When we are using convolutional neural networks, most of the time, we need to fix the input image size to feed it to the network. The usual practice is to resize the input image to the given size (the image aspect ratio is no longer kept) and then crop a fixed size patch randomly from the resized image. This practice may work well for image classification where fine details may not be necessary. But for Image retrieval, we want to keep the image aspect ration unchanged. In this post, I will summarize ways to resize an image to square shape with padding and keep its aspect ratio.
The main idea is to first resize the input image so that its maximum size equals to the given size. Then we pad the resized image to make it square. A number of packages in Python can easily achieves this.
PIL is a popular image processing package in Python. We can use either Image
module or the ImageOps module to achieve what we want.
Image module#First we create a blank square image, then we paste the resized image on it to form a new image. The code is:
from PIL import Image, ImageOps
desired_size = 368
im_pth = "/home/jdhao/test.jpg"
im = Image.open(im_pth)
old_size = im.size # old_size[0] is in (width, height) format
ratio = float(desired_size)/max(old_size)
new_size = tuple([int(x*ratio) for x in old_size])
# use thumbnail() or resize() method to resize the input image
# thumbnail is a in-place operation
# im.thumbnail(new_size, Image.ANTIALIAS)
im = im.resize(new_size, Image.ANTIALIAS)
# create a new image and paste the resized on it
new_im = Image.new("RGB", (desired_size, desired_size))
new_im.paste(im, ((desired_size-new_size[0])//2,
(desired_size-new_size[1])//2))
new_im.show()ImageOps module#The PIL ImageOps module has a expand() function
that will add borders to the 4 side of an image. We need to calculate the
padding length in 4 side of the resized image before applying this method.
delta_w = desired_size - new_size[0]
delta_h = desired_size - new_size[1]
padding = (delta_w//2, delta_h//2, delta_w-(delta_w//2), delta_h-(elta_h//2))
new_im = ImageOps.expand(im, padding)
new_im.show()In OpenCV, we have
copyMakeBorder
which is handy in making borders. The full code to resize and pad an image is
as follows:
import cv2
desired_size = 368
im_pth = "/home/jdhao/test.jpg"
im = cv2.imread(im_pth)
old_size = im.shape[:2] # old_size is in (height, width) format
ratio = float(desired_size)/max(old_size)
new_size = tuple([int(x*ratio) for x in old_size])
# new_size should be in (width, height) format
im = cv2.resize(im, (new_size[1], new_size[0]))
delta_w = desired_size - new_size[1]
delta_h = desired_size - new_size[0]
top, bottom = delta_h//2, delta_h-(delta_h//2)
left, right = delta_w//2, delta_w-(delta_w//2)
color = [0, 0, 0]
new_im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT,
value=color)
cv2.imshow("image", new_im)
cv2.waitKey(0)
cv2.destroyAllWindows()I have upload the whole script to GitHub and you can download it here.
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