.}-blur.jpg ::: *.jpg
#echo "Finish augmetation 3"

#mkdir red
#echo "Start augmentation 4"
#parallel -j 200 convert {} -channel R -separate red/{.}-red.jpg ::: *.jpg
#echo "Finish augmetation 4"

#mkdir blue
#echo "Start augmentation 5"
#parallel -j 200 convert {} -channel B -separate blue/{.}-blue.jpg ::: *.jpg
#echo "Finish augmetation 5"

#mkdir green
#echo "Start augmentation 6"
#parallel -j 200 convert {} -channel G -separate green/{.}-green.jpg ::: *.jpg
#echo "Finish augmetation 6"

#echo "Copying augmented data to main folder"
#cp flop/* flip/* blur/* red/* blue/* green/* .

#echo "Augmentation done"

from multiprocessing import Pool
from PIL import Image
import sys

def resize_image(file, size=224):
    black_background = Image.new('RGB', (size, size), "black")
    img = Image.open(file)
    img.thumbnail((size,size))
    x, y = img.size
    black_background.paste(img, (int((size - x) / 2), int((size - y) / 2)))
    black_background.save(file)
    return black_background
  
pool = Pool(6)
for i, _ in enumerate(pool.map(resize_image, glob.glob("breeds/*"))):
    if i % 10 == 0:
        sys.stdout.write('\r{0} out of {1} processed'.format(i+1, len(glob.glob("breeds/*"))))
        
sys.stdout.write('\n')
sys.stdout.flush()

display_images(glob.glob('breeds/*.jpg'))

print("Done.")